Please provide a maximum one-page summary of how we can analyze the cost of capital for our project (below) vis--visthe peer-reviewed research article attached. Consider risk as you decide. What factors should we consider? What WACC do you come up with for this project?

The question is: our US-based IT firm creates smartphone apps for a North American market,and is considering a new venture. We would bepartnering with aJapanese firm to design and market a PC-based computer game for the Japanese and South Korean markets. This is a $10 million (USD)investment for additional staff and expansion of space, and would represent a major capital investment for our small firm. The firm's current net revenues are about $2 million a year. The totalreturn to investors on the stock is about 14% a year, we can borrow at about 7% a year, our capital structure is 90:10 stocks to bonds.

Journal of Financial Economics 60 (2001) 187}243 The theory and practice of corporate "nance: evidence from the "eld John R. Graham , Campbell R. Harvey * Fuqua School of Business, Duke University, Durham, NC 27708, USA National Bureau of Economic Research, Cambridge, MA 02912, USA Received 2 August 1999; received in revised form 10 December 1999 Abstract We survey 392 CFOs about the cost of capital, capital budgeting, and capital structure. Large "rms rely heavily on present value techniques and the capital asset pricing model, while small "rms are relatively likely to use the payback criterion. A surprising number of "rms use "rm risk rather than project risk in evaluating new investments. Firms are concerned about "nancial #exibility and credit ratings when issuing debt, and earnings We thank Franklin Allen for his detailed comments on the survey instrument and the overall project. We appreciate the input of Chris Allen, J.B. Heaton, Craig Lewis, Cli! Smith, Jeremy Stein, Robert Taggart, and Sheridan Titman on the survey questions and design. We received expert survey advice from Lisa Abendroth, John Lynch, and Greg Stewart. We thank Carol Bass, Frank Ryan, and Fuqua MBA students for help in gathering the data, and Kathy Benton, Steve Fink, Anne Higgs, Ken Rona, and Ge Zhang for computer assistance. The paper has bene"ted from comments made by an anonymous referee, the editor (Bill Schwert), as well as Michael Bradley, Alon Brav, Susan Chaplinsky, Magnus Dahlquist, Gene Fama, Paul Gompers, Ravi Jagannathan, Tim Opler, Todd Pulvino, Nathalie Rossiensky, Rick Ruback, David Smith, Rene Stulz, and seminar particiH pants at the Harvard Business School/Journal of Financial Economics Conference on the interplay between theoretical, empirical, and "eld research in "nance, the 2000 Utah Winter Finance Conference, the University of Wisconsin and the 2001 American Finance Association Meetings. Finally, we thank the executives who took the time to "ll out the survey. This research is partially sponsored by the Financial Executives Institute (FEI). The opinions expressed in the paper do not necessarily represent the views of FEI. Graham acknowledges "nancial support from the Alfred P. Sloan Research Foundation. Some supplementary research results are available at http://www.duke.edu/&charvey/Research/indexr.htm. * Corresponding author. Fuqua School of Business, Duke University, Durham, NC 27708, USA. Tel.: #1-919-660-7768; fax: #1-919-660-7971. E-mail address: cam.harvey@duke.edu (C.R. Harvey). 0304-405X/00/$ - see front matter 2001 Published by Elsevier Science S.A. PII: S 0 3 0 4 - 4 0 5 X ( 0 1 ) 0 0 0 4 4 - 7 188 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 per share dilution and recent stock price appreciation when issuing equity. We "nd some support for the pecking-order and trade-o! capital structure hypotheses but little evidence that executives are concerned about asset substitution, asymmetric information, transactions costs, free cash #ows, or personal taxes. 2001 Published by Elsevier Science S.A. JEL classixcation: G31; G32; G12 Keywords: Capital structure; Cost of capital; Cost of equity; Capital budgeting; Discount rates; Project valuation; Survey 1. Introduction In this paper, we conduct a comprehensive survey that describes the current practice of corporate "nance. Perhaps the best-known "eld study in this area is John Lintner's (1956) path-breaking analysis of dividend policy. The results of that study are still quoted today and have deeply a!ected the way that dividend policy research is conducted. In many respects, our goals are similar to Lintner's. We hope that researchers will use our results to develop new theories } and potentially modify or abandon existing views. We also hope that practitioners will learn from our analysis by noting how other "rms operate and by identifying areas where academic recommendations have not been fully implemented. Our survey di!ers from previous surveys in a number of dimensions.\u0010 First, the scope of our survey is broad. We examine capital budgeting, cost of capital, and capital structure. This allows us to link responses across areas. For example, we investigate whether "rms that consider "nancial #exibility to be a capital structure priority are also likely to value real options in capital budgeting decisions. We explore each category in depth, asking more than 100 total questions. Second, we sample a large cross-section of approximately 4,440 "rms. In total, 392 chief "nancial o$cers responded to the survey, for a response rate of 9%. The next largest survey that we know of is Moore and Reichert (1983) who study 298 large "rms. We investigate for possible nonresponse bias and conclude that our sample is representative of the population. \u0010 See, for example, Lintner (1956), Gitman and Forrester (1977), Moore and Reichert (1983), Stanley and Block (1984), Baker et al. (1985), Pinegar and Wilbricht (1989), Wansley et al. (1989), Sangster (1993), Donaldson (1994), Epps and Mitchem (1994), Poterba and Summers (1995), Billingsley and Smith (1996), Shao and Shao (1996), Bodnar et al. (1998), Bruner et al. (1998) and Block (1999). J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 189 Third, we analyze the responses conditional on "rm characteristics. We examine the relation between the executives' responses and "rm size, P/E ratio, leverage, credit rating, dividend policy, industry, management ownership, CEO age, CEO tenure, and the education of the CEO. By testing whether responses di!er across these characteristics, we shed light on the implications of various corporate "nance theories related to "rm size, risk, investment opportunities, transaction costs, informational asymmetry, and managerial incentives. This analysis allows for a deeper investigation of corporate "nance theories. For example, we go beyond asking whether "rms follow a "nancial pecking order (Myers and Majluf, 1984). We investigate whether the "rms that most strongly support the implications of the pecking-order theory are also the "rms most a!ected by informational asymmetries, as suggested by the theory. Survey-based analysis complements other research based on large samples and clinical studies. Large sample studies are the most common type of empirical analysis, and have several advantages over other approaches. Most largesample studies o!er, among other things, statistical power and cross-sectional variation. However, large-sample studies often have weaknesses related to variable speci"cation and the inability to ask qualitative questions. Clinical studies are less common but o!er excellent detail and are unlikely to `average awaya unique aspects of corporate behavior. However, clinical studies use small samples and their results are often sample-speci"c. The survey approach o!ers a balance between large sample analyses and clinical studies. Our survey analysis is based on a moderately large sample and a broad cross-section of "rms. At the same time, we are able to ask very speci"c and qualitative questions. The survey approach is not without potential problems, however. Surveys measure beliefs and not necessarily actions. Survey analysis