Insider sales in IPOs: Consequences of liquidity needs

Journal of Corporate Finance 39 (2016) 1–17

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Insider sales in IPOs: Consequences of liquidity needs Ansley Chua ⁎, Tareque Nasser College of Business, Kansas State University, Manhattan, KS 66506, USA

a r t i c l e

i n f o

Article history: Received 1 April 2015 Received in revised form 18 May 2016 Accepted 20 May 2016 Available online 27 May 2016 JEL classification: G32 G30 G34

a b s t r a c t We examine the causes and consequences of insiders' liquidity needs as a motivation for the secondary sales in an initial public offering (IPO). Four main findings resulted from our analysis: (1) lower levels of pre-IPO cash holdings lead to lower levels of executive compensation, (2) smaller cash-pay results in greater levels of insider sales, (3) equity-pay levels do not directly affect the insider sales but do influence the presence of an IPO lockup period, which in turn affects the levels of insider sales, and (4) higher levels of insider sales due to liquidity needs result in lower levels of underpricing and long-run returns. Taken together these results also suggest that liquidity induced secondary sales can be a source of agency problems. © 2016 Elsevier B.V. All rights reserved.

Keywords: Initial public offering Cash Executive compensation Insider selling

1. Introduction Do insiders sell secondary shares as part of the initial public offering (IPO) for liquidity reasons?1 Anecdotal evidence suggests so. For instance, according to Facebook's May 3, 2012 U.S. Securities and Exchange Commission (SEC) submission, their initial registration with insider sales documentation, 236 million secondary shares were to be sold during the IPO, of which executive officers and directors were listed to sell 117 million shares.2 However, two weeks later on the May 18, 2012 amended prospectus, this number increased to 388 million shares, with executive officers and directors listed to sell 193 million shares.3 This prompted the media to question whether this increase in executive and director selling is due to concerns about the postIPO lockup period as suggested by some insiders (Gallagher, 2012) or due to inside knowledge of an impending price crash which motivated James Breyer, one of the directors, to double the number of shares to be sold (Marshall, 2012).4 In this paper, we argue and provide evidence that insider sales during an IPO are carried out due to insiders' liquidity needs, and study the consequences of the secondary sales for liquidity motives. ⁎ Corresponding author. E-mail addresses: [email protected] (A. Chua), [email protected] (T. Nasser). 1 During IPOs firms can issue new, primary shares, or can offer existing shares held by insiders, which are known as secondary shares. The proceeds from the sale of secondary shares go to the insiders who sell them. This may provide much needed liquidity to the insiders who may have been accepting a lower payments in a potentially cash starved entrepreneurial firm. 2 http://www.sec.gov/Archives/edgar/data/1326801/000119312512208192/d287954ds1a.htm. 3 http://www.sec.gov/Archives/edgar/data/1326801/000119312512240111/d287954d424b4.htm. 4 Secondary sale by insiders during an IPO is undeniably an intriguing affair as it can straddle both sides of the law. Clearly, securities regulation demands full disclosure and due diligence at the time of the public offering, which make secondary sale by the firm managers legal; it serves their liquidity needs. But it is also possible that insiders may have private knowledge during the time of the sale, making it illegal but hard to prove in the court of law. However, this is not the focus of our paper.

http://dx.doi.org/10.1016/j.jcorpfin.2016.05.004 0929-1199/© 2016 Elsevier B.V. All rights reserved.

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A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

We begin by examining the relationship between insider sales during the initial offering and firm and IPO characteristics. We first explore whether the level of managerial compensation during the pre-IPO years drives the insiders' liquidity needs, and if the level of compensation is largely determined by the firm's pre-IPO cash holding. If a firm is cash constrained, the cash compensation for its managers may also be lower than that of the managers of its peer firms. As a substitute for salary, firms may offer equity-pay when cash availability is limited in cash-poor start-up firms, such as high-technology firms, that have intangible assets but little cash (Core and Guay, 2001; Yermack, 1995). However, without a secondary market, equity compensation cannot satisfy the executives' immediate liquidity needs. One would expect firms to seek the lowest-cost method of funding executive compensations and thus, this “equity-pay-as-finance” explanation is plausible only if asking employees to take a discount in salary is the lowest-cost way to fund managerial pay at a new venture (Oyer and Schaefer, 2005). Also, large stakeholders of a relatively new venture may want to provide incentive to the management by granting equity-pay, but not at the cost of diluting their ownership (Baker and Gompers, 2003). It is therefore reasonable to assume that cash-constrained firms may pay both cash and equity, but at lower levels than that of comparable firms. Managers of an entrepreneurial firm are typically undiversified and have their human capital and a significant portion of their wealth tied to the firm, in the form of illiquid ownership of firm's shares (Gentry and Hubbard, 2004; Heaton and Lucas, 2000). Therefore, managers of a cash-strapped IPO-firm who receive below normal cash compensation may have high liquidity needs and would be more inclined to sell their shares. We find that insiders' selling of secondary shares, after controlling for other factors, is negatively correlated to firm's pre-IPO cash holding. To determine if the insider sales can be attributed to insiders' personal liquidity needs we hand collect data on pre-IPO executive compensation and examine its relationship with the pre-IPO cash holdings. We find that lower levels of the firms' pre-IPO cash holdings result in lower levels of insiders' total, cash and equity compensations. Second, as the probability of a firm issuing its IPO with a lockup clause may depend on insider's liquidity needs, we examine causes and consequences of having a post-IPO lockup. Majority of IPOs have lockup periods, usually for a period of six months. Under a lockup agreement, underwriters forbid company insiders from selling their shares. Managers usually do not sell any of their own shares during the IPO, but instead wait until the end of the lockup period, as majority of the firms have no secondary sales (Field and Hanka, 2001). The lockup restricts the supply of shares, helping boost IPO prices during the early trading days. Furthermore, Aggrawal et al. (2002) argue that managers strategically underprice IPOs to generate information momentum in order to sell shares at a higher price at the lockup expiration. However, if the insiders are in immediate needs of cash inflows, they might prefer not to have a lockup restriction. We find that cash-pay is uncorrelated to the presence of a lockup period, but higher levels of equity-pay significantly reduce the probability of having a lockup period. This suggests that when insiders are paid more in equity to make up for the reduced cash-pay, they may prefer to sell some of their equity holdings during the IPO rather than waiting for months. The above observation is also supported by our finding that, when cash-pay is predicted by the firm's cash holdings and cash-to-total-asset ratio in a two-stage regression, lower cash-pay decreases the likelihood of having a lockup provision. If an IPO has a lockup contract despite insiders having liquidity needs, insiders may find a need to sell a part of their pre-IPO holding and possibly at a higher level. We find that the presence of a lockup provision increases both the likelihood and the levels of insider sales. Third, we identify the factors that serve as a proxy for the insiders' liquidity needs. The earlier discussions indicate that lower levels of pre-IPO cash holdings and managerial compensations, and the absence of a lockup agreement implies a lower level of insider-liquidity and a greater need for secondary sales. The timing decision of an IPO also plays an important role in supplying liquidity to insiders. Firms are more likely to go public when the industry valuations are high (Chua, 2014; Jain and Kini, 2006; Pagano et al., 1998). If firms can time their public offerings with a higher industry valuation, each additional share offered by insiders can generate more cash for the insiders, alleviating their liquidity needs. Therefore, we expect and find a significantly negative correlation between industry valuation and secondary sales. Thus, we identify six liquidity proxies (see the liquidity related variables in Appendix Table A.1 for the variable descriptions) that explain the presence of secondary sales and their levels. Equipped with these liquidity proxies we embark on the next task. Finally, we examine how the level of insider sales that is motivated by insiders' liquidity needs influence the first-day-returns of IPOs and the long term performance of the firm. Using OLS regression, we find that insider sales is not significantly correlated to first-day returns. However, if insider sales projected on liquidity variables is used as a generated regressor in a two-stage framework, a higher level of insider sales results in a significantly lower level of first-day returns or lower underpricing. This shows that insider sales due to the insiders' liquidity needs is correlated to lower first day returns. A similar pattern emerges when the two-year post-IPO returns are regressed on insider sales using OLS and generated-regressor-2SLS methods. In particular, liquidity-induced-insider-sales have a significant negative correlation with long-run returns. Managers with greater ownership stake typically would want the IPO to be substantially underpriced to take advantage of the information momentum (Habib and Ljungqvist, 2001). However, this would not hold true if either of the following two alternatives dominates. First, managers would underprice the IPO less if the ratio of the marginal value of the IPO proceeds to long-run firm value is high (Aggrawal et al., 2002). In other words, managers' liquidity needs supersede the need for long term-value maximization. Second, maximization of proceeds at the IPO can dominate the incentive for underpricing if managers sell a greater portion of their own personal stake in the firm at the time of the IPO (Ljungqvist and Wilhelm, 2003). But the second alternative is unlikely, as less than half a percent of the IPOs in our sample have secondary sales greater than 50% of the insiders' ownership. Therefore, the results are consistent with the idea that insiders' liquidity needs drive secondary sales. This liquidity induced secondary sales can also give way to some agency problems that may impede managers from maximizing the long-run value of the firm.

