1
Unionization and IPO Underpricing
Antonios Chantziaras, Dimitrios Gounopoulos, Stergios Leventis*
* Chantziaras, [email protected], Durham University Business School, UK; Gounopoulos
(corresponding author), [email protected], University of Bath, UK; and Leventis,
[email protected], International Hellenic University, Greece. We acknowledge helpful comments by Bart
Lmbrecht, Thomas Boulton, Francesco Bova, Trevis Certo, Jason Chen, James Chyz, Richard Chung, Incheol
Kim, Sophia Hamm, John Howe, Marcin Kacperczyk, Woo-Jong Lee, Mario Levis, Winnie Leung, David
Matsa, Roni Michaeli, Ortiz Molina, Jay Ritter, Ghon Rhee, Tao Shen, Konstantinos Stathopoulos, Francisco
Santos, Xuann Tian, Ian Tonks, Nikos Vafeas and Xuejing Xing, seminars participants in the University of
Bath, International Hellenic University, Oxford Brooks University, University of Bristol, University of Sheffield
and conference participants at the European Accounting Association Conference, the Financial Management
Meeting, the Hellenic Accounting and Finance Association for their valuable feedback, and to Chen Huang and
Hang Pham, for excellent research assistance.
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Unionization and IPO Underpricing
Abstract
This paper investigates the impact of labor unionization on IPO underpricing. We
demonstrate that the existence of unions reduces underpricing by 11.20%. Unionized IPOs
are associated with downward offer-price revisions, higher cost of capital, inferior firm
operating performance, and incremental failure risk. We argue that unions’ presence
discourages investor participation, as investors discount the value of unionized IPOs. Further,
by employing Right-to-Work legislation to capture variations in the effectiveness of labor
unions, we determine that the effect that unions have on first-day returns is more noticeable
in areas with incremental union power. We conclude that labor unionization is an important
factor in IPO pricing and first-day returns. Our findings are of particular interest to managers,
labor unionists, and market participants.
I. Introduction
An initial public offering (IPO) comprises a fundamental corporate decision as it
provides companies with access to capital markets and allows owners to partake in wealth
diversification (Santos (2017)). Significant IPO first-day share returns (also known as
underpricing) constitute one of the most robust findings in corporate finance. Since at least as
early as Ibbotson (1975), the main line of thought on IPO performance has been that positive
IPO underpricing is caused primarily by information asymmetries and conflicts of interest
(e.g., Arthurs, Hoskisson, Busenitz and Johnson (2008), Bradley and Jordan (2002),
Chambers and Dimson (2009), Ljungqvist and Wilhelm (2003)). Prior studies have examined
underpricing mainly as the interplay between corporate insiders, important stakeholders, and
outside investors. However, it is surprising that very limited academic attention has been
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directed at the impact that labor has on IPO valuation. We aim to close this gap in the
literature by bringing to the fore the role of organized labor as an important element of
agency costs and enhanced cost of capital, which impacts initial IPO investor interest and
returns.
Relevant theory (Welbourne and Andrews (1996)) and the business press suggest that
organized labor should influence the overall IPO process. Indicatively, a case that received
extensive publicity and media attention was the Teamsters’ Union 10-month strike against
Overnite Transportation Co. in 2000, when the firm organized its IPO. Considering the
adversity of the strike, analysts readjusted their estimates to up to $300 million in proceeds,
$200 million less than the company’s goal. This caused the company to halt its IPO.
Consequently, Overnite’s Chairman and CEO, Leo Suggs, stated that, "1 don't see an IPO or
any given disposition in the foreseeable future” (Schulz (2000), p. 24).
Previous research has demonstrated that important market constituencies influence the
IPO valuation, namely: top executives (e.g., Higgins and Gulati (2006)) and board members
(e.g., Bell, Filatotchev and Aguilera (2014)); audit committees (e.g., Bédard, Coulombe and
Courteau (2008)) and auditors (e.g., Beatty (1989)); underwriters (e.g., Carter and Manaster
(1990)) and venture capitalists (e.g., Loughran and Ritter (2004)); regulators (e.g.,
Chaplinsky, Hanley and Moon (2017)); and the media (e.g., Bajo and Raimondo (2017)).
Although executive surveys (Welbourne and Andrews (1996)) and the previous literature on
equity valuation (e.g., Lee and Mas (2012)) both suggest that labor unionization should
influence IPO valuation, no systematic empirical investigation has yet been conducted. An
established and growing branch of research in industrial relations and corporate finance has
documented the impact of labor unionization on core economic matters, emphasizing the
impact of unions on operating performance and equity values. Labor unions are associated
with an adverse effect on corporate performance (i.e., Doucouliagos and Laroche (2009),
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Hirsch (2004)) because they foster risk-averse attitudes (Faleye, Mehrotra and Morck
(2006)), and such attitudes constrain managerial decision making (Chyz, Leung, Li and Rui
(2013)) while delaying future investments (Fallick and Hassett (1999)). Further, prior
evidence demonstrates that successful union organization campaigns reduce equity values
(e.g., Lee and Mas (2012)), while unionized, venture capital backed IPOs are less profitable
and less likely to survive (Xing, Howe, Anderson and Yan (2017)).
Motivated by the lack of empirical evidence on the subject, we revisit the role of
initial valuation in IPOs by examining the impact of unionization on IPO underpricing. We
employ a large and comprehensive sample of 1,568 IPOs floated on U.S. stock exchanges
during the estimation window of 1997-2014. We focus on a single country to obtain a sample
that is homogenous in terms of underlying financial and economic development, legal and
social structure, politics, public infrastructure, and relevant institutional characteristics. In an
important departure from prior studies, we concentrate our study on IPOs for two reasons.
First, the U.S. IPO market attracts great attention among researchers since it is a market that
exerts great influence on global markets and diffuses its underwriting methods throughout the
world (Ljungqvist, Jenkinson and Wilhelm (2003)). Second, while the IPO market in the U.S.
provides an ideal setting for our analysis, as it is characterized by large first-day returns for a
developed market (with an average “underpricing discount” of 20% over the last 40 years
(Santos (2017)), it also provides a useful half-way house between countries where
unionization is essentially not institutionalized and countries where union presence in
corporations is dominant.
Our findings reveal a negative and significant association with IPO first-day returns,
as the existence of unions reduces underpricing by 11.20%. This effect persists after
controlling for numerous variables commonly used to explain underpricing and after
employing different estimation methods and correcting for self-selection bias and mitigating
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endogeneity concerns. This evidence shows that unionization is a contributing factor in
higher cost of capital, negative offer-price revisions, inferior firm operating performance, and
a higher probability of corporate failure, all of which are clear manifestations of modest
investor participation in the issue due to lower valuations of unionized issuers. We also
demonstrate that the negative effect of unionization on IPOs is more prominent in states that
grant labor unions more power by not enacting Right-to-Work legislation. Overall, our
findings are robust to different measures of unionization and underpricing, after controlling
for various participants in the IPO market, and to specification issues related to sampling
methods.
Importantly, we document significantly higher cost of equity capital for unionized
issuers as compared to their non-unionized counterparts. Extending our investigations to three
years following the IPO date, we reveal a negative and significant impact of unionization on
firm operating performance, which confirms our expectation that modest investor demand
may stem from lower investor valuations attributable to the negative impact of unionization
on firm operating performance. Additional analyses on firm mortality further fuel our
expectation for investors’ reluctance in allocating their funds to unionized IPOs, since
unionization increases the IPO failure risk by 89.8%. Our results suggest that unionization
constitutes a significant cost for the issuing firm and that unionization status represents an
important determinant of IPO valuation.
We also consider the endogeneity of unionization and unions’ self-selection to
organize in firms, since unobservable determinants of unionization may influence pricing.
We advocate the use of a self-selection control to reveal the pure effect of unionization and
adopt the Heckman (1979) two-stage procedure and the maximum likelihood estimator
(MLE), while we mitigate endogeneity concerns using an instrumental variables (IV)
approach. Our results continue to hold after testing for self-selection biases (Shipman,
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Swanquist and Whited (2017)) by repeating our analysis using a propensity score matched
(PSM) sample (based on observable firm-level characteristics that moderate the differences
between treatment (unionized) and control (non-unionized) IPOs).
Finally, this study employs many sensitivity tests. We substitute our main
unionization proxy, the existence of collective bargaining coverage (Union), with four
alternative specifications to test the validity of our main results. We find that all coefficients
of alternative unionization proxies are negative. The percentage of a company's employees
covered by collective bargaining agreements (as reported in company filings) is strongly
statistically significant, indicating that labor unionization has a negative impact on a company
during the public offering process.
Our study contributes to the existing literature on several fronts. First, we make an
important contribution to the IPO and corporate finance literatures by bringing to the fore the
impact of labor unionization. Thus, we extend prior understandings regarding the roles of
important stakeholders in IPO pricing by documenting the significant reluctance of investors
to participate when they factor unionization into their investment decisions. Second, we
advance prior literature examining the impact of organized labor on corporate matters. We
show that the presence of organized labor translates into significant costs for IPOs, extending
prior knowledge regarding the cost of unionization for equity capital (i.e., Abowd (1989),
Chen, Kacperczyk and Ortiz-Molina (2011a), Lee and Mas (2012)). We further contribute by
showing that these costs depend on the incremental political power of unions. Third, our
empirical results also provide evidence regarding bookbuilding (e.g., Benveniste and Spindt
(1989)), as we report that unionized IPOs exhibit downward offer-price revisions.
Specifically, we lend support to the notion that initial returns are expected to be lower
(higher) when the offer price is adjusted down (up) (Hanley (1993)). Finally, our findings
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extend the relevant literature on the impact of unionization on firm operating under-
performance (i.e., Doucouliagos and Laroche (2009), Xing et al. (2017)).
The remainder of the paper is organized as follows: Section II demonstrates the
related literature and hypothesis development. We describe our sample selection process,
main unionization measurement, and research methods in Section III. The empirical findings
are reported in Section IV, while Section V outlines the potential mechanisms causing the
negative relation between labor unionization and IPO underpricing. We present sensitivity
testing and the robustness of our results in Section VI. Finally, Section VII concludes the
paper.
II. Related Literature and Hypotheses Development
A. Labor Unions and Agency Costs
Viewed through the lens of agency theory, a firm is a nexus of contracts between
principals and agents (Jensen and Meckling (1976)). Contractual relations are more complex
in the IPO setting, due to the coexistence of “conflicting voices” from various principal
groups that can ultimately give rise to agency problems (Arthurs et al. (2008)). Indeed, the
decision to go public gives rise to significant organizational transitions, emanating from the
dilution of ownership from the existing shareholders to outside institutional and retail
investors (Allcock and Filatotchev (2010)). Therefore, IPO firms have to adapt to a set of
contractual relationships between insiders, pre-IPO and public-market investors,
stakeholders, and underwriters that are potentially associated with significant agency costs
(Arthurs et al. (2008), Bell et al. (2014)).
Although the previous literature has illuminated the contractual relations between
various actors in the IPO process, it has remained silent regarding the employees who are
central to the firm. Contractual relations are even more complex when employees are
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organized into unions (Chyz et al. (2013), Xing et al. (2017)). This is because labor unions
act as agents for their members to advance their claims for better wages, hours, and working
conditions through collective bargaining, industrial action, and activism (Chen, Chen and
Liao (2011b), Faleye et al. (2006)). To increase their members’ welfare and benefits, unions
develop rent-seeking behaviors and capture economic rents from other economic agents,
which, taken to the extreme, leads to diverting resources from positive-sum activities into
zero- and even negative-sum efforts to capture transfers (Tollison (2012)). Unions’ ability to
extract rents is reflected both in the union wage premium (in the U.S. this was historically
15% (Aidt and Tzannatos (2002))) and in a reduction of the wealth of shareholders and other
economic agents. Robust academic evidence shows the unions’ ability to curtail CEO
compensation (Huang, Jiang, Lie and Que (2017)) and affect bondholder wealth, since
unionization increases a firm’s credit risk and its bond yield spreads (Chen et al. (2011b)).1
Considering the salient contractual complexities arising from unionization, previous
studies have investigated its impact on operating performance and corporate decision making.
Previous research (e.g., Doucouliagos and Laroche (2009), Hirsch (2004)) demonstrates that
unions adversely affect corporate performance. Unions foster risk-averse attitudes (Faleye et
al. (2006)), constrain managerial decisions (Chyz et al. (2013)), and impose the adoption of
conservative policies (Leung, Li and Rui (2009)). Considering this connection between
unions and risk-aversion, firms with organized labor are more likely to rank projects with
sufficient cash flows and low risk ahead of potentially higher NPV projects (Faleye et al.
(2006)). As a consequence, unionized firms tend to avoid certain types of investments, such
as capital expenditures or R&D spending (Faleye et al. (2006)); they are also more likely to
delay future investments (Fallick and Hassett (1999)) and suppress technological innovation
1 Abowd (1989) finds that an increase in union rents is associated with a dollar-for-dollar trade-off with
shareholder wealth, while Huang et al. (2017) observe that organized labor curbs CEO compensation by 9.2%.
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(Bradley, Kim and Tian (2017)). Overall, these studies imply that unionization is detrimental
to firm value.
In addition to the effect that labor has on firm performance, extensive research
provides evidence regarding how unions affect strategic corporate financial decisions. Firms
strengthen their bargaining positions against union demands using the following strategies:
reporting higher losses (DeAngelo and DeAngelo (1991)), issuing debt (Marciukaityte
(2015), Matsa (2010)), curbing cash balances (Klasa, Maxwell and Ortiz-Molina (2009)),
missing analysts’ earnings estimates (Bova (2013)), and adopting income-decreasing
accounting methods (Bowen, DuCharme and Shores (1995)) and smoother earning paths
(Hamm, Jung and Lee (2018)). Collectively, these actions indicate management’s intention to
shelter corporate income from unions and mitigate the rent-seeking capabilities of unions.
