What do U.S. multinationals’ voluntary geographical employment
disclosures tell us?
Anne Beatty
beatty.86@osu.edu Fisher College of Business
The Ohio State University
442 Fisher Hall
2100 Neil Avenue
Columbus, OH 43210
614-292-5418
Scott Liao
scott.liao@rotman.utoronto.ca
Rotman School of Management
University of Toronto
105 St. George Street
Toronto, ON M5S 3E6
416-946-8599
December 15, 2012
We would like to thank Trevor Harris, Bruce Miller, Stephen Penman and seminar
participants at Columbia, M.I.T., Michigan, Temple and UCLA.
Abstract
A recent Gallop poll found the unemployment rate to be one of the top issues that
concern U.S. voters. Although survey data indicates that U.S. multinationals decreased
their U.S. work forces by 2.9 million during the past decade while increasing their
employment overseas by 2.4 million, disclosure of the number of employees by
geographical segment is voluntary for these firms. We consider whether the voluntary
choice to disclose this information is consistent with fear of potential government or
employee actions affecting corporate reporting of non-financial information. Specifically,
we compare the associations between aggregate U.S. employment and U.S. and foreign
sales reported in the Bureau of Economic Analysis (BEA) survey data to those for the
aggregate of COMPUSTAT firms who voluntarily disclose that information, and to those
for all COMPUSTAT firms by incorporating firm-specific imputed employment data. We
find that U.S. employment is positively affected by foreign activity for firms that
voluntarily disclose employment by geography, but that foreign activity negatively
affects U.S. employment for firms who choose not to disclose. These findings are
consistent with firms voluntarily disclosing this information when it is less likely to
trigger government or employee actions. To further explore the disclosure decision, we
also examine the characteristics of U.S. multinational firms who choose to disclose the
number of domestic versus foreign employment. Our findings that firms subject to more
outsourcing press coverage, expanding their foreign operations, and operating in low
wage areas are less likely to disclose while those engaged in lobbying activities or having
more autonomous foreign subsidiaries are more likely to disclose provide further support
that voluntary disclosure is more likely when concerns about government and employee
actions are lower.
1 1. Introduction
According to Bureau of Labor Statistics data, the U.S. unemployment rate
exceeded 8% from January of 2009 to September of 2012, resulting in the longest period
of consecutive monthly unemployment above 8% since the inception of record keeping in
1948. A great deal of political and media attention has focused on this unprecedented
period of unemployment. Of particular concern is the increase in corporate profits that
have not been accompanied by a corresponding increase in jobs especially given federal
bailout money provided to stimulate job creation. One potential explanation for the
inconsistency between profits and job growth is a shift by multinationals to hire
employees overseas while downsizing the number of U.S. employees.
Data from the 2009 BEA survey indicates that U.S. multinationals decreased
their U.S. work forces by 2.9 million during the preceding decade while increasing their
employment overseas by 2.4 million.1 In an April 19, 2011 Wall Street Journal Article,
David Wessel argues that
The Commerce Department's totals mask significant differences among the big
companies. Some are shrinking employment at home and abroad while increasing
productivity. Others are hiring everywhere. Still others are cutting jobs at home
while adding them abroad2… the growth of their overseas work forces is a sensitive
point for U.S. companies.
David Wessel further argues that “Many of them don't disclose how many of their
workers are abroad. And some who do won't talk about it.” For example, IBM had
separately disclosed the number of employees in the U.S. before 2010, but only provided
a global headcount in its 2010 10-K filing. When asked about the change in disclosure
1
The survey also indicates that when multinational’s sales dropped significantly at the peak of the
recession in 2009, these firms cut 5.3% of their U.S. labor but only 1.5% abroad.
2
For example, from 2005 to 2010, GE cut 1,000 employees overseas and 28,000 in the U.S.; Cisco added
10,900 employees in the U.S. and 21,250 overseas; Honeywell cut 5,000 employees in the U.S. while
adding 19,000 jobs overseas. 1 policy by Computerworld, an IBM spokesman said ‘our competitors report headcount
globally. Going forward we will report it globally.’ Similarly, Oracle’s corporate public
relations director also declined to comment on future hiring or headcount. Although the
lack of required disclosure has been a source of political concern, approximately twenty
percent of multinational SEC filers voluntarily disclose the number of employees by
geographical segment.
Ramanna (2012) argues that voluntary labor practice disclosures likely reflect
agency relationships between shareholders and employees and between shareholders and
both local and national communities. Based on this argument, we examine firms’
voluntary geographical employment disclosures to better understand whether concerns
over potential government or employee actions affect firms’ voluntary disclosure of
geographic employment data. If the effect of foreign activity on U.S. employment
influences these government and employee actions and thus firms’ disclosure decisions,
then biased inferences are likely to result from merely dropping observations with
missing data when estimating models of the association between foreign activity and U.S.
employment. We use imputation methods to address this potential selection basis, and use
multiple imputation to avoid downward biases in standard errors caused by single
imputation when the missing data is treated as known (Little and Rubin, 2002). This
approach is particularly appealing in our setting because all SEC filers are required to
disclose the total number of employees even though the breakdown between U.S. versus
foreign employment is voluntary. This known aggregate of the two imputed numbers
provides one way to validate the use of imputation techniques.
2 We first explore the association between firms’ domestic employment and foreign
activity and whether this association affects or varies with this voluntary disclosure. This
helps us understand whether foreign activity complements or substitutes for domestic
employment and whether this relation depends on geographical employment disclosure
decisions to further our understanding of the nature of the disclosures. Specifically, we
analyze the determinants of U.S. employment using the aggregate BEA data compared to
a sample of COMPUSTAT firms that disclose geographical employment and, by
incorporating imputed firm-level employment data, to all COMPUSTAT firms. We
compare the coefficients on foreign activity in total employment or U.S. employment
regressions across the three aggregate samples. While we find no significant differences
in the estimated coefficients between the aggregate BEA and overall COMPUSTAT data
for either the reported total employment or the imputed domestic employment, we find
significant differences in these coefficients for COMPUSTAT firms that voluntarily
disclose the geographical employment breakdown. We find that foreign activity
positively affects domestic and total employment for firms that disclose geographical
employment, but for both the aggregate overall COMPUSTAT data and the BEA data we
find that foreign activity negatively affects U.S. and total employment.
In addition to analyzing the differences in the association between foreign
activities and domestic and overall employment between samples, we further link our
analysis to the government and employee concerns by comparing the time trends of
domestic versus foreign employment across these samples. We find that, in aggregate,
multinationals reporting the number of U.S. versus foreign employees display an increase
in both categories, consistent with a significant positive correlation between U.S. and
3 foreign employment rather than the opposing trend documented in the BEA survey. In
contrast, estimates of the number of U.S. versus foreign employees for those that do not
disclose suggest a decline in U.S. employment and an increase in foreign employment,
consistent with the BEA survey results. These findings suggest that the potential threat of
government or employee actions may inhibit firms’ voluntary disclosure of geographical
employment when their foreign activities are more negatively associated with U.S.
employment.
Finally, to shed more light on how concerns over government and employee
actions affect firms’ voluntary disclosure choices, we directly estimate a prediction model
of the determinants of the choice to disclose the breakout between domestic versus
foreign employees. Our analyses of the characteristics of firms that choose to provide
geographic employment disclosures further support the notion that firms’ disclosure
decisions are affected by concerns about potential government or employee actions.
Specifically, firms are less likely to disclose when they are subject to outsourcing
publicity, expanding the number of geographical segments, and operating in foreign areas
or countries where wages are consistently lower than those in the U.S. Further, firms are
more likely to disclose if they have lobbied Congress on jobs bills or have more
autonomous foreign subsidiaries.
