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Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
Financial Constraint, Liquidity Management and Investment.doc
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  • 1. Financial Constraint, Liquidity Management and Investment* Timothy J. Riddiough School of Business University of Wisconsin-Madison triddiough@bus.wisc.edu Zhonghua Wu School of Business Florida International University wuz@fiu.edu This Draft: November 2007 Abstract Investment and liquidity management are analyzed in a sector where firms are exogenously cash flow constrained. Across the entire sector, we find high investment sensitivity to both q and measures of financial market frictions. Liquidity is managed through cash retention (dividend) policy and access to short-term bank finance, in which bank line of credit smoothes variation in available cash flow and accelerates investment. We show that cash flow constraint is not equivalent to financial constraint, where more (less) financially constrained firms in our sample exhibit high (low) investment and liquidity management sensitivity to variables that measure financial market frictions. Empirical results provide support for debt overhang, free cash flow and asset tangibility as important financial market frictions that influence investment outcomes. * We thank David T. Brown, Jim Clayton, Morris Davis, Piet Eiccholtz, Erasmo Giambona, Don Hausch, François Ortalo-Magné, Steve Malpezzi, Armin Schwienbacher, James Seward, David Shulman, Ko Wang, Toni Whited and seminar participants at Baruch College, University of Amsterdam, University of Wisconsin-Madison, and the 2006 ASSA meetings for helpful comments. We gratefully acknowledge the Puelicher Center for Banking Education at University of Wisconsin-Madison for its financial support.
  • 2. Financial Constraint, Liquidity Management and Investment 1. Introduction Fazzari, Hubbard, and Petersen (1988) convincingly argue that internal versus external sources of finance are imperfect substitutes in the context of funding investment, and hence that financial constraints impede the efficient allocation of resources. Their study has had wide impact, and has come under intense scrutiny. Critics, beginning with panelists that provided comments and discussion published alongside the original Brookings paper, have generally focused on three instrumental issues: i) endogeneity of financial constraint proxies; ii) measurement error in Tobin’s q; and iii) omitted variables and channels that provide more complete information about the link between financial market frictions and real investment outcomes. Chirinko (1993) concisely summarizes these concerns by stating, ―It is unclear whether significant liquidity and net worth variables are capturing a structural element heretofore missing in the investment equation or are merely reflecting a general misspecification.‖ Previous studies have addressed one or two of these instrumental issues at a time, but none have addressed all three in a systematic and comprehensive manner. For example, Whited (1992) and Kaplan and Zingales (1997) primarily address the financial constraint issue, while Erickson and Whited (2000) focus on measurement error in q and Almeida, Campello and Weisbach (2004) emphasize the link between cash flow sensitivity of cash holdings and financial constraint. The intent of this study is to address all three issues—endogeneity of financial constraint proxies, measurement error in q, and omitted variables/channels—simultaneously and comprehensively in order to provide additional perspective on the effects of financial constraint on investment decisions. To address endogeneity in the financial constraint proxy and measurement error in q, we analyze a specific sector that provides an attractive natural economic laboratory: publicly traded 1
  • 3. firms that own commercial real estate assets in an investment vehicle called a Real Estate Investment Trust (REIT). These firms are regulated to pay out at least 90 percent of their GAAP net income as dividends, and most pay out at least 100 percent of GAAP net income to avoid negative tax effects. This implies that the entire sector is constrained in its ability to retain cash, and therefore depends heavily on external finance to fund investment, which mitigates concerns over confounding effects in identifying constrained firms. These firms also have well measured q values, due to the competitive nature of the industry and characteristics of the underlying commercial real estate assets. The third instrumental issue revolves around omitted variables and resource channels. We make two contributions in this regard. First, we recognize that cash is not a sufficient statistic for available liquidity. Firms will vary in their capacity and need to hold liquidity, and may decide to hold less internal liquidity when low-cost external sources such as bank lines of credit (L/C) exist. Consequently, firms that might appear to be liquidity constrained may in fact have more than adequate stores of liquidity when external sources are recognized. Second, we specify and estimate a structural model that accounts for endogeneity in cash flow retention, bank L/C usage, and investment decisions. Cash flow retention and bank L/C usage together account for a firm’s liquidity management policy as related to investment, where simultaneous consideration allows us to better disentangle cause and effect as well as to better assess investment-cash flow and other sensitivities that have been a focus in the literature. A unique panel data set covering the years 1990-2003 has been assembled to analyze these issues. Preliminary analysis shows that REITs retain less cash flow, have a lower stock of cash, and use more bank L/C than a broad cross-section of other publicly traded firms. In other words, based on these measures, REITs appear to be financially constrained. We also find that REIT bank L/C usage increases monotonically with investment. This suggests that, in the short run, and given their significant constraints on cash flow retention, bank L/C substitutes for internal cash in funding investment. 2
  • 4. Full sample structural 3SLS estimation produces a number of noteworthy results. First, in the investment equation, q and the liquidity flow measures of retained cash flow and bank L/C use all significantly affect investment, with coefficient estimates that imply high investment sensitivities. Investment sensitivity to q is such that the elasticity of investment with respect to q is just shy of one, which places it near what standard q-theory would predict. Given the cash flow constraints faced by REITs together with the fact that commercial real estate assets are tangible with significant debt capacity, high investment sensitivity to both retained cash flow and bank L/C use is consistent with effects of asset tangibility (Almeida and Campello (2007)) and incentives to accelerate current investment in order to create additional external financing capacity in the future (Hennesey, Levy, Whited (2007)). Across the full sample, firms are seen to invest at a rate of approximately 20 percent per year, which exceeds rates of investment by the broad cross-section of comparison industrial firms. Moreover, most REITs pay well in excess of the minimum dividend payout requirement. This raises the issue of whether these cash flow constrained and equity dependent firms are really financially constrained. In other words, why is external finance available and affordable to these firms? We conjecture that limited discretion on cash retention mitigates adverse selection costs associated with raising outside finance. This chain of reasoning implies that, contrary to conventional wisdom that emphasizes the primacy of information-based costly external finance as a premier financial market friction, cash flow constraints and equity dependence are not sufficient conditions for financial constraint. To differentiate between the effects of cash flow constraint and financial constraint on investment and liquidity management, we split the sample based on Kaplan and Zingales’ (1997) methodology for indexing financial constraint. Based on KZ index scores, we find the more constrained sub-sample invests less, generates lower cash flow and has a lower stock of cash, pays fewer dividends, employs more leverage, and is less likely to maintain relationships with bank 3
  • 5. lenders and security underwriters. In other words, the KZ index method appears to accurately classify firms in our sample as more or less financially constrained. Simultaneous equation estimation reveals substantial differences between firms that are more versus less financially constrained. Consistent with arguments of Gomes (2001), the less financially constrained firms are responsive to investment signals contained in their stock prices, while the more constrained firms are not. This outcome refines results of Baker, Stein and Wurgler (2003), who do not differentiate equity dependent firms on the basis of financial constraint. Sensitivity of investment and liquidity management to proxies for financial market frictions is generally much higher in the sub-sample of more financially constrained firms. For example, cash retention policy responds to a number of variables in the financially constrained sub-sample of firms, including investment. Establishing a statistically significant link between dividend policy and investment is a new result (see Fama and French (2002) for additional background), in which firms decrease their dividend payout when investment increases—presumably to redirect scarce cash flow away from shareholders towards capital acquisition. Variables that cause dividend payout to increase in the more constrained sub-sample include equity issuance, a positive change in bank L/C capacity and a positive change in bank L/C use. None of these variables have any effect on dividend payout in the less constrained sub-sample. Stark differences between sub-samples also exist with respect to bank L/C usage. An extra $1 of retained cash flow causes L/C usage to decrease by more than $1 in the more constrained sub- sample, whereas retained cash flow has no effect on L/C use in the sample of less constrained firms. Thus, more financially constrained firms treat cash as negative short term debt by saving cash out of cash flow, whereas cash constrained but less financially constrained firms do not. Consistent with Sufi (2007), these results suggest that financially constrained firms closely monitor their bank L/C use due to concerns over covenant violations that would impose significant additional costs. Bank L/C use is also highly responsive to investment, leverage and firm age in the more 4
