2009年2月24日学术报告.pdf

Legal Protection, Equity Dependence, and Corporate Investment: Evidence from around the World * Yuanto Kusnadi City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong Phone: (852) 3442-6212; Fax: (852) 2788-7944 E-mail: yuanto@cityu.edu.hk Sheridan Titman University of Texas at Austin and NBER Austin, Texas 78712-1179 Tel: (512)-232-2787; Fax: (512)-471-5073 Email: Sheridan.Titman@mccombs.utexas.edu K.C. John Wei Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong Tel: (852)-2358-7676; fax: (852)-2358-1749 Email: johnwei@ust.hk * We thank Malcolm Baker, Konan Chan, Chao Chen, Zhihong Chen, Cheol Eun, Ben Jacobsen, Dongwook Lee, Peter MacKay, Marco Pagano, Bill Maxwell, and seminar participants at the 2006 FMA/Asian Finance Association Conference (Auckland), the 2006 Chinese International Conference in Finance (Xi’an), the 2006 European Finance Association Annual Meeting (Zurich), the 2006 FMA Conference (Salt Lake City), the 2008 American Accounting Association Annual Meeting (Aneheim), Hong Kong University of Science and Technology, Korea University, Massey University, Nanyang Technological University, Singapore Management University, and the University of Melbourne for their helpful comments and suggestions. This paper won the Best Paper Award at the 2006 NTU International Conference on Finance (Taipei) and the 14th Conference on the Theories and Practices of the Securities and Financial Markets (Kaohsiung). The authors thank Dr. Virginia Unkefer for editorial assistance. We acknowledge financial support from an RGC Competitive Earmarked Research Grant of the Hong Kong Special Administration Region, China (Project no. HKUST6448/05H). All remaining errors are ours. Legal Protection, Equity Dependence, and Corporate Investment: Evidence from around the World ABSTRACT We investigate the effects of legal protection of investors and equity dependence on the investment-stock price sensitivity in an international setting. We find that firms in countries with strong legal protection of investors have investments that are more sensitive to stock prices than do firms in countries with weak legal protection. In addition, equity-dependent firms display higher investment-stock price sensitivities than do nonequity-dependent firms. Finally, the positive relation between legal protection and the investment-stock price sensitivity is more pronounced for equity-dependent firms than for nonequity-dependent firms. Overall, we provide evidence that both legal protection of investors and equity dependence influence managers’ corporate investment decisions. The existing literature has documented ample evidence of a positive relationship between corporate investments and stock prices. The traditional explanation for this observed positive association is the “Q-theory of investment” (Tobin (1969)). In an efficient market, a stock price (measured by Tobin’s Q) reflects the market’s information about a firm’s investment opportunities or the marginal rate of return on capital. In addition, a stock price may also contain some private information that managers do not know. For example, the theoretical models suggested by Dow and Gorton (1997) and Subrahmanyam and Titman (1999) illustrate that since stock prices also reflect a collection of information from market participants (such as traders and investors), managers can learn from the information in their firms’ stock prices about future investment opportunities (the prospective role of stock prices) as well as assess past decisions (the retrospective role of stock prices). 1 Chen, Goldstein, and Jiang (2007) formally examine this learning hypothesis by arguing that managers rely on the private information in stock prices and incorporate this information into their investment decisions. This implies that the sensitivity of corporate investments to stock prices should be higher for firms whose stock prices are more informative. They use stock price non-synchronicity (1-R2) and the probability of informed trading (PIN) as proxies for the degree of private information. Consistent with their hypothesis, they find that both measures have a positive effect on the investment-stock price sensitivity. Therefore, financial markets are not just a sideshow; they affect firms’ real activities. Recent studies in behavioral finance have suggested that stock markets may not be efficient and these studies have offered an alternative explanation for the positive relationship between corporate investments and stock prices through the equity-financing channel. More specifically, 1 Other theoretical papers that discuss the allocational role of stock prices include Dow and Rahi (2003), Dow, Goldstein, and Guembel (2007), Foucault and Gehrig (2008), and Goldstein and Guembel (2008). 1 the existence of a non-fundamental component in stock prices causes the effective cost of external equity to deviate from the cost of other forms of capital. In turn, this divergence affects a firm’s access to capital and, consequently, managers’ corporate investment decisions. Stein (1996) and Baker, Stein, and Wurgler (2003) argue that if the equity-financing channel is the cause of the positive relationship between corporate investments and stock prices, corporate investments should be more sensitive to changes in the non-fundamental component of stock prices in equity-dependent firms (i.e., those firms that have financial constraints and have to raise external equity to finance their investment projects) than in nonequity-dependent firms. The reason is that equity-dependent firms have incentives to raise equity for their corporate investments when their stock prices are overvalued (above their fundamental values); but they would forgo their investment opportunities rather than issue new equity when their stock prices are undervalued. In contrast, mispricing is unlikely to affect the investment decisions of nonequity-dependent firms. Using data from the U.S., Baker, Stein, and Wurgler (2003) find support for the equity-financing channel argument. Each of the above studies was designed to test the learning and equity-financing channels on firms’ capital investments in the U.S. Thus, an independent test of the learning and the equityfinancing channels on investments requires an analysis of markets outside the U.S. Conducting such a test is the goal of this paper. Morck, Yeung, and Yu (2000) argue that agency problems are more rampant in countries with poor investor protection, resulting in a smaller magnitude of private information (as measured by stock price non-synchronicity) embedded in the stock prices of these firms. Hence, managers’ investment decisions in these countries may not be sensitive to stock prices, resulting in inefficient allocation of investment capital. Subrahmanyam and Titman (1999) suggest that regulatory agencies (such as government) facilitate the efficiency of capital 2 allocation as strong investor protection enables the firms’ stock prices to be more informative, which, in turn, will guide managers in their investment decisions. The empirical findings from Morck, Yeung, and Yu (2000) and DeFond, Hung, and Trezevant (2007) appear to support the argument that stock prices and earnings of firms in countries with strong legal protection are more informative than those of firms in countries with weak legal protection. Based on the learning channel argument, we argue that firms in countries with strong legal protection of investors should have higher investment-stock price sensitivities than do firms in countries with weak legal protection. In addition, by combining the learning and equity-financing channels, we predict that the effect of a country’s legal protection of investors on the investment-stock price sensitivity should be stronger for equity-dependent firms than for nonequity-dependent firms. To the best of our knowledge, there has been no previous empirical study that examines these issues simultaneously. Our study offers several contributions to the literature. First, it is related to the law and finance literature. Many recent international studies have acknowledged the impact of legal protection of investors on various aspects of financial markets. 2 Our paper is also closely related to the literature on the investment-stock price sensitivity. Earlier studies by Morck, Shleifer, and Vishny (1990) and Blanchard, Rhee, and Summers (1993) find little evidence that the stock market affects corporate investment. However, recent studies by Baker, Stein, and Wurgler (2003), Chen, Goldstein, and Jiang (2007), and Polk and Sapienza (2008) find that the stock market has an important effect on corporate investments. 3 We contribute to these two strands of literature by showing that a country’s legal protection of investors is positively related to the 2 3 See Beck and Levine (2005) for an excellent review of the literature on law and finance. See also Chirinko and Schaller (2001) and Gilhirst, Himmelberg, and Hubberman (2005). 3 investment-stock price sensitivity, which is consistent with the learning channel argument as suggested by Chen, Goldstein, and Jiang (2007). 4 Second, we use three measures of equity dependence (the adjusted KZ index originally suggested by Kaplan and Zingales (1997), firm size, and a dividend dummy) and extend the tests suggested by Baker, Stein, and Wurgler (2003) to the international setting. Our results confirm the role of the equity-financing channel in corporate investments. More specifically, the investment-stock price sensitivity increases monotonically from nonequity-dependent firms to equity-dependent firms. Finally, our last test on the interaction between the learning and the equity-financing channels shows that the positive association between legal protection and the investment-stock price sensitivity is more pronounced for equity-dependent firms than for nonequity-dependent firms. The study that is closest to ours is by Chen, Jiang, and Goldstein (2007). We find that legal protection of investors plays a complementary role to price informativeness in affecting managers’ corporate investment decisions. While they emphasize the role of private information in stock prices at the firm level, we focus on the role of investor protection at the country level. Moreover, we show that the equity-financing channel reinforces the effect of legal protection on the investment-stock price sensitivity. Baker, Stein, and Wurgler (2003) argue that an increase in the investment-stock price sensitivity is often associated with greater efficiency in capital allocation. Based on their argument, our results seem to suggest that firms in countries with strong legal protection are more efficient in allocating their capital than are firms in countries with weak legal protection, in particular the equity-dependent firms. 4 Kelley and Woidtke (2006) investigate the role of investor protection in real investments. Their focus is on the foreign investments made by multinational U.S. firms, however. 