Our paper contributes to the corporate accountability reporting literature
discussed by Ramanna (2012) by focusing on whether U.S. multinationals’ disclosures of
actions that are not entirely captured in revenues and expenses as currently defined by
GAAP are affected by potential government and employee actions. Our paper also
extends the literature on outsourcing and political costs, e.g., Ramanna and
4 Roychowdhury (2010), by showing that firms that lobby more on the employment bills
tend to provide employment breakdowns. Further, our findings shed light on the debate
over multinationals’ disclosure of U.S. employment, by showing that there is a systematic
bias in the voluntary disclosures towards firms with a positive association between
foreign sales and U.S. employment and firms that increase both domestic and foreign
employment over the last decade. We also contribute to the more general disclosure
literature by highlighting the use of Bayesian bootstrapping imputation to analyses of
voluntary disclosures in SEC filings. Our study suggests that caution should be exercised
before ignoring missing data. In our setting, the relation between U.S. employees and
foreign sales depends on the disclosure decision and therefore ignoring missing data may
generate inaccurate inferences.
Finally, our study contributes to the segment disclosure literature. Previous
studies focus on segment disclosure of financial information. For example, Hope and
Thomas (2008) examine the association between segment profit disclosures and empire
building in the post SFAS 131 period. Similarly, Berger and Hann (2007) use agency
theory to interpret firms’ decisions to conceal segment profit information. Our study
differs by examining the factors affecting non-financial segment disclosures.
Background information for this study is provided in section 2. Our research
design is described in Section 3. Data and descriptive statistics are provided in Section 4.
Results are reported in Section 5 and conclusions are drawn in Section 6.
2. Background
2.1 Employment and Segment Disclosure Requirements
5 The reporting requirements under 10-K Item 1 include a discussion of a
company’s business including, among other things, identifying the number of employees.
However, there is no requirement that firms separately disclose their domestic versus
foreign headcount. Although there is no regulatory requirement to disclose the number of
employees by geographical region, some firms voluntarily provided this information in
their 10-K filings. Roughly 20% of 10-K filers provide a breakout of domestic versus
foreign employees based on the Historical Segments database within COMPUSTAT.3
Under SFAS No. 131, practicable firms must report revenues from external
customers and long-lived assets (1) attributed to the enterprise’s country of domicile and
(2) attributed to all foreign countries in total from which the enterprise derives revenues
or holds assets,. However, firms are not required to disclose segment profits by
geographic area or employment information by either geographic area or by operating
segment. Berger and Hann (2007) exploit the accounting rule change and find that firms
with higher agency costs are more inclined to conceal segment profits. Consistent with
their findings, Hope and Thomas (2008) find that in the post SFAS 131 regime, firms
concealing geographical earnings information are more likely to engage in empire
building. While segment financial information is widely studied and viewed important in
addressing agency problems, previous studies have been silent on non-financial segment
information, e.g., geographical employment disclosure.
2.2 Geographical Employment Disclosure Incentives
Ramanna (2012) discusses three explanations for firms’ voluntary disclosure of
actions that are not entirely captured in revenues and expenses as currently defined by
3 For a random selection of 100 firms, we validate the accuracy of the COMPUSTAT segments information using the 10-­‐K disclosures. 6 GAAP. The first is “window dressing” designed to earn reputational capital among one or
more constituencies. The second is internalizing negative externalities in firm disclosure
decisions, which can offset costs that are otherwise incurred by one or more firm
constituencies. The third is internalizing positive externalities, which create benefits for
one or more firm constituencies. These explanations encompass both the public choice
and market failure theories of government intervention. Specifically, Watts and
Zimmerman’s (1978) political cost hypothesis that is based on the public choice theory of
government action argues that government actions result from the self-interested
behaviors of voters and politicians who devote resources in an attempt to transfer wealth
from one group to another. In contrast, the market failure theory argues that government
intervention is required to address externalities and market failures that arise from
imperfect information and from transactions costs. Stiglitz (2008) outlines two types of
government interventions to address these market failures. The first is market based
interventions including taxes or subsidies and the second is regulation including either
mandatory disclosures or proscribed or mandated actions.
Previous research has considered each of these theories of government actions in
the outsourcing context. Based on the political cost hypothesis, Ramanna and
Roychowdhury (2010) examine the relation between accounting discretion and negative
publicity about job outsourcing. They examine two related questions: whether firms use
accounting discretion to mitigate the potential economic consequences of negative
publicity, and whether firms’ political connections provide an additional motivation to
use accounting discretion in the face of negative publicity. Guay (2010) suggests that the
applicability to other settings to accounting researchers may be greater for the first
7 question, where Ramanna and Roychowdhury (2010) find that firms receiving greater
negative outsourcing publicity are more likely to use negative accounting discretion.
Consistent with this suggestion, we consider whether negative outsourcing publicity
affects that likelihood of voluntary disclosure of non-financial labor related disclosures.
Consistent with the market failure theory, the possibility that firms’ employment
decisions may create externalities is discussed by Kochan (2012). He argues that firms
value short-term profit rather than investment in human capital that may increase social
welfare.4 He states:
As in a classic market failure, individual firms are not shouldering the true costs of
their actions. They benefit from minimizing their own labor costs while society picks
up the tab for their lack of investment in human capital: slow economic growth,
unemployment, welfare, and so on.
Kochran (2012) provides further support for this argument using the following quote
from former IBM VP and Sloan Foundation president Ralph Gomory:
The principal actors in attaining [the nation’s] economic goals must be our
corporations. But today our government does not ask U.S. corporations, or their
leaders, to build productivity here in America; much less does it provide incentives
for them to move in that direction…[Government leaders] do not realize that the
fundamental goals of the country and of our companies have diverged. The sole
focus on profit maximization, which leads to offshoring and holds down wages, does
not serve the nation…We must act to realign the goals of company and country.
This idea that outsourcing leads to market failures is modeled more formally by
Aronsson and Koskela (2007), who examine negative externalities that arise in an
economy with involuntary unemployment where firms may outsource part of their
production to other countries. They argue that the equilibrium unemployment provides an
4 Stiglitz (2008) also has a similar argument. He argues that “decisions by firms have social costs that they
do not appropriately take into account (just as firms do not take into account environmental externalities).
For instance, even without unemployment insurance benefits, firm decisions concerning layoffs do not lead
to Pareto efficiency unemployment benefits in unemployment systems that are not fully experience-rated, it
is obvious that when firms lay off an individual, it imposes a social cost on others.” 8 incentive for the government to directly tax outsourcing. This argument is consistent with
Stiglitz (2008) that “firms that have a policy of letting go of labor more easily lead to
higher labor turnover…..this means that the equilibrium unemployment rate will be
higher. More generally, it is optimal to slow this process down, for example, with
mandatory severance pay.”
2.3 Potential Government or Employee Actions
Multinational’s incentives to voluntarily provide information about the number of
U.S. versus foreign employees likely depend on the expectations about government and
employee actions. These concerns are reflected in the results of a poll of 180 corporate
executives conducted by the consulting firm Diamond Cluster International released on
PRNewire (2004). The results of this survey indicated that “85% of the executives were
concerned about legislation or political pressure against outsourcing, while 84% were
worried about backlash from employees. And 62% said they were worried about negative
corporate publicity that could be created by outsourcing.”
Consistent with the surveyed CEOs’ concerns over legislation or political
pressure, President Obama said in his 2012 State of the Union address “if you’re a
business that wants to outsource jobs, you shouldn’t get a tax deduction for doing it…no
American company should be able to avoid paying its fair share of taxes by moving jobs
and profits overseas. From now on, every multinational company should have to pay a
basic minimum tax. And every penny should go towards lowering taxes for companies
that choose to stay here and hire here… It’s time to stop rewarding businesses that ship
jobs overseas, and start rewarding companies that create jobs right here in America.”
Anecdotal evidence is also consistent with concerns expressed over employee
9 backlash. For example, based on an article “IBM layoffs incite backlash” on Network
World (2009), when IBM revealed that it would shed some 5,000 North American jobs
and potentially send more positions overseas, it “has stirred up some bad sentiment
toward Big Blue as the economy continues to languish.” Further evidence of this concern
is provided in a Washington Post article quoting Lee Conrad, national coordinator for
Alliance@IBM, a group trying to unionize IBM workers that is concerned about IBM’s
decision to stop disclosing this information, as saying that ‘IBM can do as it wishes, and
the rest of us have to guess.’