  • 6. constrained sub-sample, while there is either less or no responsiveness to these effects in the less constrained sub-sample. Thus, we show that cash flow constraint is not the same thing as financial constraint. Sub- sample estimation reveals that cash flow constrained firms that are classified as financially constrained are highly responsive to shocks in variables that proxy for financial market frictions. Higher cash flow results in a simultaneous paydown in bank L/C use and increase in investment, achieved in part through reduction in dividend payout. Less financially constrained firms, in contrast, exhibit stability in their dividend policy with no sensitivity to investment or L/C use. These findings point to the importance of agency costs over information-based costs of external finance in governing investment and liquidity management policies of financially constrained firms. Our results are also generally consistent with cash flow focused liquidity management effects emphasized in Almeida et al. (2004) and Almedia and Campello (2007). The paper is organized as follows. Section 2 provides further background on REITs and bank L/C usage. Hypothesis development and empirical model specification are addressed in section 3. Data are described and a preliminary analysis of the data are reported and analyzed in section 4. Simultaneous system equation estimation for the full sample is undertaken in section 5, and sub-sample results are considered in section 6. Section 7 concludes the paper. 2. Further Background on REITs and Bank Lines of Credit The data employed in previous studies of corporate investment generally have limited and noisy variation. One solution to the problem is to apply alternative specifications and more sophisticated econometric analysis (see, e.g., Hoshi, Kashyap and Scharfstein (1991), Erickson and Whited (2000)). A more direct solution is to try to obtain better data. We emphasize the latter approach, and examine the Real Estate Investment Trust (REIT) sector. The REIT sector, for several reasons, provide an attractive natural laboratory to study the effects of financial market frictions on firm investment. First, all REITs are cash flow constrained 5
  • 7. by regulation, as they are required to pay at least 90 percent of taxable income to shareholders in the form of dividends.1 After accounting for the effects of depreciation and the fact that most REITs pay in excess of the minimum payout requirement, 65 to 90 percent of current cash flow is typically paid out as dividends. 2 Cash flow constraints of this magnitude are typically thought to imply financially constraint due to the presumed high costs of accessing external finance. Consequently, based on this logic, exogenously imposed cash flow constraints substantially reduce endogeneity problems associated with identifying financially constrained firms. Combined adjustment and purchasing costs of investment should not exceed the shadow value of newly installed capital. Shadow value follows from investor expectations of the marginal contributions of new capital gains to future profit. In theory, marginal q provides a direct (isomorphic) measure of the shadow value of capital. Marginal q is generally unobservable in the data, however, so analysts rely on average q. If marginal q is badly measured by average q, an investment-cash flow relation may be a spurious, as current cash flow may contain information regarding investment opportunities. Hayashi (1982) has shown that average q is a sufficient statistic for investment when the following necessary conditions are satisfied: i) there is perfect competition in factor and product markets, ii) fixed capital is homogeneous, and iii) product and adjustment costs are linearly homogeneous. Commercial real estate asset markets and the firms (REITs) that own these assets satisfy these conditions to a remarkably close approximation. The factor market is primarily land and physical capital, with relatively little reliance on human capital, and these markets are generally quite competitive. Competitive market structure is important, since imperfectly competitive industries will generate quasi-rents that can cause a spurious correlation between cash flow and investment after controlling for average q (Abel and Eberly (2001), Cooper and Ejarque (2003)). 1 Prior to 2000, the dividend payout requirement was 95 percent. 2 REITs that pay out less than 100 percent of net income incur an excise tax on the difference, which causes most to pay at least 100 percent of net income. The average payout in our data and in other studies is approximately 120 percent of net income (see also Chan et al. (2003)). The annual flow of depreciation expense (a non-cash item) is generally between two and three percent of the asset’s initial book value, which equates to between 25 and 40 percent of net operating income. 6
  • 8. A large proportion of real estate asset operating expenses go to pay utilities, insurance and property taxes, which are effectively linear in scale. Investment, which in this sector is primarily the acquisition of built (productive) assets, results in adjustment costs that are linearly homogeneous. Furthermore, investment in built assets requires very little ―time-to-build‖, and also contain little option value that potentially distorts the marginal-average q relation. In addition, regulation requires REITs to be monoline (non-integrated) companies. This suggests that imperfect product substitution that confounds many multi-product firms is less problematic with REITs, which strengthens the link between average and marginal q.3 Finally, there are no taxes at the entity level to distort investment incentives. Compounding the usual marginal q–average q measurement error problem is that average q is often badly measured in the data due the reliance on asset book values to proxy for the replacement cost of firm assets. As Hartzell, Sun, and Titman (2006) and others have pointed out, however, book asset value is a relatively accurate measure of replacement cost with commercial real estate assets. For example, they report a correlation of .92 between their book asset measure of q and a net asset value measure of q that is based on market sales data. To begin to get a sense of the data, Figure 1 shows how average q varies by year for REITs in our sample, where q is defined as market value of equity plus book value of debt divided by asset book value as of the beginning of the year. Quartile cutoff values are displayed in addition to mean values. Mean and median q values generally exceed 1.0 over the sample period, but not by a large amount. It is also apparent that there is significant cross-sectional variation in q values in the early years of the sample period (particularly in 1992 and 1993), whereas this variation decreases after 1994. Figure 1 Here 3 See Hayashi and Inoue (1991) for more on the issue of imperfect asset substitution and investment. 7
  • 9. In Figure 2 the time-series of average q and rates of investment by year as a percentage of year-beginning asset value are displayed. There is a clear direct contemporaneous relation between investment and average q, with cross-correlation measured at .78. Note that investment is in the 10 percent range for most years, but that the years 1995-98 resulted in higher rates of investment that generally exceeded 20 percent of year-beginning book assets. Figure 2 Here In their analysis of the REIT sector, Ott, et al. (2005) document that only seven percent of firm-level investment was funded by retained cash earnings, as compared to 70 percent for other publicly traded firms. Because of their inability to retain cash, REITs rely on outside financing sources to facilitate investment. Seasoned equity, long-term unsecured debt, and secured mortgage debt are the claims typically issued to permanently finance acquisitions (see Brown and Riddiough (2003) for additional background). In the short term, REITs rely heavily on bank lines of credit (L/C) to fund investment. 4,5 The typical funding cycle is as follows. A firm identifies an investment opportunity, which often requires partial or full payment at closing. Anticipating these investment opportunities, the firm arranges a bank L/C with sufficient capacity to meet its liquidity needs. The bank L/C is drawn down to fund the investment, where the firm subsequently begins to work with an investment or commercial bank to secure permanent sources of finance. Once there is sufficient scale, equity or 4 We have explored whether REITs utilize the commercial paper market, and have found no evidence that they do. This is because REITs are generally younger firms without the AAA and AA credit ratings required to access this market. The ratings outcomes are in significant part because REITs are unable to retain cash flow. Thus, it appears that firms which have access to the commercial paper market are the larger, more mature firms that are able to retain cash— precisely the type of firms that are not likely to be financially constrained. In contrast, REITs, which are by definition cash constrained, almost exclusivily rely on bank L/C for external-source liquidity needs. 5 Bank L/C account for a large proportion of total firm-level bank debt in the U.S. A recent Federal Reserve Board survey reports that approximately 80 percent of commercial and industrial loans made by banks are arranged as short- term bank loan commitments or lines of credit. According to Martin and Santomero (1987) and Avery and Berger (1991), the primary stated reasons why firms use bank L/C are financial flexibility and speed of action. In practice, firms that are short on cash often use bank L/C to meet their immediate liquidity needs. See Sufi (2007) for further detail on the structure of bank lines of credit. 8
  • 10. long-term debt is issued with proceeds used to pay down bank L/C and hence recreate capacity to fund the next round of acquisitions. Table 1 compares REITs to other publicly traded firms (C-Corporations) that are not subject to dividend payout requirements. We show how five ratios vary and compare by year from 1990 to 2003. The five ratios, all as a percentage of beginning-year total book assets (K), are net investment (INV), dividends paid (DIV), net cash flow (NCF), the stock of cash and liquid securities (CS), and bank L/C capacity (L/C). We also report how investment correlates with the other reported variables over the sample period. Table 1 Here Observe the high rates of investment by REITs in the middle 1990s, and that average investment by REITs exceeded average investment by C-Corporations by almost 70 percent during the 1990-2003 sample period.6 As noted earlier, acquisitions were the largest component of net investment for REITs over the sample period, whereas capital expenditures and depreciation (a negative adjustment) were major components of net investment for C-Corporations.7 As a result of the dividend payout requirement, paid dividends are significantly higher and retained cash flow is significantly lower for REITs. Interestingly, as noted earlier, a significant fraction of REITs pay dividends in excess of the minimum 90 percent of net income required by regulation. Specifically, further analysis reveals that 70 percent of REITs pay at least 100 percent of the net income as dividends in any given year, with a median payout ratio of 120 percent. This equates to most firms retaining between 10 and 35 percent of cash flow as deployable liquidity or an addition to cash stock. 6 Average rates of investment for REITs in this table do not exactly match those reported in Figure 2 because different data sources were used to generate the respective table and figure. 7 Real estate assets are highly durable with depreciation periods that generally exceed 30 years, whereas assets held by industrial firms typically depreciate at a much faster rate. Consequently, capital expenditures are significantly higher for C-Corporations than for REITs. 9