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In order to check the robustness of our results, we identify and estimate an insider-liquidity-factor based on the liquidity proxies using a factor analysis. We observe that this liquidity factor is negatively correlated with insider sales and positively correlated with both underpricing and long-run returns. This implies that lower insider-liquidity results in a higher likelihood of secondary sales. It also suggests that the market reacts negatively to this apparent liquidity-motivated-insider-sales on the first day of trading and in the long-run. Our work contributes to the literatures on IPOs, executive compensation and cash holdings in private firms, and insider trading. First, we add to the literature on secondary sales and lockup agreement in IPOs. Our paper extends the work of Ang and Brau (2003) and Brau et al. (2007) in identifying insiders' objectives of secondary sales and examining the effect of insider selling on IPO underpricing and long-run returns. It also adds to Field and Hanka (2001), Aggrawal et al. (2002), Brav and Gompers (2003) among others, who examine the role of a lockup period in IPOs. Second, the paper makes a unique contribution to the growing literature on executive compensation and cash holdings in private firms. In that regard, our paper is closely related to Gao et al. (2013), who compares the cash policies in public and private firms, and Cronqvist and Fahlenbrach (2013) and Gao and Li (2015), which study CEO pay in private firms in comparison with that of public firms. Third, this paper contributes to research on insider trading. Although the paper does not focus on informed insider trading, it adds to the extant literature that informs about the relationship between insider trading restrictions and executives' motivation to trade (e.g., Fidrmuc et al., 2006; Roulstone, 2003). The key contribution of this paper is to show that insiders' liquidity needs is an important, if not the primary, reason for secondary sales in an IPO. In doing so, this paper provides empirical evidence to support and argue that the pre-IPO cash holding and executive compensation captures the insiders' liquidity needs, and both the argument and evidence are novel to the literature. The paper shows that: (1) lower levels of pre-IPO cash holdings lead to lower levels of executive compensation, (2) smaller cash-pay results in greater levels of insider sales, (3) equity-pay levels do not directly affect the insider sales but do influence the presence of an IPO lockup period, which in turn affects the levels of insider sales, and (4) higher levels of insider sales induced by liquidity needs result in lower levels of underpricing and long-run returns. As a whole, these results support executives' liquidity needs as a motivation for the secondary sales in an IPO and also suggest that liquidity induced secondary sales can be a source of agency problems. The rest of the paper is structured as follows: Section 2 examines the previous literature and develops the hypotheses. Section 3 describes the sample and data. Section 4 presents the empirical methods, analysis and findings; Section 5 concludes. 2. Prior literature and hypotheses 2.1. Insiders' liquidity constraints and compensations in entrepreneurial firms

“The poor man's son whom heaven in its anger has visited with ambition … pursues the idea of a certain artificial and elegant repose he may never arrive at, for which he sacrifices a real (tranquility) that is at times in his power, and which, if in the extremity of old age he shall at last attain to it, he will find it to be in no respect preferable to that humble security and contentment which he had abandoned for it” (Adam Smith, 1907, p. 259–260). An entrepreneurial venture, similar to what Adam Smith has described above, typically starts with one's desire to create something new and make a better living, but at a considerable expense of current tranquility and financial security. Only a few are able to cross the hurdle, primarily due to liquidity constraints (Holtz-Eakin et al., 1994). Entrepreneurs concentrate their wealth in their own private business far more than what is prescribed by financial theory (Gentry and Hubbard, 2004; Heaton and Lucas, 2000). They bear excessive risk for the returns they earn (Moskowitz and Vissing-Jørgensen, 2002), and accept lower median life-time earnings than similarly skilled wage-earners (Hamilton, 2000). So, it would not be surprising to find that insiders of many firms face financial and liquidity constraints before the firms go public. The success and growth of an entrepreneurial firm significantly depends on securing outside financing, such as venture capital (VC) funds. But not all start-up firms are fortunate enough to have venture capitalists provide financial and organizational support. Those firms that obtain VC finance often have founders replaced by outside CEOs to professionalize their human capital base (Hellmann and Puri, 2002). Bengtsson and Hand (2013) find that founder employees earn less cash pay and face weaker cash incentives, but have stronger equity incentives, than their hired-on counterparts. Entrepreneur-CEOs who are able to secure VC funding and keep their job have their cash pay increase proportionally to the quantity and quality of the financing secured. However, successful fundraising also increases the pay gap between the CEOs and other executives (Bengtsson and Hand, 2011). This suggests that many insiders, even at a VC-backed firm, can often be paid lower than the market rate and are in need of cash inflows. The patterns of pre-IPO executive compensation in non-VC-backed firms are not well documented as there is a dearth of studies on this issue.5 But one can conjecture about the lower levels of the executive pay in such firms. We find that there is a significant negative correlation between VC-backing and presence of secondary sales, which also supports the above assumption. 5 There are a few papers that examine CEO pay in private firms in comparison with that of public firms (see, for example, Cronqvist and Fahlenbrach (2013) and Gao and Li (2015)), but none of these examine private firms that are about to become public.

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In general, private firms have greater financial constraints and hold significantly lower levels of cash than their comparable public counterparts (Gao et al., 2013). Gao et al. (2013) examine the pre- and post-IPO cash holdings in a sample of IPOs that has only secondary sales. They find that the post-IPO cash holdings grow significantly despite no IPO proceeds add to the firms' cash holdings. One possible interpretation of this finding is that pre-IPO firms that are cash constrained also have their insider experiencing a lack of liquid assets. This leads to our first three, interrelated, hypotheses. H1. : A firm's pre-IPO cash holding is positively related to the level of its executives' compensation. H2. : The pre-IPO level of managerial compensation, which is determined by firm's cash holding, is inversely related to the insiders' liquidity needs. H3. : The level of insiders' liquidity needs is positively correlated with the presence and magnitude of the secondary sales.

2.2. Causes and consequences of insider selling Insiders usually do not sell secondary shares in an IPO, primarily due to their interest in maximizing their personal wealth, which leads them to focus on the lockup-expiration share price rather than the IPO offer price (Aggrawal et al., 2002). So, on one hand, any secondary sales by insiders during IPOs can be construed as a negative signal by investors. They may assume that the sale is motivated by private information (Ang and Brau, 2003; Leland and Pyle, 1977). On the other hand, if insiders do sell secondary shares, they would bargain hard to attain a better offer price. Habib and Ljungqvist (2001) argue that underpricing is lower when more secondary shares are offered by insiders. While the above two arguments offer explanations on why underpricing may be lower with insider selling, they do not reveal any motives behind the selling. Brau et al. (2007, hereafter BLS) argue that there are two possible reasons for insiders to sell secondary shares (i.e., insider information and insiders' liquidity needs), but neither reason leads to a lower level of underpricing. They contend that, although insiders may sell overpriced shares opportunistically, this negative information would not affect the initial return because of price support and stabilization provided by market makers. BLS further state that secondary sales motivated by diversification or liquidity needs should not have any impact on underpricing. Regardless of the motive of the secondary sales, Ang and Brau (2003) document that insiders go to a great length to minimize the effect of a possible negative signal of secondary sales. They find insiders initially underreport the number of shares they own when the company files the initial prospectus. These insiders then increase the number of shares they eventually sell through later amendments. In some cases, when demand for the IPO is high, the company reduces the primary shares offered to obscure the fact that number of secondary share offerings have increased. Finally, insiders try to confound the negative signal associated with secondary sales by committing to a longer lock-up period, which investors perceive as a positive signal. BLS, having found no significant effect of insiders' secondary sales on underpricing, focus on the long-run returns. They argue that if the insider sale is motivated by private information, particularly of the knowledge of IPO being overpriced, the stock should underperform in the long-run. If the secondary sales by insiders are diversification or liquidity motivated, then it should not be associated with poor long-term performance. BLS observe that the shares underperform in the long-run, but they measure this effect on secondary share revisions, and not on the final levels of insider selling. Since our main emphasis in this paper is to examine the liquidity motivation of secondary sales, we only examine either the presence or levels of insiders' secondary sales. We expect, on average, insider secondary sales to be unassociated with underpricing after accounting for market, firm and issue characteristics. However, if the insiders are suffering from serious lack of liquidity, the need for secondary sales becomes more pressing. In that case, one of the two things may happen: (1) insiders bargain hard to the point the issue is overpriced (Habib and Ljungqvist, 2001), or (2) additional secondary shares may drown out any excess demand that the investors may have, resulting in a tepid first-day price run-up. Under both scenarios we may observe a lower level of underpricing, which leads to the following hypothesis. H4. : A higher level of insider sales due to liquidity needs is associated with lower underpricing. Overwhelming liquidity needs may also cause insiders to sell a substantial part of their ownership stake and exacerbate agency problems. A lower level of post-IPO ownership may lead to lower managerial effort (Mehran, 1995). This leads to our last hypothesis. H5. : Insider's secondary sales motivated by liquidity needs are negatively correlated with the long-run returns.

3. Data 3.1. Sample construction and data sources The sample for this study consists of 1603 IPOs issued between 1997 and 2010 that have all insiders' secondary sales, executive compensation and pre-IPO cash holding data.6 Panel A of Table 1 shows how the final sample is put together. We collect the 6

The sample is limited to the post-1996 issuances because of the limitation of data availability in EDGAR.

