Are Publicly Held Firms Less Efficient? Evidence from the U.S. Property-Liability Insurance Industry

Transcription

1 Are Publicly Held Firms Less Efficient? Evidence from the U.S. Property-Liability Insurance Industry Submitted by Xiaoying Xie, Ph.D. Assistant Professor Department of finance College of Business and Economics California State University, Fullerton 2600 E. Nutwood Ave. #1060 Fullerton, CA92831 Phone: Fax: For Conference Performance Measurement in the Financial Services Sector: Frontier Efficiency Methodologies and Other Innovative Techniques, 2008, London, UK

2 Are Publicly Held Firms Less Efficient? Evidence from the U.S. Property-Liability Insurance Industry Abstract: This paper studies the performance of publicly held firms in the U.S. property-liability insurance industry during the period by looking at both initial public offerings (IPOs) and ongoing publicly traded firms. It investigates the determinants of firms decision to go public and the efficiency changes brought by IPOs. It also examines the differences in financial characteristics and efficiency between public and private firms. Results show that the likelihood of an IPO increases with firm size, leverage, and premiums growth rate, suggesting firms conduct IPO to raise additional capital or ease capital constraint. IPO firms do not differ significantly from private firms in cost and revenue efficiency changes, indicating they perform no worse than private firms. Public firms generally differ from private firms in that they are larger and more diversified, use more reinsurance service, have higher leverage, underwrite more business in long-tail commercial lines, and operate with higher loss ratio but lower expense ratio. Regression analysis shows that public firms do not underperform private firms in either cost efficiency or revenue efficiency measurement. The findings are mostly consistent with the theory that firms go public to explore the benefits of easier access to capital. Key Words: Publicly Held Firms; Initial Public Offerings (IPOs); Property-Liability Insurance Industry; Efficiency; Data Envelopment Analysis (DEA) 1

3 1. Introduction A substantial body of literature on corporate finance has emerged on the various issues of firms going public in the past two decades (See Brau and Fawcett, 2006; Ritter and Welch, 2002). There have been fewer studies, however, that attempted to empirically investigate the rationale behind firms decision to go public, or stay private, let alone comparing performance differences between public firms and their private peers. The lack of academic studies on this issue is largely due to the scarcity of data from private firms. The U.S. insurance industry, however, provides an excellent opportunity to investigate this issue. In this industry, comprehensive financial information is available for both publicly and privately held firms due to the regulatory requirements and the database built by National Association of Insurance Commissioners (NAIC) and A. M. Best Company (an insurance rating firm). In this paper, we analyze performance differences between public firms and private firms in the U.S. property-liability insurance industry by studying both initial public offerings (IPOs) and ongoing publicly traded firms over the period We empirically investigate the determinants and effects of IPOs and examine the differences in financial characteristics and efficiency between public firms and their private peers. We try to answer the following questions: First, what are the determinants of IPOs in the insurance industry? Second, do IPO firms experience post-issue underperformance? Third, do public firms in general underperform private firms? Only a few studies attempt to explore empirically why companies go public. Brau et al. (2003) address the factors influencing IPO decisions and takeovers (by publicly traded acquirers) by comparing the IPO firms and the private target firms and thus provide indirect evidence on firms motivations to go public. Pagano et al. (1998) examine the factors affecting a firm s 2

4 decision to go public by presenting a direct comparison of private Italian firms to public Italian firms (banking and insurance firms are excluded). Lerner (1994), using data from biotechnology industry, examines the timing of initial public offerings and private financings by venture capitalists and finds that industry market-to-book ratio is an important determinant of going public. No similar studies presently exist for the insurance industry. Several papers analyze the post-ipo operating performance of firms. Degeorge and Zeckhauser (1993) measure the change in operating performance of firms that experienced reverse leveraged buyouts (LBOs), a special class of IPOs. They find that reverse-lbo firms substantially underperform comparison firms (the matched public firms in the same industry) in the post-ipo period. 1 Jain and Kini (1994) study post-ipo operating performance of public firms (other than reverse LBOs) and find firms going public 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 show underperformance of IPO firms as well; however, they find the performance does not decline further during the second through tenth years of public trading. In almost all the operating performance studies on IPOs, the matched firms are publicly traded firms in the same industry, not the private firms. Consequently, the results may be affected by the survival bias in the analysis: the matched public firms are often established and good performance firms in the industry. Better performance of matched firms than IPO firms could also indicate that the inferior performance of IPO firms would eventually end, as documented in Mikkelson et al. (1997). The absence of direct comparison between performance of public and private firms, therefore, leaves the question about the wisdom of going public still 1 Holthausen and Larcker (1996), however, find that the reverse LBO firms generally outperform their industries for the four years following the IPO, through with some evidence of decline in performance. 3