  • 11. Cash stock is significantly lower and bank L/C capacity is significantly higher for REITs in comparison to C-Corporations. These two variables also display interesting covariation with respect to investment. Prior to 1993, equity REITs were a small sector with a total capitalization of approximately $20 billion. Rates of growth were relatively slow during this period. As a result, cash stocks were relatively high and bank L/C capacity was relatively low. Then, in 1993, rates of investment increased rapidly. In 1993 and 1994, the data indicate that REITs were able to tap their cash reserves to help fund investment. By 1995, however, cash reserves were largely depleted, and bank L/C capacity increased substantially as an apparent substitute for (deficient) internal-source liquidity. Finally, in 1999-2003, when investment rates drop off from previous high levels, bank L/C capacity likewise declines. Note that cash flow also drops of during this latter period, which limits REITs ability to replenish their cash reserves. In comparison, bank L/C capacity and cash stock display a distinct negative cross- correlation with investment for C-Corporations. Whereas cash flow constrained REITs are forced to rely on both internal and external sources of liquidity to help fund investment, the typical (cash unconstrained) C-Corporation appears to use its vastly greater internal store of liquidity to fund investment. And, because unconstrained firms don’t require external-source liquidity to meet their investment needs, C-Corporations maintain lower L/C capacity relative to the more cash constrained REITs. REITs thus appear to use bank L/C as a substitute for cash, and do so as a result of being cash flow constrained. At the same time, high levels of investment and paid dividends that exceed the minimum payout requirement suggest that many of these cash flow constrained firms are not necessarily financially constrained, in the sense that financial constraints are thought to cause low rates of investment and create strong incentives to hoard available cash flow. The availability of external-source liquidity in the form of bank L/C provides an intriguing link that may help explain these complex relations. 10
  • 12. 3. Hypothesis Development and Empirical Model Specification 3.a. Hypothesis Development To motivate our empirical model specification we appeal to a recent paper by Hennessy, Levy, and Whited (2007), who develop an internally consistent theory of dynamic investment with financial market frictions. In their model, marginal q depends on average q plus factors that account for distortions associated with costly external finance and debt overhang problems. In a dynamic setting, financial constraints and costly external finance create incentives for the firm to invest now in order to create financial capacity in the future. The dynamic investment-collateral capacity feature of Hennessy et al.’s analysis has particular relevance to firms such as REITs, which, besides experiencing significant constraints on cash flow retention, own tangible-durable collateral that offers significant debt capacity (see also Almeida and Campello (2007)). For hypothesis development, we primarily focus on the unique role of bank L/C as a low- cost liquidity source in a world with the identified financing frictions. 8,9 As a starting point, it is important to distinguish between the effects of bank L/C capacity and usage. Bank L/C capacity is a negotiated outcome that is strongly influenced by a bank that closely monitors the financial condition of the firm. We interpret changes in bank L/C capacity as a proxy for the magnitude of the firm’s debt overhang problem, where greater L/C debt capacity implies greater overall debt capacity and hence smaller debt overhang problems. This in turn implies greater investment. Conditional on bank L/C capacity, bank L/C utilization is chosen by the firm in response to current and expected future investment opportunities. Financially constrained firms will value future financial capacity created through investment, and intensely utilize their available L/C capacity to install capital when investment opportunities are available. Thus, controlling for other 8 As Joe Stiglitz noted in the original general discussion to Fazzari, Hubbard, and Petersen (1988), ―liquidity of a firm includes its lines of credit as well as its stock of cash. This is an alternative explanation of why the stock of cash has little explanatory power in the cross-sectional investment equations even if finance constraints are important.‖ 9 Banks are known to play a unique role in resolving financial market frictions, and hence can lessen (or tighten) financial constraints (Fama (1985), James (1987)). Houston and James (2001) document that multiple bank relationships relax investment-cash flow sensitivities, but they do not consider the direct impact of bank finance capacity or utilization on investment. Others, including notably Sufi (2007), have considered the link between firm financial characteristics, bank L/C availability and/or the cost of bank finance, but again a direct channel between bank finance and investment is largely unexplored. 11
  • 13. factors including q and bank L/C capacity, we posit that L/C usage proxies for preemptive investment incentives as they relate to relaxing financial constraints going forward. This leads to our first hypothesis: Hypotheses 1: An increase in bank L/C capacity is positively related to investment. Furthermore, conditional on L/C capacity, an increase in bank L/C utilization is positively related to investment. Hypothesis 1 suggests that L/C usage causes investment in a world with financial market frictions. Bank L/C usage is also important to financially constrained firms as a liquidity source to facilitate investment. This suggests that causation actually goes both ways, in that L/C usage is an important source of capital and therefore highly sensitive to actual levels of investment. This results in the following hypothesis: Hypotheses 2: Bank L/C utilization is particularly sensitive to and depends positively on actual investment. Financially constrained firms are thus hypothesized to utilize bank L/C as a substitute for scarce cash. In a dynamic setting, however, financially constrained firms must carefully manage their liquidity to ensure that financial slack is available to fund investment opportunities as they arrive over time. This is a major focus of Hennessey et al. (2007), and is also central to the analysis of Almeida, Campello and Weisbach (2004) in their paper on the cash flow sensitivity of cash. In this latter paper, cash holdings of financially constrained firms are sensitive to current cash flow as they (implicitly) relate to creating financial capacity for anticipated future investment. In our context, when cash flow is high, and because the opportunity cost of L/C usage will generally exceed that of the cash stock, constrained firms will use their cash flow to reduce their L/C usage in order to economize on cost and to recreate capacity for the next round of investment opportunities. In contrast, we would expect less financially constrained firms have a lower L/C usage rate to begin with and a greater ability to secure additional L/C capacity to meet liquidity needs. As a result, they show less propensity to reduce L/C usage. 12
  • 14. After controlling for investment, high cash flow realizations are thus predicted to be used by REITs to reduce bank L/C outstanding. That is, L/C use substitutes for cash flow. Moreover, given that external debt and equity issuances are infrequent and expensive to undertake relative to utilizing bank L/C, we expect external security issuances to be used (in part) to reduce bank L/C outstanding in order to reduce cost and replenish the liquidity stock. This results in our third hypothesis: Hypotheses 3: Bank L/C utilization depends negatively on cash flow as well as equity and debt issuances. In the short term, financially constrained firms can fund investment from two possible sources: cash or bank L/C. One possible way to increase cash available for investment is to reduce the dividend payout. Thus, dividend policy is hypothesized to respond to the high relative cost of external finance, and is endogenous as a result. We summarize this in the following hypothesis: Hypotheses 4: Financially constrained firms dynamically adjust their dividend payout downward to fund higher levels of investment. At least two factors complicate the intuition embedded in this hypothesis. First, it is important to recognize that limits to debt capacity and exogenous constraints on retaining cash reduce a firm’s discretion to select against outside equity investors. This is in turn lowers the cost of external finance and hence reduces the need to retain earnings to finance investment. Second, implications of financial capacity management as in Almeida et al. (2004) suggest that cash flow is valuable to financially constrained firms in all states of the world as a way to hedge against future income or investment shocks. This will cause dividend payouts to be smoothed, which will dampen the anticipated negative investment-dividend payout relation. 3.b. Empirical Model Specification To specify an empirical model we focus on three financial market frictions previously discussed: costly external finance, debt overhang, and collateral borrowing constraints. Furthermore, 13
  • 15. the four hypotheses stated in the previous section collectively imply a structural relation between investment and liquidity management as expressed through cash retention policy and bank L/C usage. This produces the following simultaneous system of equations to be estimated: Investment = f(Q,RCF,L/CUse,FMF) (1) RetainedCashFlow = f(Inv,L/CUse,FMF,Instrument) (2) L/CUse = f(Inv,RCF,FMF,Instrument) (3) where Q denotes average q, RCF denotes retained cash flow, FMF indicates variables employed to proxy for financial market frictions that are common across the system, and Instrument denotes exogenous instrumental variables used to identify the system. Note that retained cash flow identifies dividend payout, since the difference between gross cash flow (an instrumental variable in the RetainedCashFlow equation) and retained cash flow isolates the dividend payout. Table 2 identifies and summarizes variables that are common across the three equations, with their interpretation as related to investment. Retained cash flow and cash stock are used to proxy for costly external finance and debt overhang problems. Total leverage proxies for debt overhang problems. Its effect is complicated, however, by incentives for collateral-constrained firms to invest more today in order to create additional financial capacity in the future. Thus the relationship between investment and total leverage is unclear. Table 2 Here In terms of investment, the change in bank L/C capacity is considered as a proxy for debt overhang costs, and is of particular interest in the context of liquidity management for cash constrained firms. The change in bank L/C usage measures the firm’s actual utilization of its external-source liquidity stock. After controlling for L/C capacity and other relevant effects, intense 14