4 We recognize the possibility that there are alternative explanations that drive our main results and that there are potential endogeneity issues concerning several of our explanatory variables in the regressions. We, therefore, attempt to address these concerns by performing a series of robustness tests. Nevertheless, our main results that legal protection and equity dependence positively affect the investment-stock price sensitivity survive these robustness tests. The remainder of this paper is organized as follows. Section I develops our hypotheses. Section II describes the sources of our data. Section III presents the empirical tests of our hypotheses and discusses the results. Section IV concludes the paper. I. A. Hypothesis Development Legal Protection, the Stock Market and Corporate Investments Legal protection includes not only the rights prescribed by regulations and laws, but also the effectiveness of enforcement. In a series of papers, La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1997, 1998, and 2002) examine the various aspects of legal protection of outside investors across 49 countries. They document that countries with strong legal protection of minority shareholders have better corporate governance, more developed capital and debt markets, larger stock market capitalizations, larger numbers of listed securities per capita, higher firm valuation, and a higher rate of IPOs than do countries with weak legal protection of investors. In a more recent paper, La Porta, Lopez-de-Silanes, and Shleifer (2006) examine how securities laws affect capital market development and find that laws do matter, especially in countries that facilitate private enforcement through disclosure requirements and liability rules. Several theoretical papers, such as those by Dow and Gorton (1997) and Subrahmanyam and Titman (1999), argue that managers can learn from the private information contained in stock 5 prices in making their corporate investment decisions. Using data from the U.S. market, Chen, Goldstein, and Jiang (2007) find empirical evidence that supports this learning hypothesis. More specifically, they document that the sensitivity of corporate investments to stock prices is higher for firms whose stock prices are more informative. Morck, Yeung, and Yu (2000) find that the magnitude of private information contained in stock prices is greater for firms in countries with strong legal protection, which suggests that the stock prices of these firms are more informative than those of firms in countries with weak legal protection. Moreover, firms in countries with strong legal protection are more likely to replace poor-performing CEOs (DeFond and Hung (2004)) and provide more informative earnings announcements (DeFond, Hung, and Trezevant (2007)) than their counterparts in countries with weak legal protection. Taken together, the evidence on stock prices indicates that they should reflect investment opportunities better and managers should more efficiently allocate their capital to investment projects in countries with strong legal protection than in countries with weak legal protection.5 Based on the learning hypothesis suggested by Dow and Gorton (1997), Subrahmanyam and Titman (1999) and Chen, Goldstein, and Jiang (2007), among others, and the empirical findings by Morck, Yeung, and Yu (2000) and DeFond, Hung, and Trezevant (2007), we argue that firms in countries with strong legal protection of investors should have investments that are more responsive to stock prices than their counterpart firms in countries with weak legal protection. 6 The above discussions lead to our first hypothesis: 5 A related paper by Love (2003) documents that financial development helps to overcome the financing constraints faced in corporate investment decisions. Shleifer and Wolfenzon (2002) further discuss how an increase in legal protection will mitigate the problem of the limited pledgeability of cash flows. 6 Hartzell, Sun, and Titman (2006) derive the same implications on the effect between corporate governance and the investments of REITs. Specifically, they hypothesize that REITs with more effective firm-level governance mechanisms (such as higher institutional ownership and lower insider ownership) should have investment spending 6 Hypothesis 1: Firms in countries with strong legal protection of investors have higher investment-stock price sensitivities than do firms in countries with weak legal protection of investors. B. Equity Dependence, the Stock Market, and Corporate Investments Baker, Stein, and Wurgler (2003) extend the model by Stein (1996) and derive implications on the role of the equity-financing channel in corporate investments. They argue that stock market irrationality is unlikely to affect the investment decisions of nonequity-dependent firms (those with sufficient liquidity and no debt). In contrast, equity-dependent firms will not want to go to the external market to issue equity when their stocks are undervalued, despite their need to raise funds for investments. The opposite occurs in the case of overvaluation in that equitydependent firms are now willing to issue equity to finance their investments under such circumstances. Therefore, equity-dependent firms have investments that are more sensitive to variations in the non-fundamental component of stock prices than do nonequity-dependent firms. Baker, Stein, and Wurgler (2003) use a modified KZ index first constructed by Kaplan and Zingales (1997) as a measure of equity dependence to examine the effect of equity dependence on the relationship between corporate investments and stock prices. 7 They define a firm as equity dependent if the firm’s stock price is undervalued and its available capital is low enough that it has to issue undervalued equity to achieve the first-best level of investments. Our hypothesis on the effect of equity dependence and the investment-stock price sensitivity follows theirs. Specifically, we expect that the sensitivity of corporate investments to stock prices is higher for that is more directly related to changes in the average stock prices (Tobin’s Q) of REITs in their respective property sectors. 7 The original KZ index also includes Tobin’s Q. The original KZ index has been widely used to measure the degree of financial constraints or equity dependence. For example, Lamont, Polk, and Saa-Requejo (2001) use the original KZ index to examine the impact of financial constraints on stock returns. 7 equity-dependent firms than for nonequity-dependent firms, which leads to our second hypothesis: Hypothesis 2: Equity-dependent firms have higher investment-stock price sensitivities than do nonequity-dependent firms. C. Legal Protection, Equity Dependence, the Stock Market, and Corporate Investments In their concluding remarks, Baker, Stein, and Wurgler (2003) suggest that the presence of agency conflicts increases the incentives of managers of nonequity-dependent firms to smooth investments and that the equity-financing channel ought to mitigate managers’ inefficient investment behaviors. Wurgler (2000) demonstrates that financial markets play an important role in the efficient allocation of capital. Moreover, Almeida and Wolfenzon (2005) develop a model to show that both investor protection and equity dependence affect the efficiency of capital allocation. In the absence of financial constraints and an adequate level of investor protection, managers tend to keep average projects. Therefore, corporate investments of these nonequitydependent firms in countries with weak investor protection may not be responsive to stock prices. When the level of investor protection increases, outside investors demand that managers terminate projects with low productivity and switch to those with high productivity. The presence of financial constraints further requires that managers commit to terminate average projects. Such actions would free up useful resources that would be channeled to more productive projects. Therefore, by considering the effects of both legal protection and equity dependence discussed above and in Hypotheses 1 and 2, we posit that the positive relationship between legal protection and the investment-stock price sensitivity should be more pronounced 8 for equity-dependent firms than for nonequity-dependent firms, which leads to our final hypothesis as follows: Hypothesis 3: The effect of legal protection on the investment-stock price sensitivity is more pronounced for equity-dependent firms than for nonequity-dependent firms. II. Data and Sample Statistics We collect two sets of data. The first dataset includes measures of legal protection of investors at the country level. We measure legal protection of investors based on the following four indices from La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) and La Porta, Lopezde-Silanes, and Shleifer (2006): (1) anti-director rights, (2) private enforcement, (3) public enforcement, and (4) investor protection. The second dataset consists of firm-level financial data. We obtain our firm-level data from Worldscope and Datastream, which are provided by Thomson Financial. After excluding the U.S., we manage to retrieve firm-level data for 43 out of the 49 countries covered by La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998). For each firm, we collect financial variables that include capital expenditures, cash flow, cash balances, cash dividends, total debt, total assets, and book value of equity from Worldscope and market value of equity from Datastream. From the initial sample, we exclude firms with missing firm-year observations, firms operating in the financial industry (firms with SIC codes between 6000 and 6999), and firms with book values of total assets of less than US$10 million. 8 Overall, our filtering process yields an unbalanced panel of 110,882 firm-year observations for 17,009 firms from 43 countries. The sample period is from 1985 to 2004. The second column of Table I reports the total firm-year observations for each 8 We use the exchange rates from Datastream to convert the book value of total assets from local currencies to U.S. dollars. 9 country in the final sample. Japan and the United Kingdom dominate the sample, each with more than 17,000 firm-year observations. [Insert Table I here] A. Country-Level Legal Protection Variables Our first country-level variable, the anti-director rights index (ANTIDIR), has been widely used in many studies as a proxy for the effectiveness of corporate governance or legal protection of investors for a country. It is constructed by adding one to each of the six rights that are intended to measure the degree of minority shareholders’ involvement in corporate decisions. It ranges from 0 to 6 with a higher value indicating a stronger degree of legal protection. ANTIDIR is taken from La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998). The second to fourth legal protection variables are taken directly from the work of La Porta, Lopez-de-Silanes, and Shleifer (2006). 