5 open. The only research we document that directly compares the ex post performance of IPO firms and the matched firms that remained private is by Pagano et al. (1998). The paper shows profitability declines after the IPO and provides supports to the window of opportunity hypothesis of IPOs. Research that indirectly addresses the issue has been done by Kaplan (1989), Muscarella and Vetsuypens (1990), and Smith (1990). They study the operating performance of firms converting from public to private ownership through LBOs or management buyouts (MBOs) and find such firms experience operating efficiency gains, suggesting public firms are less efficient than private firms. In this paper, given the data advantage in insurance industry, we are able to examine directly the performance of IPO firms and public firms using private firms as benchmark. IPO in insurance industry has been investigated, with main focuses on the difference between Demutualization IPO and the normal IPO, the underpricing and the post-offering stock performance of IPO firms (Lai et al., 2007; Viswanathan, 2006). 2 They however, did not, look at the performance difference between public firms and private firms, and the efficiency changes pre- and post- IPOs, which will be the focus of this paper. This study is important in two ways: (1) It directly compares the performance of public firms and private firms to evaluate firms decision of going public. Literature on IPO in general finds that IPO firms underperform 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). However, the decision of going public can still be justifiable as long as IPO firms do not underperform their private peers. (2) We incorporate a widely accepted literature on firm performance measurement- 2 Demutualization refers to the conversion of a mutual insurer to a stock company. Demutualization IPO refers to firms that conduct demutualization and IPO simultaneously. We delete demutualization IPO firms from our analysis due to their special characteristics. 4

6 -frontier efficiency analysis into our study and therefore provide a more comprehensive measurement of firm performance. Critics on using financial ratios to measure firm operating performance include that financial ratios are subject to limitations and may incur accuracy problems and lack of foundation in economic theory (Fisher and McGowan, 1983; Watts and Zimmerman, 1983; Chaddad and Cook, 2004). In comparison, frontier efficiency analysis is based on classic microeconomic theory (Shephard, 1970) and accounts for the multidimensionality of a firm s production process. It is a more sophisticated measurement than single financial ratio and has become the state-of-the-art in measuring the performance of business firms. In this study, we employ cost and revenue efficiency estimated by using data envelopment analysis (DEA) to measure firm performance and test our hypotheses based on this measurement. By way of preview, we find that before going public, IPO firms differ significantly from private firms in many aspects of firm characteristics. Probit analysis shows that a firm s likelihood to issue an IPO is increasing in size, leverage, and premiums growth rate but decreasing in its percentage of business in short-tail commercial lines. The cost and revenue efficiency changes of IPO firms do not differ significantly from those of private firms, indicating IPO firms perform no worse than their private peers. Public firms, in general, differ from private firms in many areas. They are larger and more diversified, use more reinsurance service, have higher leverage, underwrite more business in long-tail commercial lines, and operate with higher loss ratio but lower expense ratio. Regression analysis shows that public firms do not significantly underperform private firms in either cost efficiency or revenue efficiency measurement. Our results indicate that gaining easier access to capital is the major motivation for public firms in insurance industry whose operation requires substantial capital supports. 5

7 The remainder of the paper is organized as follows. Section 2 develops the tested hypotheses. Section 3 describes the data and sample selection. Section 4 presents the methodology. Section 5 presents the probit regression analysis on the determinants of decisions to go public and the post-issue efficiency changes of IPO firms. Section 6 presents the difference between public firms and private firms and the regression results on their cost and revenue efficiency, and section 7 concludes the paper. 2. Hypotheses development Theories on firms decision to go public often reply on the assumption that firms will benefit from public trading and more diversified ownership, which outweighs the costs brought by public listing. That is to say, the decision of going public is an ex ante cost-benefit analysis of firms (Brau and Fawcett, 2006; Ritter and Welch, 2002; and Pagano et al., 1998). Going public may improve firm performance ex post if the benefits from operating as a public firm exceed the costs of going public, but the firm may suffer low efficiency if the actual costs outweigh the benefits. In this section, we discuss the motivations, costs and benefits of going public, and formulate hypotheses about the effect of going public on the efficiency of firms based on the existing literature. We also develop hypotheses about firms characteristics that are likely to be associated with their listing decisions. As stated in Pagano et al. (1998), these theories, and their predictions, are not necessarily mutually exclusive. Different motivations often work interactively to produce an IPO issue. Moreover, some theories have similar implications for the decision and performance of firms, and it is an intricate work to disentangle them. However, the design of our analysis does enable us to identify whether public firms are more or less efficient than private firms. 6

