Are publicly held firms less efficient? Evidence from the US property-liability insurance industry. This version: January 12, 2010

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1 Are publicly held firms less efficient? Evidence from the US property-liability insurance industry Xiaoying Xie* Department of Finance, California State University, Fullerton This version: January 12, 2010 Abstract This paper studies the performance of publicly held firms in the US property-liability insurance industry by analyzing companies that issued initial public offerings (IPOs) from 1994 to 2005, using private firms as the benchmark. I investigate ex ante determinants and ex post effects of IPOs on firm efficiency, operating performance, and other financials. I also analyze stock returns and follow-on SEO and acquisition activities to provide further information on IPO motivation. The paper finds that the likelihood of an IPO significantly increases with firm size and premium growth. IPO firms experience no post-issue underperformance in efficiency, operations, or stock returns; register improvement in allocative and cost efficiency; and reduce financial leverage and reinsurance usage. Moreover, IPO firms are active in follow-on SEO issues and acquisition activities. The findings are mostly consistent with the theory that firms go public for easier access to capital and to ease capital constraints. JEL classification: G22; G32 Keywords: Initial public offerings (IPOs); Property-liability insurance industry; Efficiency; Data envelopment analysis (DEA) * Corresponding author. Tel.: ; fax: address: xxie@fullerton.edu (X. Xie).

2 1. Introduction A substantial body of literature on initial public offerings (IPOs) has emerged in the past two decades (See Brau and Fawcett, 2006; Ritter and Welch, 2002); however, only a few studies attempt to empirically investigate the rationale behind a firm s decision to go public and address performance differences between public firms and their private peers. Existing empirical studies cover IPOs of US firms (Chemmanur et al., 2006; Helwege and Packer, 2003; Lerner, 1994), 1 Indian firms (Gopalan and Gormley, 2008), UK and Irish firms (Aslan and Kumar, 2007), German firms (Boehmer and Ljungqvist, 2004), and Italian firms (Pagano et al., 1998). For the US financial services industries, particularly the US insurance industry, such an empirical study has yet to be conducted. The lack of empirical study on the decision to go public is largely due to the paucity of data from private firms. The US insurance industry provides an excellent opportunity to overcome this difficulty, because special regulatory filing requirements for US insurance firms have made comprehensive financial information available for both publicly and privately held entities. The National Association of Insurance Commissioners (NAIC) and A. M. Best Company (an insurance rating firm) collect and distribute this data every year. This paper analyzes performance differences between public and private firms in the US property-liability insurance industry by looking at initial public offerings from 1994 to I empirically investigate the ex ante determinants of the decision to go public and ex post effects of IPOs on firm efficiency, operating performance, and other financials. I also analyze stock returns, follow-on seasoned equity offerings (SEOs), and acquisition activities in order to provide further information on IPO motivation. The research answers two main questions: first, 1 Lerner (1994) studies the biotechnology industry. Helwege and Packer (2003) examine a sample of nonfinancial firms that issue bonds prior to an IPO. Chemmanur et al. (2006) study US manufacturing firms with data available from the US Census Bureau. 1

3 what are the determinants of IPOs in the insurance industry? And second, do IPO firms experience post-issue underperformance? Several papers analyze post-ipo operating performance. Degeorge and Zeckhauser (1993) measure operating performance change in firms that experience reverse leveraged buyouts (LBOs), a special class of IPOs, and find that reverse-lbo firms perform substantially worse than comparison firms (the industry-matched public firms) in the post-ipo period. Jain and Kini (1994) study the post-ipo operating performance of public entities (other than reverse LBOs) and find that these firms exhibit a substantial decline in operating performance subsequent to the IPO. Mikkelson et al. (1997) examine the ownership structure and operating performance of IPO firms and also observe initial underperformance, but they find that performance declines no further during the subsequent ten years. One weakness with these studies is that they usually use industry-matched public firms rather than private companies as the control samples. Consequently, survival bias may affect their results: the matched public firms have gone through the IPO process; therefore, they may be more established and perform better. In fact, the better performance of matched public firms versus IPO firms could also indicate that the inferior performance of IPO firms would eventually end, as documented in Mikkelson et al. (1997). Without a direct comparison between IPO and private firms, the rationale and effect of public offerings cannot be fully explained. This paper utilizes data for both public and private insurance firms and directly examines the performance of IPOs by using private entities as the benchmark. 2 2 IPOs in the insurance industry have been investigated with a focus on underpricing and the post-offering stock performance of demutualization IPOs (Lai et al., 2008; Viswanathan, 2006). These studies do not look at the performance difference between public firms and private firms, the determinants of IPOs, and the effects of IPOs on firm efficiency and operating performance, which this paper examines. Studies of IPOs in the banking industry can refer to Friesen and Swift (2009) and Adams et al. (2009). 2