faces the risk that the respondents are not representative of the population of "rms, or that the survey questions are misunderstood. Overall, survey analysis is seldom used in corporate "nancial research, so we feel that our paper provides unique information to aid our understanding of how "rms operate. The results of our survey are both reassuring and surprising. On one hand, most "rms use present value techniques to evaluate new projects. On the other hand, a large number of "rms use company-wide discount rates to evaluate these projects rather than a project-speci"c discount rate. Interestingly, the survey indicates that "rm size signi"cantly a!ects the practice of corporate "nance. For example, large "rms are signi"cantly more likely to use net present value techniques and the capital asset pricing model for project evaluation than are small "rms, while small "rms are more likely to use the payback criterion. A majority of large "rms have a tight or somewhat tight target debt ratio, in contrast to only one-third of small "rms. Executives rely heavily on practical, informal rules when choosing capital structure. The most important factors a!ecting debt policy are "nancial #exibility and a good credit rating. When issuing equity, respondents are concerned 190 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 about earnings per share dilution and recent stock price appreciation. We "nd very little evidence that executives are concerned about asset substitution, asymmetric information, transactions costs, free cash #ows, or personal taxes. We acknowledge but do not investigate the possibility that these deeper implications are, for example, impounded into prices and credit ratings, and so executives react to them indirectly. The paper is organized as follows. In the second section, we present the survey design, the sampling methodology, and discuss some caveats of survey research. In the third section we study capital budgeting. We analyze the cost of capital in the fourth section. In the "fth section we examine capital structure. We o!er some concluding remarks in the "nal section. 2. Methodology 2.1. Design Our survey focuses on three areas: capital budgeting, cost of capital, and capital structure. Based on a careful review of the existing literature, we developed a draft survey that was circulated to a group of prominent academics for feedback. We incorporated their suggestions and revised the survey. We then sought the advice of marketing research experts on the survey design and execution. We made changes to the format of the questions and overall survey design with the goal of minimizing biases induced by the questionnaire and maximizing the response rate. The survey project is a joint e!ort with the Financial Executives Institute (FEI). FEI has approximately 14,000 members that hold policy-making positions as CFOs, treasurers, and controllers at 8,000 companies throughout the U.S. and Canada. Every quarter, Duke University and the FEI poll these "nancial o$cers with a one-page survey on important topical issues (Graham, 1999b). The usual response rate for the quarterly survey is 8}10%. Using the penultimate version of the survey, we conducted beta tests at both FEI and Duke University. This involved having graduating MBA students and "nancial executives "ll out the survey, note the required time, and provide feedback. Our beta testers took, on average, 17 minutes to complete the survey. Based on this and other feedback, we made "nal changes to the wording on some questions. The "nal version of the survey contained 15 questions, most with subparts, and was three pages long. One section collected demographic information about the sample "rms. The survey instrument appears on the Internet at the address http://www.duke.edu/&charvey/Research/indexr.htm. We sent out two di!erent versions with questions 11}14 and questions 1}4 interchanged. We were concerned that the respondents might "ll in the "rst page or two of the survey J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 191 but leave the last page blank. If this were the case, we would expect to see a higher proportion of respondents answering the questions that appear at the beginning of either version of the survey. We "nd no evidence that the response rate di!ers depending on whether the questions are at beginning or the end of the survey. 2.2. Delivery and response We used two mechanisms to deliver the survey. We sent a mailing from Duke University on February 10, 1999 to each CFO in the 1998 Fortune 500 list. Independently, the FEI faxed out 4,440 surveys to their member "rms on February 16, 1999. Three hundred thirteen of the Fortune 500 CFOs belong to the FEI, so these "rms received both a fax and a mailed version. We requested that the surveys be returned by February 23, 1999. To encourage the executives to respond, we o!ered an advanced copy of the results to interested parties. We employed a team of 10 MBA students to follow up on the mailing to the Fortune 500 "rms with a phone call and possible faxing of a second copy of the survey. On February 23, FEI refaxed the survey to the 4,440 FEI corporations and we remailed the survey to the Fortune 500 "rms, with a new due date of February 26, 1999. This second stage was planned in advance and designed to maximize the response rate. The executives returned their completed surveys by fax to a third-party data vendor. Using a third party ensures that the survey responses are anonymous. We feel that anonymity is important to obtain frank answers to some of the questions. Although we do not know the identity of the survey respondents, we do know a number of "rm-speci"c characteristics, as discussed below. Three hundred ninety-two completed surveys were returned, for a response rate of nearly 9%. Given the length (three pages) and depth (over 100 questions) of our survey, this response rate compares favorably to the response rate for the quarterly FEI-Duke survey.The rate is also comparable to other recent academic surveys. For example, Trahan and Gitman (1995) obtain a 12% response rate in a survey mailed to 700 CFOs. The response rate is higher (34%) in Block (1999), but he targets Chartered Financial Analysts - not senior o$cers of particular "rms. 2.3. Summary statistics and data issues Fig. 1 presents summary information about the "rms in our sample. The companies range from very small (26% of the sample "rms have sales of less than $100 million) to very large (42% have sales of at least $1 billion) (see Fig. 1A). In subsequent analysis, we refer to "rms with revenues greater than $1 billion as `largea. Forty percent of the "rms are manufacturers (Fig. 1C). The nonmanufacturing "rms are evenly spread across other industries, including 192 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 "nancial (15%), transportation and energy (13%), retail and wholesale sales (11%), and high-tech (9%). In the appendix, we show that the responding "rms are representative of the corporate population for size, industry, and other characteristics. The median price}earnings ratio is 15. Sixty percent of the respondents have price}earnings ratios of 15 or greater (Fig. 1D). We refer to these "rms as growth Fig. 1. Sample characterstics based on the survey respponses of 392 CFOs. J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 193 Fig. 1. (continued). "rms when we analyze how investment opportunities a!ect corporate behavior. We refer to the remaining 40% of the respondents as nongrowth "rms. The distribution of debt levels is fairly uniform (Fig. 1E). Approximately one-third of the sample "rms have debt-to-asset ratios below 20%, another third have debt ratios between 20% and 40%, and the remaining "rms have debt ratios greater than 40%. We refer to "rms with debt ratios greater than 30% as 194 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 highly levered. The creditworthiness of the sample is also dispersed (Fig. 1F). Twenty percent of the companies have credit ratings of AA or AAA, 32% have an A credit rating, and 27% have