How Corporations make investment decisions

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Write a 3 page paper that explains how corporations make investing decisions. In other words, corporations can reinvest in their own business or they could invest in other businesses, perhaps their suppliers. Use APA format. Please put the references in alphabetical order on the reference page. Also, on the reference page put only the references I provided but you can get info elsewhere as needed.

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NICOLÁS E. MAGUD International Monetary Fund SEBASTIÁN SOSA International Monetary Fund Corporate Investment in Emerging Markets: The Role of Commodity Prices ABSTRACT  We examine how firm-level and country-specific macroeconomic variables determine corporate investment in emerging markets. In particular, we investigate how investment decisions are affected by changes in country-specific commodity export prices, using firmlevel data from 38 emerging markets for the period 1990–2013. We show that in addition to the standard firm-level variables (such as expected future profitability, cash flows, leverage, and new debt flows), commodity export prices play a significant role in driving corporate investment. Moreover, we show that the sharp decline in commodity prices since 2011 has been a key factor explaining the sizable slowdown in private investment growth during this period, especially in regions with large net commodity exporters. JEL Codes: E2, E3, F3, F4 Keywords: Investment, emerging markets, commodity prices, capital inflows C ommodity prices have fluctuated widely over the past two decades. The macroeconomic impact of commodity price swings has been studied extensively in the literature, both empirically and theoretically. However, empirical studies on the link between commodity prices and corporate investment in emerging markets are relatively scant, particularly those based on firm-level data. This paper empirically investigates the determinants of investment at the firm level in emerging markets, with a special focus on the role of commodity export prices. As a by-product, the paper examines the factors behind the post-2011 weakening of private investment in emerging markets (in particular, commodity export prices) and the differences across emerging regions. ACKNOWLEDGMENTS   The authors are grateful to Sebnem Kalemli-Ozcan, Hamid Faruqee, Andre Meier, Gian Maria Milesi Ferretti, Bertrand Gruss, Herman Kamil, Alex Klemm, Samya Beidas-Strom, Hui Tong, Davide Furceri, and Sergejs Saksonovs for their valuable comments and suggestions. They also thank Genevieve Lindow and Ben Sutton for their excellent research assistance. 157 158 E C O N O M I A , Fall 2017 F I G U R E 1 . Real Private Investment and Commodity Export Price Growth, 2004–14 A. Asia excluding China Percent 35 30 25 20 15 10 5 0 –5 –10 –15 2004 B. Commonwealth of Independent States Percent 40 Real private investment 20 0 –20 Commodity export price 2006 2008 2010 2012 2014 –40 2004 C. Europe Percent 30 20 20 10 10 0 0 –10 –10 –20 –20 2004 2006 2008 2008 2010 2012 2014 D. Latin America and the Caribbean Percent 30 –30 2006 2010 2012 2014 –30 2004 2006 2008 2010 2012 2014 Source: International Monetary Fund (IMF), World Economic Outlook database; Gruss (2014); and IMF staff calculations. Private investment in emerging markets is highly correlated with (countryspecific) commodity export prices (figure 1). The comovement of private investment and commodity export prices is especially high in the case of Latin America and the Caribbean and the Commonwealth of Independent States, with correlation coefficients of 0.84. This reflects the fact that these regions include many of the largest commodity exporters. For emerging Europe, the correlation is also strong (0.82), while it is much lower for emerging Asia excluding China (0.36). Private investment in emerging markets has also been highly correlated with capital inflows (figure 2). We study the determinants of investment in panel regressions that combine firm-level data for about 16,000 listed firms with country-specific macroeconomic variables—notably, commodity export prices and capital Nicolás E. Magud and Sebastián Sosa 159 F I G U R E 2 . Real Private Investment Growth and Net Capital Inflows, 2004–14a B. Commonwealth of Independent States A. Asia excluding China Percent 25 20 15 10 5 0 2004 2006 2008 2010 Percent of GDP 5 4 3 2 1 0 –1 2012 2014 Percent 40 20 0 –20 –40 2004 C. Europe Percent 30 20 10 0 –10 –20 –30 2004 Percent of GDP 12 Real private investment 10 8 6 4 2 Capital inflows (right scale) 0 2006 2008 2010 2012 2014 2006 2008 2010 Percent of GDP 8 6 4 2 0 –2 –4 –6 –8 2012 2014 D. Latin America and the Caribbean Percent 20 15 10 5 0 –5 –10 –15 –20 –25 2004 Percent of GDP 4 3 2 1 0 2006 2008 2010 2012 2014 –1 Source: IMF, World Economic Outlook database; and IMF staff calculations. a. PPP-weighted average. Capital inflows are defined as the balance of the external financial account, in percent of GDP. inflows—for thirty-eight emerging markets over the period 1990–2013.1 After identifying the key factors driving firms’ investment decisions in emerging markets, we shed light on which of these factors have been the main drivers of the sharp deceleration in corporate investment growth since 2011. Our study generates four main results. First, we confirm the importance of what can be dubbed the usual suspects. In line with previous studies in the literature, we find that emerging market firms’ capital expenditure is positively associated with expected profitability (proxied by Tobin’s q), cash flows (suggesting the existence of borrowing constraints), and debt flows. It is negatively 1. Table A1 in appendix A presents the list of countries in the sample and the number of firms in each country. 160 E C O N O M I A , Fall 2017 associated with leverage. Second, and the key contribution of the paper, commodity prices matter. Conditional on the usual suspects, investment is positively associated with changes in country-specific commodity export prices, and the link is statistically and economically significant. Third, investment by emerging market firms is also influenced by the availability of foreign (international) financing. Finally, based on the first three results, we put the magnifying glass on the most recent event of a fall in commodity prices. Thus, as an extension to our main contribution, we look into whom to blame for the post-2011 investment slowdown. Factors vary across emerging market regions, with the sharp adjustment in commodity prices playing a substantial role in commodity exporter regions (such as Latin America). Another factor was lower expected profitability of firms, which partly reflects the downward revisions to potential growth in many emerging markets. The moderation in capital inflows to emerging markets and increased leverage (particularly in Asia) also played a significant role. Our paper is related to the extensive empirical literature on the determinants of corporate investment in emerging markets. It relates to a strand that studies financing constraints, typically relying on Tobin’s q investment models or Euler investment equations. Most of these studies document the importance of internal financing for firms’ investment owing to capital markets’ imperfections. Based on this framework, for example, Fazzari, Hubbard, and Petersen examine the case of U.S. manufacturing firms, while Love and Zicchino study emerging market companies.2 The sensitivity of investment to cash flows is particularly strong for smaller firms and for firms in less financially developed economies.3 The use of cash flow as a measure of financial frictions has been criticized, however.4 This has been addressed by Gilchrist and Himmelberg, who establish the existence of financial constraints by testing the significance of investment-to-cash-flow sensitivities beyond the effect of the so-called fundamental q.5 The latter is essentially a vector autoregression (VAR) of forecasting equations out of which the expected value of marginal q, conditional on observed fundamentals (including cash flow), is constructed. This implies that any additional effect picked up by cash flows should reflect financial constraints. 2. Fazzari, Hubbard, and Petersen (1988); Love and Zicchino (2006). Hubbard (1998) provides a thorough survey of this literature. 3. Fazzari, Hubbard, and Petersen (2000); Carpenter and Guariglia (2008); (Love, 2003). 4. For example, Kaplan and Zingales (1997); Gomes (2001); Abel and Eberly (2011). 5. Gilchrist and Himmelberg (1995, 1999). Nicolás E. Magud and Sebastián Sosa 161 We follow this q literature, aware of its possible shortcomings. We use the q as one important explanatory variable of firm-level investment, but we also control for other variables to mitigate, to the extent possible, other investment opportunities that could be misinterpreted as captured by the q. Harrison, Love, and McMillan document that foreign direct investment (FDI) flows to emerging markets are associated with a reduction in firms’ financing constraints.6 They examine whether—and to what extent—the availability of foreign capital helps relax financing constraints in emerging market firms by combining firm-level data on cash flows with country-specific capital flows. Forbes also finds that financing constraints relax when capital account restrictions are eased, as do Gelos and Werner.7 These studies focus on macroeconomic variables, but only on capital flows and their role in the relaxation of financial constraints. In contrast, we want to better understand another key driver of corporate investment in emerging markets: namely, commodity export prices. In another related paper, though from a macroeconomic perspective, Fernández, Gonzales, and Rodríguez show that in emerging markets, business cycles are strongly influenced by country-specific commodity prices, which are procyclical.8 Finally, Fornero, Kirchner, and Yany, and Ross and Tashu, study the link between the terms of trade and investment.9 We contribute to this literature in several ways. First, we analyze the determinants of firms’ investment decisions for a large sample of emerging markets covering a period of over two decades. This contrasts with previous studies on investment in emerging markets using firm-level data, which mostly focused on one country or a small group of countries. Our approach allows us not only to work with an extensive database, but also to explore (and exploit) the potential heterogeneity across emerging market regions. Second, in addition to firm-level data, we include some country-specific macroeconomic variables in the analysis—notably, commodity export prices. The latter is our main contribution. Finally, as a by-product, we examine the drivers of the post-2011 investment growth slowdown and how the main factors varied across emerging market regions. The rest of the paper proceeds as follows. The next section presents a theoretical framework to motivate the empirical exercise that follows. Subsequent 6. 7. 8. 9. Harrison, Love, and McMillan (2004). Forbes (2007); Gelos and Werner (2002). Fernández, Gonzales, and Rodríguez (2014). Fornero, Kirchner, and Yany (2014); Ross and Tashu (2015). 