9 The private enforcement index (PRIENF) is constructed by taking an arithmetic average of the disclosure requirements and liability standards indices. It ranges from 0 to 1. A higher value on this index suggests more effective private enforcement of securities laws. The disclosure requirements index captures regulations on the information that must be disclosed in an IPO transaction. The liability standards index measures the procedural difficulty in recovering losses from directors, distributors, and accountants. In sum, the private enforcement index measures the costs that investors need to incur to recover losses from corporate insiders, distributors of securities, and accountants. The public enforcement index (PUBENF) is constructed by taking an arithmetic average of the supervisor characteristics, rule-making power, investigative powers, orders, and criminal 9 See La Porta, Lopez-de-Silanes, and Shleifer (2006) for a more complete explanation on the various components that make up the private and public enforcement indices. 10 indices. It ranges from 0 to 1, with a higher value signifying more effective enforcement of securities laws by the regulators. The supervisor characteristics index captures three aspects of supervisors (the regulatory agency or official in charge of the securities market): its independence, its criteria for dismissal, and its focus on the securities markets. The rule-making power index measures the power of the supervisor in regulating equity-issuances and/or listing rules on the exchanges. The investigative power index measures the power of the supervisor in gathering the necessary documents and the ability to subpoena witness testimony in the case of litigation. The orders index measures the power of the supervisor in imposing sanctions on issuers, distributors, and accountants for non-criminal violations of securities laws. The criminal index measures the power to enforce sanctions for criminal violations of securities laws. In sum, the public enforcement index measures the power of the capital market supervisory agency in regulating and enforcing the securities laws. Finally, the investor protection index (INVPRT) is the principal component of the disclosure requirements, liability standards, and anti-director rights indices. In addition to the four legal protection measures, we also use the legal origin variable (LO). La Porta et al. (1998) have shown that the countries with common-law-based legal systems offer stronger legal protection to investors than do countries with other legal traditions. For convenience, we use a dummy variable that equals one for English common-law-based countries and zero for French, German, or Scandinavian civil-law-based countries. From the third column of Table I, we observe that there is wide variation in the legal origin of countries in our sample. The majority of countries in the Asia Pacific (9 out of 14) and Africa (2 out of 3) adopt the English common-law-based system. In contrast, the French civil-law-based system is followed in South America and most of the countries in Western Europe (8 out of 18). 11 The fourth to seventh columns of Table I provide the statistics on ANTIDIR, PRIENF, PUBENF, and INVPRT, respectively. ANTIDIR ranges from 0 to 4, PRIENF ranges from 0.18 (Austria) to 0.96 (Canada), PUBENF ranges from 0 (Japan) to 0.90 (Australia), and INVPRT ranges from 0 (Germany) to 0.96 (Canada). Consistent with the finding by La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998), we also document that there exists a positive correlation between a country’s legal origin and the ANTIDIR score, with common-law-based countries reporting higher ANTIDIR scores than civil-law-based countries reported. This is consistent with the notion that common-law-based countries in general provide stronger legal protection to investors than do civil-law-based countries. B. Firm-Level Financial Variables For each firm, i, our measure of corporate investments in year t (CAPXit) is calculated as capital expenditures in year t divided by total assets at the end of year t-1. Cash flow (CFit) is calculated as income before extraordinary items plus depreciation and amortization in year t divided by total assets at the end of year t-1. Finally, our measure of stock prices, Tobin’s Q (Qit), is calculated as the market value of equity (the stock price multiplied by the number of shares outstanding) plus total assets minus the book value of equity divided by total assets at the end of year t. We winsorize all financial variables at the 1st and 99th percentile levels to minimize the outlier problem. Kaplan and Zingales (1997) construct the original five-variable KZ index on a sample of 49 low-dividend manufacturing firms in the U.S. as a measure of financial constraints. They estimate the following regression equation to construct the KZ index for each firm-year observation: 12 KZ it = −1.002CFit − 39.368DIVit − 1.315CASH it + 3.139 LEVit + 0.283Qit , (1) where KZit is the KZ score for firm i in year t. CASHit is cash balances and is calculated as cash balances at the end of year t divided by total assets at the end of year t-1. LEVit is leverage and is calculated as the sum of long-term debt and debt in current liabilities divided by the sum of longterm debt, debt in current liabilities, and the book value of equity (all measured at the end of year t). DIVit is dividends and is calculated as cash dividends paid in year t divided by total assets at the end of year t-1. CFit and Qit are cash flow and Tobin’s Q in year t as defined earlier. Baker, Stein, and Wurgler (2003) argue that Tobin’s Q captures information about stock mispricing and is often used as a proxy for investment opportunities. To avoid this dual role for Q, Baker, Stein, and Wurgler use a modified four-component version of the KZ index that omits Q in the baseline specification as follows: KZ it = −1.002CFit − 39.368DIVit − 1.315CASH it + 3.139 LEVit , (2) Firms with higher modified KZ scores are considered to be more equity dependent or more reliant on external equity financing for their investment projects. One concern about the original or modified KZ index is that it is originally constructed for a small sample of manufacturing firms in the U.S., which might not be applicable for our sample of international firms. In order to alleviate this possible bias, we construct our version of the modified KZ index (called the adjusted KZ index) as follows. For each country, we reset the weights of each component of the index in equation (2) such that each variable contributes equally to the total variance of the index. Since each country will have different weights on the components of the index, the adjusted KZ index should be a better measure of equity dependence 13 in our international sample. 10 Besides the adjusted KZ index, we also use firm size (SIZE) computed as the natural logarithm of total assets and a dummy variable to represent dividendpaying firms (DIVD) as our measures of equity dependence. 11 Panel A of Table II presents the summary statistics of the financial variables. The mean (median) corporate investment measure (CAPXt) across the 43 sample countries is 7.5 (4.7) percent. The value for our international sample is slightly lower compared with the mean (median) of 8.2 (6.0) percent reported by Baker, Stein, and Wurgler (2003) on a sample of U.S. firms. The mean (median) cash flow (CFt) is 7.7 (7.8) percent; the mean (median) Tobin’s Q (Qt) is 1.4 (1.1); the mean (median) adjusted KZ index is 0.01 (0.3); and the mean (median) SIZE is 12.5 (11.3). Seventy-four percent of our sample firms are dividend-paying firms Additionally, we present Pearson correlations among the financial variables and the legal protection measures in Panel B of Table II. 12 The cross-correlations between the financial variables and the legal protection variables are generally negative (16 out of 24), with magnitudes ranging from -0.32 to 0.14. The correlations between corporate investment and Tobin’s Q and cash flow are both positive and significant at the one-percent level, which is consistent with the evidence reported in the literature. Finally, the correlations among the four legal protection variables are all in the expected direction (positive) with magnitudes ranging from 0.38 to 0.81. [Insert Table II here] 10 Baker, Stein, and Wurgler (2003) also use the same approach in one of their robustness tests and find that the main results are not influenced by the weights attached to each component of the modified KZ index. 11 Note that we first winsorize the components of the adjusted KZ index at the 1st and 99th percentile before estimating the index. We use the four-component adjusted KZ index in our subsequent empirical tests. However, in our unreported tests, we obtain similar results when we use the original five-component adjusted KZ index. 12 The country-median values of financial variables are used to compute the correlation coefficients. 14 III. Empirical Results and Discussions In this section, we empirically examine (i) if the legal protection of investors affects the investment-stock price sensitivity, (ii) if the empirical evidence found in U.S. firms on the relationship between equity dependence and corporate investment (Baker, Stein, and Wurgler (2003)) can be extended to international markets, and (iii) if equity dependence has any impact on the effect of legal protection of investors on corporate investment. Our research design closely follows that of Baker, Stein, and Wurgler (2003). A. The Role of Legal Protection of Investors on the Investment-Stock Price Sensitivity Following Fazzari, Hubbard, and Petersen (1988) and Baker, Stein, and Wurgler (2003), we estimate the following baseline investment equation for our international sample: 44 20 j =1 t =1 CAPX it = ao + bQit −1 + fCFit + ∑ b j Industryij + ∑ bt Yeart +uit , (3) where CAPXit is the corporate investment of firm i in year t, Qit-1 is firm i’s Tobin’s Q in year t-1, and CFit is its cash flow in year t. These variables are as defined earlier. Regression coefficients of b and f measure the sensitivity of corporate investments to stock prices and to cash flow, respectively. Since our measures of legal protection are country-specific variables, we use a country random-effects generalized least squares (GLS) model to estimate equation (3) for our panel data. We include industry (bj) and year (bt) dummies to control for industry and year effects. We follow Fama and French (1997) in classifying our international firms into 44 industries. The uit is an error term that is assumed to be independent of the explanatory variables. To mitigate the problems of serial auto-correlation and heteroskedasticity, heteroskedasticity-corrected robust standard errors. 