8 2.1. Theories favoring going public Literature on corporate finance suggests various motivations for going public, including both financial motivations and private reasons of management. Since the paper focuses on the financial rationale that drives the performance of firms, we leave private considerations of management out of our discussion. 3 The benefits of listing can be categorized as follows Ability to raise equity capital and overcome capital constraint The most apparent reason for firms to go public is to raise capital. Going public will enhance firms ability to raise equity capital as opposed to private equity, debt, or venture capital (Pagano et al., 1998; Chemmanur and Fulghieri, 1999). In an initial offering, firms can raise new capital without the associated risks, restrictions and costs of debt or the constraints set by venture capitalists. After the initial offering, firms may raise additional capital through seasoned equity offerings (SEOs) as needed. Firms may need to raise additional equity capital to sustain or pursue a higher growth rate, finance new investment, or reduce the level of debt in order to survive and thrive in a competitive market. In the insurance industry, equity capital provides cushion for risks taken by the insurance companies and is the backbone of their underwriting capacities. The higher the level of risk, the more demand of equity capital. The adequacy of firms capital is closely monitored by regulators, policyholders and insurance rating firms. Private insurance companies have limited ability to raise new capital in a timely fashion. They 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, 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; and Pagano et al., 1998). 7

9 especially for long-tail lines such as commercial liability insurance, making it difficult or more expensive for private insurers to raise new capital (Chamberlain and Tennyson, 1998). In contrast, it might take much less time and expense for public insurers to raise additional capital. Based on this theory, we predict going public should be more appealing for insurance firms that require more equity capital and they conduct IPOs to raise additional capital and/or ease capital constraint. They tend to be firms that engage in riskier lines of business and therefore with higher volatility in underwriting performance. They should also have higher leverage ratio, higher growth rate, use more reinsurance services and/or with higher agent balances than private firms (Viswanathan and Cummins, 2003). Firms with such characteristics are more sensitive to the shocks of capital adequacy and the ability of raising additional capital is therefore more valuable. In the property-liability insurance industry, long-tail lines usually associate with higher risks, especially lines involving commercial liability because they deal with dynamic risks in the society whose loss payment are dramatically impacted by the changes of laws, scientific discoveries and development. 4 Firms with more business in such lines will therefore desire better access to capital market to raise additional capital once their underwriting capacity is depleted by unexpected losses. Reflected in the underwriting performance, firms with higher proportion of business on riskier lines will also have higher loss ratio. Firms underwriting more business in long-tail lines need to maintain a higher level of loss reserves (liability of the company) and may therefore operate with higher leverage given the capital constraint they face as private firms. Going public and raise additional capital will help bring down the leverage and ease the capital constraint. 4 For example, the U.S. property-liability insurance industry experienced a liability crisis in the mid 1980s partially due to the unexpected claims from the Asbestos liability losses (Graham and Xie, 2007). 8

10 Insurance firms may go public to sustain and support their high growth rate. Firms may experience high growth rate as the result of their comparative advantages, competitive pressure or strategic development policy. Furthermore, if IPO firms underwrite more riskier lines of business, operate with higher leverage or experience higher growth rate, they will also tend to use more reinsurance services to cede out the risks beyond the capacity of the firm Market-timing theory (windows of opportunity) Another theory suggests that firms choose to go public in good markets when equity valuations are high and stay private using private financings in bad markets when equity valuations are low (Pagano, et al., 1998; Rajan and Servaes, 1997; Lerner, 1994; Choe, Masulis, and Nanda, 1993; Ritter, 1991; Lucas and McDonald, 1990). This theory suggests that firms usually wait to go public when a bull market offers more favorable pricing, and they tend to avoid issuing during the periods when they observe few other firms issue. According to this theory, we should observe that firms take advantage of booming stock market and tend to go public when the industry market-to-book ratio is high, and stay private if vice versa. Following Pagano et al. (1998), we proxy the windows of opportunity by the median market-to-book ratio of public companies in the insurance industry and include year dummies in the regression to control for the stock market condition. It is worth noting that this theory does not necessarily predict an improvement of the post-issue operating performance because firms that time the market may not choose to issue at the right time when equity capital is most valuable to the firms Managerial discipline (monitoring) Another potential rationale of going public is to create a managerial discipline device for firms. Going public helps create an external monitoring mechanism of firms that may not 9

11 otherwise exist in private firms. For example, publicly traded firms face the threat of hostile takeovers, and the managers of such firms face a more competitive labor market, which lead to higher pressure for management teams to perform. Moreover, the managerial decisions are more adequately exposed to the market s assessment than those of private firms. Furthermore, the shareholders of a public company can design more efficient compensation contracts by using stock options or stock-price indexed payment that help align the incentive of managers with that of shareholders (Holmström and Tirole, 1993; Schipper and Smith, 1986). We do not test this motivation directly, however, we anticipate better (improved) performance from public firms (IPO firms) than private firms if this hypothesis holds Facilitating acquisition activities Facilitating acquisition activities can also be a motivation to go public (see Brau, et al., 2003; Ritter and Welch, 2002; Zingales, 1995). The theory states that public listing is important in that it can generate public shares and trading price for the firms that make them easier to enter the takeover activities either as an acquirer or as a target. Public firms can make less expensive acquisitions by paying stocks and preserve their cash position for operation and investment. In addition, public targets are normally valued higher than private targets and obtain higher takeover premiums (Faccio, et al., 2006; Chang, 1998). Since we do not study the delisting firms and the acquisition activities, we do not explore the issue in this paper Strategic movement of the company IPOs may serve as strategic moves of companies. Going public may bring the following benefits to firms in addition to the ones mentioned above. First, Going public helps broaden the ownership base of the firm, which may reduce the bargaining power of one large investor (or 5 To get a rough idea, in the U.S. property-liability insurance industry, during , among the 573 transactions, 265 acquirers were public firms; and approximately 50 targets were publicly traded. 10