4 This study contributes to the literature in two ways: (1) it evaluates company decisions to go public by directly comparing public and private firms. The literature generally acknowledges that IPO firms perform worse than industry-matched public firms in terms of either long-run stock performance (Loughran and Ritter, 1995; Ritter, 1991) or operating performance (Mikkelson et al., 1997; Jain and Kini, 1994; Degeorge and Zeckhauser, 1993). The decision to go public can still be justified, however, if IPO firms do not underperform in comparison to their private peers. (2) This is the first paper that empirically incorporates frontier efficiency analysis into the study of IPOs to provide a more comprehensive measurement of firm performance. Based on classic microeconomic theory (Shephard, 1970), frontier efficiency analysis captures the multidimensionality of a firm s production process. It is more sophisticated than any single financial ratio and has become the state-of-the-art method of measuring the performance of businesses. Frontier efficiency methodology also has an advantage over analysis of stock prices in that it can be estimated for both public and private entities, while stock prices are only available for publicly traded firms. In this study, I estimate various types of efficiencies (scale, allocative, technical, cost, and revenue efficiency) by using the data envelopment analysis (DEA) method and test hypotheses based on these measures. I also analyze operating performance and stock returns to provide a comparison. The remainder of the paper is structured as follows. Section 2 develops the tested hypotheses. Section 3 describes the data and sample. Section 4 presents frontier efficiency methodology. Section 5 presents analyses on the determinants of the decision to go public and the post-issue effects of IPOs (this section also covers firms SEO and acquisition activities after the IPO and the analysis for delisted firms), and section 6 concludes the paper. 2. Hypotheses 3

5 Companies often base their rationale for going public on the assumption that the benefits from public trading and more diversified ownership outweigh the costs of public listing. It is essentially an ex ante cost-benefit consideration (Brau and Fawcett, 2006; Ritter and Welch, 2002; and Pagano et al., 1998). If the benefits exceed the costs, the performance of a firm will improve; otherwise, it may suffer a lower efficiency. This section discusses the motivations, costs, and benefits of going public. Hypotheses about the effect of the IPO and about firm characteristics likely to be associated with listing decisions are formulated based on the existing literature. These theories and their predictions are not necessarily mutually exclusive because different motivations often work interactively to produce an IPO issue (Pagano et al., 1998). Moreover, some theories have similar implications for company decisions and performance, so it is an intricate process to disentangle them. The testing hypotheses have been developed with regard to these observations Theories favoring going public The literature on corporate finance suggests that firms go public for both financial reasons and for management s private reasons. As an examination of financial data, this study is not well equipped to examine private considerations, so this discussion will not cover them. 3 The following sections categorize the benefits of listing Ability to raise equity capital and overcome capital constraints The most cited reason for going public is to raise capital. Going public enhances a firm s ability to raise equity capital more effectively than private equity, debt, or venture capital (Pagano et al., 1998; Chemmanur and Fulghieri, 1999). In an initial offering, a firm can raise new capital without the associated risks, restrictions, costs of debt, or the constraints set by 3 For example, the founders and shareholders of a private firm may seek to go public to cash out and diversify their personal portfolios (see summary of this issue in Brau and Fawcett, 2006; Ritter and Welch, 2002; Alavi et al., 2008). 4

6 venture capitalists. After the initial offering, a firm may raise additional capital through seasoned equity offerings as needed. The infusion of equity capital can help the firm to rebalance accounts after high growth or investment, sustain or pursue higher growth rates, finance new investments, or reduce the level of debt in order to survive and thrive in competitive markets. In the insurance industry, equity capital provides a cushion for risks taken by the insurance companies and is the backbone of their underwriting capacities. Higher levels of risk create stronger demand for equity capital. The adequacy of a firm s capital is closely monitored by regulators, policyholders, and insurance rating firms. Private insurance companies possess a limited ability to raise new capital in a timely fashion and face substantial transaction costs when raising new capital due to information asymmetries. Outside investors generally have less information about the quality of a private insurer s assets and the value of its reserve estimates for unpaid losses, especially for long-tail lines such as commercial liability insurance, making it more difficult and costly for private insurers to raise new capital (Chamberlain and Tennyson, 1998). Based on this theory, it is predicted that insurance firms in need of more equity capital are more likely to go public. These firms tend to engage in riskier lines of business and, therefore, exhibit higher volatility in their underwriting performance. They are more likely to have higher leverage, higher growth rates, more reinsurance usage, and/or higher agent balances than other private firms (Viswanathan and Cummins, 2003). Firms with such characteristics are more sensitive to capital inadequacy shocks and place more value on the ability to raise additional capital. In the property-liability insurance industry, long-tail lines are usually associated with higher risk, especially lines involving commercial liability, because they deal with dynamic risks 5

7 in society. Their loss payments are dramatically affected by alterations of law and regulation, scientific discoveries, and other developments. Firms with more business in such lines need easier access to the market for additional capital, in case unexpected losses deplete their underwriting capacities. Firms underwriting more business in long-tail lines also need to maintain a higher level of loss reserves (liability of the company) and may operate with higher leverage given the capital constraints they face as private firms. Additional capital raised from the IPO and any follow-on equity issues will help bring down the leverage and ease capital constraints. Insurance firms may go public to support and sustain high growth rates. Firms may experience high growth rates as a result of comparative advantage, competitive pressure, or strategic development policy. Such firms will have a higher demand for equity capital and tend to transfer risks beyond their underwriting capacity to reinsurers Market-timing theory (windows of opportunity) Market-timing theory suggests that firms go public when markets are good and equity valuations are high and remain private when markets are bad and equity valuations are low (Pagano et al., 1998; Lerner, 1994). In this way, firms can take advantage of the bull market and issue IPOs when the industry market-to-book ratio is high 4. Following Pagano et al. (1998), I proxy the windows of opportunity by the median market-to-book ratio of public companies in the insurance industry and/or include year dummies in the regression to control for stock market conditions Managerial discipline (monitoring) 4 This theory does not necessarily predict post-issue performance improvement because firms that intend to time the market may not always issue IPOs when equity capital is most valuable to them. Colaco et al. (2009) also show that a firm may be willing to issue an IPO without following clusters of similar firms if the firm needs to raise capital or if the insiders need portfolio diversification. 6