162 E C O N O M I A , Fall 2017 sections describe the empirical approach and present the results, while the final section provides concluding remarks. Theoretical Framework This section presents an augmented q model of investment for a small open economy, which we use as a framework for the empirical analysis below. We develop a basic frictionless model to illustrate how commodity prices can affect investment decisions. Adding frictions to this model is unlikely to result in different firm-level decisions; however, we test for their impact in the empirical section below. The problem of firm i in period t over an infinite horizon is to maximize the present discounted value of the flow of dividends, Dt, given by ∞ D  Et  ∑ t +i 1  ,  i =1 R  (1) where R represents the gross interest rate. In turn, the firm’s dividend flow is given by (2) Dt = π ( K t , θt ) − pt I t − c ( I t , K t ) , where p is the firm’s profit function, Kt the stock of capital, qt the level of technology, and pt the price of capital in units of domestic goods. It denotes investment, and c(It, Kt) is a function that captures the adjustment cost of investment. The profit function is assumed to be increasing in capital and level of technology, and concave. The adjustment cost of installing new capital is an increasing and convex function in the value of (It /Kt), defined below, and qt is a stationary first-order Markov process. Given a constant rate of depreciation, d, the stock of capital equation changes over time, as (3) K t +1 = I t + (1 − δ ) K t . Assume that firms in this small open economy purchase their capital abroad.10 Since capital is imported, the domestic price of investment depends on the real exchange rate. In turn, the real exchange rate increases with the 10. Assuming that only a share of the capital stock is imported does not alter the results. Nicolás E. Magud and Sebastián Sosa 163 country’s terms of trade, that is, the relative price of exports to imports ( pX /pM). We normalize the real exchange rate, e, to the unit circle, taking a value of zero when the terms of trade equal their long-run value. Thus, the domestic price of importing capital is given by   p  pX  p  M M  (4) If the terms of trade are at their long-run value (denoted by an overbar), so is the real exchange rate (equaling zero). In this case we have the typical closed economy example, in which the domestic price of capital equals one. When the economy’s terms of trade are above their long-run value, the economy is richer, so the real exchange appreciates (that is, it increases), and the price of new capital in terms of domestic goods decreases. Likewise, for terms of trade lower than their long-term value, the economy is poorer, the real exchange rate depreciates, and the price of investment is higher. Therefore, the firm’s problem is to maximize equation 1 subject to equations 2–4. The Bellman equation for the firm’s problem is given by (5) V ( K t , θt , et ) = max I ,K t t +1     p  π ( K t , θt ) − 1 − et  X   I t    pM       .   1 − c ( I t , K t ) + Et V( K t +1 , θt +1 , et +1 )   R   Equivalently, (6) V( K t , θt , et ) = max I t   p  π ( K t , θt ) − 1 − et  X   I t − c ( I t , K t )  pM    . 1 + Et V  I t + (1 − δ ) K t , θt +1 , et +1  R { } 164 E C O N O M I A , Fall 2017 Optimizing over the control variable It, while Kt is the state variable, implies the following first-order condition: (7)   pX   1 1 1 − et    + cI ( I t , K t ) = R Et V( K t +1 , θt +1 , et +1 )  = R Et qt +1 . p  M   On the right-hand side of equation 7, as usual in the literature, we define Tobin’s q as the discounted shadow price of capital—marginal q—which equals the replacement cost of capital plus the adjustment cost of installing new capital, that is, the effective price of new capital. Assume that a constantreturns-to-scale adjustment cost of capital is given by 2  1  I c ( I t , K t ) = b  t − µ K t , 2  Kt  (8) in which µ denotes the investment-to-capital ratio in steady state, which is associated with no adjustment costs. Intuitively, µK is the level of investment necessary to maintain a constant stock of capital in the steady state. Substituting equation 8 in equation 7, we get (9)   1  It  pX   1 − µ  = Et V( K t +1 , θt +1 , et +1 )  = Et qt +1 . 1 − et   + b  R  pM    R  Kt  Rearranging equation 9 yields (10)   p  It 1  1 =  Et qt +1 + et  X  − 1 + µ, Kt b  R  pM   which shows the standard positive association between Tobin’s q and investment. As shown in the literature, an increase in marginal q (that is, a higher shadow price of capital, which implies a larger present discounted value of the flow of dividends, as shown below), causes the firm to optimally increase investment. The latter can be shown by using the envelope condition out of equation 6: (11) qt =  π K ( K t , θt ) − cK ( I t , K t )  + 1 (1 − δ ) Et [ qt +1 ]. R Nicolás E. Magud and Sebastián Sosa 165 Updating (11) one period, forwarding it, taking expectations as of period t, applying the law of iterated expectations and substituting back in (11), and finally iterating forward and using the transversality condition yields (12) i  ∞  1 − δ    π K ( K t + i , θt + i ) − cK ( I t + i , K t + i )   , VK ( K t , θt ) = Et ∑    i = 0  R   which shows that the marginal value of an additional unit of capital should equal the discounted flow of marginal profits, net of adjustment costs. Crucially for our empirical analysis, equation 10 also shows that, all else equal, an improvement in the terms of trade (that is, the relative price of exports to imports) results in real appreciation, which increases investment—consistent with the lower costs of importing capital—and vice versa. Appendix B presents the phase diagram corresponding to the saddle-path equilibrium and the effects of (transitory and permanent) terms-of-trade shocks. Econometric Approach Based on the model presented in the previous section, we estimate a panel regression model of investment with time and firm-level fixed effects, combining firm-level data and country-specific macroeconomic variables to identify the main determinants of corporate investment in emerging markets. The analysis focuses on factors that, for theoretical reasons, are thought to affect firms’ investment decisions. These factors include firm-specific variables such as expected future profitability, cash flows, cost of debt, leverage, and debt flows. We also include country-specific macroeconomic variables—notably commodity export prices, but also net capital inflows and uncertainty. We then look at the recent deceleration of private investment growth in emerging markets to examine the key factors explaining the slowdown and the main differences across emerging market regions. Empirical Model Our empirical specification is a variation of the traditional Tobin’s q investment model, augmented to include other possible determinants identified in the literature on corporate investment. In a neoclassical model, the marginal benefit from an extra unit of investment and the cost of capital should be 166 E C O N O M I A , Fall 2017 sufficient statistics to explain investment behavior. The q theory of investment basically reformulates the neoclassical theory, such that firms’ investment decisions are based on the ratio between the market value of the firm’s capital stock and its replacement cost.11 Much of the literature on corporate investment published over the last decades, however, highlights the importance of financing constraints. In the presence of financial frictions, access to external financing for investment projects that would in principle be profitable may be limited. Therefore, firms’ investment decisions would be determined not only by investment opportunities, but also by the availability of internal funds. Evidence of financial constraints is largely based on the sensitivity of investment to different measures of internal funds—typically cash flow or cash stock. A firm’s higher dependence on internal funding is interpreted as a sign of tighter financial constraints.12 However, this interpretation of the correlation between cash flow and investment as evidence of financial constraints is far from uncontroversial. A strand of the literature argues that rather than financing constraints, the relationship between cash flows and investment may reflect the correlation between cash flow and investment opportunities that are not captured well by traditional measures of investment opportunities, in particular Tobin’s q. Nevertheless, a number of studies address these criticisms, and most empirical studies continue to use the investment-to-cash-flow sensitivity as a measure of financial frictions.13 We also follow this approach, using both cash flow measures and Tobin’s q. Beyond corporate financial indicators, we also include key country-specific macroeconomic variables that may affect corporate investment. Specifically, we consider commodity export prices (which drive the terms of trade), capital inflows, and uncertainty. We estimate linear panel regressions allowing for both time and firm-level fixed effects.14 Given that our specification contains both firm-level and country-level data, we use clustered (by country) robust standard errors to address the risk of standard-error bias. As is common in the literature, we use the lagged dependent variable as an additional explanatory 11. Tobin (1969); Hayashi (1982). For instance, investment would increase whenever the value of q is larger than one, an indicator that the present discounted value of the flow of expected dividends outweighs the replacement cost of capital. 12. See, for example, Fazzari, Hubbard, and Petersen (1988); Blanchard, Rhee, and Summers (1993); and Fazzari, Hubbard, and Petersen (2000). 13. For example, Gilchrist and Himmelberg (1995, 1999); Carpenter and Guariglia (2008). 14. As discussed later, the results are robust to also allowing for country fixed effects. Nicolás E. Magud and Sebastián Sosa 167 variable. Thus, the baseline specification, consistent with equation 10 above, is as follows: (13) I ic,t I ic,t −1 CFic,t =α+λ + β1Qic,t + β 2 + β3 LEVic,t −1 K ic,t −1 K ic,t − 2 K ic,t −1 + β4 ∆DEBTic,t + β5 INTic,t + β6 Picx,t −1 + β 7KI c,t K ic,t −1 + β8 UNCc,t + di + d t + ε ic,t , where the subscripts (ic,t) stand for firm i in country c during period t. I is fixed investment (excluding inventories) and K the stock of capital. The variable q represents the standard Tobin’s q, where average q, measured as the firm’s price-to-book-value ratio, is used as a proxy for (unobservable) marginal q.15 CF denotes the firm’s cash flow; LEV is leverage; DDEBT is the change in total debt since the previous period; and INT is a measure of the firm’s cost of capital, to account for the opportunity cost of funds. KI denotes (net) capital inflows; Px, (the log difference of) the commodity export price index; and UNC, aggregate uncertainty. The variables di and dt represent firm and trend (or alternatively time, see discussion below) fixed effects. Finally, e is the error term. Intuitively, this specification is based on the idea that investment is determined by the flow of (discounted) future dividends. As shown in equation 10 above, we should expect a positive coefficient associated with q, indicating that firms that expect to be more profitable should invest more, which is a common finding in the literature. As also discussed above, the cash flow coefficient should exhibit a positive sign if firms face financial constraints, since firms would need to rely on internal funds to finance investment projects. Debt stock and debt flows, in turn, are expected to have opposite effects on corporate investment. While higher leverage is expected to be negatively associated with investment, the flow of debt would be positively related to capital expenditure because financing investment is one of the main reasons to incur new debt. A higher cost of debt, in turn, is expected to be associated with lower investment. Regarding the country-level variables, commodity export prices are expected to be positively related to capital spending. Net capital inflows should also be positively related to corporate investment, because 15. See Hayashi (1982) for a discussion of the conditions under which the two measures are equivalent. 