  • 16. utilization of available L/C capacity is interpreted to be consistent with incentives to install tangible collateral today (accelerate investment) in order to relax financial constraints in the future. Additional variables to control for the effects of financial market frictions include Firm age (years after IPO), which proxies for unspecified financial market frictions such as time-varying costly external finance or collateral capacity effects. For example, firms begin their life without any sort of managerial track record, and therefore may be more financially constrained than seasoned firms. Equity and debt issuance dummies are included as controls for major financing events that affect short-run liquidity management decisions. The purpose of equation (2) is to isolate determinants of cash flow retention policy as it interacts with investment and bank L/C utilization. We would expect cash flow retention policy to be sensitive to proxies for financial market frictions if firms are truly financially constrained. In addition to gross cash flow, lagged gross and retained cash flow are included as instrumental variables in the specification to account for inertia in the firm’s dividend policy.10 Change in L/C use is specified in equation (3). The relation between L/C usage and investment is of particular interest, as is the relation between L/C usage and retained cash flow. Instrumental variables in this equation are year-beginning total L/C capacity and year-beginning total L/C use. We would expect a positive relation between the change in L/C use and available total L/C capacity and a negative relation between the change in L/C use and the outstanding L/C debt level. This system of equations that account for interactions between liquidity management and investment allows us to collectively assess hypotheses 1 through 4. Critical relations between investment and L/C use or capacity, as articulated in hypotheses 1 and 2, are contained in equations (1) and (3). If REITs are truly financially constrained, we would expect high sensitivity of L/C use to investment given the importance of bank L/C in facilitating investment for these cash- constrained firms. Predictions associated with Hypothesis 3, which addresses the relation between 10 The inclusion of lagged retained cash flow as an instrumental variable in the net cash flow equation creates concerns regarding estimation consistency and bias in the system of equations. This issue will be addressed in detail in the estimation section of the paper. 15
  • 17. bank L/C use and cash, are captured by equation (3). The relation between investment and dividend payout as articulated in Hypothesis 4 is contained in equation (2), where the maintained hypothesis predicts that cash flow retention (dividend payout) moves directly (inversely) with investment. 4. Data and Preliminary Results 4.a. Data Our primary data source for model estimation is the SNL REIT database. This database provides detailed information on REIT investment, bank L/C availability and usage, and firm financial characteristics. To be included in the sample, a firm has to meet the following criteria: (1) listed on NYSE, AMEX or NASDAQ; (2) elected REIT tax status at the beginning of each sample year; (3) registered with the National Association of Real Estate Investment Trusts (NAREIT), the industry’s trade association; and (4) classified as an equity REIT by NAREIT.11 Given a sample period of 1990 through 2003, the original sample from the SNL REIT database has 3,667 firm-year observations. Capital offering data (equity and public debt issuance) from the NAREIT Capital Offering database is hand-matched into the SNL data set. These data consist of 156 equity IPOs, 1,401 seasoned equity offerings, and 950 public debt offerings. We also obtain firm-level REIT bank L/C information from Loan Pricing Corporation’s DealScan database. We eliminate observations that do not fit within the following bounds: 0.3<Qt 4.0; −1.0 Investmentt 3.0; 0.0≤CashStockt≤0.5; 0.0 Leveraget≤1.0 where all variables are scaled by year-beginning total book asset value. The wide bounds on investment result because some firms grew significantly in a given year due to a merger or other significant investments. We limit cash stock to 50 percent of book assets due to regulation that constrains REIT holdings in assets other 11 Equity REITs, which is the type of firm we are interested in in this study, are firms that hold ownership positions in the underlying real estate assets (as opposed to debt positions, which are firms that are referred to as mortgage REITs). 16
  • 18. than real estate interests.12,13 The final data set produces an unbalanced panel consisting of 1,257 firm-year observations from 1990 to 2003. Summary statistics are presented in Table 3. Average investment of just under 20 percent over the sample period indicates significant growth at the firm and sector level. 14 The mean Q value is 1.217, which is not terribly high given the rapid rate of investment by many firms during the sample period. One explanation for the lower Q values is that Q is better measured in our data due to book asset values that more accurately reflect replacement cost. Average cash flow net of dividends is only 1.64 percent of assets, while the average stock of cash and marketable securities is only 1.89 percent of assets.15,16 This reaffirms that REITs are unable to retain significant amounts of cash as a result of dividend payout requirements. Note that, even with severe constraints on cash retention, both variables exhibit significant cross-sectional variation. Total leverage as measured by long-term debt is approximately 40 percent of firm assets on average. Table 3 Here Median firm age is only eight years, reflecting the fact that a significant number of IPOs occurred over the sample period.17 Equity and public debt issuances occur in 36.3 and 21.7 percent of firm-years during the sample period, respectively. These percentages are well above rates observed with C-Corporations, and reflect REITs’ substantial growth during the sample period combined with their inability to retain significant amounts of cash. 12 For example, one observation which we eliminated had a cash stock value of .993. This firm was apparently in liquidation mode, as its asset base was declining significantly in the two years leading up to the .993 observation. 13 In addition, one observation with an annual change in L/C capacity of 17.35 times year-beginning book assets was eliminated. This firm experienced close to a 100-fold increase in book assets in one year, with total L/C capacity increasing from zero to 20 percent of year-end book assets. 14 Note that data used in this section, culled primarily from the SNL REIT data base, is different from the data used in Table 1 to compare REITS and C-Corporations. That data were derived primarily from Compustat and DealScan data bases, where we also did not apply the selection-screening criteria stated above. 15 Retained cash flow is defined as GAAP net income plus depreciation and amortization minus paid dividends. 16 Significant increases and decreases in assets in a given year caused by (dis)investment are the reasons for the relatively high max and min values, particularly in the flow variables. 17 As of 2003, a firm could have been in existence a total of 43 years as a REIT (original REIT legislation was passed in 1960). The SNL REIT database counts age from the firm’s IPO date, so some firms that initially went public as a C- Corporation converted to REIT status at some later date prior to the start of our sample period in 1990. 17
  • 19. The average changes in bank L/C capacity and actual bank L/C usage as a percentage of assets are 7.20 and 1.54 percent per year, respectively. 18 Note that the median value for both variables is zero. There are significant differences in the composition of the two median values, however. The change in L/C use is zero 14.2 percent of the time, where a significant proportion of those observations are from earlier in the sample period by firms that do not actively utilize their bank lines. In comparison, the change in L/C capacity is zero 42.7 percent of the time. This larger percentage reflects the fact that capacities are not always renegotiated on an annual basis. 4.b. Erickson−Whited Test of Q Measurement Quality Investment models that account for financial market frictions often posit that average q is informative and well measured, in the sense that the necessary conditions stated in Hayashi (1982) are satisfied and data used for empirical testing accurately describe the true current value of the capital stock. Although these presumptions are typically problematic, we have argued that the REIT data are both informative and well measured. This in turn implies that: i) only variables that measure financial market frictions are required to augment the classical investment equation specification (no additional controls for real market effects are required), and ii) coefficient estimates should more accurately reflect true economic magnitudes associated with the relevant variables, including Q. To test our assertions, we apply a generalized threshold test supplied by Erickson and Whited (2005) that allows us to assess coefficient sign robustness of regressors as they depend on the measurement quality of our proxy for Tobin’s q. The basic idea is to posit that Tobin’s q is unobservable while other variables are observable. A proxy is chosen for the unobservable 18 As noted earlier, one observation with a change in L/C capacity value of 17.35 was eliminated from the sample. The next largest observation, which is the maximum in our data set used for model estimation, is 4.68. In this case there was approximately a ten-fold increase in book assets over the year, accompanied with L/C capacity that roughly doubled. Thus, extremely rapid firm-level growth that sometimes occurred is the primary cause of the large percentage increases in L/C capacity. There are six other observations in the sample with a change in L/C capacity that was at least twice year-beginning book assets. 18
  • 20. regressor, thus causing the true value of q to be measured with error. For our purposes, we assume that the measurement error in q is uncorrelated with the disturbance term from an OLS regression. Threshold estimates are used to assess whether the signs of other regressors might be affected by the errors-in-variable problem. A threshold estimate near zero implies that the hypothesis that the coefficient of interest has the incorrect sign can be rejected, whereas a coefficient estimate near one makes it hard to reject the hypothesis that the coefficient of interest is zero. A coefficient in between zero and one is indeterminate. Thus, for our purposes, the null hypothesis is that the coefficient sign of the variable of interest (cash flow, for example) is zero and therefore not robust to the errors-in-variable problem. If the test does not reject the null hypotheses, then one can infer that Tobin’s q is sufficiently well measured so as to produce a reliable coefficient sign in an OLS investment equation estimation. Column A of Table 4 reports OLS estimation results for the investment equation. Note that the coefficient for average q is statistically as well as economically significant. The size of the coefficient at .139 is particularly noteworthy, and is several magnitudes larger than q coefficient estimates generated by OLS investment models that utilize industrial data (see, e.g., analysis contained in Erickson and Whited (2005)). Indeed, the size of the coefficient is such that the elasticity of investment with respect to Q is near one, which is what theory would predict.19 Table 4 Here Using estimates supplied to us by Toni Whited, in columns (B) and (C) we report threshold value estimates together with standard errors of the estimates. We find that all of the statistically significant variable coefficients reported in Table 4 pass the Erickson and Whited (2005) robustness test based on partial correlations.20 That is, the test allows us to reject the null hypothesis that the 19 Using the coefficient estimate of .139, a mean investment rate of .20 and a mean q value of 1.22, an elasticity measure of 0.848 is obtained. 20 A simple correlation test can be used as an alternative to the partial correlation test. The partial correlation test is more appropriate to our setting, as Erickson and Whited (2005) state: ―…individuals who prefer or require the 19