8 Going public enables firms to utilize an external monitoring mechanism that may not otherwise exist in private firms. Managers of publicly traded firms face the threat of hostile takeover and high pressure to perform in a competitive labor market. Managerial decisions in such firms are more exposed to market assessment than those of private firms. Shareholders can also design more efficient compensation packages and use stock options or stock-price indexed payments to align the incentive of managers with that of the owners (Holmström and Tirole, 1993). This paper does not test this hypothesis directly, but, for it to hold true, improved performance of IPO firms is anticipated Facilitating acquisition activities Firms may go public to facilitate acquisition activities (Zingales, 1995). A public listing can generate public shares and trading prices for a firm and facilitate its participation in takeover activities either as an acquirer or as a target. Public firms can make less expensive acquisitions by paying stocks while preserving the company cash position for operation and investment. In addition, public targets are normally valued higher than private targets and have higher takeover premiums (Faccio et al., 2006; Chang, 1998). In this paper, I test this theory by examining the acquisition activities of IPO firms after their public offerings Strategic movement An IPO may serve as a firm s strategic movement. First, going public helps broaden the ownership base of the company, which could reduce the bargaining power of large investors considerably (Chemmanur and Fulghieri, 1999). Second, going public helps enhance the reputation and visibility of a firm and capture a first-mover advantage (Ritter and Welch, 2002; Maksimovic and Pichler, 2001). The performance of IPO firms may therefore compare more 7

9 favorably against private firms because their added prestige and visibility is valued by customers, suppliers, employees, and the financial community Theories against going public Agency costs and signaling costs Going public may help create a better external monitoring system, but the dilution of management ownership may increase agency costs and create a moral hazard problem (Jensen and Meckling, 1976). After going public, more dispersed share ownership and more serious free-rider problems may impair internal monitoring by shareholders. The performance of public firms could also suffer due to increased agency conflicts between managers and shareholders. Kaplan (1989) and Smith (1990) find improvements in the operating performance of firms that transfer from public to private through management buyouts (MBOs) and credit the reduced conflicts of interests between managers and owners in private firms for the improvement. As management s ownership interests dilute and agency costs increase, the performance of IPO firms is expected to decline. The high signaling costs of going public may also discourage a firm from listing. Investors are generally less informed than insiders about the true value of companies seeking to go public. Consequently, the market may expect an adverse selection of IPOs. To send a positive signal to the market about the high quality of the firm, the original owners may retain a significant ownership stake in the firm (Leland and Pyle, 1977) or underprice their IPO shares (Allen and Faulhaber, 1989; Welch, 1989). Both signals, among others, such as using a longer lockup period and certification, 5 are expensive. As Chemmanur and Fulghieri (1999) suggest, small and young firms are more subject to the costs of information asymmetry, so successful 5 See Brau and Fawcett (2006) for a summary of literature on the use of certification. 8

10 listing firms tend to be old and large firms. Based on this theory, the probability of going public relates positively to firm size High pre-offering expenses and fees High expenses and fees may discourage firms from going public. Such fees include, but are not limited to, underwriter commissions, initial and pre-offering expenses (such as fees for attorneys and accountants), and significant housekeeping and cleanup costs for the Securities and Exchange Commission (SEC) registration. Overall, the direct costs of going public are estimated to be about $250,000, plus an additional 7% of the gross proceeds of the IPO (Ritter, 1987), and the costs are not sensitive to the size of the issue (Chen and Ritter, 2000). The high costs of IPOs and their insensitivity to firm size suggest that large firms are more likely to go public Burden of post-offering duties Firms face additional costs after they become public. First, they must comply with federal securities laws and various filing requirements. Administrative and compliance expenses and efforts toward developing public and investor relations are by no means negligible. For instance, in the US, the insurance industry is heavily regulated, with the Statutory Accounting Principles (SAP) as the dominant accounting rule. After going public, firms must also file based on Generally Accepted Accounting Principles (GAAP), which is not required of private companies. Second, firms must disclose information about their operational and financial situations where secrecy may be crucial for competitive advantage, such as investment projects and merger and acquisition strategies. Finally, firms lose flexibility in making prompt strategic decisions, because they face scrutiny from, and have fiduciary duties to, a more diversified group of public 6 It is ideal to include the age of firms in the model; however, in the insurance industry, it is common for many individual firms to run under one group ownership, and the members of a group change over time. It is, therefore, difficult to define the precise year of incorporation of a group holding company. 9