168 E C O N O M I A , Fall 2017 they may play a role in relaxing firms’ financing constraints in emerging markets.16 Finally, economic theory predicts that higher uncertainty should be associated with lower investment as firms enter a wait-and-see mode, especially to the extent that investment decisions are irreversible.17 Data We use annual firm-level data from Worldscope. The sample includes 16,000 publicly traded firms from thirty-eight emerging markets, covering the period 1990–2013. Table A1 in appendix A presents the list of countries in the sample and the number of firms per country.18 The number of firms varies significantly across countries as well as across time, with a smaller number in most countries during the first half of the 1990s.19 f i r m - l e v e l d a t a . Investment (I) is measured as the purchase of fixed assets by the firm. The stock of capital (K) is measured as the total net value of property, plant, and equipment. Tobin’s q is given by average q. Cash flow (CF) is computed as the firm’s net profits from operating activities; leverage (LEV) is measured as the ratio of total debt obligations to total assets; new debt (DDEBT) is defined as the change in total debt obligations since the previous period; and the cost of funds (INT) is defined as the firm’s effective interest rate paid on total debt obligations. To avoid the presence of outliers and coding errors that would bias the estimation, observations with inconsistent data are dropped from the sample.20 The country-specific distribution is then calculated for each of the variables, and 16. Harrison, Love, and McMillan (2004). 17. See, for instance, Bloom, Bond, and van Reenen (2001); Magud (2008); Baum, Caglayan, and Talavera (2008); and Dixit and Pindyck (1994). More recently, Li, Magud, and Valencia (2015) document how firm heterogeneity matters in the response of investment to interest rate versus uncertainty shocks, as the balance sheet dimension can identity if either a financial channel or a wait-and-see channel dominates the firm’s investment reaction to the shock. 18. We consider countries that were classified as emerging markets at the start of the sample. 19. The share of total private investment accounted for by corporate investment ranges, for example, between 70 and 75 percent across countries in Latin America and the Caribbean (although disaggregated data are not available for many countries). Moreover, the recent downturn has mainly been driven by corporate investment (although residential investment has also been trending downward in some countries). The firm-level data in the sample represent about 12 percent of aggregate private investment (in the national accounts), with correlation coefficients varying by country but averaging over 30 percent. 20. For example, negative book values for the capital stock, debt, or the price-to-book value of equity. Nicolás E. Magud and Sebastián Sosa 169 T A B L E 1 . Summary Statistics Variable Investment/capital stock (t-1) q Cash flow/capital stock (t-1) Leverage Interest expense ratio Change in debt/capital stock (t-1) Commodity export price growth Capital inflows/GDP No. observations Mean Standard deviation 389,977 435,454 410,693 493,919 355,256 357,397 367,748 497,058 0.25 1.81 0.06 0.68 0.08 0.27 4.32 -0.49 1.46 1.59 4.67 1.05 0.08 6.69 13.18 5.39 Source: Authors’ calculations. the bottom and top 5 percent of each variable’s observations are excluded from the analysis. Table 1 reports the summary statistics for the firm-level data.21 m a c r o e c o n o m i c d a t a . We use the country-specific gross commodity export price indexes constructed by Gruss.22 Capital inflows (measured using the financial account balance, in percent of GDP) and real GDP series come from International Financial Statistics and World Economic Outlook, both published by the International Monetary Fund (IMF). Finally, we use data from Bloomberg to construct our measure of country-specific uncertainty based on the average monthly volatility of stock market returns, computed as the standard deviation of daily stock market returns over a month. Results Table 2 reports the results of the baseline specification (equation 13). Columns 1–3 show that all the coefficients for the firm-level variables have the expected sign and are statistically significant at the one percent level. Following the theoretical model above, the dependent variable is the investment-tocapital ratio (ICR), with the stock of capital lagged one period. Consistent with the theory and findings in previous empirical studies, Tobin’s q is positively related to investment. Also in line with previous studies, we find robust evidence of financial constraints, reflected in a positive relationship between a firm’s cash flow and capital spending. Moreover, more leveraged firms tend 21. Using only listed firms restricts the sample of firms, imposing some limitations to the data. 22. Gruss (2014). 170 E C O N O M I A , Fall 2017 T A B L E 2 . Baseline Resultsa Explanatory variable ICR (t-1) q (1) (2) (3) (4) (5) (6) 0.0967*** (0.0126) 0.0207*** (0.0045) 0.0966*** (0.0124) 0.0200*** (0.0044) 0.0069*** (0.0019) 0.1070*** (0.0154) 0.0190*** (0.0045) 0.0125*** (0.0023) -0.0337*** (0.0035) -0.0793*** (0.0273) 0.0033*** (0.0009) 0.0949*** (0.0188) 0.0182*** (0.0045) 0.0117*** (0.0022) -0.0324*** (0.0029) -0.0712** (0.0283) 0.0029*** (0.0010) 0.0004*** (0.0001) 0.0929*** (0.0187) 0.0178*** (0.0043) 0.0117*** (0.0022) -0.0318*** (0.0030) -0.0685** (0.0292) 0.0029*** (0.0010) 0.0005*** (0.0001) 0.0023*** (0.0007) 8.8320*** (1.0170) 8.8720*** (0.9980) 9.8130*** (0.9630) 9.3580*** (0.8300) 9.1640*** (0.8540) 0.0905*** (0.0191) 0.0176*** (0.0042) 0.0115*** (0.0021) -0.0318*** (0.0031) -0.0663** (0.0298) 0.0029*** (0.0010) 0.0005*** (0.0001) 0.0024*** (0.0007) -1.39e–06 (1.31e–06) 8.7850*** (0.9890) 94,183 0.030 16,512 38 94,157 0.036 16,511 38 83,327 0.059 15,102 38 63,799 0.051 12,262 36 63,799 0.053 12,262 36 62,632 0.052 12,190 36 Cash flow Leverage (t-1) Interest expense ratio (t-1) Change in debt Commodity export price (t-1) Net capital inflows Uncertainty Constant Summary statistic No. observations R2 No. firms No. countries ** Statistically significant at the 5 percent level. *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period.The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. to exhibit lower investment in the following period, while an increase in debt is associated with higher capital expenditure. Finally, the coefficient on the cost of debt is negative, as expected. We then introduce the country-specific macroeconomic variables (table 2, columns 4–6). The magnitude and significance of the coefficients of Tobin’s q, cash flow, leverage, cost of debt, and change in debt do not change. We find robust evidence that an increase in a country’s commodity export prices is associated with higher investment in firms in that country. This result is consistent with previous studies that document the positive impact of improving terms of trade on investment even beyond firms in the export sector.23 It also is 23. For example, Fornero, Kirchner, and Yany (2014) for Chile; Ross and Tashu (2015) for Peru. Nicolás E. Magud and Sebastián Sosa 171 consistent with Fernández, Gonzales, and Rodríguez, who document that, on average, emerging markets are commodity exporters and country-specific commodity prices are procyclical.24 The impact of commodity export prices could be transmitted through direct channels affecting commodity sectors (and other sectors, such as manufacturing and services, related to commodities) or indirectly through income effects affecting aggregate demand and activity in other sectors, as well.25 Investment in emerging market firms is also influenced by the availability of foreign (cross-border) financing. The larger the net capital flows an emerging market economy receives, the larger its firms’ capital expenditure. Both coefficients (on commodity export prices and capital inflows) are positive and strongly statistically significant. Interestingly, we do not find market uncertainty to be a significant determinant of capital expenditure at the firm level. This result is consistent with previous studies showing that although uncertainty has a negative effect on investment, the effect generally disappears when Tobin’s q is introduced.26 The estimated coefficients are not only statistically but also economically significant in most cases. A one-standard-deviation change in each of the main independent variables would be associated with the following changes in the investment-to-capital ratio (in percentage points): Tobin’s q: 2.9; cash flow: 5.3; leverage: 3.3; change in debt: 1.9; commodity export growth: 0.63; and capital inflows: 1.4 (see figure 3). As indicated in table 1, the investment-to-capital ratio has a mean of 0.25 and a standard deviation of 1.46. We then explore whether the overall results are mostly explained by one emerging market region or if they hold across regions. Table 3 reports the results of splitting the sample by regions. The results for the main explanatory variables hold for most regions.27 In particular, the coefficient on commodity export prices is positive and statistically significant for all regions. Extension: The Post-2011 Private Investment Weakening Private investment exhibited strong growth in emerging markets in the period 2003–11, except in 2009, when the global financial crisis hit. After peaking 24. Fernández, Gonzales, and Rodríguez (2014). 25. See Druck, Magud, and Mariscal (2015). 26. For example, Leahy and Whited (1996). 27. An exception is emerging Europe, where a few regressors (such as cash flow, leverage, and cost of debt) show the correct sign but are not statistically significant. 