15 we estimate White’s Column (1) of Table III presents the regression coefficients for the baseline investment equation (3). We find that both regression coefficients of b and f are positive and statistically significant at the one-percent level. The finding for our international sample corroborates the prevailing general results that corporate investments are positively correlated to both stock prices and cash flow. Our next task is to test the role of the learning channel in the investment-stock price sensitivity. Morck, Yeung, and Yu (2000) and DeFond, Hung, and Trezevant (2007) find that stock prices and earnings of firms in countries with strong legal protection of investors, respectively, reflect a greater magnitude of private information than do those of firms in countries with weak legal protection. Therefore, in this paper, we use the legal protection of investors as our measure of the information embedded in stock prices from which managers can learn. To test Hypothesis 1, we modify equation (3) to include our measures of legal protection as follows: CAPX it = ao + bQit −1 + c(Qit −1 × LEGALi ) + dLEGALi 44 20 + fCFit + ∑ b j Industryi + ∑ bt Yeart +uit , j =1 j (4) t =1 where LEGALi is one of the measures of legal protection of investors for firm i. Note that firms from the same country will have the same value of LEGAL. The other variables are as defined previously. The coefficient of interest in this case is the coefficient on the interaction term, c. Hypothesis 1 predicts that c is positive. In other words, we posit that the legal protection of investors increases the sensitivity of corporate investments to stock prices. We estimate equation (4) by including the interaction of each of the four measures of legal protection (ANTIDIR, PRIENF, PUBENF, and INVPRT) with Tobin’s Q as an additional 16 independent variable. 13 The results of the country random-effects regressions are reported in Columns (2) to (5) of Table III. Although the b coefficients (on Q) in Columns (2) to (5) are smaller in magnitude when compared with the b coefficient in Column (1), they continue to be positive and statistically significant at the one-percent level. The magnitudes of the f coefficients (on CF) are also stable across Columns (2) to (5). In addition, LEGAL (i.e., the coefficient d) is negatively and significantly associated with corporate investments in three of the four models, which suggests that firms in countries with strong legal protection tend to undertake fewer investment projects than do firms in countries with weak legal protection. More importantly, we find that the coefficient of the interaction term, c, is positive and significant at the one-percent level in all four models (with t-statistics of 6.32, 7.52, 7.52, and 7.55, respectively), which is supportive of Hypothesis 1 (i.e., the learning channel argument). Moreover, the economic significance of the result is quite substantial. A one standard deviation increase in ANTIDIR increases the investment-stock price sensitivity by about 22 percent. Similarly, a one standard deviation increase in INVPRT leads to about a 61 percent increase in the investment-stock price sensitivity. 14 Since legal origin is correlated with the legal protection variables, we replace LEGAL with the legal origin dummy (LO), which equals zero in civil-law-based countries and one in common-law-based countries, and re-estimate equation (4). As shown in Column (6), the result is consistent with the earlier specifications given that the coefficient of the interaction term is significant with the expected positive sign. In fact, firms in countries with English common-law- 13 Note that except for ANTIDIR and LO, the other measures of legal protection have been standardized to the range of between 0 and 5 in all the regression specifications. 14 For ANTIDIR, the increase in the sensitivity of corporate investment to stock price is [(1.31×0.002)/0.005]×100 =52%. For INVPRT, the value is [(1.22×0.002)/0.0004]×100=61%. 17 based traditions display substantially higher investment-stock price sensitivities by about 64 percent than do firms in countries with civil-law-based traditions. 15 Since La Porta et al. (2006) report that INVPRT can explain about 70 percent of all variations in the components of PRIENF and ANTIDIR, we use INVPRT as our representative measure of legal protection in the subsequent tables. 16 We further include the interaction term between INVPRT and CF as an additional explanatory variable and estimate equation (5) below: CAPX it = ao + bQit −1 + c(Qit −1 × INVPRTi ) + dINVPRTi + fCFit + g (CFit × INVPRTi ) 44 20 j =1 t =1 + ∑ b j Industryij + ∑ bt Yeart +uit . (5) We present the estimation results of equation (5) in Column (7) of Table III. The coefficient of the interaction term between INVPRT and Q still displays a positive association with corporate investments. Although it is not the focus of our paper, we find that the coefficient of the interaction term between INVPRT and CF is negative and significant at the one-percent level. Our interpretation is that firms in countries with strong legal protection face fewer constraints in raising external capital to finance their investment projects than firms in countries with weak legal protection (Levine (2005)) and that strong legal protection helps to overcome the information asymmetry between managers and outside shareholders (Myers and Majluf (1984)). 17 Hence, corporate investments of firms in countries with strong legal protection are not so much affected by liquidity constraints, resulting in these firms exhibiting smaller investmentcash flow sensitivities than firms in countries with weak legal protection. [Insert Table III here] 15 The increase in the investment-stock price sensitivity is (0.005/0.008)×100 = 63%. We find that the results are consistent for the other legal protection measures and they are available upon request. 17 A related paper by Love (2003) documents that financial development helps to overcome the financing constraints faced in corporate investment decisions. Shleifer and Wolfenzon (2002) further discuss how an increase in legal protection will mitigate the problem of the limited pledgeability of cash flows. 16 18 B. The Role of Legal Protection in the Investment-Stock Price Sensitivity: Sensitivity Analysis In this section, we perform a series of sensitivity tests to examine if our results are robust to alternative specifications. Our main regression specification is based on our unbalanced panel data and, as such, our results can be influenced by within-industry effects as well as cross-time effects. We attempt to address these issues in two ways. In the first test, we adopt the FamaMacBeth (1973) approach in estimating equation (5) for each year and report the means and standard errors of the coefficient estimates of the cross-sectional regressions in Column (1) of Table IV. Although the coefficient on investor protection, d, becomes insignificant and the sign of the coefficient on Q, b, changes to negative (and significant), we observe that our main result on the coefficient of the interaction term, c, remains intact since it continues to be positive and significant (t-statistic = 5.10) at the one-percent level. Examining the results of the crosssectional regressions further reveals that the coefficient of the interaction term, c, is significantly positive in 13 out of the 20 years of our sample period. In the second test, following Wurgler (2000) and Rajan and Zingales (1998), we examine if our results can also hold at the industry level. For each country, we compute the equally weighted yearly mean values of all our explanatory variables across all firms within each industry. We then re-estimate equation (5) using the country random-effects model with industry and year dummies and present the results in Column (2) of Table IV. We observe that the main result persists even at the industry level. That is, the coefficient of the interaction term, c, is significantly positive. Next, we exclude Japan and the United Kingdom from our sample to check if our results hold after dropping observations from these two countries that dominate our sample. The results are reported in Column (3) of Table IV. We show that the coefficient of the interaction term, c, 19 remains positive and significant (t-statistic = 6.84) at the one-percent level. Therefore, our main finding is not driven by observations from these two countries with the most firm-year observations. In the above analysis, the legal protection measures are taken to be constant for the entire sample period. However, a recent study by Pagano and Volpin (2005) documents that antidirector rights have evolved in the past decade due to legal reforms. By relying on questionnaire answers by respondents including legal experts and business practitioners, Pagano and Volpin (2005) manage to compile a list of updated anti-director rights for 47 out of the original 49 countries in La Porta, Lopez-de-Silanes, Shleifer, and Vishny’s (1998) survey for the period from 1993 to 2002. We combine the original anti-director rights variable for the period from 1985 to 1992 with the updated anti-director rights variable for the period from 1993 to 2004 and rename it as the “new anti-directors rights index” (NEW_ANTIDIR). 18 We then replace ANTIDIR with NEW_ANTIDIR to re-estimate equation (5). As shown in Column (4) of Table IV, our results are robust regardless of whether we use the original or the updated anti-director rights. Stock prices of firms with greater earnings management should be less informative and firms in countries with weak investor protection are more likely to be subject to greater earnings management. To test the robustness of our investor protection measure, we replace the INVPRT index in Equation (5) with the earnings management index (EMGMT) constructed by Leuz, Nanda, and Wysocki (2003). Unlike the other measures of legal protection, a higher score on the index implies that firms in a specific country are more prone to earnings management, indicating that legal protection is likely to be low for that country. Therefore, our prediction is that the coefficient, c, should be negative and the coefficient, g, should be positive. We find that the results (unreported) are consistent with our prediction. Firms in countries with a high EMGMT 18 We thank Marco Pagano for providing us with the updated anti-director rights data. 20 score, which are more likely to engage in earnings manipulation, have investments that are less (more) sensitive to stock prices (cash flow) than their counterparts in countries with a low EMGMT score have. We also acknowledge that our regression specification might suffer from a potential endogeneity problem for a couple of reasons. First, La Porta, Lopez-de-Silanes, and Shleifer (2006) document that investor protection matters for financial development, which implies that our measure of legal protection (INVPRT) could be endogenous. Second, several papers have questioned the use of Tobin’s Q as a proxy for investment opportunities since Q cannot be measured without errors. 