11 shareholders. Brau and Fawcett (2006) demonstrate that the desire to maintain decision-making control is the most important concern for firms to stay private. Because the costs of operating as a public firm are decidedly high, especially for smaller firms, it is predicted that size is positively related to the probability of going public. In addition, a public firm s performance may suffer if the costs outweigh the benefits. In summary, it is hypothesized that IPO firms will not underperform in comparison to private firms if the benefits of going public outweigh the related costs, and the characteristics associated with a firm s likelihood of going public will be consistent with the theories under consideration. 3. Data and sample selection 3.1. Data source I extract the list of publicly traded property-liability insurance firms from SNL DataSource, Center for Research in Security Prices (CRSP), and A.M. Best. 7 From these resources, I obtain a complete list of publicly traded firms (both current and historical), including their primary insurance sector information, 8 as well as the starting and ending date of listings. The decision-making units in this analysis are groups and unaffiliated firms. 9 Each insurer s group code (NAIC group code) and its public holding company are matched using information collected from the NAIC, A.M. Best, Factiva reports, Hoover s online company profiles, and 7 Several Bermuda-based insurance holding firms that list in US stock exchanges are deleted from the sample because their US insurance subsidiaries cannot be identified. 8 In this paper, financial guaranty, mortgage guaranty, and surety holding firms are included in the analysis because NAIC considers all these types of firms as property-liability firms. Multiline firms (such as AIG) are also analyzed. Property-liability operations of other public insurance firms, such as public life-health and managed care firms are also included in the sample. Excluding these firms does not change the results. 9 See Cummins and Xie (2008) for a discussion of company affiliations in the insurance industry. 10

12 various other resources. 10 Finally, I collect the financial data of firms (covering the years ) from the regulatory annual statements maintained by the NAIC. Table 1 shows the sample size. During the period of 1993 to 2005, 218 publicly traded insurance companies engage in the property-liability insurance business. Among them, 89 are continuously traded old listing firms (firms that started listing before 1994 and were publicly traded throughout ); 52 are continuously traded new listing firms (firms that started listing between 1994 and 2005, and were publicly traded at least until the end of 2005); and 77 are historical firms (firms that were delisted between 1993 and 2005, no matter when they started the listing). 11 The continuously traded new listing firms include nine demutualization IPO firms and 43 non-demutualization IPO firms. The number of new listing firms and delisted firms by year is also reported in the table Study sample The study sample of the paper is the IPO firm sample. 12 This sample consists of stock firms that issued IPOs between 1994 and 2005, which include continuously traded new listing firms (43 non-demutualization IPOs) and delisted IPO firms (12 historical firms that issued 10 In the insurance industry, it is common for several subsidiaries to be operated under common ownership and management. In a few cases, both the holding company and some of its controlled subsidiaries are listed (e.g. The American International Group and its controlled firm Transatlantic Holdings, Inc. are both listed). Research on IPO decisions, such as Pagano et al. (1998), treats the listed independent firms and the listed subsidiaries of public parents separately. Since this analysis is at the group and unaffiliated firm level and only a very few cases involve the public subsidiaries of publicly traded companies, the separation is less necessary. The listed parent and the subsidiary are considered as one observation that is represented by the parent company here. 11 Among them, 17 firms issued IPOs between 1994 and 2005; 12 were non-demutualization firms and five were demutualization firms. 12 To investigate whether public firms underperform in the long run and shed some light on the future development of the IPO firms, I also look at public firms that started listing before 1994 (Non-IPO public firm sample), using private firms as the benchmark. The Non-IPO public firm sample includes 88 continuously traded old listing firms and 59 non-ipo historical firms. The overall finding is that non-ipo public firms in general do not underperform in comparison with private firms (in terms of cost efficiency, revenue efficiency, technical efficiency, and return on assets), but public firms differ significantly when compared to private firms in certain characteristics such as size, leverage, usage of reinsurance, agents balances ratio, business mix, and diversification level. 11

13 non-demutualization IPOs between 1994 and 2005). 13 Demutualization IPO firms are excluded from the sample because these firms operate under the mutual organizational form before going public Control sample The control sample in this study includes private firms in the US property-liability insurance industry. I select the private firm sample by using the following procedures: (1) Identify all the groups and unaffiliated firms that report property-liability data to the NAIC from 1993 to (2) Exclude the 218 public firms from the sample in all years. (3) Exclude the insurance groups and unaffiliated firms whose ultimate parent companies primary businesses are not insurance, but are publicly traded. 14 (4) Exclude mutual firms and keep stock firms. Stock firms and mutual firms differ in many areas such as capital structure, business focus, corporate governance, and production techniques (Harrington and Niehaus, 2002; Cummins et al., 1999a), so mutual firms do not serve as a proper control sample for this study and are therefore excluded. (5) Keep private firms that continuously operated for at least 12 years out of the 14-year period ( ). In other words, the private firms in general do not exit the market during the study period. This requirement for relatively continuous operation implies that these firms have the potential to go public (Pagano et al., 1998). The above selection criteria return 270 private stock firms (with 3,598 firm years) Delisted firms are included in the sample to avoid any survivorship bias. Excluding these firms from the analyses does not change the results. 14 For example, GE Global insurance group is excluded because its ultimate parent (General Electric Company) is a publicly traded conglomerate. 12