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Table 1 Sample construction and distribution. Panel A shows the sample 1603 IPOs from 1997 to 2010, collected from the SDC Global New Issues database, after excluding non-U.S. firms, financial (sic: 6000-6411, 6722-6799) and utilities (sic: 4900-4949) firms, closed-end funds/trusts, unit offers, and IPOs with an offer price below $5 per share. Panels B and C show the time and industry distributions, respectively. Industry breakdown is based on Fama–French's 12 industry classification. Panel A: Sample Number of firm-year in the sample

Number of IPOs dropped

Number of IPOs remaining

Reason for dropping firm-years from the sample IPO available in SDC during 1997–2006, after initial screening IPOs with missing level of secondary sales

1993 62 1931

Accounting data missing in Compustat

327 1604

Compensation data missing Number of IPOs in the final sample

1 1603

Panel B: Distribution by year Year

Number of IS-IPOs

Number of Non-IS-IPOs

Total number of IPOs

% of Total

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 All

219 134 303 250 46 33 32 89 63 73 61 7 8 6 1324

55 29 26 6 1 13 13 29 32 30 31 3 5 6 279

274 163 329 256 47 46 45 118 95 103 92 10 13 12 1603

17.09 10.17 20.52 15.97 2.93 2.87 2.81 7.36 5.93 6.43 5.74 0.62 0.81 0.75 100%

Panel C: Industry distribution Industry

Number of IS-IPOs

Number of Non-IS-IPOs

Number of IPOs

% of Total

1. Consumer non-durables 2. Consumer durables 3. Manufacturing 4. Energy 5. Chemicals 6. Business Equipment 7. Telecom 8. Utilities 9. Shops 10. Healthcare 11. Finance (Real Estate) 12. Other All

35 13 73 35 13 469 88 0 118 225 11 244 1324

16 4 18 9 6 104 7 0 36 16 2 61 279

51 17 91 44 19 573 95 0 154 241 13 305 1603

3.18 1.06 5.68 2.74 1.19 35.75 5.93 0.00 9.61 15.03 0.81 19.03 100%

initial sample of IPOs from the Securities Data Company (SDC) using a common filtering practice that excludes 1) issues under $5, 2) utilities, financial, and insurance firms, and 3) IPOs of foreign firms, closed-end mutual funds, unit trusts, and REITs (e.g., see Ljungqvist and Wilhelm, 2003). We also collect cross-sectional data of IPO characteristics from SDC. Balance sheet and income statement data are taken from Compustat. The balance sheet values are from the year-end information prior to the IPO. In order to account for inflation, all cash values are adjusted to 1980 levels, based on the Consumer Price Index.7 First-day closing price, shares outstanding, and post-IPO returns are obtained from the Center for Research in Security Prices (CRSP). Industry classifications are obtained from Kenneth French's website.8 Insiders' compensation, ownership and secondary sales data are hand collected from the final IPO prospectus that are filed with the SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system. We examine the final prospectus of each IPO

7 8

http://www.bls.gov/cpi/cpid1412.pdf - Table 27. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

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firm to obtain the executive compensation data of last fiscal year prior to the IPO. The prospectus reports the top five compensated executives who made more than $100,000.9 It also provides the breakdown of the type of compensation and whether the executive is a CEO and/or the chairman of the board. The level of cash compensation is computed as the sum of the executives' salary, bonus and other non-equity based compensations, such as 401 K contributions. The equity based compensation is calculated as the sum of: 1) the reported or estimated value of restricted shares, and 2) the stated option value or [the number of options granted × estimated call option value]. Stock value is estimated as either the company assigned stock value as given in the prospectus or proxy, or as the number of shares granted × IPO offer price. Because the values of the options are not provided in the prospectus before 2006, we also estimate the pre-IPO value of the option grants for these IPOs. In order to estimate the option value, we assume that the strike price for the pre-IPO options is the offer price. We further assume that the offer price is the underlying asset price on the premise that the executives have knowledge of the impending public offering and expected offer price. In order to estimate the volatility of the underlying asset, we calculate the prior year's equally weighted volatility of all listed stocks in the firms' industry using Fama and French's 49 industry (FF-49) breakdown. We assume the standard ten-year employee stock option to expiration. The dollar value of the compensations is also adjusted to the 1980 level using the Consumer Price Index. Although the secondary sales data is available in SDC, we hand collect the pre-IPO holdings and insider sales of all reported executives and directors from the IPO prospectus.10 In this study we examine the presence of insiders' secondary sales or the ratio of executives and director sales and their holdings, calculated as: Insider Ratioi ¼

Insider Salesi Total Pre−IPO Holdingsi

ð1Þ

We calculate the Scaled Industry (FF-49) P/E ratio, as mentioned in Section 1, to control for high industry valuation. This measures the issue month's industry valuation relative to its five year historical average. First, the industry-specific marketvalue-weighted average price-to-earnings ratio (P/Eindus , T) is computed for each calendar month end. Then for each calendar month, the historical valuation is estimated as the five year average price-to-earnings ratios beginning with six months prior to the current month. Finally, the Scaled Industry P/E ratio for the issue month is computed as: P=Eindus;T Scaled Industry P=Eindus;T ¼ X66 P=Eindus;T−t t¼7

ð2Þ

60 The IPO founding year and underwriter reputation data are obtained from Jay Ritter's website.11 Firm age is computed as the difference between the year of issue and the founding year. Underwriters, as given in SDC, are matched with the ranking score in Jay Ritter's database. Underwriters with a reputational ranking of eight and higher are classified as “top-tier underwriters” (Loughran and Ritter, 2004). Panel B of Table 1 delineates a yearly breakdown of the number of IPOs and the Fama and French 12 industry (FF-12) distribution. During our sample period, approximately 27%, 37% and 36% of the IPOs belong to pre-bubble (1997–1998), bubble (1999–2000) and post-bubble (2001–2010) periods, respectively. About 79% of the IPOs come from four FF-12 industries, of which business equipment industry is the largest, accounting for 36% of IPOs. We have no IPOs from utilities and financial firms by design. However, there are 13 IPOs of real estate firms that are placed with the finance industry, as these firms share the same FF-12 industry classification. Since there are significant variations in number of IPOs across industries and over different periods which IPO characteristics and outcomes (Loughran and Ritter, 2004), we control for industry fixed effects and include pre-bubble and bubble period dummies in our subsequent regression based analysis, when appropriate. 3.2. Descriptive statistics Table 2 presents the univariate statistics for the following categories: 1) variables of interest, 2) liquidity related variables, 3) offer characteristics, and 4) firm characteristics. Constructions and data sources of all these variables are described in Appendix Table A.1. For all four categories, the mean, median, and standard deviation of the distributions are listed for IPOs with insider sales (abbreviated as IS-IPOs), without insider sales (abbreviated as non-IS-IPOs) and for the whole sample. About 17.4% of the 1603 IPOs are IS-IPOs, with average insider ratio of 13.7%. Although there is no significant difference in cash holding between IS-IPOs and non-IS-IPOs, both the mean and median cash to total asset ratio is significantly lower in IPOs with secondary sales. This suggests that firms that require large investment in capital assets have insiders with greater liquidity needs. This is consistent with the observation that median total asset is significantly higher in IS-IPOs. In fact, IS-IPOs are, on average, older firms. So, it is not surprising to find that IS-IPOs have higher median sales level, ROA and net income. 9 Although IPO firms are required to disclose the compensation of only the top five executives who earned more than $100,000, the number of executives whose compensation is reported by the IPO firms varies from one to ten. So, instead of using sum of all reported executives' compensations, in this study we utilize the per-firm mean of all executives' compensation (except for Table 4, where the unit of observation is individual insiders). For each firm we compute the average cash, equity and total compensations. 10 We specifically exclude blockholders and other primary investors because their liquidity is not intrinsically tied to the firm's liquidity. 11 http://bear.warrington.ufl.edu/ritter/ipodata.htm

A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

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Table 2 Descriptive statistics. This table reports univariate comparisons of mean and median values of various variables of interest. The sample consists of 1603 IPOs, as described in Table 1. All variables are defined in Appendix Table A.1. The last two columns of this table report the t-statistics for differences in means and z-statistics of the Wilcoxon test for differences in medians, between firms with and without insider sales. Statistical significance at the 1%, 5%, and 10% levels in two-tailed tests is indicated by ***, **, and *, respectively. All variables that have a dollar value for the unit of measurement have been adjusted for inflation in 1980's dollar value. With insider sale N Variables of interest Insiders sales (1/0) Insider ratio First day return Two year return

279 279 278 264

Liquidity related variables Cash ($B) 279 Cash/Total asset 279 Average cash pay 279 ($M) Average equity pay 279 ($M) Lockup (1/0) 279 Scaled industry P/E 279 Offer characteristics Shares overhang VC backed (1/0) High IB reputation (1/0) Hi-tech (1/0) Revise up (1/0) Bubble period (1/0) Firm characteristics Firm age Sales ($M) Total assets ($M) Leverage ROA Net income ($M)