19 We employ instrumental variables as a partial means to address these concerns. We follow La Porta, Lopez-de-Silanes, and Shleifer (2006) by using the legal origin dummy, the efficiency of the judiciary score, and the natural logarithm of GDP per capita as instruments for INVPRT. Likewise, we use the lagged one- and two-period Q values, the legal origin dummy, and other financial variables as instruments for Q. We then obtain the predicted values of INVPRT and Q from the first-stage regressions. Subsequently, we examine whether or not our main results are influenced by the level of capital market development. We use the external market capitalization measure (computed as the ratio of stock market capitalization held by small shareholders to gross domestic product and obtained from La Porta, Lopez-de-Silanes, and Shleifer (2006)) as a proxy for the level of financial development of a country’s capital market (DEV). We then include DEV and the predicted INVPRT as well as their interactions with both the predicted Q and CF as additional control variables to re-estimate equation (5). We present the results in Column (5) of Table IV. The results indicate that the interaction-term coefficient between the predicted INVPRT and the 19 For example, Q is also commonly used as a measure of firm valuation and La Porta, Lopez-de-Silanes, Shleifer, and Vishny (2002) have found that firms in countries with stronger legal protection tend to have higher valueation than their counterparts in countries with weaker legal protection do. 21 predicted Q remains positive and significant (t-statistic = 2.99) at the one-percent level. Thus, the legal protection of investors increases the investment-stock price sensitivity, even after accounting for the level of capital market development and addressing the endogeneity of Q and INVPRT. A recent paper by Bekaert, Harvey, Lundblad, and Siegel (2007) introduces several exogenous measures of a country’s growth opportunities. They find that these measures can help to predict future investments. Bekaert et al. (2007; p. 1082) further comment that “such a measure should prove useful in numerous empirical studies seeking to avoid endogeneity problems”. We replace Q with two of their measures, local growth opportunities (LGO) and global growth opportunities (GGO), and interact them with INVPRT and re-estimate equation (5). 20 The results in Columns (6) and (7) of Table IV suggest that the use of these exogenous measures of growth opportunities do not alter our main results. Both interaction-term coefficients of LGO and GGO with INVPRT are positive and significant at the one-percent level (with tstatistics of 4.40 and 3.49, respectively). Finally, we replace Q by ΔQ (i.e., the change in Q), which is calculated as the change between contemporaneous Q and lagged one-period Q and re-estimate equation (5). The result in Column (8) of Table IV shows that the coefficient on the interaction term between ΔQ and INVPRT remains positive and significant. [Insert Table IV here] 20 See Bekaert, Harvey, Lundblad, and Siegel (2007) for details on the construction of the exogenous measures of country growth opportunities. We thank Stephan Siegel for providing us with the monthly data. The data are only available for 40 out of our 43 sample countries. 22 To summarize, our findings so far highlight the important role that legal protection plays in the relationship between corporate investments and stock prices. 21 In general, firms in countries with strong legal protection of investors have higher investment-stock price sensitivities than do firms in countries with weak legal protection. C. The Role of Equity Dependence on the Investment-Stock Price Sensitivity After establishing that legal protection matters in the sensitivity of a firm’s corporate investments to stock prices, we now explore the role of the equity-financing channel. As elaborated earlier, we use the adjusted KZ index, firm size (SIZE), and a dividend dummy (DIVD) as our measures of equity dependence to test Hypotheses 2 and 3. It is noted that the degree of equity dependence increases (decreases) with the adjusted KZ (SIZE) scores. Similarly, we also classify those firms that do not pay dividends (DIVD = 0) as being equity-dependent. We first assign firms to quintile portfolios where Quintile 1 represents the portfolio of firms in the bottom 20% of the adjusted KZ and SIZE scores. Correspondingly, Quintile 5 represents the portfolio of firms in the top 20% of the adjusted KZ and SIZE scores. Following Baker, Stein, and Wurgler (2003), the assignment of firms is based on the firm’s median-adjusted KZ and SIZE scores over the whole sample period. 22 We then estimate the baseline investment equation (3) separately for each KZ or SIZE quintile portfolio. As before, we use a country random-effects model that controls for industry and year effects, with White’s heteroskedasticity-corrected robust standard errors. Hypothesis 2 predicts that the coefficient, b, (on Tobin’s Q) increases with KZ quintiles and decreases with 21 In our unreported tests, we have also estimated several other specifications such as including lagged values of CAPX to account for the possibility that a firm’s investment often occurs with a lag (Lamont (2000)), excluding Q or including Q2 in the specification, removing all non-manufacturing firms (SIC codes between 3000 and 4000), and dividing our sample into two sub-periods (1985 to 1994 and 1995 to 2004). The results are robust to these additional controls. 22 Alternatively, we assign firms based on the firm-year adjusted KZ and SIZE scores and obtain similar results. 23 SIZE quintiles and DIVD. In other words, we posit that the investment-stock price sensitivity should increase with the degree of equity dependence. Panels A and B of Table V present the estimation results of equation (3) for Quintile 1 to Quintile 5 portfolios formed using the adjusted KZ and SIZE scores, respectively, and Panel C presents the results based on DIVD. We observe that the coefficient, b, (on Q) increases (decreases) from 0.003 (0.013) in the bottom KZ (SIZE) quintile to 0.023 (-0.002) in the top KZ (SIZE) quintile. Meanwhile, the coefficient, f, (on CF) does not appear to follow any meaningful pattern. Likewise, the coefficient, b, (on Q) decreases from 0.018 for firms that do not pay dividends (DIVD = 0) to 0.000 for those that pay dividends (DIVD = 1). Hence, firms classified as equity dependent display larger investment-stock price sensitivities than do firms that are classified as nonequity dependent. These empirical results are consistent with Hypothesis 2 and extend the findings by Baker, Stein, and Wurgler (2003) into an international sample. Thus, we interpret our result as supportive of the equity-financing channel as a potential explanation for the positive relationship between corporate investments and stock prices among international firms. [Insert Table V here] In terms of economic significance, a one standard deviation change in Tobin’s Q changes corporate investment by 0.3 to 2 percentage points. 23 The economic effect is quite sizeable, considering that the median corporate investment over the whole sample period is 4.7 percent. This analysis further suggests that the effect of stock prices on corporate investments outweighs the effect of cash flow for firms in the top KZ quintile (those that are considered to be the most 23 For firms in the bottom KZ quintile, the change in corporate investment is (0.858×0.003)×100 = 0.3%. Correspondingly, for firms in the top adjusted KZ quintile, the value is (0.858×0.023)×100 = 2%. 24 dependent on external equity). 24 This finding is again similar to that found by Baker, Stein, and Wurgler (2003) in a sample of U.S. firms. D. The Role of Equity Dependence on the Investment-Stock Price Sensitivity: Sensitivity Analysis An alternative specification to test Hypothesis 2 is to estimate the following equation country-by-country: CAPX it = ao + bQit −1 + c(Qit −1 × KZ it ) + dKZ it + fCFit 44 20 j =1 t =1 + ∑ b j Industryij + ∑ bt Yeart +uit , (6) where KZit is the adjusted KZ score for firm i at time t. The other variables are defined previously. The coefficient of interest in this case is c. We expect c to be positive. That is, corporate investments are more sensitive to stock prices for equity-dependent firms than for nonequitydependent firms. We include the interaction of Q with KZ, SIZE, or DIVD as an additional regressor and estimate equation (6) for each country using the fixed-effects model that controls for industry and year effects and White’s heteroskedasticity-corrected robust standard errors, clustered by industry. 25 Panel A of Table VI presents the results for the country-by-country analysis. As shown in Column (1) for KZ, the regressions yield positive coefficients for c (on Q×KZ) in 38 out of 42 countries and, among them, the coefficients for 24 countries are significant at the tenpercent level or better. 26 We find similar results for DIVD in Column (3). The regression coefficients for c are negative in 34 out of 42 countries and, among them, the coefficients for 19 24 For firms in the top KZ quintile, the effect of a one standard deviation change in cash flow on corporate investment is (0.116×0.107)×100 = 1.2%. 25 Since this is a country-by-country regression, we follow Baker, Stein, and Wurgler (2003) and use the fixedeffects model rather than the random-effects model. 26 Zimbabwe is deleted because of too few observations for the fixed effect regressions. 25 countries are significant at least at the ten-percent level. However, the results for SIZE in Column (3) are much weaker than those with KZ or DIVD. Only half of the regression coefficients for c are negative and, among them, only 10 are significant at the ten-percent level or better. In Panel B of Table VI, we observe that the results are very robust in that all regression coefficients, c, are highly significant with the predicted signs when we adopt the Fama-MacBeth (1973) approach as well as for emerging, developed, and European countries; excluding Japan and UK, for manufacturing firms, and for both sub-periods (1985 to 1994 and 1995 to 2004). [Insert Table VI here] E. The Roles of Legal Protection of Investors and Equity Dependence To explore the interaction between legal protection and equity dependence on the investment-stock price sensitivity, we estimate the following equation separately for portfolios formed using KZ, SIZE, or DIVD: CAPX it = ao + bQit −1 + c(Qit −1 × INVPRTi ) + dINVPRTi + fCFit 44 20 + ∑ b j Industryi + ∑ bt Yeart +uit . j =1 j (7) t =1 We present the estimation results using the country random-effects model in Panels A to C of Table VII. For portfolios of firms based on KZ, we observe that the regression coefficient, b, (on Q) continues to display the same monotonically increasing pattern with the KZ quintiles as demonstrated in Table V with b increasing from -0.001 for the bottom KZ quintile to 0.018 for the top KZ quintile. The coefficient, b, is also statistically significant at least at the one-percent level for every KZ quintile, except for Quintile 1. The results from SIZE and DIVD portfolios are consistent with those reported earlier in that the coefficient, b, decreases from the smallest firms to the largest firms or from firms that do not pay dividends to firms that pay dividends. 