  • 21. non-q explanatory variable coefficients are zero as a result of errors-in-variables problems associated with our q measure. These test results thus provide additional evidence supporting our claim that Tobin’s q is well measured using data from the REIT sector, and that investment equation results are more reliable than those encountered in many other studies of the effects of market frictions on investment. 5. Full Sample Structural Model Estimation This paper has two primary objectives. The first is to analyze the endogenous effects of liquidity management on investment, and the second is to assess whether cash flow constraint is the same thing as financial constraint. This section emphasizes the former objective by focusing on full sample results, while the following section emphasizes the latter objective by analyzing sub- samples. Concern over the effects of endogenous dividend payout policy on investment has existed since Fazzari, Hubbard, and Petersen’s (1988) initial work. Although it is true that, in our data, REITs are constrained to pay out a significant portion of their gross cash flow as dividends, most REITs actually pay well in excess of the required minimum, with significant variation in actual paid dividends (see Chan et al. (2003)). This suggests that cash retention policy is potentially endogenous as it relates to investment. Bank L/C usage is also likely to be endogenous, since we know that REITs use their bank L/C to fund investment (Brown and Riddiough (2003)). We estimate a linear system based on equations (1) through (3) using a 3SLS procedure. The endogenous variables—investment, retained cash flow, and L/C use—are estimated in a first stage. Because two of the endogenous variables are used to estimate the third endogenous variable in each of the three equations, we pool all other variables to be used as instruments in the first stage estimation (including all fixed effects). This approach is conservative, in the sense that the pooling of all non-endogenous variables as instruments can increase the standard errors of the endogenous conceptual device of holding all else constant in order to form prior opinions about the relationship between two variables may be more comfortable dealing with the partial correlation.‖ 20
  • 22. variables to bias against statistical significance. To address this latter issue, we also estimated the system using an iterated 3SLS procedure (see Hausman (1975)). Three stage least squares and iterated 3SLS estimation results for the sample are displayed in Table 5. All model specifications include year and firm property type fixed effects in addition to an intercept term. Table 5 Here Investment equation results are reported in panel A. The coefficient estimate on Q decreases slightly from the OLS model coefficient estimate reported in Table 4, but remains relatively large and statistically significant. High investment-q sensitivity is consistent with results of Baker, Stein and Wurgler (2003), who find that equity dependent firms display a higher sensitivity of investment to q than non-equity dependent firms. Our finding makes sense in that equity-dependent firms like REITs repeatedly access external capital markets, and therefore must be careful to react appropriately to capital cost-investment signals contained in the firm’s stock price. At the same time that investment shows significant sensitivity to Q, a number of variables meant to proxy for financial market frictions, including variables measuring the endogenous effects of liquidity on investment, also exert a strong influence on investment. In particular, the coefficient on retained cash flow implies extreme investment sensitivity to available cash flow. This sensitivity of investment to retained cash flow can be explained by the tangibility of real estate as loan collateral and its effects on financial constraint. 21 Almeida and Campello (2007) show that 21 We have also considered the possibility of data or specification issues in explaining the high sensitivity of investment to retained cash flow. After examining histograms of all variables reported in Table 5 for the potential distorting effects of outliers, and eliminating 31 observations that might influence the results, we find that our results are robust to the potential effects of outliers (this holds for retained cash flow as well as all other variables). We also examine the model specification, in which we use lagged retained cash flow as an instrument in the retained cash flow equation. This variable is included because dividend policy is known to be smoothed, where current dividend payouts typically depend on the previous period’s dividend payout (see Lintner (1956)). The use of lagged endogenous variables as instruments is, however, known to potentially produce inconsistent and biased coefficient estimates in simultaneous equation estimation, since the lagged instrument in question may be correlated with the system disturbances. To address this issue, we conduct two diagnostic tests. First, we conduct a Sargan-Hansen misspecification test that assesses the effect of the lagged retained cash flow instrumental variable on the investment (as well as L/C use) equation. The test does not reject the null hypothesis that the instrument is an over-identifying restriction, thus lending credibility that the 21
  • 23. investment sensitivity to cash flow for financially constrained firms is increasing in asset tangibility. Because REITs hold very high percentages of highly tangible real estate, their credit multiplier can be expected to exceed that of most industrial firms that primarily hold plant, equipment and human capital (see Giambona and Schwienbacher (2007) for analysis of the effects of harder versus softer forms of collateral on debt capacity). The coefficient on cash stock is significant with a positive coefficient sign that exceeds unity. Investment is therefore quite sensitive to this variable, which is perhaps not surprising given firm constraints on retaining earnings. Firms that issue equity during the year invest over 14 percent more than firms that do not issue equity. Public debt issuance, on the other hand, has no affect on investment, supporting results in Brown and Riddiough (2003) who find that equity issuance with REITs is more often used to fund investment while public debt issuance is more often used to reconfigure the firm’s capital structure. Bank L/C capacity is intended to measure the change in the external-source liquidity stock available to fund investment. Hypothesis 1 asserts that this variable proxies for debt overhang effects, in the sense that the bank lender closely monitors the firm and sets L/C capacity accordingly. This variable enters significantly with a positive coefficient sign, consistent with the hypothesized relation. Bank L/C use has a positive and statistically significant coefficient sign. The interpretation of the L/C usage result is, conditional on controlling for changes in L/C capacity in anticipation of investment, firms that more intensely utilize their available L/C capacity invest more. We interpret this result as supporting predictions of Hennessey et al. (2007), who argue that constrained firms have incentives to invest in debt capacity in order to relax financial constraints in the future. Retained cash flow equation estimation results are displayed in panel B of Table 6. As noted earlier, dividend payout as a percentage of net assets is implicit in the estimation results, where one lagged instrumental variable does not unduly affect the results. The second test assesses serial correlation in the system disturbance term. A Durbin-Watson test fails to reject the hypothesis that there is no serial correlation in error terms. 22
  • 24. simply substitutes one minus the coefficient on current gross cash flow (1−β) and multiplies all other coefficients by minus one (−1) to obtain coefficient estimates in a dividend payout equation. Hypothesis 4 asserts that costly external finance causes financially constrained firms to lower their dividend payout when investment opportunities are greater. Investment is found to have no significant statistical effect on dividend policy in the full sample estimation, however. That is, estimation results imply that retained cash flow causes investment for these cash constrained/equity dependent firms, but that investment itself does not cause a reduction in dividends in order to increase retained cash flow. System estimation indicates that firms with a greater stock of cash simultaneously invest more and pay more dividends, suggesting that dividend policy may be used in part to moderate free cash flow concerns of outside investors. Firm age is weakly statistically significant, where older firms pay lower dividends. This is consistent with firm age proxying for reputation or other effects that moderate information or agency frictions between management and outside investors. Interestingly, dividend policy does not react to an equity or long-term debt issuance in the current period, suggesting that issuances are used to directly address investment and balance sheet concerns rather than to modify dividend policy in the short term. The coefficient estimate on current period gross cash flow indicates the marginal propensity to retain cash flow, where an incremental dollar of gross cash flow results in 87 cents being paid as dividends and 13 cents being retained internally. Given a required payout percentage of approximately 60 percent of total gross cash flow, and presuming that the payout constraint is not binding at the margin, our results suggest dividend payout on a marginal dollar of gross cash flow that is well above the required minimum. Why do these cash flow constrained/equity dependent firms behave this way? One plausible explanation is that free cash flow combines with muted pecking order effects (which reduces the cost of external finance) in such a way that shareholders prefer that management go through the public securities issuance process to validate investment 23