Mean

No insider sale

Median

SD

N

0.137 0.088 0.133 0.116 0.193 −0.104

1324 0.163 1324 0.141 1324 1.477 1295

0.015 0.166 0.191

0.005 0.090 0.135

0.469 90.68% 1.067

279 279 279

3.422 44.09% 74.55%

279 279 279

55.19% 39.78% 11.47%

278 19.38 278 250.37 279 216.72 278 0.242 278 0.010 278 4.884

Mean

Full sample Median

SD

N

Mean

Median

SD

z-test

0.172 0.116 0.236 0.040 −0.512 2.473

1603 1603 1602 1559

0.174 0.000 0.024 0.000 0.165 0.116 0.066 −0.445

0.030 1324 0.183 1324 0.259 1324

0.018 0.245 0.154

0.005 0.067 0.154 0.254 0.108 0.209

1603 1603 1603

0.017 0.231 0.161

0.005 0.062 0.132 0.245 0.112 0.219

−1.235 1.295 −6.105⁎⁎⁎ −3.775⁎⁎⁎ ⁎⁎⁎ 2.200 6.862⁎⁎⁎

0.056

2.602 1324

0.640

0.108 1.702

1603

0.610

0.092 1.890

−1.054

0.974

1324 0.342 1324

69.61% 1.263

1.218 0.454

1603 1603

73.11% 1.229

1.180 0.442

9.870⁎⁎⁎ −8.161⁎⁎⁎ −7.116⁎⁎⁎

1.454 1324 1324 1324

4.520 54.83% 75.45%

1603 1603 1603

4.329 52.96% 75.30%

1324 1324 1324

71.75% 38.14% 41.76%

1603 1603 1603

68.87% 38.43% 36.49%

3.135

11 21.62 78.86 778.05 59.68 652.72 0.148 0.290 0.048 0.321 2.911 47.585

4.004 2.536

1319 13.99 7 20.802 1300 259.47 21.70 1292.38 1324 298.21 29.08 1314.61 1317 0.243 0.075 0.387 1300 −0.484 −0.160 1.392 1300 −3.573 −4.107 90.022

0.379 0.085 0.223 2.335

t-test

2.289 2.420

1597 14.93 8 21.04 1603 257.86 28.50 1217.44 1578 284.02 31.84 1225.62 1595 0.243 0.083 0.371 1578 −0.397 −0.064 1.284 1578 −2.083 −2.470 84.164

−3.700⁎⁎⁎ 1.340

1.282 6.554⁎⁎⁎

−2.336⁎⁎

−9.845⁎⁎⁎ −7.802⁎⁎⁎ −3.280⁎⁎⁎ −0.314 −5.127⁎⁎⁎ 0.509 −12.930⁎⁎⁎

3.803⁎⁎⁎ −0.155 −1.531 −0.033 11.444⁎⁎⁎ 2.230⁎⁎

8.293⁎⁎⁎ 10.629⁎⁎⁎ 5.687⁎⁎⁎ 1.903 12.311⁎⁎⁎ 11.092⁎⁎⁎

The average cash and equity compensations for the whole sample are $160,505 and $610,337, respectively. Interestingly, the average cash compensation of $190,527 for IS-IPOs is significantly higher compared to $154,179 for non-IS-IPOs. However, this comparison does not take into account of the fact that the firms with insider sales are significantly older and more profitable. Although there is no statistical difference in average equity compensation for IPOs with and without insider sales, the median equity compensation is significantly lower in IS-IPOs. Firms with insider sales have a lower proportion of VC backing but similar leverage (measured by debt to total asset ratio), which implies lower access to outside capital pre-IPO. About 91% of the IS-IPOs have a lockup clause compared to 73% of nonIS-IPOs. This leads us to question if the presence of a higher proportion of lockup agreement is a cause or an effect of the insiders' decision to sell secondary shares. It is possible the insiders may want to dampen the negative effect of the secondary sales through committing to a lockup period, but committing to one makes the insiders' liquidity needs more acute. Therefore, we treat the presence of lockup agreement as one of the symptoms of insiders' liquidity needs. Finally, we observe that IS-IPOs, compared to non-IS-IPOs, are not timed to take advantage of favorable market valuations; both the average and median scaled industry P/E ratios are significantly lower for IS-IPOs. For IS-IPOs the median value of this ratio being less than 1 further suggests bad timing of the issuances, possibly due to liquidity needs. 4. Empirical methods, analysis and results In this section, we examine whether insiders' liquidity needs play a substantial role in insiders' secondary selling during IPOs and then study the consequences, particularly on underpricing and long-term returns. 4.1. Pre-IPO cash holdings and secondary sales We first examine the effect of pre-IPO cash holdings after controlling for other variables that could influence both the presence and levels of insiders' secondary sales. The variables that we include in multivariate models are motivated based on prior literature. We use the following base regression (Eq. (3)) and employ OLS, Probit or Logit regressions when insider sales is a binary

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variable (1/0) and employ Tobit regression when we use insider ratio as the dependent variable. OLS regression serves as the linear probability model when the dependent variable is binary. We do not use OLS regression when the dependent variable is insider ratio because the variable takes the value between 0 and 1, and only 17.4% observations have non-zero values. Regression results are reported in Table 3. Insider salesi ¼ β0 þ β1 Cashi þ β2 Lockupi þ β3 Scaled industry P=Ei þ β4 Sahres overhangi þ β5 VC backedi þ β6 Ln agei þ β7 Leveragei þ β8 ROAi þ β9 High IB reputationi þ β10 High−techi þ β11 Ln total assetsi þ β12 Ln salesi þ β13 Revise upi þ β14 Pre−bubble period þ β15 Bubble period þ εi

ð3Þ

Larger firms and firms that are able carry a greater debt load have more access to capital. Therefore, these firms may be less constrained by cash and are able to compensate their insiders appropriately. Consequently, insiders of these firms may have fewer needs to sell secondary shares. We include log of total asset and as a proxy for the pre-IPO firm size and debt to asset ratio (leverage) as proxies for access to outside capital. We find leverage has a significant negative correlation with both the presence and levels of insider sales (abbreviated as IS). While total asset is negatively correlated with the IS presence, it is uncorrelated with IS levels. Firm's longevity and the presence of VC backing and highly reputed underwriters may mitigate information asymmetry, such that insiders feel comfortable selling secondary shares without negatively affecting the offer price and first-day trading price (Megginson and Weiss, 1991; Carter et al., 1998). Hence, we include log of firm age, VC backing dummy, and a dummy variable for high reputation underwriters in the regressions. Our findings indicate that VC baking is positively correlated in two model specifications and firm age is in one specification, otherwise they are largely unrelated to IS. Typically older firms that decide to go public are large and have better accounting performance. Hence, we use sales and ROA as additional covariates and find both to be significantly and positively correlated with IS. Following Hanley (1993), we include an offer price revised-up dummy variable (1/0), which may capture the level of demand for IPOs. With greater demand for the shares of an IPO, the market can accommodate secondary selling beyond the financial needs of the issuing firms. We find that an upward revision of the offer price significantly increases the occurrence of IS. If an IPO has a large shares overhang, then that implies only a small portion of its outstanding shares is offered in the IPO. This also means that the insiders would have large shareholdings, and insiders would be more inclined to maximize the share price in the long run and have less interest in secondary sales (Aggrawal et al., 2002). We use shares overhang as an explanatory variable and find that it is significantly negatively correlated to both presence and levels of IS. High-tech firms often compensate insiders in equity pay relatively more than in cash, which may cause their insider to some of their equity holdings as secondary shares. Therefore, we use hi-tech dummy as an independent variable but find it to be uncorrelated with insiders' secondary sales. Finally, we use cash, lockup dummy and scaled industry P/E as regressors. As discussed earlier, all three variables are intricately related to insiders' liquidity needs. A greater level of pre-IPO cash holdings may allow sufficient financial flexibility to compensate the executives generously. So a higher level cash holding may mean less liquidity needs for insiders and Table 3 Insider sales. This table presents OLS, Probit and Logit regressions of the insider sales dummy, and Tobit regression of the insider sales ratio on several explanatory variables. P-values are obtained based on the Huber (1967) and White (1980) sandwich estimators. All regressions contain Fama–French 12 industry fixed-effects and a constant. All variables are defined in Appendix Table A.1 and the sample is described in Table 1. Insider sales (1/0)

Cash ($B) Lockup (1/0) Scaled industry P/E Shares overhang VC backed (1/0) Ln age Leverage ROA High IB reputation (1/0) Hi-tech Ln total asset Ln sales Revise up Pre-bubble period (1/0) Bubble period (1/0) Industry dummy N Adjusted/Pseudo R2

Insider sales (%)

OLS

Probit

Logit

Tobit

(1)

(2)

(3)

(4)

Coef.

p-Value

Coef.

p-Value

Coef.

p-Value

Coef.

p-Value

−0.362 0.075 −0.055 −0.016 0.024 0.025 −0.052 0.017 0.017 −0.012 −0.028 0.034 0.040 −0.082 −0.165 Yes 1494 0.144

0.000 0.000 0.013 0.000 0.285 0.054 0.025 0.032 0.535 0.703 0.003 0.000 0.052 0.008 0.000

−2.486 0.483 −0.314 −0.106 0.194 0.086 −0.244 0.455 0.089 −0.051 −0.172 0.224 0.180 −0.166 −0.570 Yes 1494 0.214

0.017 0.000 0.025 0.000 0.057 0.120 0.067 0.002 0.445 0.703 0.005 0.000 0.052 0.172 0.000

−4.435 0.890 −0.530 −0.191 0.367 0.144 −0.395 0.896 0.144 −0.077 −0.336 0.427 0.326 −0.277 −1.051 Yes 1494 0.214

0.017 0.000 0.041 0.000 0.041 0.124 0.080 0.003 0.487 0.744 0.003 0.000 0.048 0.192 0.000

−0.753 0.076 −0.058 −0.031 0.018 0.019 −0.065 0.098 0.014 −0.017 −0.024 0.042 0.027 −0.051 −0.120 Yes 1494 0.313

0.010 0.013 0.080 0.000 0.388 0.150 0.039 0.001 0.584 0.601 0.165 0.010 0.169 0.080 0.001