172 E C O N O M I A , Fall 2017 F I G U R E 3 . Investment-Capital Ratio Response to One-Standard-Deviation Shock to Independent Variables Percentage points 6 5 4 3 2 1 0 Tobin’s q Cash flow Leverage Change in debt Commodity export prices Capital inflows Source: IMF staff calculations. in 2011, however, investment growth has gradually slowed (figure 4). Most emerging market regions have shared a similar pattern of investment dynamics, with strong growth in the precrisis period, a sharp contraction in 2009 followed by a rapid and strong recovery, and a sustained deceleration since 2011. The latter was particularly pronounced in emerging Europe, where growth has stalled, and “Other” economies, where it actually turned negative in 2014. But, which of the factors identified above play the biggest role in explaining the recent investment deceleration? Have the key factors varied across emerging market region? To answer these questions, we add to the equation a dummy variable (RECENT) that takes the value of one for all observations during the post-2011 period. Here, we control for time effects through a time trend rather than year dummy variables (to mitigate multicolinearity problems).28 We also add interaction terms, interacting the RECENT dummy variable with the main factors determining investment, in order to assess whether the marginal effect of any of the latter changed in the most recent period—both in the full sample and for each region. Specifically, we estimate the following specification: 28. Analysis of time effects through year dummy variables points to a clear downward trend, which supports the substitution for a time trend in the regression. Nicolás E. Magud and Sebastián Sosa 173 T A B L E 3 . Regional Decompositiona Explanatory variable ICR (t-1) q Cash flow Leverage (t-1) Interest expense ratio (t-1) Change in debt Commodity export price (t-1) Net capital inflows Uncertainty Constant Summary statistic No. observations R2 No. firms No. countries Full sample (1) LAC (2) Asia (3) Europe (4) Other (5) 0.0905*** (0.0191) 0.0176*** (0.0042) 0.0115*** (0.0021) -0.0318*** (0.0031) -0.0663** (0.0298) 0.0029*** (0.0010) 0.0005*** (0.0001) 0.0024*** (0.0007) -1.39e–06 (1.31e–06) 8.785*** (0.989) 0.1900*** (0.0353) 0.0129*** (0.0030) 0.0136** (0.0051) -0.0450*** (0.0089) -0.0114 (0.0214) 0.0026* (0.0013) 0.0006** (0.0002) 0.0019 (0.00104) -9.01e–07** (3.39e–07) 1.844 (1.349) 0.0787*** (0.0221) 0.0162** (0.0051) 0.0191*** (0.0039) -0.0329*** (0.0035) -0.0803* (0.0402) 0.0027* (0.0014) 0.0005*** (0.0001) 0.0024** (0.0009) -4.66e–06** (1.61e–06) 9.360*** (1.119) 0.0776** (0.0310) 0.0230*** (0.0059) 0.00137 (0.0012) -0.0133 (0.0089) 0.0026 (0.0768) 0.0008 (0.0016) 0.0004*** (6.81e–05) 0.0040*** (0.0012) -2.90e–06 (2.29e–06) 9.094* (5.046) 0.1520*** (0.0357) 0.0268*** (0.0020) 0.00839*** (0.0011) -0.0291* (0.0119) -0.1330* (0.0604) 0.0075** (0.0021) -5.00e–05 (0.0004) 0.0012 (0.0012) 7.98e–06 (5.47e–06) 14.04*** (2.912) 47,506 0.049 8,894 10 6,404 0.044 1,615 13 62,632 0.052 12,190 36 4,622 0.085 775 7 4,100 0.142 906 6 * Statistically significant at the 10 percent level. ** Statistically significant at the 5 percent level. *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period.The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. (14) I ic,t I ic,t −1 CFic,t =α+λ + β1Qic,t + β 2 + β3 LEVic,t −1 K ic,t −1 K ic,t − 2 K ic,t −1 + β4 ∆DEBTic,t + β5 INTic,t −1 + β6 P icx ,t −1 + β 7 KI c ,t K ic,t −1 + δRECENT + ηh RECENT × X th + di + d t + ε ic,t ,   CFic,t ∆DEBTic,t x for X th =  , LEVic,t −1 , , Pic,t −1, KI c,t  , respectively. K ic,t −1   K ic,t −1 174 E C O N O M I A , Fall 2017 F I G U R E 4 . Real Private Investment Growth, 2001–14 Percent 40 30 20 10 0 –10 Latin America and the Caribbean Europe Asia excl. China Other –20 –30 –40 2002 2004 2006 2008 2010 2012 2014 Source: IMF, World Economic Outlook database; and IMF staff calculations. Table 4 presents the results for the full sample. The coefficient on the RECENT dummy variable is negative and statistically significant, pointing to weaker corporate investment during this period (column 1), while all the regressors (both firm-level and country-specific macroeconomic variables) retain their sign and statistical significance. Regarding the interaction terms, financial constraints relaxed in the recent slowdown (column 3), while the negative relationship between leverage and firm-level investment became stronger (column 4). At the same time, firms’ investment sensitivity to changes in capital inflows and debt flows weakened in the post-2011 period (columns 5–6). To focus on the contribution of each factor in each emerging market region during the recent slowdown we run specification 14 for each region’s firms separately. The results are shown in the appendix (tables A2–A4). Notably, corporate investment has become more sensitive to commodity export prices in Latin America and less so in emerging Asia (columns 5–6 in table A2), while leverage’s role in explaining investment increased in emerging Asia and dropped in Latin America (columns 1–2 in table A3). Finally, the sensitivity to q increased in emerging Europe (column 7 in table A3), while in Asia the relationship between capital inflows and firm-level investment weakened (column 6 in table A4). Recent Uncertainty Net capital inflows Commodity export price (t-1) Change in debt Interest expense ratio (t-1) Leverage (t-1) Cash flow q ICR (t-1) Explanatory variable 0.0907*** (0.0191) 0.0175*** (0.0043) 0.0114*** (0.0021) -0.0316*** (0.0031) -0.0638** (0.0293) 0.0029*** (0.0010) 0.0004*** (9.46e–05) 0.0025*** (0.0007) -2.11e–06 (1.39e–06) -0.0084* (0.0042) (1) 0.0905*** (0.0191) 0.0170*** (0.0045) 0.0114*** (0.0021) -0.0317*** (0.0031) -0.0644** (0.0299) 0.0029*** (0.0010) 0.0004*** (9.39e–05) 0.0025*** (0.0007) -2.02e–06 (1.34e–06) -0.0136* (0.0069) (2) (3) 0.0911*** (0.0191) 0.0174*** (0.0043) 0.0130*** (0.0021) -0.0315*** (0.0031) -0.0639** (0.0294) 0.0029*** (0.0010) 0.0004*** (9.42e–05) 0.0025*** (0.0007) -2.13e–06 (1.37e–06) -0.0065 (0.0040) T A B L E 4 . The Role of the Main Factors in the Post–2011 Slowdowna 0.0906*** (0.0191) 0.0174*** (0.0043) 0.0115*** (0.0021) -0.0313*** (0.0031) -0.0638** (0.0292) 0.0029*** (0.0010) 0.0004*** (9.94e–05) 0.0025*** (0.0007) -2.24e–06 (1.46e–06) -0.0072 (0.0044) (4) 0.0904*** (0.0191) 0.0174*** (0.0043) 0.0114*** (0.0021) -0.0315*** (0.0031) -0.0641** (0.0294) 0.0029*** (0.0010) 0.0004*** (9.40e–05) 0.0026*** (0.0007) -2.09e–06 (1.36e–06) -0.0097** (0.0039) (5) 0.0909*** (0.0189) 0.0174*** (0.0043) 0.0116*** (0.0021) -0.0312*** (0.0032) -0.0638** (0.0293) 0.0033*** (0.0009) 0.0004*** (9.41e–05) 0.0025*** (0.0007) -2.09e–06 (1.39e–06) -0.0076* (0.0042) (6) 0.0906*** (0.0191) 0.0175*** (0.0043) 0.0114*** (0.0021) -0.0316*** (0.0031) -0.0637** (0.0291) 0.0029*** (0.0010) 0.00037*** (9.65e–05) 0.0025*** (0.0007) -2.29e–06 (1.63e–06) -0.0069 (0.0046) (continued) (7) 62,632 0.052 12,190 36 7.809*** (0.960) (1) 62,632 0.052 12,190 36 7.868*** (0.966) 0.0038 (0.0026) (2) 62,632 0.053 12,190 36 7.799*** (0.959) -0.0075** (0.0036) (3) 62,632 0.052 12,190 36 7.710*** (0.941) -0.0045* (0.0025) (4) 62,632 0.052 12,190 36 7.862*** (0.978) -0.0013*** (0.0004) (5) 62,632 0.052 12,190 36 7.809*** (0.968) -0.0023* (0.0012) (6) 62,632 0.052 12,190 36 0.0004 (0.0005) 7.691*** (0.936) (7) * Statistically significant at the 10 percent level. ** Statistically significant at the 5 percent level. *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period. The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. Summary statistic No. observations R2 No. firms No. countries Constant Recent * commodity export prices Recent * change in debt Recent * capital inflows Recent * leverage (t-1) Recent * cash flow Recent * q Explanatory variable T A B L E 4 . The Role of the Main Factors in the Post–2011 Slowdowna (Continued) Nicolás E. Magud and Sebastián Sosa 177 The contribution of each of the determinants to the post-2011 downturn in the investment-to-capital ratio in the average firm is computed by multiplying this period’s change in each factor by its corresponding estimated marginal effect. Based on these regional regressions, the marginal effect of each variable in the post-2011 period is computed as the sum of the coefficient associated with that variable and the coefficient on the interaction term (of that variable with the RECENT dummy), if the latter is statistically significant. Then, this marginal effect is multiplied by the change in the explanatory variable since 2011 to compute the overall contribution of the latter to the recent slowdown. Formally, the contribution of each factor X in region j (conditional on being statistically significant) is given by (β + η ) ∆ X h j h j j 2011–13   CF ∆DEBTj,t x for X j =  j,t , LEV j,t −1 , , P j,t −1, KI j,t  , K j,t −1   K j,t −1 j = LAC, ASIA, EUR, Other. The recent weakening in business investment in the average firm can largely be explained by the evolution of its main explanatory factors (figure 5).29 However, our results suggest that the relative contribution of each of the determinants has varied across regions. Lower commodity export prices emerge as the largest contributor to the slowdown in Latin American and Caribbean economies. The substantial contributions of weaker commodity prices to the decline in private investment growth observed since 2011 is not surprising given the large share of commodity sectors in private investment in this region. Lower expectations of firms’ future profitability (as measured by Tobin’s q) were the primary factor behind the weakening of investment in emerging Europe, other emerging markets, and emerging Asia. This is likely to reflect, at least partly, the downward revisions to potential growth observed in many emerging markets during this period, as well as a general sense of leaner times associated with weaker external demand and tighter global financial conditions.30 Corporate investment has also been influenced by the declining availability of international financing in recent years, particularly in emerging Asia. A 29. The sum of the contributions of each variable adds to the fitted value presented in the figure. Thus, the illustrated fitted value does not include the impact of fixed effects. 30. Potential GDP growth has slowed considerably in emerging markets as a whole, by about 1.2 percentage points since 2011. See IMF (2015, chap. 3). 178 E C O N O M I A , Fall 2017 F I G U R E 5 . Contributions to the Post–2011 Slowdowna Percent 50 0 –50 –100 –150 LAC Asia Europe Other Tobin’s q Cash flow Leverage Change in debt Net capital inflows Commodity export prices EMs total Interest expense ratio Source: Authors’ calculations. a. Relative contribution of each factor to the 2011–13 investment slowdown. number of economies have seen a moderation in capital inflows since 2012.31 Our firm-level regressions suggest that this explains a nonnegligible share of the investment slowdown. Higher corporate leverage (presumably increasing the external finance premium) and lower internal cash flow have also played a role, especially in Asian emerging markets.32 Robustness We check the robustness of our results in several ways. First, we estimate the model using the Arellano-Bond difference-in-differences approach. The results for the baseline specification remain broadly unchanged (table 5). Second, we use cash stock rather than cash flow to measure the availability of internal funds. Some previous studies use the cash stock because they assume it is less likely to be associated with future growth opportunities 31. See IMF (2013, chap. 4; 2014b). 32. The result for leverage is in line with IMF (2014a, chap. 2). 