Overall, the previous literature highlights the antithesis between labor’s aims and the interests
of principals and agents, while suggesting the existence of incremental agency costs in
unionized contexts.
B. Labor Unions and Company Valuation
Determining the value of an IPO firm is a vexing problem for investors because they
have limited knowledge about the issuing firm. In order to assess firm value, investors place
reliance on the prospectus prepared by the new issuer (Bédard et al. (2008)), on firm
fundamentals (Aggarwal, Bhagat and Rangan (2009), Field and Lowry (2009)), and on firm’s
future performance (Borochin and Yang (2017)). When investors become aware of high
agency conflicts, typical of unionized contexts, they tend to price protect themselves and
discount IPO firm value (Bruton, Filatotchev, Chahine and Wright (2010), Roosenboom and
Schramade (2006)). Investors will further discount IPO firm value when organized labor is
present in a corporate context because unionization negatively impacts firm profitability
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(Doucouliagos and Laroche (2009)), stock returns (Lee and Mas (2012)), dividend payouts
(DeAngelo and DeAngelo (1991)), and investment cash flows (Chen and Chen (2013)). For
example, it is evident that successful union elections are followed by a subsequent decline
(by almost 10%) of a firm’s stock market value (Lee and Mas (2012)) and dividend payout
ratios (He, Tian, Yang and Zuo (2018)). Unionization also induces greater spreads in newly-
originated loans (Qiu and Shen (2017)) and sharpens the volatility of investment cash flows
(Chen and Chen (2013)).
Overall, firm value reflects the present value of the firm’s future cash flows, with the
discount rate dependent on risk (Marciukaityte (2018)). Unionization can have an adverse
effect on firm value by reducing the firm’s future cash flows and increasing risk (e.g., Chen
et al. (2011a)). Indicatively, the previous literature suggests that unionization is associated
with higher average salaries (Aidt and Tzannatos (2002)), employee protection from layoffs
(Abraham and Medoff (1984)), plant closures (Chen et al. (2011a)), labor strikes (Kleiner and
Bouillon (1988)), and other restrictions on operating flexibility (Chen et al. (2011a)).
Consequently, unionization is associated with lower profitability (Doucouliagos and Laroche
(2009)) and higher risk (Chen et al. (2011a)). Thus, based on prior evidence, we posit that
investors will discount IPO firm value when factoring unionization into their investment
decision models.
C. Hypothesis
Previous research suggests the existence of incremental agency costs in unionized
contexts (Chyz et al. (2013), Xing et al. (2017)), while implies that unionization is
detrimental to firm value (e.g., Chen et al. (2011a), Doucouliagos and Laroche (2009), Faleye
et al. (2006)). Drawing upon IPO literature, we anticipate that investors discount agency costs
and firm prospects, and price-protect themselves through a larger discount on IPO firm value
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(e.g., Bruton et al. (2010), Roosenboom and Schramade (2006)). This is because union
presence increases the cost of capital (Chen et al. (2011a)) and the probability of corporate
failure (Xing et al. (2017)), and decreases firm operating performance (Doucouliagos and
Laroche (2009), Xing et al. (2017)), all of which are crucial factors in investment decisions
(Lerner and Tåg (2013)). Against this background, we expect investors to penalize unionized
issuers through greater discounts in IPO firm value and to become more hesitant about
allocating their funds to unionized firms. Overall, we predict that investors will act rationally
and lower their valuations and bids for unionized issuers (Ljungqvist (2007)), which in turn
should be translated into lower underpricing. Thus, we hypothesize that:
H: Ceteris paribus, labor unionization is negatively associated with IPO underpricing.
III. Research Design
A. Data
We collected a sample of IPOs floated on U.S. stock exchanges from January 1, 1997,
to December 31, 2014, from the Securities Data Company (SDC) database. Following prior
studies, we cleaned our sample by excluding the following: IPOs with a share price of less
than $5, ADRs, reverse leverage buy-outs, limited partnerships, and spinoffs (e.g., Santos
(2017)). Although we retained financial companies in our sample, we eliminated those with
Standard Industrial Classification (SIC) codes ranging from 6723 to 6999 (i.e., closed-end
funds, real estate investment trusts (REITs), royalty trusts, and special-purpose investment
vehicles (Gounopoulos, Kallias, Kallias and Tzeremes (2017))). We further excluded foreign
issuers (e.g., Lowry and Murphy (2007)) by using the historical business addresses registered
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in 10-K filings (e.g., Marciukaityte (2015)).2 Next, we required that our sample firms be
covered by both the Compustat database and the Center for Research in Security Prices
(CRSP) database, from which databases we obtain, respectively, accounting and aftermarket
data. This entire process yielded 2,401 IPOs. We then eliminated 833 issuers that did not
disclose union-related expressions in their 10-K filings (see section III.B for a description) in
order to avoid the inclusion of firms arbitrarily defined as non-unionized. Thus, we ended up
with a sample that is comprised of 1,568 IPOs, of which 208 are unionized.
B. Measuring Labor Unionization
We operationalize a firm-level unionization measure, as this entails lower
measurement errors (Cheng (2017)), while we further sensitivity test for alternative
definitions (see subsection VI.A). We rely on Item 1 (Business) of 10-K company filings to
determine whether employees are covered by a collective bargaining agreement. First, we
download 10-K filings from the Securities Exchange Commission FTP server. Second, we
develop a PERL Script, similar to Cheng (2017), which allows us to parse sentences related
to union coverage. We employ a battery of keyword combinations in our code, such as:
bargaining agreement(s); bargaining unit(s); collective agreement(s); collective bargain(ing);
labo(u)r agreement(s); labo(u)r organization(s); labo(u)r union(s); organized labo(u)r;
organiz(s)ed employee(s)/staff/personnel/workforce; work council(s); trade union(s); trade-
union(s); union(’s) activity(ies); union(’s) agreement(s); union contract(s); union
organization(s); unioniz(s)ed and union(s). Finally, we manually verify and identify
observations of firms disclosing that their employees are covered under a collective
bargaining agreement (Union) and, for a control group, observations of companies that firmly
2 We obtain each firm’s historical business address through its 10-K filings, as databases tend to backfill
business addresses (Marciukaityte (2015)). We download company filings, as available through the Securities
Exchange Commission FTP server, and develop a PERL script that parses state code, state name, city, and zip
code.
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report no union representation. We provide examples of the scoring process of 10-K filings
for employees covered by bargaining agreements in Appendix C.
C. Research Methods
To shed light on the effect of unionization on IPO pricing, we use a treatment effects
model in which the dependent variable is the natural logarithm of one plus the first-day return
(estimated as the difference between the first aftermarket price and the IPO offer price
divided by the IPO offer price), specified as follows:
Ln(1 + underpircing) = α + βX + γUnion + ε (1)
where X represents a vector of firm- and IPO-specific characteristics; Union is a
dichotomous variable which signifies that company employees are covered by a collective
bargaining agreement; and ε stands for the error term. Initially, we conduct our analysis
employing a multivariate OLS regression setting, but we cannot eliminate the possibility that
this method will generate biased coefficients because the coefficient γ of our main
independent variable may be influenced by feedback effects and/or be correlated with the
error term. Arguably, endogeneity issues could affect the sign, magnitude, or statistical
significance of our results since unions may self-select to organize in firms. Unobservable
determinants of unionization may influence pricing.
In our attempt to mitigate the concern that labor unions self-select to organize in
firms, we adopt the Heckman (1979) two-stage procedure. First, we model a firm’s likelihood
to become unionized using a vector of determinants of unionization. In the second stage, we
add Heckman's LAMBDA (the inverse Mills ratio) obtained in the first stage to the original
regressions. Adding Heckman's LAMBDA to the second-stage regression ensures that our
coefficient estimates for the variables of interest are conditional on the unionization status of
the company.
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We further test our inferences by employing an MLE approach and a two-stage IV
approach. Under the MLE approach, we strengthen our inferences regarding the bivariate
normality of the error terms between the outcome and selection equations (correlated error
adjustment), similar to Gounopoulos et al. (2017). The advantage of the MLE resides in its
ability to simultaneously process all the available information, while equipping us with a
Wald test for the independence of residuals. Conversely, the IV approach relaxes the
assumptions of normality in the distribution of residuals and treats the endogenous variable in
the second stage as the fitted probabilities obtained from the first-stage. Crucial to our
analysis is the fact that these properties allow for flexibility in the selection of explanatory
variables. Finally, applying the IV approach equips us with the ability to conduct a Hausman
test for endogeneity.
1. Determinants of Unionization
Based on the previous discussion, the first stage of our econometric model depends on
the probability of a firm becoming unionized. We model this probability by drawing upon
previous studies that claim that unionized companies are likely to differ from non-unionized
companies in terms of size and cash reserves (Klasa et al. (2009)), inventory levels (Matsa
(2010)), debt issuance (Marciukaityte (2015), Matsa (2010)), profitability (Doucouliagos and
Laroche (2009)), tangible and intangible assets (Faleye et al. (2006)), and age (Hirsch
(2004)). In our first-stage model, we account for size, measured as the natural logarithm of
total assets (LnAssets); total cash and investment securities over total assets (Cash);
inventories over total assets (Inventory); return on assets (ROA); Tangibility of the firm,
expressed as net property, plant, and equipment over total assets; the ratio of total liabilities
to total assets (Leverage); and the natural logarithm of the number of years elapsed from the
firm’s foundation to the year of the IPO (LnAge). We maintain the variables Leverage and
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LnAge in our second-stage model, since previous studies indicate that these are important
determinants of IPO underpricing (i.e., Ritter (1984), Ritter (1991)).
To satisfy the exclusion restriction, we follow Chino (2016) and operationalize Union
by the natural logarithm of the percentage of part-time workers in each industry (PTWork) in
every year, using data from the NBER CPS Merged Outgoing Rotation Groups File
(http://www.nber.org/morg/annual/).3 Part-time employees, compared to full-time employees,
have fewer incentives to join unions (see Hernández (1995)) because they work fewer hours
and have shorter tenure in a workplace, and thus may anticipate few benefits from organizing
unions at a workplace. For instance, coordinating the benefits and objectives of part-time
employees at a workplace through union membership might be difficult, as part-time
employees often hold multiple positions at multiple employers. Prior studies also document
that the propensity to unionize is smaller among part-time employees, as compared to full-
time employees (e.g., Hernández (1995)). While we anticipate an inverse relationship
between PTWork and unionization, it is unlikely that it would directly affect underpricing.
Thus, the fraction of part-time workers in each industry could be a reasonable instrument for
identifying the effects of unionization on IPO underpricing.
2. Determinants of IPO Underpricing
We rely on the relevant literature and include key indicators (see Appendix A for
variable definitions) that have been shown to account for the variability in underpricing as
control variables in our second-stage regressions. We operationalize firm size by the natural
logarithm of the total proceeds raised in the IPO (LnProceeds) (Gounopoulos et al. (2017)),
3 The NBER CPS Merged Outgoing Rotation Groups File data are available in Census Industry Classification
(CIC) codes. We follow the methodology described in Chino (2016) and transform each firm’s primary CIC into
Standard Industrial Classification (SIC) codes, using a crosswalk list available through the U.S. Census Bureau
(https://www.census.gov/people/io/methodology/).
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thus anticipating an inverse relation with underpricing on the grounds that smaller offerings
tend to be more speculative than larger ones (Beatty and Ritter (1986)).
Firm Age enters into the model as a surrogate for risk (Ritter (1984), Ritter (1991)).
Investors view older firms as safer investments because older firms are resilient during
market swings. Here we expect a negative coefficient. Intrigued by the riskier and harder-to-
value nature of IPOs floated on the NASDAQ (Lowry and Shu (2002)), we include an
indicator variable for NASDAQ listings while we also control for excessive first-day returns
related to Internet and Technology firms (e.g., Aggarwal, Krigman and Womack (2002)).
Vc represents a binary variable set to 1 for venture capital backed IPOs, and 0
otherwise. We do not form any strong expectation about the sign of this variable because of
the contrasting evidence in the previous literature (see Megginson and Weiss (1991) and
Loughran and Ritter (2004)). We augment the model with measures for underwriting
(Underwriter) and audit (BIG4) quality, where Underwriter and BIG4 indicate the highest
prestige ranking and Big-4 audit firm, respectively. Reputable underwriters act as positive
signals for market participants, while their engagement is associated with potential abnormal
first-day returns (Carter and Manaster (1990)). In line with Beatty (1989), we argue that the
engagement of reputable audit firms increases the quality and credibility of financial
statements, and thus may reduce the money to be left on the table.
We capture the equity dilution caused by the issuance with the ratio of retained shares
to issued shares (Overhang) (Bradley and Jordan (2002)). Lowry and Murphy (2007)
highlight the fact that greater levels of overhang escalate initial returns because the costs of
underpricing are shared proportionately among investors, who retain ownership after the firm
goes public. Because underpricing and dilution costs can be higher for firm owners
liquidating their shares on the immediate IPO date (Habib and Ljungqvist (2001)), we include
an indicator signaling whether the offering is exclusively primary (Primary).