a BBB rating. The remaining 21% have speculative debt with ratings of BB or lower. Though our survey respondents are CFOs, we ask a number of questions about the characteristics of the chief executive o$cers. We assume that the CFOs act as agents for the CEOs. Nearly half of the CEOs for the responding "rms are between 50 and 59 years old (Fig. 1I). Another 23% are over age 59, a group we refer to as `mature.a Twenty-eight percent of the CEOs are between the ages of 40 and 49. The survey reveals that executives change jobs frequently. Nearly 40% of the CEOs have been in their jobs less than four years, and another 26% have been in their jobs between four and nine years (Fig. 1J). We de"ne the 34% who have been in their jobs longer than nine years as having `long tenurea. Forty-one percent of the CEOs have an undergraduate degree as their highest level of educational attainment (Fig. 1K). Another 38% have an MBA and 8% have a non-MBA masters degree. Finally, the top three executives own at least 5% of the common stock of their "rm in 44% of the sample. These CEO characteristics allow us to examine whether managerial incentives or entrenchment a!ect the survey responses. We also study whether having an MBA a!ects the choices made by corporate executives. Fig. 1M shows that 36% of the sample "rms seriously considered issuing common equity, 20% considered issuing convertible debt, and 31% thought about issuing debt in foreign markets. Among responding "rms, 64% calculate the cost of equity, 63% have publicly traded common stock, 53% issue dividends, and 7% are regulated utilities (Fig. 1N). If issuing dividends is an indication of a reduced informational disadvantage for investors relative to managers (Sharpe and Nguyen, 1995), the dividend issuance dichotomy allows us to examine whether the data support corporate theories based on informational asymmetry. Table 1 presents correlations for the demographic variables. Not surprisingly, small companies have lower credit ratings, a higher proportion of management ownership, a lower incidence of paying dividends, a higher chance of being privately owned, and a lower proportion of foreign revenue. Growth "rms are likely to be small, have lower credit ratings, and have a higher degree of management ownership. Firms that do not pay dividends have low credit ratings. Below, we perform univariate analyses on the survey responses conditional on each separate "rm characteristic. However, because size is correlated with a number of di!erent factors, we perform a robustness check for the nonsize characteristics. We split the sample into large "rms versus small "rms. On each size subsample, we repeat the analysis of the responses conditional on "rm characteristics other than size. We generally only report the "ndings with respect to nonsize characteristics if they hold on the full sample and the two size J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 195 196 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 subsamples. We also perform a separate robustness check relative to public versus private "rms and only report the characteristic-based results if they hold for the full and public samples. The tables contain the full set of results, including those that do not pass these robustness checks. All in all, the variation in executive and "rm characteristics permits a rich description of the practice of corporate "nance, and allows us to infer whether corporate actions are consistent with academic theories. We show in the appendix that our sample is representative of the population from which it was drawn, fairly representative of Compustat "rms, and not adversely a!ected by nonresponse bias. 3. Capital budgeting methods 3.1. Design This section studies how "rms evaluate projects. Previous surveys mainly focus on large "rms and suggest that internal rate of return (IRR) is the primary method for evaluation. For example, Gitman and Forrester (1977), in their survey of 103 large "rms, "nd that only 9.8% of "rms use net present value as their primary method and 53.6% report IRR as primary method. Stanley and Block (1984) "nd that 65% of respondents report IRR as their primary capital budgeting technique. Moore and Reichert (1983) survey 298 Fortune 500 "rms and "nd that 86% use some type of discounted cash #ow analysis. Bierman (1993) "nds that 73 of 74 Fortune 100 "rms use some type of discounted cash #ow analysis. These results are similar to the "ndings in Trahan and Gitman (1995), who survey 84 Fortune 500 and Forbes 200 best small companies, and Bruner et al. (1998), who interview 27 highly regarded corporations. (See http://www.duke.edu/ &charvey/Research/indexr.htm for a review of the capital budgeting literature.) Our survey di!ers from previous work in several ways. The most obvious di!erence is that previous work almost exclusively focuses on the largest "rms. Second, given that our sample is larger than all previous surveys, we are able to control for many di!erent "rm characteristics. Finally, we go beyond NPV versus IRR analysis and ask whether "rms use the following evaluation techniques: adjusted present value (see Brealey and Myers, 1996), payback period, discounted payback period, pro"tability index, and accounting rate of return. We also inquire whether "rms bypass discounting techniques and simply use earnings multiples. A price-earnings approach can be thought of as measuring the number of years it takes for the stock price to be paid for by earnings, and therefore can be interpreted as a version of the payback method. We are also interested in whether "rms use other types of analyses that are taught in many MBA programs, such as simulation analysis and value at risk (VaR). Finally, we J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 197 are interested in the importance of real options in project evaluation (see Myers, 1977). 3.2. Results Respondents are asked to score how frequently they use the di!erent capital budgeting techniques on a scale of 0 to 4 (0 meaning `nevera, 4 meaning `alwaysa). In many respects, the results di!er from previous surveys, perhaps because we have a more diverse sample. An important caveat here, and throughout the survey, is that the responses represent beliefs. We have no way of verifying that the beliefs coincide with actions. Most respondents select net present value and internal rate of return as their most frequently used capital budgeting techniques (see Table 2); 74.9% of CFOs always or almost always (responses of 4 and 3) use net present value (rating of 3.08); and 75.7% always or almost always use internal rate of return (rating of 3.09). The hurdle rate is also popular. These results are summarized in Fig. 2. The most interesting results come from examining the responses conditional on "rm and executive characteristics. Large "rms are signi"cantly more likely to use NPV than small "rms (rating of 3.42 versus 2.83). There is no di!erence in techniques used by growth and nongrowth "rms. Highly levered "rms are signi"cantly more likely to use NPV and IRR than are "rms with small debt Fig. 2. Survey evidence on the popularity of di!erent capital budgeting methods. We report the percentage of CFOs who always or almost always use a particular technique. IRR represents internal rate of return, NPV is net present value, P/E is the price-to-earnings ratio, and APV is adjusted present value. The survey is based on the responses of 392 CFOs. (b) Internal rate of return (a) Net present value (f) Payback period (c) Hurdle rate (j) Sensitivity analysis (e.g., `gooda vs. `faira vs. `bada) (d) Earnings multiple approach (g) Discounted payback period (l) We incorporate the `real optionsa of a project when evaluating it (i) Accounting rate of return (or book rate of return on assets) (k) Value-at-risk or other simulation analysis (e) Adjusted present value (h) Pro"tability index 3.09 3.08 2.53 2.48 2.31 1.89 1.56 1.47 1.34 0.95 0.85 0.83 38.92 29.45 26.59 20.29 13.66 10.78 11.87 Mean 75.61 74.93 56.74 56.94 51.54 % always or almost always 0.76 0.93 0.88 1.41 1.79 1.58 1.40 2.87 2.83 2.72 2.13 2.13 Small 1.22*** 0.72* 0.75 1.25 2.01* 1.55 1.57 3.41*** 3.42*** 2.25*** 2.95*** 2.56*** Large Size 0.84 0.97 0.73 1.43 1.97 1.52 1.31 3.36 3.30 2.55 2.78 2.35 Growth P/E 0.86 0.69** 0.81 1.19 2.11 1.67 1.55 3.36 3.27 2.41 2.87 2.41 Non-G 3.36*** 3.39*** 2.46 2.63** 2.56*** High 0.78 1.10*** 0.87 0.80 0.74 0.96* 1.34 1.32 1.67 2.12*** 1.49 1.64 1.50 1.41 2.85 2.84 2.58 2.27 2.10 Low Leverage 1.09 0.80 0.66 1.22 1.90 1.84 1.34 3.52 3.47 2.48 3.01 2.60 Yes 1.04 0.79 0.67 1.21 2.22* 1.49* 1.61 3.35 3.38 2.36 2.92 2.62 No Investment grade 2.68*** 2.76*** 2.63 2.06*** 2.17** No 1.04 0.82** 0.80 0.91 0.81 0.83 1.40 1.27 1.88 1.88 1.54 1.62 1.37 1.52 3.43 3.35 2.46 2.84 2.42 Yes Pay dividends Table 2 Survey responses to the question: how frequently does your "rm use the following techniques when deciding which projects or acquisitions to pursue? 