72,049 72,016 23.1700*** (1.0710) -0.2310*** (0.0075) 0.0151*** (0.0013) 0.00649*** (0.0015) -0.2330*** (0.0075) 0.0155*** (0.0013) 23.2300*** (1.0790) (2) (1) 72,001 23.6700*** (1.0860) -0.2330*** (0.0075) 0.0151*** (0.0013) 0.00653*** (0.0015) -0.0801*** (0.0058) (3) 63,098 22.4900*** (1.1000) -0.2280*** (0.0080) 0.0139*** (0.0014) 0.0140*** (0.0026) -0.0800*** (0.0062) -0.0245 (0.0254) (4) 63,090 22.3400*** (1.0960) -0.2280*** (0.0080) 0.0137*** (0.0014) 0.0140*** (0.0025) -0.0737*** (0.0062) -0.0233 (0.0255) 0.0026*** (0.00077) (5) 48,459 17.4000*** (1.2710) -0.2610*** (0.0094) 0.0132*** (0.0016) 0.0132*** (0.0030) -0.0714*** (0.0074) -0.0274 (0.0280) 0.0021*** (0.0008) 0.0005*** (5.09e–05) (6) 48,459 17.3900*** (1.2710) -0.2620*** (0.0094) 0.0132*** (0.0016) 0.0131*** (0.0030) -0.0704*** (0.0073) -0.0240 (0.0280) 0.0021*** (0.0008) 0.0005*** (5.08e–05) 0.0023*** (0.0003) (7) 47,742 -0.2620*** (0.0095) 0.0126*** (0.0016) 0.0127*** (0.0030) -0.0701*** (0.0073) -0.0289 (0.0285) 0.0021*** (0.0008) 0.0004*** (5.10e–05) 0.0025*** (0.0003) 7.57e–06*** (1.74e–06) 17.1300*** (1.2820) (8) *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period. The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. Summary statistic No. observations Constant Uncertainty Net capital inflows Commodity export price (t-1) Change in debt Interest expense ratio (t-1) Leverage (t-1) Cash flow q ICR (t-1) Explanatory variable T A B L E 5 . Robustness: Arellano–Bond Specifcationa 180 E C O N O M I A , Fall 2017 T A B L E 6 . Cash Stocka Explanatory variable ICR (t-1) q (1) (2) (3) (4) (5) (6) 0.0967*** (0.0126) 0.0207*** (0.0045) 0.0934*** (0.0134) 0.0201*** (0.0046) 0.00065*** (0.0002) 0.1060*** (0.0164) 0.0196*** (0.0048) 0.0027** (0.0010) -0.0390*** (0.0035) -0.0662** (0.0282) 0.0036*** (0.0010) 0.0933*** (0.0198) 0.0186*** (0.0047) 0.0024** (0.0010) -0.0374*** (0.0030) -0.0585* (0.0289) 0.0033*** (0.0012) 0.0005*** (0.0001) 0.0916*** (0.0198) 0.0183*** (0.0045) 0.0024** (0.0010) -0.0368*** (0.0032) -0.0568* (0.0300) 0.0033*** (0.0012) 0.0005*** (0.0001) 0.0021*** (0.0007) 8.8320*** (1.0170) 8.7800*** (1.0700) 9.7000*** (1.0890) 9.0860*** (0.9670) 8.9470*** (0.9730) 0.0889*** (0.0201) 0.0181*** (0.0044) 0.0023** (0.0010) -0.0369*** (0.0033) -0.0541* (0.0304) 0.0033*** (0.0012) 0.0005*** (0.0001) 0.0022*** (0.0007) -1.71e–06 (1.52e–06) 8.5010*** (1.1110) 94,183 0.030 16,512 38 88,273 0.032 15,281 36 79,319 0.056 14,126 36 60,541 0.048 11,414 34 60,541 0.050 11,414 34 59,398 0.048 11,344 34 Cash stock Leverage (t-1) Interest expense ratio (t-1) Change in debt Commodity export price (t-1) Net capital inflows Uncertainty Constant Summary statistic No. observations R2 No. firms No. countries * Statistically significant at the 10 percent level. ** Statistically significant at the 5 percent level. *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period.The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. than the cash flow measure.33 The results are reported in table 6. Using cash stock rather than cash flow does not alter the results. Specifically, Tobin’s q, lagged leverage, the change in debt, commodity export prices, and the availability of foreign financing all have similar coefficients as before, in terms of both magnitude and statistical significance. Cash stock is also a significant explanatory variable of firms’ capital spending, with a positive and statistically significant coefficient. As a third test, we include additional controls (table 7). In particular, real GDP growth is added as a proxy for aggregate economic activity—with the 33. For example, Harrison, Love, and McMillan (2004). See Love (2003) for further discussion. 181 Nicolás E. Magud and Sebastián Sosa T A B L E 7 . Other Robustness Checksa Explanatory variable ICR (t-1) q Cash flow Leverage (t-1) Interest expense ratio (t-1) Change in debt Commodity export price (t-1) Net capital inflows Uncertainty Commodity import price (t-1) (1) (2) (3) 0.0904*** (0.0191) 0.0177*** (0.0043) 0.0115*** (0.0021) -0.0318*** (0.0031) -0.0661** (0.0298) 0.0029*** (0.0010) 0.0003** (0.0001) 0.0024*** (0.0007) -1.11e–06 (1.44e–06) 0.0002 (0.0003) 0.0912*** (0.0192) 0.0168*** (0.0043) 0.0114*** (0.0021) -0.0315*** (0.0031) -0.0622** (0.0288) 0.0028*** (0.0010) 0.0005*** (9.87e–05) 0.0023*** (0.0006) -9.56e–07 (1.21e–06) 0.0913*** (0.0190) 0.0180*** (0.0043) 0.0115*** (0.0021) -0.0324*** (0.0029) -0.0691** (0.0291) 0.0029*** (0.0010) 0.0004*** (0.0001) Real GDP growth (t-1) -9.69e–07 (1.22e–06) 0.0004*** (2.96e–05) 0.0020*** (0.0001) -5.09e–06*** (7.43e–07) 0.0010* (0.0006) Change in debt (t-1) Summary statistic No. observations R2 No. firms No. countries 0.0025*** (0.0004) 0.0277*** (0.0004) 0.0038*** (0.0002) -0.0294*** (0.0007) 0.0849*** (0.0073) 0.0014* (0.0007) Net capital inflows (t-1) Constant (4) 8.8310*** (1.0210) 8.4800*** (0.9700) 8.7870*** (1.006) 0.0007*** (9.60e–05) 5.3430*** (0.1840) 62,632 0.052 12,190 36 62,632 0.052 12,190 36 62,632 0.050 12,190 36 209,726 0.036 35,047 36 * Statistically significant at the 10 percent level. ** Statistically significant at the 5 percent level. *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period.The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. previous results also holding. Commodity import prices are included as additional regressors, since they may affect the firms’ cost of inputs, particularly in commodity-importer economies. However, this variable is not statistically significant with all the other coefficients unchanged. We also lagged capital inflows and the change in debt to mitigate potential endogeneity problems, and the results again remain unaltered. In all these alternative specifications, the 182 E C O N O M I A , Fall 2017 T A B L E 8 . Excluding Countries with the Most Firmsa Explanatory variable ICR (t-1) q (1) (2) (3) (4) (5) (6) 0.0850*** (0.0150) 0.0250*** (0.0020) 0.0849*** (0.0148) 0.0243*** (0.0019) 0.00581*** (0.0017) 0.0964*** (0.0197) 0.0231*** (0.0020) 0.0115*** (0.0024) -0.0301*** (0.0034) -0.0458* (0.0242) 0.0033*** (0.0011) 0.0919*** (0.0232) 0.0238*** (0.0019) 0.0110*** (0.0021) -0.0319*** (0.0037) -0.0486 (0.0289) 0.0029** (0.0012) 0.0004*** (0.0002) 0.0898*** (0.0230) 0.0231*** (0.0021) 0.0110*** (0.0021) -0.0312*** (0.0039) -0.0438 (0.0280) 0.0029** (0.0012) 0.0005*** (0.0001) 0.0020*** (0.0007) 7.1560*** (0.8940) 7.2070*** (0.8760) 8.2370*** (0.9810) 8.5020*** (0.9300) 8.2740*** (0.9670) 0.0859*** (0.0232) 0.0230*** (0.0021) 0.0107*** (0.0020) -0.0313*** (0.0040) -0.0396 (0.0279) 0.0029** (0.0012) 0.0005*** (0.0001) 0.0021*** (0.0007) -3.59e–08 (1.15e–06) 7.8330*** (1.1850) 57,851 0.029 10,372 35 57,837 0.035 10,372 35 50,580 0.061 9,392 35 44,416 0.059 8,558 34 44,416 0.061 8,558 34 43,249 0.059 8,486 34 Cash flow Leverage (t-1) Interest expense ratio (t-1) Change in debt Commodity export price (t-1) Net capital inflows Uncertainty Constant Summary statistic No. observations R2 No. firms No. countries * Statistically significant at the 10 percent level. ** Statistically significant at the 5 percent level. *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period.The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. positive relationship between commodity export prices and firms’ investment remains statistically and economically significant. Fourth, we estimate the model without the countries with the largest number of firms, such as China, Korea, and Taiwan, to rule out the possibility that these countries are driving the results (table 8). Our results hold when we exclude these countries from the sample. Although not shown here, results also hold if we add firm-specific sales as a control. Fifth, we exclude firms in the lower decile of capital stock levels, to ensure that they are not biasing the results, and the results remain robust. We also run quantile regression, with the results again holding. Another extension to Nicolás E. Magud and Sebastián Sosa 183 check the performance of the model was to control for firm size and for the degree of internationalization of the firm. Once again, our main results did not change.34 In our last set of robustness tests, we consider a specification including country fixed effects, and the results remain unaltered. To control for time effects, we use year dummy variables, which reveal a negative trend in investment-to-capital ratios. Thus, we then use a trend variable rather than year dummy variables, and the baseline results do not change.35 Finally, we also estimate the model including country-time dummy variables instead of the country-specific macroeconomic variables. The coefficients on the firmlevel variables do not change substantially (in terms of both statistical and economic significance).36 To sum up, we find that beyond the standard firm-level variables used to explain investment, country-specific macroeconomic variables—notably commodity export prices—are important determinants of firms’ investment decisions, and this result appears to be quite robust. Concluding Remarks We find that commodity export prices are key to explaining firm-level investment decisions, an aspect that appears to have been overlooked in the past. As commodity export prices rise, private sector firms increase their investment ratios. This finding is based on an analysis of business investment 34. We find that larger firms and firms that are highly integrated with international financial markets, all else equal, tend to invest more. These results are available on request. 35. In the extension incorporating the RECENT dummy variable, the trend variable is used to capture time effects, since having both year dummy variables and the RECENT dummy variable one would entail identification and interpretation issues. 36. These country-time dummy variables capture time-varying idiosyncratic domestic factors, which are positively correlated with our country-specific macroeconomic variables— particularly commodity export prices. Our baseline specification given by equation 13 does not necessarily capture all possible domestic factors that may influence firms’ investment. This does not affect the interpretation of our results on commodity export prices, however, since these are mostly exogenous to the country and most likely are not affected by any other domestic variables not included in the model. That is, there may be other relevant domestic factors, such as a political cycle, but this should not be correlated with commodity export prices and therefore should not be biasing the estimated coefficient of the latter. 