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We further control for the ratio of total liabilities to total assets (Leverage) and
include an indicator for positive earnings per share (DEPS), both measured during the year
trailing the IPO (Gounopoulos et al. (2017)). High levels of debt financing can impose
discipline on management, whereas the existence of positive accounting returns alleviates
uncertainty. We anticipate that both measures have a negative loading with our dependent
variable. We augment our model to account for market timing effects and include a “market
sentiment” variable, similar to Santos (2017). First, we compute the equally-weighted
average underpricing for each month in our sample (Market underpricing). Second, we
compare each monthly underpricing with the sample distribution of Market underpricing, and
classify a month as Hot_Month if the relevant underpricing is in the top quartile (above 19%),
as Santos (2017). We also account for periods of turbulence and control for the 2007-2008
period (Crunch), when financial markets faced turbulence from the subprime mortgage crisis.
Throughout our analysis, we control for industry and year-fixed effects since potential IPO
clustering could undermine the reliability of our findings (e.g., Cao and Shi (2006)).
IV. Empirical Results
A. Univariate Analysis and IPO Characteristics
TABLE 1 summarizes all relevant statistics, as well as the composition of the entire
sample of unionized and non-unionized IPOs. Panel A depicts the dispersion of IPOs across
time, and Panel B shows the sample composition according to SIC codes and firm-specific
characteristics. Fluctuations in the number of IPOs reveal three peaks: before the Dotcom
period (1997-2000), before the recent credit crunch (2007-2008), and during the 2013-2014
period. Despite intensive listing activity during the pre-Dotcom period, which reached its
highest level in March 2000 (Aggarwal et al. (2002)), the relative number of unionized IPOs
is lower than non-unionized IPOs (especially for the years 1999 and 2000). One explanation
for this is the “Internet craze” of 1999-2000 (Cao and Shi (2006)) and the relatively
18
underrepresented figures of internet and technology IPOs in the unionized sample (lower part
of Panel B in TABLE 1). The post-Dotcom period, including the burst bubble in early 2001,
saw an overall reduction in IPOs until 2003, followed by an upward trend until the credit
crunch of 2007-2008. There was only one unionized IPO during 2008, but the number of
unionized IPOs gradually rose in subsequent years until the end of our sample period.
[Insert TABLE 1 about here]
The right side of Panel A shows yearly fluctuations in two core SIC divisions, which
together account for more than half of the sample of unionized IPOs. This fact is clearly
illustrated in Panel B (TABLE 1), where the largest number of unionized IPOs belong to the
Manufacturing (44.23%) and Services (17.79%) divisions, while the least unionized sector is
that of Finance, Insurance and Real Estate (where white-collar employees predominate and
95.1% of total IPOs in this sector are non-unionized). Such findings reflect the trends in
unionization across industries (see, e.g., Division of Labor Force Statistics (2015)).
TABLE 2 shows the selected characteristics of unionized IPOs separated into IPOs
with high percentages (Panel A) and low percentages (Panel B) of unionized employees. The
vast majority of these unionized IPOs operate in the manufacturing sector, with just under
half having a century of operational experience. In addition, unionized IPOs exhibit lower
first-day returns when compared to the annual averages4; this phenomenon is more prominent
for the highly-unionized IPOs (Panel A) than for the low-unionized IPOs (Panel B). Orchids
Paper Products Co., a manufacturing company with 84.85% of its employees unionized,
experienced a low underpricing of 6.25%. On the other hand, American Reprographics Co,
4 The annual averages of first-day returns are derived through Jay Ritter's website
(https://site.warrington.ufl.edu/ritter/ipo-data/).
19
with only 0.53% of its employees associated with labor unions, realized an underpricing of
6.15%.
[Insert TABLE 2 about here]
TABLE 3 provides descriptive statistics for each subsample (Panel A), as well as for
the entire sample. Univariate analysis lends support to our hypothesis. First, unionized IPOs
exhibit inferior first-day returns compared with non-unionized IPOs, since the mean (median)
value of First-day return is 13.594 (7.5) and 38.671 (16.855), respectively. The difference in
means is statistically significant at 1%. Second, the subsamples differ in the pattern of price
revisions since unionized IPOs are, on average, associated with downward revisions,
significant at the 5% level. The modest offer price for unionized IPOs lends support to our
speculations of lower valuations by investors, as Jain and Kini (1999) predict that modest
offer price signals little demand, little value, or both.
[Insert TABLE 3 about here]
Moving on to the IPO characteristics in Panel A, we observe that unionized IPOs are
considerably larger, with an average of $372 million proceeds raised, in contrast to $114
million for their non-unionized counterparts. The average unionized IPO firm is almost 28
years older and operates with higher levels of leverage than its average counterpart. Our
results corroborate findings from previous research that unionization is more pronounced in
older firms (Hirsch (2004)) and that firms facing union bargaining positions strategically
amass more debt (Matsa (2010)). Additionally, the fact that unionized issuers appear to be
more mature partially explains why they exhibit less underpricing, as more mature firms are
easier to value (Chambers and Dimson (2009)). Comparing the quality image of the two
groups, we find that unionized IPOs are more likely to engage Big-4 auditors and top-ranked
underwriters but are less likely to rely on venture capital financing. Considering the internet
or the broader technology sector membership, we notice significant differences (at 1%
20
significance) between the subgroups. This may partially explain the absence of unionized
IPOs from the NASDAQ exchange, which is the technology issuers' favorite listing platform.
Retained equity during an IPO serves as a signal of post-issue performance and prospects of
the firm (Daily, Certo, Dalton and Roengpitya (2003)). The significantly lower percentage (at
10%) of retained ownership (Overhang) of unionized IPOs sends puzzling signals of pre-IPO
shareholders about the firm’s prospects, making new investors to value low-ownership firms
(i.e., unionized firms) less than their high-ownership counterparts (Aggarwal et al. (2009)).
Ultimately, the Pearson pairwise correlations of variables used in the study (see TABLE 3
Panel B) indicate that multicollinearity is not likely to influence our results.
B. Multivariate Analysis
We report our empirical results regarding the effect of unionization on underpricing in
TABLE 4. We tabulate the resulting coefficients of all estimation methods as follows: the
OLS regression in Column 1, the Heckman two-stage procedure in Column 2, the MLE two-
equation treatment model in Column 3, and the IV method in Column 4. We include OLS
estimates to facilitate benchmarking. Our results show that, across all estimation methods,
unionization strongly reduces first-day returns. The Union coefficients are statistically
significant at 1%, and the coefficient magnitudes are consistent with one another and sharply
contrast with the OLS benchmark, while multicollinearity is not likely to affect our results
(VIF=1.28). Overall, the results support our hypothesis for a negative and statistically
significant effect of unionization on IPO underpricing. For example, drawing upon the OLS
estimations (Column 1 in TABLE 4), if a company becomes unionized it will exhibit a
decrease in underpricing by 31.68% (e-0.381 - 1 = -0.3168), or in absolute terms 11.20% less
money left on the table.5
5 This is estimated as 31.68% * 35.34% (35.34% is the average First-day return obtained from TABLE 3).
21
[Insert TABLE 4 about here]
For the remaining control variables, our results are generally consistent with the
previous research, except for our prediction regarding the relation between underpricing and
LnProceeds. Although we anticipated a negative coefficient, there are previous studies that
argue and empirically demonstrate a positive association (see Daily et al. (2003)).6
Underpricing also increases with Internet and Technology IPOs (Ljungqvist and Wilhelm
(2003)), as well as with the engagement of reputable underwriters and venture capital
financing (Loughran and Ritter (2004)). The positive and statistically-significant Overhang
coefficient corroborates the findings of Bradley and Jordan (2002), since lower dilution costs
(meaning greater overhang) result in escalated immediate aftermarket performance. The
positive and highly-significant coefficient of Hot_Month verifies the excessive funds left on
the table when the market sentiment is high (Santos (2017)), while the credit crunch period
leaves underpricing unaffected.
Next, we examine our concern regarding the endogenous nature of unionization,
which we instrument with the natural logarithm of the percentage of part-time workers in
each industry (PTWork), for each estimation method separately. First, the high statistical
significance of 1% for the inverse Mills ratio supports the idea that unions self-select in
companies. Second, the Wald test of the MLE indicates a significant correlation of the error
terms between the outcome and selection equations, at 1%. Third, the Hausman test from the
IV framework indicates feedback effects at 1%. Taking everything into consideration, these
findings lead to the rejection of the null hypothesis of no endogeneity.
6 The positive coefficient LnProceeds corroborates the meta-analyses of Daily et al. (2003), as the authors
evidence a positive and significant relationship between underpricing and gross proceeds.
22
V. The Causes of Less Underpricing in Unionized IPOs
In this section, we discuss the potential causes of the significant negative relation
between unionization and IPO underpricing. According to our hypothesis development, this
negative relation stems from higher cost of capital, lower operating performance and
incremental corporate failure risk associated with unionized contexts, which lead investors to
form less-favorable valuations and to discount the IPO firm value of unionized issuers. We
examine the cost-of capital channel of labor unions (subsection V.A), the IPO pricing through
the bookbuilding process (subsection V.B), IPO operating performance (subsection V.C), and
IPO mortality (subsection V.D).
A. Unionization, IPO Valuation and Cost of Capital
In this section we explore the cost-of-capital channel of labor unions’ impact. Ceteris
paribus, a higher cost of capital implies a lower IPO offer price. We follow Çolak, Durnev
and Qian (2017) to measure the offer price relative to its intrinsic value, where the intrinsic
value is valuation based on industry peers’ price multiples. Similar to Çolak et al. (2017), we
use a PSM method and create matching pairs between the IPOs in our sample and seasoned
firms in the same year and industry. In the first step, we run a probit regression (1 if the firm
belongs to our sample, and 0 if it is a matching candidate) on six covariates: sales; EBITDA
margin (measured as earnings before interest, taxes, depreciation and amortization over
sales); net capital expenditures over total assets; Tangibility, Cash and Leverage as
previously defined (see subsection III.C.1). We then use the propensity score from this probit
regression and perform a nearest-neighbor matching approach without replacement in the
same industry-year.
For every IPO firm in our sample, we calculate three P/V ratios in which P is the IPO
offer price and V is the “fair/intrinsic value” of the matched seasoned firm. We employ three
23
price multiple, namely price-to-sales, price-to-EBITDA; and price-to-earnings. For the IPO
firm, P represents the offer price multiplied with shares outstanding prior to the IPO, whereas
for the matching firm we use the closing price and shares outstanding on the IPO date. The
values of price-to-EBITDA and price-to-earnings are set to missing if EBITDA or earnings
are negative (similar to Çolak et al. (2017)), respectively. The following equations illustrate
the construction of the P/V ratios:
P/V(Sales) =(P/Sales)IPO
(P/Sales)Matching Firm
(2)
P/V(EBITDA) =(P/EBITDA)IPO
(P/EBITDA)Matching Firm
(3)
P/V(Earnings) =(P/Earnings)IPO
(P/Earnings)Matching Firm
(4)
Panel A of TABLE 5 compares the mean and median P/V ratios of unionized and
non-unionized IPOs. We observe that the P/V ratios of unionized IPOs are significantly lower
than those of their non-unionized counterparts, either using tests for equality of the means
(t-test) or the medians (Wilcoxon rank-sum test). In particular, P/V (EBITDA), and P/V
(Earnings) are less than 1 and suggest that the offer price of unionized firms is set lower,
which is further translated as higher cost of capital (Çolak et al. (2017)). In Panel B of
TABLE 5 we repeat comparisons using a PSM sample of unionized and non-unionized IPOs.
According to Rosenbaum and Rubin (1985), when units receiving a treatment share as many
characteristics as possible with non-treated units, their between-outcome comparisons are
least affected by self-selection. Thus, we perform comparisons using a nearest-neighbor
matching approach (see subsection VI.B for a description of the matching process), since
conducting comparisons between treatment (unionized) and control (non-unionized) units
that share so many characteristics are less likely to be affected by self-selection (Rosenbaum
and Rubin (1985)). We observe that unionized issuers continue to have significantly lower
24
P/V ratios as compared to their non-unionized counterparts, with the differences been
statistically significant at 5% or higher. Collectively, these results suggest an increased cost-
of-capital explanation of the effect of unions on the pricing of IPOs.
[Insert TABLE 5 about here]
We take a step further and conduct multivariate analyses to examine the effect of
unionization on the offer price relative to its intrinsic value. We adopt the same model
specification as in Chemmanur and Krishnan (2012), and we control for LnAssets, Vc,
Underwriter and Overhang, as previously defined, while we use natural logarithm
transformations of all P/V ratios (same as Chemmanur and Krishnan (2012)). TABLE 6
presents the results of the multivariate analyses and reports the coefficients of all estimation
methods as follows: the Heckman two-stage procedure (Columns 1, 4 and 7), the MLE two-
equation treatment model (Columns 2, 5 and 8), and the IV method (Columns 3, 6 and 9).
Our results indicate a negative impact of unionization on P/V ratios, the coefficient of Union
is negative and statistically-significant coefficient at 1% (across all estimation methods, apart
from IV estimates in column 9). The results are consistent with the idea that the existence of
unions is associated with lower valuations and higher cost of capital, strengthening our
argument for less favorably valuations by investors when confronting unionized issuers.
[Insert TABLE 6 about here]
B. Unionization and IPO Pricing
In the previous section we demonstrated that the existence of unions is associated
with lower valuations by investors. We take a step further and investigate any association
between unionization and IPO pricing during the bookbuilding period. Given that higher
agency costs and less-favorable firm prospects affect first-day returns, they will also instigate
investors to price protect themselves and discount IPO firm value (Bruton et al. (2010),
25
Roosenboom and Schramade (2006)). On this basis, we anticipate that investors will lower
their bidding prices in the case of unionized issuers, attributable to lower valuations
(Ljungqvist (2007)), and that this, in turn, will suppress the average limit price of IPOs during
the bookbuilding process. Such attitudes will affect the offer price of the issue because
underwriters set a more conservative price when the average limit price is low (Cornelli and
Goldreich (2003)). In a nutshell, we posit that the reluctance of investors to participate in
unionized IPOs, as expressed through modest bidding for IPOs, will drive the offer price
down (Hanley (1993)).
Beyond the demonstrated negative offer-price revisions among unionized IPOs (see
TABLE 3), we take a further step and conduct multivariate analyses to examine the effect of
unionization on offer price revisions. Following relevant studies, we operationalize
bookbuilding turbulence in terms of offer-price deviation from the midpoint of the initial
filing price range (Benveniste and Spindt (1989), Cornelli and Goldreich (2003), Hanley
(1993)). We maintain the same covariates as in our main regressions since the pricing for
bookbuilding participants and aftermarket investors is driven by the same factors (refer for a
proof to Lowry and Schwert (2004)).
[Insert TABLE 7 about here]
TABLE 7 reports the coefficients of all estimation methods as follows: the Heckman
two-stage procedure in Column 1, the MLE two-equation treatment model in Column 2, and
the IV method in Column 3. Our results indicate a downward price adjustment for unionized
IPOs, as Union attains a negative and statistically-significant coefficient at 1% across all
estimation methods. This negative effect lends support to our inferences for lower bidding
prices on behalf of investors, attributable to lower valuations in the presence of unions, that
ultimately suppress the offer price. An immediate consequence of the lower offer price is that
unionized firms will fail to raise the capital they were expecting to implement their
26
investment agenda. In other words, our results lend support to the notion that when the offer
price is adjusted down the initial returns are expected to be lower (Hanley (1993)).
C. Unionization and IPO Operating Performance
Additionally, we examine the impact of unionization on the future operating
performance of IPOs, employing an estimation window of three years following the IPO date
(e.g., Ritter (1991)). We employ TobinsQ as a proxy for firm operating performance, defined
as the market value of assets divided by the book value of assets at the fiscal year-end (where
the market value of assets is calculated as the book value of assets plus the market value of
common stock less the sum of the book value of common stock and balance-sheet-deferred
taxes) (Xing et al. (2017)). To analyze the impact of unions on firm operating performance,
we use the same model specification as in Xing et al. (2017). As control variables, we employ
(1) Profitability, measured as EBITDA over total assets; (2) Dividends, representing the ratio
of dividends to the book value of equity; (3) Cap_Exp, calculated as net capital expenditures
to total assets; (4) Lev, representing the ratio of total liabilities over total assets; and (5)
LnAssets and LnAge as previously defined.
[Insert TABLE 8 about here]
TABLE 8 presents analyses of the impact of unionization on firm operating
performance, using a sample of 1,094 IPOs for the time period 1997-20147. We report the
coefficients of all estimation methods as follows: the Heckman two-stage procedure in
Column 1, the MLE two-equation treatment model in Column 2, and the IV method in
Column 3. Our results indicate a negative impact of unionization on IPO operating
performance as Union exhibits a negative and statistically-significant coefficient at 1% across
7 The sample employed in this section is smaller than our initial sample (1,568 IPOs) for two reasons: (1) 226
IPOs have been liquidated, acquired, merged, or delisted within our estimation window and (2) financial
information for the three consecutive years following the IPO date was not available for 248 IPOs.
27
all estimation methods. This negative effect lends support to our anticipation for lower
valuations for unionized issuers, since investors’ valuations and prospects of unionized IPOs
are indeed realized in the long run.
D. Unionization and IPO Mortality
Given the negative effect of unionization on firm operating performance, we move a
step further and investigate the impact of unionization on firm mortality, arguably the
ultimate measure of long-run firm performance (i.e., Klepper (2002)). We track each firm
from the IPO date to the end of 2017, or to the delisting date if it is earlier. We obtain the
status of the issuing firm (delisting code8) from CRSP and classify our sample firms as
involuntarily delisted (i.e., delisted for negative reasons such as financial distress, liquidation,
failure to meet listing standards, etc.) if their delisting codes are greater than or equal to 300
(e.g., Gounopoulos and Pham (2018)). Our sample is comprised of 291 (18.2%)
involuntarily-delisted IPOs, of which 30 are unionized and 261 are non-unionized.
To estimate the impact of unionization on firm mortality, we employ the Cox
proportional hazard model (similar to Gounopoulos and Pham (2018), Xing et al. (2017));
since the baseline hazard function can take any functional form, it accounts for right
censoring and requires no assumptions about the distribution of event dates (Alhadab,
Clacher and Keasey (2015)). We conduct our analysis by employing the same model
specification as in Xing et al. (2017) and include in our model: (1) the natural logarithm of
total sales (LnSales); (2) the market-to-book ratio (MB); (3) the Z-score (Altman (1968)); and
(4) LnAge. We also consider an extended version of the model as presented in Gounopoulos
and Pham (2018), which captures various firm and offering characteristics. Specifically, we
account for (1) firm Diversification, measured as the number of business segments the firm
8 The CRSP delisting codes consist of the following six categories: active (100-171), mergers (200-290),
exchanges (300-390), liquidations (400-490), dropped (500-591), and expirations (600-610).
28
operates; (2) indicator variables signaling research and development (DR&D) and advertising
expenses reporting (DAdvert);9 and (3) LnAge, LnSales, Underwriter, BIG4, Vc, Profit, Lev,
MB, Cap_Exp, LnProceeds, and Underpricing as previously defined.
[Insert TABLE 9 about here]
TABLE 9 presents the results of the Cox proportional hazards model of probability of
failure and time-to-failure, which assesses the impact of unionization on IPO mortality. In
particular, the positive and significant (at the 1% level) coefficient of Union indicates that
unionized IPO firms have a higher probability of failure in the periods following the offering.
The hazard ratio of 1.898 implies that the failure risk of unionized IPOs increases by 89.8%
from IPOs without organized labor (see column 1 of TABLE 9). This effect is persistent even
after controlling for various firm factors influencing IPO survivability.
VI. Sensitivity Testing
A. Alternative Definition of Unionization
We substitute our main unionization proxy (i.e., existence of collective bargaining
coverage (Union)) with four alternative specifications. First, we measure unionization based
on the percentage of unionized employees (Pct_Union) as derived from company filings.
This percentage, however, is underreported when compared to Union. Second, we extract
firm-level unionization from a firm’s registration statement S-1 or its amended form (S-1/A),
similar to Xing et al. (2017). To this end, we employ the same process as for our main
unionization measure (see subsection III.B) and identify firms that report union
representation (Union_Prosp) and those that report no representation, as well as the
percentage of unionized employees (Pct_Union_Prosp) reported in company prospectuses
9 We include indicator variables for research and development and advertising expenses reporting instead of
their ratio over total assets, as in Gounopoulos and Pham (2018), since including the ratios results in a material
drop in the number of observations.
29
(i.e., the company’s S-1 statement or its amended form S-1/A). Third, we use data from the
Union Membership and Coverage Database (UMCD)10 in order to estimate an industry-level
unionization proxy (Union_Ind) by multiplying the percentage of employees covered by
collective bargaining in the industry with the number of employees over lagged total assets
(see, for example Chen et al. (2011a), Chyz et al. (2013), Hilary (2006)).
Running the models again, we find that all coefficients of alternative unionization
proxies are negative and statistically significant at 1% across all estimation methods, apart
from Union_Ind, which is insignificant (see TABLE 10). The results for Pct_Union and
Pct_Union_Prosp indicate that the higher the percentage of unionized employees in a
company at the stage of going public, the lower the returns to the investors in the immediate
aftermarket. This finding provides support to our hypotheses for negative association between
unionization and underpricing. However, the insignificant results for Union_Ind cast doubt
on the reliability of this industry-level proxy, revealing potential material measurement errors
between industry- and firm-level unionization proxies.
[Insert TABLE 10 about here]
B. Propensity Score Matching
Although we primarily rely on the Heckman, MLE, and IV approaches to mitigate
endogeneity concerns, we repeat our analysis employing a PSM approach. We use observable
firm-level characteristics in order to moderate the differences between treatment (unionized)
and control (non-unionized) samples (Shipman et al. (2017)). First, we use the same set of
unionization determinants as described in subsection III.C.1 to fit a probit model that
estimates the likelihood of each IPO being unionized (propensity score). Next, we employ a
10 Industry-level data are available through the UMCD, which is compiled from the Current Population Survey
(CPS). Since UMCD data are available in CIC codes, we employed the methodology described in footnote 3 to
transform CIC into SIC codes.
30
nearest-neighbor matching approach without replacement to match firms that are unionized
and non-unionized, based on closeness to the predicted value from the first step, but with the
restriction that matching pairs belong to the same year and to the same two-digit SIC
industry. This process yields 201 matching pairs11. When we conduct additional analyses
using the PSM sample, we verify that the magnitude of Union remains unaffected, though
statistically significant at 5% (see Column 1 in TABLE 11). The effect of unionization on
IPO underpricing persists when employing alternative unionization proxies (as described in
subsection VI.A) since all proxies attain a negative coefficient and remain statistically
significant at 1% for Pct_Union, Union_Prosp and Pct_Union_Prosp and at 5% for Union.
C. Right-to-Work Legislation, Unionization and IPO Underpricing
In order to provide further insights into the effect of unionization on IPO
underpricing, we use Right-to-Work (RTW) legislation at the state level to capture variations
in the effectiveness of labor unions (Chino (2016), Marciukaityte (2015)). According to the
National Labor Relations Act (Wagner Act, 1935), when a union receives more than 50% of
the votes in a bargaining unit, that union is entitled to represent all employees of the unit and
to demand union fees and dues from them. This union entitlement, however, has been toppled
by the passage of RTW laws since the mid-1940s, according to which unions may collect
payments from union members on a voluntary basis. Arguably, RTW laws constrain unions
by limiting organizing activity, curtailing financial resources, and weakening power in states
in which such legislation has been enacted (Marciukaityte (2015)).
11 Despite the fact that our PSM design successfully moderates the differences between unionized and non-
unionized IPOs, we do not achieve a sufficient reduction in standardized differences across covariates, as their
values lie outside the threshold of ±20 (Rosenbaum and Rubin (1985)). We redesigned the PSM and included
various caliper constraints (ranging from [0.2-0.01]) that yielded: 1) standardized differences outside the
threshold of ±20 and 2) extremely small samples. Although the PSM may not be perfect, many studies suggest
that it facilitates the conducting of more accurate analyses (see for e.g., Conniffe, Gash and O'Connell (2000)).
We use the PSM sample only as a complement to our main analysis.
31
[Insert TABLE 12 about here]
We divide our sample according to the effective year of RTW law inaction at the state
level (available through the National Right to Work Committee) in order to analyze the effect
of incremental union power on IPO underpricing. Among the first states to adopt RTW laws
were Arkansas and Florida (in 1944), followed by 19 more states by the mid-1980s. During
our sample period of 1997-2014, the number of states further increased by three, as
Oklahoma (2001), Indiana (2012), and Michigan (2013) adopted RTW laws. TABLE 12
reports the coefficients of all estimation methods as follows: the Heckman two-stage
procedure in Columns 1 and 4, the MLE two-equation treatment model in Columns 2 and 5,
and the IV method in Columns 3 and 6. We observe that, whether or not a state has enacted
RTW legislation, the effect of unionization is negative, though more prominently negative in
regions with incremental union power. Union is statistically significant at 1% for states
without RTW laws, but insignificant for states with such laws (apart from MLE and IV
estimates in Column 2 and 3, which are, respectively, statistically significant at 10% and 5%).
The Wald tests for homogeneity in the pairwise estimated coefficients (Columns 7 to 9) show
that the difference in coefficients between RTW and non-RTW states is statistically
significant at 1% across all estimation methods. Thus, we demonstrate that incremental union
power reduces underpricing.
[Insert TABLE 11 about here]
D. Alternative Sampling and Measurements
We conduct additional robustness exercises as follows: (1) we measure underpricing
at the end of the eleventh trading day and first trading month (Chambers and Dimson (2009));
(2) we exclude all IPOs that belong to the Finance, Insurance, and Real Estate sectors (2-digit
SIC 60-67) (Lowry and Shu (2002)); (3) we exclude Internet and Technology IPOs, as such
firms are characterized by excessive first-day returns (e.g., Aggarwal et al. (2002)) and are
32
less labor intensive; (4) we remove all observations during the bubble period of 1999-2000
(e.g., Çolak et al. (2017)); (5) we restrict our sample to two distinct core SIC divisions that
account for the vast majority (or 70.03%) of the total number of IPOs (both unionized and
non-unionized) in our sample, namely Services (35.46%) and Manufacturing (34.57%); (6)
we repeat analyses for the effect of unionization on IPO underpricing, P/V ratios and price
revisions after controlling for various participants in the IPO market (post-IPO ownership by
institutional investors and number of analysts following the firm) (e.g., Chemmanur and
Krishnan (2012)); and (7) we winsorize all continuous variables at the 5th and 95th
percentiles. None of these variations change our results.
VII. Summary and Conclusion
In this paper, we bring to the fore the role of organized labor in the IPO process by
investigating its impact on pricing behavior during the first trading day. Our empirical
evidence suggests a significant negative relation between labor unionization and IPO
underpricing, as the existence of unions reduces underpricing by 11.20%. Our additional
analyses indicate that this negative relationship reflects investors’ lower valuations of
unionized issuers, since the salient agency costs and higher cost of capital associated with
unionized firms, discourage investor participation. This effect is more pronounced for firms
headquartered in states without RTW laws, which empower unions.
To understand the potential mechanisms causing the negative relation between labor
unionization and IPO underpricing, we examine both the pre- and post-IPO periods. Our
results indicate that unionization increases cost of capital, as unionized firms have
significantly lower P/V ratios as compared to their non-unionized counterparts, attributable to
lower intrinsic value as compared to comparable peer firms. We reveal that, although
underwriters commence the price-discovery process from a high starting point, investors’
lower valuations and prospects for unionized IPOs instigate them towards price-protection,
33
which in turn drives offer prices down. Extending the examination period up to three years
following the IPO date, we find a negative and significant impact of unionization on firm
operating performance and firm survivorship, which affirms our speculation that modest
investor demand may stem from investors’ less-favorable valuations of the negative impact of
unionization on firm performance, which ultimately increases the firm’s failure risk. Our
results suggest that unionization constitutes a significant cost for the issuing firm and that a
company’s unionization status represents an important determinant of IPO valuation.
Our study carries important implications for managers and market participants such as
shareholders and underwriters. Our findings suggest that unionization affects a firm’s access
to public financing by reducing its expected influx of capital. Therefore, managers and
shareholders should factor unionization into their decision to bring the firm to the public
arena, as it entails additional costs for the issuing firm. By indicating the significant impact of
unionization on firm value, offering price and first-day returns, underwriters could benefit
from our results and adjust their IPO pricing strategy in accordance with the presence or
absence of organized labor in a firm. In addition, market participants should factor
unionization into their investment analyses, as unionization is a contributing factor to inferior
firm operating performance and to a higher probability of failure. Finally, market participants
should be aware that the negative relation between labor unionization and IPO underpricing
is more prominent in regions where legislation is supportive of the organizing activities of
unions.
34
Appendix A
Variable Definition
Panel A: IPO pricing
First-day return The difference between the first secondary market closing price available on CRSP
and IPO offer price, divided by IPO offer price. This variable is transformed into the
regression models by adding 1 and taking the natural logarithm (LnUnderpricing).
Revision The difference between IPO offer price and the midpoint of the initial filing price
range, divided by the midpoint of initial filing price range. This variable is
transformed into the regression models by adding 1 and taking the natural logarithm
(LnRevisions).
Panel B: Unionization
Union Binary indicator that equals 1 if the company's employees are covered by a collective
bargaining agreement, as reported in company filings, and 0 otherwise.
Pct_Union The percentage of a company's employees covered by collective bargaining
agreements, as reported in company filings.
Union_Prosp Binary indicator that equals 1 if the company's employees are covered by a collective
bargaining agreement, as reported in the company’s registration statement S-1 or its
corresponding amended form S-1/A, and 0 otherwise.
Pct_Union_Prosp The percentage of a company's employees covered by collective bargaining
agreements, as reported in the company’s registration statement S-1 or its
corresponding amended form S-1/A.
Union_Ind Industry level unionization, calculated as the product of the percentage of unionized
employees, from the Union Membership and Coverage Database (UMCD), in the
industry with the number of the company’s employees, over lagged total assets.
Panel C: IPO characteristics
Proceeds Gross proceeds, in millions of U.S. dollars, raised by the IPO. This variable is
estimated as shares offered times the offer price. The variable is transformed into the
regression models by adding 1 and taking the natural logarithm (LnProceeds).
Firm Age The number of years elapsed since the firm’s foundation to the IPO date, using
foundation dates from the Field-Ritter database. This variable is transformed into the
regressions by adding 1 and taking the natural logarithm (LnAge).
NASDAQ Binary indicator that equals 1 for NASDAQ listings, and 0 otherwise.
Internet Binary indicator that equals 1 for IPOs of internet firms, and 0 otherwise. Internet
firms are classified as those with business description sections in Thomson Financial
SDC containing any of the words “Internet”, “Online”, “eBusiness”, “eCommerce”,
and “Website”.
Technology Binary indicator that equals 1 for IPO firms with the SIC codes: 3571, 3572, 3575,
3577 or 3578 (i.e. computer hardware); 3661, 3663 or 3669 (i.e. communications
equipment); 3671, 3672, 3674, 3675, 3677, 3678 or 3679 (i.e. electronics); 3812 (i.e.
navigation equipment); 3823, 3825, 3826, 3827 or 3829 (i.e. measuring and
controlling devices); 3841 or 3845 (i.e. medical instruments); 4812 or 4813 (i.e.
telephone equipment); 4899 (i.e. communications services); and 7371, 7372, 7373,
7374, 7375, 7378 or 7379 (i.e. software), and 0 otherwise.
Vc Binary indicator that equals 1 for firms with venture capital backing, and 0 otherwise.
Underwriter Binary indicator that equals 1 for new listings employing underwriters of the highest
prestige ranking, following Loughran and Ritter (2004), and 0 otherwise.
BIG4 Binary indicator that equals 1 for the existence of a reputable auditor, and 0
otherwise. Reputable auditors are considered as the Big-4 (namely, Price Waterhouse
Coopers, Deloitte, Ernst and Young, and KPMG).
Overhang The ratio of the shares that pre-IPO shareholders retain over the number of new
shares issued in the offering.
Primary Binary indicator that equals 1 if the offering is exclusively primary, and 0 otherwise.
Hot_Month Binary indicator that equals 1 for the month being classified in the top quartile (above
19%) in terms of market underpricing, and 0 otherwise.
Crunch Binary indicator that equals 1 for IPOs within the financial (credit crunch) crisis of
2007-2008, and 0 otherwise.
(continued on next page)
35
(continued)
D: Firm fundamentals
DEPS Binary indicator that equals 1 for positive earnings per share during the last fiscal
year prior to IPO, and 0 otherwise.
Leverage The ratio of total liabilities over total assets in the last fiscal year prior to IPO.
Panel E: P/V ratios
P/V (Sales) The ratio of (𝑃/𝑆𝑎𝑙𝑒𝑠)𝐼𝑃𝑂/(𝑃/𝑆𝑎𝑙𝑒𝑠)𝑀𝑎𝑡𝑐ℎ𝑖𝑛𝑔 𝐹𝑖𝑟𝑚, where (P/Sales) is the price
multiplied with shares outstanding over sales. For the IPO firm, we use offer price
and shares outstanding prior to the IPO. For the matching firm, we use the closing
price and shares outstanding on the IPO date.
P/V (EBITDA) The ratio of (𝑃/EBITDA)𝐼𝑃𝑂/(𝑃/EBITDA)𝑀𝑎𝑡𝑐ℎ𝑖𝑛𝑔 𝐹𝑖𝑟𝑚, where (P/EBITDA) is the
price multiplied with shares outstanding over EBITDA. For the IPO firm, we use
offer price and shares outstanding prior to the IPO. For the matching firm, we use the
closing price and shares outstanding on the IPO date. The value of P/EBITDA is set
to missing if EBITDA is negative.
P/V (Earnings) The ratio of (𝑃/Earnings)𝐼𝑃𝑂/(𝑃/Earnings)𝑀𝑎𝑡𝑐ℎ𝑖𝑛𝑔 𝐹𝑖𝑟𝑚, where (P/Earnings) is the
price multiplied with shares outstanding over Earnings. For the IPO firm, we use
offer price and shares outstanding prior to the IPO. For the matching firm, we use the
closing price and shares outstanding on the IPO date. The value of P/Earnings is set
to missing if Earnings are negative.
Panel F: Firm operating performance determinants
TobinsQ The market value of assets divided by the book value of assets at the fiscal year-end
(where the market value of assets is calculated as the book value of assets plus the
market value of common stock less the sum of the book value of common stock and
balance-sheet-deferred taxes).
Profitability Earnings before interest, tax, depreciation, and amortization over total assets.
Dividends Dividends over the book value of equity.
Cap_Exp Net capital expenditures over total assets.
Lev The ratio of total liabilities over total assets.
Panel G: Firm mortality determinants
LnSales Natural logarithm of total sales.
MB Market to book value of equity.
Z-score Proxy for the probability of bankruptcy (Altman (1968)), calculated as [0.6 × [Market
value of equity/Book value of total debt] + [3.3 × Earnings before interest and taxes +
Sales + 1.4 × Retained earnings + 1.2 × Working capital]/Book value of assets].
DR&D Binary indicator that equals 1 if the company reports research and development
expenses, and 0 otherwise.
DAdvert Binary indicator that equals 1 if the company reports advertising expenses, and 0
otherwise.
Diversification Number of business segments in which the firm operates.
Panel H: Unionization determinants
PTWork Natural logarithm of the percentage of part-time workers per CIC industry.
Cash Total cash and investment securities over total assets.
LnAssets Natural logarithm of total assets.
Inventory Levels of inventory over total assets.
ROA Return on assets, measured as the ratio of income before extraordinary items to total
assets at year-end, multiplied by 100.
Tangibility Net property plant and equipment over total assets.
36
Appendix B
TABLE B 1
First stage results of main analysis
Determinants of unionization for IPO firms. This table reports the first-stage results of TABLE 4, for the
Heckman (Column 1), maximum likelihood estimation (Column 2) and IV approaches (Column 3). The
probability of unionization is regressed on all TABLE 4 covariates and a list of unionization determinants (see
subsection III.C.1). The sample consists of 1,568 U.S. IPOs over the period 1997-2014. The z-statistics in
parentheses are based on standard errors adjusted for heteroskedasticity. * indicates significance at the 10%
level; ** at the 5% level; and *** at the 1% level. Numbers are rounded up to the third decimal place. All
variables are defined in Appendix A.
Variables (1) (2) (3)
Heckman MLE IV
PTWork -3.104*** -2.996*** -0.588**
(-4.33) (-4.10) (-2.10)
LnAssets 0.104* 0.085 0.062***
(1.71) (1.50) (5.40)
Cash -1.280*** -1.643*** -0.081**
(-3.85) (-5.22) (-2.34)
Inventory 0.723* 0.623* 0.104
(1.87) (1.73) (0.96)
ROA 0.003 0.001 -0.000
(0.91) (0.28) (-0.76)
Tangibility 0.634*** 0.575*** 0.122**
(3.81) (3.79) (2.15)
LnProceeds -0.050 0.001 -0.012
(-0.62) (0.01) (-0.91)
LnAge 0.242*** 0.202*** 0.042***
(4.95) (4.00) (3.91)
NASDAQ -0.496*** -0.425*** -0.092***
(-4.22) (-3.56) (-4.21)
Internet -0.869 -0.764** -0.001
(-1.44) (-2.22) (-0.08)
Technology -0.568*** -0.491*** -0.083***
(-3.40) (-3.29) (-2.63)
Vc -0.577*** -0.455** -0.060***
(-3.17) (-2.57) (-3.78)
Underwriter 0.178 0.254* -0.001
(1.40) (1.94) (-0.06)
BIG4 0.259* 0.249* 0.018
(1.88) (1.85) (1.04)
Overhang -0.004 -0.000 -0.001**
(-0.44) (-0.02) (-2.10)
Primary 0.174 0.153 0.022
(1.51) (1.39) (1.23)
Leverage 0.049 0.041 -0.002
(0.50) (0.65) (-0.20)
DEPS -0.124 -0.069 -0.032
(-1.09) (-0.65) (-1.65)
Hot_Month 0.196 0.306** 0.036
(1.58) (2.46) (1.24)
Crunch -0.249 -0.199 -0.126
(-0.98) (-0.81) (-0.78)
(intercept) -1.485*** -1.541*** 0.053
(-3.74) (-3.82) (0.51)
Year and Ind. Dummies Yes Yes Yes
Pseudo R2 0.386 0.386 0.384
Observations 1,568 1,568 1,568
37
Appendix C
We provide examples of sentences parsed from company filings and the relevant scoring for
collective bargaining representation (in bold).
1. Metaldyne Performance Group (IPO date December 12, 2014)
Form 10-K (for the fiscal year ending December 31, 2014)
Item 1. Business
Employees
As of December 31, 2014, we employed approximately 12,000 total employees in 13
countries. As of December 31, 2014, approximately 38% of our employees were employed
under the terms of collective bargaining agreements with industrial trade unions or
employed under international workers councils.
2. Stock Building Supply Holdings (IPO date September 8, 2013)
Form 10-K (for the fiscal year ending December 31, 2013)
Item 1. Business
Employees
At January 31, 2014, we had approximately 3,025 full-time equivalent employees, none of
whom were represented by a union. We believe that we have good relations with our
employees. Additionally, we believe that the training provided through our ongoing
development programs to our professional employees and an entrepreneurial, performance-
based culture provide significant benefits to our customers.
38
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TABLE 1
Summary statistics for unionized vs. non-unionized IPOs
This table reports the summary statistics for a sample of 1,568 U.S. IPOs over the period 1997-2014, along with
the sub-samples of unionized and non-unionized IPOs. IPO deals are retrieved from the Securities Data
Company (SDC) Database. The left-hand side of Panel A reports the distribution across time, both in absolute
numbers and percentages of the total sample. The right side presents the allocation of IPOs across Standard
Industrial Classification (SIC) divisions with a high participation of unionized IPOs. Panel B describes IPOs by
SIC division and company-specific information.
Panel A: Distribution across time of unionized and non-unionized IPOs
Year Entire Sample Non-Unionized
IPOs Unionized IPOs
Unionized IPOs -
Manufacturing
Sector
Unionized IPOs -
Services Sector
No. % No. % No. % No. % No. %
1997 140 8.93 118 8.68 22 10.58 10 10.87 4 10.81
1998 98 6.25 78 5.74 20 9.62 9 9.78 3 8.11
1999 202 12.88 195 14.34 7 3.37 1 1.09 3 8.11
2000 183 11.67 174 12.79 9 4.33 4 4.35 1 2.70
2001 47 3.00 37 2.72 10 4.81 5 5.43 0 0.00
2002 38 2.42 28 2.06 10 4.81 3 3.26 3 8.11
2003 37 2.36 32 2.35 5 2.40 2 2.17 0 0.00
2004 103 6.57 87 6.40 16 7.69 7 7.61 2 5.41
2005 95 6.06 71 5.22 24 11.54 11 11.96 4 10.81
2006 101 6.44 82 6.03 19 9.13 14 15.22 3 8.11
2007 74 4.72 67 4.93 7 3.37 4 4.35 2 5.41
2008 6 0.38 5 0.37 1 0.48 1 1.09 0 0.00
2009 23 1.47 21 1.54 2 0.96 0 0.00 2 5.41
2010 65 4.15 58 4.26 7 3.37 5 5.43 0 0.00
2011 56 3.57 47 3.46 9 4.33 1 1.09 3 8.11
2012 69 4.40 62 4.56 7 3.37 4 4.35 1 2.70
2013 98 6.25 82 6.03 16 7.69 4 4.35 4 10.81
2014 133 8.48 116 8.53 17 8.17 7 7.61 2 5.41
Total 1,568 100.00 1,360 100.00 208 100.00 92 100.00 37 100.00
Panel B: Allocation of unionized and non-unionized IPOs by SIC division and company specific
information
Entire Sample
(N = 1,568)
Unionized IPOs
(N = 208)
Non-Unionized
IPOs
(N = 1,360)
SIC Division No. % No. % No. %
Agriculture., Forestry and Fishing (2-digit SIC
01-09) 2 0.13 0 0.00 2 0.15
Mining (2-digit SIC 10-14) 51 3.25 6 2.88 45 3.31
Construction (2-digit SIC 15-17) 15 0.96 4 1.92 11 0.81
Manufacturing (2-digit SIC 20-39) 542 34.57 92 44.23 450 33.09
Transportation, Communication. & Utilities (2-
digit SIC 40-49) 117 7.46 36 17.31 81 5.96
Wholesale trade (2-digit SIC 50-51) 30 1.91 14 6.73 16 1.18
Retail trade (2-digit SIC 52-59) 110 7.02 12 5.77 98 7.21
Finance, Insurance. and Real Est. (2-digit SIC
60-67) 144 9.18 7 3.37 137 10.07
Services (2-digit SIC 70-89) 556 35.46 37 17.79 519 38.16
Public Administration (2-digit SIC 91-99) 1 0.06 0 0.00 1
Company specifics No. % No. % No. %
Internet IPOs 154 9.82 1 0.48 153 11.25
Technology IPOs 599 38.20 15 7.21 584 42.94
VC-backed IPOs 750 47.83 10 4.81 740 54.41
NASDAQ IPOs 1,107 70.60 67 32.21 1,040 76.47
45
TABLE 2
Top-15 and bottom-15 IPOs based on level of unionization
Selected characteristics of the top-15 (Panel A) and bottom-15 IPOs (Panel B) in terms of unionization, for a
sample of 1,568 U.S. IPOs over the period 1997-2014. IPO deals are retrieved from the Securities Data
Company (SDC) Database. IPO first-day returns are calculated as the percentage changes from first-day closing
price to offer price. The annual averages of first-day returns are derived through Jay Ritter's website
(https://site.warrington.ufl.edu/ritter/ipo-data/).
Panel A: Top-15 unionized IPOs
IPO date Company Sector Company
unionization
Age
at
IPO
1st-day
return
(company)
1st-day
return
(annual
avg.)
7/14/2005 Orchids Paper Products
Co Manufacturing
84.85% 30 6.25% 10.10%
11/20/2006 Spirit AeroSystems
Holdings Manufacturing
81.00% 56 11.54% 11.60%
9/8/2007 Horsehead Holding Corp Manufacturing 79.00% 159 3.33% 14.50%
5/26/2004 Republic Airways
Holdings Inc
Transp., Commun.
& Utilities 78.13% 8 5.92% 12.30%
5/16/2005 Xerium Technologies Inc Manufacturing 76.81% 105 0.00% 10.10%
9/3/1998 Ladish Co Inc Manufacturing 76.55% 11 5.56% 21.40%
10/14/2010 Tower International Inc Manufacturing 70.51% 18 0.00% 9.40%
4/17/2002 ExpressJet Holdings Inc Transp., Commun.
& Utilities 70.00% 6 0.00% 8.70%
6/4/2006 Sealy Corp Manufacturing 68.00% 125 9.38% 11.60%
9/26/2005 Horizon Lines Inc Transp., Commun.
& Utilities 67.73% 49 7.50% 10.10%
7/21/2004 Dex Media Inc Manufacturing 67.00% 100 2.63% 12.30%
11/17/2010 General Motors Co Manufacturing 64.00% 102 3.61% 9.40%
11/13/2000 Orion Power Holdings
Inc
Transp., Commun.
& Utilities 63.68% 2 0.00% 57.00%
7/27/2000 Lexent Inc Services 63.45% 2 0.00% 57.00%
2/11/2006 Innophos Holdings Inc Manufacturing 60.22% 18 0.58% 11.60%
Panel B: Bottom-15 unionized IPOs
IPO date Company Sector Company
unionization
Age
at
IPO
1st-day
return
(company)
1st-day
return
(annual
avg.)
12/15/2010 Swift Transportation Co Transp., Commun.
& Utilities 3.89% 44 0.91% 9.40%
11/3/1998 CSK Auto Inc Retail trade 3.78% 81 17.80% 21.40%
11/18/2010 Aeroflex Holding Corp Manufacturing 3.45% 73 0.00% 9.40%
7/27/2000 Genencor International
Inc Manufacturing
3.06% 18 20.14% 57.00%
11/5/2011 NGL Energy Partners LP Wholesale trade 2.70% 1 0.00% 13.60%
1/12/1997 US Vision Inc Retail trade 2.47% 30 0.00% 13.80%
11/22/2004 UAP Holding Corp Manufacturing 2.42% 26 2.00% 12.30%
9/6/1998 School Specialty Inc Wholesale trade 2.05% 38 2.42% 21.40%
12/18/2013 AMC Entertainment
Holdings Services
2.00% 93 5.00% 21.00%
9/24/2014 Smart & Final Stores Inc Retail trade 1.84% 143 0.08% 14.30%
10/8/2005 CF Industries Holdings
Inc Manufacturing
1.73% 59 1.25% 10.10%
11/4/2012 MRC Global Inc Wholesale trade 1.67% 31 0.19% 18.10%
9/5/2007 AECOM Technology
Corp Services
1.03% 27 5.50% 14.50%
6/27/2013 HD Supply Holdings Inc Wholesale trade 1.00% 80 3.67% 21.00%
3/2/2005 American Reprographics
Co Services 0.53% 45 6.15% 10.10%
46
TABLE 3
Descriptive statistics and correlation matrix of IPO firms.
This table provides descriptive statistics (Panel A) and pairwise correlations (Panel B) for a sample of 1,568 U.S. IPOs over the period 1997-2014, along with the sub-
samples of unionized and non-unionized IPOs. All IPOs come from the Securities Data Company (SDC) database. Share price data is from CRSP; accounting data is from
Compustat. In Panel A, we report descriptive statistics and include the mean, median, minimum, maximum and standard deviation for the dependent variables and all control
variables used in the subsequent regressions. We also provide a t-test for difference in means between sub-samples. Panel B reports pairwise correlations of the variables used
in the study. First-day return is expressed in percentage and proceeds are in millions of U.S. dollars. * indicates significance at the 10% level; ** at the 5% level; and *** at
the 1% level. Numbers in Panel A and B are rounded up to the third and second decimal place respectively. All variables are defined in Appendix A.
Panel A: Descriptive statistics
Variable Entire sample (N = 1,568) Unionized IPOs (N = 208) Non-unionized IPOs (N = 1,360) Mean
diff. Mean Median Min Max StDev Mean Median Min Max StDev Mean Median Min Max StDev
First-day return 35.344 15 -0.88 605.6 58.594 13.594 7.5 -0.83 180 22.643 38.671 16.855 -0.88 605.6 61.621 25.077***
Revision 0.019 0.024 -0.5 0.455 0.136 -0.006 0 -0.292 0.4 0.119 0.023 0.031 -0.5 0.455 0.138 0.029**
Union 0.133 0 0 1 0.339 1 1 1 1 0 0 0 0 0 0 -
Proceeds 148.57 76.935 2.65 15774 460.499 372.08 160.395 2.65 15774 1170.92 114.38 72 3.37 1876 163.899 -257.697***
Firm Age 18.7 9 0 165 25.781 43.75 31 0 165 40.08 14.869 8 0 149 20.273 -28.881***
NASDAQ 0.706 1 0 1 0.456 0.322 0 0 1 0.468 0.765 1 0 1 0.424 0.443***
Internet 0.098 0 0 1 0.298 0.005 0 0 1 0.069 0.113 0 0 1 0.316 0.108***
Technology 0.382 0 0 1 0.486 0.072 0 0 1 0.259 0.429 0 0 1 0.495 0.357***
Vc 0.478 0 0 1 0.5 0.048 0 0 1 0.214 0.544 1 0 1 0.498 0.496***
Underwriter 0.508 1 0 1 0.5 0.688 1 0 1 0.465 0.48 0 0 1 0.5 -0.207***
BIG4 0.786 1 0 1 0.41 0.846 1 0 1 0.362 0.777 1 0 1 0.416 -0.069*
Overhang 5.385 3.005 -0.1 114.9 10 3.843 2.445 0 68.17 6.385 5.62 3.07 -0.1 114.9 10.425 1.778*
Primary 0.666 1 0 1 0.472 0.615 1 0 1 0.488 0.674 1 0 1 0.469 0.059
Hot_Month 0.351 0 0 1 0.478 0.245 0 0 1 0.431 0.368 0 0 1 0.482 0.122***
Crunch 0.051 0 0 1 0.22 0.038 0 0 1 0.193 0.053 0 0 1 0.224 0.014
Leverage 0.561 0.424 0 11.79 0.672 0.588 0.585 0 1.736 0.328 0.557 0.377 0 11.79 0.71 -0.031
DEPS 0.327 0 0 1 0.469 0.495 0 0 1 0.501 0.301 0 0 1 0.459 -0.194***
47
Panel B: Correlation Matrix
Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)
1. Union 1.00
2. LnProceeds 0.26*** 1.00
3. LnAge 0.30*** 0.24*** 1.00
4. NASDAQ -0.33*** -0.39*** -0.20*** 1.00
5. Internet -0.12*** -0.07*** -0.21*** 0.10*** 1.00
6. Technology -0.25*** -0.17*** -0.21*** 0.18*** 0.20*** 1.00
7. Vc -0.34*** -0.24*** -0.31*** 0.33*** 0.17*** 0.36*** 1.00
8. Underwriter 0.14*** 0.41*** 0.05* -0.27*** 0.01 0.03 0.02 1.00
9. BIG4 0.06** 0.21*** 0.04* -0.07*** 0.03 0.07*** 0.12*** 0.21*** 1.00
10. Overhang -0.06** -0.26*** -0.07*** 0.04* 0.08*** 0.11*** 0.13*** 0.13*** 0.03 1.00
11. Primary -0.04* -0.25*** -0.23*** 0.10*** 0.03 0.02 0.13*** -0.11*** -0.05* 0.08*** 1.00
12. Leverage 0.02 -0.02 0.03 0.01 -0.04* -0.04 -0.04 -0.01 -0.01 -0.01 0.01 1.00
13. DEPS 0.14*** 0.16*** 0.28*** -0.22*** -0.12*** -0.14*** -0.31*** 0.04 0.03 -0.09*** -0.29*** -0.08*** 1.00
14. Hot_Month -0.09*** -0.23*** -0.21*** 0.16*** 0.21*** 0.25*** 0.16*** -0.07*** -0.02 0.19*** 0.21*** -0.04 -0.14*** 1.00
15. Crunch -0.02 0.11*** 0.00 0.00 -0.03 0.00 0.00 0.03 0.01 -0.06** -0.09*** -0.01 0.07*** -0.13***
48
TABLE 4
Effect of unionization on IPO underpricing.
This table reports the results of regressions of IPO underpricing (dependent variable) on a Union dummy variable
and other control variables for a sample of 1,568 U.S. IPOs over the period 1997-2014. Union takes the value of 1 if
the company’s employees are covered by a collective bargaining agreement, and 0 otherwise. All variables are
defined in Appendix A. Four estimation procedures are used: ordinary least-squares (Column 1), Heckman two-
stage (Column 2), maximum likelihood estimation (Column 3) and generated IV approach (Column 4). The first-
stage results are reported in Appendix B. The t-statistics (column 1) and z-statistics (columns 2 to 4) in parentheses
are based on standard errors adjusted for heteroskedasticity. The dependent and control variables are winsorized at
the 1st and 99th percentiles. The lower part of the table provides the Wald and Hausman statistics, based on the
MLE and IV estimations respectively. All regressions include industry (at the 2-digit level of SIC code) and
calendar year dummies. * indicates significance at the 10% level; ** at the 5% level; and *** at the 1% level.
Numbers are rounded up to the third decimal place.