A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

9

consequently a reduced need for insider sales. We find cash holdings is significantly and negatively correlated with IS in all four regression models. The probability of insider sales decreases by about 36% to 54% for a billion dollar of increase of cash holdings from its average value.12 Furthermore, for every one billion dollars of additional cash holding, the insiders sell 76% fewer shares from their pre-IPO ownership. We find that the lockup dummy is positively and significantly correlated with insider sales. IPOs with a lockup provision have 7.5% to 10.7% (the average marginal effect of the Probit regression) higher probability of secondary sales and a 7.6% higher level of insider selling than IPOs without a lockup provision. This is consistent with the idea that insiders may sell shares to protect against liquidity needs and possible shocks during the lockup period. IPOs may time the issuance during a higher industry valuation. The ability of timing the issuance may suggest sufficient financial flexibility of the issuer, which in turn may imply less liquidity needs for its insiders. Moreover, if the insiders do sell shares during high valuation period, they may be able to sell less and meet their liquidity needs. So, we expect a significant negative correlation between the scaled industry P/E ratio and IS, which we observe in all four regressions. 4.2. Cash holdings and executive compensation The link between insiders' liquidity needs and their secondary sales depends on the premise that cash poor firms inadequately compensate their executives during the pre-IPO period. Furthermore, cash constrained firms, given their resource requirements, may rely more on equity-pay than cash-pay. Indeed we find a highly significant negative correlation (ρ = − 0.286, with a p-value of 0.000) between cash-pay to total-pay ratio and cash-to-total asset ratio. In this section, we examine how executives' pay is affected by the firms' pre-IPO cash holdings. Table 4 shows regressions of executive compensation on cash, cash-to-total asset, or both in the presence of other control variables. These regressions are run on the same sample as shown in Table 1, but the observations are on the executive level rather than the firm level. We employ this approach to control for executive characteristic such as if the insider is the CEO or chairman of the board.13 We also run these regressions at the firm level and the results are essentially the same.14 We use pre-IPO cash holdings and cash-to-total asset separately as regressors in Models 1 and 2, respectively. Both the variables are significantly and positively correlated with total (Panel A), cash (Panel B) and equity (Panel C) compensations. The total compensation increases by $5648 and the cash compensation increases by about $1300 for every $1 million increase pre-IPO cash holdings from the average value. If the cash-to-total asset ratio increases by one standard deviation from its mean value, the total pay increases by about $470,000, of which about $450,000 comes in the form of equity pay. In Model 3, when both cash and cashto-total assets are employed together, cash-to-total asset loses significance in the cash pay regression, otherwise the results are similar to Models 1 and 2. CEOs receive a significantly greater compensation compared to other executives, but there is no significant additional compensation for the chairmanship. CEOs on average receive $1.9 million of additional compensation, and the majority of which is received in the form of equity pay; the additional cash pay is in the range of $200,000. Larger firms, in terms of total assets, on average pays pay more to their executives. Interestingly, net income and sales, in presence of other variables, are negatively correlated with the total compensation. VCs seem to prefer paying less cash but more equity to the executives of the firms that they are backing. 4.3. Compensation and insider's secondary sales The main takeaway from the above section is that the liquidity of the firm, measured in cash holdings and cash-to-total asset ratio, significantly influences the executive pay. That is, a cash poor firm would pay its executives rather poorly, which is consistent with our first hypothesis that a firm's pre-IPO cash holding is positively related to the level of its executives' compensation. In this section, we test our second hypothesis, using a two-step procedure (see Wooldridge, 2010, p. 123). In the first step, we regress executive compensation (either cash-pay or equity-pay) on pre-IPO cash holdings and cash-to-total asset ratio. The second step is similar to Eq. (3), except we use the predicted compensation as an explanatory variable instead of cash holdings. Since the predicted compensation is a generated regressor, we implement the Murphy and Topel (1985) correction for the standard errors following Hardin (2002). The second stage regression takes the following form (Eq. (4)): OLS or Logit regressions when insider sales is a binary variable (1/0) and Tobit regression when insider ratio is the dependent variable. Table 5 reports the results.

̂ Insider salesi ¼ β0 þ β1 Compensation i þ β 2 Lockupi þ β 3 Scaled industry P=E i þ β 4 Sahres overhang i þ β5 VC backedi þ β6 Ln agei þ β7 Leveragei þ β8 ROAi þ β9 High IB reputationi þ β10 High−techi þ β11 Ln total assetsi þ β12 Ln salesi þ β13 Revise upi þ β14 Pre−bubble period þ β15 Bubble period þ εi

12

ð4Þ

The average marginal effects of Cash are −0.526 and −0.547 for Probit and Logit models, respectively. We compute the robust standard errors for all regressions. Since, in this section, the unit of analysis is individual insiders rather than firms, we also compute the standard errors clustered by firms. The standard errors are adjusted for 1490 clusters (i.e., number of firms), and results remain unchanged. 14 Although these results are not included in the paper for brevity, the authors can provide these upon request. 13

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A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

Table 4 Cash and compensation. This table shows regressions of pre-IPO compensation of all executives; the data sample is the same as Table 1, except instead of firm level average compensations it uses the insider level compensation data. Panels A, B and C show OLS regressions on the total, cash and equity compensations, respectively. Cash compensation is defined as the sum of salary bonus and other non-equity compensation. Equity compensation is defined as the sum of stock grants and the value of option grants. CEO Dummy is 1 if the executive is a CEO and 0 otherwise. Chairman Dummy is 1 if the executive is the Chairman and 0 otherwise. P-values are obtained based on the Huber (1967) and White (1980) sandwich estimators. All regressions contain Fama–French 12 industry fixed-effects and a constant. All other variables are defined in Appendix Table A.1. (1)

Panel A: Total pay Cash ($B) Cash/Total asset CEO (1/0) Chairman (1/0) Ln total asset Ln sales VC backed (1/0) Net income Adjusted R2 Panel B: Cash pay Cash ($B) Cash/Total asset CEO (1/0) Chairman (1/0) Ln total asset Ln sales VC backed (1/0) Net income Adjusted R2 Panel C: Equity pay Cash ($B) Cash/Total asset CEO (1/0) Chairman (1/0) Ln total asset Ln sales VC backed (1/0) Net income Adjusted R2 Industry dummy N

(2)

Coef.

p-Value

5.648

0.000

1.920 0.060 0.324 −0.144 0.314 −0.004 0.040

0.000 0.878 0.000 0.012 0.011 0.000

1.282

0.000

0.200 0.209 0.058 0.042 −0.043 −0.001 0.195

0.000 0.000 0.000 0.000 0.002 0.003

4.366

0.001

1.720 −0.149 0.267 −0.186 0.357 −0.003 0.030 Yes 6632

0.000 0.698 0.001 0.001 0.003 0.001

(3)

Coef.

p-Value

Coef.

p-Value

1.919 1.936 0.053 0.407 −0.074 0.148 −0.003 0.040

0.000 0.000 0.892 0.000 0.212 0.242 0.002

4.731 1.609 1.923 0.060 0.312 −0.067 0.140 −0.004 0.043

0.002 0.000 0.000 0.877 0.000 0.259 0.262 0.000

0.076 0.204 0.207 0.084 0.040 −0.040 −0.001 0.177

0.000 0.000 0.000 0.000 0.000 0.005 0.020

1.287 −0.008 0.200 0.209 0.058 0.042 −0.042 −0.001 0.195

0.000 0.723 0.000 0.000 0.000 0.000 0.003 0.003

1.843 1.732 −0.154 0.324 −0.114 0.188 −0.002 0.031 Yes 6632

0.000 0.000 0.689 0.000 0.054 0.131 0.004

3.444 1.617 1.722 −0.149 0.254 −0.108 0.182 −0.003 0.033 Yes 6632

0.010 0.000 0.000 0.698 0.001 0.065 0.139 0.001

We find that cash compensation, conditioned on firm's liquidity, is significantly and negatively correlated with insider sales. This is consistent with our second hypothesis that the pre-IPO level of managerial compensation, which is determined by firm's cash holding, is inversely related to the insiders' liquidity needs. For one standard deviation (about $0.25 million) increase in cash compensation, the probability of secondary sales by the insiders in an IPO reduces by 11% to 21% (marginal effect of Logit is −0.866) and the magnitude of the decrease of sales is about 24%. However, the level of equity compensation, predicted by cash holdings, is uncorrelated with both the incidence and magnitude of IS. This is not surprising because two different and opposite forces are in work here. On one hand, the increase in equity compensation expands the insiders' pre-IPO ownership and makes them more focused on the long term value of their holdings, therefore not participating in the secondary sales. On the other hand, if the higher level of equity-pay is a substitution of lower cash pay, insiders would have great incentive to unload some of their holdings during the IPO. This analysis indicates that the relationship between equity pay and insiders' secondary sales is greatly affected by the presence of a lockup period. So, next, we turn our attention to the relationship between executive compensation and the lockup decision. 4.4. Compensation and the lockup decision In this section, we examine if the level of compensation is a determinant of lockup period presence and if so, we examine whether it is due to the insiders' liquidity needs. Insiders who have received a significant portion of the pre-IPO compensation in the form of equity-pay are very likely to cash in during or immediately after the public offering. However, new investors may perceive the sales by insiders as a negative signal. Therefore, a lockup period can serve as a commitment mechanism to alleviate the investors' concerns (Ang and Brau, 2003). But if other credible mechanisms—such as past superior performance, VC backing or presence of highly reputed underwriters—are in place to mitigate moral hazard, IPOs may opt not to have a lockup