In addition, evidence consistent with multinationals’ concerns about negative
publicity due to offshoring is provided in the Washington Post quote of Jeff Immelt, GE
CEO and President Obama’s job council head, who said “firms should be ready to answer
questions from the public” and that “if you want to be an admired company, you better
know, you better have accountability, and you better think through where the jobs are.”
Although much of the focus has been on potential payments that might be
required from offshoring firms, there have also been some calls to provide payments to
firms that commit to reducing offshoring. Political columnist Harold Myerson argues:
In an impressive display of industrial-strength chutzpah, corporate America is now
demanding lower tax rates even as it daily disinvests in its home country. Worse yet,
the new Congress seems likely to grant its wish—lowering taxes indiscriminately on
those rare corporations that invest in America and on those more numerous
corporations that abandon it. Is it too much to ask of the government that it
discriminate between friend and foe? How about rewarding companies that pledge,
as Siemens, Daimler, and BMW have in their own country, to keep or create a
specified number of highly skilled jobs here at home? How about mandating, as
Germany has, that companies put worker representatives on their boards, as a
means of slowing corporate flight? America’s economic decline is at bottom
institutional, and reversing it requires institutional solutions that change the
structure of American corporations.
Consistent with this call for rewards for firms that hire domestic employees, in September
10 of 2010 the Creating American Jobs and Ending Offshoring Act was introduced in the
U.S. Congress. Although not passed by the Senate, the proposed bill would have given
firms a two-year payroll tax holiday, reducing the amount of Social Security taxes they
would have to pay, for new employees who replace workers doing similar jobs overseas.5
2.3.1 Proposed Mandatory Disclosure Requirements
Congress has also considered the possibility of enacting a mandatory outsourcing
disclosure requirement in an effort to lower domestic unemployment. The Washington
Post reported on February 1, 2012 that U.S. House Representative Gary Peters (D-Mich.)
introduced a bill requiring U.S. firms with revenues over $1 billion to disclose how many
of their jobs are based on U.S. soil and how many are based abroad, and to track the
increase or decrease of these figures from the previous year, in an attempt to shed light on
the number of American jobs being outsourced. The article goes on to say that “such data
is closely guarded by some of the country’s biggest multinationals, including Pfizer,
Apple and IBM. Public filings by these firms disclose their total number of employees,
but don’t specify where those jobs are located.” The purpose of this bill is to “incentivize
U.S. companies to keep jobs in the U.S.” by allowing lawmakers and the public to use
geographical employment disclosures to track which firms are adding U.S. jobs.
A second proposed mandatory disclosure bill, the “Stop Outsourcing and Create
American Jobs Act of 2010,” was introduced by Rep. Jerry Cranwell (D., Calif.) on June
29, 2010. The bill would require all Federal government departments and agencies to
request each bidder for a Federal contract to provide information regarding whether the
offeror engaged in “outsourcing” during the fiscal year before awarding the contract. The
5 It
also would have revoked the provisions of the tax code that Democrats say encourage firms to
outsource their work force.
11 bill attacks the restructuring of government suppliers who terminate the employment of a
United States worker from a job and hire (or contract for) the same job to be performed in
a foreign country. The bill would punish bidders by debarment from future Federal
government contracts and impose criminal fraud penalties under 18 U.S.C. 1001 (false
statements to the Government).
3. Research Design
3.1 U.S. Employment Model
Alejandro et al. (2011) report that previous studies examining the relation
between multinationals’ foreign activities and domestic employment have found evidence
of both complementarity and substitution between international activity and home
country employment. For example, Desai, Foley, and Hines (2008) find that U.S.
employment grows with foreign employment, while Brainard and Riker (1997) find that
employment at foreign affiliates substitutes for U.S. employment. Based on Alejandro et
al.’s (2011) model examining how U.S. employment varies with U.S. and foreign sales
for aggregate service industry data, we investigate whether the relation between foreign
sales and U.S. employment differs for multinationals that voluntarily disclose U.S
employment versus those that choose not to disclose this information.
If multinationals are growing or shrinking their workforces both at home and
abroad then we would expect the number of U.S. employees to be positively related to
foreign activity. This finding would be consistent with multinationals’ foreign activities
serving market demand for U.S. goods and services. On the other hand, if the number of
U.S. employees is negatively associated with foreign sales, this would be consistent with
multinationals increasing the number of foreign employees while cutting their U.S.
12 employment. This possibility might arise when foreign activities reflect that use of
cheaper labor abroad. To test these arguments, we estimate the following OLS model,
using aggregate data.
U.S. Emp = β0 + β1*U.S. Sales + β2* Foreign Sales + ε
(1)
Where:6
U.S. Emp:
number of employees in the U.S. (COMPUSTAT item “emps” for
segment identified as ‘2’),
U.S. Sales:
U.S. revenues (COMPUSTAT item “sales” for segment identified as ‘2’)
deflated by the CPI to year 2000 levels,
Foreign Sales: foreign sales (COMPUSTAT ‘Revt’ – U.S. Sales) deflated by the CPI to
year 2000 levels.
Further, based on the argument that the threat of employee or government actions
may make firms whose foreign activities are associated with fewer U.S. jobs less likely to
disclose their domestic versus foreign employment, we expect the coefficient on foreign
sales to be negative and lower for firms that choose not to disclose geographical
employment than for disclosing firms. For non-disclosers, we employ the imputation
technique to construct their U.S. employment as discussed in the following sections.
3.2 Addressing Missing Data Bias
Accounting and finance research often relies on datasets where data is missing for
one or more variables for some sample observations. A typical approach taken to address
missing data is to drop these observations from the analysis; however, the
appropriateness of this approach depends on the pattern of missing data as well as the
reason why the data is missing.
Imbens and Wooldridge (2007) detail the conditions under which ignoring
observations does not cause biased inferences. Specifically, they note that ignoring
6
While estimating the model in logs produces similar statistical inferences we estimate the model in nonlogarithmic form to facilitate the economic interpretation of the coefficients.
13 missing data will not produce biased estimates when E(y|x,s) = E(y|x), where y is the
dependent variable , x is the independent variable, and s is a binary selection indicator for
missing data. However, if this condition is violated, then simply ignoring observations is
not appropriate and produces biased inferences. In this case, imputation techniques may
be used to correct this bias.
Data imputation methods are appropriate when the selection probability
conditional on the independent variables is the same as the selection probability
conditional on both the dependent and independent variables, i.e. P(s=1|y,x) = P(s =1|x).
This assumption is called unconfoundedness, selection on observables, or conditional
ignorability. The imputation approach uses non-missing observations to impute missing
data and then analyzes the resulting complete dataset. When data is missing only for the
dependent variable, the imputation method is appropriate because the unconfoundedness
assumption in this case is equivalent to missing at random (MAR) assumption required
by most imputation models.
3.2.1 Imputation Method
Although our reported employment models are estimated using aggregate data to
allow for statistical comparisons with the aggregate BEA data, we impute missing data at
the firm level. We also estimate the employment models for the COMPUSTAT sample
using firm level data to ensure that our results do not differ due to the aggregation and to
allow for the inclusion of control variables in the employment model.7
Depending on the missing data pattern, either parametric or nonparametric
methods of imputation are suitable. The mechanism for multiple imputations depends on
the missing data pattern. For monotone missing data, where there is an ordering of the
7 The results of using the firm level data for analysis are similar to that of using aggregate data. 14 missing data such that if Yj is missing then all variables, where Yk, k>j are also missing,
either parametric or nonparametric approaches can be used. For arbitrary missing data
patterns Markov Chain Monte Carlo methods can be used, which does not require a
restrictive data pattern. We adopt the Markov Chain Monte Carlo (MCMC) approach in
imputing the missing data as our primary imputation method. The MCMC method
repeatedly samples from probability distributions, each of which is determined by the
previous step and by some random noise approximating the true sampling distribution.
To match the normally distributed data assumption for MCMC estimation, we take logs
of employment in the imputation process. Alternatively, we use a nonparametric
propensity imputation method that does not require a distributional assumption but
requires a monotone missing data pattern as a robustness check.8
Generally in the imputation model we should include as many variables as
possible (Rubin 1996). However, Barnard and Meng (1999) argue it is important to keep
the number of variables in control. On a related note, Schafe (1997) and van Buuren et al.