14 4. Frontier efficiency methodology This paper employs frontier efficiency methodology to measure firm performance. Frontier efficiency is rooted in classic microeconomic theory (Shephard, 1970). It accounts for the multidimensionality of a firm s production process and therefore provides a more sophisticated measure of firm performance than any single financial ratio, such as return on assets or return on equity. The frontier efficiency methodology also has an advantage over analysis of stock prices in that it allows the study to include both public and private firms, while stock prices are only available for publicly traded companies (Cummins et al., 1999b; Cummins and Xie, 2008). 16 The application of this method contributes significantly to the IPO performance literature. The essence of frontier efficiency is distinguishing well-performing production units from those that perform poorly. The technique estimates the best practice efficient frontier that consists of the most efficient firms in an industry, and compares all firms in the industry to the frontier. Firms operating on the frontier are fully efficient (with efficiency scores of one), and firms not on the frontier are inefficient (with efficiency scores between zero and one). DEA, a non-parametric technique, is adopted to estimate firm efficiency (Cooper et al., 2000). Non-parametric techniques are superior to econometric efficiency measurements because they require no assumptions to be made regarding the production or cost function and the probability distribution of the error terms, which prevents potential estimation bias caused by model misspecification. Previous studies have shown that DEA estimation has good asymptotic 15 Some firm years are dropped in the probit and other regression analyses because of the further constraints applied when estimating firm efficiency and missing values of other control variables. 16 This paper complements the efficiency analyses with operating performance and stock returns to see whether results would be different if using alternative measures and to make conclusions more accessible to people not familiar with frontier efficiency methodology. Literature has shown that frontier efficiency scores capture valuable information, as compared to operating performance and market returns (Cummins and Zi, 1998; Cummins and Xie, 2009). 13

15 statistical properties (Banker, 1993) and better accuracy than econometric approaches in estimating efficiency in the presence of heteroscedasticity (Banker et al., 2004). DEA also yields consistent estimators for contextual variables in a two-stage regression with DEA efficiency as the dependent variable (Banker and Natarajan, 2008). Classic microeconomic theory presents the primary goal of companies as maximizing revenues while minimizing costs. Accordingly, this analysis estimates both cost and revenue efficiency for sample firms, as well as technical efficiency, scale efficiency, and allocative efficiency in order to identify the sources of cost (in)efficiency, such as economies of scale and resource allocation in the production process. The appendix to this paper shows the relationship between various types of efficiency and associated mathematical programming. The first step in estimating DEA efficiency is to specify the inputs, outputs, input prices, and output prices for firms. Four inputs are used in this study: administrative labor, agent labor, materials and business services, and financial equity capital. A modified version of the valueadded approach is adopted to define insurance outputs (Cummins and Weiss, 2000; Cummins and Nini, 2002; Cummins and Xie, 2008). Four insurance outputs (personal lines short-tail insurance, personal lines long-tail insurance, commercial lines short-tail insurance, and commercial lines long-tail insurance) are defined for risk pooling and risk bearing, and real financial services One intermediary output (invested assets) is defined for the financial intermediary services provided by insurance companies. Following Cummins and Xie (2008), this paper defines the quantities of administrative labor, agent labor, and business services as the current dollar expenditures related to these inputs divided by their current prices. The prices for administrative labor and agent labor inputs are defined as the US Department of Labor (DOL) average weekly wage (AWW) for employees in 14

16 insurance companies (SIC 6331 and NAICS ) and the DOL AWW for insurance agents (SIC 6411 and NAICS ), respectively. The price for materials and business services is calculated by taking the weighted average of price indices for business services from the expense page of Best s Aggregates and Averages. The base year for all price indices is the year The quantity of financial equity capital input is measured by using the average of the beginning and end-of-year equity capital, deflated by the CPI. The ideal cost of capital measure is the expected market return on equity capital. Unfortunately, expected market returns cannot be calculated for most insurers because the majority of insurers are not publicly traded. After considering several measures of cost of capital in the literature, the paper selects the size adjusted capital asset pricing model based on data from Ibbotson Associates (2008). That is, the cost of capital for year t is calculated as the 30-day Treasury bill rate at the end of year t-1, plus the long-term (1926 to the end of year t-1) average market risk premium on large company stocks, plus the long-term (1926 to the end of year t-1) average size premium from Ibbotson Associates. All insurers in the industry are classified into four size groups based on their equity capital. The largest size category receives no size premium, and each of the smaller size categories receives the Ibbotson long-term average size premium. 17 The quantity of insurance output is measured by the present value of losses incurred. The price of each insurance output is defined as the difference between real premiums earned and the 17 See Cummins and Xie (2008) for more details of the method. The other two methods for cost of capital estimation used in the literature include: (1) omitting the size premium and assigning the same cost of capital to each firm in a given year (Cummins and Nini, 2002) and (2) the three-tier approach: estimating the cost of capital for traded insurers in various A.M. Best financial rating categories and assigning costs of capital to non-traded insurers based on their A.M. Best ratings (Cummins et al., 1999b). Previous literature reveals the robustness of the cost efficiency results with alternative cost of capital assumptions (Cummins et al., 1999b; Cummins and Nini, 2002; Cummins and Xie, 2008). The size adjusted capital asset pricing model is adopted because, theoretically, it better addresses the concern that the cost of capital for public firms should be different from that of private firms. Since public firms on average are larger than private firms, they are assigned lower cost of capital under the size adjusted CAPM approach, while the three-tier approach assigns the same cost of capital to public and private firms based on the A.M. Best ratings. An earlier version of this paper tests the three-tier approach; the overall conclusions are the same. 15