A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

11

Table 5 Compensation and insider sales. This table shows the second stage regressions of 2SLS regressions of the insider sales. OLS or Logit regressions are used when insider sales is a binary variable (1/0) and Tobit regression when insider ratio is the dependent variable. The first stage is the firm average compensation (Cash pay in Panel A and equity pay in Panel B) instrumented by the pre-IPO cash holding and cash to total asset ratio. P-values are obtained based on Murphy and Topel (1985) standard errors. All regressions contain Fama–French 12 industry fixed-effects and a constant. All variables are defined in Appendix Table A.1 and the sample is described in Table 1. OLS

Logit

Tobit

Coef.

p-Value

Coef.

p-Value

Coef.

p-Value

Panel A: Cash pay Cash pay Lockup (1/0) Scaled industry P/E Shares overhang VC backed (1/0) Ln age Leverage ROA High IB reputation (1/0) Hi-tech Ln total asset Ln sales Revise up Pre-bubble period (1/0) Bubble period (1/0) Adjusted/Pseudo R2

−0.474 0.076 −0.055 −0.017 0.020 0.025 −0.047 0.016 0.012 −0.015 −0.028 0.037 0.040 −0.078 −0.164 0.145

0.008 0.002 0.046 0.000 0.386 0.061 0.095 0.067 0.650 0.622 0.008 0.000 0.052 0.008 0.000

−7.228 0.923 −0.509 −0.202 0.316 0.142 −0.339 0.906 0.073 −0.117 −0.312 0.471 0.329 −0.219 −1.044 0.217

0.007 0.000 0.058 0.000 0.091 0.165 0.170 0.000 0.723 0.627 0.004 0.000 0.044 0.337 0.000

−0.983 0.079 −0.055 −0.033 0.011 0.018 −0.052 0.101 0.006 −0.022 −0.023 0.048 0.028 −0.043 −0.119 0.316

0.004 0.011 0.098 0.000 0.662 0.177 0.116 0.001 0.833 0.471 0.089 0.000 0.192 0.148 0.001

Panel B: Equity pay Equity pay Lockup (1/0) Scaled industry P/E Shares overhang VC backed (1/0) Ln age Leverage ROA High IB reputation (1/0) Hi-tech Ln total asset Ln sales Revise up Pre-bubble period (1/0) Bubble period (1/0) Adjusted/Pseudo R2 Industry dummy N

−0.041 0.073 −0.055 −0.016 0.025 0.024 −0.050 0.019 0.021 −0.012 −0.033 0.033 0.040 −0.086 −0.167 0.114 Yes 1494

0.425 0.003 0.046 0.000 0.281 0.071 0.080 0.027 0.425 0.685 0.002 0.000 0.052 0.004 0.000

−0.371 0.858 −0.534 −0.191 0.359 0.142 −0.354 0.924 0.191 −0.090 −0.386 0.405 0.320 −0.317 −1.079 0.210 Yes 1494

0.336 0.001 0.049 0.000 0.052 0.163 0.158 0.000 0.344 0.708 0.000 0.000 0.049 0.159 0.000

−0.077 0.072 −0.059 −0.031 0.019 0.019 −0.060 0.102 0.021 −0.018 −0.032 0.039 0.025 −0.057 −0.124 0.310 Yes 1494

0.192 0.020 0.080 0.000 0.437 0.155 0.079 0.000 0.432 0.550 0.015 0.002 0.234 0.058 0.001

period (Brav and Gompers, 2003). So, the presence of a lockup period may be negatively correlated with the level of equity pay, but uncorrelated with cash pay. But if we condition the executive pay on firm's cash holdings, the presence of lockup period is likely to be positively correlated with the level of cash pay, but uncorrelated with equity pay. This is precisely what we observe from the results in Table 6. Model 1 in Table 6 is a linear probability regression (OLS) of lockup dummy on cash pay (Panel A) or equity pay (Panel B) in presence of other factors based on prior literature. Model 2 is a two-stage least-square regression. The first stage is the regression of executive compensation (cash-pay or equity-pay) on pre-IPO cash holdings and cash-to-total asset ratio. The OLS and the second stage of the 2SLS takes the following form (Eq. (5)), where Xi represents compensation for OLS and predicted compensation for 2SLS. Lockup dummyi ¼ β0 þ β1 X i þ β2 Scaled industry P=Ei þ β3 Sahres overhang i þ β4 VC backedi þ β5 Ln firm agei þ β6 Levaragei þ β7 ROAi þ β8 High IB reputaioni þ β9 High−techi þ β10 Ln total assetsi þ β11 Ln salesi þ β12 Revise upi þ β13 Pre−bubble period þ β14 Bubble period þ εi

ð5Þ

The findings from these models indicate that the level equity pay is negatively correlated with the lockup dummy in the OLS regression and the level of cash pay is positively correlated in the 2SLS regression. Taken together, the findings suggest that the presence of a lockup period provides additional information about the nature of the liquidity needs of the insider.

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A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

Table 6 Compensation and lockup decision. This table shows the regressions of the lockup clause during the IPO. Model (1) is OLS regression of lockup dummy variable; p-values are obtained based on the Huber (1967) and White (1980) sandwich estimators. Model (2) is a two stage regression of the lockup dummy. The first stage is the firm average compensation instrumented by the pre-IPO cash holding and cash to total asset ratio. P-values are obtained based on Murphy and Topel (1985) standard errors. All regressions contain Fama–French 12 industry fixed-effects and a constant. All other variables are defined in Appendix Table A.1. OLS

2SLS

(1)

(2)

Coef.

p-Value

Coef.

p-Value

Panel A: Cash pay Cash pay Scaled industry P/E Shares overhang VC backed (1/0) Ln firm age Leverage ROA High IB reputation (1/0) Hi-tech (1/0) Ln total asset Ln sales Revise up (1/0) Pre-bubble period (1/0) Bubble period (1/0) Adjusted/Pseudo R2

0.021 −0.004 −0.023 −0.010 −0.003 0.021 0.016 −0.177 −0.023 −0.061 0.020 −0.087 −0.326 −0.435 0.287

0.659 0.899 0.000 0.694 0.839 0.424 0.116 0.000 0.471 0.000 0.015 0.000 0.000 0.000

0.311 −0.004 −0.022 −0.008 −0.003 0.022 0.018 −0.171 −0.023 −0.065 0.018 −0.086 −0.330 −0.437 0.277

0.095 0.897 0.000 0.729 0.830 0.461 0.044 0.000 0.478 0.000 0.035 0.000 0.000 0.000

Panel B: Equity pay Equity pay Scaled industry P/E Shares overhang VC backed (1/0) Ln firm age Leverage ROA High IB reputation (1/0) Hi-tech (1/0) Ln total asset Ln sales Revise up (1/0) Pre-bubble period (1/0) Bubble period (1/0) Adjusted/Pseudo R2 Industry dummy N

−0.009 −0.004 −0.022 −0.010 −0.003 0.021 0.015 −0.175 −0.023 −0.059 0.020 −0.083 −0.324 −0.431 0.288 Yes 1494

0.072 0.881 0.001 0.672 0.852 0.417 0.143 0.000 0.472 0.000 0.015 0.000 0.000 0.000

0.008 −0.004 −0.023 −0.010 −0.002 0.022 0.016 −0.177 −0.024 −0.061 0.020 −0.086 −0.326 −0.435 0.287 Yes 1494

0.881 0.897 0.000 0.662 0.869 0.458 0.071 0.000 0.461 0.000 0.020 0.000 0.000 0.000

4.5. Liquidity proxies, the liquidity factor and secondary sales In this section, we bring together the variables that directly or indirectly embody the liquidity needs of the insiders in an IPO, and then construct a single liquidity factor. Based on the analyses in Sections 4.1 to 4.4, we identify the following six variables that fit the criteria: cash, cash to total asset, cash-pay, equity-pay, lockup dummy and scaled industry P/E ratio. We perform a factor analysis on these variables with the hope of creating a valid factor. If a combination of all six variables were to represent the liquidity needs of the insiders then the factor loading's sign of lockup should be opposite to that of other variables. Unfortunately, we are not able to have this condition met with all six liquidity proxies. So, we try with different subsets of these variables, and we find one with cash to total asset, cash pay, equity pay, lockup dummy and scaled industry P/E ratio that meets the criteria. Panel A of Table 7 shows the eigenvalues of the factor analysis. We use principal-component factor method and assume communalities are 1. The factor loadings of first two orthogonal factors are shown in Panel B. The signs of the second factor's loadings are consistent with a liquidity factor. We then use the least square regression approach to predict the factor scores, and label it as the liquidity factor.15 Note that the regression approach computes factor scores such that the factor variable has a mean of zero and standard deviation of one. Panel C of Table 7 shows the regressions of insider sales on the liquidity factor. The regressions are similar to Eq. (3), except cash is replaced with the liquidity factor. We find that an increase in insiders' liquidity significantly decreases the occurrence but

15 The correlations (both Pearson and Spearman rank correlations) between insider secondary sales variables, liquidity proxies and liquidity factor are reported in Panel A of Appendix Table A.2.