12 small group of large investors) considerably (Chemmanur and Fulghieri, 1999). Second, going public helps enhance the reputation and publicity of a firm and capture a first-mover advantage (Ritter and Welch, 2002; Maksimovic and Pichler, 2001). The industry analysts and the media pay more attention on public firms than the private firms, which help publicize the company. Moreover, there is evidence that the financial analyst recommendations are often biased upward after an IPO which enhance the reputation of firms (Bradley, et al., 2003). The performance of public firms (IPO firms) may therefore be better (more improved) than private firms because their publicity adds prestige and visibility, as valued by customers, suppliers, employees and the financial community Theories against going public Theories against going public often contend the benefits of going public must be weighted against the challenges and burdens brought by it. These challenges and burdens emerge from various areas, such as the market (information asymmetry), the pre-filing operations, fees and expenses and the post-filing requirements (regulations). The major arguments against not going public are as follows Agency costs and signaling costs Although going public may help create a better external monitoring system, going public means the dilution of the entrepreneur's ownership. When a firm transit from private to public ownership, the management ownership is reduced. Lower management ownership may lead to increased agency costs and create a moral hazard problem (Jensen and Meckling, 1976). In addition, the internal monitoring from shareholders may be impaired after a firm has gone public because the share ownership on a public firm is more dispersed and free-rider problem becomes more serious. The performance of public firms (IPO firms) would therefore suffer 11

13 because of the increased conflict between mangers and shareholders, and the weakened internal monitoring system. Kaplan (1989) and Smith (1990) document improvement in operating performance of firms that transfer from public to private through management buyouts (MBOs), which is explained by the reduced conflicts of interests between managers and owners in a private firm. The operating performance of IPO firms, therefore, are expected to decline as a result of increased conflicts of interests between managers and shareholders resulting from the dilution of the entrepreneur's ownership interests. In addition to the potential increase in agency costs, high signaling costs of going public may also discourage a firm from listing. It is acknowledged that investors are less informed than the insiders about the true value of the companies seeking going public, an adverse selection of going public is therefore expected by the market. To send a positive signal to the market about the high quality of the firm, the original owners may have to retain a significant ownership stake in the firm (Leland and Pyle, 1977), or underprice their IPO shares (Allen and Faulhaber, 1989; Grinblatt and Hwang, 1989; Welch, 1989; Ritter, 1987; Beatty and Ritter, 1986; Rock, 1986). Both signals, among others such as using longer lockup period and certification 6 are expensive and may hurt the objective of diversifying the ownership of the firm and the portfolio diversification of initial owners. As suggested by Chemmanur and Fulghieri (1999) on the going public decision of a firm, small and young firms are more subjected to the costs of information asymmetry, and therefore, successful listing firms tend to be old and large firms. Based on this theory, we predict that the probability of going public is positively related to firm size High pre-offering expenses and fees 6 See Brau and Fawcett (2006) for a summary of literature on the use of certification. 7 We had planned to include age in the model; however, in insurance industry it is common that many individual firms 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). 12

14 Firms may decide not going public because of the high expenses and fees. Such fees would include, but not limited to, the underwriters commission, initial, pre-offering expenses such as expenses for attorneys and accountants, and the significant housekeeping and cleanup work for the Securities and Exchange Commission (SEC) registration. These costs are not trivial to the firms. For example, Ritter (1987) documented that the direct costs of going public are about $250,000 plus additional 7 percent of the gross proceeds of the IPO. Furthermore, Chen and Ritter (2000) found that the costs are not very sensitive to the issue size. The high costs of IPO and their insensitivity to firm size further imply that large firms are more likely to go public than small ones Burden of post-offering duties Firms face additional costs after they become public in, but not limited to, the following areas. First, they have to comply with federal securities laws and various filing requirements. Expenses on administrative and compliance matters and efforts on developing public and investor relations are not trivial. In the U.S., the insurance industry is heavily regulated, with the dominant accounting rule the Statutory Accounting Principles (SAP). After going public, firms have to file based on both SAP and Generally Accepted Accounting Principles (GAPP) that is not required of non-public companies. Second, firms have to disclose details about their operational and financial situations whose secrecy may be crucial for their competitive advantages and potentially valuable to their competitors, such as the investment projects and merger and acquisition strategies (Maksimovic and Pichler, 2001; Yosha, 1995). Third, public firms lose flexibility because they are subject to the scrutiny from the public shareholders and have fiduciary duties to public shareholders. Consequently, such firms may not be able to make prompt strategic decisions because they need to get approval from a diversified group of public 13