  • 25. value rather than modify dividend payouts so that a greater percentage of investment can be internally funded. A negative sign on lagged gross cash flow and a positive sign on lagged retained cash flow indicates persistence in dividend payout policy, in the sense that firms with higher lagged gross cash flows pay higher current dividends and firms with higher lagged retained cash flows (lower lagged dividend payouts) pay lower current dividends. More generally, these results combined with the insignificance of most other variables in this equation implies that REITs manage their dividend payout policy to a significant extent by distributing free cash flow but otherwise maintaining stable payouts over time. Lastly, consider L/C use equation estimation results displayed in panel C of Table 6. Investment is seen to exert a strong positive effect on L/C utilization, where a $1 increase in investment results in a $.32 increase in L/C use. This result is consistent with Fama and French (2002), who find that firms use short-term debt to absorb short-term variation in investment. Causality between investment and L/C use therefore goes in both directions, supporting assertions contained in Hypotheses 1 and 2. Retained cash flow displays a negative sign and is statistically significant. The magnitude of the coefficient implies an almost exact one-to-one substitution relation between cash flow and L/C usage, supporting assertions contained in Hypothesis 3. Structural estimation therefore suggests an intuitively appealing channel between liquidity and investment, in which cash is a strong substitute for L/C use, while, at the same time, cash and L/C use accelerate investment in tangible assets. Dividend policy, on the other hand, seems unresponsive to investment, suggesting a complex interplay between free cash flow, debt overhang and tangible asset/preemptive investment effects for these cash constrained firms. Other L/C use relations are as follows. Cash stock has a strong economic but marginally statistically significant substitution effect on L/C use. Leverage (as measured by long-term debt) is seen to have no effect on L/C use, however. This finding is better understood in the context of total 24
  • 26. L/C capacity and availability, both of which significantly impact L/C usage. A significant positive relation between beginning-period L/C availability and current period L/C usage suggests that less remaining slack in bank L/C capacity creates debt overhang that constrains usage going forward. Thus, once L/C capacity is set, L/C usage responds to available short-term (bank L/C) debt, but not to long-term debt. Equity and long-term debt issuance cause a reduction in L/C use. This is consistent with Brown and Riddiough (2003), who find that equity issuances are more often used to fund investment while long-term unsecured debt issuances are more often used to reconfigure the firm’s liability structure. Equity issuance thus reduces the need to use bank L/C to fund investment, whereas a long-term debt issuance is more often used to reduce bank L/C outstanding to prepare for further investment in the future. Summarizing, we wish to highlight the following results from the full sample simultaneous system equation estimation procedure: 1) Investment is highly responsive to signals contained in stock prices, as measured by average q; 2) Investment is also highly responsive to retained cash flow, where asset tangibility and debt capacity can explain the result; 3) Bank L/C plays an important role in investment through both capacity creation and incremental usage, suggesting that debt overhang and preemptive investment are relevant financial market frictions; 4) Dividend policy does not (statistically) respond to investment, firms distribute available cash at a rate that is well above the payout requirement, and dividend payouts display significant inertia as related to previous period payout policy; and 5) Bank L/C use is highly responsive to investment, and cash flow is a direct substitute for L/C use. 6. Is Cash Flow Constraint the Same Thing as Financial Constraint? Some confusion exists in the literature as to what exactly constitutes financial constraint. Generally speaking, for our purposes, a financially constrained firm is one that experiences financial market frictions that distort investment decisions. Financial market frictions are believed 25
  • 27. to reduce the responsiveness of investment to signals contained in stock prices, and to simultaneously increase responsiveness to other factors such as available liquidity. With the literature’s emphasis on cash as the sole source of firm liquidity, cash is often synonymous with financial constraint, implying that cash flow constrained firms experience greater financial market frictions and therefore are less responsive to q. Our data suggest that cash flow constraint does not necessarily imply financial constraint. Full sample estimation results demonstrate high sensitivity of investment to Q. Furthermore, as seen in Table 1, meager retained cash flows and cash stocks did not prevent these firms from undertaking significant amounts of investment and distributing dividends well in excess of the required minimum level. Consequently, the existence of cash flow constrained firms that may or may not be financially constrained provides us the opportunity to conduct new and refined assessments of the effects of financial market frictions. There are now two competing approaches to classifying firms as financially constrained. Kaplan and Zingales (1997) gather financial data over the 1970 to 1984 time period on the 49 ―constrained‖ firms in the Fazzari et al. (1988) sample. They then run an ordered probit to estimate a model that identifies and weights factors that produce a measure of the degree of financial constraint. Lamont et al. (2001) dub this the ―KZ index‖, and summarize the method in the appendix to their paper. More recently, Whited and Wu (2006) criticize the KZ index approach, and provide a classification scheme of their own which they argue is superior to the KZ index. Exogenously imposed dividend payout requirements and the fact that our data come from a single sector classification rather than across a number of different sectors do not lend themselves to the Whited and Wu approach. Consequently, we apply the KZ index approach to ordering firms in our sample. Table 6 displays summary statistics for the 50 percent of REITs in our sample that are classified as more versus less financially constrained. Firms that are classified as less financially constrained are seen to invest more (even though Q ratios are similar between the two sub-samples), generate higher cash flow, have greater cash stocks, pay higher dividends, are less leveraged, are 26
  • 28. more likely to have longer bank and security underwriter relationships, and have greater access to bank lines of credit. Interestingly, less financially constrained firms are approximately the same age on average and are smaller than firms classified as more financially constrained. 22 We conclude from this exercise that, based on a simple analysis of factors that are typically thought to indicate greater or lesser financial constraint, the KZ index works well at classifying firms in our sample. Table 6 Here Sub-samples are created by sorting firms into the top and bottom 50 percent of firms based on their KZ index score.23 Simultaneous equation estimation using 3SLS is used to generate the results, which are reported in Table 7. In comparing the results, clear differences between the two sub-samples are immediately apparent. First, observe that the explanatory power of the system (as measured by the weighted R-squared) of the more financially constrained sub-sample of REITs exceeds that of the less constrained REITs. Ignoring the effects of Q on investment for the moment, this outcome suggests that investment and financing policies of the more constrained REITs react more systematically to financial market frictions than do the less constrained REITs. Table 7 Here Consider investment equation results, as seen in columns (1) and (4) of Table 7. Investment sensitivity to q is significant and positive in the less constrained sub-sample, whereas it is insignificantly different from zero in the more constrained sub-sample. This is consistent with predictions of Gomes (2001), who argues that financial constraints reduce investment sensitivity to q. In contrast, investment is highly sensitive to retained cash flow in the more constrained sub- 22 To calculate KZ index scores we follow the methodology of Lamont et al. (2001), which results in using lagged values (t-1) for gross cash flow, cash stock, dividend ratio, and debt ratio as well as beginning period total book assets (t-2) to scale each of these values. This approach causes mean values for these variables to differ from means reported in Table 3. 23 We also examined the top versus bottom one-third of firms as sorted by KZ index score, with similar results. 27
  • 29. sample, and is insignificant in the less constrained sub-sample. Leverage as measured by long-term debt to total book assets is statistically significant in the more constrained sub-sample, in which more levered firms invest more. Changes in bank L/C capacity and use are significant in both sub- samples, but investment sensitivity to these variables is much higher in the more constrained sub- sample. The combined effects of retained cash flow and L/C usage in the more constrained sub- sample suggest that investment responds quickly and strongly to the liquidity flows of these firms. This finding is consistent with predictions of Almeida and Campell (2007) in their static analysis of the effects of asset tangibility and financial constraint on investment as well as Hennessey, Levy, and Whited (2007) in their dynamic analysis in which investment today increases debt capacity and hence relaxes financial constraints in the future. Differences between the two sub-samples are also apparent in the retained cash flow equations seen in columns (2) and (5). Other than the prior period’s dividend payout policy, less constrained firms react only to cash stock in determining the current period’s cash retention level. Even current period gross cash flow has an insignificant effect on retained cash flow, indicating that less constrained firms effectively pay out 100 percent of marginal gross cash flow as a dividend. In contrast, payout-retention policy of the more constrained firms is seen to depend on a number of factors, including investment. Consistent with the proposed relation in hypothesis 4, investment has a positive effect on cash retention (negative effect on dividend payout) in this sub- sample, while, simultaneously, higher retained cash causes greater investment. Our finding of a causative relation between short-term variation in investment and dividend payout is notable, as other studies have been unable to isolate the commonly hypothesized relation (see, e.g., Fama and French (2002)). Older firms pay lower dividends and firms that issue equity pay higher dividends in the more constrained sub-sample. These more constrained firms also utilize liquidity gained through 28