184 E C O N O M I A , Fall 2017 using standard panel regression models and firm-level data for about 16,000 firms for thirty-eight emerging markets over the period 1990–2013. We also include a simple investment model consistent with this finding. Moreover, we find that other country-specific macroeconomic variables such as profitability, debt stocks and flows, the availability of external financing, and financial constraints also affect private-sector investment decisions, in line with the existing literature. We document which of all these factors drove the recent episode of weak investment and how the contribution of each factor varied across regions. Commodity export prices were particularly important in Latin America and the Caribbean. Appendix A: Supplemental Tables T A B L E A 1 . List of Countries and Number of Firms in Sample Country No. firms Country No. firms Argentina Brazil Bulgaria Chile China Colombia Croatia Czech Republic Egypt Hungary India Indonesia Israel Jordan Kazakhstan Korea (South) Lithuania Malaysia Mexico 1,073 3,100 1,164 3,103 22,799 753 545 511 1,227 563 17,480 4,355 3,618 1,538 223 17,245 225 12,814 2,096 Morocco Pakistan Peru Philippines Poland Romania Russian Federation Serbia Singapore Slovakia Slovenia South Africa Sri Lanka Taiwan Thailand Turkey Ukraine Venezuela Vietnam 538 2,342 1,436 2,708 3,602 770 4,998 534 7,982 237 361 5,381 1,551 17,997 7,065 2,453 375 378 3,515 Net capital inflows Commodity export price (t-1) Change in debt Interest expense ratio (t-1) Leverage (t-1) Cash flow q ICR (t-1) Explanatory variable 0.1900*** (0.0362) 0.0131*** (0.0030) 0.0133** (0.0049) -0.0444*** (0.0089) -0.0096 (0.0224) 0.0026* (0.0013) 0.0006** (0.0002) 0.0030** (0.0009) LAC (1) 0.0790*** (0.0221) 0.0160** (0.0051) 0.0200*** (0.0046) -0.0327*** (0.0035) -0.0782* (0.0391) 0.0027* (0.0014) 0.0004*** (0.0001) 0.0025** (0.0009) Asia (2) Europe (3) 0.0773** (0.0320) 0.0224*** (0.0057) 0.00654 (0.0037) -0.0130 (0.0084) 0.00788 (0.0789) 0.0014 (0.0012) 0.0004*** (0.0001) 0.0040*** (0.0012) Cash flow 0.1500*** (0.0362) 0.0272*** (0.0020) 0.00868*** (0.0012) -0.0285* (0.0118) -0.134* (0.0572) 0.0075** (0.0022) -0.0002 (0.0004) 0.0019 (0.0012) Other (4) 0.1920*** (0.0351) 0.0131*** (0.0030) 0.0136** (0.0051) -0.0444*** (0.0089) -0.0113 (0.0214) 0.0026* (0.0013) 0.0006** (0.0002) 0.0029** (0.0009) LAC (5) T A B L E A 2 . Regional Decomposition: Interaction of RECENT with Cash Flow and Commodity Export Pricesa 0.0784*** (0.0219) 0.0160** (0.0051) 0.0190*** (0.0038) -0.0326*** (0.0034) -0.0747* (0.0376) 0.0027* (0.0014) 0.0003** (0.0001) 0.0025** (0.0009) Asia (6) 0.0777** (0.0314) 0.0229*** (0.0058) 0.0014 (0.0012) -0.0132 (0.0088) 0.00356 (0.0772) 0.0008 (0.0016) 0.0004*** (0.0001) 0.0040*** (0.0012) Europe (7) Commodity export prices 0.1500*** (0.0361) 0.0264*** (0.0021) 0.0084*** (0.0010) -0.0283* (0.0119) -0.1300* (0.0581) 0.0076** (0.0021) -0.0002 (0.0004) 0.0018 (0.0013) (continued) Other (8) 8.7240*** (0.9790) -0.0877 (1.9610) 47,506 0.049 8,894 10 -5.26e–06*** (1.49e–06) -0.0046 (0.0049) -0.0034 (0.0057) -1.91e–06** (6.34e–07) -0.0228 (0.0121) 0.0092 (0.0061) 4,622 0.087 775 7 Asia (2) LAC (1) 6,404 0.047 1,615 13 8.7060 (6.0600) -3.15e–06 (3.11e–06) -0.0006 (0.0118) -0.0086 (0.0051) Europe (3) 4,100 0.145 906 6 10.3600*** (2.5480) 2.04e–06 (4.75e–06) -0.0272*** (0.0042) -0.0091** (0.0026) Other (4) 4,622 0.087 775 7 0.0021*** (0.0005) 0.16800 (1.8850) -1.67e–06** (6.33e–07) -0.0095 (0.0133) LAC (5) 47,506 0.050 8,894 10 -0.0012*** (0.0003) 8.2090*** (0.9830) -7.28e–06*** (1.44e–06) -0.0158** (0.0058) Asia (6) 6,404 0.044 1,615 13 0.0004 (0.0003) 8.8400 (6.1120) -3.03e–06 (3.18e–06) 0.00035 (0.0128) Europe (7) Commodity export prices 4,100 0.144 906 6 -0.0003 (0.0008) 10.4200** (2.5870) 2.59e–06 (5.24e–06) -0.0328*** (0.0060) Other (8) * Statistically significant at the 10 percent level. ** Statistically significant at the 5 percent level. ***S tatistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period. The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. Summary statistic No. observations R2 No. firms No. countries Constant Recent * commodity export prices Recent * cash flow Recent Uncertainty Explanatory variable Cash flow T A B L E A 2 . Regional Decomposition: Interaction of RECENT with Cash Flow and Commodity Export Pricesa (Continued) Net capital inflows Commodity export price (t-1) Change in debt Interest expense ratio (t-1) Leverage (t-1) Cash flow q ICR (t-1) Explanatory variable 0.1920*** (0.0350) 0.0131*** (0.0030) 0.0135** (0.0050) -0.0454*** (0.0089) -0.0100 (0.0221) 0.0026* (0.0013) 0.0006** (0.0002) 0.0029** (0.0009) LAC (1) 0.0788*** (0.0221) 0.0160** (0.0052) 0.0191*** (0.0039) -0.0322*** (0.0035) -0.0778* (0.0391) 0.0027* (0.0014) 0.0004** (0.0001) 0.0025** (0.0009) Asia (2) Q 0.0774** (0.0311) 0.0230*** (0.0059) 0.00138 (0.0012) -0.0132 (0.0087) 0.00282 (0.0773) 0.0008 (0.0016) 0.0004** (0.0001) 0.0040*** (0.0012) Europe (3) 0.1500*** (0.0361) 0.0264*** (0.0022) 0.00840*** (0.0010) -0.0282* (0.0117) -0.1300* (0.0584) 0.0076** (0.0021) -0.0002 (0.0004) 0.0019 (0.0013) Other (4) T A B L E A 3 . Regional Decomposition: Interaction of RECENT with Q and Leveragea 0.1920*** (0.0346) 0.0139*** (0.0032) 0.0136** (0.0051) -0.0443*** (0.0088) -0.00996 (0.0216) 0.0026* (0.0013) 0.0005** (0.0002) 0.0030** (0.0009) LAC (5) 0.0787*** (0.0220) 0.0155** (0.0053) 0.0190*** (0.0038) -0.0328*** (0.0035) -0.0790* (0.0405) 0.0027* (0.0014) 0.0004*** (0.0001) 0.0024** (0.0009) Asia (6) Leverage 0.0771** (0.0313) 0.0213*** (0.0058) 0.00136 (0.0012) -0.0138 (0.0087) 0.00298 (0.0775) 0.0008 (0.0016) 0.0004*** (0.0001) 0.0040*** (0.0012) Europe (7) 0.1500*** (0.0360) 0.0266*** (0.0017) 0.00840*** (0.0010) -0.0282* (0.0118) -0.1300* (0.0583) 0.0076** (0.0022) -0.0002 (0.0004) 0.0019 (0.0012) (continued) Other (8) 4,622 0.087 775 7 47,506 0.049 8,894 10 8.5680*** (0.9630) -5.73e–06*** (1.56e–06) -0.0038 (0.0053) -0.0065* (0.0034) -1.78e–06** (6.49e–07) -0.0234* (0.0111) 0.0102* (0.0050) 0.0863 (1.9420) Asia (2) LAC (1) 6,404 0.045 1,615 13 8.4280 (6.0420) -3.31e–06 (3.27e–06) -0.0006 (0.0106) -0.0076 (0.0061) Europe (3) 4,100 0.144 906 6 10.3900*** (2.5060) 2.39e–06 (5.18e–06) -0.0306*** (0.0027) -0.0024 (0.0098) Other (4) 4,622 0.087 775 7 -0.0047 (0.0057) -0.0949 (1.9390) -1.96e–06** (5.82e–07) -0.0126 (0.0149) LAC (5) 47,506 0.049 8,894 10 0.0044 (0.0028) 8.7820*** (0.9910) -5.11e–06*** (1.53e–06) -0.0116 (0.00824) Asia (6) Leverage 6,404 0.045 1,615 13 0.0105*** (0.0029) 9.0410 (6.1760) -2.86e–06 (3.20e–06) -0.0144 (0.00936) Europe (7) 4,100 0.144 906 6 -0.0020 (0.0094) 10.3600** (2.7380) 2.31e–06 (4.84e–06) -0.0285 (0.0170) Other (8) * Statistically significant at the 10 percent level. ** Statistically significant at the 5 percent level. *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period. The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. Summary statistic No. observations R2 No. firms No. countries Constant Recent * q Recent * leverage (t-1) Recent Uncertainty Explanatory variable Q T A B L E A 3 . Regional Decomposition: Interaction of RECENT with Q and Leveragea (Continued) Net capital inflows Commodity export price (t-1) Change in debt Interest expense ratio (t-1) Leverage (t-1) Cash flow q ICR (t-1) Explanatory variable 0.1930*** (0.0348) 0.0131*** (0.0029) 0.0139** (0.0053) -0.0444*** (0.0089) -0.0107 (0.0218) 0.0027 (0.0015) 0.0005** (0.0002) 0.0029** (0.0009) LAC (1) 0.0790*** (0.0220) 0.0160** (0.0052) 0.0191*** (0.0038) -0.0326*** (0.0036) -0.0780* (0.0394) 0.0030** (0.0011) 0.0004*** (0.0001) 0.0025** (0.0009) Asia (2) Europe (3) 0.0767** (0.0320) 0.0228*** (0.0057) 0.0023 (0.0016) -0.0104 (0.0069) 0.00146 (0.0772) 0.0024 (0.0015) 0.0004*** (0.0001) 0.0040*** (0.0012) Capital inflows 0.1510*** (0.0358) 0.0263*** (0.0021) 0.0084*** (0.0011) -0.0283* (0.012) -0.1300* (0.0578) 0.0077** (0.0023) -0.0002 (0.0004) 0.0019 (0.0012) Other (4) 0.1920*** (0.0348) 0.0131*** (0.0030) 0.0136** (0.0051) -0.0443*** (0.0089) -0.0101 (0.0222) 0.0026* (0.0013) 0.0005** (0.0002) 0.0029** (0.0009) LAC (5) T A B L E A 4 . Regional Decomposition: Interaction of RECENT with Capital Inflows and Change in Debta 0.0786*** (0.0221) 0.0160** (0.0051) 0.0191*** (0.0039) -0.0327*** (0.0034) -0.0772* (0.0394) 0.0027* (0.0014) 0.0004*** (0.00012) 0.0026** (0.0009) Asia (6) Europe (7) 0.0777** (0.0311) 0.0230*** (0.0059) 0.0014 (0.0012) -0.0134 (0.0087) 0.00282 (0.0777) 0.0008 (0.0016) 0.0004*** (0.0001) 0.0040*** (0.0012) Change in debt 0.1500*** (0.0362) 0.0261*** (0.0023) 0.0084*** (0.0010) -0.0282* (0.0117) -0.1300* (0.0590) 0.0076** (0.0021) -0.0002 (0.0004) 0.0022 (0.0019) (continued) Other (8) 8.7290*** (0.9870) -0.0678 (1.9760) 47,506 0.050 8,894 10 -5.26e–06*** (1.49e–06) -0.0048 (0.0051) -0.0015 (0.0009) -1.90e–06** (6.37e–07) -0.0197 (0.0129) -0.0032 (0.0067) 4,622 0.087 775 7 Asia (2) LAC (1) 6,404 0.047 1,615 13 8.8170 (6.1700) -3.13e–06 (3.13e–06) -0.0023 (0.0115) -0.0036 (0.0027) Europe (3) 4,100 0.144 906 6 10.3800*** (2.5450) 2.28e–06 (4.88e–06) -0.0311*** (0.0038) -0.0022 (0.0040) Other (4) 4,622 0.087 775 7 0.0006 (0.0073) -0.0612 (2.0630) -1.89e–06** (7.10e–07) -0.0222 (0.0246) LAC (5) 47,506 0.050 8,894 10 -0.0014** (0.0005) 8.7790*** (1.0080) -5.11e–06*** (1.49e–06) -0.0082 (0.0051) Asia (6) Europe (7) 6,404 0.044 1,615 13 0.0013 (0.0025) 8.6590 (6.1320) -3.15e–06 (3.15e–06) -0.0033 (0.0089) Change in debt 4,100 0.144 906 6 -0.0008 (0.0020) 10.5100*** (2.3650) 2.02e–06 (5.04e–06) -0.0276* (0.0126) Other (8) * Statistically significant at the 10 percent level. ** Statistically significant at the 5 percent level. *** Statistically significant at the 1 percent level. a. The dependent variable is the investment-capital ratio (ICR), with the stock of capital lagged one period. The regressions control for time and firm-level fixed effects. Robust standard errors (clustered by country) are in parentheses. Summary statistic No. observations R2 No. firms No. countries Constant Recent * capital inflows Recent * change in debt Recent Uncertainty Explanatory variable Capital inflows T A B L E A 4 . Regional Decomposition: Interaction of RECENT with Capital Inflows and Change in Debta (Continued) Nicolás E. Magud and Sebastián Sosa 191 Appendix B: Effects of Terms-of-Trade Shocks We start by replicating equation 11: (A1) 1 qt =  π K K t , θt − cK I t , K t  + 1 − δ Et  qt +1  . R ( ) ( ) ( ) After subtracting qt+1 from both sides, we can rearrange the equation as follows: (A2)  1− δ + R Et ∆qt +1 = π K K t , θt − cK I t , K t +   Et  qt +1  , R  ( ) ( ) where DEtqt+1 = qt+1 - qt. In steady state, DEtqt+1 = 0 holds for (A3) Et  qt +1  = R  c I , K − π K K t , θt  K t .  1− δ + R  K t t ( ) ( ) Thus, the slope of the EtDqt+1 = 0 line is given by (A4) ∂ Et qt +1 R  c I , K − π KK K t , θt  > 0 =  ∂ Kt 1 − δ + R  KK t t ( ) ( ) given that cKK (It, Kt) > 0 and pKK(Kt, qt) < 0. From equations 10 and 3, (A5)   1  E q  K t +1 − K t = ∆Κ t +1 =  µ − δ +  t  t +1  + et − 1  . b R    ( ) Thus, (A6)  1  E q  ∆Κ t +1 = 0 µ − δ =  t  t +1  + et − 1 , b R  implying a zero slope. Figure B1 shows the phase diagram, which uses the facts that (A7) ∂ Et ∆qt +1 ∂ Kt ( ) ( ) = π KK K t , θt − cKK I t , K t < 0 ∆Et qt +1=0 192 E C O N O M I A , Fall 2017 F I G U R E B 1 . Phase Diagram Q Qt+1 = 0 A’ Kt+1 = 0 A A’ K and (A8) ∂∆K t +1 K t = > 0. ∂ Et qt +1 bR A real appreciation (that is, an increase in et), shifts the Et Dqt+1 = 0 schedule upward, while DKt +1 = 0 remains unaltered. 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Tobin, James. 1969. “A General Equilibrium Approach to Monetary Theory.” Journal of Money, Credit, and Banking 1(1): 15–29. Copyright of Economia is the property of Brookings Institution Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. 