17 real present value of losses incurred for the output divided by the real present value of losses incurred. A smoothing procedure is applied to the four insurance outputs and their prices for each firm in the sample to control for the potential errors in variables problem in output price measurement due to the randomness of losses (Cummins and Xie, 2008). The quantity of intermediation output is measured by the average of the beginning and end-of-year invested assets (deflated to 2000 by CPI). The price of the intermediation output is defined as the weighted average of the expected return on stocks and the realized return on other interestbearing invested assets. The decision-making units of the efficiency estimation consist of group and unaffiliated single property-liability insurers. I estimate the year-by-year efficiency scores for every eligible firm during the period 1993 to Originally, the sample for efficiency estimation includes all group and unaffiliated insurers with data available from the NAIC, but I eliminate the following firms: firms with zero or negative net worth or premiums, firms with extremely high premiumto-surplus ratios, and small firms with assets below $1 million. I also exclude risk retention groups, US Lloyds, and state worker s compensation fund programs from the sample. 5. Results for IPO firms 5.1. Determinants of the decision to go public Based on the discussions of motivations of going public, I conduct a univariate analysis and a probit regression for the determinants of issuing IPOs. The probit model is: Pr( IPO = 1) = F( α + β SIZE + β LEVERAGE + β REINSURE + β AGENTBAL it, 1 it, 1 2 it, 1 3 it, 1 4 it, 1 + β GROWTH + β PLT + β CLT + β PHERF + β GHERF + β ROA 5 it, 6 it, 1 7 it, 1 8 it, 1 9 it, 1 10 it, 1 + β LOSSRATIO + β EXPRATIO + β UNAFFILIATEDit, 1+ β14 MTBit, 1 11 it, 1 12 it, β EFF + γ YEAR 15 it, 1 t t) (1) where i represents insurer i, t represents year t and, 16

18 IPOi,t = zero if a company i stays private in year t and one if it goes public in that year; SIZE = Logarithm value of total admitted assets; LEVERAGE = Liabilities divided by policyholders surplus; REINSURE = Reinsurance ratio, defined as: Reinsurance ceded / (Direct premiums written + reinsurance assumed); AGENTBAL = Agents' balances ratio, defined as: Agents' balances / Direct premiums written; GROWTH = Premium growth, defined as: Net premiums written (t) / Net premiums written (t-1); PLT = Percentage of premiums in personal lines long-tail; CLT = Percentage of premiums in commercial lines long-tail; PHERF = Herfindahl index of product lines based on net premiums written; GHERF = Herfindahl index across states based on net premiums written; ROA = Return on asset; LOSSRATIO = Loss ratio; EXPRATIO = Expense ratio; UNAFFILIATED = one if an unaffiliated single firm, zero if an insurance group; MTB = Median market-to-book value of equity of insurance industry; EFF = Efficiency (scale, allocative, technical, cost, or revenue efficiency) generated by DEA; and YEAR = Calendar year dummies. Similar to Pagano et al. (1998), at any time t with IPO issues, the IPO=0 sample includes all the private companies in that year as selected in Section 3.3 and other IPO firms remaining private in that year. A firm is dropped from the IPO=0 sample once it goes public. 18 Lagged values of the regressors are used in the regression, except for the growth variable, where the premium growth rate in year t is used. 19 Table 2 provides univariate statistics for IPO firms and private firms for the year prior to the IPO. As predicted by the theories, IPO firms are much larger than private firms in terms of assets, premium revenue, policyholder surplus, and net income. IPO firms have higher leverage than private firms (1.85 vs. 1.69). Additionally, IPO firms use more reinsurance services (0.43 vs. 18 That is, the private firms (IPO=0 sample) include firms that stayed private during the sample period and IPO firm-years before they went public. A robustness test applying NASDAQ SmallCap market initial listing requirements to the IPO=0 sample returns a similar quantitative result. 19 There are a few firms whose t-1 financials are missing, so I use their year t value to retain them in the sample. Dropping these firms returns similar results. 17

