Paid for connections or too connected to be good  Paid for connections or too connected to be good? Social Networks and Executive and Non-Executive Director Compensation   Joanne Horton, Yuval Millo London School of Economics George Serafeim Harvard Business School

Motivation: role of directors in current capitalism Is there a demand for people that are better ‘connected’? Most economic based research views social connections as a mechanism for rent extraction (Larcker et al. 2005; Barnea and Guedj 2007; Brown et al. 2008), following the managerial power paradigm of Bebchuk Social capital and management theory suggests social connections can potentially be an important strategic tool for competitive advantage (Burt 2005)

Research Questions Do social network measures exhibit an association with executive and non-executive directors’ compensation? Do firms with better connected directors have better or worse future performance?

Directors: monitors and advisors Corporate directors monitor management on behalf of the shareholders: such monitoring may reduce agency costs and improve firm performance (Fama and Jensen 1983) Directors may provide resources to the firm. For example board members can serve as ‘boundary spanners’ (Zahra and Pearce 1989) providing access to communication channels with the external environment (Pfeffer and Salancik 1978) and thereby provide their firm with access to new information (Allen 1974, Burt 1979). These resources can reduce dependency between the firm and external contingencies (Pfeffer and Salancik 1978), enhance organizational legitimacy (Zahra and Pearce 1989), reduce uncertainty (Pfeffer 1972), lower transaction costs (Williamson 1984), and thereby impact firm decision making (Mizruchi 1996) and improve a company’s performance and solvency (Stokman et al. 1985, Mizruchi and Stearns 1988).

Predictions for director compensation Directors receive higher compensation the better monitoring or advising services they provide Since monitoring role is performed by NEDs better connected executives will have higher compensation For NEDs the relation with pay is not clear. Better connected NEDs may or may not be better monitors Better connected directors may create additional agency costs through leakage of proprietary information Less connected directors may demand higher pay to compensate for inability to diversify their human capital risk

Network measures Directors Degree Closeness Dyadic Constraint Normalized Aggregate Directors Degree Closeness Dyadic Constraint A 2 0.4583 0.7003 B 6 0.5500 0.3588 C 5 0.6111 0.4097 D 4 0.5000 0.5448 E 4 0.5000 0.5702 F 4 0.5500 0.4752 G 3 0.4074 0.9028 H 2 0.3793 0.9028 I 4 0.5500 0.3126 J 1 0.3667 1.0000 K 2 0.3928 0.5000 L 1 0.2895 1.0000 Network measures G A B C H E D F I K L J

Sample: companies and directors All companies listed at the London Stock Exchange (LSE) and the Alternative Investment Market (AIM) between 2000-2007 4,278 firms, 31,495 directors and 111,114 directorship-year pairs The sample includes 3,477 firms, 22,585 directors and 79,439 directorship-year pairs Total compensation for each director is the sum of salary, bonus and other benefits (also examine stock options in section 5.3.1)

Results (in a nutshell) Executive directors are rewarded for being socially connected Non-executive directors are rewarded for having constrained social connections The predicted component from high executive connectedness and low non-executive connectedness is positively associated to future firm performance

Research design: network measures and compensation Compensation = f(Social networkit, Controlsijt) Compensation is the compensation for director i, directorship j at year t Social networkit is either the closeness or dyadic constraint measure for director i at year t. We cluster standard errors at the company level to mitigate serial and cross-director correlation within a firm (we tried several alternative estimation methods in 5.3.5)

Association between compensation and closeness   Dependent variable: Compensation CEO CFO Executive Chairman Non-Executive Intercept -184.94 -151.42 -227.70 -86.71 -6.42 (-1.71) (-3.41) (-2.38) (-3.28) Closeness 354.47 148.57 319.05 -109.58 -25.62 (3.26) (2.73) (2.63) (-2.52) (-3.03) MTB -0.34 0.16 -0.47 -0.37 -0.08 (-0.27) (0.24) (-0.36) (-1.06) (-1.85) ROA -51.90 -35.45 -74.43 -5.25 -2.10 (-4.90) (-4.85) (-3.84) (-1.74) (-1.95) Firm size 98.12 58.91 75.09 15.82 2.82 (12.73) (16.84) (7.28) (9.01) Stock return -17.23 -13.02 -15.41 -3.15 -1.25 (-3.96) (-3.77) Growth -14.87 -7.11 -5.89 -3.52 -0.65 (-2.26) (-0.68) (-1.34) Board size 45.57 31.34 45.30 31.95 8.34 (1.39) (1.98) (1.26) (4.52) Director tenure 60.66 66.31 55.26 26.21 4.29 (6.38) (10.90) (6.11) (9.82) Male -27.81 13.30 64.86 15.49 1.52 (0.96) (3.66) (2.10) Index, Industry and Year f.e. Yes N 7,940 6,031 7,889 9,169 26,839 Adj-R squared 54.8% 62.9% 40.6% 29.1% 22.5%