Variables Exp Sign (1) (2) (3) (4)
OLS Heckman MLE IV
Union - -0.381*** -1.442*** -1.397*** -2.464***
(-2.72) (-4.18) (-4.99) (-4.08)
LnProceeds - 0.152*** 0.176*** 0.174*** 0.237***
(3.11) (3.71) (3.57) (4.23)
LnAge - -0.015 0.056 0.054 0.104*
(-0.33) (1.18) (1.14) (1.87)
NASDAQ + 0.108 -0.009 -0.004 -0.119
(1.18) (-0.09) (-0.04) (-0.99)
Internet + 0.223* 0.220* 0.227* 0.214*
(1.69) (1.69) (1.78) (1.66)
Technology + 0.489*** 0.424*** 0.425*** 0.333**
(3.37) (3.29) (2.98) (2.12)
Vc ? 0.480*** 0.377*** 0.380*** 0.271**
(4.95) (3.92) (3.82) (2.47)
Underwriter + 0.349*** 0.396*** 0.394*** 0.401***
(4.09) (4.72) (4.67) (4.52)
BIG4 - -0.100 -0.052 -0.054 -0.052
(-1.00) (-0.55) (-0.54) (-0.50)
Overhang + 0.015*** 0.015*** 0.015*** 0.016***
(4.13) (3.80) (4.15) (4.22)
Primary + -0.162** -0.126 -0.129 -0.097
(-2.00) (-1.49) (-1.60) (-1.10)
Leverage - -0.009 -0.014 -0.013 -0.031
(-0.18) (-0.26) (-0.25) (-0.56)
DEPS - -0.072 -0.081 -0.080 -0.120
(-0.84) (-0.95) (-0.94) (-1.30)
Hot_Month + 0.347** 0.388*** 0.394*** 0.420***
(2.40) (3.04) (2.79) (2.85)
Crunch ? 0.558 0.477 0.514 0.260
(1.08) (0.86) (0.97) (0.35)
(intercept)
0.405 0.206 0.186 0.113
(1.28) (0.15) (0.58) (0.33)
Inverse Mills ratio
0.668***
(3.32)
Year and Ind. Dummies
Yes Yes Yes Yes
R2 (OLS)
0.301
Adj. R2 (OLS)
0.257
Mean VIF
1.280
Wald test
13.030***
Hausman test
14.817***
Observations 1,568 1,568 1,568 1,568
49
TABLE 5
Unionization and cost of capital: Price-to-Value ratio analysis.
This table compares the price-to-value (P/V) ratios of unionized IPOs and non-unionized IPOs, relative to
matching comparable firms using a Propensity Score Matching method. The three P/V ratios are P/V (Sales),
P/V (EBITDA), and P/V (Earnings), and their construction is explained in section V.A. P/V ratios are
winsorized at the 1st and 99th percentiles. We provide descriptive statistics and include the mean, median, and
standard deviation, accompanied with tests for equality of the means (t -test) and the medians (Wilcoxon rank-
sum test) between the unionized and non-unionized IPOs subsamples, both for the full sample (Panel A) and a
propensity score matched sample (Panel B). ** indicates significance at the 5% level, and *** at the 1% level.
Numbers are rounded up to the third decimal place.
Panel A: Summary statistics for full sample
Variable Unionized IPOs Non-Unionized IPOs Mean
diff.
Median
diff. Obs Mean Median StDev Obs Mean Median StDev
P/V (Sales) 207 1.697 0.265 8.088 1,280 5.736 0.588 14.793 4.039*** 0.323***
P/V (EBITDA) 184 0.464 0.243 0.922 665 1.279 0.397 2.868 0.815*** 0.154***
P/V (Earnings) 124 0.849 0.227 2.1 482 1.297 0.394 2.899 0.448 0.167***
Panel B: Summary statistics for propensity score matched sample
Variable Unionized IPOs Non-Unionized IPOs Mean
diff.
Median
diff. Obs Mean Median StDev Obs Mean Median StDev
P/V (Sales) 200 1.157 0.262 3.697 192 2.738 0.435 6.479 1.581** 0.173***
P/V (EBITDA) 177 0.462 0.238 0.89 127 0.829 0.418 1.132 0.367** 0.18***
P/V (Earnings) 119 0.694 0.224 1.261 88 0.894 0.463 1.187 0.199 0.239***
50
TABLE 6
Unionization impact on P/V ratios.
This table reports the results of regressions of the natural logarithm of P/V ratios on a Union dummy variable and other control
variables over the period 1997-2014. The three dependent variables are P/V (Sales) - (columns 1-3), P/V (EBITDA) - (columns 4-6),
and P/V (Earnings) - (columns 7-9), and their construction is explained in section V.A. Union takes the value of 1 if the company’s
employees are covered by a collective bargaining agreement, and 0 otherwise. All variables are defined in Appendix A. Three
estimation procedures are used: Heckman two-stage (Columns 1, 4 and 7), maximum likelihood estimation (Columns 2, 5 and 8) and
generated IV approach (Columns 3, 6 and 9). The z-statistics in parentheses are based on standard errors adjusted for
heteroskedasticity. The dependent and control variables are winsorized at the 1st and 99th percentiles. The lower part of the table
provides the Wald and Hausman statistics, based on the MLE and IV estimations respectively. All regressions include industry (at the
2-digit level of SIC code) and calendar year dummies. * indicates significance at the 10% level; ** at the 5% level; and *** at the 1%
level. Numbers are rounded up to the third decimal place.
Variables
(1) (2) (3) (4) (5) (6) (7) (8) (9)
LnP/V(Sales) LnP/V(EBITDA) LnP/V(Earnings)
Heckman MLE IV Heckman MLE IV Heckman MLE IV
Union -1.368*** -2.877*** -4.735*** -1.327*** -1.585*** -2.294*** -1.376*** -1.605*** -0.809*
(-2.87) (-12.38) (-4.12) (-3.99) (-8.81) (-3.14) (-3.23) (-4.63) (-1.76)
LnAssets -0.225*** -0.130** 0.062 -0.213*** -0.195*** -0.102 -0.192*** -0.191*** -0.206***
(-3.87) (-2.45) (0.56) (-4.57) (-4.53) (-1.27) (-3.44) (-3.78) (-3.45)
Vc 0.162 -0.016 -0.198 0.328*** 0.313*** 0.287** 0.034 -0.007 0.133
(1.18) (-0.12) (-1.04) (2.78) (2.61) (2.08) (0.22) (-0.05) (0.82)
Underwriter -0.030 -0.001 -0.059 0.105 0.137 0.093 0.337** 0.364*** 0.303**
(-0.26) (-0.01) (-0.43) (1.03) (1.29) (0.81) (2.48) (2.69) (2.25)
Overhang -0.029*** -0.031*** -0.033*** -0.023*** -0.024*** -0.023*** -0.021** -0.022*** -0.021***
(-5.48) (-4.92) (-5.12) (-3.65) (-3.93) (-3.45) (-2.49) (-2.84) (-2.90)
(intercept) 0.410 0.122 0.156 2.135* 2.175*** 2.132*** 1.975 1.900*** 2.110***
(0.22) (0.45) (0.44) (1.79) (8.86) (6.92) (1.50) (4.85) (5.51)
Inverse Mills ratio 0.632**
0.761***
0.837***
(2.30)
(3.96)
(3.42)
Year and Ind.
Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
R2 (OLS)
Adj. R2 (OLS)
Mean VIF
Wald test
111.726***
81.844***
22.649***
Hausman test
24.144***
13.047***
1.155
Observations 1,487 1,487 1,487 849 849 849 606 606 606
51
TABLE 7
Unionization impact on price revisions.
This table reports the results of regressions of IPO price revisions (dependent variable) on a Union dummy
variable and other control variables for a sample of 1,568 U.S. IPOs over the period 1997-2014. Union takes the
value of 1 if the company’s employees are covered by a collective bargaining agreement, and 0 otherwise. All
variables are defined in Appendix A. Three estimation procedures are used: Heckman two-stage (Column 1),
maximum likelihood estimation (Column 1) and generated IV approach (Column 3). The z-statistics in
parentheses are based on standard errors adjusted for heteroskedasticity. The dependent and control variables are
winsorized at the 1st and 99th percentiles. The lower part of the table provides the Wald and Hausman statistics,
based on the MLE and IV estimations respectively. All regressions include industry (at the 2-digit level of SIC
code) and calendar year dummies. * indicates significance at the 10% level; * at the 5% level; and *** at the 1%
level. Numbers are rounded up to the third decimal place.
Variables (1) (2) (3)
Heckman MLE IV
Union -0.129*** -0.168*** -0.271***
(-3.98) (-9.68) (-4.24)
LnProceeds 0.042*** 0.043*** 0.050***
(9.47) (9.29) (8.59)
LnAge -0.006 -0.003 0.001
(-1.29) (-0.72) (0.20)
NASDAQ -0.009 -0.013 -0.024**
(-0.92) (-1.42) (-1.99)
Internet 0.012 0.014 0.012
(1.01) (1.18) (0.98)
Technology 0.002 0.002 -0.010
(0.21) (0.17) (-0.71)
Vc 0.008 0.004 -0.006
(0.91) (0.39) (-0.60)
Underwriter 0.021*** 0.023*** 0.022**
(2.65) (2.80) (2.58)
BIG4 -0.006 -0.005 -0.005
(-0.71) (-0.57) (-0.57)
Overhang 0.002*** 0.001*** 0.002***
(4.11) (4.15) (4.03)
Primary -0.011 -0.010 -0.007
(-1.39) (-1.34) (-0.79)
Leverage -0.011** -0.011* -0.013**
(-2.14) (-1.85) (-2.22)
DEPS -0.018** -0.018** -0.023**
(-2.26) (-2.14) (-2.46)
Hot_Month 0.048*** 0.051*** 0.053***
(4.02) (4.19) (3.86)
Crunch -0.053 -0.049 -0.080
(-1.02) (-1.03) (-1.12)
(intercept) 0.018 -0.001 0.004
(0.14) (-0.02) (0.11)
Inverse Mills ratio 0.069***
(3.69)
Year and Ind. Dummies Yes Yes Yes
Wald test
57.704***
Hausman test
24.826***
Observations 1,568 1,568 1,568
52
TABLE 8
Unionization impact on firm operating performance.
This table reports the results of regressions of Tobin’s Q (dependent variable) on a Union dummy variable and
other control variables for a sample of 1,094 U.S. IPOs over the period 1997-2014. Union takes the value of 1 if
the company’s employees are covered by a collective bargaining agreement, and 0 otherwise. All variables are
defined in Appendix A. Three estimation procedures are used: Heckman two-stage (Column 1), maximum
likelihood estimation (Column 2) and generated IV approach (Column 3). The z-statistics in parentheses are
based on standard errors adjusted for heteroskedasticity. The dependent and control variables are winsorized at
the 1st and 99th percentiles. The lower part of the table provides the Wald and Hausman statistics, based on the
MLE and IV estimations respectively. All regressions include industry (at the 2-digit level of SIC code) and
calendar year dummies. * indicates significance at the 10% level; ** at the 5% level; and *** at the 1% level.
Numbers are rounded up to the third decimal place.
Variables (1) (2) (3)
Heckman MLE IV
Union -2.162*** -2.784*** -5.803***
(-7.59) (-27.63) (-10.47)
Profitability 0.412*** 0.472** 0.282
(2.88) (2.48) (1.41)
LnAssets 0.072** 0.089*** 0.182***
(2.30) (2.89) (3.57)
LnAge -0.130*** -0.080** 0.236***
(-2.85) (-2.17) (3.19)
Lev -0.650*** -0.428** 0.565*
(-3.59) (-2.52) (1.91)
Dividends 1.776*** 1.793*** 3.117***
(3.18) (2.76) (3.13)
Cap_Exp 0.596 0.393 -1.048*
(1.19) (0.76) (-1.85)
(intercept) 1.736* 1.407*** 2.459***
(1.84) (5.36) (6.79)
Inverse Mills ratio 1.048***
(6.41)
Year Dummies Yes Yes Yes
Ind. Dummies Yes Yes Yes
Wald test
509.547***
Hausman test
206.545***
Observations 4,376 4,376 4,376
53
TABLE 9
Unionization impact on firm mortality.
This table reports the estimation of the Cox proportional hazards model of the probability of failure and time to
failure for a sample of 1,568 U.S. IPOs over the period 1997-2014. Union takes the value of 1 if the company’s
employees are covered by a collective bargaining agreement, and 0 otherwise. The t-statistics are shown in
parentheses below the coefficient estimates. All variables are defined in Appendix A. Control variables are
winsorized at the 1st and 99th percentiles. The lower part of the table provides the Chi-square and Chi-square
test probability. All regressions include industry dummies (at the 2-digit level of SIC code). * indicates
significance at the 10% level; ** at the 5% level; and *** at the 1% level. Numbers are rounded up to the third
decimal place.
Variables (1) (2)
Coefficient Hazard ratio Coefficient Hazard ratio
Union 0.641*** 1.898 0.526** 1.692
(2.60)
(2.07)
LnAge -0.228*** 0.796 -0.215*** 0.807
(-2.74)
(-2.60)
LnSales -0.469*** 0.626 -0.284*** 0.753
(-11.18)
(-4.76)
MB -0.017 0.983 -0.020 0.980
(-0.47)
(-0.41)
Z-score 0.069*** 1.072
(3.44)
Underwriter
-0.735*** 0.479
(-4.76)
BIG4
0.198 1.219
(1.26)
Vc
0.128 1.136
(0.80)
Profitability
-0.063 0.939
(-0.22)
Lev
0.705** 2.023
(2.25)
DR&D
-0.537*** 0.585
(-2.90)
DAdvert
-0.133 0.875
(-0.91)
Cap_Exp
0.853 2.346
(1.60)
Diversification
-0.173 0.841
(-0.90)
LnProceeds
-0.535*** 0.585
(-6.77)
Underpricing
0.003*** 1.003
(3.76)
Ind. Dummies Yes
Yes
Chi-square 304.029
409.942
Chi-square test p-value 0.000
0.000
Observations 1,568 1,568
54
TABLE 10
Sensitivity analysis - alternative measures of unionization.