(1999) argue that imputation models should include variables that are correlated with the
imputed variable (i.e., determinants of domestic or foreign employment in our context),
and variables that are associated with the missingness of the imputed variable (i.e.,
determinants of the geographical employment disclosure). Based on Schafe (1997) and
Buuren et al.’s suggestion, the variables included in our imputation model include both
the variables in the employment model and the variables in our geographical segment
employment disclosure choice model, which is discussed in section 3.3.
3.2.2 Multiple Imputation
8
The inferences from our employment model do not differ for these two imputation methods. 15 Imputing a single value for the missing data can address the bias caused by
selection that is ignorable after conditioning on covariates. However, Little and Rubin
(2002) argue that the variance of the estimates caused by the single imputation method is
biased downward. To address the bias in the variance they suggest adding a random error
term to the imputed value and use an averaging technique on multiple imputed values to
derive an appropriate sampling error.
The number of imputations required to obtain an efficient estimator will depend
on the extent to which the data is missing. Specifically, Rubin (1987) states that the
relative efficiency (RE) of using m imputations depends on the fraction of missing data λ
based on the following formula: RE= (1+ λ/m)-1. This formula suggests that only 2
imputations are needed to achieve 95% efficiency when only 10% of the data is missing
versus 19 imputations to achieve the same relative efficiency when 90% of the data is
missing.9 The m completed datasets created by imputations are analyzed using the same
procedures that would be used in the absence of missing data. The results from the
analyses of the m datasets are then combined to produce unbiased variance estimates.
3.3 Geographical Segment Employment Disclosure Choice
To understand how concerns over government and employee actions affect firms’
voluntary disclosure choices, we estimate a prediction model of the determinants of the
choice to disclose the breakout between domestic versus foreign employees.
Consistent with Ramanna and Roychowdhury’s (2010) finding that outsourcing
publicity affects firms accounting discretion, we predict that outsourcing publicity may
be related to firms’ voluntary domestic versus foreign headcount disclosures. Assuming
9
We use 20 imputations in our analysis, which, based on Rubin (1987), should be relatively efficient given
that in our sample,19.25% of firms disclose geographical employment, ,
16 that geographical employment disclosures are informative about the extent rather than the
mere existence of outsourcing, we expect that firms that withhold this information would
be more likely to be subject to negative press publicity about their outsourcing policies.
This is especially likely if it is suspected that firms withhold this information when
foreign activities are associated with a decline in U.S. employment. Alternatively,
negative publicity might induce firms to disclose if their foreign activities complement
their U.S activities and generate additional U.S. jobs. We measure the press outsourcing
publicity as an indicator that equals 1 if the firm was one of the top 100 firms in the
number of press mentions of the firm’s name and the words “outsource,” “outsourcer,” or
“outsourcing” during any year from 2000-2009.
We expect that firms who are concerned about government actions related to
unemployment and offshoring would be more likely to lobby about bills addressing these
issues. For example, we expect these firms are more likely to lobby about bills such as
the “Hiring Incentives to Restore Employment Act,” which provides tax benefits to
employers who hire previously unemployed workers, and the “Employee Free Choice
Act, 2009,” which enables employees to form, join, or assist labor organizations to
provide for mandatory injunctions for unfair labor practices during organizing efforts. We
argue that firms that hire more domestically are more likely to benefit from these bills,
and therefore we expect these firms are more likely both to lobby and to disclose
information about their domestic versus foreign employment. To capture this propensity to lobby, we use an indicator that equals 1 if the firm lobbied on either the
“Hiring Incentives to Restore Employment Act” or the “Employee Free Choice Act,
2009;” 0 otherwise.
17 The negative reaction to increased foreign employment may also be lessened if
the foreign employees are hired by a relatively autonomous foreign subsidiary. Following
Robinson and Stocken (2011), we measure foreign subsidiary autonomy using the
subsidiaries’ functional currency designated in their financial reports. If foreign
subsidiaries use their local currency as the functional currency, then parents will have to
include the currency adjustment gain or loss in their comprehensive income. Therefore,
we use an indicator variable that equals 1 if firm has non-missing value on
COMPUSTAT “recta”; 0 otherwise. We expect firms will be more likely to disclose their
geographic employment when the foreign currency is the GAAP functional currency.
We further argue that potential government and employee actions designed to reduce
offshoring employment by U.S. firms may depend on the reason for and extent of the
foreign activities. The Council on Foreign Relations notes that “Thea Lee, policy director
for the AFL-CIO, says much of the economic data supporting the link between overseas
investment and domestic job growth fails to distinguish between foreign investment used
to serve market demand for U.S. goods and services and foreign investment used to buy
cheaper labor abroad.” That is, some firms hire foreign workers to sell products produced
in the U.S., but others hire foreign workers to shift production from the U.S. We capture
foreign investment designated to buy cheaper labor abroad using an indicator variable for
firms that operate in countries and areas with wages lower than U.S. wages. We expect
firms that operate in these areas and countries to be less willing to disclose the number of
employees by geographical segment because the foreign activities of these firms are more
likely to reduce U.S. jobs. Further, controlling for overall growth, measured as total
revenue growth and an indicator for merger activity, we expect firms with higher foreign
18 growth, indicated by the change in the number of geographical segments, to be less likely
to voluntarily disclose the breakout of their employment.
We include several other control variables that we expect might be related to the
effect of foreign activities on U.S. employment. Based on Pfaffermayr’s (1997) argument
that domestic and foreign activities are complements for vertically integrated
multinationals we expect the effect of foreign activities on U.S. employment and the
likelihood of disclosure of geographical employment to differ based on the extent of
vertical integration. We include two measures
of vertical integration suggested by
Adelman (1955). Specifically, we use the ratio of Income to Sales and the ratio of
Inventory to Sales as the proxies for vertical integration. He argues that the farther the
firm carries its processing of the product the larger will be the value added by the firm, or
income originating within the firm. He further argues that the more successive processes
performed the higher will be the Inventory to Sales ratio. We also control for the
existence of pensions or post-retirement benefits on the disclosure decision, since we
expect the incentive to hire U.S. employees to differ based on these benefits. We also
expect that firms that pay higher foreign taxes would be more likely to voluntarily
disclose U.S. versus foreign employment, captured by Repatriation defined as -1*
(marginal tax rate minus either zero or foreign effective tax rate, whichever higher).
In addition, we control for whether firms disclose geographical operating profits
and geographical capital expenditures to ensure that we are not capturing the overall
geographical disclosure tendencies. We also incorporate a variable indicating whether the
firm discloses information about employee unionization to control for a general tendency
to provide employee information.
19 Further, we control for variables in prior literature that are associated with
voluntary disclosure, such as use of a Big 4 auditor, firm age, firm size, leverage, and
tangibility. We also control for fixed Fama/French industry and year effects.
To test the above arguments, we estimate firms’ geographical employment
disclosure choice using the following Logit model:
Disclose = β0 + β1*Lobby + β2*Press + β3*Autonomy + β4* Low Wage + β5*∆Rev
+ β6*Merge + β7* ∆#Seg + β8*Income/Sales + β9*Inventory/Sales
+ β10* Pension + β11*Repatriation + Σβc* Other Controls
+ ΣβI* IFE +Σ βy* YFE +ε
(2)
Where:
Disclose:
An indicator that equals 1 if the firm provides geographical segment
employee data; 0 otherwise.
Lobby:
An indicator that equals 1 if the firm lobbied on either the “Hiring
Incentives to Restore Employment Act” or the “Employee Free Choice
Act, 2009;” 0 otherwise.
Press:
An indicator that equals 1 if the firm was one of the top 100 firms
based on number of press mentions of the firm’s name and the words
“outsource,” “outsourcer,” or “outsourcing” during any year from
2000-2009.
Autonomy:
An indicator variable that equals 1 if firm has non-missing value on
COMPUSTAT “recta”; 0 otherwise.
Low Wage: An indicator variable that equals 1 if the firm identifies its segments in
any of the following areas or countries where the wage is constantly
lower that in the U.S.: Asia (including China, India, Malaysia, etc.,
Latin America, Africa, Middle East, Caribbean, Mediterranean, Italy or
Spain; 0 otherwise.
∆Rev:
Growth of total revenues, measured as the change in revenue
(Compustat “revt”) divided by lagged revenue.
Merge:
An indicator variable that equals 1 if the growth of total assets
(Compustat “at”) is greater than 10%; 0 otherwise.
∆#Seg:
Annual change in the number of segments reported by the firm.
Income/Sales: Quintile ranking of income (Compustat “ebit”) divided by total
revenues (Compustat “revt”).
Inventory/Sales: Quintile ranking of inventory (Compustat “invt”) divided by total
revenues (Compustat “revt”).
Pension:
An indicator variable that equals 1 if the firm has either pension
employer contribution (Compustat “pbec”) or postretirement service
cost (Compustat “prc”); 0 otherwise.
20 Repatriation: Measured as -1* (marginal tax rate minus either zero or foreign effective
tax rate, whichever higher), where foreign tax rate is defined as
Compustat “txfo” over “pifo”)
Other Control Variables:
OP Dis:
An indicator variable that equals 1 if the firm discloses geographic
operating profit (Compustat “ops”); 0 otherwise.
Capx Dis:
An indicator variable that equals 1 if the firm discloses geographic
capital expenditure (Compustat “capxs”); 0 otherwise.
Union Dis:
An indicator variable that equals 1 if the firm has disclosed union
information in their 10Ks.
Big4 :
An indicator variable that equals 1 if the firm uses one of the big 4
auditors:PWC, Earnst & Young, Deloitte and KPMG; 0 otherwise.
US Sale:
Natural log of sales made in the U.S. (Compustat “sales”).
Foreign Sale: Natural of log of foreign sales (Compustat “revt”-“sales”).
Size:
Natural log of total assets (Compustat “at”).
EMP:
Total number of employees (Compustat “emp”).
Age:
Number of years that the firm has been covered by Compustat up to the
data date.
Lev:
Ratio of total debt (Compustat “dlc” + “dltt”) to total assets (Compustat
“at”).
PPE:
Ratio of property, plants and equipment (Compustat “ppent”) to the
number of employees (Compustat “emp”) divided by 1,000.
CFO:
Ratio of operating cash flow (Compustat “oancf”) to lagged total assets
(Compustat “at”).
4. Data and Descriptive Statistics
A sample of U.S. firms that disclose geographical segment sales data for two or
more segments is drawn from the Historical Segment database within COMPUSTAT for
the period from 2000 to 2009.10 This results in a sample of 20,836 firm year observations
for 3,760 separate firms. After requiring non-missing COMPUSTAT test and control
variables, we end up having 19,010 firm-year observations for 3,365 firms. Of these,
3,574 observations disclose the number of employees by segment and 15,436 do not.11
10
We delete observations if any segment sales are greater than the total sales or if segment sales are less
than zero. We also delete observations if the countries or areas where the foreign segments are located are missing. 11 COMPUSTAT reports geographical segments considered to be the primary segments as operating segments. Less than 3% of the sample firms had primary geographical segments. 21 Table 1 provides descriptive statistics for domestic versus foreign employment
and sales for the aggregate BEA data by year. Aggregate domestic employment declined
over the 10 year period, while aggregate foreign employment and sales grew significantly
over the decade. Figure 1 provides graphical depictions of these trends.
Domestic versus foreign employment trends for our COMPUSTAT sample firms
partitioned by whether they provide geographical employment disclosures are provided in
Table 2. For those that provided geographical employment disclosures, both aggregate
domestic and aggregate foreign employment grew over the decade. In contrast, the
imputed employment data for those who did not disclose indicate a decline in aggregate
domestic employment and flat aggregate foreign employment.12 This finding is consistent
with the notion that firms more likely to be subject to political and employee pressure are
less likely to disclose geographical employment. Figures 2-3 provide graphical depictions
of these trends.
Before we discuss the imputation results, it is important to point out that data
imputation methods require common support for model variables between those with
missing and available data. We provide evidence about this assumption in Appendix A,
which provides descriptive statistics of firm characteristics partitioned by whether firms
provide geographical employment disclosures. The table also tests the differences in
means of model variables between the two samples and provides the normalized
differences that can be used to assess the adequacy of the overlap between the covariates
for the two groups. We find significant differences in most firm characteristics between
12 We aggregate the overall and imputed domestic employment of COMPUSTAT firms, because we are trying to compare the pattern of aggregate BEA data to that of disclosers versus non-­‐disclosers of geographical employment. One advantage of using aggregate imputed data for our analyses is that the measurement error may cancel out and be less of a concern. 22 the two groups using the traditional t-statistics, which is ideal for these variables to be
able to explain the variation in the employment disclosure choice or employment
decisions. On the other hand, none of the normalized differences exceeds the .25
threshold suggested by Wooldridge (2012), indicating adequate overlap, and therefore
distributions with common support, for all of the characteristics for the imputation
models.
5. Regression Results
Table 3 provides the results of our regression models of employment on aggregate
domestic and foreign sales for the BEA data, the aggregate overall COMPUSTAT data,
and the aggregate COMPUSTAT discloser sample. In Panel A the dependent variable is
aggregate worldwide employment, which is a required disclosure for all SEC filers, in
Panel B the dependent variable is aggregate U.S. employment, which includes imputed
employment numbers for non-disclosers.
The results in Panel A indicate a significantly positive association between
foreign sales and worldwide employment for the sample of COMPUSTAT firms that
disclose geographical segment employment in contrast to a negative (though
insignificant) association between foreign sales and total employment for both the
aggregate BEA data and the aggregate overall COMPUSTAT data. In addition, the
coefficient on foreign sales for the overall COMPUSTAT sample is significantly
different from the coefficient for the sample of firms that disclose geographical segment
data, but not significantly different from the coefficient for the BEA data. The lack of
significant difference between the aggregate overall COMPUSTAT and BEA data
suggests that the difference in the composition of these two samples does not affect our
23 inferences. The significant difference between the aggregate overall COMPUSAT data
and the disclosers suggests that firms are more willing to disclose the geographical
segment data when their foreign activities are positively associated with the number of
total employees.
Panel B shows that the negative coefficients on foreign sales for both the
aggregate BEA and overall COMPUSTAT data more than double compared to Panel A
and become more significantly different from zero, although the coefficients for the BEA
and aggregate COMPUSTAT sample remain insignificantly different from each other.
These findings suggest a substitution relation between international activity and domestic
employment when examining the overall samples. The lack of significant difference in
the coefficients between these two samples using either reported employees or imputed
domestic employees suggests that the data imputation did not provide biased estimates
and provides some assurance of the accuracy of the data imputation technique. The
coefficient on foreign sales for the COMPUSTAT discloser sample is smaller than that
for worldwide employment, though remains significantly positive and the coefficient
remains significantly greater than for the overall COMPUSTAT sample. This finding is
again consistent with the idea that firms are more willing to disclose geographical
segment data when their foreign activities are positively associated with the number of
domestic employees. These findings along with those in Panel A, suggest that firms may
be concerned about the potential costs that the government or employees may impose on
them for the negative effect of foreign activities on U.S. employment, and therefore
choose not to disclose geographical employment.
24 Table 4 provides the results of our geographical employment disclosure choice
model. We find a significantly positive coefficient on the Lobby variable suggesting that
firms concerned with labor and employment legislation are more likely to disclose their
geographical headcount. The coefficient on the Press variable is significantly negative
suggesting that firms that withhold this information may be more likely to be subject to
negative publicity about their outsourcing policies. We also find that when the foreign
subsidiaries are relatively more autonomous we find that it is more likely for the firm to
disclose. In addition, we find a negative association between low wage and disclosure,
suggesting that firms are less willing to disclose geographical employment when their
foreign activities represent a movement of jobs to low wage countries and areas.