15 shareholders. Brau and Fawcett (2006), through surveying a sample of private firms found that the desire to maintain decision-making control is a CFO s (Chief Financial Officer s) most important concern to stay private. Since the costs of operating as public firms are sufficiently high, especially for small size firms, we predict that size is negatively related to the probability of going public. In addition, public firms performance may suffer as the burdens outweigh the benefits. As a summary, we hypothesize that: IPO firms will not underperform private firms if the benefits of going public exceed the related costs; and the signs on the characteristics associated with a firm s likelihood of going public will be consistent with the related theories. In the long run, public firms do not underperform private firms. Because of the limitation of data span ( ), we test the second hypothesis by comparing the public firms that conduct IPO before 1993 with private firms. 3. Data and Sample Selection 3.1. Data source Data used in this analysis come from several sources. The list of publicly traded propertyliability insurance firms is mainly extracted from SNL DataSource, and supplemented by Center for Research in Security Prices (CRSP) and A.M. Best. 8 From these resources, we get a complete list of publicly traded firms (both current and historical) with their primary insurance sector information. 9 We then check with CRSP to get their starting and ending date of listings. The 8 Several Bermuda-based insurance holding firms that list in the U.S. stock exchanges are deleted from the sample because we cannot identify their U.S. insurance subsidiaries. 9 In this paper, we include financial guaranty, mortgage guaranty and surety holding firms in the analysis because NAIC considers all these types of firms as property-liability firms. Multiline firms (such as AIG) are also 14

16 decision-making units in this analysis are groups and unaffiliated firms. 10 If a holding company is publicly traded, the related insurance group of this holding company (with all of its affiliated subsidiaries) is identified as a publicly held firm. We match the insurance group code (NAIC group code) and the public holding company by using information collected from NAIC, A.M. Best, Factiva reports, and Hoovers online company profiles and various other resources. 11 Finally, we collect the financial data of firms from the regulatory annual statements (propertyliability insurance industry) maintained by the National Association of Insurance Commissioners (NAIC). In this study, financial data from are used in the analysis. Table 1 shows the sample size. During the period , we identify 218 publicly traded insurance companies that have property-liability insurance business. Among them, 89 are old listing firms (firms that started listing before 1993 and are publicly traded throughout ); 52 are new listing firms (firms that started listing after 1993 and are publicly traded at least until the end of year 2005); and 77 are delisting firms (firms that are delisted between 1993 and 2005). The new listing firms include 9 demutualization IPO firms and 43 nondemutualization IPO firms. The number of new listing firms and delisting firms by year is also reported in the table Study sample included in the analysis. We also include in the sample property-liability operations of other public insurance firms such as public life-health and managed care firms. Deleting these firms from the sample does not change our results. 10 See Cummins and Xie (2008) for discussion of company affiliations in the insurance industry. 11 In insurance industry, it is common that several subsidiaries are 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). Since our analysis is at group and unaffiliated firm level, we consider the listed parent and the subsidiary as one observation that is represented by the parent company. Research on IPO decision, such as Pagano et al. (1998) treats the listed independent firms and the listed subsidiaries of public parents separately. We think the separation is less demanded in this paper because our analysis is at the group level, and only a couple of cases involve the public subsidiaries of publicly traded companies. 15

17 In this study, because our focus is the performance of public firms and the rationale for going public, we exclude the delisting firms from our sample. 12 We also exclude demutualization IPOs from our sample because demutualization IPO firms convert from mutual organizational form rather than from private stock organizational form. Viswanathan (2006) and Lai et al. (2007) study the two types of IPOs and find significant difference in stock performance between them. Some public insurance holding companies that are classified by SNL as life-health or managed care also have property-liability insurance operation. We keep these firms in our study sample. A robustness test shows that excluding them from the sample do not affect the results. Based on the above selection criteria, our study sample include 88 old listing firms and 43 new listing (Non-Demutualization IPO) firms. The final study sample consists of 74 old listing firms and 33 Non-Demutualization IPO firms for which we could estimate efficiency scores with DEA methodology. 13 The sample size for efficiency change of IPO firms is further reduced because we require financial data for the year prior to IPO and the one or two years following IPO to calculate efficiency change (the (t-1, t+1) and (t-1,t+2) window). Twenty-four IPO firms in our sample have efficiency change value over (t-1, t+1) window and 16 IPO firms have the value for (t-1, t+2) window, where t represents the year in which a firm conducts IPO Private firm sample The control sample in our analysis contains the private firms in the U.S. property-liability insurance industry. We select the private firm sample by the following procedures: 12 The reason of delisting could be another interesting research topic, which is not the focus of this paper. 13 We lose several observations for both old listing sample and new listing sample because of the data do not allow us to calculate the DEA efficiency (which is relatively data intensive). See section 4 for data selection criteria for efficiency estimation. From now on, the IPO firms in the paper refer to Non-demutualization IPO firms. 16