A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

13

Table 7 Insider sales and liquidity. Panel A shows the eigenvalue of the factor analysis with the variables listed in Panel B. We use principal-component factor method and assumed communalities are 1. The factor loadings shown in Panel B. Panel C of this table shows the regressions of insider sales on the liquidity factor. OLS, Probit and Logit regressions are used when the insider sales is a dummy (1/0) variable, and Tobit is used when insider sales is the percentage of the insider's pre-IPO ownership. P-values are obtained based on the Huber (1967) and White (1980) sandwich estimators. All regressions contain Fama–French 12 industry fixed-effects and a constant. All variables are defined in Appendix Table A.1. Panel A: Factor analysis

Eigenvalue

Factor 1

Factor 2 (Liquidity factor)

1.6747

1.3186

Panel B: Factor Loading

Cash/Total asset Cash pay Equity pay Lockup (1/0) Scaled industry P/E

Factor 1

Factor 2 (Liquidity factor)

−0.0772 0.8892 0.8859 0.2272 −0.2041

0.4717 0.1887 0.1996 −0.7198 0.7088

Panel C: Liquidity factor and insider sales OLS

Liquidity factor Shares overhang VC backed (1/0) Ln age Leverage ROA High IB reputation (1/0) Hi-tech Ln total asset Ln sales Revise up Pre-bubble period (1/0) Bubble period (1/0) Industry dummy N Adjusted/Pseudo R2

Probit

Logit

Tobit

Coef.

p-Value

Coef.

p-Value

Coef.

p-Value

Coef.

p-Value

−0.033 −0.017 0.027 0.023 −0.049 0.020 0.017 −0.014 −0.036 0.033 0.036 −0.104 −0.192 Yes 1494 0.139

0.003 0.000 0.244 0.067 0.031 0.018 0.527 0.659 0.000 0.000 0.085 0.001 0.000

−0.203 −0.108 0.203 0.083 −0.228 0.479 0.102 −0.070 −0.218 0.204 0.148 −0.302 −0.712 Yes 1494 0.204

0.002 0.000 0.045 0.134 0.078 0.002 0.382 0.599 0.000 0.000 0.110 0.009 0.000

−0.343 −0.198 0.376 0.136 −0.355 0.956 0.176 −0.113 −0.421 0.392 0.286 −0.522 −1.318 Yes 1494 0.204

0.004 0.000 0.035 0.145 0.105 0.001 0.395 0.630 0.000 0.000 0.081 0.010 0.000

−0.026 −0.032 0.018 0.019 −0.056 0.106 0.018 −0.023 −0.037 0.039 0.019 −0.083 −0.158 Yes 1494 0.300

0.123 0.000 0.387 0.165 0.062 0.001 0.487 0.469 0.023 0.013 0.335 0.006 0.000

Probit margins, dydx(*) for −0.043; Logit margin, dydx(*) is −0.042.

not the magnitude of the secondary sales. A one standard deviation increase in liquidity factor reduces the probability of a participation in secondary sales by 3.3% to 4.3% (marginal effect of the Probit regression).16 The overall findings from Sections 4.1 to 4.5 support our third hypothesis that the level of insiders' liquidity needs is positively correlated with the presence and magnitude of the secondary sales. In the next two sections we turn our attention to the consequences of IS due to insiders' liquidity needs. 4.6. First day returns Ljungqvist and Wilhelm (2003) document a negative relationship between the level of secondary sales and first day returns during the bubble period, but they attribute this to the lack of insiders' incentive to negotiate the highest offer price when a small fraction of the insiders' holdings are sold. Brau et al. (2007) examine the increases in the secondary shares offered by insiders prior to the IPOs, possibly due to private information. But they do not find any correlation between secondary share increases and initial returns, and conjecture that underwriters' market stabilization activity is a reason for the absence of a negative correlation. However, there is no study that tests the relation between underpricing and secondary sales induced by liquidity

16 Note that these regressions include pre-bubble and bubble dummy to account for the structural changes in the IPO market. However, the liquidity factor is highly significant and negatively correlated with IS in all four specifications; if we run the regressions without these two time-period dummies, we find that a one standard deviation increase in liquidity factor reduces the probability of a participation in secondary sales by 7.6% to 8.3% and reduces the magnitude of IS by 6.5%. This also suggests the levels of tension between insiders' liquidity needs and long-term goal change over time.

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A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

Table 8 First day returns. This table shows the regression of the first day returns. Models (1) and (3) are OLS regressions. Models (2) and (4) show only the second stage of 2SLS regressions; in the first stage, insider sales is regressed on the six liquidity variables shown in Appendix Table A.1. Model (5) uses a liquidity factor as a regressor, obtained through factor analysis (see Table 7) of the liquidity variables. P-values for Models (1), (3) and (5) are obtained based on the Huber (1967) and White (1980) sandwich estimators. Pvalues for Models (2) and (4) are obtained based on Murphy and Topel (1985) standard errors. All regressions contain Fama–French 12 industry fixed-effects and a constant. All variables are defined in Appendix Table A.1. Insider sales (1/0) (1)

Insider sales Predicted insider sales Liquidity factor Share overhang VC backed (1/0) Ln firm age High IB reputation (1/0) Hi-tech (1/0) Ln total asset Ln sales Revise up (1/0) Inverse proceeds Bubble period (1/0) Industry dummy N Adjusted R2

Coef.

p-Value

−0.009

0.365

0.013 0.016 −0.009 0.020 −0.025 −0.020 −0.001 0.167 −0.796 0.075 Yes 1502 0.402

Insider sales (%) (2)

0.000 0.153 0.074 0.097 0.069 0.000 0.884 0.000 0.000 0.000

(3)

Coef.

p-Value

−0.267

0.000

0.012 0.009 −0.010 0.018 −0.032 −0.019 0.002 0.159 −0.457 0.062 Yes 1502 0.410

0.000 0.325 0.055 0.098 0.008 0.000 0.627 0.000 0.009 0.000

Coef.

p-Value

−0.058

0.176

0.013 0.015 −0.009 0.020 −0.025 −0.019 −0.001 0.167 −0.801 0.076 Yes 1502 0.402

Liquidity factor (4)

0.000 0.171 0.076 0.099 0.065 0.000 0.845 0.000 0.000 0.000

(5)

Coef.

p-Value

−1.647

0.000

0.012 0.011 −0.008 0.016 −0.028 −0.020 0.003 0.165 −0.793 0.061 Yes 1502 0.408

0.000 0.302 0.176 0.198 0.044 0.000 0.501 0.000 0.000 0.000

Coef.

p-Value

0.021 0.012 0.014 −0.009 0.015 −0.024 −0.020 0.001 0.163 −0.803 0.058 Yes 1502 0.408

0.001 0.000 0.189 0.082 0.217 0.075 0.000 0.838 0.000 0.000 0.000

needs. We hypothesize that there is a negative correlation between liquidity motivated insider sales and underpricing. We test this using the following base regression (Eq. (6)) and report the results in Table 8. First day ret i ¼ β0 þ β1 X i þ β2 Shares Overhang i þ β3 VC backedi þ β4 Ln firm agei þ β5 High IB reputationi þ β6 Hi−techi þ β7 Ln total assetsi þ β8 Ln salesi þ β9 Revise upi þ β10 Inverse proceedsi þ β11 Bubble periodi þ εi

ð6Þ

All five models include the standard control variables that have been shown to affect first day returns (e.g., see Loughran and Ritter, 2004). Depending on the model, Xi represents different variables. Models (1) and (3) are OLS regressions where Xi is an insider sales dummy and insider ratio, respectively. Models (2) and (4), as reported in Table 8, are the second stage regressions of 2SLS models, and Xi is the first-stage predicted value of the appropriate insider sales (dummy or ratio) variable regressed on the six liquidity proxies that we identify in Section 4.5. The first stage regression is not shown for brevity, but is available upon request. Finally, the Xi variable in Model (5) is the liquidity factor, which is delineated in the previous section. We see no significant correlation between insider sales and underpricing based on Models (1) and (3). However, when we use predicted insider sales, we find that insider sales is negatively correlated with underpricing. If the probability of liquidity-inducedinsider-sales presence increases by one standard deviation, the first-day returns are about 2.5% lower. Similarly, if the liquidity motivated insider selling increases by one standard deviation, the first-day returns are about 2.47% lower.17 These findings are consistent with our fourth hypothesis that a higher level of insider sales due to liquidity needs is associated with lower underpricing. To buttress the findings that this lower underpricing is attributable to insiders' liquidity needs, we turn to Model (5). Here too we find a significant correlation; if the liquidity factor decreases by one standard deviation, the underpricing is about 2.1% lower. Overall, the above findings suggest that the market negatively reacts to any insider sales induced by liquidity needs, perhaps because of the fear that insiders' objective is not properly aligned with that of the new investors. We, therefore, examine the impact of liquidity motivated IS on the long-run performance in the next section. 4.7. Long-run performance The fact that insiders sell secondary shares to fulfill the immediate liquidity need may not be inherently bad for the firm. If these sales do deviate managers from its long-term goal, the impact can be detected in the future returns of the IPO firm. We use the following base regressions with identical model specifications as in Section 4.6. The raw two-year post-IPO return is used as the dependent variable.18 17

Panel B of Appendix Table 2.1 reports the descriptive statistics of the predicted insider sales variables. In order to calculate the two year post-issue returns, the stock returns are obtained from CRSP and used to calculate the buy-and-hold return for the two years after issue. If the firm is delisted in the two year time period and was not acquired, the final delisting return is included and adjusted by 30% as per Shumway (1997). Raw Returns are used because we find that one of the variable of interest, insider ratio, is correlated with the scaled industry P/E, which could fundamentally affect any industry-adjusted returns. However, we also examined the industry-adjusted returns as the dependent variable and find consistent results. 18