0.95 0.78 0.90 1.36 1.85 1.61 1.49 3.19 3.23 2.68 2.60 2.35 0.92 0.92 0.76 1.34 2.00 1.50 1.45 2.94** 2.82*** 2.33*** 2.29** 2.24 Manu. Others Industry 0.95 0.79 0.81 1.30 1.85 1.49 1.40 3.34 3.35 2.39 2.70 2.37 Low 0.86 0.99* 0.98 1.44 2.04 1.76* 1.52 2.85*** 2.77*** 2.70** 2.12*** 2.18 High Management own 198 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 1.89 1.56 1.47 1.34 0.95 0.85 0.83 38.92 29.45 26.59 20.29 13.66 10.78 11.87 1.07 1.18 0.87 1.49 2.25 1.94 1.68 3.08 2.83 2.88 2.20 3.21 '59 2.90 2.80 2.39 2.20 2.97 Long 1.39 0.90 0.92 0.75*** 0.88 0.83 0.95 1.33 0.93 0.80 0.77* 1.34 1.86 1.46* 1.36 3.17** 2.37*** 2.51 2.37 3.16* Short CEO tenure 1.79** 1.93 1.48*** 1.72 1.40* 1.56 3.09 2.43*** 2.38*** 2.36 3.06 Ynger CEO age 0.99 0.74 0.83 1.42 1.98 1.68 1.49 3.17 2.48 2.57 2.41 3.17 Yes 0.88 0.91* 0.85 1.29 1.86 1.49 1.39 3.00* 2.55 2.42 2.25 3.03 No CEO MBA No 3.07** 2.56** 2.42** 2.26*** 1.76 0.89* 0.67 0.86 0.57 0.85 1.76 1.30* 1.62 1.90 1.52 1.60 0.95 1.48* 3.50 2.05 3.18 3.14 3.76 3.04*** Yes Regulated 0.77 0.88 0.75 1.30 1.85 1.57 1.44 2.99 2.65 2.33 2.24 3.03 No 1.31 2.08 1.56 1.40 3.24 2.45 2.70 2.37 3.27 Yes 3.38 2.62 2.56 2.36 3.31 Yes 1.01 0.90 1.00** 1.43 0.90 0.74 0.81 1.27 0.96 0.89 0.83 1.38 1.84 1.53 1.43 2.95*** 2.49 2.43 2.28 3.01** No Foreign sales 1.56*** 1.98 1.60 1.62 1.59 1.53 2.78*** 2.67* 2.10*** 2.18 2.77*** No Public corp. 1.12*** 0.89 0.81 0.83 0.99** 0.76 1.39 1.96 1.61 1.46 3.23** 2.43* 2.64** 2.43 3.18 Yes Target debt ratio Yes 3.60*** 2.35 3.28*** 2.76*** 0.86 1.36*** 0.86 0.80 0.85 0.75 1.36 1.26 1.83 2.15* 1.51 1.84* 1.44 1.57 2.97 2.57 2.30 2.22 3.00 3.57*** No Fortune 500 mailing Respondents are asked to rate on a scale of 0 (never) to 4 (always). We report the overall mean as well as the % of respondents that answered 3 (almost always) or 4 (always). ***, **, * denotes a signi"cant di!erence at the 1%, 5%, and 10% level, respectively. All table columns are de"ned in Table 1. 3.08 2.53 2.48 2.31 74.93 56.74 56.94 51.54 (a) Net present value (f) Payback period (c) Hurdle rate (j) Sensitivity analysis (e.g., `gooda vs. `faira vs. `bada) (d) Earnings multiple approach (g) Discounted payback period (l) We incorporate the `real optionsa of a project when evaluating it (i) Accounting rate of return (or book rate of return on assets) (k) Value-at-risk or other simulation analysis (e) Adjusted present value (h) Pro"tability index 3.09 75.61 Mean (b) Internal rate of return % always or almost always J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 199 200 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 ratios. This is not just an artifact of "rm size. In unreported analysis, we "nd a signi"cant di!erence between high- and low-leverage small "rms as well as high- and low-leverage large "rms. Interestingly, highly levered "rms are also more likely to use sensitivity and simulation analysis. Perhaps because of regulatory requirements, utilities are more likely to use IRR and NPV and perform sensitivity and simulation analyses. We also "nd that CEOs with MBAs are more likely than non-MBA CEOs to use net present value, but the di!erence is only signi"cant at the 10% level. Firms that pay dividends are signi"cantly more likely to use NPV and IRR than are "rms that do not pay dividends. This result is also robust to our analysis by size. Public companies are signi"cantly more likely to use NPV and IRR than are private corporations. As the correlation analysis indicates in Table 1, many of these attributes are correlated. For example, private corporations are also smaller "rms. Other than NPV and IRR, the payback period is the most frequently used capital budgeting technique (rating of 2.53). This is surprising because "nancial textbooks have lamented the shortcomings of the payback criterion for decades. (Payback ignores the time value of money and cash #ows beyond the cuto! date; the cuto! is usually arbitrary.) Small "rms use the payback period (rating of 2.72) almost as frequently as they use NPV or IRR. In untabulated analysis, we "nd that among small "rms, CEOs without MBAs are more likely to use the payback criterion. The payback is most popular among mature CEOs (rating of 2.83). In separate examinations of small and large "rms, we "nd that mature CEOs use payback signi"cantly more often than younger CEOs. Payback is also frequently used by CEOs with long tenure (rating of 2.80). Few "rms use the discounted payback (rating of 1.56), a method that eliminates one of the payback criterion's de"ciencies by accounting for the time value of money. It is sometimes argued that the payback approach is rational for severely capital constrained "rms: if an investment project does not pay positive cash #ows early on, the "rm will cease operations and therefore not receive positive cash #ows that occur in the distant future, or else will not have the resources to pursue other investments during the next few years (Weston and Brigham, 1981, p. 405). We do not "nd any evidence to support this claim because we "nd no relation between the use of payback and leverage, credit ratings, or dividend policy. Our "nding that payback is used by older, longer-tenure CEOs without MBAs instead suggests that lack of sophistication is a driving factor behind the popularity of the payback criterion. McDonald (1998) notes that rules of thumb such as payback and hurdle rates can approximate optimal decision rules that account for the option-like features of many investments, especially in the evaluation of very uncertain investments. If small "rms have more volatile projects than do large "rms, this could explain why small "rms use these ad hoc decision rules. It is even possible that small "rms use these rules not because they realize that they approximate the optimal rule but simply because the rules have worked in the past. J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 201 A number of "rms use the earnings multiple approach for project evaluation. There is weak evidence that large "rms are more likely to employ this approach than are small "rms. We "nd that a "rm is signi"cantly more likely to use earnings multiples if it is highly levered. The in#uence of leverage on the earnings multiple approach is also robust across size (i.e., highly levered "rms, whether they are large or small, frequently use earnings multiples). In summary, compared to previous research, our results suggest increased prominence of net present value as an evaluation technique. In addition, the likelihood of using speci"c evaluation techniques is linked to "rm size, "rm leverage, and CEO characteristics. In particular, small "rms are signi"cantly less likely to use net present value. They are also less likely to use supplementary sensitivity and VaR analyses. The next section takes this analysis one step further by detailing the speci"c methods "rms use to obtain the cost of capital, the most important risk factors, and a speci"c capital budgeting scenario. 