Association between compensation and dyadic constraint   Dependent variable: Compensation CEO CFO Executive Chairman Non-Executive Intercept -71.37 -70.61 -317.25 -119.82 -15.77 (-0.54) (-1.03) (-2.31) (-4.77) (-4.09) Dyadic Constraint -81.92 -62.23 74.41 53.93 9.86 (-1.85) (-1.67) (1.05) (6.94) (7.51) MTB -0.34 0.17 -0.44 -0.08 (-0.28) (0.25) (-0.34) (-0.98) (-1.93) ROA -51.11 -35.87 -74.98 -4.83 -2.02 (-4.79) (-4.86) (-3.86) (-1.63) (-1.87) Firm size 99.52 59.09 77.99 16.11 2.84 (12.95) (16.72) (7.36) (6.60) (9.15) Stock return -17.77 -13.07 -15.76 -3.25 -1.29 (-4.08) (-4.87) (-3.45) (-1.96) Growth -14.93 -7.41 -6.43 -3.86 -0.67 (-2.25) (-1.77) (-0.74) (-1.48) (-1.38) Board size 23.86 11.62 81.57 42.53 10.22 (0.59) (0.49) (1.73) (4.17) (5.40) Director tenure 59.34 66.04 54.49 24.69 4.23 (6.24) (10.91) (6.03) (6.95) (9.72) Male -27.59 13.47 65.99 13.55 1.56 (-0.53) (0.96) (3.73) (1.04) (2.14) Index, Industry and Year f.e. Yes N 7,940 6,031 7,889 9,169 26,839 Adj-R squared 54.8% 62.9% 40.4% 29.5% 22.8%

Within firm-year association between compensation and network measures   Dependent variable: Compensation Executive directors Non-Executive directors Intercept -58.96 -58.70 -60.96 -60.88 -23.37 -21.69 -24.78 -23.96 (-6.25) (-6.23) (-6.33) (-6.31) (-12.65) (-11.86) (-12.90) (-12.56) Closeness 4159.85 4738.79 -782.78 -530.90 (2.30) (2.97) (-8.54) (-5.65) Dyadic constraint -99.93 -106.97 38.63 32.35 (-1.41) (-1.65) (8.68) (6.90) Director tenure 78.34 78.47 78.61 78.63 18.44 18.54 18.31 18.34 (13.21) (13.06) (13.07) (13.05) (13.09) (12.86) (12.87) Male 14.04 14.46 12.77 12.85 8.62 8.49 9.17 9.07 (1.69) (1.74) (1.53) (1.54) (5.11) (5.02) (5.34) (5.28) Busy Director -21.77 -5.47 -6.18 -2.91 (-0.88) (-0.21) (-5.52) (-2.71) Firm x Year f.e. Yes N 23,648 42,887 Adj R-squared 18.5% 18.2% 19.4% 18.8% # of firms 2,181 3,032

Research design: network measures and firm performance (1) We estimate the model for 2000-2001 (estimation period) and we test the predictions in period 2002-2007 (test period). We also used 2000-2004 as the test period and 2005-2007 as the estimation period. The results were qualitatively similar to the ones reported here. After estimating the predicted component for each directorship-year we average this variable by firm and then over the years used for estimation to construct a firm level variable Intuition: if better connected executives are better advisors and less connected NEDs better monitors then future performance should increase in the predicted component of compensation

Research design: network measures and firm performance (2) Then we include this firm-level variable in a regression where the dependent variable is future performance. Performanceit = f(Pcompit-1, Performanceit-1, Controls Where Performanceit is average stock return, MTB, ROA or sales growth for firm i over 2002-2007 and Performanceit-1 is average stock return, MTB, ROA or sales growth for firm i over 2000-2001. By including lag performance we control for other potential omitted variables that can affect performance persistently and for the endogeneity of the director selection (firms with low or high performance may systematically choose directors with high social capital)

Association between predicted compensation from closeness and future performance Dependent Variables: Stock Return MTB ROA Sales growth Intercept 0.098 0.132 -0.709 -0.612 -0.092 -0.097 -0.002 0.075 (1.74) (2.33) (-1.79) (-1.57) (-2.09) (-2.22) (-0.02) (0.50) Lag Return -0.016 -0.015 (-0.52) (-0.50) Lag MTB 0.557 (15.88) (15.98) Lag ROA 0.508 0.509 (13.54) (13.52) Lag 3-year Growth 0.282 0.284 (8.21) (8.29) Mean Predicted SN Comp 0.116 0.234 -0.014 0.196 (2.93) (1.45) (-1.35) (2.89) Sum Predicted SN Comp 0.021 -0.001 0.030 (2.84) (2.38) (-0.70) (2.30) Firm size -0.003 0.062 0.064 0.013 0.014 0.018 0.015 (-0.21) (-0.45) (2.03) (2.07) (7.65) (7.92) (1.38) (1.13) 0.032 (5.24) (5.25) Board size -0.081 -0.099 -0.164 -0.253 -0.025 -0.028 -0.049 (-2.51) (-2.88) (-0.99) (-1.47) (-2.49) (-2.35) (-0.44) (-0.75) % