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Traditional capital structure theory predicts that reducing banks' leverage reduces the risk and cost of equity but does not change the weighted average cost of capital, and thus the rates for borrowers. We confirm that the equity of better-capitalized banks has lower beta and idiosyncratic risk. However, over the last 40 years, lower risk banks have not had lower costs of equity (lower stock returns), consistent with a stock market anomaly previously documented in other samples. A calibration suggests that a binding ten percentage point increase in Tier 1 capital to risk-weighted assets could double banks' risk premia over Treasury bills.

This paper explores the nature of fluctuations in world bulk shipping by quantifying the impact of time to build and demand uncertainty on investment and prices. We examine the impact of both construction lags and their lengthening in periods of high investment activity, by constructing a dynamic model of ship entry and exit. A rich dataset of secondhand ship sales allows for a new estimation strategy: resale prices provide direct information on value functions and allow their nonparametric estimation. We find that moving from time-varying to constant to no time to build reduces prices, while significantly increasing both the level and volatility of investment. (JEL G31, L11, L62, L92)

This paper estimates the response of investment to changes in uncertainty using data on oil drilling in Texas and the expected volatility of the future price of oil. Using a dynamic model of firms’ investment problem, I find that: (i) the response of drilling activity to changes in price volatility has a magnitude consistent with the optimal response prescribed by theory, (ii) the cost of failing to respond to volatility shocks is economically significant, and (iii) implied volatility data derived from futures options prices yields a better fit to firms’ investment behavior than backward-looking volatility measures such as GARCH. (JEL C58, D92, G13, G31, L71, Q31) The real options literature, beginning with Marschak (1949) and Arrow (1968) and developed in Bernanke (1983), Pindyck (1991), and Dixit and Pindyck (1994), explains how firms should make decisions about investments that involve sunk costs. Real options theory views such investments as options in that, at any point in time, a firm may choose to either invest immediately or delay and observe the evolution of the investment’s payoff. A key insight is that the option to delay has value when future states of the world with positive returns to investing and states with negative returns are both possible, even holding the expected future return constant at its present level. Thus, in the presence of irreversibility and uncertainty, a naive investment timing rule—proceed with an investment if its expected benefit even slightly exceeds its cost—is suboptimal because it does not account for the value of continuing to hold the option. Instead, firms should delay irreversible investments until a significant gap develops between the investments’ expected benefits and costs. Moreover, as uncertainty increases, real options theory tells us that the incentive to delay should grow stronger and the gap between the expected benefit and cost necessary to trigger investment should widen. While real options theory therefore prescribes how firms should carry out irreversible investments in uncertain environments, it is not empirically well-known

I study a dynamic economy featuring adverse selection in asset markets. Borrowing constrained entrepreneurs sell past projects to finance new investment, but asymmetric information creates a lemons problem. I show that this friction is equivalent to a tax on financial transactions. The implicit tax rate responds to aggregate shocks, generating amplification in the response of investment and cyclical variation in liquidity. (JEL D82, D92, E32, E44, G31, L15)

What is the impact of real estate prices on corporate investment? In the presence of financing frictions, firms use pledgeable assets as collateral to finance new projects. Through this collateral channel, shocks to the value of real estate can have a large impact on aggregate investment. Over the 1993-2007 period, the representative U.S. corporation invests 6 cents out of each additional dollar of collateral. To compute this sensitivity, we use local variations in real estate prices as shocks to the collateral value of firms that own real estate. We address the endogeneity of local real estate prices using the interaction of interest rates and local constraints on land supply as an instrument. We address the endogeneity of the decision to own land (1) by controlling for observable determinants of ownership and (2) by looking at the investment behavior of firms before and after they acquire land. The sensitivity of investment to collateral value is stronger the more likely a firm is to be credit constrained.

