Monetary policy, social capital, and corporate investment

Abstract

We study the effect of monetary policy and social capital on the efficiency of corporate investments among the public US firms. We use social capital of CEOs at the peer- and non-peer levels to build proxies for the CEO networking and the effective federal funds rate and the spread between the rate and the 10-years US Treasury note as our proxies for monetary policy. We capture investment inefficiency from the residuals of Richardson (2006)‘s investment efficiency model. Our results show that both innovations in monetary policy and social capital of CEOs influence corporate investment inefficiency significantly. Stronger social ties amongst CEOs and their peers lead to greater inefficiency in investments while stronger social ties between CEOs and their non-peer directors lead to lower inefficiency in investments. The results strengthen our belief on social capital’s spillover influence on investment decision at the firm level. Our evidence indicates that CEO social ties function as another channel that affects their investment decision and that the influence of social ties also depends on the role of the other parties CEOs are affiliated with—whether they have titles of CEOs or titles of directors.

Appendix

Variable definition:

INVEFF residuals from the investment efficiency model following Richardson (2006);

FYFF the effective federal funds rate. We use the annual average rate directly available from the Federal Reserve of St. Louis website, the FRED;

FFSPR the spread between the federal funds rate and the yield on the 10-years US Treasury note. We obtain this spread directly from the Federal Reserve of St. Louis website, the FRED;

Btwn_CEOCEO, Clsn_CEOCEO, Dgr_CEOCEO The prefixes “Btwn_”, Clsn_”, and “Dgr_” indicate betweenness, closeness, and degree, respectively. The connections that we examine follow these prefixes. For instance, the betweenness between two CEOs are identified using the first variable in this list. The two occupational titles are jointly used to mark their distinct social capital between title-based peers from those non-title-based peers. We use Pajek, the social network analysis software, to obtain compute these measures amongst CEOs (Batagelj et al. 2012; de Nooy et al. 2005);

Btwn_CEODir, Clsn_CEODir, Dgr_CEODir The prefixes “Btwn_”, Clsn_”, and “Dgr_” indicate betweenness, closeness, and degree, respectively. The connections between CEOs (those with title of CEOs) and directors (those with title of directors). We use Pajek, the social network analysis software, to obtain compute these measures amongst CEOs’ connections to directors (Batagelj et al. 2012; de Nooy et al. 2005);

Btwn_CEOIndepdir, Clsn_CEOIndepdir, Dgr_CEOIndepdir The prefixes “Btwn_”, Clsn_”, and “Dgr_” indicate betweenness, closeness, and degree, respectively. The connections between CEOs (those with title of CEOs) and independent directors (those with title of directors but not sit on the board of the same firms). We use Pajek, the social network analysis software, to obtain compute these measures amongst CEOs and independent directors (Batagelj et al. 2012; de Nooy et al. 2005);

ROA net income scaled by the average of current total assets and the most historical recent fiscal year end’s total assets;

ROASQ square root of ROA defined above;

Cratio Current ratio, measured by current assets divided by current liabilities;

CFOA Cash flow from operations, which we compute as operating cash flow divided by lagged total assets;

AltmanZ A business failure prediction model for firms using the estimated parameters explained by Altman et al. (1995): AltmanZ = −17.862 + 1.472*log(total assets) + 3.041*log(sales over assets) + 14.839*(retained earnings over assets) + 1.516*(book value of equity / total liability);

BIG4 an indicator variable taking a value of 1 if the auditor is one of Big 4 CPA firm and 0 otherwise;

LOSS an indicator variable taking a value of 1 if the firm realize loss in the past two fiscal years.