4. Cost of capital 4.1. Methodology Our "rst task is to determine how "rms calculate the cost of equity capital. We explore whether "rms use the capital asset pricing model (CAPM), a multibeta CAPM (with extra risk factors in addition to the market beta), average historical returns, or a dividend discount model. The results in Table 3 and summarized in Fig. 3 indicate that the CAPM is by far the most popular method of estimating the cost of equity capital: 73.5% of respondents always or almost always use the CAPM (rating of 2.92; see also Fig. 1H). The second and third most popular methods are average stock returns and a multibeta CAPM, respectively. Few "rms back the cost of equity out from a dividend discount model (rating of 0.91). This sharply contrasts with the "ndings of Gitman and Mercurio (1982) who survey 177 Fortune 1000 "rms and "nd that only 29.9% of respondents use the CAPM `in some fashiona but "nd that 31.2% of the participants in their survey use a version of the dividend discount model to establish their cost of capital. More recently, Bruner et al. (1998) "nd that 85% of their 27 best-practice "rms use the CAPM or a modi"ed CAPM. While the CAPM is popular, we show later that it is not clear that the model is applied properly in practice. Of course, even if it is applied properly, it is not clear that the CAPM is a very good model (see Fama and French, 1992). The cross-sectional analysis is particularly illuminating. Large "rms are much more likely to use the CAPM than are small "rms (rating of 3.27 versus 2.49, respectively). Smaller "rms are more inclined to use a cost of equity capital that 2.92 1.72 1.56 0.91 0.86 0.44 39.41 34.29 15.74 13.93 7.04 Mean 73.49 1.22 0.37 0.96 1.80 1.39 2.49 0.90 1.65 1.62 3.19 0.87 0.47 0.76 0.32 1.02 0.39 1.54*** 1.70 1.48* 1.66 0.82** 1.05 2.83 Long 2.43 1.91 1.21 Ynger 0.44** 0.32* 1.02 1.78 1.48 3.03 Non-G 0.79 0.43 1.73 1.49 0.83 2.96 Short CEO tenure 2.93 '59 CEO age P/E Growth 0.54*** 0.76 0.50 0.56 0.87 1.65 1.70* 3.27*** Large 2.85 Small Size 0.72 0.41 1.53 1.62 0.78 3.08 Yes Yes 1.67 1.71 0.88 0.36 0.99* 0.47 1.90* 1.48 1.02* 2.77* No 0.79* 0.44 0.98 1.48 1.76 0.69 2.19 1.60 2.17 1.20 3.00 Yes 2.83 No 0.87 0.94 0.28*** 0.49 0.98 1.60 1.69 3.02 0.81 0.43 1.80 1.49 0.92 3.03 Yes 0.67 0.49 1.65 1.56 0.99 3.13 Yes 1.62 1.57 0.81 3.23 Yes 1.80 1.38 0.90 2.78 No 0.97** 0.96 0.55*** 0.37 1.78 1.52 0.97 2.75** No 0.46** 0.71** 1.38* 2.17*** 0.95 3.46*** Yes Fortune 500 mailing 1.23*** 0.41 1.10 1.87 1.44 2.36*** High Foreign sales 0.65 0.51 0.97 1.66 1.59 3.26 Low Management ownership 1.53*** 0.65 0.27* 0.20 1.91 1.53 0.68* 2.13*** No Public corp. 0.97 0.44 0.80 1.84 1.49 2.87 Manu. Others Industry 1.12** 0.80 0.24** 0.44 0.95 1.63 1.54 2.76 No Target debt ratio 0.70 0.54 0.90 1.77 1.51 3.00 Yes Pay dividends 1.70 1.64 1.41** 1.53 0.88 0.93 2.87 No Regulated 0.48 0.51 1.05** 0.92 1.56 1.45 3.34 No Investment grade 3.23*** 3.13 High CEO MBA 0.92 0.48 0.72 1.80 1.57 2.57 Low Leverage Respondents are asked to rate on a scale of 0 (never) to 4 (always). We report the overall mean as well as the % of respondents that answered 3 (almost always) and 4 (always). ***, **, * denotes a signi"cant di!erence at the 1%, 5% and 10% level, respectively. All table columns are de"ned in Table 1. (b) using the capital asset pricing model (CAPM, the `betaa approach) (a) with average historical returns on common stock (c) using the CAPM but including some extra `risk factorsa (f) back out from discounted dividend/earnings model, e.g., Price"Div./(cost of cap. } growth) (d) whatever our investors tell us they require (e) by regulatory decisions (b) using the capital asset pricing model (CAPM, the `betaa approach) (a) with average historical returns on common stock (c) using the CAPM but including some extra `risk factorsa (f) back out from discounted dividend/earnings model, e.g., Price"Div./(cost of cap. } growth) (d) whatever our investors tell us they require (e) by regulatory decisions % always or almost always Table 3 Survey responses to the question: does your "rm estimate the cost of equity capital? (If `noa, please go to next question). If `yesa, how do you determine your "rm's cost of equity capital? 202 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 203 Fig. 3. Survey evidence on the popularity of di!erent methods of calculat the cost of equity capital. We report the percentage of CFOs who always or almost always use a particular technique. CAPM represents the capital asset pricing model. The survey is based on the responses of 392 CFOs. is determined by `what investors tell us they requirea. CEOs with MBAs are more likely to use the single-factor CAPM or the CAPM with extra risk factors than are non-MBA CEOs, but the di!erence is only signi"cant for the singlefactor CAPM. We also "nd that "rms with low leverage or small management ownership are signi"cantly more likely to use the CAPM. We "nd signi"cant di!erences for private versus public "rms (public more likely to use the CAPM). This is perhaps expected given that the beta of the private "rm could only be calculated via analysis of comparable publicly traded "rms. Finally, we "nd that "rms with high foreign sales are more likely to use the CAPM. Given the sharp di!erence between large and small "rms, it is important to check whether some of these control e!ects just proxy for size. It is, indeed, the case that foreign sales proxy for size. Table 1 shows that that there is a signi"cant correlation between percent of foreign sales and size. When we analyze the use of the CAPM by foreign sales controlling for size, we "nd no signi"cant di!erences. However, this is not true for some of the other control variables. There is a signi"cant di!erence in use of the CAPM across leverage that is robust to size. The public/private e!ect is also robust to size. Finally, the di!erence in the use of the CAPM based on management ownership holds for small "rms but not for large "rms. That is, among small "rms, CAPM use is inversely related to managerial ownership. There is no signi"cant relation for larger "rms. 204 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 4.2. Specixc risk factors Table 4 investigates sources of risk other than market risk, and how they are treated in project evaluation. The list of risk factors includes the fundamental factors in Fama and French (1992), and momentum as de"ned in Jegadeesh and Titman (1993), as well as the macroeconomic factors in Chen et al. (1986) and Ferson and Harvey (1991). The format of Table 4 is di!erent from the others. We ask whether, in response to these risk factors, the "rm modi"es its discount rate, cash #ows, both, or neither. We report the percentage of respondents for each category. In the cross-tabulations across each of the demographic factors, we test whether the `neithera category is signi"cantly di!erent conditional on "rm characteristics. Overall, the most important additional risk factors are interest rate risk, exchange rate risk, business cycle risk, and in#ation risk (see Fig. 4). For the calculation of discount rates, the most important factors are interest rate risk, size, in#ation risk, and foreign exchange rate risk. For the calculation of cash #ows, many "rms incorporate the e!ects of commodity prices, GDP growth, in#ation, and foreign exchange risk. Interestingly, few "rms adjust either discount rates or cash #ows for book-tomarket, distress, or momentum risks. Only 13.1% of respondents consider the book-to-market ratio in either the cash #ow or discount rate calculations. Momentum is only considered important by 11.1% of the respondents. Small and large "rms have di!erent priorities when adjusting for risk. For large "rms, the most important risk factors (in addition to market risk) are foreign exchange risk, business cycle risk, commodity price risk, and interest rate risk. This closely corresponds to the set of factors detailed in Ferson and Harvey (1993) in their large-sample study of multibeta international asset pricing models. Ferson and Harvey "nd that the most important additional factor is foreign exchange risk. Table 4 shows that foreign exchange risk is by far the most important nonmarket risk factor for large "rms (61.7% of the large "rms adjust for foreign exchange risk; the next