Investment efficiency: Dual-class vs. Single-class firms

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Investment efficiency: Dual-class vs. Single-class firms Xiaoyan Chenga, Heminigild Mpundub, , Huishan Wanb ⁎

a b

University of Nebraska at Omaha, College of Business Administration, Mammel Hall 300, 6708 Pine Street, Omaha, NE 68182, United States University of Northern Iowa, College of Business Administration, 1227 West 27th Street, CBB 325, Cedar Falls, IA 50614-0123, United States

ARTICLE INFO

ABSTRACT

JEL classification: G31 M41

This study examines the effects of a dual-class structure on investment efficiency. Agency theory suggests that a dual-class structure exacerbates agency problems, leading to under- or overinvestment, but another view posits that the dual-class structure insulates managers from the pressure of the marketplace or activist investors seeking short-term profits. We find that dualclass firms invest more efficiently than single-class peers. This effect is more pronounced among firms with less transparent investments such as R&D. Our findings are robust to a propensity score matching approach and a setting where single-class firms recapitalize with dual-class shares. Furthermore, we find that among firms most at risk of overinvestment, dual-class firms have higher future accounting profitability and less volatile future returns.

Keywords: Dual-class Investment efficiency Overinvestment Underinvestment Agency theory Stewardship theory

1. Introduction This study is motivated by the ongoing debates among governance researchers (Baran, Forst, & Via, 2018; Bebchuk, Kraakman, & Triantis, 1999; Gompers, Ishii, & Metrick, 2004; Jordan, Kim, & Liu, 2016) on the efficacy of a dual-class structure. A dual-class firm typically has two classes of shares with identical cash flow rights but different voting rights: a publicly traded “inferior” class of shares with one vote per share (held mainly by outsiders) and a “superior” class of shares with multiple (typically ten) votes per share (held mainly by insiders) that are not publicly traded. This innovation of separating cash flow rights from voting rights enables insiders to gain control of a firm without having to put up a significant amount of cash. But how does it affect investment efficiency? One stream of research concentrates on the potential conflict between principals and agents (Jensen & Meckling, 1976). The dualclass structure may exacerbate agency problems, since in this setting principals cannot easily remove “shirking” agents. However, the prevalence of prestigious dual-class firms (e.g., Google, Facebook, Ford Motor, Snap Inc., LinkedIn, and Groupon) raises the question of why a presumably value-destroying structure would persist in the economy.1 A second stream of research (Baran et al., 2018; Chemmanur & Jiao, 2012; DeAngelo & DeAngelo, 1985; Stein, 1988) argues that dual-class share structures actually enable managers to engage in value-adding activities, mainly by protecting their control. This could partly explain why the dual-class structure seems to be gaining popularity among innovative entrepreneurial firms. Yet other research posits that firms may use different governance mechanisms to mitigate agency problems and thereby increase shareholder value, and dual-class structure is one such mechanism.2

Corresponding author. E-mail addresses: [email protected] (X. Cheng), [email protected] (H. Mpundu), [email protected] (H. Wan). 1 According to Gompers et al. (2004), about 6% of all Compustat firms are dual class. Ritter's IPO data (https://site.warrington.ufl.edu/ritter/ipodata/) suggest that about 14% of all new IPOs from 2007 to 2016 had a dual-class structure. 2 For example, Jordan, Liu, and Wu (2014) find that dual-class firms use dividend payout policy as a precommitment device to protect shareholders' investments against managerial expropriation. ⁎

https://doi.org/10.1016/j.gfj.2019.100477 Received 25 October 2018; Received in revised form 9 May 2019; Accepted 12 May 2019 1044-0283/ © 2019 Elsevier Inc. All rights reserved.

Please cite this article as: Xiaoyan Cheng, Heminigild Mpundu and Huishan Wan, Global Finance Journal, https://doi.org/10.1016/j.gfj.2019.100477

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There is little evidence related to investment efficiency in dual-class firms, even though investment efficiency is a major determinant of the return on capital (Biddle, Hilary, & Verdi, 2009). We test how dual-class share structure affects firms' investment behaviors under circumstances in which firms are especially likely to either over- or underinvest. Following Biddle et al. (2009), we define investment efficiency as investment behavior characterized by undertaking positive net present value (NPV) projects and passing up negative NPV projects. First, we examine investment behavior in dual-class firms that are likely to underinvest (cash-constrained, highly levered firms) and those that are likely to overinvest (cash-rich, unlevered firms), using Heckman's (1979) two-stage regression models and a measure of total investment (capital expenditures plus R&D expenditures plus acquisitions less sales of PPE), and also investigating the components of investment. Second, we model the expected level of investment given a firm's growth opportunities, to examine how the dual-class structure affects deviation from that level. In additional analyses, we repeat our tests by employing a propensity score matching procedure, and we investigate whether recapitalization of single-class into dual-class status affects investment efficiency. We also examine whether industry concentration affects investment efficiency in dual-class firms, by partitioning our sample into two subsamples with relatively high and low concentrations of dual-class firms. Finally, we explore the incremental economic consequences of investment efficiency. We add to the existing literature in at least three ways. First, we augment and complement research on dual-class structures and investment efficiency (e.g., Bebchuk et al., 1999; Chemmanur & Jiao, 2012; Gompers et al., 2004; Masulis, Wang, & Xie, 2009) by addressing a unique setting in which the investing behavior of managers is at odds with the opportunism predicted by agency theory. Second, we examine R&D expenditures, capital expenditures and acquisitions to understand the channel through which investment efficiency is effected in dual-class firms (Lara, Osma, & Penalva, 2016). Finally, our study should be of interest to both investors and regulators, and contribute to the debate in the investing community about the surge of dual-class share structure.3 Furthermore, we complement the work of Lara et al. (2016) on how settings conducive to overinvestment or underinvestment condition the association between accounting conservatism and future performance. The remainder of the paper proceeds as follows. Section 2 reviews the related literature and develops the hypotheses that we test. Section 3 discusses the research design, including our sample. Section 4 presents the results. Section 5 outlines the robustness checks, and Section 6 concludes. 2. Hypothesis development The separation of ownership and control leads to various agency costs that are exacerbated by the information asymmetry between managers and owners (Jensen & Meckling, 1976). To be specific, information asymmetry leads to moral hazard and adverse selection, which in turn lead to over- or underinvestment. Outsiders who are unable to observe the firm's investment opportunities or to monitor managers' actions may withhold funds, exacerbating underinvestment by resource-constrained firms (Jensen & Meckling, 1976). Biddle et al. (2009) argue that firms with low cash and high leverage have a high risk of insolvency. In financially constrained firms, managers forgo valuable investment opportunities (underinvest) because the firm has risky debt that inevitably increases the costs of investment in new projects. On the other hand, overinvestment arises when managers prefer investing in projects with negative NPV. It is exacerbated in cash-rich firms, where insiders may pursue negative-NPV investments that generate private benefits at the expense of shareholders (Jensen & Meckling, 1976; Smart & Zutter, 2003). A growing literature examines the separation of ownership and control in the unique context of dual-class firms, in which there is a wedge between the cash flow rights and the voting rights of shareholders. This wedge gives insiders disproportionate control. Studies of dual-class firms draw heavily from agency theory, which assumes that managers are self-interested individuals whose interests are inherently in conflict with those of shareholders, and that managers behave opportunistically. In this view, dual-class structure inevitably leads to serious agency problems since it allows managers to entrench themselves. Bebchuk et al. (1999) present theoretical analyses of how dual-class structures could result in suboptimal investment behavior but do not directly test their theory. Gompers et al. (2004) document that firm value increases with insiders' cash flow rights but decreases with insiders' voting rights, and they argue that entrenchment leads to underinvestment. Masulis et al. (2009) provide evidence suggesting that managers at dualclass firms tend to waste corporate resources to extract private benefits of control at the expense of shareholders. On this line of reasoning, we argue that dual-class firms will either underinvest or overinvest: Hypothesis 1a. Dual-class firms suffer more overinvestment than single-class firms do. Hypothesis 1b. Dual-class firms suffer more underinvestment than single-class firms do. An alternative view argues that a dual-class share structure might enhance shareholder value. A seminal work by DeAngelo and DeAngelo (1985) suggests that private firms with high potential investments are most likely to benefit from going public with a dualclass structure, which gives them access to capital markets while maintaining managerial control.4 The authors argue that information asymmetry between insiders and outsiders may constrain the investment behavior of insiders when there is a divergence 3

The stock structure debate, available at http://www.equilar.com/blogs/13-stock-structure-ipos. html. NASDAQ expresses this view in The promise of market reform: Reigniting America's economic engine (Nasdaq, 2017): “Each publicly traded company should have flexibility to determine a class structure that is most appropriate and beneficial for them, so long as this structure is transparent and disclosed up-front so that investors have complete visibility into the company. Dual-class structures allow investors to invest side-by-side with innovators and high-growth companies, enjoying the financial benefits of these companies' success.” 4