Although we find no significant association between the disclosure decision and overall
firm growth, measured either by revenue growth or merger activity, we find that firms
expanding their foreign operations, captured by an increase in the number of
geographical segments, are less likely to disclose. These associations of disclosure with
low wage and foreign growth are consistent with firms being less willing to disclose
when the effects of foreign activities on U.S. employees are more negative.13
In addition to our test variables, we find that several of our control variables are
significantly correlated with the disclosure choice. We find a significantly positive
coefficient on our first vertical integration measure, Income /Sales, consistent with firms
being more willing to disclose geographical employment numbers when foreign activities
are complementary with domestic employment. Furthermore, consistent with our
prediction, we find that firms that provide pension or post-retirement benefits are more
13
The economic magnitudes of these determinants are also large relative to an average disclosure rate less
than 20%. E.g., firms operating in low wage areas or countries are 3.8% less likely to disclose geographical
employment and firms providing pension and post-retirement benefits are 3.5% more likely to disclose.
25 likely to disclose geographical employment. We also find that firms are less likely to
disclose when the firm age is greater. Finally, firms are more likely to disclose
geographical employment data when they also disclose geographical operating
performance and capital expenditures.
5.2 Validation of Imputation Methodology
To validate our imputation technique, we first compare the sum of imputed
domestic and international headcounts for firms that do not disclose segment employment
by geography to the total employment, which these firms do disclose. Because we
separately impute the numbers of domestic and foreign employees for firms that do not
disclose employment geographically, if our multiple imputation technique generates
accurate numbers, then the sum of these two imputed numbers should be close to the total
employment. The results of this comparison suggest that the imputation technique seems
to be reasonably accurate. The mean of non-disclosers’ total employment and the sum of
imputed domestic and foreign employment are 10,565 and 10,264 people, respectively.
The Pearson (Spearman) correlation between total employment and the sum of imputed
employment is 94% (99%). We also find the high correlations hold across the propensity
to disclose geographical employment.14 While this approach does not speak to how well
the imputed numbers map into the actual domestic versus foreign employment
breakdown, it provides some confidence that the imputation technique generates
reasonable aggregates.15
14
When we compare the actual total employment and the sum of imputed domestic and foreign
employment across the quintiles based on the propensity to disclose using Equation (2), we continue to find
high correlations; for example, the Spearman correlations are constantly between 0.97 and 0.99.
15
To address the concern that the total employment in the imputation model drives this convergence
between total employment and the sum of imputed numbers, we remove total employment from the model,
and find similar results, suggesting this does not drive the results.
26 Second, we use an out-of-the-sample prediction technique to validate the
imputation method. Specifically, we randomly assign half of the sample firms that
disclose geographical employment as pseudo-non-disclosers and then run the imputation
model as described above to generate imputed domestic and foreign headcounts for these
firms as if they did not disclose geographical employment. Using this approach, we are
able to validate the imputation technique by comparing the imputed headcounts with the
actual employment. The Pearson (Spearman) correlation between the imputed domestic
employment and the actual domestic employment is 92% (96%); the Pearson (Spearman)
correlation between the imputed foreign employment and the actual foreign employment
is 94% (96%). In addition, the mean imputed (actual) domestic employment for these
pseudo-non-disclosers is 5,851 (6,107) people while the mean imputed foreign
employment is 5,497 (5,480) people. These statistics suggest that imputed numbers are
reasonably close to the actual headcounts. More importantly, using the imputed domestic
employment to run Equation (1) generates very similar results to using the actual
numbers. For example, the coefficients on domestic sales and foreign sales using imputed
(actual) domestic headcounts as the dependent variable are 0.0020 (0.0014) and -0.0002
(0.0000), respectively. 16 One caveat to this approach, however, is that the random
assignment of pseudo-non-disclosers does not capture the geographical disclosure choice
made by firms.
To partially address the issue that random assignment may not capture the
disclosure choice, we assign half of the sample firms that disclose geographical
employment as pseudo-non-disclosers based on the propensity to disclose using Equation
(2). Specifically, disclosing firms whose propensity to disclose is lower than the sample
16
None of the difference in coefficient between using imputed vs. actual headcounts is significant.
27 median are assigned as pseudo-non-disclosers. Following the same procedure, we find
very similar results to the random assignment. For example, the Spearman correlations
between actual domestic (foreign) headcounts and imputed domestic (foreign)
headcounts for these pseudo-non-disclosers are 0.96 (0.93). In addition, the coefficients
on domestic sales and foreign sales using imputed domestic employment (actual
employment) as the dependent variable are 0.0030 (0.0028) and -0.0003 (0.0005),
respectively. 17 We further address the selection issue by comparing our imputed
domestic numbers to those provided in Fortune Magazine’s annual “Best 100 Companies
to Work for” list. This list includes the non-disclosers in our sample (249 firm-years), so
we are able to compare the actual domestic employment numbers acquired via Fortune’s
surveys of these companies with the imputed numbers for this small sample.
The
Spearman correlations between actual domestic headcounts and imputed domestic
headcounts for these non-disclosers are 0.94. While inclusion in this best company list is
not random, the selection process likely differs from the disclosure choice providing
further validation of our imputation estimation.
5.3 Robustness Tests
To address potential concerns about the stickiness of the disclosure choice we
estimate our disclosure choice model separately for two subsamples. Specifically, we
separately examine the choice to discontinue disclosure for multinationals that previously
disclosed and the choice to initiate disclosure for multinationals that had previously not
disclosed. The predicted signs of the coefficients for the decision to stop disclosing are
the opposite of those for the decision to start disclosing. The inferences from these
17 Again,
none of the difference in coefficient between using imputed vs. actual employment is significant. 28 models are largely the same as those from the model reported in Table 4 except for the
low wage variable, which is insignificant in both the stop and start models.
We address concerns about the indirect nature of the explanatory variables that
may capture correlated omitted variables in our prediction model by conducting a
falsification test on business segment employment disclosures, instead of geographical
segment. If our arguments are valid and our test variables do not capture omitted
variables, we would not expect our test variables related to likely government and
employee actions due to foreign activity to be related to the business segment
employment disclosure decision. However, if these proxies are capturing other omitted
variables that are generally related to employment disclosures then we would expect that
they predict both business and geographical disclosure choices. We do not observe
significant coefficients on our proxies for lobbying, press publicity about outsourcing, or
subsidiary autonomy, nor do we find significant coefficients on the change in
geographical segments, suggesting that these variables are capturing something specific
to the geographical segment disclosure choice. The coefficients on the pension and low
wage variables are significant but have opposite signs in the business segment versus the
geographical segment disclosure models. These findings suggest that our test variables do
not capture the general geographical disclosure propensity; instead, they are more likely
to proxy for the potential for government and employee actions in response to firms’
geographical employment disclosures.
6. Conclusions
We consider whether the voluntary choice by U.S. multinational firms to disclose
geographical employment data is affected by potential government or employee actions.
29 Specifically, we compare the associations between aggregate U.S. employment and U.S.
and foreign sales reported in the BEA survey data to those for aggregate COMPUSTAT
data for those that voluntarily disclose that information and to those for all
COMPUSTAT firms by incorporating imputed employment data. Consistent with a
concern for potential government or employee actions, we find that U.S. employment is
positively affected by foreign activity for those that voluntarily disclose geographical
employment, but that foreign activity negatively affects U.S. employment for those who
choose not to disclose.
To further explore the link between voluntary disclosure and potential
government and employee actions, we also examine the characteristics of U.S.
multinational firms that choose to disclose the number of employees by geographical
segment. Our findings that firms that are more concerned about employment legislation
and that have more autonomous foreign subsidiaries are more likely to disclose and firms
that have attracted more publicity about outsourcing, operating in low wage areas and
expanding their foreign operations are less likely to disclose provide further support for
less voluntary disclosure when the effect of foreign activities on U.S. employment is
more negative.
Our paper contributes to the corporate accountability reporting literature by
focusing on the debate over the association between U.S. employment and U.S.
multinationals’ foreign activities and over required disclosure of the number of U.S.
employees. We show that there is a systematic bias in the voluntary disclosures towards
firms with a positive association between foreign sales and U.S. employment numbers
and with both increased domestic and foreign employment over the past decade.
30 We also contribute to the more general disclosure literature by highlighting the
use of Bayesian bootstrapping imputation on analyses of voluntary disclosures in SEC
filings, in a setting that provides the potential to partially validate these estimations.