This paper focuses on the importance of equity markets in facilitating the exit of entrepreneurs investing in technology. Entrepreneurs' willingness to invest and aggregate output is affected in two opposite ways. First, uncertainty about equity price or lack of market liquidity discourages technology adoption. This can explain slow technology adoption and limited participation by venture capitalists in underdeveloped equity markets. Second, fast adoption is a positive signal to imperfectly informed equity market participants. This provides a rational explanation for overpricing technology stocks and overinvestment in developed markets. Fast adoption is most probable at an intermediate quality of information. (JEL D82, E23, G12, G31, G32, O33)

We propose a theoretically motivated procedure for measuring heterogeneity in firms’ growth opportunities and document its empirical properties. The term “growth opportunities” refers to the component of a firm’s market value that cannot be attributed to its assets in place. This decomposition of firm value underpins many of the theoretical models describing cross-sectional differences in firms’ investment and stock return behavior. However, successful applications of such models depend on the quality of empirical measures of growth opportunities. Our procedure identifies economically significant differences in firms’ growth opportunities which are not captured by the commonly used empirical measures. We base our approach on a theoretical model incorporating investment specific productivity shocks and heterogenous firms. Productivity shocks in the capital goods sector account for a significant fraction of observed growth variability, according to the literature on the real determinants of economic growth (Jeremy Greenwood, Zvi Hercowitz and Per Krusell 1997, Jonas D. M. Fisher 2006). Greenwood, Hercowitz and Krusell (1997) show that, empirically, investment specific shocks are negatively correlated with aggregate investment, both at business cycle and lower frequencies. Our model predicts that the sensitivity of firm stock returns to investment specific productivity shocks (z-shocks) is greater for firms that derive Growth Opportunities and Technology Shocks

To address how technological progress in financial intermediation affects the economy, a costly-state verification framework is embedded into the standard growth model. The framework has two novel ingredients. First, firms differ in the risk/return combinations that they offer. Second, the efficacy of monitoring depends upon the amount of resources invested in the activity. A financial theory of firm size results. Undeserving firms are over financed, deserving ones under funded. Technological advance in intermediation leads to more capital accumulation and a redirection of funds away from unproductive firms toward productive ones. With continued progress, the economy approaches its first-best equilibrium. An extended version of the paper containing some quantitative analysis is available at: http://ssrn.com/abstract=996263

Entrepreneurs bear substantial risk, but empirical evidence shows no sign of a positive premium. This paper develops a theory of endogenous entrepreneurial risk taking that explains why self-financed entrepreneurs may find it optimal to invest in risky projects offering no risk premium. Consistently with empiri cal evidence, the model predicts that poorer entrepreneurs are more likely to undertake risky projects. It also finds that incentives for risk taking are stronger when agents are impatient. (JEL G31, G32, L25, L26) Entrepreneurial activity is risky and poorly diversified. Most economic models would suggest that the high degree of entrepreneurial risk should be compensated by a significant premium in returns.1 Yet empirical evidence finds that the premium to entrepreneurial activity is surprisingly low,2 which raises the question of why people become entrepreneurs. A number of hypotheses have been offered to answer this question, mostly based on the idea that entrepreneurs have a different set of preferences or beliefs (e.g., risk tolerance or overoptimism). This paper provides an alternative theory of endogenous entrepreneurial risk taking that does not rely on individual heterogeneity of preferences or beliefs. We incorporate endogenous choice of entrepreneurial risk in a simple dynamic occupational choice model. A borrowing constrained agent chooses whether to be a worker or an entrepreneur. The occupational choice is discrete, in the sense that each activity requires full-time involvement. A worker receives fixed wage income, while an entrepreneur gets access to an entrepreneurial technology and decides how much to invest in it. Borrowing constraints induce endogenous sepa ration into different occupations: the rich, who have sufficient funds for investment, choose to be entrepreneurs, and the poor prefer to receive fixed pay by becoming workers. The indivisibility of occupational choice may create a nonconcavity in the agent's value function as a function of

A large empirical literature has documented that firm-level differences in productivity, size, ownership status, and other characteristics are crucial to understanding differences in firms’ decisions to export. The evidence strongly supports the self-selection of more productive firms into export markets, but there has been more mixed evidence on the subsequent feedback effects of exporting on the future path of firm productivity. Several recent papers have introduced a new dimension into this export-productivity relationship: firm-level investments in productivity-enhancing activities such as R&D. James A. Costantini and Marc J. Melitz (2007), Alla Lileeva and Daniel Trefler (2007), and Paula Bustos (2006) explore the linkages between investments in innovation, productivity, and the decision to export in the context of the liberalization of trade regimes. Aw, Roberts, and Tor Winston (2007) have also found a significant role for firm R&D investments in explaining Taiwanese firm export patterns, as well as interaction effects between firm R&D and export choices in explaining productivity change. In this paper we summarize some empirical results from our research project to develop an estimable structural model of the joint exportinvestment decision. In the theoretical model, firms invest in R&D and physical capital, which can affect the path of future productivity for the firm. R&D investment, through its effect on future productivity, increases the profits from exporting, and participation in the export market raises the return to R&D investments. The theoretical model yields equations for the policy functions for R&D investment, physical investment, and the exporting decision, as well as the evolution of firm-level profitability, that can be estimated with micro datasets containing R&D Investments, Exporting, and the Evolution of Firm Productivity