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between what insiders believe to be optimal investment choices and the preferences of a less informed, dominant outside shareholder. For example, tech-savvy entrepreneurial insiders might wish to invest a large amount of cash in a groundbreaking project whose rationale might at best be gibberish to uninformed outsiders. In a single-class firm, insiders facing such a dilemma might underinvest to appease outsiders and thus avoid a proxy fight or a hostile takeover. Furthermore, when projects are funded with the issuance of new shares, the newly issued shares dilute managers' voting power and weaken their ability to resist takeover attempts. In response, they pass up investment opportunities that would have positive NPV. Dual-class structure, an extreme example of anti-takeover provisions, attenuates the control dilution and mitigates underinvestment. Dual-class structure also has the potential to limit overinvestment. The existence of dual-class stock today is a consequence of a market dysfunction that focuses on short-term increases in shareholders' value. By insulating managers from undue market pressure from less informed outsiders, the dual-class structure may empower managers to focus on long-term strategy and reduce investment myopia and thus overinvestment (DeAngelo & DeAngelo, 1985).5 Stein (1988) and He and Tian (2013) provide empirical evidence that dual-class structure may lessen managerial myopia or short-term orientation. More fundamentally, Donaldson and Davis (1991) argue that managers aim to be good stewards of the assets they control on behalf of shareholders.6 If managers do not behave opportunistically (Barney, 1990; Donaldson, 1990a, 1990b), the predictions of agency theory may not hold, and the dual-class structure may improve investment efficiency. Indeed, Nguyen and Xu (2010) provide evidence suggesting that managers of dual-class firms do not behave opportunistically, by documenting smaller absolute abnormal accruals in dual-class firms than in single-class ones. Likewise, Wan (2013) documents lower real earnings management in dual-class firms than in single-class ones. Therefore, we suggest that insider control in dual-class firms might encourage insiders to make better investment decisions, reducing both overinvestment and underinvestment: Hypothesis 2a. Dual-class firms suffer less overinvestment than single-class firms do. Hypothesis 2b. Dual-class firms suffer less underinvestment than single-class firms do. 3. Research design 3.1. Sample selection Our initial sample of dual-class firm-years is based on the 1995 to 2002 dual-class sample by Gompers, Ishii, and Metrick (2010). We extend the list of dual-class firm-years by drawing on Ritter's listing of IPOs with multiple share classes outstanding (1980–2016)7 and on annual 10-K SEC filings. Where a firm's 10-K filing is not detailed enough, we examine the 8-A12B registration of new securities filings, which specify the timing, numbers, and classes of new share issues. This process expands our list to 8146 firm-years covering the period from 1981 to 2016. Next, we exclude 604 firm-years in the financial (SIC 6000–6900) and utility (SIC 4400–5000) industries from our sample. We also delete 2194 firm-year observations with missing key variables. These screenings leave 5348 firm-year observations, representing 751 unique dual-class firms spanning the period 1987 to 2016.8 Table 1, panel A summarizes the selection of our dual-class sample. To constitute our matching single-class sample, we start with all Compustat firms using the same 2-digit SIC codes and the same period as our dual-class sample (1987 to 2016). We then remove 93,145 observations with missing key variables, and 5348 dual-class observations. Our final sample consists of 93,709 single-class firm-year observations, representing 13,238 unique single-class firms. We summarize the sample selection of single-class firms in panel B of Table 1. 3.2. Models and variable definitions Our study examines how a dual-class structure affects investment efficiency. A common approach, known as the treatment effect model, suggests a linear regression of investment efficiency (our dependent variable) on a set of independent variables, including an indicator variable to represent dual-class status (the treatment) and some control variables (Lennox, Francis, & Wang, 2012). However, this approach presents several challenges that need to be addressed before we can make any meaningful inferences. First, managers of firms issuing shares are faced with a binary choice (Tucker, 2010) of whether to issue one class of shares (single-class 5 Howell (2010) argues that a dual-class structure mitigates concerns about tenure and thus creates incentives for managers to invest in developing firm-specific human capital. 6 Donaldson and Davis (1991, pp. 51–52) describe stewardship theory as follows: “The executive manager, under this theory, far from being an opportunistic shirker, essentially wants to do a good job, to be a good steward of the corporate assets. Thus, stewardship theory holds that there is no inherent, general problem of executive motivation. Given the absence of an inner motivational problem among executives, there is the question of how far executives can achieve the good corporate performance to which they aspire. Thus, stewardship theory holds that performance variations arise from whether the structural situation in which the executive is located facilitates effective action by the executive. The issue becomes whether or not the organisation (sic) structure helps the executive to formulate and implement plans for high corporate performance (Donaldson, 1985). Structures will be facilitative of this goal to the extent that they provide clear, consistent role expectations and authorise (sic) and empower senior management.” 7 https://site.warrington.ufl.edu/ritter/ipo-data/. 8 The final sample comprises only post-1986 years because our tests require cash flow data, which are available only after 1986.

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Table 1 Sample selection. Panel A: dual-class firm-years (751 unique dual firms) Initial sample (based on Gompers et al., 2010, updated to 2016, and on Ritter's IPO data) Less: firm-years in the financials and utilities Less: firm-years missing key regression variables

8146 (604) (2194)

Final dual-class firm-year sample (1987–2016)

5348

Panel B: single-class firm-years (13,238 unique single-class firms) Initial sample (All Compustat firm-years with same 2-digit SIC as dual-class firm-years in panel A and covering the period 1987 to 2016) Less: firm-years missing key regression variables Less: dual firm-years (see panel A)

192,202 (93,145) (5348)

Final single-class firm-year sample (1987–2016)

93,709

status) or multiple classes of shares (dual-class status). This choice is not random and therefore raises issues of self-selection bias (Lennox et al., 2012; Tucker, 2010). We model some key factors (observable to us as researchers) that are likely to factor into the decision, but we are never privy to the full information set that goes into that decision. Furthermore, some of the unobservable (to us) factors that affect this choice (single- or dual-class shares) are also likely to ultimately affect the ex post investing behavior of managers. For example, if managers seek a dual-class structure in order to resist excessive market pressures, their ex post investing behavior will be conditional on successfully implementing the preferred dual-class status. In other words, there is potential endogeneity in the sense that the error term in the treatment model (our unobservable factors) is related to the independent variables (especially our dual indicator variable). To address potential bias of regression coefficients due to unobservable factors, we employ Heckman's (1979) two-stage regression approach. In the first stage, we model the binary choice (dual vs. single) using a probit regression as follows:

DUAL i,t =

0

+

1

MEDIA +

n

CONTROL VARIABLESi,t +

i,t .

(1)

We define the variables as follows. DUAL an indicator variable that takes the value of 1 for dual-class firms, and 0 otherwise MEDIA an indicator variable that takes the value of 1 for firms in the media industry, and 0 otherwisea CONTROL VARIABLES LOGASSET the natural logarithm of the total assets of the firm LOGAGE the natural logarithm of the age of the firm MTB market-to-book ratio of the firm Z_SCORE a measure of bankruptcy risk TANGIBILITY the ratio of property, plant, and equipment (PPE) to total assets IND_K an average measure, calculated for firms in the same 3-digit SIC industry, of market leverage, computed as the ratio of long-term debt to the sum of long-term debt and market value of equity CFOSALE the ratio of cash flows from operations (CFO) to sales SLACK the ratio of cash to property, plant, and equipment DIVIDEND an indicator variable that takes the value of 1 if the firm paid a dividend, and 0 otherwise OP_CYCLE the natural logarithm of the operating cycle of a firm. Operating cycle is calculated as [(receivables/sales) + (inventory/COGS)] × 360 LOSS an indicator variable that takes the value of 1 if the net income before extraordinary items is negative, and 0 otherwise a Following Gompers et al. (2010), we define media companies as those with SIC codes 2710–11, 2720–21, 2730–31, 4830, 4832–33, 4840–41, 7810, 7812, and 7820.

Following previous researchers (Gompers et al., 2010; Nguyen & Xu, 2010), in modeling the selection, we include characteristics linked with a firm's class structure decision. The dependent variable DUAL is equal to one for firms with a dual-class structure and zero otherwise. We use MEDIA as the instrumental variable. The intuition is that being in the media industry affects the likelihood of a firm's choosing a dual-class structure (stage one),9 while being in that industry is unlikely to directly affect investment efficiency (stage two).10 Since our hypotheses discuss investment efficiency in the context of underinvestment and overinvestment, we generate the variable OVER to distinguish firms at risk of underinvesting or overinvesting. Following Biddle et al. (2009), we first partition our 9 DeAngelo and DeAngelo (1985) suggest that media firms are more likely to choose dual-class structure, since the control of a media company provides private benefits with a dual-class structure. 10 Our unreported statistics in Pearson correlation analysis show that MEDIA is not significantly correlated with our proxies for investment efficiency.

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sample by cash and leverage,11 variables previously shown to be associated with over- and underinvestment (Jensen, 1986; Myers, 1977). We then create decile ranks (rescaled to be between 0 and 1) for cash and the modified leverage variable. The composite variable OVER averages the scaled cash and leverage ranks. According to Biddle et al. (2009), averaging mitigates the measurement error in the composite variable. In the second stage regression, we include, among the list of independent variables, a DUAL indicator variable (=1 for dual-class firm-years; 0 otherwise), OVER (to capture a firm's tendency to overinvest or underinvest), and an interaction of the two (DUAL*OVER). We also include a bias correction factor, the inverse Mills ratio (IMR, defined in Eq. (2) and derived from the first stage), to address potential bias from unobservable factors (Lennox et al., 2012; Tucker, 2010). Thus, we estimate the following linear regression model:

INVESTi,t + 1 =

0

+

OVERi,t +

1

DUAL i,t +

2

3

DUAL

OVERi,t +

n

CONTROL VARIABLESi,t +

m

IMR +

i,t .

(2) INVEST is total investment (capital expenditures plus R&D expenditures plus acquisitions less sales of PPE) scaled by lagged total assets. IMR is calculated as a ratio of the standard normal probability density function (p.d.f.) to the standard normal cumulative distribution function (c.d.f.) if dual = 1 or a ratio of minus the standard normal p.d.f. to 1 minus the standard normal c.d.f. if dual = 0. The control variables are as defined in Eq. (1) above. We do not include the variable MEDIA in the second stage because it does not affect investment efficiency except through the IMR variable. We include the control variables that are related to firms' investment behaviors (Biddle et al., 2009; Biddle & Hilary, 2006). Before discussing the combination of coefficients that would render support for our hypotheses, we note that in Eq. (2) above, β0 (β0 + β1) represents the baseline investment for single-class firms as underinvestment (overinvestment) becomes most likely. In a similar way, (β0 + β2) (β0 + β1 + β2 + β3) is the baseline investment for dual-class firms as underinvestment (overinvestment) becomes most likely. The estimated coefficient on DUAL (β2) captures the investment differential between single- and dual-class firms when underinvestment is most likely. A negative (positive) coefficient (β2) on the DUAL indicator variable supports Hypothesis 1b (2b) by suggesting that dual-class firms are more (less) likely than single-class peers to underinvest. Stated differently, a negative (positive) coefficient (β2) on the DUAL variable means that dual-class firms have a lower (higher) investment among firms susceptible to underinvestment. Since the coefficient (β3) on the interaction term DUAL*OVER captures the incremental impact of dual-class structure on investment as overinvestment becomes more likely, the sum of the coefficients (β2 + β3) measures the investment differential between single-class and dual-class firms when overinvestment is most likely. A positive (negative) joint coefficient (β2 + β3) on the DUAL and DUAL*OVER terms indicates that compared to single-class firms, dual-class firms have higher (lower) investment among firms more at risk of overinvestment, which supports Hypothesis 1a (2a). Next, we perform a test using the residuals as a proxy for investment efficiency. Following Biddle et al. (2009), we first estimate a firm-specific model of investment as a function of sales growth. Thus,

INVESTi,t + 1 =

0

+

Sales Growthi,t +

1

(3)

i,t + 1.