18 (1) We identify all the groups and unaffiliated firms that report property-liability data to NAIC during ; (2) Exclude the 218 public firms from the sample in all years; (3) Exclude the insurance groups and unaffiliated firms whose ultra parents primary businesses are not insurance but are publicly traded; 14 (4) Keep only in the sample the firms operating with stock organizational form. This is because the non-demutualization IPO firms were private stock firms before they went public. Literature on insurance organizational form has shown that 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, Mayers and Smith, 1988), so mutual firms will not serve as proper control sample for stock firms that conduct IPOs. (5) We require that the private firms have continuous operation during our sample period. That means the private firms do not exit the market during our study period. In this way, we control the potential survival bias since we require the old listing firms and the new IPO firms are current firms in the industry. Moreover, continuous operation means these firms have the option to go public if they wish. The above selection criteria return us 195 private stock firms (with 2340 firm years). The number of firm year varies when we come to the efficiency and efficiency change analysis, as shown in the tables because of further constraints we apply when estimating firm efficiency. 4. Methodology This section presents our methodology. After an introduction to the frontier efficiency 14 For example, GE Global insurance group is excluded because its ultra parent (General Electric Company) is a publicly traded conglomerate. 17

19 measurement, including the discussion of DEA efficiency, inputs and prices, outputs and prices, and data criteria for efficiency estimation, we present the model specification on performance of IPO firms and old listing firms Frontier efficiency measurement DEA cost and revenue efficiency The essence of efficiency analysis is to separate well-performed production units from those that perform poorly. This is done by estimating best practice efficient frontiers consisting of the dominant firms in an industry and comparing all firms in the industry to the frontier. Firms operating on the frontier are fully efficient (with efficiency scores of 1) and firms not on the frontier are inefficient (with efficiency scores between 0 and 1). We use data envelopment analysis (DEA), a non-parametric technique, to estimate firm efficiency (Cooper et al., 2000). Non-parametric techniques are superior to econometric efficiency measurement in that it needs no assumptions regarding the production function or cost function and the probability distribution of the error terms, which avoids potential estimation bias caused by model misspecification. Previous study has shown that DEA estimation has good asymptotic statistical properties (Banker, 1993) and is more accurate than econometric approaches in estimating efficiency in the presence of heteroscedasticity (Banker et al., 2004). Moreover, DEA also yields consistent estimators for contextual variables in the two-stage regression with DEA efficiency as the dependent variable (Banker and Natarajan, 2007). 15 We measure cost efficiency and revenue efficiency for each company in the industry. Cost efficiency measures whether a company uses more inputs than a best-practice firm does and whether it selects the optimal combinations of inputs, holding constant input prices and 15 Banker and Natarajan (2007) show that the DEA based two-stage method can still obtain quite useful results when the input variables and contextual variables correlate but the correlation level is between -0.2 to 0.4. The variables used in this study satisfy this condition. 18

20 output quantity. Suppose, for example, there are N firms in the industry, and each firm produces m m outputs = (,,..., ) R with k inputs (,,..., ) y y1 y2 y m + k x x1 x2 x k + = R. Denote the input price k vector as = (,,..., ) R, and the output price vector as (,,..., ) w w1 w2 w k + We calculate firm 0 solving the following linear programming problem: m p p1 p2 p m + = R. * * * * * j s cost-minimizing input vectors x j { x1j, x2 j,..., xrj,..., xkj } = by k * C ( x, y) Min * w, rj x λ x j 0 rj0 0 r= 1 = (1) Subject to N j= 1 N j= 1 λ y y i = 1,..., m λ j ij ij0 = 1,..., * jxrj x r k rj0 λ 0 j = 1,..., N j Cost efficiency (CE) is then defined as k k * rj rj rj rj r= 1 r= 1 CE( x, y) w x w x =, where { 1, 2,...,,..., } x = x x x x is the observed input quantity vector of the firm. j j j rj kj Similarly, a firm s revenue efficiency is estimated by solving the following program: m * R ( xy, ) Max * p, ij y λ y j 0 ij0 0 i= 1 = (2) Subject to N j=1 N j=1 * j ij ij0 j rj rj0 i=1,...,m r=1,...,k λ 0 j=1,...,n j λ y λ x y x 19