  • 30. positive changes in bank L/C capacity and usage to increase dividend payouts. Interestingly, these more constrained firms pay out only 42 percent of the marginal change in gross cash flow as dividends. All of this occurs at the same time that there is strong reference to previous period’s retention-payout decisions. Finally consider the L/C use equations in columns (3) and (6). Again, there are clear differences between the two sub-samples, in which more constrained firms display greater sensitivity to the system-wide effects. For example, L/C use is much more sensitive to investment and net cash flow in the more constrained sub-sample. This is consistent with the need of financially constrained firms to simultaneously fund investment with available liquidity as well as manage their available debt capacity to avoid costly covenant violations (Sufi (2007)). The more constrained firms are also sensitive to leverage and firm age. The only variable that generates higher L/C use sensitivity among the less constrained firms is public debt issuance. The interpretation of this latter result is that less constrained firms use the public debt issuance proceeds to reduce L/C use, whereas the more constrained firms are unable or unwilling to reduce their L/C use with issuance proceeds. All together, our findings show that cash flow constraint is not the same thing as financial constraint. All firms in our sample are cash flow constrained and equity dependent. Cash flow constrained firms that experience fewer financial constraints as measured by KZ index score are highly responsive to investment signals contained in their stock prices, but are mostly unresponsive to other variables that proxy for financial market frictions—including cash flow. There does appear to be interdependence between L/C usage and investment with the less constrained firms, but the effects are much less pronounced than with the more constrained firms. In comparison, the more constrained firms are unresponsive to investment signals contained in their stock prices, but display extreme sensitivity in all three structural equations to a number of variables that proxy for financial market frictions. 29
  • 31. Thus, firms that are equity dependent and financially unconstrained firms are sensitive to signals contained in stock prices, which refines earlier findings of Baker et al. (2003). In contrast, financial market frictions overwhelm stock price signals to drive investment, cash retention and bank L/C use policies of severely constrained firms. Predictions contained in hypotheses 1 through 4, which collectively state that investment, dividend policy, and L/C usage are endogenously determined and that there are significant interactions between variables, are supported by the sub-sample of more financially constrained REITs. For example, more financially constrained firms pay out less cash as dividends and pay down bank L/C faster with available cash, which is consistent with precautionary saving motives identified by Almeida et al. (2004). Consequently, our findings show that more financially constrained firms are more short-term focused than the less constrained firms and, in effect, take what they can get. In contrast, the sub-sample of less constrained firms show a longer-term focus with less sensitivity to proxies of financial market frictions. 7. Conclusion We examine the role of liquidity management as it affects investment by cash constrained firms. By employing a structural model to account for endogeneity, we find that investment and liquidity management interact in interesting and heretofore unexplored ways. For example, bank lines of credit are found to smooth variation in cash flow and accelerate investment. We also show that cash flow constraint is not equivalent to financial constraint, as firms that are both cash flow constrained and financially constrained behave very differently from firms that are cash flow constrained but are not financially constrained. The sub-sample of more constrained firms shows high sensitivity to variables that proxy for financial market frictions, lending support for agency- based explanations of costly external finance over information-based explanations. 30
  • 32. References Abel and Eberly. 2001. Q Theory Without Adjustment Costs and Cash Flow Effects Without Financing Constraints. Unpublished Manuscript, University of Pennsylvania. Almeida, Heitor and Murillo Campello. 2007. Financial Constraints, Asset Tangibility, and Corporate Investment. Review of Financial Studies 20, 1429-1460. Almeida, Heitor, Murillo Campello, and Michael S. Weisbach. 2004. The Cash Flow Sensitivity of Cash. Journal of Finance 59, 1777-1804. Avery, B. Robert, and Allen N. Berger. 1991. Loan Commitments and Bank Risk Exposure. Journal of Banking and Finance 15, 173-192. Baker, Malcolm, Jeremy C. Stein, and Jeffrey Wurgler. 2003. When Does the Market Matter? Stock Prices and the Investment of Equity Dependent Firms. Quarterly Journal of Economics 118, 969-1006. Brown, David T. and Timothy J. Riddiough. 2003. Financing Choice and Liability Structure of Real Estate Investment Trusts. Real Estate Economics 31, 313-346. Chan, Su Han, John Ecrickson, Ko Wang. 2003. Real Estate Investment Trusts: Structures, Performance, and Investment Opportunities. Oxford University Press, New York, NY. Chirinko. 1993. Business Fixed Investment Spending: Modeling Strategies, Empirical Results, and Policy Implications. Journal of Economic Literature 31, 1875-1911. Cooper, Russell and Joao Ejarque. 2003. Financial Frictions and Investment: Requiem in q. Review of Economic Dynamics 6, 710-728. Erickson, Timothy, and Toni Whited. 2000. Measurement Error and the Relationship between Investment and Q. Journal of Political Economy 108, 1027-1057. __________. 2005. Proxy Quality Thresholds: Theory and Applications. Financial Research Letters 2, 131-151. Fama, Eugene F. 1985. What’s Different about Banks? Journal of Monetary Economics 15, 29-39. Fama, Eugene F. and Kenneth R. French. 2002. Testing Trade-off and Pecking Order Predictions about Dividends and Debt. Review of Financial Studies 15, 1-33. Fazzari, M. Steven, R. Glenn Hubbard, and Bruce C. Petersen. 1988. Financing Constraints and Corporate Investment. Brookings Paper on Economic Activity 141-206. Giambona, Erasmo and Armin Schwienbacher. 2007. Debt Capacity of Tangible Assets: What is Collateralizable in the Debt Market? Unpublished Manuscript, University of Amsterdam. Gomes, Joao F. 2001. Financing Investment. American Economic Review 91, 1263-1285. Ham, J.C. and A. Melnik. 1987. Loan Demand: An Empirical Analysis Using Micro Data. Review of Economics and Statistics 69, 704-709. 31
  • 33. Hartzell, Jay C., Libo Sun, Sheridan Titman. 2006. The Effect of Corporate Governance on Investment: Evidence from Real Estate Investment Trusts. Real Estate Economics 34, 343-376. Hausman. 1975. An Instrumental Variable Approach to Full Information Estimators for Linear and Certain Nonlinear Econometric Models. Econometrica 43, 727-738. Hayashi, Fumio. 1982. Tobin’s Marginal and Average q: A Neoclassical Interpretation. Econometrica 50, 213-224. Hayashi, Fumio and Tohru Inoue. 1991. The Firm Growth–Q Relationship with Multiple Goods: Theory and Evidence From Panel Data on Japanese Firms. Econometrica 59, 731-754. Hennessy, Christopher A., Amnon Levy, and Toni M. Whited. 2007. Testing Q Theory with Financing Frictions. Journal of Financial Economics 83, 691-717. Hoshi, Takeo, Anil Kashyap, and David Scharfstein. 1991. Corporate Structure, Liquidity, and Investment: Evidence From Japanese Industrial Groups. Quarterly Journal of Economics 106, 33- 60. Houston, Joel, and Christopher James. 2001. Do Relationship Have Limits: Banking Relationships, Financial Constraints, and Investment. Journal of Business 74, 347-374. James, Christopher. 1987. Some Evidence on the Uniqueness of Bank Loans. Journal of Financial Economics 19, 217-235. Kaplan, Steven, and Luigi Zingales. 1997. Do Financing Constraints Explain why Investment is Correlated with Cash Flow? Quarterly Journal of Economics 112, 169-215. Lamont, Owen, Christopher Polk, and Jesús Saá-Requejo. 2001. Financial Constraints and Stock Returns. Review of Financial Studies 14, 529-554. Lintner, John. 1956. Distribution of Incomes of Corporations Among Dividends, Retained Earnings, and Taxes. American Economic Review 46, 97-113. Martin, Spenser, and Anthony Santomero. 1987. Investment Opportunities and Corporate Demand for Lines of Credit. Journal of Banking and Finance 21, 1331-1350. Ott, Steven, Timothy J. Riddiough, and Ha-Chin Yi. 2005. Finance, Investment, and Investment Performance: Evidence from the REIT Sector. Real Estate Economics 33, 203-235. Sufi, Amir. 2007. Bank Lines of Credit in Corporate Finance: An Empirical Analysis. Forthcoming, Review of Financial Studies. Whited, Toni M. 1992. Debt, Liquidity Constraints, and Corporate Investment: Evidence from Panel Data. Journal of Finance 16, 469-478. Whited, Toni M. and Guojun Wu. 2006. Financial Constraints Risk. Review of Financial Studies 19, 531-559. 32
  • 34. Figure 1. Distribution of Tobin's Q from 1992 to 2003 4 3.5 3 2.5 2 1.5 1 0.5 0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 MinQ P25Q MedianQ MeanQ P75Q MaxQ This figure displays the distribution of Q values by year for firms in the sample. Tobin’s Q is defined as the market-to-book ratio, i.e., (market equity + book debt) / total book assets. All values are as of the beginning the year. MinQ and MaxQ are the minimum and Maximum Q in a given year; P25Q and P75Q are Q at the 25 and 75 percentile; and MedianQ and MeanQ are the mean and median Q. The data source is from the SNL's REIT Financial database. 33
  • 35. Figure 2. Q and Investment from 1992 to 2003 1.5 1 1.4 0.8 1.3 0.6 1.2 1.1 0.4 1 0.2 0.9 0.8 0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Q (left axis) Investment (right axis) This figure displays the average Q and average rate of investment by year for firms in the sample. Q is defined as (market equity + book debt) / total book assets as of the beginning of the year. Investment is calculated as net investment / total book assets. Q is shown on the left axis and investment is shown on the right axis. The data source is from the SNL's REIT Financial database. 34
  • 36. Table 1. Financial Characteristics of REITs and C-Corporations REITs C-Corporations Year INV DIV NCF CS L/C INV DIV NCF CS L/C 1990 7.6 5.2 0.6 5.6 1.8 11.3 2.0 6.3 5.8 1.5 1991 4.1 4.2 0.8 3.6 4.4 13.2 1.9 5.0 5.5 1.9 1992 9.9 4.7 1.3 4.7 3.4 15.3 1.8 5.1 5.8 2.1 1993 28.5 7.3 1.2 3.9 2.8 17.4 1.8 5.1 6.2 3.1 1994 32.1 6.7 1.4 4.6 8.2 14.9 1.7 6.8 5.8 3.7 1995 24.6 6.8 1.6 2.7 14.7 9.4 1.9 7.5 6.6 5.3 1996 28.6 6.4 2.3 2.4 14.1 8.6 1.8 7.5 6.4 6.3 1997 39.1 5.8 2.2 2.5 15.7 8.9 1.8 7.4 6.3 6.7 1998 33.2 5.3 2.5 2.0 22.8 12.0 1.7 6.9 6.4 7.6 1999 11.8 4.3 2.0 1.3 15.2 11.3 1.7 7.0 5.4 4.9 2000 9.0 4.1 2.2 1.2 4.5 8.9 1.4 6.7 5.3 4.8 2001 7.8 4.0 1.6 1.7 8.7 10.8 1.4 5.1 5.2 4.8 2002 10.1 4.0 1.7 1.6 4.2 6.1 1.4 5.2 7.3 5.1 2003 10.6 4.1 1.4 1.4 4.8 4.7 1.3 5.5 8.4 3.9 Average 18.4 5.2 1.6 2.8 9.0 10.9 1.7 6.2 6.2 4.4 Correlation -- 0.78 0.49 -0.13 0.63 -- 0.55 -0.22 -0.60 -0.43 This table compares financial characteristics of REITs and C-Corporations. C-Corporations are selected from SIC codes 1000- 3999 and 5000-6000. Total year-beginning assets of firms are used to scale the numbers, and are reported as percentages. INV is the net investment; DIV is cash dividends paid; NCF is cash flow net of paid dividends; CS is the stock of cash and short- term investments as of the beginning of the year; and L/C is the total credit line capacity as of the beginning of the year. The bank L/C information is obtained from the Loan Pricing Corporation's DealScan database, and the firm financial information is from the Compustat Industrial Annual database. Correlation indicates the cross-correlation between investment and each of the other variables over the sample period. 37