This table reports the results of regressions of IPO underpricing (dependent variable) on unionization and other
control variables for a sample of U.S. IPOs over the period 1997-2014. Unionization proxies are defined as: (1)
Union_Prosp takes the value of 1 if the company’s employees are covered by a collective bargaining agreement,
as reported in the company’s registration statement (prospectus), and 0 otherwise (Columns 1 to 3); (2)
Pct_Union is the percentage of a company's employees covered by a collective bargaining agreement, as
reported in the company filings (Column 4); (3) Pct_Union_Prosp is the percentage of a company's employees
covered by a collective bargaining agreement, as reported in the company’s registration statement (Column 5);
and Union_Ind is the product of the percentage of unionized employees in the industry with the number of
employees of each company, scaled by lagged total assets (Column 6). All variables are defined in Appendix A.
Three estimation procedures are used: Heckman two-stage (Column 1), maximum likelihood estimation
(Column 1) and generated IV approach (Columns 3 to 6). The z-statistics in parentheses are based on standard
errors adjusted for heteroskedasticity. The dependent and control variables are winsorized at the 1st and 99th
percentiles. The lower part of the table provides the Wald and Hausman statistics, based on the MLE and IV
estimations respectively. All regressions include industry (at the 2-digit level of SIC code) and calendar year
dummies. * indicates significance at the 10% level; ** at the 5% level; and *** at the 1% level. Numbers are
rounded up to the third decimal place.
Variables Exp
Sign
(1) (2) (3) (4) (5) (6)
Heckman MLE IV IV IV IV
Union_Prosp - -1.291*** -1.220*** -2.684***
(-3.72) (-3.98) (-4.10)
Pct_Union -
-0.066***
(-3.09)
Pct_Union_Prosp -
-0.079***
(-3.11)
Union_Ind -
-0.270
(-0.93)
LnProceeds - 0.167*** 0.165*** 0.232*** 0.216*** 0.234*** 0.205***
(3.38) (3.21) (3.93) (3.70) (3.61) (3.03)
LnAge - 0.030 0.025 0.114* 0.046 0.071 -0.322**
(0.58) (0.49) (1.84) (0.85) (1.14) (-2.16)
NASDAQ + -0.061 -0.051 -0.224* -0.053 -0.211 -0.140
(-0.57) (-0.50) (-1.65) (-0.46) (-1.43) (-0.90)
Internet + 0.198 0.202 0.207 0.230* 0.228* 0.273
(1.49) (1.55) (1.56) (1.79) (1.66) (1.24)
Technology + 0.483*** 0.484*** 0.398** 0.450*** 0.483*** 0.475***
(3.67) (3.27) (2.43) (3.09) (3.06) (2.79)
Vc ? 0.400*** 0.407*** 0.243** 0.408*** 0.346*** 0.443***
(4.04) (3.94) (2.04) (3.99) (2.87) (3.61)
Underwriter + 0.371*** 0.368*** 0.397*** 0.367*** 0.345*** 0.159
(4.37) (4.29) (4.32) (4.28) (3.77) (1.21)
BIG4 - -0.114 -0.117 -0.122 -0.087 -0.192* -0.412**
(-1.18) (-1.11) (-1.11) (-0.87) (-1.70) (-2.57)
Overhang + 0.014*** 0.014*** 0.015*** 0.016*** 0.015*** 0.022*
(3.51) (3.83) (3.82) (4.17) (3.76) (1.92)
Primary + -0.086 -0.090 -0.056 -0.165* -0.096 -0.114
(-1.00) (-1.09) (-0.61) (-1.93) (-1.00) (-1.13)
Leverage - -0.008 -0.007 -0.033 -0.065 -0.093 -0.131
(-0.13) (-0.12) (-0.58) (-1.00) (-1.19) (-1.40)
DEPS - -0.072 -0.070 -0.122 -0.094 -0.115 -0.338**
(-0.82) (-0.81) (-1.25) (-1.02) (-1.10) (-2.23)
Hot_Month + 0.409*** 0.412*** 0.423*** 0.281* 0.415*** 1.166***
(3.14) (2.85) (2.79) (1.89) (2.60) (3.22)
Crunch ? 0.580 0.611 0.385 0.194 0.258 0.409
(1.04) (1.16) (0.51) (0.36) (0.47) (0.72)
(intercept)
0.247 0.242 0.063 0.198 0.006 1.689***
(0.18) (0.73) (0.18) (0.58) (0.02) (2.78)
Inverse Mills ratio
0.580***
(2.86)
Year and Ind. Dummies Yes Yes Yes Yes Yes Yes
Wald test
7.455***
Hausman test
16.292*** 7.091*** 9.569*** 9.884***
Observations 1,472 1,472 1,472 1,525 1,430 1,552
55
TABLE 11
Sensitivity analysis - Propensity Score Matched Sample.
This table reports the results of the cross-sectional OLS regression analysis of IPO underpricing (dependent
variable) on unionization and other control variables for a PSM sample of 402 U.S. IPOs over the period 1997-
2014. Unionization proxies are defined as: (1) Union takes the value of 1 if the company’s employees are
covered by a collective bargaining agreement, and 0 otherwise (Column 1); (2) Pct_Union is the percentage of a
company's employees covered by a collective bargaining agreement, as reported in company filings (Column 2);
(3) Union_Prosp takes the value of 1 if the company’s employees are covered by a collective bargaining
agreement, as reported in the company’s registration statement (prospectus), and 0 otherwise (Column 3); and
(4) Pct_Union_Prosp is the percentage of a company's employees covered by a collective bargaining agreement,
as reported in the company’s registration statement (Column 4). All variables are defined in Appendix A. The t-
statistics in parentheses are based on standard errors adjusted for heteroskedasticity. The dependent and control
variables are winsorized at the 1st and 99th percentiles. All regressions include industry (at the 2-digit level of
SIC code) and calendar year dummies. * indicates significance at the 10% level; ** at the 5% level; and *** at
the 1% level. Numbers are rounded up to the third decimal place.
Variables Exp Sign (1) (2) (3) (4)
Union - -0.399**
(-2.06)
Pct_Union -
-0.016***
(-3.28)
Union_Prosp -
-0.458**
(-2.28)
Pct_Union_Prosp -
-0.013***
(-2.88)
LnProceeds - 0.050 0.031 0.014 0.033
(0.49) (0.29) (0.13) (0.29)
LnAge - 0.068 0.082 0.076 0.115
(0.83) (0.94) (0.87) (1.28)
NASDAQ + 0.225 0.243 0.201 0.197
(1.28) (1.31) (1.09) (1.00)
Internet + 0.241 0.426 0.512 0.653
(0.53) (0.93) (0.97) (1.31)
Technology + 0.515 0.520 0.524 0.669*
(1.43) (1.44) (1.40) (1.77)
Vc ? 0.138 0.241 0.041 0.194
(0.59) (0.99) (0.17) (0.77)
Underwriter + 0.309* 0.327* 0.318* 0.320
(1.65) (1.67) (1.66) (1.58)
BIG4 - -0.092 -0.046 -0.104 -0.196
(-0.46) (-0.22) (-0.49) (-0.87)
Overhang + 0.021** 0.022** 0.020* 0.022*
(2.01) (1.98) (1.92) (1.92)
Primary + -0.373** -0.418** -0.357** -0.417**
(-2.17) (-2.34) (-1.98) (-2.21)
Leverage - -0.059 -0.107 0.005 -0.110
(-0.52) (-0.90) (0.04) (-1.04)
DEPS - -0.129 -0.077 -0.117 -0.157
(-0.73) (-0.41) (-0.65) (-0.82)
Hot_Month + 0.185 0.176 0.177 0.286
(0.66) (0.58) (0.63) (0.96)
Crunch ? 1.772 1.676 1.724 1.676
(1.57) (1.47) (1.43) (1.41)
(intercept)
3.510*** 3.543*** 3.660*** 3.620***
(4.15) (4.09) (4.22) (4.08)
Year and Ind. Dummies
Yes Yes Yes Yes
R2
0.255 0.289 0.245 0.284
Adj. R2
0.083 0.101 0.061 0.082
Mean VIF
1.260 1.239 1.261 1.231
Observations 402 360 383 342
56
TABLE 12
Sensitivity analysis - Right to Work legislation, unionization and IPO first-day returns.
This table reports the results of regressions of IPO underpricing (dependent variable) on a Union dummy variable and other control
variables for a sample of 1,568 U.S. IPOs over the period 1997-2014, after dividing the sample into states with and without Right to
Work legislation (Columns 1-3 and 4-6 respectively). All variables are defined in Appendix A. Three estimation procedures are used:
Heckman two-stage (Columns 1&4), maximum likelihood estimation (Columns 2&5) and generated IV approach (Columns 3&6). We
also test for homogeneity in the pairwise estimated coefficients for the Heckman two-stage (Column 7), maximum likelihood estimation
(Column 8) and generated IV approach (Column 9), using a Wald test. The z-statistics in parentheses are based on standard errors
adjusted for heteroskedasticity. The dependent and control variables are winsorized at the 1st and 99th percentiles. The lower part of the
table provides the Wald and Hausman statistics, based on the MLE and IV estimations respectively. All regressions include industry (at
the 2-digit level of SIC code) and calendar year dummies. * indicates significance at the 10% level; ** at the 5% level; and *** at the 1%
level. Numbers are rounded up to the third decimal place.
Variables
RTW States Non-RTW States Difference in coefficients
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Heckman MLE IV Heckman MLE IV (1) - (4) (2) - (5) (3) - (6)
Union -0.586 -2.187* -2.059** -1.520*** -1.321*** -2.071*** 0.934*** -0.867*** 0.012***
(-0.92) (-1.77) (-2.05) (-4.01) (-4.35) (-2.95) (16.058) (18.882) (8.727)
LnProceeds 0.086 0.128 0.183 0.228*** 0.222*** 0.262*** -0.142*** -0.094* -0.08
(0.91) (1.20) (1.42) (4.15) (3.97) (4.25) (6.688) (2.807) (1.668)
LnAge -0.011 0.101 0.060 0.045 0.033 0.062 -0.056 0.068 -0.001
(-0.13) (1.11) (0.64) (0.79) (0.56) (0.96) (0.958) (1.374) (0)
NASDAQ -0.096 -0.338* -0.211 0.098 0.116 0.025 -0.194 -0.454*** -0.235
(-0.53) (-1.77) (-1.17) (0.81) (0.98) (0.17) (2.59) (14.847) (2.694)
Internet -0.154 -0.029 -0.041 0.236* 0.246* 0.238* -0.39*** -0.276** -0.28**
(-0.49) (-0.09) (-0.13) (1.67) (1.78) (1.71) (7.609) (3.978) (4.047)
Technology 0.536** 0.503* 0.440 0.345** 0.359** 0.317* 0.19 0.144 0.122
(2.16) (1.68) (1.40) (2.31) (2.16) (1.76) (1.617) (0.742) (0.462)
Vc 0.392** 0.306 0.273 0.310*** 0.328*** 0.244* 0.082 -0.022 0.03
(2.19) (1.61) (1.40) (2.69) (2.71) (1.79) (0.504) (0.035) (0.048)
Underwriter 0.285* 0.307* 0.274 0.419*** 0.408*** 0.404*** -0.134 -0.1 -0.129
(1.85) (1.77) (1.60) (4.27) (4.10) (4.02) (1.873) (1.019) (1.66)
BIG4 -0.308* -0.195 -0.329** 0.031 0.022 0.052 -0.339*** -0.216* -0.381***
(-1.91) (-1.05) (-1.99) (0.28) (0.17) (0.40) (9.092) (3.083) (8.824)
Overhang 0.014* 0.014 0.015 0.015*** 0.015*** 0.016*** -0.001 -0.001 -0.001
(1.68) (1.20) (1.41) (3.40) (4.33) (4.52) (0.076) (0.078) (0.022)
Primary -0.342** -0.257 -0.229 -0.101 -0.107 -0.098 -0.241** -0.15 -0.131
(-2.28) (-1.63) (-1.33) (-0.98) (-1.09) (-0.96) (5.462) (2.347) (1.663)
Leverage -0.282* -0.244 -0.272 0.029 0.031 0.022 -0.311*** -0.275*** -0.294***
(-1.86) (-1.39) (-1.51) (0.50) (0.58) (0.40) (28.207) (26.77) (29.731)
DEPS -0.052 -0.112 -0.106 -0.140 -0.141 -0.147 0.088 0.029 0.04
(-0.35) (-0.68) (-0.63) (-1.34) (-1.34) (-1.34) (0.71) (0.076) (0.135)
Hot_Month -0.218 -0.145 -0.105 0.748*** 0.743*** 0.745*** -0.966*** -0.888*** -0.85***
(-0.99) (-0.59) (-0.42) (4.80) (4.29) (4.15) (38.481) (26.253) (22.441)
Crunch -1.008 -0.969 -1.346* 1.389** 1.441*** 1.211* -2.397*** -2.411*** -2.557***
(-1.07) (-1.57) (-1.80) (2.09) (3.13) (1.71) (12.953) (27.486) (12.989)
(intercept) 0.694 0.464 -0.094 0.000 0.025 0.005
(0.49) (0.57) (-0.10) (0.00) (0.06) (0.01)
Inverse Mills ratio 0.127
0.756***
(0.35)
(3.38)
Year and Ind.
Dummies Yes Yes Yes Yes Yes Yes
Wald test
8.208*** 11.323***
Hausman test
3.318*
6.470***
Observations 460 460 460 1,108 1,108 1108
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