INVESTi, t+1 is the total investment of a firm (capital expenditures plus R&D expenditures plus acquisitions less sales of PPE) scaled by lagged total assets, and Sales Growthi,t is the percentage change in sales from year t-1 to t. We estimate Eq. (3) for each industry-year, applying Fama and French's 48-industry classification (Fama & French, 1997), and use firm-specific residuals from the estimated model as a measure of deviation from the expected level of investment. Each year, we partition the residuals for all sample firms (dual-class and single-class) into quartiles. The top (most positive) quartile of residuals represents firms that are most likely to be overinvesting, while the lowest (most negative) quartile represents underinvesting firms. The two middle quartiles are our benchmark. Thus, we create a dependent variable (INV_STATE) with three states (2 = overinvestment; 1 = underinvestment; and 0 = benchmark). There are 1174 dual-class vs. 24,517 single-class firm-years in the overinvestment group, 1038 dual vs. 24,652 single in the underinvestment group, and 3136 dual vs. 44,540 single in the benchmark (normal investment) group. To predict the likelihood that a firm is in the extreme quartiles (overinvesting or underinvesting), we estimate a multinomial logit model as follows:

INV_STATEi,t =

0

+

1

DUAL i,t +

n

CONTROL VARIABLESi,t +

i,t .

(4)

where INV_STATEi,t is as just defined. DUAL is an indicator variable that takes the value of 1 for dual-class firms, and 0 otherwise. Our explanatory and control variables are the same ones used in estimating Eq. (2), but we also control for cash and leverage. Hypothesis 1a (1b) predicts that dual-class firms are more likely to be in the top (bottom) quartile of unexplained investment, while Hypothesis 2a (2b) predicts that dual-class firms are less likely to be in the top (bottom) quartile of unexplained investment. The coefficient γ1 in Eq. (4) captures the odds that a firm lies in the extreme quartiles (2 = overinvestment; 1 = underinvestment) as opposed to the middle quartiles (see discussion of the dependent variable INV_STATE in the previous paragraph). A significantly positive (negative) value for γ1 supports Hypotheses 1a and 1b (2a and 2b). In other words, the residuals will deviate more (less) from the benchmark middle quartiles for dual-class firms than they do for single-class firms if Hypotheses 1a and 1b (2a and 2b) hold. 3.3. Descriptive statistics Panel A of Table 2 presents the distribution of dual-class vs. single-class status by year, while panel B compares the distribution of 11

We first multiply leverage by −1 so that we have a variable that increases with the likelihood of overinvestment (Biddle et al., 2009). 5

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Table 2 Sample distribution. Panel A: distribution of sample over the years Year

Single-class firm-years Dual-class firm-year Percent (dual: single) Year

Single-class firm-years Dual-class firm-years Percent (dual: single)

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

13 724 809 637 645 1774 1754 3665 4610 4990 4954 4723 4852 4852 4612

4403 4087 3934 3834 3642 3632 3543 3317 3131 3076 2748 3095 3126 2968 1559 93,709

2 10 14 10 11 17 21 97 287 313 314 327 326 322 295

15.38% 1.38% 1.73% 1.57% 1.71% 0.96% 1.20% 2.65% 6.23% 6.27% 6.34% 6.92% 6.72% 6.64% 6.40%

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ALL YEARS

268 254 230 231 206 192 203 169 168 172 173 199 189 189 139 5348

6.09% 6.21% 5.84% 6.03% 5.66% 5.29% 5.73% 5.09% 5.37% 5.59% 6.30% 6.43% 6.05% 6.37% 8.92% 5.71%

Panel B: distribution of sample by industry Industry

Single-class firm-years

Dual-class firm-years

Percent (dual: single)

Communications Retail Business services Electronic equipment Printing and publishing Wholesale Food products Pharmaceutical products Entertainment Construction materials Transportation Apparel Automobiles and trucks Healthcare Trading Machinery Computers Restaurants, hotels, motels Consumer goods Electrical equipment Beer & liquor Personal services Petroleum and natural gas Business supplies Textiles Miscellaneous ALL INDUSTRIES

3343 4700 11,943 6652 520 3780 1998 6588 1351 2079 1729 1216 1607 2385 1677 4435 4373 1611 1572 1786 419 591 6619 896 244 19,595 93,709

618 475 472 255 213 212 210 199 197 184 179 151 132 127 119 118 118 118 108 106 94 93 85 68 58 639 5348

18.5% 10.1% 4.0% 3.8% 41.0% 5.6% 10.5% 3.0% 14.6% 8.9% 10.4% 12.4% 8.2% 5.3% 7.1% 2.7% 2.7% 7.3% 6.9% 5.9% 22.4% 15.7% 1.3% 7.6% 23.8% 3.3% 5.7%

dual-class vs. single-class status by industry. On average, our sample dual-class firm-years are about 6% of the single-class firm-years. The top dual-class industries are communications, retail, business services, electronic equipment, printing and publishing, wholesale, food products, pharmaceuticals, and entertainment, which account for about half of the sample. Table 3 panel A reports key characteristics of single-class and dual-class firms.12 Panel B of Table 3 presents the results of equality tests—whether the means and medians for single- vs. dual-class firms differ statistically. Notably, dual-class firms are older than single-class firms (median of 15 years for dual-class ones vs. 11 years for single-class ones). In accord with the findings of Smart, Thirumalai, and Zutter (2008) and Cremers, Lauterbach, and Pajuste (2018), we find that compared to single-class firms, dual-class firms tend to have larger firm size, more leverage, higher profitability, and lower investments. The medians of total assets, leverage, and investment are about 612, 0.35, and 7.47% in dual-class firms vs. 133, 0.151, and 9.97% in single-class firms, respectively. The mean of loss is 0.279 in dual-class firms vs. 0.404 in single-class firms. On average, a larger proportion of dual-class firms pay dividends (49% of dual firms vs. 33% of 12

We winsorize all variables at the 1% and 99% levels. 6

18.469 3,066.264 15.999 0.180 0.473 2.317 2.577 0.283 0.185 -0.416 3.930 0.334 4.683 0.397

9.789 147.247 11.000 0.090 0.161 1.465 2.585 0.204 0.173 0.059 0.418 0.000 4.739 0.000

7

9.967 132.964 11.000 0.092 0.151 1.472 2.574 0.203 0.172 0.057 0.425 0.000 4.747 0.000

13.996 2,919.609 19.448 0.136 0.739 1.840 3.355 0.270 0.216 -0.041 1.990 0.489 4.522 0.279

Mean

Mean

18.725 3,074.633 15.803 0.183 0.458 2.344 2.533 0.284 0.183 -0.438 4.041 0.325 4.693 0.404

Dual-class firms

Single-class firms

21.334 878.132 22.000 0.256 0.653 2.346 4.827 0.426 0.236 0.138 2.182 1.000 5.185 1.000

7.472 611.836 15.000 0.069 0.350 1.367 2.734 0.210 0.204 0.083 0.326 0.000 4.590 0.000

Median

18.725 3,074.633 15.803 0.183 0.458 2.344 2.533 0.284 0.183 -0.438 4.041 0.325 4.693 0.404

26.766 15,243.877 13.763 0.217 1.575 2.820 11.308 0.248 0.095 2.545 11.683 0.469 0.827 0.491

Std Dev 4.178 25.910 6.000 0.025 0.000 1.071 0.904 0.083 0.093 -0.029 0.083 0.000 4.259 0.000

Q1 7.472 611.836 15.000 0.069 0.350 1.367 2.734 0.210 0.204 0.083 0.326 0.000 4.590 0.000

Median 3.475 176.485 7.000 0.018 0.017 1.030 1.441 0.104 0.149 0.027 0.071 0.000 4.138 0.000

Q1

15.182 1,875.787 29.000 0.193 0.913 2.036 4.544 0.390 0.303 0.144 1.268 1.000 5.013 1.000

Q3

⁎⁎⁎

2.495⁎⁎⁎ -478.872⁎⁎⁎ -4.000⁎⁎⁎ 0.023⁎⁎⁎ -0.199⁎⁎⁎ 0.105⁎⁎⁎ -0.160⁎⁎⁎ -0.007⁎⁎⁎ -0.032⁎⁎⁎ -0.026⁎⁎⁎ 0.099⁎⁎⁎ 0.000⁎⁎⁎ 0.157⁎⁎⁎ 0.000⁎⁎⁎

21.187 11,219.887 14.954 0.166 2.106 1.599 5.949 0.211 0.093 1.183 7.060 0.5000 0.771 0.448

Std Dev

4.729 155.024 -3.645⁎⁎⁎ 0.047⁎⁎⁎ -0.281⁎⁎⁎ 0.504⁎⁎⁎ -0.822⁎⁎⁎ 0.014⁎⁎⁎ -0.033⁎⁎⁎ -0.397⁎⁎⁎ 2.051⁎⁎⁎ -0.164⁎⁎⁎ 0.171⁎⁎⁎ 0.125⁎⁎⁎

13.996 2,919.609 19.448 0.136 0.739 1.840 3.355 0.270 0.216 -0.041 1.990 0.489 4.522 0.279

Mean

Kruskal-Wallis test of difference of medians (single – dual)

21.681 804.925 21.000 0.261 0.638 2.367 4.845 0.428 0.236 0.138 2.260 1.000 5.196 1.000

Q3

T-test of difference of means (single – dual)

Tests of equality

9.967 132.964 11.000 0.092 0.151 1.472 2.574 0.203 0.172 0.057 0.425 0.000 4.747 0.000

Median

Dual-class firms

Notes: INVEST is total investment (capital expenditures plus research and development expenditures plus acquisitions less sales of property, plant, and equipment) scaled by lagged total assets. AT is total assets. AGE is the age of the firm. CASH is cash and short-term investments deflated by lagged total assets. LEV is the book value of long-term debt (total) divided by common/ordinary equity (total). MTB is the market-to-book ratio of the firm. Z_SCORE is a measure of bankruptcy risk. TANGIBILITY is the ratio of property, plant, and equipment (PPE) to total assets. IND_K is an average measure, calculated for firms in the same 3-digit SIC industry, of market leverage, computed as the ratio of long-term debt to the sum of long-term debt and market value of equity. CFOSale is the ratio of cash flows from operations (CFO) to sales. SLACK is the ratio of cash to property, plant, and equipment. DIVIDEND is an indicator variable that takes the value of 1 if the firm paid a dividend and 0 otherwise. OP_CYCLE is the natural logarithm of the operating cycle of a firm. Operating cycle is calculated as [(receivables/sales) + (inventory/COGS)] x 360. LOSS is an indicator variable that takes the value of 1 if the net income before extraordinary items is negative and 0 otherwise. Q1 and Q3 are the lower and upper quartiles respectively.

INVEST, % AT AGE CASH LEV MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS

4.129 28.037 6.000 0.024 0.000 1.069 0.935 0.084 0.093 -0.024 0.082 0.000 4.251 0.000

Median

Panel B: Test of equality of means and medians

INVEST, % AT AGE CASH LEV MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS

Q3

Mean

Q1

Mean

Median

Single-class firms

Combined

Panel A: Descriptive statistics.

Table 3 Comparing the characteristics of dual-class and single-class firms.