  • 37. Table 2. Variables Common Across the System: Definitions and Economic Interpretation as Applied to Investment Expected Coefficient Variable Definition Economic Interpretation Sign RetainedCashFlowt Cash flow in year t net of dividends paid, Proxy for costly external finance and debt + scaled by beginning-year book assets overhang problems CashStockt Year-beginning stock of cash and marketable Proxy for costly external finance and debt + securities, scaled by book assets overhang problems Leveraget Year-beginning total long-term debt, Control variable and proxy for scaled by book assets debt overhang problems LnAget Natural logarithm of beginning-year firm age Control variable for unspecified financing ? frictions associated with firm age EDumt Dummy variable that equals 1 if an Control variable ? equity offering occurs during year t DDumt Dummy variable that equals 1 if a long-term Control variable ? unsecured debt offering occurs during year t L/CCapacityt-1 Change in L/C capacity during year t-1, Proxy for + scaled by beginning-year book assets debt overhang problems L/CUsaget Change in L/C use during year t, Proxy for preemptive investment/ + scaled by beginning-year book assets collateral capacity effects 38
  • 38. Table 3. Descriptive Statistics Variable N Min Max Mean Median Std Investmentt 1257 -0.902 2.806 0.197 0.078 0.340 Qt 1257 0.442 3.714 1.217 1.164 0.326 RetainedCashFlowt 1257 -0.590 0.125 0.0164 0.020 0.040 CashStockt 1257 0 0.427 0.0189 0.0076 0.040 Leveraget 1257 0 1.0 0.395 0.399 0.203 Aget 1257 1 51 11.142 8 9.846 EDumt 1257 0 1 0.363 0 0.481 DDumt 1257 0 1 0.217 0 0.412 L/CCapacityt-1 1257 -1.227 4.677 0.072 0 0.321 L/CUsaget 1257 -0.563 0.911 0.0154 0 0.103 The table presents summary statistics for the full sample, with observations covering the years 1990 to 2003. Investmentt is net investment over total book assets in year t; Qt is the market value of equity plus book value of debt divided by the book value of assets in year t; RetainedCashFlowt is net cash flow (total cash flow minus dividend payout) over book assets in year t; CashStockt is cash and cash equivalents over book assets in year t; Leveraget is total long-term debt over book assets in year t; Aget is the age of the firm in years in year t; EDumt indicates whether a REIT issues equity during year t; DDumt indicates whether a REIT issues long-term public debt during year t. L/CCapacityt-1 is the net increase in bank L/C capacity over book assets in year t-1; L/CUsaget is the net increase of bank L/C debt outstanding over book assets in year t; The data are from the SNL’s REIT financial database. 39
  • 39. Table 4. OLS Investment Equation and Erickson-Whited Threshold Test Results OLS Estimation Results Partial Correlation Threshold Partial Correlation Threshold (A) (B) (C) Qt 0.139 0.000 0.000 (4.79)*** (0.000) (0.000) NetCashFlowt 0.608 0.119 0.119 (2.73)*** (0.123) (0.123) CashStock t 0.634 0.000 0.000 (2.78)*** (0.000) (0.000) Leveraget -0.044 0.000 0.000 (-0.91) (0.000) (0.000) Lnaget -0.015 0.000 0.000 (-1.24) (0.000) (0.000) EDumt 0.150 0.028 0.028 (7.62)*** (0.020) (0.020) DDumt 0.030 0.086 0.086 (1.32) (0.086) (0.086) L/CCapacityt-1 0.139 0.000 0.000 (4.93)*** (0.000) (0.000) L/CUsaget 1.355 0.017 0.017 (15.15)*** (0.018) (0.018) N 1257 Adj. R2 0.410 This table presents OLS investment equation estimation and associated Erickson-Whited (2005) threshold test results. Regressors are as defined in Table 2. T-statistics are stated in parentheses for the OLS regression results. ***, **, * indicate statistical significance at 1%, 5%, and 10% levels, respectively. Standard errors are reported below the threshold estimates in columns (B) and (C). In column (B) it is assumed that the measurement error in q may be correlated with one or more regressors, but is uncorrelated with the disturbance term, whereas in column (C) it is assumed that the measurement error in q is uncorrelated with all other variables. The data are an unbalanced panel from the SNL’s REIT financial database covering the years 1990-2003. All the models include an intercept term as well as year and firm property type fixed effects as part of the specification. 40
  • 40. Table 5. 3SLS and IT3SLS Simultaneous Equation Estimation Results For Investment, Retained Cash Flow, and Bank L/C Use Panel A : Investment Equation Panel B : Retained Cash Flow Equation Panel C : L/C Use Equation (1) 3SLS (2) IT3SLS (3) 3SLS (4) IT3SLS (5) 3SLS (6) IT3SLS Qt 0.118 0.107 (4.01)*** (4.33)*** RetainedCashFlowwt 2.58 2.67 -0.938 -0.975 (3.05)*** (3.28)*** (-2.87)*** (-2.88)*** CashStock t 1.22 1.22 -0.203 -0.206 -0.236 -0.247 (3.94)*** (4.09)*** (-5.40)*** (-5.53)*** (-1.88)* (-1.91)* Leveraget -0.038 -0.035 0.0078 0.0081 -0.012 -0.0098 (-0.74) (-0.71) (1.16) (1.21) (-0.69) (-0.53) Lnaget -0.018 -0.018 0.0028 0.0029 0.0081 0.0084 (-1.32) (-1.37) (1.75)* (1.79)* (1.73)* (1.69)* EDumt 0.144 0.144 -0.0016 -0.0026 -0.041 -0.043 (6.67)*** (6.87)*** (-0.31) (-0.52) (-4.32)*** (-4.37)*** DDumt 0.010 0.012 0.0040 0.0034 -0.026 -0.026 (0.40) (0.51) (1.34) (1.16) (-3.22)*** (-3.04)*** L/CCapacityt-1 0.196 0.200 -0.0030 -0.0051 (6.56)*** (7.14)*** (-1.34) (-0.97) L/CUsaget 0.625 0.673 0.061 0.046 (1.95)* (2.28)** (1.41) (1.09) Investmentt 0.023 0.029 0.317 0.326 (0.78) (1.06) (7.60)*** (7.95)*** GrossCashFlowt 0.128 0.131 (2.70)*** (2.89)*** RetainedCashFlowt-1 0.263 0.254 (7.65)*** (7.48)*** GrossCashFlowt-1 -0.065 -0.062 (-4.08)*** (-4.09)*** TotalL/CCapt 0.051 0.060 (2.97)*** (3.93)*** TotalL/COutt -0.175 -0.175 (-7.72)*** (-7.43)*** N 1257 1257 1257 1257 1257 1257 Weighted R2 0.263 0.276 0.263 0.276 0.263 0.276 41
  • 41. This table presents 3SLS and Iterated (IT)3SLS simultaneous equation estimation results for firm investment, retained cash flow and bank L/C use. Regressors common to the investment and other equations are as defined in Table 2. Other exogenous variables are: GrossCashFlowt-1,t, the ratio of cash flow prior to dividend payout over book assets in years t-1 and t, respectively; RetainedCashFlowt-1, the ratio of net cash flow to beginning period book assets in year t-1; TotalL/CCapt, total L/C capacity at year-beginning t over book assets; and TotalL/COutt, total L/C debt outstanding at year-beginning t over book assets. T-statistics are listed in the parentheses. ***, **, * indicate statistical significance at 1%, 5%, and 10% level, respectively. The data are from the SNL’s REIT financial database. All the models include an intercept term as well as year and firm property type fixed effects as part of the specification. 42
  • 42. Table 6. Descriptive Statistics of the Sub-samples based on KZ Index Score Less Constrained Group (with lower KZ index) More Constrained Group (with higher KZ index) Variable Mean Median STD Mean Median STD It 0.249 0.127 0.423 0.145 0.052 0.338 Qt 1.250 1.210 0.367 1.185 1.144 0.275 CFt-1 0.092 0.085 0.049 0.055 0.057 0.031 CSt-1 0.023 0.007 0.052 0.015 0.008 0.022 DivRatiot-1 0.089 0.069 0.101 0.035 0.036 0.018 DivPayoutt-1 0.833 0.807 0.312 0.623 0.604 0.369 Levt-1 0.361 0.347 0.241 0.610 0.585 0.206 Aget 11.5 7 10.37 10.77 8 9.289 Bank Relationt .591 1 .492 .554 1 .497 U/W Relationt .337 0 .473 .162 0 .369 Total L/C Capt .215 .183 .199 .135 .128 .108 TotalAssett 879 575 1,037 1,791 853 2,816 This table presents summary statistics for the sub-samples classified by KZ index score. The less constrained group consists of 629 observations that are from lower-half of the full sample based on KZ index score, and the more constrained group consists of 628 observations that are from the upper-half of the full sample based on KZ index score. It is net investment over year-beginning book assets in year t; Qt is the market value of equity plus book value of debt divided by the book value of total assets at the beginning of year t; CFt-1 is gross cash flow (prior to dividend payout) over year-beginning book assets in year t-1; CSt-1 is cash and cash equivalents over book assets at the beginning of year t-1; DivRatiot-1 is total cash dividend over year-beginning book assets in year t-1; DivPayoutt-1 is total cash dividend over Funds From Operations (FFO) in year t-1, where FFO is defined as net income plus depreciation and amortization; Levt-1 is the ratio of long-term debt over total assets in year t-1; Aget is the age of the REIT in year t; and TotalAssett is total book assets in millions of dollars at the beginning of year t. The data are from the SNL’s REIT financial database. 43
  • 43. Table 7. KZ Index Sub-sample Results: 3SLS Simultaneous Equation Estimations For Investment, Retained Cash Flow, and Bank L/C Use Low KZ Index Score (Less Constrained) High KZ Index Score (More Constrained) (1) Investment (2) Ret’d Cash Flow (3) L/C Use (4) Investment (5) Ret’d Cash Flow (6) L/C Use Qt 0.128 -0.026 (3.23)*** (-0.92) RetainedCashFlowt 1.23 -0.232 4.44 -1.74 (1.03) (-0.57) (6.69)*** (-5.55)*** CashStock t 1.01 -0.259 -0.041 0.177 -0.031 -0.037 (2.29)** (-4.82)*** (-0.26) (0.29) (-1.09) (-0.18) Leveraget -0.033 -0.0091 0.0082 0.216 -0.0069 -0.092 (-0.34) (-0.60) (0.27) (2.33)** (-1.61) (-2.92)*** Lnaget -0.011 0.0034 0.0048 -0.035 0.0034 0.014 (-0.59) (1.16) (0.77) (-1.78)* (3.84)*** (2.00)** EDumt 0.164 0.0010 -0.043 0.128 -0.0067 -0.052 (5.17)*** (0.13) (-3.42)*** (4.32)*** (-3.95)*** (-4.57)*** DDumt 0.025 0.007 -0.037 0.019 -0.0001 -0.0158 (0.65) (1.22) (-3.52)*** (0.55) (-0.03) (-1.38) L/CCapacityt-1 0.173 0.0007 0.280 -0.0173 (4.49)*** (0.09) (5.38)*** (-5.80)*** L/CUsaget 0.870 0.132 1.516 -0.062 (1.78)* (1.56) (5.07)*** (-3.89)*** Investmentt 0.027 0.279 0.037 0.435 (0.77) (5.71)*** (3.90)*** (9.34)*** GrossCashFlowt -0.041 0.578 (-0.82) (13.11)*** RetainedCashFlowt-1 0.216 0.50 (4.34)*** (15.18)*** GrossCashFlowt-1 -0.061 -0.406 (-2.58)*** (-13.62)*** TotalL/CCapt 0.032 0.120 (1.50) (3.62)*** TotalL/COutt -0.126 8 -0.245 (-4.68)*** (-6.49)*** N 629 629 629 628 628 628 Weighted R2 0.281 0.281 0.281 0.605 0.605 0.605 44
  • 44. This table presents 3SLS simultaneous equation estimation results for investment, retained cash flow and bank L/C use based on sub-samples. Sub-samples are created by, first, calculating a Kaplan-Zingales (KZ) measure of the degree of financial constraint (see Lamont, Polk, and Saá-Requejo (2001, appendix) for a compact explanation of how the measure is calculated) and, second, splitting the full sample in half based on the ordered KZ index score. A higher KZ index score indicates a more financially constrained firm. Regressors common to the investment and other equations are as defined in Table 3. Other exogenous variables are: GrossCashFlowt-1,t, the ratio of cash flow prior to dividend payout over book assets in years t-1 and t, respectively; RetainedCashFlowt-1, the ratio of net cash flow to beginning period book assets in year t-1; TotalL/CCapt, total L/C capacity at year-beginning t over book assets; and TotalL/COutt, total L/C debt outstanding at year-beginning t over book assets. T-statistics are listed in the parentheses. ***, **, * indicate statistical significance at 1%, 5%, and 10% level, respectively. The data are from the SNL’s REIT financial database. All the models include an intercept term as well as year and firm property type fixed effects as part of the specification. 45

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