19 0.28) than private firms, suggesting that their underwriting capacity constrains their ability to retain risks. IPO firms also have a higher agents balances ratio (0.27 vs. 0.15), indicating that such firms may face more capital constraints. The premium growth of IPO firms is much higher than that of private firms (1.53 vs. 1.19), indicating that IPO firms are expanding and need stronger capital support. These findings altogether suggest that firms conduct IPOs to raise additional capital or ease capital constraints after periods of high growth. With respect to business mix, IPO firms write significantly more business in long-tail commercial lines (0.54 vs. 0.42) and less business in short-tail commercial lines (0.18 vs. 0.30), which suggests that IPO firms engage more intensively in riskier lines. IPO firms are more diversified across business lines and geographical areas than private firms, implying that the potential reputation benefit from going public may be significant. Surprisingly, IPO firms on average do not have higher returns on assets than private firms before going public (0.033 vs. 0.05). 20 IPO firms have a higher loss ratio (0.74 vs. 0.65) and a slightly higher expense ratio (0.42 vs. 0.38), which may be due to the greater demand in services from commercial customers. I find no significant difference in firms technical, cost, and revenue efficiency between IPO firms and private firms, even though IPO firms have lower scale and allocative efficiency scores. Table 3 presents probit regression results, which are largely consistent with the findings from univariate analyses. Large firms and firms with a higher premium growth rate and a larger proportion of long-tail commercial lines are more likely to go public. The reinsurance usage and agent balance ratios are also positively related to IPO issues. These findings suggest that capital need is a motivation for IPOs. The size effect is consistent with arguments regarding the burdens 20 The median return on assets of IPO firms and private firms is vs The median test is significant at the 5% level. Most of the existing literature on IPOs finds that pre-issue performance of IPO firms is better than industry-matched (public) firms (Mikkelson et al., 1997; Jain and Kini, 1994; Degeorge and Zeckhauser, 1993, etc.). 18

20 and challenges of going public, such as the cost of information asymmetry, high pre-offering expenses and fees, and the burden of post-offering duties. Variables measuring the pre-ipo performance of firms include return on assets, loss ratio, expense ratio, and various types of efficiency. Among them, expense ratio carries a significant positive sign, and scale and allocative efficiency carry a significant negative sign, while other performance variables are more or less insignificant. This suggests that IPO firms do not necessarily perform better in all dimensions than private firms and may go public to seek improvements. The industry market-to-book ratio is positive but not statistically significant, providing little support for the window of opportunity or market-timing theory in the insurance industry Effect of IPO on firm performance and financials To evaluate the IPO decision, it is essential to compare the ex post performance and financials of IPO firms with those of firms that remain private. Following Pagano et al. (1998), I investigate this issue by constructing an unbalanced panel sample for all IPO firms and private firms as selected in Section 3.3 for the years I then estimate fixed-effects regressions, and the effects of the decision to go public are captured by dummy variables for the year of the IPO and the three subsequent years. The model specification is as follows: 3 y it, = α + β j IPO t j + β4 IPO t n + μ i + d t + ε it, j= 0 (2) 21 Year dummies are excluded in table 3 because none of them are significant and a potentially high correlation exists between market-to-book ratio and year dummies. A robustness test with year dummies in the model returns similar results. In order to address whether a firm s IPO decision is affected by the Sarbanes-Oxley Act of 2002, I conduct robustness checks for the probit analyses by splitting the overall sample period into two parts, and The results remain stable over time. 22 In regard to IPO firms that delisted during the sample period, the firm years after delisting are excluded from the sample. 19

21 where i represents insurer i, t represents year t, μ i and d t are firm-specific and calendar year specific effects, respectively. IPOt j are dummy variables equal to one if year t jis the IPO year; IPOt nis a dummy variable equal to one if the IPO took place more than three years before t. The design of this model allows use of a pre-ipo firm as a control for itself after the deal. It also allows separation of the short-term and long-term effects, which could be mutually offsetting. 23 The dependent variables y it, include efficiency (scale, allocative, technical, cost, and revenue efficiency), ROA, LOSSRATIO, EXPRATIO, LEVERAGE, NCEQUIP, FINANCIAL1, FINANCIAL2, GROWTH, REINSURE, and AGENTBAL, where NCEQUIP = FINANCIAL1 = FINANCIAL2 = Investment in equipment and EDP: Expenditures over equipment and Electronic Data Processing Equipment/Total non-commission expenses; Financial investments - all: Total invested assets/total assets; and Financial investments - long-term: (Total invested assets - Cash and short-term invested assets)/total assets. Other variables are defined as in the Probit model Efficiency, operating, and underwriting performance The first section (Efficiency) of Table 4 shows that IPO firms experience no significant improvement in post-ipo scale efficiency and technical efficiency. However, IPOs significantly help improve firms allocative efficiency, suggesting that management with better access to capital markets is more efficient in allocating the available resources. Driven by improvement in 23 In a robustness test, I control the status of whether a firm satisfies listing requirements in year t-j by using NASDAQ SmallCap market initial listing requirements to construct qualification dummy variables. The results of the IPO dummies are not affected. 24 I conduct a robustness test by including control variables in the regressions to address the concerns that changes in performance and financials may also be affected by other variables, such as previous performance and financials. The variables controlled include: firm size, lagged values of the dependent variable, and lagged values of other variables that are found relevant by the literature, such as business mix and firm diversification level. In addition, lagged values of ROA, growth, leverage, reinsurance ratio, and agents balances ratio are added in every regression. The results are largely consistent with the ones reported in the paper. 20