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1.000 0.265 -0.040 -0.054 -0.193 0.213 -0.007⁎⁎ 0.288 -0.018 0.006⁎ -0.168 -0.209 0.088 -0.142 0.030 0.131

1.000 -0.042 -0.069 -0.175 0.188 -0.058 0.231 -0.026 -0.024 -0.178 -0.120 0.066 -0.144 -0.057 0.077

INV_STATE

1.000 -0.002 0.059 -0.049 0.039 -0.041 0.017 -0.012 0.078 0.036 -0.040 0.079 -0.047 -0.058

DUAL

1.000 0.200 -0.089 -0.050 -0.062 -0.003 0.076 0.130 0.047 -0.055 0.226 -0.024 -0.108

AT

1.000 -0.207 0.062 -0.154 -0.008⁎⁎ 0.115 0.224 0.118 -0.110 0.411 -0.016 -0.233

AGE

1.000 -0.141 0.258 0.209 -0.405 -0.391 -0.284 0.581 -0.234 0.005 0.201

CASH

1.000 -0.109 -0.006⁎⁎ 0.124 0.178 0.058 -0.080 0.079 -0.055 -0.033

LEV

1.000 -0.161 -0.155 -0.218 -0.316 0.150 -0.151 0.012 0.194

MTB

1.000 -0.067 -0.030 0.129 0.101 0.071 0.085 -0.244

Z_SCORE

1.000 0.508 0.100 -0.330 0.198 -0.283 -0.089

TANGIBILITY

1.000 0.145 -0.212 0.302 -0.261 -0.186

IND_K

1.000 -0.208 0.142 -0.152 -0.261

CFOSale

1.000 -0.135 -0.008⁎⁎ 0.122

SLACK

1.000 -0.060 -0.340

DIVIDEND

1.000 0.017

OP_CYCLE

1.000

LOSS

Notes: The table above lists the Pearson correlation coefficients. All coefficients are significant at the 1% level except those marked*, which are significant at the 10% level, those marked **, which are significant at the 5% level, and those in boxed borders, which are insignificant. INVEST is total investment (capital expenditures plus research and development expenditures plus acquisitions less sales of property, plant, and equipment) scaled by lagged total assets. INV_STATE is investment state, ranked in quartiles of investment efficiency (residuals) within the sample so that 1=underinvestment (first quartile), 2=overinvestment (fourth quartile), and 0=benchmark (quartiles 2 and 3). DUAL is an indicator variable that takes the value of 1 for dual-class firms and 0 otherwise. AT is total assets. AGE is the age of the firm. CASH is cash and short-term investments deflated by lagged total assets. LEV is the book value of long-term debt (total) divided by common/ordinary equity (total). MTB is the marketto-book ratio of the firm. Z_SCORE is a measure of bankruptcy risk. TANGIBILITY is the ratio of property, plant, and equipment (PPE) to total assets. IND_K is an average measure, calculated for firms in the same 3-digit SIC industry, of market leverage, computed as the ratio of long-term debt to the sum of long-term debt and market value of equity. CFOSale is the ratio of cash flows from operations (CFO) to sales. SLACK is the ratio of cash to property, plant, and equipment. DIVIDEND is an indicator variable that takes the value of 1 if the firm paid a dividend and 0 otherwise. OP_CYCLE is the natural logarithm of the operating cycle of a firm. Operating cycle is calculated as [(receivables/sales) + (inventory/COGS)] x 360. LOSS is an indicator variable that takes the value of 1 if the net income before extraordinary items is negative and 0 otherwise.

INVEST INV_STATE DUAL AT AGE CASH LEV MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS

INVEST

Table 4 Correlation matrix.

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Table 5 Distribution of sample across the groups (overinvestment vs. normal investment vs. underinvestment). INV_STATE

Dual-class firms Single-class firms Statistical Test

Overinvestment

Normal investment

1,174 21.95% 24,517 26.16% Overinvestment vs. Normal p-value < 0.0001

3,136 44,540

Underinvestment 58.64% 1,038 47.53% 24,652 Normal vs. Underinvestment p-value < 0.0001

Total 19.41% 26.31%

5,348 93,709

Notes: INV_STATE is investment state, ranked in quartiles of investment efficiency (residuals) within the sample so that 1=underinvestment (first quartile), 2=overinvestment (fourth quartile), and 0=benchmark (quartiles 2 and 3).

single ones). We present a correlation matrix of key variables in Table 4. Our two dependent variables (INVEST and INV_STATE) are positively and significantly related, with a correlation coefficient of 0.265. Notably, the dual-class indicator variable (DUAL) is significantly and negatively correlated with deviation from the expected level of investment (INV_STATE), indicating that dual-class firms are less likely to deviate from that level. As we expected, the DUAL variable is positively correlated (0.079) with DIVIDEND, confirming that dualclass firms in our sample were more likely to pay dividends (see previous paragraph). In addition, we find that DUAL and LOSS are negatively correlated (−0.058), indicating that a smaller proportion of dual-class firms have losses in the sample period. In short, the findings in Table 4 provide univariate evidence that dual-class firms tend to invest more efficiently. In the analysis using the residuals as a proxy for investment efficiency, we first examine the distribution of our sample across the groups (overinvestment, normal investment, and underinvestment) before performing a multivariate test. Table 5 reports the distribution of dual-class and single-class firm-years across the three groups. As Table 5 shows, dual-class firms are less likely to overinvest (21.95% of dual-class vs. 26.16% of single-class firms) and to underinvest (19.41% of dual-class vs. 26.31% of single-class firms). Overall, the results in Table 5 provide preliminary evidence that compared to single-class firms, dual-class firms are less likely to overinvest or underinvest. 4. Results We present the results of our binary choice model in panel A of Table 6. Except for CFOSALE (ratio of cash flows from operations to sales), all our independent variables are significant at the 1% level. Notably, the MEDIA indicator variable (=1 if a firm is in the media industry; 0 otherwise) has a coefficient of 1.934 (t = 34.09), supporting our choice of the variable as a suitable instrument in the Heckman (1979) regressions. In panel B of Table 6, we present the results of the second stage Heckman regression model. To be specific, we examine the level of investment conditional on dual-class status and the firm's likelihood of overinvesting and underinvesting. To examine the specific channel used to enhance investment efficiency in a dual-class structure, we use the subcomponents of total investment (INVEST): research and development expenditures (R&D), capital expenditures (CAPEX), and acquisitions less sales of property, plant, and equipment (ACQN) as alternative measures of investment. We depict the results in Table 6(panel B). We first discuss the results of using total investment (INVEST) as our dependent variable in this paragraph and then discuss the results of using alternative measures of investment (R&D, CAPEX, ACQN) in the next paragraph. Recall that the constant term (β0 = 22.598, t = 28.50) represents the baseline investment for single-class firms most likely to underinvest. The baseline investment for dual-class firms in the underinvestment category is 29.660 (β0 + β2 = 22.598 + 7.062). The significantly positive coefficient on the DUAL indicator variable (β2 = 7.062; t = 4.44) indicates that among firms likely to underinvest (cash-constrained, highly levered firms), dual-class firms have a higher investment level, thus reflecting higher investment efficiency (Hypothesis 2b is supported). To test the hypothesis regarding overinvestment, we need to evaluate the difference (β2 + β3) between the baseline investment for dual-class firms (β0 + β1 + β2 + β3) and the baseline for single-class peers (β0 + β1). The coefficient, β3, of the term DUAL*OVER is −7.215 (t = −5.12), suggesting that the incremental impact of dual-class structure on investment is to reduce investment as overinvestment becomes more likely. However, among firms in the overinvesting group, the overall investment differential between single-class and dual-class firms (measured by the sum of the coefficients on the variables DUAL and DUAL*OVER) is negative (−0.153) but not statistically significant (t = −0.12). Such findings show that, while dual-class firms at risk of overinvestment tend to invest less than their single-class peers, the difference is not significant. We investigate this issue further in Table 7. In sum, the findings in Table 6(panel B) provide support for Hypothesis 2b, that dual-class firms in the underinvestment-risk category invest more efficiently than their single-class peers. In Table 6(panel B), we also present the results using research and development expenditures (R&D), capital expenditures (CAPEX,) and acquisitions less sales of property, plant and equipment (ACQN) as dependent variables. Notably, the coefficient on the DUAL variable is 2.011 (t = 4.66) for R&D, while it is insignificant for both CAPEX and ACQN. This finding suggests that dual-class firms at risk of underinvestment increase their investment efficiency by enhancing R&D expenditures. Among firms at risk of overinvestment, the sum of the coefficients on DUAL and DUAL*OVER is significant and negative for R&D (−6.625; t = −18.13) and CAPEX (−6.953; t = −3.59) and is insignificant for ACQN (1.520; t = 1.82). Such findings indicate that, among firms most likely to overinvest, dual-class firms invest less in both R&D and capital expenditures than do their single-class peers. In sum, cash-poor dual9

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Table 6 Selection model and the effect of dual-class status on investment efficiency. Panel A: Binary choice model (Heckman [1979] first stage). DUALi,t = α0 + α 1*MEDIA + α n*CONTROL VARIABLESi,t + εi,t. CONSTANT

-2.468⁎⁎⁎ (-20.21) 1.934⁎⁎⁎ (34.09) 0.148⁎⁎⁎ (21.46) 0.104⁎⁎⁎ (5.89) -0.032⁎⁎⁎ (-3.53) 0.010⁎⁎⁎ (5.11) -1.454⁎⁎⁎ (-18.46) 1.367⁎⁎⁎ (7.52) 0.009 (0.80) -0.026⁎⁎⁎ (-7.73) 0.114⁎⁎⁎ (3.02) -0.290⁎⁎⁎ (-13.96) -0.149⁎⁎⁎ (-4.06) 99,057 0.10

MEDIA LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS N Pseudo R-squared

Panel B: Conditional relation between investment and dual-class status (Heckman [1979] second stage) DEPVARi,t = β0 + β1*OVERi,t + β2*DUALi,t + β3*DUAL*OVERi,t + βn*CONTROL VARIABLESi,t + βm*IMRi,t + εi,t DEPVAR

INVEST

R&D

CONSTANT

22.598 (28.50) -2.278⁎⁎⁎ (-4.44) 7.062⁎⁎⁎ (4.44) -7.215⁎⁎⁎ (-5.12) 0.9011 1.062⁎⁎⁎ (25.00) -5.123⁎⁎⁎ (-46.43) 2.231⁎⁎⁎ (36.98) 0.045⁎⁎⁎ (3.39) 15.774⁎⁎⁎ (33.20) -43.356⁎⁎⁎ (-42.02) -1.153⁎⁎⁎ (-18.21) 0.087⁎⁎⁎ (8.64) -2.530⁎⁎⁎ (-13.96) 0.338⁎⁎ (2.60) 1.284⁎⁎⁎