closest is 51.4% adjusting for business cycle risk). The ordering is di!erent for small "rms. Small "rms are more a!ected by interest rate risk than they are by foreign exchange risk. This asymmetry in risk exposure is consistent with the analysis of Jagannathan and Wang (1996) and Jagannathan et al. (1998). They argue that small "rms are more likely to be exposed to labor income risk and, as a result, we should expect to "nd these "rms relying on a di!erent set of risk factors, and using the CAPM less frequently, when estimating their cost of capital. As might be expected, "rms with considerable foreign sales are sensitive to unexpected exchange rate #uctuations. Fourteen percent of "rms with substantial foreign exposure adjust discount rates for foreign exchange risk, 22% adjust J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 205 Fig. 4. Survey evidence on types of multibeta risk that are important for adjusting cash #ows or discount rates. We report the percentage of CFOs who always or almost always adjust for a particular type of risk. The survey is based on the responses of 392 CFOs. cash #ows, and 32% adjust both. These "gures represent the highest incidence of `adjusting somethinga for any type of non-market risk, for any demographic. There are some interesting observations for the other control variables. Highly levered "rms are more likely to consider business cycle risk important; surprisingly, however, indebtedness does not a!ect whether "rms adjust for interest rate risk, term structure risk, or distress risk. Growth "rms are much more sensitive to foreign exchange risk than are nongrowth "rms. (Table 4 only reports the results for four control variables; A full version of Table 4 is available on the Internet at http://www.duke.edu/&charvey/Research/indexr.htm.) 4.3. Project versus xrm risk Finally, we explore how the cost of equity models are used. In particular, we consider an example of how a "rm evaluates a new project in an overseas market. We are most interested in whether corporations consider the companywide risk or the project risk in evaluating the project. Table 5 contains some surprising results. Remarkably, most "rms would use a single company-wide discount rate to evaluate the project; 58.8% of the respondents would always or almost always use the company-wide discount rate, even though the hypothetical project would most likely have di!erent risk characteristics. However, 51% of the "rms said they would always or almost (b) Interest rate risk (change in general level of interest rates) (f) Foreign exchange risk (d) GDP or business cycle risk (a) Risk of unexpected in#ation (h) Size (small "rms being riskier) (e) Commodity price risk (c) Term structure risk (change in the long-term vs. short-term interest rate) (g) Distress risk (probability of bankruptcy) (i) `Market-to-booka ratio (ratio of market value of "rm to book value of assets) (j) Momentum (recent stock price performance). 8.78 15.34 18.80 14.45 6.00 18.86 3.71 6.27 1.99 2.86 15.30 10.80 6.84 11.90 14.57 2.86 8.57 7.41 3.98 3.43 4.86 7.10 4.84 10.86 12.57 18.75 18.80 11.90 13.43 24.65 Disc. rate Cash #ow Both Overall 88.86 86.93 81.48 67.43 75.14 55.11 55.56 61.76 66.00 51.27 Neither Both 3.98 4.46 5.94 2.49 10.45 2.70 3.36 9.40 3.38 6.08 7.43 15.44 6.93 6.76 13.43 9.93 14.43 14.67 17.33 12.67 2.99 1.49 4.95 2.70 2.68 8.05 6.47 2.70 86.57 8.91 4.70 85.15 6.93 2.01 82.18 12.94 27.03 9.45 12.84 75.12 2.99 4.73 14.93 9.46 71.64 9.90 22.82 15.35 23.49 67.33 12.87 27.03 19.80 17.57 60.40 9.95 20.53 14.93 7.95 61.69 7.46 4.00 16.92 8.67 61.19 7.43 10.67 29.70 17.33 45.54 91.89 89.26 79.87 56.76*** 79.73* 38.26*** 48.65** 61.59 71.33** 59.33** Large Neither Small Large Small Large Small Large Small Discount rate Cash Flow Size 3.15 2.38 6.98 3.12 7.03 10.24 6.98 12.40 14.84 13.39 Cash Flow 4.94 8.43 15.85 4.94 6.10 18.75 7.41 9.64 15.66 7.06 2.36 3.17 6.98 20.31 3.12 14.96 24.03 14.73 7.03 7.09 4.94 1.20 6.10 24.69 6.10 22.50 18.52 16.87 3.61 16.47 Non-G Growth Non-G Discount rate Growth 4.72 5.56 6.98 12.50 10.94 22.83 22.48 10.08 17.19 22.83 n/a 1.23 6.02 7.41 17.07 23.75 14.81 12.05 9.64 18.82 Non-G Both Growth P/E 89.76 88.89 79.07 64.06 78.91 51.97 46.51 62.79 60.94 56.69 88.89 84.34 76.83 62.96 70.73 35.00** 59.26* 61.45 68.67 57.65 Growth Non-G Neither Table 4 Survey responses to the question: when valuing a project, do you adjust either the discount rate or cash #ows for the following risk factors? (Check the most appropriate box for each factor). Percentage of respondents choosing each category is reported 8.45 4.32 3.55 3.61 3.68 2.45 3.61 6.63 12.88 13.66 10.91 6.71 14.29 6.17 12.88 7.09 6.83 4.96 13.94 10.71 10.37 15.60 1.24 4.32 6.17 11.43 4.82 10.71 14.29 18.12 3.55 0.72 6.34 18.44 28.37 16.43 5.67 26.62 2.14 4.91 6.63 4.82 17.18 16.15 8.48 17.68 12.42 10.49 57.06 63.35 66.67 65.24 72.05 77.16 4.26 88.96 7.19 86.14 4.23 83.73 21.99 24.82 13.57 9.93 8.63 15.71 6.52 24.40 23.19 50.60 88.65 87.77 80.99 52.48 41.84*** 59.29 68.09 60.43** 70.71 52.17 High Neither 4.26 4.26 9.38 13.83 6.45 7.29 12.77 3.23 6.45 13.54 Yes 3.19 3.95 6.75 9.52 7.14 13.55 15.02 2.79 9.52 15.94 No Discount rate 3.19 5.32 7.29 22.34 26.88 19.79 7.45 26.88 4.30 8.33 Yes 2.79 0.79 5.95 12.30 15.87 12.75 5.53 15.14 3.57 8.76 No Cash Flow 4.26 5.32 2.08 31.91 16.13 13.54 11.70 10.75 13.98 19.79 Yes Both Foreign sales 5.18 7.91 5.95 13.49 19.44 11.55 14.23 10.76 12.30 26.29 No 88.30 85.11 81.25 31.91 50.54 59.38 68.09 59.14 75.27 58.33 Yes 88.84 87.35 80.95 64.68*** 57.54 62.15 64.43 71.31** 74.60 49.00 No Neither Percentage of respondents choosing each category is reported. The percentages for discount rate, cash #ow, both and neither should sum to 100. ***, **, * denotes a signi"cant di!erence at the 1%, 5%, and 10% level, respectively. All table columns are de"ned in Table 1. (b) Interest rate risk (change in general level of interest rates) (f) Foreign exchange risk (d) GDP or business cycle risk (a) Risk of unexpected in#ation (h) Size (small "rms being riskier) (e) Commodity price risk (c) Term structure risk (change in the long-term vs. short-term interest rate) (g) Distress risk (probability of bankruptcy) (i) `Market-to-booka ratio (ratio of market value of "rm to book value of assets) (j) Momentum (recent stock price performance) Both Low High Low High Low High Low Discount rate Cash Flow Leverage 1.65 0.95 0.66 34.52 15.61 9.87 0.68 0.82 1.49 2.50 1.86 0.64 1.09** 1.82** 2.50 2.36*** 2.50 2.09 1.65 0.95 0.66 58.79 50.95 34.52 15.61 9.87 0.72 1.18 1.80 2.54 2.31 0.62 0.87** 1.61 2.49 2.02* CEO age % always or almost always Mean '59 Ynger 2.50 2.09 58.79 50.95 0.55 0.99 1.49 2.18 2.11 Long 0.88 1.54 2.45 1.99 0.68 0.92 1.73* 2.64*** 2.06 Short 0.68 1.08* 1.81* 2.58 2.30** 0.59 0.88 1.77 2.49 2.20 Yes 0.67 0.98 1.60 2.51 1.99 No CEO MBA 0.85*** 0.61 1.04 1.69 2.37** 2.26 CEO tenure 0.49 1.12 1.84 2.76 2.20 High Leverage Growth Non-G Low P/E 0.58 1.05 2.01 2.83** 2.25 No 0.38 1.27 1.50 2.00 2.55 Yes 0.67 0.89* 1.66 2.52* 2.03* No Regulated 0.75 1.17 1.82 2.41 2.43 Yes Investment grade 0.64 0.84* 1.52* 0.67 0.91 1.70 2.39 1.90 No 0.57 1.01 1.58 2.64* 2.25** Yes 0.61 1.08 1.78 2.55 2.24 Yes 2.61 2.22 Low 0.65 0.90 Yes 1.81 0.79* 0.63 0.66*** 0.94 1.41** 2.42 2.87 1.79*** 2.21 No 0.85** 1.08 0.68 0.93 1.58 2.33*** 2.02 No Foreign sales 0.56 0.96 1.52 2.41 2.01 High Management ownership 1.42*** 1.70 Public corp. 0.68 1.01 1.86 2.32* 2.01 Manu. Others Industry 2.53 2.56 1.82*** 2.22 No Target debt ratio 0.68 1.05 1.75 2.46 2.31 Yes Pay dividends 0.71 0.89 1.58 2.57 1.97 No 0.46* 1.17* 1.92* 2.20** 2.61*** Yes Fortune 500 mailing Respondents are asked to rate on a scale of 0 (never) to 4 (always). We report the overall mean as well as the % of respondents that answered 3 (almost always) and 4 (always). ***, **, * denotes a signi"cant di!erence at the 1%, 5%, and 10% level, respectively. All table columns are de"ned in Table 1. (a) The discount rate for our entire company (d) A risk-matched discount rate for this particular project (considering both country and industry) (b) The