21 * * * * * where y = { y, y,..., y,..., y j } and y 0 1j0 2 j0 ij0 mj { } 0 j y1j, y2 j,..., yij,..., ymj = represent firm j s observed output quantity and the revenue-maximizing output quantity vector, respectively. A firm s revenue efficiency is then defined as RE( x, y) m m * p y p y ij0 ij0 ij0 ij0 i= 1 i= 1 =. Fully cost efficient firms have CE = 1 and cost inefficient firms have a CE estimate falling somewhere between zero and one. Similarly, RE is also bounded by zero and one with RE = 1 for fully revenue efficient firms Inputs and input prices To estimate DEA cost and revenue efficiency, we need to specify the inputs, outputs, input prices and output prices for firms. Following previous studies, four inputs are used in this study: administrative labor, agent labor, materials and business services, and financial equity capital (Cummins and Weiss, 2000; Cummins and Nini, 2002; Cummins and Xie, 2008). We impute the quantities of administrative labor, agent labor and business services from the dollar value of expenses. The price for administrative labor and agent labor inputs are defined as the U.S. Department of Labor (DOL) average weekly wage for employees in insurance companies (SIC 6331 and NAICS ) and the DOL average weekly wage 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 price indices for related items are obtained from the DOL and the U.S. Department of Commerce Bureau of Economic Analysis (BEA). We use the year 2000 as the base year for all price indices. The price for financial equity capital is estimated using a similar method described in Cummins et al. (1999b). We estimate the cost of capital for traded insurers in different Best s rating categories using the Fama-French three-factor 20

22 model (Cummins and Phillips, 2005) and assign the cost of capital to non-traded insurers accordingly based on their ratings Outputs and output prices We adopt a modified version of the value-added approach to define insurance outputs (Cummins and Weiss, 2000; Cummins and Xie, 2008). Four insurance outputs are defined for insurance services (risk pooling and risk bearing, and real financial services such as risk management and claim payments), and one intermediary output is defined for the financial intermediary services provided by the insurance company. Following previous studies, we use the present value of losses incurred as insurance output quantity to proxy for the service of risk pooling, risk bearing and real financial services. We classify insurance outputs into four categories: personal lines short-tail losses, personal lines long-tail losses, commercial lines short-tail losses and commercial lines long-tail losses as these lines differ in their risk level and loss development durations. Consistent with theory on risk premium, we define the price for each insurance output as the real loading of the insurance PEi LLEi premium (base year=2000). In other words, output price is defined as Pi =, where P i LLE denotes the price of insurance output i, PE i denotes the real premiums earned from insurance output i, and LLE i denotes the quantity of output i, i.e., real present value of losses and loss adjustment expenses incurred of insurance output i. To control for the potential errors in variables problem when measuring output prices due to the randomness of losses, we apply a smoothing procedure to the four insurance outputs and their prices for each firm in the sample, following Cummins and Xie (2008). i 16 Previous studies have shown that cost efficiency is not sensitive to alternative cost of capital definitions (Cummins et al., 1999b; Cummins and Nini, 2002; Cummins and Xie, 2008). 21

23 The output quantity for intermediation services is measured by the amount of real invested assets. As in Cummins and Xie (2008), 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 Data selection for efficiency estimation The decision-making units of our efficiency analysis consist of group and unaffiliated single insurers in the property-liability insurance industry during the period Originally, the sample consisted of all group and unaffiliated insurers for which data are available from the NAIC. We then eliminated firms with zero or negative net worth, premiums, or inputs, firms with an unrealistic premiums-to-surplus ratios (e.g., ratio greater than 6.2), and firms whose organizational forms are not identified by the NAIC or by A.M. Best. Risk retention groups, U.S. Lloyds, and state worker s compensation fund programs are also excluded from the sample. Since extremely small firms are atypical and may bias the estimation, we eliminated firms whose assets are below $1.5 million. The final sample used to estimate efficiency consists of 9,449 firms over the entire sample period Analysis on firm performance Our analysis on performance of IPO firms and old listing firms proceeds in three steps. The first step examines the factors determining IPO decisions. The objective is to help reveal the motivations underlying IPO issues in the property-liability insurance industry. We estimate a probit model using IPO dummy as a dependent variable, and use firm financial characteristics and other characteristics prior to IPOs (lagged one year value) as independent variables, as specified in the hypothesis section. 22

24 The second step analyzes changes in the efficiency of IPO firms to investigate whether IPO firms underperform private firms. The analysis includes a multivariate regression with efficiency changes as dependent variables and using the IPO dummy as the key explanatory variable, with company characteristics controlled. We estimate the efficiency change in two windows: One year prior to IPO (t-1) to one year after IPO (t+1); and one year prior to IPO (t-1) to two years after IPO (t+2) to see the short-run and relatively long-run effects on firm performance of IPOs. We follow Cummins et al. (1999b) and Cummins and Xie (2008) to define efficiency changes. The final step analyzes whether public firms underperform private firms in the long run by comparing the cost and revenue efficiency of public firms ( old listing firms) and private firms during the whole sample period. We present an OLS regression on firm efficiency, with firm cost efficiency (revenue efficiency) as dependent variable, and public firm dummy as the key explanatory variable, along with other control variables. We realize that a firm s public trading status not only affects insurer efficiency, but may also be affected by other characteristics of firms, thereby raising concerns for a possible endogeneity issue. To address the potential estimation bias created by endogeneity problem, we run a 2SLS regression as a robustness check. We first estimate a probit regression that regresses public firm dummy on exogenous variables similar to the model specification in the probit regression for IPO firms, and enter the predicted value of public firm dummy from the first stage regression into the second stage efficiency regression as an instrumental variable. 5. Results on IPO firms 5.1. Determinants of decisions to go public Summary statistics 23