5.795 (13.37) 7.500⁎⁎⁎ (33.03) 2.011⁎⁎⁎ (4.66) -8.636⁎⁎⁎ (-15.73) 0.0000 0.010 (0.52) -0.315⁎⁎⁎ (-6.63) 1.370⁎⁎⁎ (35.89) -0.045⁎⁎⁎ (-5.20) -2.416⁎⁎⁎ (-15.06) -31.743⁎⁎⁎ (-66.54) -1.117⁎⁎⁎ (-26.94) 0.131⁎⁎⁎ (18.75) -0.850⁎⁎⁎ (-12.53) -0.094 (-1.29) 3.209⁎⁎⁎

OVER DUAL DUAL*OVER Joint Significance LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS

⁎⁎⁎

⁎⁎⁎

CAPEX

ACQN

64.100 (33.20) 16.933⁎⁎⁎ (14.92) -2.942 (-1.43) -4.011 (-1.53) 0.0003 -0.749⁎⁎⁎ (-7.23) -13.325⁎⁎⁎ (-50.13) 2.878⁎⁎⁎ (21.73) 0.817⁎⁎⁎ (26.31) -12.592⁎⁎⁎ (-12.47) -14.106⁎⁎⁎ (-5.39) -0.689⁎⁎⁎ (-4.52) 0.241⁎⁎⁎ (8.31) -5.012⁎⁎⁎ (-12.26) 0.479 (1.51) -0.295

9.331⁎⁎⁎ (29.51) -9.769⁎⁎⁎ (-37.64) 1.621 (1.49) -0.101 (-0.12) 0.0689 0.948⁎⁎⁎ (43.70) -1.573⁎⁎⁎ (-30.92) 0.061⁎⁎⁎ (5.35) -0.006⁎⁎ (-2.31) -6.376⁎⁎⁎ (-31.86) -2.698⁎⁎⁎ (-5.58) 0.105⁎⁎⁎ (11.01) -0.034⁎⁎⁎ (-12.64) -0.488⁎⁎⁎ (-4.96) 0.124⁎⁎ (2.73) -0.764⁎⁎⁎

⁎⁎⁎

(continued on next page) 10

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Table 6 (continued) Panel B: Conditional relation between investment and dual-class status (Heckman [1979] second stage) DEPVARi,t = β0 + β1*OVERi,t + β2*DUALi,t + β3*DUAL*OVERi,t + βn*CONTROL VARIABLESi,t + βm*IMRi,t + εi,t DEPVAR IMR N R-squared

INVEST

R&D

CAPEX

ACQN

(6.68) -1.507⁎⁎⁎ (-3.53) 99,057 0.159

(35.11) 0.564⁎⁎⁎ (4.43) 99,057 0.344

(-0.63) 0.106 (0.18) 99,057 0.132

(-8.57) -0.455 (-1.57) 99,057 0.050

Notes: INVEST is total investment (capital expenditures plus research and development expenditures plus acquisitions less sales of property, plant, and equipment) scaled by lagged total assets. R&D is 100 times the research and development expenditures, deflated by lagged total assets. CAPEX is 100 times the capital expenditures, deflated by lagged net property, plant, and equipment. ACQN is 100 times the difference between acquisitions and sale of property, plant, and equipment, deflated by lagged total assets. OVER is a ranked variable based on the average of a ranked (decile) measure of cash and leverage, which increases with the likelihood of overinvestment (leverage is multiplied by minus one before ranking so that both variables are increasing with the likelihood of overinvestment). DUAL is an indicator variable that takes the value of 1 for dual-class firms and 0 otherwise. LOGASSET is the natural logarithm of the total assets of the firm. LOGAGE is the natural logarithm of the age of the firm. MTB is the market-to-book ratio of the firm. Z_SCORE is a measure of bankruptcy risk. TANGIBILITY is the ratio of property, plant, and equipment (PPE) to total assets. IND_K is an average measure, calculated for firms in the same 3-digit SIC industry, of market leverage, computed as the ratio of long-term debt to the sum of long-term debt and market value of equity. CFOSale is the ratio of cash flows from operations (CFO) to sales. SLACK is the ratio of cash to property, plant, and equipment. DIVIDEND is an indicator variable that takes the value of 1 if the firm paid a dividend and 0 otherwise. OP_CYCLE is the natural logarithm of the operating cycle of a firm. Operating cycle is calculated as [(receivables/sales) + (inventory/COGS)] x 360. LOSS is an indicator variable that takes the value of 1 if the net income before extraordinary items is negative and 0 otherwise. IMR (the inverse Mills ratio) is a bias correction factor calculated as a ratio of the standard normal p.d.f. to the standard normal c.d.f. (if dual=1) or a ratio of minus the standard normal p.d.f. to 1 minus the standard normal c.d.f. (if dual=0). We present t-statistics in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. All regressions use firm and year clusters to address possible correlation of residuals across firms and across time (Petersen, 2009).

class firms avoid underinvestment mainly by increasing R&D, and cash-rich ones avoid overinvestment by reigning in both R&D and capital expenditures. In Table 7, we present the results of a multinomial regression that examines deviations from the expected level of investment for our sample firms. To be specific, we regress our dependent variable INV_STATE, a measure of deviation from the expected level of investment, on the indicator variable DUAL and some control variables. Recall that INV_STATE has three levels: 2, the highest quartile, representing overinvestment; 1, the lowest quartile, representing underinvestment; 0, the middle quartiles, our benchmark or expected level of investment. Thus, a negative (positive) coefficient on the DUAL variable implies that dual-class firms are less (more) likely to deviate from the expected level of investment. As Table 7 shows, the coefficient of the indicator variable DUAL is −0.109 (t = −2.90) for the overinvestment group (overinvestment versus normal investment) and −0.202 (t = −5.22) for the underinvestment group (underinvestment versus normal investment). This means that dual-class firms have about 10.3% (18.3%) lower odds of overinvesting (underinvesting) than single-class firms do.13 Stated differently, dual-class firms have higher investment efficiency as measured by lower deviations from expected levels of investment though they seem better at avoiding underinvestment than at avoiding overinvestment. This is further empirical evidence supporting both Hypotheses 2a and 2b. 5. Robustness checks 5.1. Propensity score matching In the previous section, we address the endogeneity issue using Heckman (1979) two-stage regressions. In this section, we employ an alternative approach, propensity score matching, to address the potential selection bias.14 Propensity score matching is widely used to minimize the difference in observable characteristics between two groups, in order to disentangle the treatment effect from a selection effect. To perform propensity score matching, we first run a probit model to calculate the propensity score (predicted likelihood) that a firm selects a dual-class structure given the observed independent variables. The probit model includes variables to control for firm characteristics that differ significantly between dual-class and single-class firms. To ensure a close match of propensity scores, we apply radius matching with a caliper of 0.01. The resulting propensity score sample includes 5271 dual-class observations and 29,804 matched single-class observations. We then re-estimate the test of the conditional relation between investment and dual-class status in this sample as follows:

INVESTi,t + 1 = a 0 + a1

OVERi,t + a2

DUAL i,t + a3

DUAL

13

OVERi,t + an

CONTROL VARIABLES i,t +

i,t .

(5)

The dual:single odds ratio is 0.897 (e−0.109) for overinvestment and 0.817 (e−0.202) for underinvestment. We are grateful to an anonymous reviewer for suggesting the use of propensity score matching in addition to the Heckman (1979) two-stage approach. 14

11

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Table 7 Dual-class status and deviations from the expected investment level. INV_STATEi,t = γ0 + γ1*DUALi,t + γn*CONTROL VARIABLESi,t + εi,t

CONSTANT DUAL CASH LEV LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS N Pseudo R-squared

Overinvestment

Underinvestment

0.789*** (11.18) -0.109*** (-2.90) 1.842*** (33.79) 0.020*** (3.83) 0.005 (1.18) -0.254*** (-26.19) 0.176*** (34.38) -0.009*** (-8.29) 1.252*** (30.62) -2.998*** (-27.34) -0.038*** (-9.20) -0.014*** (-13.22) -0.272*** (-13.05) -0.234*** (-21.65) -0.336*** (-17.27) 99,057 0.189

2.232*** (31.04) -0.202*** (-5.22) -0.876*** (-15.09) -0.031*** (-5.71) -0.041*** (-9.06) -0.136*** (-13.53) 0.057*** (10.47) -0.004*** (-3.89) 0.173*** (3.95) -7.600*** (-57.06) -0.012** (-2.62) 0.009*** (10.92) -0.126*** (-6.08) -0.223*** (-19.90) 0.203*** (10.67) 99,057

Notes: The dependent variable, INV_STATE, is investment state, ranked in quartiles of investment efficiency (residuals) within the sample so that 1=underinvestment (first quartile), 2=overinvestment (fourth quartile), and 0=benchmark (quartiles 2 and 3). DUAL is an indicator variable that takes the value of 1 for dual-class firms and 0 otherwise. CASH is cash and short-term investments deflated by lagged total assets. LEV is the book value of long-term debt (total) divided by common/ordinary equity (total). LOGASSET is the natural logarithm of the total assets of the firm. LOGAGE is the natural logarithm of the age of the firm. MTB is the market-to-book ratio of the firm. Z_SCORE is a measure of bankruptcy risk. TANGIBILITY is the ratio of property, plant, and equipment (PPE) to total assets. IND_K is an average measure, calculated for firms in the same 3-digit SIC industry, of market leverage, computed as the ratio of long-term debt to the sum of long-term debt and market value of equity. CFOSale is the ratio of cash flows from operations (CFO) to sales. SLACK is the ratio of cash to property, plant, and equipment. DIVIDEND is an indicator variable that takes the value of 1 if the firm paid a dividend and 0 otherwise. OP_CYCLE is the natural logarithm of the operating cycle of a firm. Operating cycle is calculated as [(receivables/sales) + (inventory/COGS)] x 360. LOSS is an indicator variable that takes the value of 1 if the net income before extraordinary items is negative and 0 otherwise. We present the t-statistics in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. All regressions use firm and year clusters to address possible correlation of residuals across firms and across time (Petersen, 2009).