Even modest reductions in the after-tax cost of capital purchases provide strong incentives for increased investment. Indeed, for tax subsidies that are temporary, and for capital goods that are very long-lived, the incentive to invest when the after-tax price is temporarily low is essentially infinite. Firms that would have purchased new capital equipment in the future, instead make their purchases during the period of the subsidy. For tax increases, the effects are the opposite. Firms, that would have normally invested now, delay until the tax rate returns to normal. We present a model of the equilibrium effects of temporary investment tax incentives. The model reveals a simple relationship between the shadow price of investment goods and the size of a temporary investment tax incentive. Specifically, for sufficiently long-lived capital goods (goods with very low rates of economic depreciation) and for sufficiently short-lived investment tax subsidies, the shadow value of capital should be nearly unchanged, and thus the pre-tax shadow price of capital goods should fully reflect the magnitude of the tax subsidy. This result holds regardless of the elasticity of investment supply and regardless of the underlying demand for capital. Instead, it relies only on the firm’s ability to arbitrage predictable movements in the after-tax price of long-lived capital over time. Two conclusions immediately follow. First, observ ing price increases following a temporary tax incentive is not evidence that investment supply is relatively inelastic. A temporary investment tax subsidy can substantially affect investment even if it bids up the price of investment sharply. Second, because economic theory dictates that the shadow price of investment moves one-for-one with a temporary tax subsidy, the elasticity of supply can be inferred from quantity data alone. Recent changes in US tax law allow us to use the model and its implications to estimate struc tural parameters that govern the supply of investment. The 2002 and 2003 tax bills provided temporarily accelerated tax depreciation called bonus depreciation for certain types of qualified capital goods. Under the 2002 bill, firms could immediately deduct 30 percent of investment purchases and then depreciate the remaining 70 percent under standard depreciation schedules.

We examine investment behavior when firms face costs in the access to external funds. We find that despite the existence of liquidity constraints, standard investment regressions predict that cash flow is an important determinant of investment only if one ignores q. Conversely, we also obtain significant cash flow effects even in the absence of financial frictions. These findings provide support to the argument that the success of cash-flow-augmented investment regressions is probably due to a combination of measurement error in q and identification problems. (JEL E22, E44, G31)

In an influential paper, David S. Scharfstein and Jeremy C. Stein (1990) modeled sequential investment by agents concerned about their reputation as good forecasters. Consider an agent who acts after observing the behavior of another ex ante identical agent. Scharfstein and Stein argue that reputational herding requires that better agents have more correlated signals conditionally on the state of the world. They claim that without correlation the second agent would have no incentive to attempt to manipulate the market inference about ability by imitating the behavior of the first agent. In this Note we show that in their model, correlation is not necessary for herding, other than in degenerate cases. Our clarification exploits a parallel with statistical herding, introduced by Abhijit V. Banerjee (1992) and Sushil Bikhchandani et al. (1992) (henceforth, BHW). BHW feature investors who maximize expected profits in a common-value environment and have access to conditionally independent private signals of bounded precision, while still observing the behavior of others. Eventually, the evidence accumulated from observing earlier decisions is sufficiently strong to swamp the private information of a single decision maker. Thereafter, everyone rationally copies the prevailing behavior. We notice that payoffs have a common-value nature in both the statistical and the reputational model. The observed behavior of other agents possibly affects the probability belief attached to different states of the world as well as the payoff conditional on each state. Herding arises from the interaction of these two channels affecting the expected payoff, be it physical or reputational. Positive differential conditional correlation of signals in the reputational model is tantamount to the introduction of positive payoff externalities in the statistical model. This reinforces the tendency to herd already present with independence. The fact that differential conditional correlation is not needed for herding is a clear strength of the reputational herding model. It is not necessary to assume common unpredictable components of returns at the individual level in order to rationalize the empirical findings that individual prediction errors of security analysts are correlated. After setting up Scharfstein and Stein’s model in Section I, we summarize their findings in Section II and provide a unified definition of herd behavior in Section III. Section IV contains our critique of their line of argument and clarifies the role of differential conditional correlation. In Section V we propose alternative robust scenarios where herding would indeed be driven by correlation. Section VI concludes.