discount rate for the overseas market (country discount rate) (c) A divisional discount rate (if the project line of business matches a domestic division) (e) A di!erent discount rate for each component cash #ow that has a di!erent risk characteristic (e.g. depreciation vs. operating cash #ows) (a) The discount rate for our entire company (d) A risk-matched discount rate for this particular project (considering both country and industry) (b) The discount rate for the overseas market (country discount rate) (c) A divisional discount rate (if the project line of business matches a domestic division) (e) A di!erent discount rate for each component cash #ow that has a di!erent risk characteristic (e.g. depreciation vs. operating cash #ows) Size % always or almost always Mean Small Large Table 5 Survey responses to the question: How frequently would your company use the following discount rates when evaluating a new project in an overseas market? To evaluate this project we would use 208 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 209 always use a risk-matched discount rate to evaluate this project. These results are related to Bierman (1993) who "nds that 93% of the Fortune 100 industrial "rms use the company-wide weighted average cost of capital for discounting, 72% use the rate applicable to the project based on the risk or the nature of the project, and 35% use a rate based on the division's risk. The reliance of many "rms on a company-wide discount rate might make sense if these same "rms adjust cash #ows for foreign exchange risk when considering risk factors (i.e., in Table 4). However in untabulated results, we "nd the opposite: "rms that do not adjust cash #ows for foreign exchange risk are also relatively less likely (compared to "rms that adjust for foreign exchange risk) to use a risk-matched discount rate when evaluating an overseas project. Large "rms are signi"cantly more likely to use the risk-matched discount rate than are small "rms (rating of 2.34 versus 1.86). This is also con"rmed in our analysis of Fortune 500 "rms, which are much more likely to use the riskmatched discount rate than the "rm-wide discount rate to evaluate the foreign project (rating of 2.61 versus 1.97). Very few "rms use a di!erent discount rate to separately value di!erent cash #ows within the same project (rating of 0.66), as Brealey and Myers (1996) suggest they should for cash #ows such as depreciation. The analysis across "rm characteristics reveals some interesting patterns. Growth "rms are more likely to use a company-wide discount rate to evaluate projects. Surprisingly, "rms with foreign exposure are signi"cantly more likely to use the company-wide discount rate to value an overseas project. Public corporations are more likely to use a risk-matched discount rate than are private corporations; however, this result is not robust to controlling for size. CEOs with short tenures are more likely to use a company-wide discount rate (signi"cant at the 5% level for both large and small "rms). 5. Capital structure Our survey has separate questions about debt, equity, debt maturity, convertible debt, foreign debt, target debt ratios, credit ratings, and actual debt ratios. Instead of stepping through the responses security by security, this section distills the most important "ndings from the capital structure questions and presents the results grouped by theoretical hypothesis or concept. These groupings are neither mutually exclusive nor all-encompassing; they are intended primarily to organize the exposition. 5.1. Trade-ow theory of capital structure choice 5.1.1. Target debt ratios and the costs and benexts of debt One of the longest-standing questions about capital structure is whether "rms have target debt ratios. The trade-o! theory says that "rms have optimal 210 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 debt}equity ratios, which they determine by trading o! the bene"ts of debt with the costs (e.g., Scott, 1976). In traditional trade-o! models, the chief bene"t of debt is the tax advantage of interest deductibility (Modigliani and Miller, 1963). The primary costs are those associated with "nancial distress and the personal tax expense bondholders incur when they receive interest income (Miller, 1977). In this section we discuss the traditional factors in the trade-o! theory, namely distress costs and tax costs and bene"ts. Many additional factors (e.g., informational asymmetry, agency costs) can be modeled in a trade-o! framework. We discuss these alternative costs and bene"ts in separate sections below. Table 6 and Fig. 5 show the factors that determine the appropriate amount of debt for the "rm. The CFOs tell us that the corporate tax advantage of debt is moderately important in capital structure decisions: Row a of Table 6 shows that the mean response is 2.07 on a scale from 0 to 4 (0 meaning not important, 4 meaning very important). The tax advantage is most important for large, regulated, and dividend-paying "rms } companies that probably have high corporate tax rates and therefore large tax incentives to use debt. Desai (1998) shows that "rms issue foreign debt in response to relative tax incentives, so we investigate whether "rms issue debt when foreign tax treatment is favorable. We "nd that favorable foreign tax treatment relative to the U.S. is fairly important (overall rating of 2.26 in Table 7). Big "rms (2.41) with large foreign exposure (2.50) are relatively likely to indicate that foreign tax treatment is an important Fig. 5. Survey evidence on some of the factors that a!ect the decision to issue debt. The survey is based on the responses of 392 CFOs. J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 211 factor. This could indicate that "rms need a certain level of sophistication and exposure to perform international tax planning. In contrast, we "nd very little evidence that "rms directly consider personal taxes when deciding on debt policy (rating of 0.68 in Table 6) or equity policy (rating of 0.82 in Table 8, the least popular equity issuance factor). Therefore, it seems unlikely that "rms target investors in certain tax clienteles (although we can not rule out the possibility that investors choose to invest in "rms based on payout policy, or that executives respond to personal tax considerations to the extent that they are re#ected in market prices, see Graham, 1999a). When we ask "rms directly about whether potential costs of distress a!ect their debt decisions, we "nd they are not very important (rating of 1.24 in Table 6), although they are relatively important among speculative-grade "rms. However, "rms are very concerned about their credit ratings (rating of 2.46, the second most important debt factor), which can be viewed as an indication of concern about distress. Among utilities and "rms that have rated debt, credit ratings are a very important determinant of debt policy. Credit ratings are also important for large "rms (3.14) that are in the Fortune 500 (3.31). Finally, CFOs are also concerned about earnings volatility when making debt decisions (rating of 2.32), which is consistent with the trade-o! theory's prediction that "rms reduce debt usage when the probability of bankruptcy is high (Castanias, 1983). We ask directly whether "rms have an optimal or `targeta debt}equity ratio. Nineteen percent of the "rms do not have a target debt ratio or target range (see Fig. 1G). Another 37% have a #exible target, and 34% have a somewhat tight target or range. The remaining 10% have a strict target debt ratio (see Fig. 6). These overall numbers provide mixed support for the notion that companies trade o! costs and bene"ts to derive an optimal debt ratio. However, untabulated analysis shows that large "rms are more likely to have target debt ratios: 55% of large "rms have at least somewhat strict target ratios, compared to 36% of small "rms. Targets that are tight or somewhat strict are more common among investment-grade (64%) than speculative "rms (41%), and among regulated (67%) than unregulated "rms (43%). Targets are important if the CEO has short tenure or is young, and when the top three o$cers own less than 5% of the "rm. Finally, the CFOs tell us that their companies issue equity to maintain a target debt}equity ratio (rating of 2.26; Row e of Table 8), especially if their "rm is highly levered (2.68), "rm ownership is widely dispersed (2.64), or the CEO is young (2.41). Overall, the survey evidence provides moderate support for the trade-o! theory. 