22 allocative efficiency, cost efficiency of IPO firms improves as well, particularly in the long run (as shown by year +2 to year > +3 dummies). IPO firms also marginally improve their revenue efficiency in the year of IPO and the third year after the IPO, but the overall effect for all the post-ipo years is not significant (as shown by the p-value of the F-test). The second and third sections demonstrate that the profitability (return on assets) and underwriting performance (loss ratio and expense ratio) of insurers do not change significantly after the IPO. In summary, I find no ex post efficiency or operating underperformance for IPO firms in the insurance industry Leverage, investment, growth, and other financials IPO firms deleverage within the first three years after the IPO, but not in the long run. The overall effect of all IPO dummies is significant at the 10% level, suggesting that IPO eases firms capital constraints in the short run. IPO has no significant impact on capital expenditures, as shown in the regression for investment in equipment and EDP (electronic data processing equipment), investment in financial assets, and long-term financial investment. Premium growth declines after the IPO (particularly in the long run), but the overall effect is not significant. The reinsurance ratio also declines in the early years after the IPO, with overall effects significant at the 10% level. This also confirms the easing of capital constraints as a motivation for IPOs because the additional capital infusion through IPO enables firms to retain more risks and seek less reinsurance usage. Agents balances ratio is not significantly impacted by the IPO. A complete picture can be drawn when combining the effect of the IPOs with their ex ante determinants. Prior to an IPO, firms tend to experience high growth and use more reinsurance services. After an IPO, these firms reduce their leverage and cede less risk to reinsurers. These findings suggest that the major motivation of IPOs is to ease capital constraints, 21

23 increase firms underwriting capacity, and rebalance their capital structure following a period of rapid growth in business Long-run stock performance of IPO firms As a robustness check, I estimate the abnormal returns for IPOs during the period 1994 to 2005 to find out whether the long-run stock performance of these firms shows a different pattern than what efficiency and operating performance measures demonstrate. The analysis follows Ritter (1991) to compute the long run performance of IPOs. The benchmark-adjusted stock return for firm i in event month t is defined as: ARit = rit rbt (3) where r it is the monthly return for stock i and r bt is the return for the benchmark in month t. The average benchmark-adjusted stock return on a portfolio of n stocks for month t is: AR t 1 n n i = 1 = AR (4) it The cumulative benchmark-adjusted stock return from month one to month s is the summation of the average benchmark-adjusted stock return: 25 CAR 1, s s = AR (5) t= 1 t Table 5 reports the average monthly raw returns of IPO firms, the average matching firmadjusted returns (AR (t)), and the cumulative average matching firm-adjusted returns (CAR (1,t)) for 36 months after the offering date. 26 Seven out of the 36 monthly raw returns are negative, but 25 Another way to calculate CAR 1,s is to first calculate the cumulative abnormal return for month 1 through s for firm i and then take an average across the n firms (Lai et al., 2008). 26 As in Ritter (1991), this study calculates the post-issue returns for 36 months after the IPO, excluding the first day of trading. Months are defined as successive 21-trading-day periods relative to the IPO date, i.e., month 1 consists of event days 2-22, month 2 consists of event days 23-43, etc. For IPOs that are delisted before the 36 th month, stock return calculation ends with the CRSP's last listing. The corresponding 21-trading-day period is also applied to the benchmark return r bt. Two benchmarks are used: (1) the CRSP value-weighted NYSE/AMEX/NASDAQ index and 22

24 are statistically insignificant. Sixteen out of the 36 monthly average adjusted returns are negative, with one of them having t-statistics higher than 2.0. The cumulative average adjusted return gradually declines over time, reaching its minimum at % in the 34 th month, but is statistically insignificant. As such, unlike Ritter (1991), this study finds little evidence of underperformance in stock returns for insurance IPOs. This finding squares with the observations in section 5.2 that IPO insurers experience no significant post-issue increase in investments, where literature suggests that firms investing more will have lower expected returns and experience underperformance (Carlson et al., 2006). Figure 1 plots the CAR of raw returns, the CRSP value-weighted NYSE/AMEX/NASDAQ index adjusted returns, and the matching firm-adjusted returns for month zero to month 36, with month zero as the initial return. 27 The evidence of underperformance is even less significant when using the CRSP value-weighted NYSE/AMEX/NASDAQ index as the benchmark Seasoned equity offer of IPO firms To further investigate whether capital need is the major motivation for issuing IPOs, the study examines whether IPO firms conduct follow-on SEOs to have further access to equity capital. The SEO information is collected from SDC Platinum on capital issues of insurers. Table 6 reports the corresponding result, which provides further evidence of the capital need motivation of IPOs. Until the end of November 2008, about 52.7% of the IPO firms (29 out of 55 IPO firms) have issued at least one SEO, and among them, 72.4% of firms (21 out of 29 firms that issued SEOs) have had their first SEO issued within three years of the IPO. (2) listed firms matched by size. The matching insurance firms have the closest market capitalization to the issuers on the day of (or before) the IPO, selected by using a similar procedure described in Ritter s (1991) appendix. 27 The initial return is calculated as (First trading day closing price Offer price)/offer price. The offer price comes from SDC Platinum. For this sample, the initial raw return is 7.37%, the initial CRSP VW index-adjusted return is 7.23%, and the initial matching-firm adjusted return is 6.43%. 23