25 Table 2 provides summary statistics for IPO firms and private firms at the year prior to IPO issue. IPO firms show significant differences from private firms in many aspects. 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 significantly higher leverage (measured by total liability / policyholder surplus) than private firms (2.11 vs. 1.68). IPO firms use more reinsurance services (0.41 vs. 0.25) than private firms, suggesting that they underwrite a larger amount of riskier lines of business and need to expand underwriting capacity. IPO firms also have higher agents balances ratio (0.25 vs. 0.16), which indicates such firms face more capital constraint. These findings are consistent with the hypothesis that firms conduct IPO to raise additional capital or ease capital constraint. We checked the business mixture of IPO firms and private firms, and find IPO firms write significantly more business on long-tail commercial lines (0.55 vs. 0.37) and less business on short-tail commercial lines (0.13 vs. 0.32), which suggests IPO firms engage more in riskier lines. IPO firms are more diversified across business lines (measured by Herfindal index across product line based on net premiums written) and geographical areas (measured by geographical Herfindahl index based on net premiums written) than private firms, implying potential reputation benefit from going public may be more important and dramatic for them. The premium growth of IPO firms is much higher than private firms (1.62 vs. 1.17), indicating their business is expanding and needs stronger capital support. Surprisingly, IPO firms on average have lower return on assets than private firms before going public (0.02 vs. 0.04). 17 Reflecting the risk of their businesses, IPO firms have higher loss ratio (0.73 vs. 0.66), but they 17 The median return on assets of IPO firms and private firms is vs , significant at 1% level. Most of the existing literature on IPO finds IPO firms are better performed than industry-matched (public) firms before the issues (Mikkelson et al., 1997; Jain and Kini, 1994; Degeorge and Zeckhauser, 1993, etc.). 24

26 manage to have a lower expense ratio (0.32 vs. 0.45). We find no significant difference in firm efficiency between the IPO firms and the private firms. The efficiency change statistics show that IPO firm show no significant difference from private firms in the short run, except that their revenue efficiency change is lower than private firms over the (t-1, t+2) period Likelihood of IPO Table 3 presents probit regression for the determinants of issuing IPOs. We estimate two models. The distinction among them is the inclusion of efficiency as an explanatory variable. The three regressions return very consistent results. Consistent with the univariate analysis, large firms, firms with higher leverage, firms with small proportion of short-tail commercial lines and firms with higher premium growth rate are more likely to issue IPO. Agents balances ratio and percentage of business on long-tail commercial lines are positive as expected but not statistically significant. These findings suggest the pursuit/need of capital is a motivation of IPO. The size effect is also consistent with the arguments on burdens and challenges of going public, such as the cost of information asymmetry, high pre-offering expenses and fees and the burden of postoffering duties. Variables measuring the pre-ipo performance of firms include return on assets, firm underwriting performance such as loss ratio and expense ration, and/or cost efficiency (or revenue efficiency). These variables bear little explanatory power on firms decision to go public. The industry market-to-book ratio is not significant, providing no support for window of opportunity or market-timing theory in the insurance industry Efficiency change of IPO firms 25

27 Table 4 presents the multivariate regression analysis on the relationship between efficiency change and IPO to investigate whether IPO firms underperform private firms by controlling the firm characteristics. The dependent variable is the change in firm cost efficiency or revenue efficiency over the (t-1, t+1) window or (t-1, t+2) window. Independent variables are included in the regressions to control for firm characteristics that may be related to efficiency changes (Cummins et al., 1999b; Cummins and Xie, 2008). The overall finding of the regressions is that IPO firms do not underperform private firms after they go public. IPO dummy is not significant in all four regressions. Except for cost efficiency change over (-1, +1) window, IPO dummy has positive signs in the other three regressions. In summary, while the existing IPO literature finds IPO firms underperform ex post (Pagano, et al., 1998), we find no evidence of performance deterioration of IPO firms compared to private firms from our sample. The major rational that motivates IPOs in the property-liability insurance industry is to raise additional capital and ease capital constraints to support firm operation Robustness tests To test the robustness of the results and to provide support for the findings that IPO firms are no less efficient in terms of frontier efficiency, we conducted several robustness checks. The first test involved an expansion of IPO firm sample. Since the delisting firms ( ) sample includes new listings that conducted IPO during the period (but delisted before 2005), we include these firms (6 firms with frontier efficiency available) into the IPO sample. We rerun the summary statistics, the probit analysis and the efficiency change regression. The results are consistent with the findings in this section. We report in the appendix the probit 26