26 The coefficient of the interaction term, c, (on Q×INVPRT) is perhaps of more interest in this case. We detect that the coefficient, c, increases from 0.001 for the bottom KZ quintile to 0.004 for the second largest KZ quintile, before dropping back to 0.002 for the top KZ quintile. All interaction terms are positive and statistically significant at the one-percent level. In addition, we also find that the coefficient, c, is only significant for firms in SIZE Quintiles 1 and 3 and for those firms that do not pay dividends (DIVD = 0). In general, our results appear to be consistent with Hypothesis 3 that the effect of legal protection on the investment-stock price sensitivity is more significant for firms that are classified as equity dependent. 27 Next, we estimate the following equation using the whole sample: CAPX it = ao + bQit −1 + c(Qit −1 × INVPRTi ) + d (Qit −1 × INVPRTi × EDit ) + eCFit + f (CFit × INVPRTi ) + g (CFit × INVPRTi × EDit ) 44 20 j =1 t =1 (8) + hINVPRTi + kEDit + ∑ b j Industryij + ∑ bt Yeart +uit , where EDit is one of the measures of equity dependence for firm i in year t. The coefficient of interest is d. Hypothesis 3 predicts that d is positive when ED = KZ and negative when ED = SIZE or DIVD. Explicitly, the positive relationship between legal protection and the investmentstock price sensitivity should be more pronounced for equity-dependent firms than for nonequity-dependent firms. We estimate equation (8) using the country random-effects model and report the results in Panel D of Table VII. Columns (1) to (3) present the results using KZ, SIZE, and DIVD, respectively. We find that even after controlling for the effect of legal protection, the coefficient of the interaction term on Q×INVPRT×ED, d, is positive (negative) and significant at the one-percent 27 In contrast, Chen, Jiang, and Goldstein (2007) find that the magnitude of the interaction term between their measures of price informativeness and Tobin’s Q is larger in the bottom KZ quintile than that in the top KZ quintile, which suggests that managers of financially unconstrained firms have more flexibility in responding to the changes in information in market prices when making corporate investment decisions. 27 level for ED = KZ (SIZE and DIVD), which is consistent with the prediction of Hypothesis 3. That is, equity-dependent firms in countries with strong legal protection exhibit higher investment-stock price sensitivities than do nonequity-dependent firms in countries with weak legal protection. The signs and significance levels of other control variables are similar to those found in the previous tables. Furthermore, we also find that the coefficient of the interaction term on CF×INVPRT×ED, g, is negative (positive) and significant for ED = KZ (SIZE and DIVD). 28 [Insert Table VII here] F. The Roles of Legal Protection of Investors and Equity Dependence: Sensitivity Analysis Finally, we repeat the sensitivity tests on the roles of legal protection and investor protection in corporate investments similar to those conducted previously in Table IV. For the sake of brevity, we report only the results for the coefficients of the interaction terms, c and d, in Table VIII. 29 We find that both coefficients display the expected signs and are highly significant in all the specifications, providing additional support for Hypothesis 3. Taken together, our empirical tests provide fresh evidence that both legal protection and equity dependence are important determinants of the investment-stock price sensitivity. [Insert Table VIII here] IV. Conclusion We examine the effects of legal protection and equity dependence on the sensitivity of corporate investments to stock prices in an international sample that covers 43 countries. We find 28 In addition, we use INVPRT and each of our measures of equity dependence to perform a two-way sort and obtain four subsamples of firms. In our unreported results, we find that equity-dependent firms in countries with strong legal protection display the largest investment-stock price sensitivity, while nonequity-dependent firms in countries with weak legal protection countries display the smallest sensitivity. These results are consistent with the prediction of our Hypothesis 3. 29 The results for the other coefficients are essentially unchanged from the previous table. 28 that firms in countries with strong legal protection of investors display higher investment-stock price sensitivities than do firms in countries with weak legal protection. The results are consistent with the learning channel argument as suggested by Chen, Goldstein, and Jiang (2007) that stock prices are more informative and managers can learn from their firms’ stock prices in making investment decisions in countries with strong legal protection of investors. In addition, the effect of stock prices on investments increases with the degree of equity dependence, which is consistent with the equity-financing channel argument suggested by Baker, Stein, and Wurgler (2003). By examining the interplay of these two effects, we observe that the positive relationship between legal protection and the investment-stock price sensitivity is more pronounced for equity-dependent firms than for nonequity-dependent firms. By understanding that the legal environment affects both capital market development and the investment-stock price sensitivity, regulatory agencies can facilitate efficiencies in capital allocation by choosing the appropriate level of shareholder rights and enforcement of securities laws afforded to minority shareholders. Subsequently, minority shareholders’ rights and securities laws influence managers’ investment decisions by serving as effective mechanisms to restrain managers, particularly managers of equity-dependent firms, from engaging in firm value-destroying activities. As a conclusion, we provide corroborating evidence that helps to explain the cross-country determinants of corporate investment decisions. In addition, by using the investment-stock price sensitivity as a measure of the efficiency of capital allocation as argued by Baker, Stein, and Wurgler (2003), we have shown that the learning and equityfinancing channels interact with each other, with the objective of attaining more efficient allocation of capital to investment projects. 29 REFERENCES Almeida, Heitor, and Daniel Wolfenzon, 2005, The effect of external capital on the equilibrium allocation of capital, Journal of Financial Economics 75, 133-164. 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. Beck, Thornsten, and Ross Levine, 2005, Legal institutions and financial development, In C. Menard and M. Shirley (edited), Handbook of Institutional Economics, Netherlands: Springer. Bekaert, Geert., Campbell R. Harvey, Christian Lundblad, and Stephan Siegel, 2007, Global growth opportunities and market integration, Journal of Finance 62, 1081-1137. Blanchard, Oliver, Chanyong Rhee, and Lawrence Summers, 1993, The stock market, profit, and investment, Quarterly Journal of Economics 108, 115-136. Chen, Qi, Itay Goldstein, and Wei Jiang, 2007, Price informativeness and investment sensitivity to stock price, Review of Financial Studies 20, 619-648. Chirinko, Robert S. and Huntley Schaller, 2001, Business fixed investment and ‘bubbles’: The Japanese case, American Economic Review 91, 663-680. DeFond, Mark, and Mingyi Hung, 2004, Investor protection and corporate governance: Evidence from worldwide CEO turnover, Journal of Accounting Research 42, 269-312. DeFond, Mark, Mingyi Hung, and Robert Trezevant, 2007, Investor protection and the information content of annual earnings information, Journal of Accounting and Economics 43, 37-67. Dow, James, and Gary Gorton, 1997, Stock Market Efficiency and Economic Efficiency: Is there a connection?, Journal of Finance 52, 1087-1129. Dow, James, and Rohit Rahi, 2003, Informed trading, investment, and efficiency, Journal of Business 76, 439-454. Dow, James, Itay Goldstein, and Alexander Guembel, 2007, Incentives for information production in markets where price affects real investment, Wharton School Working Paper. Fama, Eugene F., and James D. MacBeth, 1973, Risk, return and equilibrium: empirical tests, Journal of Political Economy 81, 607-636. Fazzari, Steven M., R. Glenn Hubbard and Bruce C. Petersen, 1988, Financing constraints and corporate investment, Brookings Papers on Economic Activity 1, 141-205. 30 Foucault, Thierry, and Thomas Gehrig, 2008, Stock price informativeness, cross-listings, and investment decisions, Journal of Financial Economics 88, 146-168. Gilchrist, Simon, Charles Himmelberg, and Gur Hubberman, 2005, Do stock price bubbles influence corporate investment, Journal of Monetary Economics 52, 805-827. Goldtein, Itay, and Alexander Guembel, 2008, Manipulation and the allocation role of prices, Review of Economic Studies 2008, 133-164. Hartzell, Jay C., Libo Sun, and Sheridan Titman, 2006, The effect of corporate governance on investment: Evidence from Real Estate Investment Trusts (REITs), Real Estate Economics 34, 343-376. Kaplan, Steven N., and Luigi Zingales, 1997, Do investment-cash flow sensitivities provide useful measures of financial constraints? Quarterly Journal of Economics 112, 169-215. Kelley, Eric, and Tracy Woidtke, 2006, Investor protection and real investment by U.S. multinationals, Journal of Financial and Quantitative Analysis 41, 541-572. La Porta, Rafael, Florencio Lopez-de-Silanes, and Andrei Shleifer, 2006, What works in securities laws? Journal of Finance 61, 1-32. La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert Vishny, 1997, Legal determinants of external finance, Journal of Finance 52, 1131-1150. La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert Vishny, 1998, Law and finance, Journal of Political Economy 106, 1113-1155. La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert Vishny, 2002, Investor protection and corporate valuation, Journal of Finance 57, 1147-1170. Lamont, Owen, Christopher Polk, and Jesus Saa-Requejo, 2001, Financial constraints and stock returns, Review of Financial Studies 14, 529-554. Levine, Ross, 2005, Finance and Growth: Theory and Evidence, Phillipe Aghion and Stevan Durlauf, edited, Handbook of the Economic Growth, The Netherlands: Elsevier Science. Leuz, Christian, Dhananjay Nanda, and Peter D. Wysocki, 2003, Earnings management and investor protection: An international comparison, Journal of Financial Economics 69, 505527. Love, Inessa, 2003, Financial development and financing constraints: International evidence from the structural investment model, Review of Financial Studies 16, 765-791. Morck, Randall, Andrei Shleifer, and Robert Vishny, 1990, The stock market and investment: Is the market a side-show? Brookings Papers on Economic Activity 2, 157-215. 