Finally, our study suggests that researchers need to be cautious about whether missing
data can be ignored when analyzing variables that are sometimes missing. In our setting,
the relation between U.S. employees and foreign sales depends on the disclosure decision
and therefore ignoring missing data may generate inaccurate inferences.
31 Appendix A
Firms that disclose segment Firms that do not disclose Difference Normalized
employment
segment employment
between
difference
means
Variables
Mean
(𝑋! )
Lobby
0.057
Press
0.045
Autonomy
0.850
Low Wage
0.476
∆Rev
0.170
Merge
0.366
∆#Seg
0.082
Income/Sales
2.111
Inventory/Sales 2.039
Pension
0.395
Repatriation
-0.092
OP Dis
0.219
Capx Dis
0.116
Union Dis
0.371
Big4
0.818
US Sale
5.331
Foreign Sale
4.796
Size
6.168
Emp
0.577
Age
20.167
Lev
0.240
PPE
0.119
CFO
0.039
N
Median
Standard Mean Median Standard
Deviation (𝑋! )
Deviation
(S1)
(S0)
0.000
0.231
0.044
0.000
0.206
0.000
0.207
0.059
0.000
0.235
1.000
0.357
0.757
1.000
0.429
0.000
0.499
0.466
0.000
0.499
0.066
2.510
0.218
0.065
6.021
0.000
0.482
0.346
0.000
0.476
0.000
0.729
0.135
0.000
0.875
2.000
1.421
1.974
2.000
1.411
2.000
1.424
1.991
2.000
1.412
0.000
0.489
0.360
0.000
0.480
-0.118
0.229
-0.109 -0.134
0.228
0.000
0.414
0.125
0.000
0.331
0.000
0.320
0.062
0.000
0.241
0.000
0.483
0.314
0.000
0.464
1.000
0.386
0.834
1.000
0.372
5.321
2.290
5.415
5.546
2.324
4.721
2.324
4.474
4.575
2.488
6.161
2.153
6.091
6.093
2.253
0.469
1.970
0.469
0.510
2.077
14.000
15.185 21.134 15.000
15.390
0.169
0.331
0.253
0.165
1.523
0.033
0.578
0.296
0.033
5.577
0.072
0.241
0.022
0.070
0.540
3,574
15,436
t-statistics
(∆𝑥=
3.14***
-3.29***
12.11***
1.07
-0.47
2.26**
-3.34***
5.24***
1.84*
3.82***
4.21***
14.63***
11.35***
6.58***
-2.30**
-1.97**
7.07***
1.86*
2.81***
-3.42***
-0.54
-1.90*
1.80*
!! !!!
0.042
-0.045
0.167
0.014
-0.007
0.030
-0.047
0.069
0.024
0.051
0.053
0.177
0.134
0.085
-0.030
-0.026
0.095
0.025
0.038
-0.045
-0.008
-0.032
0.029
Descriptive Statistics Partitioned by Segment Employment Disclosure
Note: ***, **, and * represent the 1%, 5% and 10% significance levels, respectively.
Variable Definitions:
Lobby: An indicator that equals 1 if the firm lobbied on either the “Hiring Incentives to
Restore Employment Act” or the “Employee Free Choice Act, 2009;” 0
otherwise.
Press: An indicator that equals 1 if the firm was one of the top 100 firms based on
number of press mentions of the firm’s name and the words “outsource,”
“outsourcer,” or “outsourcing” during any year from 2000-2009.
Autonomy: An indicator variable that equals 1 if firm has non-missing value on
COMPUSTAT “recta”; 0 otherwise.
32 !!! !!!!
)
Low Wage: An indicator variable that equals 1 if the firm identifies its segments in any of
the following areas or countries where the wage is constantly lower that in the
U.S.: Asia (including China, India, Malaysia, etc., Latin America, Africa, Middle
East, Caribbean, Mediterranean, Italy or Spain; 0 otherwise.
∆Rev: Growth of total revenues, measured as the change in revenue (Compustat “revt”)
divided by lagged revenue.
Merge: An indicator variable that equals 1 if the growth of total assets (Compustat “at”)
is greater than 10%; 0 otherwise.
∆#Seg: Annual change in the number of segments reported by the firm.
Income/Sales: Quintile ranking of income (Compustat “ebit”) divided by total revenues
(Compustat “revt”).
Inventory/Sales: Quintile ranking of inventory (Compustat “invt”) divided by total
revenues (Compustat “revt”).
Pension: An indicator variable that equals 1 if the firm has either pension employer
contribution (Compustat “pbec”) or postretirement service cost (Compustat
“prc”); 0 otherwise.
Repatriation: Measured as -1* (marginal tax rate minus either zero or foreign effective
tax rate, whichever higher), where foreign tax rate is defined as Compustat “txfo”
over “pifo”).
OP Dis: An indicator variable that equals 1 if the firm discloses segment operating profit
(Compustat “ops”); 0 otherwise.
Capx Dis: An indicator variable that equals 1 if the firm discloses segment capital
expenditure (Compustat “capxs”); 0 otherwise.
Union Dis: An indicator variable that equals 1 if the firm has disclosed union information
in their 10Ks.
Big4: An indicator variable that equals 1 if the firm uses one of the big 4 auditors: PWC,
Earnst & Young, Deloitte and KPMG; 0 otherwise.
US Sale: Natural log of sales made in the U.S. (Compustat “sales”).
Foreign Sale: Natural of log of foreign sales (Compustat “revt”-“sales”).
Size: Natural log of total assets (Compustat “at”).
EMP: Total number of employees (Compustat “emp”).
Age: Number of years that the firm has been covered by Compustat up to the data date.
Lev: Ratio of total debt (Compustat “dlc” + “dltt”) to total assets (Compustat “at”).
PPE: Ratio of property, plants and equipment (Compustat “ppent”) to the number of
employees (Compustat “emp”) divided by 1,000.
CFO: Ratio of operating cash flow (Compustat “oancf”) to lagged total assets
(Compustat “at”).
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36 Table 1: Descriptive Statistics of BEA Data (in thousands)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Domestic
Employment
23,885.2
22,735.1
22,117.6
21,104.8
21,176.5
21,472.0
21,615.8
21,548.9
20,901.0
22,211.4
% change
compared
to 2000
0.00%
-4.82%
-7.40%
-11.64%
-11.34%
-10.10%
-9.50%
-9.78%
-12.49%
-7.01%
Foreign
Employment
8,171.4
8,194.1
8,255.6
8,242.2
8,666.7
9,101.3
9,617.4
10,012.1
10,025.8
10,395.6
% change
compared
to 2000
0.00%
0.28%
1.03%
0.87%
6.06%
11.38%
17.70%
22.53%
22.69%
27.22%
37 Domestic
Sales
6,695,166
6,667,428
6,094,018
6,173,525
6,395,421
6,863,827
7,084,381
7,221,121
6,929,947
7,187,533
% change
compared
to 2000
0.00%
-0.41%
-8.98%
-7.79%
-5.01%
2.52%
5.81%
7.75%
3.51%
7.35%
Foreign
Sales
2,507,433
2,474,960
2,418,886
2,703,043
2,984,262
3,351,210
3,593,967
3,985,378
4,185,206
3,769,108
% change
compared
to 2000
0.00%
-1.30%
-3.53%
7.80%
19.02%
33.65%
43.33%
58.94%
66.91%
50.32%
Table 2: Descriptive Statistics of COMPUSTAT data Partitioned by Disclosers versus
Non-Disclosers of Aggregate Geographical Employment
Number of
Observations
2000
355
2001
332
2002
353
2003
347
2004
348
2005
342
2006
372
2007
378
2008
378
2009
369
Aggregate Change
from 2000 to 2009
Disclosers
Domestic
Employment
Foreign
Employment
Number of
Observations
2,151.42
2,062.53
2,283.27
2,041.85
1,979.14
2,225.94
2,234.53
2,231.53
2,389.96
2,617.14
+21.65%
1,716.81
1,613.34
1,826.35
1,595.19
1,611.72
1,604.56
2,201.84
2,341.18
2,595.90
2,333.23
+35.90%
1,789
1,766
1,710
1,637
1,638
1,603
1,479
1,382
1,245
1,187
38 Non-Disclosers
Imputed
Domestic
Employment
9,545.73
9,335.96
8,941.58
9,424.94
9,526.25
8,971.16
8,687.59
8,420.73
7,216.34
7,002.18
-26.65%
Imputed
Foreign
Employment
6,889.81
7,125.45
6,018.25
6,476.66
6,654.74
7,344.73
8,235.84
7,679.74
7,886.81
6,961.37
1.03%
Table 3: Analysis of Associations between Employment and Domestic and Foreign Sales
Panel A: Regression of Total Employment
Prediction
Intercept
+/-
Domestic Sales
+
Foreign Sales
+/-
N
R-Squared
BEA data
Overall
COMPUSTAT
Sample
Coefficients
(t-stats)
13,710.05
(6.49)***
0.0030
(4.01)***
-0.0009
(-2.18)*
10
0.7150
Difference in the
coefficient on
Foreign Sales
Coefficients
(t-stats)
12,347.48
(2.26)**
0.0024
(5.55)***
-0.0001
(-0.37)
10
0.7771
Between BEA
and
COMPUSTAT
sample:
χ2 = 1.79
p-value=0.2012
COMPUSTAT
Firms that
disclose
geographical
employment
Coefficients
(t-stats)
1,532.18
(4.20)***
0.0008
(0.65)
0.0053
(5.49)***
10
0.9139
Between overall
COMPUSTAT
sample and
disclosers:
χ2 = 28.62
p-value=0.0001
Note: ***, ** and * represent 1%, 5% and 10% significance, respectively. All standard
errors are heteroskadasticity adjusted.