A. Chua, T. Nasser / Journal of Corporate Finance 39 (2016) 1–17

15

Table 9 Two-year post-IPO returns and insider sales. This table shows the regression of the two-year post-IPO returns. Models (1) and (3) are OLS regressions. Models (2) and (4) show only the second stage of 2SLS regressions; in the first stage, insider sales is regressed on the six liquidity variables shown in Appendix Table A.1. Model (5) uses a liquidity factor as a regressor, obtained through factor analysis (see Table 7) of the liquidity variables. P-values for Models (1), (3) and (5) are obtained based on the Huber (1967) and White (1980) sandwich estimators. P-values for Models (2) and (4) are obtained based on Murphy and Topel (1985) standard errors. All regressions contain Fama–French 12 industry fixedeffects and a constant. All variables are defined in Appendix Table A.1. Insider sales (1/0) (1)

Insider sales Instrumented insider sales Liquidity factor First day returns VC backed (1/0) Ln firm age High IB reputation (1/0) Hi-tech (1/0) Ln total asset Ln sales Bubble period (1/0) Industry dummy N Adjusted R2

Coef.

p-Value

−0.141

0.281

0.123 0.249 −0.103 0.219 0.189 −0.006 0.063 −1.117 Yes 1462 0.046

Insider sales (%) (2)

0.712 0.023 0.120 0.068 0.048 0.885 0.080 0.000

(3)

Coef.

p-Value

−3.452

0.001

−0.100 0.221 −0.086 0.139 0.155 −0.021 0.092 −1.364 Yes 1462 0.058

0.803 0.131 0.314 0.378 0.404 0.727 0.074 0.000

Coef.

p-Value

0.384

0.605

0.127 0.246 −0.110 0.222 0.190 −0.002 0.056 −1.085 Yes 1462 0.046

Liquidity factor (4)

0.702 0.022 0.105 0.066 0.047 0.963 0.108 0.000

(5)

Coef.

p-Value

−16.608

0.002

−0.015 0.196 −0.090 0.178 0.144 −0.011 0.093 −1.248 Yes 1462 0.052

0.961 0.152 0.262 0.248 0.436 0.850 0.065 0.000

Two year ret i ¼ β0 þ β1 X i þ β2 First day ret i þ β3 VC backedi þ β4 Ln firm agei þ β5 High IB reputationi þ β6 Hi−techi þ β7 Ln total assetsi þ β8 Ln salesi þ β9 Bubble periodi þ εi

Coef.

p-Value

0.313 −0.077 0.226 −0.094 0.142 0.184 −0.020 0.086 −1.368 Yes 1462 0.058

0.004 0.798 0.031 0.148 0.207 0.062 0.657 0.027 0.000

ð7Þ

We include, in all five models, covariates that have been shown to affect the long-run return (e.g., see Brau et al., 2007). Results are reported in Table 9. After controlling for other factors, we find insider sales and long-run return are uncorrelated in Models (1) and (3), but are significantly and negatively correlated in Models (2) and (4). These findings suggest that that insiders' liquidity motivated secondary sales is a symptom of agency problem. We find that two-year buy-and-hold return is about 32% lower if probability of liquidity-induced-insider-sales presence increases by one standard deviation. Similarly, the long-run return is about 25% lower if the liquidity motivated insider selling increases by one standard deviation. Result from Model (5) also reinforces the above findings. If the liquidity factor decreases by one standard deviation, the two-year buy and hold return is about 31% lower. Overall, we find support for our fifth hypothesis that insider's secondary sales motivated by liquidity needs are negatively correlated with the long-run returns. This suggests that insiders' liquidity motivated secondary sales is a symptom of agency problems.

5. Conclusion In this paper we argue and provide evidence that insider sales during an IPO are carried out due to insiders' liquidity needs, and study the consequences of the secondary sales for liquidity motives. In particular, this paper shows that: (1) lower levels of pre-IPO cash holdings lead to lower levels of executive compensation, (2) smaller cash-pay results in greater levels of insider sales, (3) equity-pay levels do not directly affect the insider sales but do influence the presence of an IPO lockup period, which in turn affects the levels of insider sales, and (4) higher levels of insider sales due to liquidity needs result in lower levels of underpricing and long-run returns. As a whole, these results highlight the executives' liquidity needs as a motivation for the secondary sales in an IPO. The key contribution of this paper is to show that the insiders' liquidity needs is an important, if not the primary, reason for secondary sales in an IPO. This liquidity induced secondary sales can also manifest some agency problems which may hinder managers from maximizing the long-run value of the firm. Acknowledgements We are grateful to an anonymous referee, an anonymous associate editor, Jen Chua, Mary Anne Majadillias, Abrar Nasser, Jeff Netter (Managing Editor), Sabuhi Sardarli and seminar and conference participants at 2015 CELS-Wash U, St. Louis, 2013 FMAChicago, and Kansas State University.

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Appendix A Appendix Table A.1 Variable definitions This table provides the descriptions and data sources of the variables that are used in this paper. Variables of interest

Definitions and sources of data

Insiders sales (1/0) Insider ratio First day return Two year return Liquidity related variables Cash ($M) Cash/total asset Average cash pay ($M) Average equity pay ($M) Lockup (1/0) Scaled industry P/E

Dummy variable that equals one if executives or directors sell shares during the IPO. (Source: Prospectus) Number of shares executives or directors sell during IPO/Total ownership pre-IPO (Source: Prospectus) First day closing price / Offer price (Source: SDC and CRSP) Two year buy and hold post-IPO return. (Source: CRSP) Pre-IPO cash holding (Source: Compustat) Cash to total asset (Source: Compustat) Average pre-IPO cash pay of all named executives (Source: Prospectus) Average pre-IPO equity pay for all named executives (Source: Prospectus) Dummy variable that equals one if the IPO has a lockup period. (Source: SDC) Month-end industry P/E scaled by six-month historical industry average P/E, lagged 6 months (Source: CRSP/Compustat)

Offer characteristics Shares overhang VC backed (1/0) High IB reputation (1/0) Hi-tech (1/0) Revise up (1/0) Pre-bubble period (1/0) Bubble period (1/0)

Number of shares outstanding / Number of shares offered (Source: SDC) Dummy variable that equals one if the IPO has venture backing, zero otherwise (Source: SDC) Dummy variable that equals one if the underwriter has a reputation score of eight or greater. (Source: Professor Ritter's website) Dummy variable that equals one if IPO is defined to be in High Tech, zero otherwise (Source: SDC) Dummy variable that equals one if the final offer price is greater than the top range of the initial filing price. (Source: SDC) Dummy variable that equals one if the issue year is in 1997 or 1998. (Source: SDC) Dummy variable that equals one if the issue year is in 1999 or 2000. (Source: SDC)

Firm characteristics Firm age Sales ($M) Total assets ($M) Leverage ROA Net income ($M)

Issue year minus founding year (Source: Professor Ritter's website) Pre-IPO Sales (Source: Compustat) Pre-IPO Total Assets (Source: Computstat) Pre-IPO Total Liabilities / per-IPO Total Assets (Source: Compustat) Pre-IPO Net Income / pre-IPO Total Assets (Source: Compustat) Pre-IPO Net Income (Source: Compustat)

Appendix Table A.2 Liquidity factors Panel A of Appendix Table A.2 reports the correlations (both Pearson and Spearman rank correlations) between insider secondary sales variables, liquidity proxies and liquidity factor. The Pearson correlations are reported in the bottom-left corner of the table and Spearman rank correlations are on the top-left. Correlations that are not significant at least at the 10% level are italicized. Panel B reports the descriptive statistics of the predicted insider sales variables. The predicted value is obtained by regressing insider sales (dummy or ratio) variable on the six liquidity related variables that are in Appendix Table A.2. Panel A:Pairwise correlations among insider sales and liquidity variables Spearman rank correlation

Insider sales %

Insiders sale (1/0)

Cash

Cash/total asset

Cash pay

Equity pay

Lockup (1/0)

Scaled industry P/E

Liquidity factor

0.179 0.172 0.342 −0.208

−0.064 −0.058 0.256 0.272 0.072

0.182 0.183 −0.096 −0.098 0.074 −0.175

−0.178 −0.178 −0.186 0.091 −0.269 0.120 −0.304

−0.244 −0.241 0.112 0.449 −0.255 0.240 −0.692 0.723

Pearson (simple) correlation Insider sales % Insider sales (1/0) Cash Cash/total asset Cash pay Equity pay Lockup (1/0) Scaled industry P/E Liquidity factor

0.994 0.609 −0.014 −0.128 0.065 −0.022 0.081 −0.101 −0.125

−0.019 −0.123 0.063 −0.034 0.182 −0.168 −0.229

0.030 −0.105 0.032 −0.094 0.520 0.027 0.242 −0.162 0.070 0.086 −0.012 −0.118 −0.072 0.095 −0.019 0.472

0.121 0.000 −0.088 −0.107

−0.164 0.100 0.172

−0.273 −0.720

0.709

Panel B: Descriptive statistics

Predicted insider sales (1/0) Predicted insider sales (%)

N

Mean

SD

1603 1603

0.174 0.024

0.094 0.015

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