1. Zona, F. (2012). Corporate Investing as a Response to Economic Downturn: Prospect Theory, the Behavioural Agency Model and the Role of Financial Slack. British Journal of Management, 23, S42�S57. https://doi-org.prxkeiser.lirn.net/10.1111/j.1467-8551.2012.00818.x 2. MAGUD, N. E., & SOSA, S. (2017). Corporate Investment in Emerging Markets: The Role of Commodity Prices. Economia, 18(1), 157�195. Retrieved from http://prx-keiser.lirn.net/login?url=http%3a%2f%2fsearch.ebscohost.com %2flogin.aspx%3fdirect%3dtrue%26db%3dbth%26AN%3d126541296%26site%3dehost-live 3. Hyunseob Kim, & Howard Kung. (2017). The Asset Redeployability Channel: How Uncertainty Affects Corporate Investment. Review of Financial Studies, 30(1), 245�280. https://doi-org.prx-keiser.lirn.net/10.1093/rfs/hhv076 The Asset Redeployability Channel: How Uncertainty Affects Corporate Investment Hyunseob Kim Cornell University Howard Kung London Business School Received August 29, 2014; editorial decision July 13, 2016 by Editor Itay Goldstein. There are active secondary markets for corporate assets. The aggregated value of transactions in these markets amounts to more than a $100 billion in a typical year (e.g., Warusawitharana 2008; Gavazza 2011).1 Importantly, activities in used asset markets vary widely across industries. For example, the retail and electronics manufacturing industries exhibit active trading in used asset markets, while the computer and pulp & paper manufacturing industries show relatively infrequent transactions. This variation in trading activity suggests that We thank two anonymous referees and the editor Itay Goldstein for their comments and guidance. We are grateful for comments from Einar Bakke (discussant), Ravi Bansal, Effi Benmelech, Nittai Bergman, Nick Bloom, Michael Brandt, Alon Brav, Murillo Campello, Steven Davis, Francisco Gomes, Dirk Hackbarth, Emi Nakamura, John Graham, Andrew Karolyi, Seoyoung Kim (discussant), Mauricio Larrain, Jean-Marie Meier, Roni Michaely, Justin Murfin, Manju Puri, Adriano Rampini, David Robinson, Norman Schürhoff (discussant), S. Viswanathan, Ivo Welch, Toni Whited, and seminar and conference participants at Cornell University, CUNY Baruch, Duke University, EFA, FMA Asia, SFS Finance Cavalcade, Rutgers University, Shanghai Advanced Institute of Finance (SAIF), Stanford Institute for Theoretical Economics (SITE), and University of British Columbia. We also thank Dawoon Kim, Young Jun Song, and Ercos Valdivieso for excellent research assistance. Kim gratefully acknowledges financial support from the Kwanjeong Educational Foundation. Any errors are our own. Supplementary data can be found on The Review of Financial Studies web site. Send correspondence to Hyunseob Kim, Samuel Curtis Johnson Graduate School of Management, Cornell University, Sage Hall, 114 East Ave, Ithaca, NY 14853; telephone: (607) 255-8335. E-mail: hk722@cornell.edu. 1 Using the SDC Platinum mergers and acquisitions database, we find that in 2012, U.S. firms were involved in more than 6,000 transactions of used asset sales and the total value of trading was $167 billion. © The Author 2016. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. doi:10.1093/rfs/hhv076 Advance Access publication August 29, 2016 Downloaded from http://rfs.oxfordjournals.org/ at :: on December 23, 2016 This paper examines how uncertainty affects corporate investment under varying degrees of asset redeployability. We develop new measures of asset redeployability by accounting for the usability of assets within and across industries. We identify plausibly exogenous shocks to economic uncertainty by using major economic and political events. We find that after an increase in uncertainty, firms using less redeployable capital reduce investment more. More redeployable assets exhibit higher recovery rates and are traded more actively in secondary markets. Overall, our results suggest that frictions in redeploying assets affect liquidation values and therefore make firms cautious about investment decisions under uncertainty. (JEL G31, D22, D92) The Review of Financial Studies / v 30 n 1 2017 2 In the financial economics and industrial organizations literature, “asset redeployability” (Williamson 1988; Shleifer and Vishny 1992) is often referred to as “asset salability” or “asset market liquidity.” (e.g., Benmelech 2009; Gavazza 2011). We use the term “asset redeployability” throughout this paper for consistency. 3 Using the number of potential users (or buyers) is consistent with other approaches of measuring liquidity in financial markets (e.g., Demsetz 1968; Amihud, Mendelson, and Uno 1999). These papers generally find lower bid-ask spreads for assets with a larger number of shareholders or float. 246 Downloaded from http://rfs.oxfordjournals.org/ at :: on December 23, 2016 frictions affecting the redeployability of assets may vary significantly across firms due to search costs for potential buyers and sellers, and firms’ financial constraints, among other factors (e.g., Ramey and Shapiro 2001; Hennessy, Levy, and Whited 2007; Gavazza 2011). In this paper, we investigate how variation in asset redeployability—the extent to which assets have alternative uses—affects firm investment under uncertainty.2 Costs associated with redeploying assets are an important source of investment irreversibility (i.e., the wedge between purchase and liquidation values of capital). Notably, costly capital reversibility creates incentives for firms to delay investment when there is uncertainty regarding profitability (e.g., Dixit and Pindyck 1994). Consider an increase in uncertainty in which both very bad and very good states of the world become equally more likely, but the mean of the distribution stays the same. With investment irreversibility, firms are more sensitive to bad states because disinvestment is more costly than investment. From a real options perspective, higher irreversibility implies lower asset liquidation values, and therefore such assets offer less protection against negative outcomes (e.g., Caballero 1991; Bloom 2009). Thus, even when expected investment opportunities (first moment) stay the same, an increase in uncertainty regarding the payoffs (second moment) induces more irreversible firms to purchase more protection by delaying investment than less irreversible firms. To test this theoretical prediction, we need to construct a measure of asset redeployability and to identify changes in uncertainty. We measure asset redeployability using the Bureau of Economic Analysis (BEA) capital flow table, which breaks down capital expenditures into a variety of asset categories for a broad cross-section of industries. First, we compute the asset-level redeployability score as the proportion of firms (or industries) that use a given asset. Thus, the redeployability score would be higher when more firms (or industries) in the economy use a given asset.3 This approach to measuring redeployability incorporates the notions of asset specificity (e.g., Williamson 1988) and asset market thickness (e.g., Gavazza 2011). As an extension, we incorporate the financial constraints of potential buyers and the correlation of output within industries to capture the deleterious effects of potential buyers’ illiquidity on redeployability, especially during economic downturns (Shleifer and Vishny 1992). Second, we compute the industry-level redeployability index as the value-weighted average of each asset’s redeployability score. The resulting redeployability index exhibits considerable variation across the The Asset Redeployability Channel: How Uncertainty Affects Corporate Investment 4 Based on this measure, the industries with the least redeployable assets include materials, transportation, and manufacturing-related, while those with the most redeployable assets are service-related. 5 See Figure 1 for fluctuations of measures of aggregate uncertainty over our sample periods. 247 Downloaded from http://rfs.oxfordjournals.org/ at :: on December 23, 2016 industries.4 Given that industry peers are likely to suffer from the same operational and financial difficulties as the firm liquidating assets (e.g., Shleifer and Vishny 1992; Ramey and Shapiro 2001), accounting for the salability of corporate assets across as well as within industries is an important advantage of our redeployability measures. Firms in industries with a higher redeployability index exhibit higher recovery rates and are more actively involved in asset sales. To identify sudden and dramatic increases in economic uncertainty, we study major economic and political events, such as Gulf War I in 1990 and the 9/11 terrorist attacks in 2001. As both quantitative and anecdotal evidence suggests, uncertainty about consumer demand and profitability, which drive firms’ investment opportunities, increased significantly after these events (e.g., Bloom 2009). In addition, the economy-wide nature of these shocks implies that they are likely to affect economic uncertainty similarly across firms, which we verify using stock return volatility as a proxy for firm-level uncertainty (e.g., Leahy and Whited 1996). Exploiting these major events as shocks to uncertainty and using Compustat quarterly data on firm-level investment, we find that asset redeployability has an economically sizable effect on corporate investment when uncertainty varies. Our baseline estimates indicate that in response to a sharp increase in uncertainty during Gulf War I and following 9/11,5 a one-standard-deviation (SD) decrease in our measure of asset redeployability leads to a 0.06- to 0.14-percentage-point decrease in the quarterly investment rate (i.e., capital expenditure divided by total assets). This magnitude is 5% to 19% of the median quarterly investment rate and accounts for 37% to 47% of average drops in investment after the shocks. These estimates imply that the typical variation in asset redeployability leads to a wide dispersion across firms in their investment response to fluctuating uncertainty. More generally, using a panel of firm quarters from 1989 to 2009, we find that the investment of firms with less redeployable capital decreases (increases) significantly more when proxies for aggregate economic uncertainty increase (decrease). Our paper contributes to the empirical literature on investment irreversibility. Despite a large theoretical literature on irreversible investment (e.g., Bernanke 1983; McDonald and Siegel 1986; Abel and Eberly 1996), empirical papers have focused on examining the average relation between uncertainty and investment (e.g., Leahy and Whited 1996; Julio and Yook 2012; Gulen and Ion 2016). However, relatively little attention has been paid to testing important cross-sectional predictions regarding irreversibility, which is partly due to The Review of Financial Studies / v 30 n 1 2017 6 Guiso and Parigi (1999) find that the negative effect of uncertainty on the sensitivity of investment to demand conditions is more pronounced for more irreversible investments using a simple dichotomous measure of the existence of used asset markets for Italian firms. However, their main focus is on testing the average uncertainty– investment relationship. Relatedly, Durnev (2012) finds that the investment–stock price sensitivity decreases in election years compared with non-election years. 