1

Unionization and IPO Underpricing

Antonios Chantziaras, Dimitrios Gounopoulos, Stergios Leventis*

* Chantziaras, antonios.chantziaras@durham.ac.uk, Durham University Business School, UK; Gounopoulos

(corresponding author), d.gounopoulos@bath.ac.uk, University of Bath, UK; and Leventis,

s.leventis@ihu.edu.gr, 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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44

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