31 Morck, Randall, Bernard Yeung, and Wayne Yu, 2000, The information content of stock markets: Why do emerging markets have synchronous stock price movement? Journal of Financial Economics 58, 215-260. Myers, Stewart, and Nicholas Majluf, 1984, Corporate financing and investment decisions when firms have information that investors do not have, Journal of Financial Economics 13, 187221. Pagano, Marco, and Paulo F. Volpin, 2005, The political economy of corporate governance, American Economic Review 96, 1005-1030. Polk, Christopher, and Paola Sapienza, 2008, The stock market and corporate investment: a test of catering theory, forthcoming in Review of Financial Studies. Rajan, Raghuram R., and Luigi Zingales, 1998, Financial dependence and growth, American Economics Review 88, 559-586. Shleifer, Andrei, and Daniel Wolfenzon, 2002, Investor protection and the equity markets, Journal of Financial Economics 66, 3-27. Subrahmanyam, Avindhar and Sheridan Titman, 1999. The going-public decision and the development of financial markets, Journal of Finance 54, 1045-1082. Stein, Jeremy C., 1996, Rational capital budgeting in an irrational world, Journal of Business 69, 429-455. Stein, Jeremy C., 2003, Agency, information and corporate investment, in George Constantinites, Milton Harris, and Rene Stulz, edited, Handbook of the Economics and Finance, New York: Elsevier Science. Tobin, James, 1969, A general equilibrium approach to monetary theory, Journal of Money, Credit and Banking 1, 15-29. Wurgler, Jeffrey, 2000, Financial markets and the allocation of capital, Journal of Financial Economics 58, 187-214. 32 Table I Legal Protection Variables This table presents the legal protection variables for our sample. LO refers to the legal origin of the company law or commercial code of each country and is taken from La Porta et al. (1998). ANTIDIR is an index of shareholder protection also taken from La Porta et al. (1998). PRIVENF is an index calculated as the average of the disclosure requirements and liability standards indices from La Porta et al. (2006). PUBENF is an index calculated as the average of the supervisor characteristics, rule-making power, investigative powers, orders and criminal indices also from La Porta et al. (2006). INVPRT is the investor protection index, which is the principal component of the disclosure requirements, liability standards, and anti-director rights indices from La Porta et al. (2006). The sample consists of 43 countries and covers the period from 1985 to 2004. Country Argentina Australia Austria Belgium Brazil Canada Chile Colombia Denmark Egypt Finland France Germany Greece Hong Kong India Indonesia Ireland Israel Italy Japan Malaysia Mexico Netherlands New Zealand Norway Pakistan Peru Philippines Portugal Singapore South Africa Firm-year observations 366 4,221 834 1,104 1,664 7,034 952 179 1,665 21 1,235 6,530 5,450 271 3,565 2,163 1,382 768 287 2,289 19,005 4,166 925 2,123 651 1,381 546 290 699 544 2,426 2,577 LO (3) French English German French French English French French Scandinavian French Scandinavian French German French English English French English English French German English French French English Scandinavian English French French French English English ANTIDIR (4) 4 4 2 0 3 5 5 3 2 2 3 3 1 2 5 5 2 4 3 1 4 4 1 2 4 4 5 3 3 3 4 5 33 PRIVENF (5) 0.36 0.71 0.18 0.43 0.29 0.96 0.46 0.26 0.57 0.36 0.58 0.49 0.21 0.41 0.79 0.79 0.58 0.55 0.66 0.44 0.71 0.79 0.35 0.69 0.55 0.48 0.48 0.50 0.92 0.54 0.83 0.75 PUBENF (6) 0.58 0.90 0.17 0.15 0.58 0.80 0.60 0.58 0.37 0.30 0.32 0.77 0.22 0.32 0.87 0.67 0.62 0.37 0.63 0.48 0.00 0.77 0.35 0.47 0.33 0.32 0.58 0.78 0.83 0.58 0.87 0.25 INVPRT (7) 0.48 0.78 0.10 0.07 0.44 0.96 0.61 0.35 0.36 0.20 0.47 0.47 0.00 0.32 0.85 0.77 0.51 0.48 0.59 0.20 0.42 0.36 0.10 0.54 0.73 0.44 0.46 0.66 0.63 0.57 0.81 0.60 Table I - Continued Country South Korea Spain Sri Lanka Sweden Switzerland Taiwan Thailand Turkey United Kingdom Venezuela Zimbabwe Firm-Year Observations 3,859 1,480 90 2,053 2,062 2,712 2,176 679 17,559 91 8 LO (3) German French English Scandinavian German German English French English French English ANTIDIR (4) 2 4 3 3 2 3 2 2 5 1 3 34 PRIVENF (5) 0.71 0.58 0.57 0.43 0.55 0.71 0.57 0.36 0.75 0.19 0.47 PUBENF (6) 0.25 0.33 0.43 0.50 0.33 0.52 0.72 0.63 0.68 0.55 0.42 INVPRT (7) 0.77 0.55 0.40 0.39 0.30 0.55 0.37 0.34 0.78 0.22 0.42 Table II Univariate Analysis Panel A presents the summary statistics of the financial variables. CAPXt is a measure of investments and is calculated as capital expenditures in year t divided by total assets at the end of year t-1. Qt-1 is Tobin’s Q and is calculated as the market value of equity plus total assets minus total equity at the end of year t-1. CFt is cash flow and is calculated as income before extraordinary items plus depreciation and amortization in year t divided by total assets at the end of year t-1. KZ is the adjusted KZ index such that each component contributes equally to the total variance of the index. SIZE is calculated as the book value of total assets (in US dollars). DIVD is a dummy variable that equals 1 for dividend-paying firms and 0 otherwise. All financial variables are winsorized at the 1st and 99th percentiles. Panel B presents the Pearson correlation matrix between the country-median financial variables and the legal protection variables. The sample period is from 1985 to 2004. a, b, c denote statistical significance at the 10, 5, and 1 percent levels, respectively. . Variable CAPXt Qt-1 CFt KZ SIZE DIVD N 110,082 110,082 110,082 110,882 110,082 110,882 Mean 0.075 1.393 0.077 0.008 12.461 0.735 Qt-1 CFt KZ SIZE DIVD ANTIDIR PRIVENF PUBENF INVPRT CAPXt 0.496c 0.555c 0.103 -0.050 0.199 -0.070 -0.214 -0.198 -0.160 Qt-1 1.000 0.252a -0.276a -0.052 0.052 0.078 0.137 -0.141 0.012 Panel A: Summary statistics Median Std Dev Min 0.047 0.092 0.000 1.140 0.858 0.474 0.078 0.116 -0.405 0.265 2.582 -8.217 12.298 1.743 9.211 1 0.442 0 Panel B: Correlation matrix CFt KZ SIZE DIVD 1.000 0.234 -0.031b 0.195 -0.084 -0.296b -0.085 -0.182 1.000 -0.420c -0.057 0.107 0.080 0.077 0.150 35 1.000 0.020 -0.230 -0.260a -0.318b -0.325b 1.000 0.008 -0.308b -0.171 -0.206 1st Quartile 0.021 0.929 0.034 -1.357 11.159 0 Max 0.578 6.008 0.453 6.131 19.407 1 3rd Quartile 0.091 1.527 0.130 1.640 13.578 1 ANTIDIR PRIVENF PUBENF 1.000 0.548c 0.375c 0.810c 1.000 0.396c 0.802c 0.710c Table III The Role of Legal Protection in the Investment-Stock Price Sensitivity This table presents the coefficients of random-effects investment regressions. The dependent variable is CAPXt. CAPXt is a measure of investments and is calculated as capital expenditures in year t divided by total assets at the end of year t-1. Qt-1 is Tobin’s Q and is calculated as the market value of equity plus total assets minus total equity at the end of year t-1 CFt is cash flow and is calculated as income before extraordinary items plus depreciation and amortization in year t divided by total assets at the end of year t-1. ANTIDIR is an index of shareholder protection also taken from La Porta et al. (1998). PRIVENF is an index calculated as the average of the disclosure requirements and liability standards indices from La Porta et al. (2006). PUBENF is an index calculated as the average of the supervisor characteristics, rule-making power, investigative powers, orders and criminal indices also from La Porta et al. (2006). INVPRT is the investor protection index, which is the principal component of the disclosure requirements, liability standards, and anti-director rights indices from La Porta et al. (2006). LO refers to the legal origin of the company law or commercial code of each country and is taken from La Porta et al. (1998). All the legal protection variables (except ANTIDIR and LO) have been normalized to between 0 and 5. Standard errors are reported in parentheses and are heteroskedasticially robust. a, b, c denote statistical significance at the 10, 5, and 1 percent levels, respectively. (1) Variables Qt-1 CFt (2) ANTIDIR 0.005c (0.001) 0.183c (0.004) -0.004c (0.000) 0.002c (0.000) (3) PRIVENF 0.000 (0.001) 0.182c (0.004) -0.007c (0.001) 0.003c (0.000) (4) PUBENF 0.005c (0.001) 0.182c (0.004) 0.001 (0.000) 0.002c (0.000) (5) INVPRT 0.004c (0.001) 0.183c (0.004) -0.002c (0.000) 0.002c (0.000) (6) LO 0.008c (0.001) 0.183c (0.004) -0.005c (0.001) 0.005c (0.001) (7) INVPRT 0.003b (0.001) 0.267c (0.001) -0.001 (0.000) 0.003c (0.000) -0.027c (0.003) Yes Yes Yes Yes Yes Yes Yes 0.175 110,082 0.176 110,082 0.176 110,082 0.178 110,082 0.176 110,082 0.176 110,082 0.178 110,082 0.011c (0.000) 0.183c (0.004) LEGAL Qt-1 × LEGAL CFt × LEGAL Industry and Year dummies included Adjusted R-square Number of observations 36 Table IV The Role of Legal Protection in the Investment-Stock Price Sensitivity: Sensitivity Analysis This table presents the coefficients of random-effect investment regressions. The dependent variable is CAPXt. CAPXt is a measure of investments and is calculated as capital expenditures in year t divided by total assets at the end of year t-1. Qt-1 is Tobin’s Q and is calculated as the market value of equity plus total assets minus total equity at the end of year t-1. ΔQt is calculated as the change between Qt and Qt-1. CFt is cash flow and is calculated as income before extraordinary items plus depreciation and amortization in year t divided by total assets at the end of year t-1. INVPRT is the investor protection index, which is the principal component of the disclosure requirements, liability standards, and anti-director rights indices from La Porta et al. (2006). NEW_ANTIDIR is the updated antidirector rights index from Pagano and Volpin (2005). DEV is a measure of financial development from La Porta et al. (2006). LGO (GGO) is the exogenous local (global) country growth opportunity measure from Bekaert et al. (2007). Standard errors are reported in parentheses and are heteroskedasticially robust. a, b, c denote statistical significance at the 10, 5, and 1 percent levels, respectively. Variables Q t-1 (1) -0.007c (0.003) (2) 0.004c (0.001) (3) 0.004c (0.001) (4) 0.003c (0.001) (5) -0.002 (0.002) (6) (7) -0.009c (0.001) ΔQt -0.024c (0.002) LGO GGO CFt INVPRT Qt-1 × INVPRT 0.421c (0.046) -0.001 (0.001) 0.005c (0.001) 0.419c (0.031) 0.002 (0.002) 0.003c (0.001) 0.245c (0.011) -0.002c (0.001) 0.002c (0.000) 0.270c (0.014) 0.392c (0.020) 0.004c (0.001) 0.003c (0.001) 0.266c (0.010) -0.008c (0.003) -0.028c (0.005) 0.277c (0.010) -0.013c (0.005) 0.004c (0.001) LGO × INVPRT -0.023c (0.003) 0.005c (0.002) -0.025c (0.003) -0.027c (0.003) GGO × INVPRT -0.053c (0.010) -0.049c (0.010) -0.020c (0.003) -0.063c (0.008) -0.002c (0.000) 0.002c (0.000) -0.022c (0.003) NEW_ANTIDIR Qt-1 × NEW_ANTIDIR CFt-1 × NEW_ANTIDIR -0.009c (0.002) 0.001 (0.001) -0.037c (0.012) DEV Qt-1 × DEV CFt-1 × DEV Industry and Year dummies included Adjusted R-square Number of observations 0.278c (0.010) 0.003c (0.001) 0.004c (0.000) ΔQt × INVPRT CFt-1 × INVPRT (8) Yes Yes Yes Yes Yes Yes Yes 0.211 16,522 0.162 73,518 0.176 110,882 0.185 110,882 0.171 103,450 0.166 103,515 0.173 110,882 37 Table V The Role of Equity Dependence in the Investment-Stock Price Sensitivity Panels A, B, and C present the coefficients of random-effects investment regressions for each quintile portfolio formed according to the firm-median KZ score, SIZE, and DIVD respectively. The dependent variable is CAPXt. CAPXt is a measure of investments and is calculated as capital expenditures in year t divided by total assets at the end of year t-1. Qt-1 is Tobin’s Q and is calculated as the market value of equity plus total assets minus total equity at the end of year t-1. CFt is cash flow and is calculated as income before extraordinary items plus depreciation and amortization in year t divided by total assets