39 Panel B: Regression of Domestic Employment
Prediction
Intercept
+/-
Domestic Sales
+
Foreign Sales
+/-
N
R-Squared
BEA data
Overall
COMPUSTAT
Sample
Coefficients
(t-stats)
10,901.65
(3.48)***
0.0026
(3.81)***
-0.0020
(-3.99)***
10
0.7200
Difference in the
coefficient on
Foreign Sales
Coefficients
(t-stats)
6,364.98
(8.65)***
0.0025
(9.39)***
-0.0020
(-7.00)***
10
0.8789
Between BEA
and
COMPUSTAT
sample:
χ2 = 0.00
p-value=0.9574
COMPUSTAT
Firms that
disclose
geographical
employment
Coefficients
(t-stats)
959.98
(4.23)***
0.0013
(2.75)***
0.0011
(2.65)***
10
0.8385
Between overall
COMPUSTAT
sample and
disclosers:
χ2 = 38.19
p-value=0.0001
Note: ***, ** and * represent 1%, 5% and 10% significance, respectively. All standard
errors are heteroskadasticity adjusted. For non-disclosers, domestic employment is based
on imputed values.
40 Table 4: Logit Model of Disclosure of Employment by Geographic Segment
Variables
Predictions
Coefficients
Clustered z-stats
Intercept
?
-0.778
-1.69*
Lobby
+
0.306
2.15**
Press
+/-0.237
-1.94*
Autonomy
+
0.253
4.23***
Low Wage
-0.112
-2.19**
∆Rev
+
-0.001
-0.26
Merge
+
0.033
1.16
∆#Seg
-0.040
-3.26***
Income/Sales
+
0.037
2.15**
Inventory/Sales
+
-0.011
-0.46
Pension
+
0.163
2.33**
Repatriation
+
0.041
0.56
OP Dis
+
0.334
4.79***
Capx Dis
+
0.186
1.94*
Union Dis
?
0.125
2.65***
Big4
?
-0.124
-1.87*
US Sale
?
-0.146
-5.64***
Foreign Sale
?
0.077
3.17***
Size
?
0.015
0.24
Size^2
?
-0.000
-0.01
Emp
?
0.074
2.03**
Age
?
-0.006
-3.00***
Lev
?
-0.011
-0.30
PPE
?
-0.019
-0.64
CFO
?
0.020
0.25
Industry Fixed
YES
Effects
Year Fixed Effects
YES
N
19,010
Pseudo R-Squared
0.0622
Note: ***, **, and * represent the 1%, 5% and 10% significance levels, respectively.
Variable Definitions:
Disclose: An indicator that equals 1 if the firm provides geographical segment employee
data; 0 otherwise.
Lobby: An indicator that equals 1 if the firm lobbied on either the “Hiring Incentives to
Restore Employment Act” or the “Employee Free Choice Act, 2009;” 0
otherwise.
Press: An indicator that equals 1 if the firm was one of the top 100 firms based on
number of press mentions of the firm’s name and the words “outsource,”
“outsourcer,” or “outsourcing” during any year from 2000-2009.
41 Autonomy: An indicator variable that equals 1 if firm has non-missing value on
COMPUSTAT “recta”; 0 otherwise.
∆Rev: Growth of total revenues, measured as the change in revenue (Compustat “revt”)
divided by lagged revenue.
Merge: An indicator variable that equals 1 if the growth of total assets (Compustat “at”)
is greater than 10%; 0 otherwise.
∆#Seg: Annual change in the number of segments reported by the firm.
Low Wage: An indicator variable that equals 1 if the firm identifies its segments in any of
the following areas or countries where the wage is constantly lower that in the
U.S.: Asia (including China, India, Malaysia, etc., Latin America, Africa, Middle
East, Caribbean, Mediterranean, Italy or Spain; 0 otherwise.
Income/Sales: Quintile ranking of income (Compustat “ebit”) divided by total revenues
(Compustat “revt”).
Inventory/Sales: Quintile ranking of inventory (Compustat “invt”) divided by total
revenues (Compustat “revt”).
Pension: An indicator variable that equals 1 if the firm has either pension employer
contribution (Compustat “pbec”) or postretirement service cost (Compustat
“prc”); 0 otherwise.
Repatriation: Measured as -1* (marginal tax rate minus either zero or foreign effective
tax rate, whichever higher), where foreign tax rate is defined as Compustat “txfo”
over “pifo”).
OP Dis: An indicator variable that equals 1 if the firm discloses segment operating profit
(Compustat “ops”); 0 otherwise.
Capx Dis: An indicator variable that equals 1 if the firm discloses segment capital
expenditure (Compustat “capxs”); 0 otherwise.
Union Dis: An indicator variable that equals 1 if the firm has disclosed union information
in their 10Ks.
Big4: An indicator variable that equals 1 if the firm uses one of the big 4 auditors: PWC,
Earnst & Young, Deloitte and KPMG; 0 otherwise.
US Sale: Natural log of sales made in the U.S. (Compustat “sales”).
Foreign Sale: Natural of log of foreign sales (Compustat “revt”-“sales”).
Size: Natural log of total assets (Compustat “at”).
EMP: Total number of employees (Compustat “emp”).
Age: Number of years that the firm has been covered by Compustat up to the data date.
Lev: Ratio of total debt (Compustat “dlc” + “dltt”) to total assets (Compustat “at”).
PPE: Ratio of property, plants and equipment (Compustat “ppent”) to the number of
employees (Compustat “emp”) divided by 1,000.
CFO: Ratio of operating cash flow (Compustat “oancf”) to lagged total assets
(Compustat “at”).
42 Figure1: Time-Series Change in Aggregate Employment Based on BEA Data
30.00% 25.00% 20.00% 15.00% 10.00% Domestic 5.00% Foreign 0.00% -­‐5.00% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 -­‐10.00% -­‐15.00% Figure 2: Time-Series Change in Employment for Disclosers of Geographical
Employment using COMPUSTAT Data
60.00% 50.00% 40.00% 30.00% Domestic 20.00% Foreign 10.00% 0.00% -­‐10.00% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 -­‐20.00% 43 Figure 3: Time-Series Change in Employment for Non-Disclosers of Geographical
Employment Using Imputed Data
15.00% 10.00% 5.00% 0.00% -­‐5.00% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Domestic -­‐10.00% Foreign -­‐15.00% -­‐20.00% -­‐25.00% -­‐30.00% -­‐35.00% 44