In addition to total investment, we use alternative measures of investment (R&D, CAPEX, and ACQN) to examine the conditional relationship between dual-class structure and investment efficiency in situations conducive to overinvestment or underinvestment. Table 8 depicts the results. Panel A of Table 8 shows that with total investment (INVEST) as our dependent variable, the coefficient of the DUAL variable is 2.706 (t = 3.16), suggesting that among firms most likely to underinvest, dual-class firms have a higher level of total investment (Hypothesis 2b is supported). Furthermore, the F-test of DUAL + DUAL*OVER shows that the joint effect has a value of −2.890 (t = −3.82), suggesting that among firms most likely to overinvest, dual-class firms invest less or have higher investment efficiency (Hypothesis 2a is supported). In sum, in the propensity score matched sample, dual-class firms invest more efficiently regardless of whether their circumstances promote underinvestment or overinvestment. Panel A of Table 8 also depicts the results of using alternative measures of investment with the propensity score matched sample. The DUAL coefficient is 3.355 (t = 12.95) for R&D and insignificant for both CAPEX (−1.809; t = −1.29) and ACQN (−0.134; t = −0.25). This is consistent with the notion that dual-class firms at risk of underinvestment use R&D as the main channel to 12

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Table 8 The effect of dual-class status on investment efficiency (propensity score matching sample). Panel A: Conditional relation between investment and dual-class status DEPVARi,t = a0 + a1*OVERi,t + a2*DUALi,t + a3*DUAL*OVERi,t + an*CONTROL VARIABLESi,t + εi,t DEPVAR

INVEST

R&D

CAPEX

ACQN

CONSTANT

20.603 (16.64) -4.204⁎⁎⁎ (-5.41) 2.706⁎⁎⁎ (3.16) -5.596⁎⁎⁎ (-3.73) 0.0001 0.938⁎⁎⁎ (14.61) -4.230⁎⁎⁎ (-25.34) 2.450⁎⁎⁎ (18.62) 0.035 (1.17) 10.285⁎⁎⁎ (14.49) -31.806⁎⁎⁎ (-19.03) -1.575⁎⁎⁎ (-10.12) 0.093⁎⁎⁎ (3.55) -1.918⁎⁎⁎ (-7.61) 0.155 (0.80) 0.891⁎⁎⁎ (2.92) 35,075 0.129

3.601 (5.90) 6.141⁎⁎⁎ (19.48) 3.355⁎⁎⁎ (12.95) -6.528⁎⁎⁎ (-11.21) 0.0000 -0.131⁎⁎⁎ (-4.72) -0.142⁎⁎ (-2.23) 1.467⁎⁎⁎ (18.05) -0.039⁎⁎ (-2.04) 0.088 (0.38) -28.141⁎⁎⁎ (-39.84) -1.408⁎⁎⁎ (-13.49) 0.171⁎⁎⁎ (9.94) -0.845⁎⁎⁎ (-9.90) 0.098 (0.98) 2.704⁎⁎⁎ (19.22) 35,075 0.340

66.125 (23.37) 11.842⁎⁎⁎ (7.21) -1.809 (-1.29) -2.806 (-0.98) 0.0059 -0.657⁎⁎⁎ (-4.09) -11.190⁎⁎⁎ (-29.03) 3.273⁎⁎⁎ (11.30) 0.820⁎⁎⁎ (12.54) -23.987⁎⁎⁎ (-15.67) -13.028⁎⁎⁎ (-3.24) -0.910⁎⁎ (-2.49) 0.263⁎⁎⁎ (3.87) -4.867⁎⁎⁎ (-8.87) -0.683 (-1.55) 0.219 (0.30) 35,075 0.136

10.413⁎⁎⁎ (17.43) -10.895⁎⁎⁎ (-24.60) -0.134 (-0.25) 0.581 (0.65) 0.2649 0.970⁎⁎⁎ (27.20) -1.544⁎⁎⁎ (-17.39) 0.161⁎⁎⁎ (5.34) -0.015⁎⁎ (-2.55) -7.706⁎⁎⁎ (-21.36) -1.786⁎⁎ (-2.03) 0.124⁎⁎⁎ (4.90) -0.041⁎⁎⁎ (-5.05) -0.361⁎⁎ (-2.34) 0.003 (0.03) -1.055⁎⁎⁎ (-6.65) 35,075 0.053

OVER DUAL DUAL*OVER Joint Significance LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS N R-squared

⁎⁎⁎

⁎⁎⁎

⁎⁎⁎

Panel B: Dual-class status and deviations from expected investment level INV_STATEi,t = b0 + b1*DUALi,t + bn*CONTROL VARIABLESi,t + εi,t

CONSTANT DUAL CASH LEV LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK

Overinvestment

Underinvestment

0.963 (7.67) -0.121⁎⁎⁎ (-2.97) 1.885⁎⁎⁎ (18.47) 0.003 (0.43) 0.027⁎⁎⁎ (3.72) -0.263⁎⁎⁎ (-15.57) 0.284⁎⁎⁎ (19.34) -0.005⁎ (-1.80) 0.808⁎⁎⁎ (10.84) -1.975⁎⁎⁎ (-10.59) -0.056⁎⁎⁎ (-5.42) -0.020⁎⁎⁎

2.665⁎⁎⁎ (20.65) -0.144⁎⁎⁎ (-3.53) -0.565⁎⁎⁎ (-5.27) -0.030⁎⁎⁎ (-3.39) -0.007 (-0.90) -0.175⁎⁎⁎ (-10.10) 0.106⁎⁎⁎ (7.30) -0.005⁎⁎ (-2.00) -1.048⁎⁎⁎ (-11.56) -6.607⁎⁎⁎ (-28.89) -0.023⁎⁎ (-2.08) -0.001

⁎⁎⁎

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Table 8 (continued) Panel B: Dual-class status and deviations from expected investment level INV_STATEi,t = b0 + b1*DUALi,t + bn*CONTROL VARIABLESi,t + εi,t

DIVIDEND OP_CYCLE LOSS N Pseudo R-squared

Overinvestment

Underinvestment

(-7.22) -0.203⁎⁎⁎ (-6.31) -0.360⁎⁎⁎ (-18.94) -0.537⁎⁎⁎ (-15.20) 35,075 0.184

(-0.60) -0.144⁎⁎⁎ (-4.38) -0.349⁎⁎⁎ (-17.61) 0.180⁎⁎⁎ (5.46) 35,075

Notes: The dependent variable, INV_STATE, is investment state, ranked in quartiles of investment efficiency (residuals) within the sample so that 1=underinvestment (first quartile), 2=overinvestment (fourth quartile), and 0=benchmark (quartiles 2 and 3). DUAL is an indicator variable that takes the value of 1 for dual-class firms and 0 otherwise. CASH is cash and short-term investments deflated by lagged total assets. LEV is the book value of long-term debt (total) divided by common/ordinary equity (total). LOGASSET is the natural logarithm of the total assets of the firm. LOGAGE is the natural logarithm of the age of the firm. MTB is the market-to-book ratio of the firm. Z_SCORE is a measure of bankruptcy risk. TANGIBILITY is the ratio of property, plant, and equipment (PPE) to total assets. IND_K is an average measure, calculated for firms in the same 3digit SIC industry, of market leverage, computed as the ratio of long-term debt to the sum of long-term debt and market value of equity. CFOSale is the ratio of cash flows from operations (CFO) to sales. SLACK is the ratio of cash to property, plant, and equipment. DIVIDEND is an indicator variable that takes the value of 1 if the firm paid a dividend and 0 otherwise. OP_CYCLE is the natural logarithm of the operating cycle of a firm. Operating cycle is calculated as [(receivables/sales) + (inventory/COGS)] x 360. LOSS is an indicator variable that takes the value of 1 if the net income before extraordinary items is negative and 0 otherwise. We present the t-statistics in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. All regressions use firm and year clusters to address possible correlation of residuals across firms and across time (Petersen, 2009).

improve investment efficiency. The joint effect of DUAL + DUAL*OVER is −3.173 (t = −8.65) for R&D, −4.615 (t = 2.75) for CAPEX, and insignificant for ACQN (0.447; t = 1.11). This suggests that among firms most likely to overinvest, dual-class firms have lower levels of both R&D and CAPEX expenditures. Overall, the results in Table 8 panel A provide findings consistent with those we obtained using Heckman's (1979) two-stage regression. Next, in Table 8, panel B, we present the results of examining the deviation from expected level of investment for the propensity score matched sample. Notably, the coefficient on the DUAL indicator variable is −0.121 (t = −2.97) for the overinvestment group and − 0.144 (t = −3.53) for the underinvestment group. For this sample, our findings indicate that dual-class firms have 11.4% (13.4%) lower odds than their single-class peers of being in the extreme quartiles among firms most likely to overinvest (underinvest).15 This is consistent with dual-class firms' having higher investment efficiency (both Hypotheses 2a and 2b are supported). 5.2. Change in investment efficiency as a firm changes status from single-class to dual-class As a further robustness check, we examine the deviation from the expected level of investment for single-class firms that adopted a dual-class structure within the sample period (1987 to 2016).16 The advantage of examining the investing behavior of firms before and after adopting a dual-class status is to control for all stable characteristics of the firms, whether observable or not, yielding more robust inferences (Allison, 2005). We thus estimate the following model:

INV_STATEi,t =

0

+

1

SINGLE_DUAL i,t +

n

CONTROL VARIABLESi,t +

i,t .

(6)

where INV_STATE is the investment state defined in quartiles of investment efficiency (residuals) within the sample, ranked so that 1 = underinvestment (first quartile), 2 = overinvestment (fourth quartile), and 0 = benchmark (quartiles 2 and 3). SINGLE_DUAL is an indicator variable that takes the value of 0 for the firm-year before the firm adopts a dual-class structure and 1 afterwards. Our control variables are the same ones we used in estimating Eq. (4). If investment efficiency is higher for dual-class firms (Hypotheses 2a and 2b), then we expect single-class firms to deviate less from the expected investment level after they switch to the dual-class structure. Thus Hypotheses 2a and 2b are supported if θ1 is significantly negative. Table 9 presents the results of our examination of single-class firms adopting a dual-class structure. Notably, for firms likely to overinvest (cash-rich, unlevered firms), the coefficient on the SINGLE_DUAL indicator variable (=1 if a firm-year is after a firm 15

The dual:single odds ratio is 0.886 (e−0.121) for overinvestment and 0.866 (e−0.144) for underinvestment. 387 firms (out of 751) first became dual-class firms during the sample period. For each firm, we examine investment efficiency in the last year of single-class status and in the first year of dual-class status. This process results in 774 firm-year observations split equally between dual = 0 and dual = 1. 16

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Table 9 Effect of change in share structure (single to dual) on investment efficiency (dual firms only). INV_STATEi,t = θ0 + θ

CONSTANT SINGLE_DUAL CASH LEV LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS N Pseudo R-squared

1⁎

SINGLE_DUALi,t + θ

n⁎CONTROL

VARIABLESi,t + εi,t

Overinvestment

Underinvestment

0.549 (0.56) -0.555*** (-3.04) 0.054 (0.06) 0.018 (0.49) -0.202*** (-3.00) -0.070 (-0.71) 0.269*** (3.50) -0.027 (-1.43) 0.538 (0.96) 2.550** (2.07) -0.213 (-1.06) -0.068 (-1.22) 0.321 (1.46) -0.222 (-1.48) 0.036 (0.15) 774 0.154