at the end of year t-1. KZ is the adjusted KZ index such that each component contributes equally to the total variance of the index. SIZE is calculated as the book value of total assets (in US dollars). DIVD is a dummy variable that equals 1 for dividend-paying firms and 0 otherwise. Q1 to Q5 refer to KZ (SIZE) quintiles 1 to 5. Standard errors are reported in parentheses and are heteroskedasticially robust. a, b, c denote statistical significance at the 10, 5, and 1 percent levels, respectively. Variables Qt-1 CFt Industry and Year dummies included Adjusted R-square Number of observations Variables Qt-1 CFt Industry and Year dummies included Adjusted R-square Number of observations Variables Qt-1 CFt Industry and Year dummies included Adjusted R-square Number of observations Panel A: Results from investment regressions using KZ Q1 Q2 Q3 Q4 0.003c 0.015c 0.018c 0.022c (0.001) (0.001) (0.001) (0.001) c c c 0.259 0.252 0.269 0.224c (0.010) (0.010) (0.012) (0.012) Yes Yes Yes Yes 0.190 0.223 0.247 0.177 22,018 22,030 22,010 22,014 Panel B: Results from investment regressions using SIZE Q1 Q2 Q3 Q4 c c c 0.013 0.011 0.009 0.003c (0.001) (0.001) (0.001) (0.001) c c C 0.100 0.175 0.249 0.284c (0.005) (0.008) (0.011) (0.013) 0.149 22,010 Yes Yes Yes Q5 0.023c (0.001) 0.107c (0.008) Yes Yes Yes 0.138 0.195 0.232 0.209 22,025 22,020 22,009 22,013 Panel C: Results from investment regressions using DIVD DIVD=0 DIVD=1 c 0.018 -0.000 (0.001) (0.000) 0.107c 0.346c (0.006) (0.006) Yes Yes 0.217 29,224 0.205 80,858 38 Q5 -0.002 (0.001) 0.406c (0.019) 0.262 22,015 Table VI The Role of Equity Dependence in the Investment-Stock Price Sensitivity: Sensitivity Analysis This table presents the regression coefficients of the interaction terms between Q and the measures of equity dependence (ED) from the fixed-effects investment regressions. Panel A presents the results for each country and Panel B presents the results for sub-samples. The dependent variable is CAPXt. CAPXt is a measure of investments and is calculated as capital expenditures in year t divided by total assets at the end of year t-1. KZ is the adjusted KZ index such that each component contributes equally to the total variance of the index. SIZE is calculated as the book value of total assets (in US dollars). DIVD is a dummy variable that equals 1 for dividend-paying firms and 0 otherwise. Standard errors are reported in parentheses and are heteroskedasticially robust. The sample consists of 43 countries. The sample period is from 1985 to 2004. a, b, c denote statistical significance at the 10, 5, and 1 percent levels, respectively. Country Argentina Australia Austria Belgium Brazil Canada Chile Colombia Denmark Egypt Finland France Germany Greece Hong Kong India Indonesia Ireland Israel Italy Japan Korea Malaysia Mexico Netherlands New Zealand Norway Pakistan Peru Philippines Portugal Singapore South Africa Spain Sri Lanka Sweden Panel A: Country-by-country analysis (1) (2) Qt-1 × KZ Qt-1 × SIZE 0.000 (0.002) 0.004 (0.003) c 0.003 (0.001) -0.002 (0.002) c (0.001) 0.004 (0.004) 0.007 (0.001) -0.002 (0.002) 0.003b 0.000 (0.002) 0.004 (0.003) (0.001) -0.002 (0.002) 0.003c (0.003) -0.002 (0.004) 0.006c (0.002) 0.016c (0.005) 0.005b (0.002) 0.002 (0.001) -0.005c -0.043 (0.044) -0.200 (0.122) 0.002 (0.001) -0.006c (0.002) c (0.001) -0.004c (0.001) 0.003 b c 0.002 (0.001) -0.003 (0.001) 0.000 (0.001) 0.004 (0.006) (0.001) -0.001 (0.001) 0.002c 0.001 (0.001) 0.000 (0.001) 0.001 (0.001) 0.002 (0.002) 0.001 (0.003) -0.003 (0.004) 0.003 (0.003) 0.001 (0.002) (0.001) 0.000 (0.001) 0.002a 0.002c (0.000) -0.002c (0.000) c (0.001) 0.000 (0.001) 0.004 0.001b (0.001) 0.000 (0.001) (0.001) -0.002 (0.003) 0.003b 0.003c (0.001) 0.001 (0.001) (0.003) -0.010a (0.006) 0.009c b 0.004 (0.002) 0.000 (0.003) (0.002) 0.011a (0.006) 0.005c -0.002 (0.003) 0.005 (0.005) (0.003) -0.001 (0.003) 0.008c 0.000 (0.001) 0.011c (0.003) (0.001) -0.004c (0.001) 0.003b c 0.003 (0.001) 0.000 (0.001) 0.002 (0.002) -0.002 (0.002) b (0.010) 0.017 (0.011) -0.025 (0.001) -0.001 (0.001) 0.002c 39 (3) Qt-1 × DIVD -0.031c (0.011) b -0.014 (0.006) -0.020 (0.012) -0.025c (0.009) 0.007 (0.019) -0.016b (0.008) -0.016 (0.027) -0.003 (0.048) b -0.022 (0.009) (0.117) -0.287b -0.002 (0.008) -0.017b (0.005) -0.014b (0.003) 0.022 (0.017) -0.011a (0.007) -0.012 (0.007) -0.003 (0.008) -0.003 (0.015) (0.015) 0.034b -0.017 (0.012) -0.007b (0.003) 0.007 (0.008) -0.010a (0.005) -0.005 (0.012) -0.029c (0.005) -0.038 (0.023) -0.006 (0.012) -0.041c (0.013) 0.005 (0.018) 0.008 (0.011) -0.018 (0.009) -0.015a (0.008) -0.019c (0.005) -0.010 (0.007) c -0.115 (0.038) -0.013c (0.004) Table VI - Continued Country Switzerland Taiwan Thailand Turkey United Kingdom Venezuela Fama-MacBeth approach Emerging countries Developed countries EU countries Exclude Japan and UK Manufacturing firms 1985 to 1994 1995 to 2004 (2) (1) Qt-1 × SIZE Qt-1 × KZ 0.002b (0.001) -0.003b (0.001) 0.004c (0.001) -0.001 (0.002) b 0.003 (0.001) 0.002 (0.002) (0.003) -0.002 (0.002) 0.005b c c 0.002 (0.000) -0.002 (0.001) 0.001 (0.009) 0.014 (0.023) Panel B: Sub-sample analysis c (0.001) -0.004c (0.001) 0.004 0.001c (0.000) -0.002c (0.001) c c 0.003 (0.000) -0.002 (0.000) (0.000) -0.002c (0.000) 0.003c c c 0.003 (0.000) -0.002 (0.000) (0.000) -0.004c (0.000) 0.003c c c (0.000) -0.005 (0.001) 0.006 (0.000) -0.001c (0.000) 0.002c 40 (3) Qt-1 × DIVD -0.002 (0.006) -0.014c (0.005) -0.006 (0.011) 0.006 (0.012) -0.015c (0.004) 0.042 (0.125) -0.024c -0.018c -0.017c -0.016c -0.019c -0.016c -0.030c -0.016c (0.004) (0.002) (0.001) (0.001) (0.001) (0.001) (0.004) (0.001) Table VII The Role of Legal Protection and Equity Dependence in the Investment-Stock Price Sensitivity Panels A, B and C present the coefficients of random-effects investment regressions for each quintile portfolio formed according to the firm-median KZ score and SIZE, and DIVD respectively. Panel D presents the results for the whole sample. The dependent variable is CAPXt. CAPXt is a measure of investments and is calculated as capital expenditures in year t divided by total assets at the end of year t-1. Qt-1 is Tobin’s Q and is calculated as the market value of equity plus total assets minus total equity at the end of year t-1. CFt is cash flow and is calculated as income before extraordinary items plus depreciation and amortization in year t divided by total assets at the end of year t-1. KZ is the adjusted KZ index such that each component contributes equally to the total variance of the index. SIZE is calculated as the book value of total assets (in US dollars). DIVD is a dummy variable that equals 1 for dividendpaying firms and 0 otherwise. Q1 to Q5 refer to KZ (SIZE) quintiles 1 to 5. Standard errors are reported in parentheses and are heteroskedasticially robust. a, b, c denote statistical significance at the 10, 5, and 1 percent levels, respectively. Variables Qt-1 Qt-1 × INVPRT Industry and Year dummies included Adjusted R-square Number of observations Variables Qt-1 Qt-1 × INVPRT Industry and Year dummies included Adjusted R-square Number of observations Variables Qt-1 Qt-1 × INVPRT Industry and Year dummies included Adjusted R-square Number of observations Panel A: Results from investment regressions using KZ Q1 Q2 Q3 c -0.001 0.006 0.008c (0.001) (0.002) (0.001) c c 0.001 0.003 0.003c (0.000) (0.001) (0.001) Yes Yes Yes Q4 0.008c (0.003) 0.004c (0.001) Q5 0.018c (0.003) 0.002c (0.001) Yes Yes 0.191 0.224 0.249 0.179 22,018 22,030 22,010 22,014 Panel B: Results from investment regressions using SIZE Q1 Q2 Q3 Q4 c c c 0.008 0.010 0.007 0.004b (0.001) (0.001) (0.001) (0.001) c c 0.002 0.000 0.001 -0.000 (0.000) (0.000) (0.000) (0.000) Yes Yes Yes Yes Q5 -0.003b (0.001) 0.000 (0.000) Yes 0.140 0.195 0.233 0.209 22,025 22,020 22,009 22,013 Panel C: Results from investment regressions using DIVD DIVD=0 DIVD=1 c 0.011 -0.000 (0.002) (0.001) 0.002c 0.000 (0.000) (0.000) Yes Yes 0.220 29,224 0.205 80,858 41 0.151 22,010 0.262 22,015 Table VII - Continued Variables Qt-1 CFt EDt INVPRT Qt-1 × INVPRT CFt-1 × INVPRT Qt-1 × INVPRT × EDt CFt-1 × INVPRT × EDt Industry and Year dummies included Adjusted R-square Number of observations Panel D: Whole sample (2) (1) EDt = SIZE EDt = KZ 0.005c 0.004c (0.001) (0.001) 0.294c 0.241c (0.011) (0.010) -0.002c 0.001c (0.000) (0.000) -0.001c -0.004c (0.000) (0.000) 0.010c 0.004c (0.000) (0.001) c -0.174c -0.013 (0.003) (0.010) c -0.001c 0.001 (0.000) (0.000) c 0.014c -0.006 (0.000) (0.001) (3) EDt = DIVD 0.007c (0.001) 0.240c (0.010) 0.001 (0.001) -0.000 (0.000) 0.004c (0.000) -0.029c (0.003) -0.006c (0.000) 0.051c (0.003) Yes Yes Yes 0.196 110,082 0.186 110,082 0.194 110,082 42 Table VIII The Role of Equity Dependence and Legal Protection in The Investment-Stock Price Sensitivity: Sensitivity Analysis This table presents the coefficients of the interactions among Q, the investor protection index (INVPRT) and the measures of equity dependence (ED) from the random-effects investment regressions. The dependent variable is CAPXt. CAPXt is a measure of investments and is calculated as capital expenditures in year t divided by total assets at the end of year t-1. KZ is the adjusted KZ index such that each component contributes equally to the total variance of the index. SIZE is calculated as the book value of total assets (in US dollars). DIVD is a dummy variable that equals 1 for dividend-paying firms and 0 otherwise. ΔQt is calculated as the change between Qt and Qt-1, NEW_ANTIDIR is the updated anti-director rights index from Pagano and Volpin (2005). LGO (GGO) is the exogenous local (global) growth opportunity measure from Bekaert et al. (2007). Standard errors are reported in parentheses and are heteroskedasticially robust. a, b, c denote significance at the 10%, 5%, and 1% levels, respectively. 43 Table VIII - Continued 1. Fama-Macbeth approach Qt-1 × INVPRT Qt-1 × INVPRT × EDt 2. Industry-level analysis Qt-1 × INVPRT Qt-1 × INVPRT × EDt 3. Dropping Japan and UK Qt-1 × INVPRT Qt-1 × INVPRT × EDt 4. Time-series anti-directors rights Qt-1 × NEW_ANTIDIR Qt-1 × NEW_ANTIDIR × EDt 5. Accounting for financial development and using instrumental variables for INVPRT and Q Qt-1 × INVPRT Qt-1 × INVPRT × EDt 6. Exogenous local growth opportunity LGOt-1 × INVPRT LGOt-1 × INVPRT × EDt 7. Exogenous global growth opportunity GGOt-1 × INVPRT GGOt-1 × INVPRT × EDt 8. ΔQt ΔQt × INVPRT ΔQt × INVPRT × EDt 44 EDt = KZ EDt = SIZE EDt = DIVD 0.006c (0.001) 0.001c (0.000) 0.015c (0.002) -0.001c (0.000) 0.006c (0.001) -0.007c (0.001) 0.005c (0.001) 0.002c (0.000) 0.020c (0.003) -0.002c (0.000) 0.006c (0.001) -0.009c (0.001) 0.004c (0.000) 0.001c (0.000) 0.010c (0.001) -0.001c (0.000) 0.003c (0.000) -0.006c (0.000) 0.003c (0.000) 0.001c (0.000) 0.005c (0.001) -0.000c (0.000) 0.003c (0.000) -0.004c (0.000) 0.004c (0.001) 0.001c (0.000) 0.003b (0.001) -0.000 (0.000) 0.004c (0.001) -0.006c (0.000) 0.004c (0.001) 0.000a (0.000) 0.012c (0.002) -0.001c (0.000) 0.007c (0.001) -0.005c (0.000) 0.006c (0.002) 0.000a (0.000) 0.014c (0.002) -0.001c (0.000) 0.010c (0.002) -0.005c (0.000) 0.006c (0.000) 0.002c (0.000) 0.038c (0.003) -0.003c (0.000) 0.012c (0.001) -0.016c (0.001)