5.1.2. Deviations from target debt ratios Actual debt ratios vary across "rms and through time. Such variability might occur if debt intensity is measured relative to the market value of equity, and yet "rms do not rebalance their debt lock-step with changes in equity prices. Our (g) Financial #exibility (we restrict debt so we have enough internal funds available to pursue new projects when they come along) (d) Our credit rating (as assigned by rating agencies) (h) The volatility of our earnings and cash #ows (a) The tax advantage of interest deductibility (e) The transactions costs and fees for issuing debt (c) The debt levels of other "rms in our industry (b) The potential costs of bankruptcy, near-bankruptcy, or "nancial distress (i) We limit debt so our customers/suppliers are not worried about our "rm going out of business (n) We restrict our borrowing so that pro"ts from new/future projects can be captured fully by shareholders and do not have to be paid out as interest to debtholders (j) We try to have enough debt that we are not an attractive takeover target (f) The personal tax cost our investors face when they receive interest income (k) If we issue debt our competitors know that we are very unlikely to reduce our output (m) To ensure that upper management works hard and e$ciently, we issue su$cient debt to make sure that a large portion of our cash #ow is committed to interest payments (l) A high debt ratio helps us bargain for concessions from our employees 2.59 2.46 2.32 2.07 1.95 1.49 1.24 1.24 1.01 0.73 0.68 0.40 0.33 0.16 59.38 57.10 48.08 44.85 33.52 23.40 21.35 18.72 12.57 4.75 4.79 2.25 1.69 0.00 0.16 0.33 0.41 0.59 0.57 1.16 1.20 1.36 1.29 2.07 1.77 2.29 1.92 2.54 0.15 0.32 0.37 0.72* 0.91*** 0.80*** 1.30 1.10** 1.77*** 1.81** 2.44*** 2.36 3.14*** 2.65 Large Size or very important Mean Small % important 0.18 0.32 0.48 0.53 0.95 1.09 1.43 1.29 1.72 1.98 2.36 2.41 2.89 2.61 Growth P/E Low 1.16 1.36 1.94 1.99 2.25 2.29 2.61 0.62 0.13 0.28 0.32* 0.13 0.22 0.33 0.80** 0.68 0.86 0.69*** 1.18 1.00*** 1.34 1.02* 1.52 1.80 2.27 2.25 2.81 2.75 Non-G 2.71 Yes 2.11 1.85 1.23 0.87 0.19* 0.14 0.49*** 0.28 0.47** 0.38 0.63 0.90*** 0.84 0.83*** 0.77 1.20 1.37** 0.99 1.70*** 1.80 1.87 2.26** 2.32 2.32 2.73 Yes 1.63 1.91 2.35 0.76 0.95 1.19 0.17 0.38 0.51 0.13 0.32 0.38 0.51*** 0.71 0.96 0.85 1.14 1.40** 1.27 1.71 2.06 2.54 2.44** 2.33 0.19* 0.34 0.41 0.55* 0.66 1.06 1.30 1.21 1.34** 2.02 1.65*** 2.28 2.04*** 2.40*** No Pay dividends 3.11** 2.76 2.59 No Investment grade 2.64** 3.36 2.60 High Leverage Table 6 Survey responses to the question: What factors a!ect how you choose the appropriate amount of debt for your "rm? 0.18 0.40 0.46 0.65 0.83 1.08 1.21 1.31 1.38 1.89 2.30 2.35 2.52 2.67 2.32 2.81 2.68 1.88 0.37 0.65 0.85 0.78 1.17 1.30 0.15 0.17 0.26** 0.33 0.36 0.63 0.66* 0.97 1.40* 1.22 1.66** 1.57 1.95 1.79*** 2.27 2.31 2.39 2.52 0.18 0.35 0.52** 0.72 0.74 1.30*** 1.45** 1.33 1.37* 2.02 1.89*** 2.41 1.99*** 2.41** High Management ownership Manu. Others Low Industry 212 J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 2.59 2.46 2.32 2.07 1.95 1.49 1.24 1.24 1.01 0.73 0.68 0.40 0.33 0.16 59.38 57.10 48.08 44.85 33.52 23.40 21.35 18.72 12.57 4.75 4.79 2.25 1.69 0.00 0.14 0.38 0.45 0.56 0.82 0.99 1.32 1.12 1.43 1.95 2.15 2.38 2.52 2.54 Mean '59 0.16 0.32 0.39 0.68 0.70 1.00 1.23 1.29 1.52 1.98 2.05 2.33 2.44 2.59 Ynger CEO age 0.16 0.42 0.48 0.67 0.78 1.05 1.39 1.37 1.46 2.22 1.92 2.40 2.28 2.68 Long 2.11 2.22 2.37 2.51 Yes 0.15 0.28** 0.34** 0.63 0.70 0.97 1.17** 1.20 1.53 0.16 0.30 0.37 0.65 0.76 1.04 1.23 1.24 1.61 0.16 0.36 0.42 0.65 0.73 0.98 1.25 1.25 1.45 1.97 2.07 2.40* 2.50 2.64 No CEO MBA 1.83*** 2.03 2.14* 2.29 2.56** 2.52 Short CEO tenure 0.14 0.14 0.38 0.67 0.71 0.86 1.33 1.38 2.32 1.71 2.64 2.27 3.59 2.76 Yes 2.63 No 2.34 2.02 0.16 0.34* 0.38 0.62 0.71 1.02 1.23 1.25 0.16 0.34 0.44 0.73 0.71 1.03 1.27 1.32 1.40*** 1.37 1.95 1.98** 2.03 2.31 2.68 Yes 1.92 2.24 2.34 0.18 0.34 0.36 0.58* 0.77 0.99 1.24 1.19 0.17 0.31 0.43 0.65 0.94 0.95 1.27 1.15 1.60** 1.63 1.89 2.13 2.26 2.91 Yes 2.43 1.98 1.01 1.20 0.15 0.36 0.35 0.64 0.16 0.27 0.42 0.78 0.34*** 0.93 1.10 1.16 1.42** 1.29 1.27*** 1.41 2.03 1.76*** 2.45 2.31 2.60 No 2.32 2.00 1.12 1.30 1.27 1.41 0.16 0.35 0.39 0.61* 0.17 0.37 0.40 0.67 0.64*** 0.70 1.00 1.26 1.22 1.51 1.94 1.91*** 1.97 2.27 0.14 0.17** 0.36 0.72 0.88* 0.48*** 0.98** 1.08 1.86*** 1.70** 2.53*** 2.30 3.31*** 2.55 Yes Fortune 500 mailing 2.30*** 2.26 2.45*** No Foreign sales 1.68*** 2.77 2.40** No Public corp. 2.73*** 2.86 2.54 Yes Target debt ratio 2.32*** 2.19 2.57 No Regulated Respondents are asked to rate on a scale of 0 (not important) to 4 (very important). We report the overall mean as well as the % of respondents that answered 3 and 4 (very important). ***, **, * denotes a signi"cant di!erence at the 1%, 5%, and 10% level, respectively. All table columns are de"ned in Table 1. (g) Financial #exibility (we restrict debt so we have enough internal funds available to pursue new projects when they come along) (d) Our credit rating (as assigned by rating agencies) (h) The volatility of our earnings and cash #ows (a) The tax advantage of interest deductibility (e) The transactions costs and fees for issuing debt (c) The debt levels of other "rms in our industry (b) The potential costs of bankruptcy, near-bankruptcy, or "nancial distress (i) We limit debt so our customers/suppliers are not worried about our "rm going out of business (n) We restrict our borrowing so that pro"ts from new/future projects can be captured fully by shareholders and do not have to be paid out as interest to debtholders (j) We try to have enough debt that we are not an attractive takeover target (f) The personal tax cost our investors face when they receive interest income (k) If we issue debt our competitors know that we are very unlikely to reduce our output (m) To ensure that upper management works hard and e$ciently, we issue su$cient debt to make sure that a large portion of our cash #ow is committed to interest payments (l) A high debt ratio helps us bargain for concessions from our employees or very important % important J.R. Graham, C.R. Harvey / Journal of Financial Economics 60 (2001) 187}243 213 3.15 2.26 2.19 0.63 63.39 52.25 44.25 5.50 3.15 2.67 2.26 2.19 0.63 85.84 63.39 52.25 44.25 5.50 0.64 2.11 2.41** 2.52** 3.22 0.77 2.30 2.13 2.57 3.30 0.57 2.16 2.30 2.71 3.13 Ynger CEO age 0.60 2.33 1.94 3.09 3.06 % important or very important Mean '59 2.67 85.84 0.29** 2.03 2.29 2.35* 3.29 0.50 2.26 2.00 2.74 3.39 Long 0.69 2.17 2.39* 2.67 3.13 Short CEO tenure 0.75 2.27 2.27 2.73 2.98 0.72 2.13 2.39 2.79 3.32 0.60 2.22 2.42 2.77 3.33 Yes 0.58 2.14 2.04* 2.66 3.06 No CEO MBA 0.55 2.22 2.26 2.70 3.20 High Leverage Growth Non-G Low P/E 0.57 2.48 2.40 2.70 3.23 No 1.11 1.67 2.11 3.33 3.33 Yes 0.57* 2.14 2.22 2.66* 3.14 No Regulated 0.65 2.20 2.37 2.38 3.06 Yes Investment grade 0.57 2.40 2.44 2.78 3.30 No 3.32 0.73 2.40 2.08 0.64 1.93** 2.12 2.64 3.17 Yes 0.66 2.10 2.13 0.61 2.18 2.37 2.65 3.21 Yes 0.56 2.26 0.61 2.54** 2.33 2.74 3.28 2.72 3.34 Yes 0.59 2.25 0.64 2.08 1.94** 2.65 2.92** No Foreign sales 0.59 2.04 2.16 1.67** 2.50 2.95 2.95 No Public corp. 0.64 2.22 2.36 3.00 Low High Management ownership 2.23*** 2.55 2.94* Manu. Others Industry 3.12** 2.92 3.36 No Target debt ratio 0.63 2.08 2.29 2.57 3.12 Yes Pay dividends 0.64 2.28 2.34 2.85 3.22 No 0.62 2.03 2.11 2.30** 3.00 Yes Fortune 500 mail Respondents are asked to rate on a scale of 0 (not important) to 4 (very important). We report the overall mean as well as the % of respondents that answered 3 and 4 (very important). ***, *