1.647 (1.26) -0.313** (-2.19) -0.764 (-0.68) -0.047 (-1.12) -0.062 (-0.87) 0.142 (1.25) -0.009 (-0.09) -0.011 (-0.58) -1.568** (-2.23) -1.107 (-0.75) -0.028 (-0.10) 0.023 (0.38) -0.561** (-2.40) -0.278 (-1.41) 0.379 (1.60) 774

adopted the dual-class structure, 0 otherwise) is −0.555 (t = −3.04). This means that for firms most likely to overinvest, the odds of being in the extreme quartile (quartile 4) of deviations are 0.57 (=e−0.555) times (or 43% lower) once the firm adopts a dual-class structure. In other words, investment efficiency is higher after the firm adopts a dual-class structure (Hypothesis 2a is supported). For firms likely to underinvest (resource-constrained, highly levered firms), the coefficient on the SINGLE_DUAL variable is −0.313 (t = −2.19). This means that a firm's odds of being in the extreme quartile (quartile 1) are 0.73 (=e−0.313) times (or 27% lower) once it adopts a dual-class structure. Thus, the hypothesis that dual-class firms are more likely to avoid underinvestment than their single-class peers (Hypothesis 2b) is supported. 5.3. The effect of industry concentration among dual-class firms Our qualitative analyses in panel B of Table 2 reveal that the concentration of firms with dual-class structure varies widely among industries. Next, we examine whether this variance affects investment efficiency in dual-class firms. We partition our sample into two groups: industries with a relatively high concentration of dual-class firms (printing & publishing, textiles, beer & liquor, communications, personal services, entertainment, apparel, food products, transportation, and retail) and those with a low concentration of dual firms (others). We rerun the regressions separately for the two groups. Table 10 presents the results of the second stage of a Heckman (1979) two-stage regression. As Table 10 shows, the coefficient on the DUAL indicator variable is 38.631 (t = 13.34) for high-concentration industries and −2.314 (t = −0.49) for low-concentration industries. Thus, it seems that among firms most at risk of underinvestment, dual-class firms are more likely to have a higher level of investment if they are in industries with a high concentration of dual firms. Notably, the sum of the coefficients on DUAL and DUAL*OVER is −16.242 (t = −8.97) for industries with a high concentration and − 7.110 (t = −1.82) for industries with a low concentration of dual firms. These findings show that among firms at risk of overinvestment, dual firms in both groups tend to invest less, though the effect of dual-class structure on investment efficiency may be stronger in industries with a high concentration of dual-class firms. 5.4. Future profitability in single-class and dual-class firms Given that dual-class firms exhibit superior investment efficiency, what are the economic consequences? If dual-class structure helps managers reduce investment myopia by curbing overinvestment in poor projects, then we expect greater future profitability for 15

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Table 10 Conditional relation between investment and dual-class status (by concentration of dual-class firms). INVESTi,t = δ0 + δ1*OVERi,t + δ2*DUALi,t + δ3*DUAL*OVERi,t + δn*CONTROL VARIABLESi,t + δm*IMRi,t + εi,t

CONSTANT OVER DUAL DUAL*OVER Joint Significance LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS IMR N R-squared

Industries with high concentration of dual-class firms

Other industries

8.142⁎⁎⁎ (6.75) 4.880⁎⁎⁎ (8.39) 38.631⁎⁎⁎ (13.34) -54.873⁎⁎⁎ (-16.35) 0.0000 0.232⁎⁎⁎ (3.87) -3.482⁎⁎⁎ (-22.41) 1.915⁎⁎⁎ (13.16) 0.200⁎⁎⁎ (3.93) 11.352⁎⁎⁎ (17.41) 2.571 (1.61) -5.891⁎⁎⁎ (-7.73) -0.063 (-1.24) -0.845⁎⁎⁎ (-3.64) -0.147 (-0.85) -0.568⁎ (1.79) -1.541⁎⁎ (-2.40) 18,401 0.160

22.622⁎⁎⁎ (26.53) -1.390⁎⁎ (-2.41) -2.314 (-0.49) -4.796⁎⁎ (-2.40) 0.0689 1.023⁎⁎⁎ (21.88) -5.106⁎⁎⁎ (-41.77) 2.133⁎⁎⁎ (34.99) 0.036⁎⁎ (2.63) 17.373⁎⁎⁎ (32.91) -48.202⁎⁎⁎ (-42.84) -1.118⁎⁎⁎ (-17.75) 0.092⁎⁎⁎ (8.78) -2.780⁎⁎⁎ (-13.51) 0.461⁎⁎⁎ (3.28) 1..518⁎⁎⁎ (7.12) 1.269 (0.98) 80,656 0.166

Notes: The dependent variable, INVEST, is total investment (capital expenditures plus research and development expenditures plus acquisitions less sales of property, plant, and equipment) scaled by lagged total assets. OVER is a ranked variable based on the average of a ranked (decile) measure of cash and leverage, which increases with the likelihood of overinvestment (leverage is multiplied by minus one before ranking so that both variables are increasing with the likelihood of overinvestment). DUAL is an indicator variable that takes the value of 1 for dual-class firms and 0 otherwise. LOGASSET is the natural logarithm of the total assets of the firm. LOGAGE is the natural logarithm of the age of the firm. MTB is the market-to-book ratio of the firm. Z_SCORE is a measure of bankruptcy risk. TANGIBILITY is the ratio of property, plant, and equipment (PPE) to total assets. IND_K is an average measure, calculated for firms in the same 3-digit SIC industry, of market leverage, computed as the ratio of long-term debt to the sum of long-term debt and market value of equity. CFOSale is the ratio of cash flows from operations (CFO) to sales. SLACK is the ratio of cash to property, plant, and equipment. DIVIDEND is an indicator variable that takes the value of 1 if the firm paid a dividend and 0 otherwise. OP_CYCLE is the natural logarithm of the operating cycle of a firm. Operating cycle is calculated as [(receivables/sales) + (inventory/COGS)] x 360. LOSS is an indicator variable that takes the value of 1 if the net income before extraordinary items is negative and 0 otherwise. IMR (the inverse Mills ratio) is a bias correction factor calculated as a ratio of the standard normal p.d.f. to the standard normal c.d.f. (if dual=1) or a ratio of minus the standard normal p.d.f. to 1 minus the standard normal c.d.f. (if dual=0). We present t-statistics in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. All regressions use firm and year clusters to address possible correlation of residuals across firms and across time (Petersen, 2009). The industries with high concentration of dual firms are printing and publishing (41%), textiles (23.8%), beer and liquor (22.4%), communications (18.5%), personal services (15.7%), entertainment (14.6%), apparel (12.4%), food products (10.5%), transportation (10.4%), and retail (10.1%).

dual-class firms at risk of overinvestment. On the other hand, if dual-class structure encourages managers to undertake additional projects with positive NPV, then we expect greater future profitability for dual-class firms at risk of underinvestment. To shed light on the economic consequences, we further investigate the association between dual-class structure and the riskiness of investment projects. If dual-class structure motivates managers to focus on long-term business strategy, then managers might pursue low-risk (prudent) investment projects, resulting in less volatile future returns regardless of whether a situation is conducive to overinvestment or underinvestment. Using a propensity score matched sample, we model the conditional relation between dual-class status and two dependent variables: future performance and the riskiness of future returns. Because this analysis requires data on future ROA and its volatility, our final sample size is reduced slightly. In panel A of Table 11, we present the results of regressing future performance (the three16

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Table 11 Association between dual-class status and future accounting performance (propensity score matching). Panel A: Conditional relation between performance and dual-class status PERFORMANCEi, = d0 + d1*OVERi,t + d2*DUALi,t + d3*DUAL*OVERi,t + dn*CONTROL VARIABLESi,t + εi,t CONSTANT

-0.071⁎⁎⁎ (-5.38) 0.004 (0.56) -0.007 (-1.33) 0.024⁎⁎ (2.18) 0.0088 0.016⁎⁎⁎ (22.41) 0.013⁎⁎⁎ (10.04) -0.029⁎⁎⁎ (-19.68) 0.007⁎⁎⁎ (18.12) 0.016⁎⁎ (2.62) -0.025 (-1.55) 0.040⁎⁎⁎ (21.26) -0.001⁎⁎⁎ (-4.21) 0.005⁎⁎⁎ (2.89) -0.001 (-0.28) -0.121⁎⁎⁎ (-41.72) 27,460 0.440

OVER DUAL DUAL*OVER Joint Significance LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND OP_CYCLE LOSS N R-squared

Panel B: Conditional relation between the volatility of future returns and dual-class status VOLATILITYi, = c0 + c1*OVERi,t + c2*DUALi,t + c3*DUAL*OVERi,t + cn*CONTROL VARIABLESi,t + εi,t CONSTANT

0.212⁎⁎⁎ (16.75) 0.079⁎⁎⁎ (11.05) -0.001 (-0.11) -0.026⁎⁎ (-2.73) 0.0000 -0.024⁎⁎⁎ (-35.41) -0.009⁎⁎⁎ (-7.40) 0.033⁎⁎⁎ (24.11) -0.008\⁎⁎⁎ (-22.78) -0.027⁎⁎⁎ (-4.15) 0.005 (0.35) -0.021⁎⁎⁎ (-11.05) 0.001⁎⁎ (3.06) -0.004⁎⁎ (-2.57)

OVER DUAL DUAL*OVER Joint Significance LOGASSET LOGAGE MTB Z_SCORE TANGIBILITY IND_K CFOSale SLACK DIVIDEND

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Table 11 (continued) Panel B: Conditional relation between the volatility of future returns and dual-class status VOLATILITYi, = c0 + c1*OVERi,t + c2*DUALi,t + c3*DUAL*OVERi,t + cn*CONTROL VARIABLESi,t + εi,t OP_CYCLE

-0.006⁎⁎⁎ (-2.87) 0.056⁎⁎⁎ (20.60) 27,460 0.396

LOSS N R-squared

Notes: The dependent variable, VOLATILITY, is the standard deviation of the future return on investment (ROA), measured from t + 1 to t + 5, adjusted by subtracting the Fama-French 48-industry-year mean. OVER is a ranked variable based on the average of a ranked (decile) measure of cash and leverage, which increases with the likelihood of overinvestment (leverage is multiplied by minus one before ranking so that both variables are increasing with the likelihood of overinvestment). DUAL is an indicator variable that takes the value of 1 for dual-class firms, and 0 otherwise. LOGASSET is the natural logarithm of the total assets of the firm. LOGAGE is the natural logarithm of the age of the firm. MTB is the market-to-book ratio of the firm. Z_SCORE is a measure of bankruptcy risk. TANGIBILITY is the ratio of property, plant, and equipment (PPE) to total assets. IND_K is an average measure, calculated for firms in the same 3-digit SIC industry, of market leverage, computed as the ratio of long-term debt to the sum of long-term debt and market value of equity. CFOSale is the ratio of cash flows from operations (CFO) to sales. SLACK is the ratio of cash to property, plant, and equipment. DIVIDEND is an indicator variable that takes the value of 1 if the firm paid a dividend and 0 otherwise. OP_CYCLE is the natural logarithm of the operating cycle of a firm. Operating cycle is calculated as [(receivables/sales) + (inventory/COGS)] x 360. LOSS is an indicator variable that takes the value of 1 if the net income before extraordinary items is negative, and 0 otherwise. IMR (the inverse Mills ratio) is a bias correction factor calculated as a ratio of the standard normal p.d.f. to the standard normal c.d.f. (if dual=1) or a ratio of minus the standard normal p.d.f. to 1 minus the standard normal c.d.f. (if dual=0). We present t-statistics in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. All regressions use firm and year clusters to address possible correlation of residuals across firms and across time (Petersen, 2009).