When a production process requires two extremely complementary inputs, conventional wisdom holds that a firm would always upgrade them simultaneously. We show, however, that if upgrading each input involves a fixed cost, the firm may upgrade them at different dates, “asynchronously.” This insight helps us understand why productivity rises with the age of a plant, why investment in structures is more spiked than equipment investment, and why plants have spare capacity. The bigger point of the paper is that complementarity does not necessarily imply comovement—not even for a single decision maker. (JEL E22, O31, P11)

A straightforward generalization of the simple net present value rule that correctly predicts when to invest in two classes of projects that can be delayed is derived. The first class consists of projects for which the option to delay derives its value exclusively from uncertainty about interest rates. It is shown that the optimal rule for investing in such projects is to simply multiply the discount rate of the project by the ratio of the mortgage rate to the riskless rate and then use this new rate as the discount rate in a standard net present value analysis. The other class of investment opportunities that is considered is the firm's option to expand. It is shown that it is only optimal for the firm to expand when a particular call option on the firm's stock has no time value. The fact that mortgage bonds (in the form of GNMAs) and stock options are actively traded implies that these rules have potentially important practical and empirical value. Besides their simplicity, the rules have the added advantage that they do not depend on a maintained assumption on the dynamics of interest rates in the economy.

This erratum corrects two errors in the results presented in Cummins, Hassett, and Oliner (2006). First, the dating of one series in the bottom panel of Figure 2 was off by one year. The value we showed for the percent change in the ratio of cash flow to investment (CF/K) for year t actually represented the value for the previous year in our dataset. The corrected version of this panel is shown below. Similarly, the GMM estimates in Table 3 for the sample of unrated firms (columns 3 and 6) reflected data for CF/K that were inadvertently lagged by one year. The corrected version of Table 3 is shown below. We thank Senay Agca for bringing these errors to our attention. In the course of checking the results in the paper, we also discovered that the datasets posted on the AER website for replicating the results in Table 6 do not contain the correct data. Unfortunately, we cannot post the appropriate datasets for Table 6 because we did not save the source data files. We have revised the “readme” file in the posted documentation to alert readers to this problem.

In our 1990 paper, we showed that managers concerned with their reputations might choose to mimic the behavior of other managers and ignore their own information. We presented a model in which “smart” managers receive correlated, informative signals, whereas “dumb” managers receive independent, uninformative signals. Managers have an incentive to follow the herd to indicate to the labor market that they have received the same signal as others, and hence are likely to be smart. This model of reputational herding has subsequently found empirical support in a number of recent papers, including Judith A. Chevalier and Glenn D. Ellison’s (1999) study of mutual fund managers and Harrison G. Hong et al.’s (2000) study of equity analysts. We argued in our 1990 paper that reputational herding “requires smart managers’ prediction errors to be at least partially correlated with each other” (page 468). In their Comment, Marco Ottaviani and Peter Sorensen (hereafter, OS) take issue with this claim. They write: “correlation is not necessary for herding, other than in degenerate cases.” It turns out that the apparent disagreement hinges on how strict a definition of herding one adopts. In particular, we had defined a herding equilibrium as one in which agent B always ignores his own information and follows agent A. (See, e.g., our Propositions 1 and 2.) In contrast, OS say that there is herding when agent B sometimes ignores his own information and follows agent A. The OS conclusion is clearly correct given their weaker definition of herding. At the same time, however, it also seems that for the stricter definition that we adopted in our original paper, correlated errors on the part of smart managers are indeed necessary for a herding outcome—even when one considers the expanded parameter space that OS do. We will try to give some intuition for why the different definitions of herding lead to different conclusions about the necessity of correlated prediction errors. Along the way, we hope to convince the reader that our stricter definition is more appropriate for isolating the economic effects at work in the reputational herding model. An example is helpful in illustrating what is going on. Consider a simple case where the parameter values are as follows: p 5 3⁄4; q 5 1⁄4; z 5 1⁄2, and u 5 1⁄2. In our 1990 paper, we also imposed the constraint that z 5 ap 1 (1 2 a)q, which further implies that a 5 1⁄2. The heart of the OS Comment is the idea that this constraint should be disposed of—i.e., we should look at other values of a. Without loss of generality, we will consider values of a above 1⁄2, and distinguish two cases.