248 Downloaded from http://rfs.oxfordjournals.org/ at :: on December 23, 2016 the difficulty of measuring investment irreversibility.6 Our paper differs from previous research along two key dimensions. First, we develop new measures of asset redeployability that vary significantly across industries, suggesting that accounting for this heterogeneity is potentially important for testing the theory. Second, we use these measures and plausibly exogenous shocks to aggregate uncertainty to show that there is indeed a wide dispersion in the effect of uncertainty on investment depending on the redeployability of capital. Our paper also relates to the growing empirical literature that uses asset redeployability to measure asset liquidation values in studying firm policies. This line of research has focused on the role of asset redeployability in capital structure outcomes such as debt maturity (e.g., Benmelech 2009), cost of capital (e.g., Benmelech and Bergman 2009; Ortíz-Molina and Phillips 2014), and leverage (e.g., Campello and Giambona 2013). In addition, Almeida, Campello, and Hackbarth (2011) and Gavazza (2011) examine the effect of asset redeployability on asset reallocation through mergers and trading in secondary markets. Beutler and Grobety (2013) use asset redeployability as a proxy for liquidation values to examine the sensitivity of industry growth to collateral values. Our paper is among the first to empirically investigate the relation between asset redeployability and corporate investment. Also, our asset redeployability measures are applicable economy-wide rather than for only a specific industry. More broadly, our paper connects to the empirical literature testing neoclassical investment models. Fazzari, Hubbard, and Petersen (1988) document that investment is strongly correlated with cash flows (especially for financially constrained firms), which runs contrary to the prediction that marginal q is a sufficient statistic for investment (e.g., Hayashi 1982). Gomes (2001) theoretically shows that cash flows should matter for investment only if q is ignored or measured with errors. Consistent with this result, Erickson and Whited (2000) find support for the q theory after accounting for measurement errors in marginal q and using a generalized method of moments estimator. Chen, Goldstein, and Jiang (2007) document that the degree of private information in stock prices is important in determining the investmentto-q sensitivity. In line with Abel and Eberly (1996) and Barnett and Sakellaris (1998), our paper provides suggestive evidence that there are nonconvexities in investment adjustment costs, which implies a nonlinear relation between q and investment. Finally, the results in this paper support the argument of policymakers and academics that uncertainty has a negative effect on the economy. We show that uncertainty, combined with limited redeployability of capital, can The Asset Redeployability Channel: How Uncertainty Affects Corporate Investment significantly dampen capital accumulation and ultimately economic growth. Thus, we add to the growing literature examining the impact of uncertainty on the macroeconomy by identifying a microeconomic channel through which the effect operates, namely, frictions in reallocating capital.7 In particular, our findings suggest that firms using less redeployable capital such as transportation and manufacturing are affected more by an increase in uncertainty relative to those using more redeployable capital such as wholesale and retail. 1. Measurement and Data 1.1 Redeployability of corporate assets We construct measures of asset redeployability using the 1997 Bureau of Economic Analysis (BEA) capital flow table (hereafter referred to as the “BEA table”). The table breaks down expenditures on new equipment, software, and structures by 180 assets for 123 industries,8 covering virtually all economic sectors in the United States.9 While the BEA table is also available for 1982 and 1992, we employ the 1997 table throughout the analysis for the following reasons. First, it provides the most detailed breakdown of asset categories and industries.10 Second, it is based on the most relevant and up-to-date information on asset usage by industry for our main sample periods, which range from 1989 to 2002. Third, by using the same table, we can maintain the consistency of asset and industry classifications in our analysis. Nonetheless, we find qualitatively similar results when we use the 1992 capital flow table. In order to reduce the influence of sector-level heterogeneity on investment, we confine our main analysis to the manufacturing industries (NAICS 310000–339999) but use the full set of industries when examining the external validity of the main results (see Section 3). 1.1.1 Constructing the asset-level redeployability score. We construct the main firm-level measures of asset redeployability in three steps. In the first 7 See, for example, Bloom (2009), Fernández-Villaverde et al. (2015), and Baker, Bloom, and Davis (2016). 8 Land is not included in the BEA asset categories. However, we find quantitatively similar results when incorporating expenditures on land into our measure by assuming land has complete redeployability across industries. 9 The industry classification employed by the BEA is based on the 1997 North American Industry Classification System (NAICS). Therefore, we match the 123 BEA industries with Compustat firms using the NAICS code. 10 The 1982 (1992) capital flow table is based on 79 (64) industries and 52 (163) assets. In addition, both tables are based on SIC industry classifications, while the 1997 table is based on the more modern NAICS classifications. 249 Downloaded from http://rfs.oxfordjournals.org/ at :: on December 23, 2016 This section describes how we (i) construct the measures of asset redeployability based on an economic link in asset usage across industries, (ii) identify variation in aggregate uncertainty, and (iii) construct firm samples for empirical analysis. The Review of Financial Studies / v 30 n 1 2017 step, we define an asset’s “redeployability score” as the sum of weights of industries that use the asset among the 123 industries in the BEA table. We employ various measures of the weights for industries to ensure robustness of the empirical results. In particular, we use one of the following variables as the weight of an industry in the economy in a given year: (i) one over the total number of BEA industries, (ii) the number of all public (i.e., Compustat) firms in an industry over the total number of all public firms, and (iii) the sum of market capitalization of all public firms in an industry over the sum of market capitalization across all public firms. The resulting asset-level measure is: Redeployability Scorea,t = 123  j =1 Ia,j (use)×Valuej,t / 123  V aluej,t , (1) j =1 11 If an industry’s expenditure on a given asset constitutes a negligible fraction (less than 0.1%) of the total expenditure on the asset in the economy, we set Ia,j (use) to zero. Our empirical results are robust to varying this cutoff from 0% (no cutoff) to 0.5%. 250 Downloaded from http://rfs.oxfordjournals.org/ at :: on December 23, 2016 where Redeployability Scorea,t is the redeployability of asset a in year t; Ia,j (use) is a dummy variable equal to one if asset a is used by industry j in a meaningful amount in the BEA table;11 and Valuej,t is either 1 (equal weight for each industry), Nj,t (number of firms), or MCAPj,t (market capitalization) of all Compustat firms in industry j in year t. The redeployability score can vary as these measures of industries’ weights change over time. In addition, if the description of an asset implies that it is highly specialized for a given firm (industry), and thus would have virtually no salability outside the firm (industry), we set the redeployability score to zero (the industry’s weight). Examples of specialized assets are “custom computer programming” and “new industrial plants construction.” The value of the asset-level redeployability score we compute appears reasonable. For example, using MCAPj,t as the weight in 1997, the score is 0.66 for “industrial trucks, trailers, and stackers,” which are used in a wide range of industries, while it is 0.02 for “drilling oil and gas wells,” which are predominantly used in the oil and gas industries. To conceptually distinguish between these measures, it is useful to decompose the notion of asset redeployability into asset specificity and liquidity components. Broadly, our measures capture the usability or specialization of capital across different purposes (e.g., industries). Therefore, in the transaction costs framework of Williamson (1988), our measures relate closely to the notion of asset specificity. In particular, our measure that only uses an industry count to categorize capital goods aligns most closely with the degree of asset specificity. However, the industry count does not fully account for market thickness (mass of potential buyers), and therefore this measure is less related to the liquidity dimension. Consequently, in our main measure we account for the liquidity component by using market capitalization of an industry as a proxy for market thickness to construct the industry’s weight. For example, an The Asset Redeployability Channel: How Uncertainty Affects Corporate Investment 1.1.2 Constructing industry- and firm-level redeployability measures. In the second step for each of these measures, we value-weight the asset-level redeployability scores in Equation (1) across the 180 assets in the BEA table to give us an industry-level redeployability index: Redeployabilityj,t = 180  wj,a ×Redeployability Scorea,t (2) a=1 where Redeployabilityj,t is a measure of asset redeployability for industry j in year t, wj,a represents industry j’s expenditure on asset a divided by its total capital expenditure from the BEA table, and Redeployability Scorea,t is the redeployability score of asset a in year t computed as in Equation (1). The resulting redeployability index represents a relative redeployability ranking of each industry’s asset composition. A key advantage of our redeployability measure is that it accounts for the salability of corporate assets across as well as within industries. While industry peers are more likely to be higher-valuation potential buyers of capital than outsiders, they are also more likely to suffer from the same operational and financial difficulties as the firm liquidating assets (e.g., Shleifer and Vishny 1992; Ramey and Shapiro 2001). Therefore, cross-industry salability is crucial in measuring asset redeployability.12 12 Consistent with this notion, we find that more than 60% of asset sales in secondary markets occur between firms in different BEA industries by using asset-level transaction data from the SDC Platinum M&A database from 1980 to 2013. Maksimovic and Phillips (2001) also find that from 1978 to 1992, 37% to 52% of buyers of plants in asset sales are from outside a given three- or four-digit SIC industry. Similarly, using data on closed plants in the aerospace industry in the early 1990s, Ramey and Shapiro (2001) find that more than three-quarters of used asse...
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Running Head: CORPORATE INVESTMENT DECISIONS

How Corporations make Investing Decisions.
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CORPORATE INVESTMENT DECISIONS

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How Corporations make Investing Decisions.
Financial planning is one of the most essential tasks a corporation must undertake. This
involves deciding and projecting the available funds, where and how they should be spent, and
how the funds can be invested. In fact, the process of investing is a significant means for the
corporation to raise the funds and acquire higher financial returns in the future (Zona, 2012).
However, before making the investment decisions, the company should analyze all risks and
determine what investments are appropriate for the corporation. Investing suitably implies that
an organization can easily meet its financial objectives. As discussed in this paper, there are
various ways in which corporations can make investing decisions.
Investment decisions characteristically comprise the commitment of massive funds with
an aim to maximize the capital of the shareholders by attaining assets and further generating
profit. As such, the company begins by evaluating the most effective way of investing the capital
funds to ensure that the returns are...


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