year average of industry-adjusted ROA in t + 1, t + 2, and t + 3) on DUAL (an indicator variable that is 1 for dual-class firms and 0 otherwise), OVER (a variable increasing with the likelihood of overinvestment), and other control variables. As Table 11 panel A shows, the coefficient on the DUAL variable is insignificant (−0.007; t = −1.33). However, the joint effect of DUAL + DUAL*OVER is significantly positive (0.017; t = 2.62), implying that, among firms most susceptible to overinvestment, dual-class firms have higher future performance. These findings are consistent with the expectation that dual-class structure prompts managers at risk of overinvestment to abandon poor projects, thus leading to greater future profitability. Panel B of Table 11 depicts the conditional relation between the volatility of future returns and dual-class status. The volatility of future returns is measured as the standard deviation of future ROA over the period from t + 1 to t + 5. Notably, the coefficient on the DUAL variable is insignificant (−0.001; t = −0.11), suggesting that, among firms most susceptible to underinvestment (cash-constrained, highly levered firms), volatility does not differ significantly between single-class and dual-class firms. On the other hand, the joint effect of DUAL + DUAL*OVER is significant and negative (−0.027; t = −6.78), suggesting that compared to single-class peers, dual-class firms tend to undertake more prudent investment projects in situations conducive to overinvestment. Overall, our results in Table 11 show that the investment-related effects of dual-class structure on future performance hold only in scenarios where overinvestment is more likely. Our findings complement the related evidence documented by Lara et al. (2016) that the association between accounting conservatism and future performance is conditional on settings conducive to overinvestment or underinvestment. 6. Conclusion Under dual-class share structure, the cash flow rights of “preferred” shares align insiders' incentives with those of outsiders, but their superior voting rights have often been associated with entrenchment. A worldview that assumes divergence between insider and outsider goals and incentives frowns upon entrenchment. However, we invoke an alternative view that suggests that insiders might seek control of firms for legitimate, even noble purposes—specifically, to use their superior information to make efficient investment decisions free of undue pressure from less informed outsiders. Upon comparing the investment efficiency of dual-class vs. single-class firms, we find that, among firms susceptible to underinvestment (cash-constrained, highly levered firms), dual-class firms tend to invest more in R&D than single-class firms do. We also find some evidence that, among firms susceptible to overinvestment (generally high-cash, unlevered firms), investment in R&D and CAPEX is lower for dual-class firms. In addition, dual-class firms exhibit significantly lower deviations from the expected level of investment than single-class firms, irrespective of whether a firm is more likely to underinvest or to overinvest. Finally, we document strong evidence of improved investment efficiency for single-class firms adopting a dual-class structure. Taken together, our results indicate that dual-class structure increases investment efficiency and support the view that the dual-class structure shields insiders from short-term market pressure and thus reduces managers' investment myopia. A caveat: our results seem to be stronger in industries with a high concentration of dual-class firms. Recent studies (Baran et al., 2018; Cremers et al., 2018) suggest that life-cycle affects firm valuation differences between dualclass and single-class firms. In this study, we use firm age to control for trends over time. Future research might investigate 18

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differences in the investment efficiency of dual-class firms during and after the tenure of founding management teams. Another avenue of future research would be to examine the relative importance of cash flow rights and voting rights in determining investment efficiency. References Allison, P. D. (2005). Fixed effects regression methods for longitudinal data using SAS. Cary, NC: SAS Institute Inc. Baran, L., Forst, A., & Via, M. T. (2018). Dual class share structure and innovation (Working paper)Kent State University. Barney, J. B. (1990). The debate between traditional management theory and organizational economics: Substantive differences or intergroup conflict? Academy of Management Review, 15, 382–393. https://doi.org/10.5465/amr.1990.4308815. Bebchuk, L., Kraakman, R., & Triantis, G. (1999). Stock pyramids, cross-ownership, and dual class equity: The creation and agency costs of separating control and cash flow rights (NBER Working Paper Series No. w6951). https://www.nber.org/papers/w6951. Biddle, G. C., & Hilary, G. (2006). Accounting quality and firm-level capital investment. The Accounting Review, 81, 963–982. https://doi.org/10.2308/accr.2006.81.5. 963. Biddle, G. C., Hilary, G., & Verdi, R. S. (2009). How does financial reporting quality relate to investment efficiency? Journal of Accounting and Economics, 48, 112–131. https://doi.org/10.1016/j.jacceco.2009.09.001. Chemmanur, T., & Jiao, Y. (2012). Dual class IPOs: A theoretical analysis. Journal of Banking and Finance, 36, 305–319. https://doi.org/10.1016/j.jbankfin.2011.07. 010. Cremers, M., Lauterbach, B., & Pajuste, A. (2018). The life-cycle of dual 9class firms (European Corporate Governance Institute [ECGI] Finance Working Paper No. 550/2018). https://ecgi.global/working-paper/life-cycle-dual-class-firms. DeAngelo, H., & DeAngelo, L. (1985). Managerial ownership of voting rights. A study of public corporations with dual classes of common stock. Journal of Financial Economics, 14, 33–69. https://doi.org/10.1016/0304-405X(85)90043-1. Donaldson, L. (1985). In defence of organization theory: A reply to the critics. Cambridge, UK: Cambridge University Press. Donaldson, L. (1990a). The ethereal hand: Organizational economics and management theory. Academy of Management Review, 15, 369–381. https://doi.org/10.5465/ amr.1990.4308806. Donaldson, L. (1990b). A rational basis for criticisms of organizational economics: A reply to Barney. Academy of Management Review, 15, 394–401. https://doi.org/10. 5465/amr.1990.4308821. Donaldson, L., & Davis, J. H. (1991). Stewardship theory or agency theory: CEO governance and shareholder returns. Australian Journal of Management, 16, 49–64. https://doi.org/10.1177/031289629101600103. Fama, E., & French, K. (1997). Industry costs of equity. Journal of Financial Economics, 43, 153–193. https://doi.org/10.1016/S0304-405X(96)00896-3. Gompers, P. A., Ishii, J., & Metrick, A. (2004). Incentives vs. control: An analysis of U.S dual-class companies (NBER Working Paper Series No. w10240). https://www. nber.org/papers/w10240. Gompers, P. A., Ishii, J., & Metrick, A. (2010). Extreme governance and equity prices. The Quarterly Journal of Economics, 118, 107–155. https://doi.org/10.1162/ 00335530360535162. He, J., & Tian, X. (2013). The dark side of analyst coverage: The case of innovation. Journal of Financial Economics, 109, 856–878. https://doi.org/10.1016/j.jfineco. 2013.04.001. Heckman, J. (1979). The sample selection bias as a specification error. Econometrica, 47(1), 153–162. https://doi.org/10.2307/1912352. Howell, J. W. (2010). The dual class stock structure in the United States: A new dataset and an examination of firms who leave the structure (Retrieved from) https:// getd.libs.uga.edu/pdfs/howell_jason_w_201005_phd.pdf. Jensen, M. (1986). Agency costs of free cash flow, corporate finance, and takeovers. American Economic Review, 76, 323–329. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X. Jordan, B., Kim, S., & Liu, M. (2016). Growth opportunities, short-term market pressure, and dual-class share structure. Journal of Corporate Finance, 41, 304–328. https://doi.org/10.1016/j.jcorpfin.2016.10.003. Jordan, B., Liu, M., & Wu, Q. (2014). Corporate payout policy in dual-class firms. Journal of Corporate Finance, 26, 1–19. https://doi.org/10.1016/j.jcorpfin.2014.02. 004. Lara, J. M. G., Osma, B. G., & Penalva, F. (2016). Accounting conservatism and firm investment efficiency. Journal of Accounting and Economics, 61, 221–238. https:// doi.org/10.1016/j.jacceco.2015.07.003. Lennox, C. S., Francis, J. R., & Wang, Z. (2012). Selection models in accounting research. The Accounting Review, 87(2), 589–616. https://doi.org/10.2308/accr-10195. Masulis, R., Wang, C., & Xie, F. (2009). Agency problems at dual-class companies. Journal of Finance, 64(4), 1697–1727. Myers, S. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5, 147–175. https://doi.org/10.1016/0304-405X(77)90015-0. Nasdaq (2017). The promise of market reform: Reigniting America's economic engine, Nasdaq progress in process report. https://business.nasdaq.com/media/ Revitalize%20Progress%20Report%20May%202018_Edits_tcm5044-61798.pdf. Nguyen, V. T., & Xu, L. (2010). The impact of dual class structure on earnings management activities. Journal of Business Finance & Accounting, 37(3–4), 456–485. https://doi.org/10.1111/j.1468-5957.2010.02203.x. Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies, 22(1), 435–480. https://doi.org/10. 1093/rfs/hhn053. Smart, S., & Zutter, C. (2003). Control as a motivation for underpricing: A comparison of dual and single-class IPOs. Journal of Financial Economics, 69, 85–110. https:// doi.org/10.1016/S0304-405X(03)00109-0. Smart, S. B., Thirumalai, R. S., & Zutter, C. J. (2008). What's in a vote? The short- and long-run impact of dual-class equity on IPO firm values. Journal of Accounting and Economics, 45, 94–115. https://doi.org/10.1016/j.jacceco.2007.07.002. Stein, J. (1988). Takeover threats and managerial myopia. Journal of Political Economy, 96, 61–80. https://doi.org/10.1086/261524. Tucker, J. W. (2010). Selection bias and econometric remedies in accounting and finance research. Journal of Accounting Literature, 29, 31–57. Wan, H. (2013). Managerial incentives and real earnings management: Evidence from dual-class firms. International Research Journal of Applied Finance, 4(2), 220–241. https://nebula.wsimg.com/93808c76289746a78808c0701cde16e1?AccessKeyId=A83663472B839ECDD54B&disposition=0&alloworigin=1.

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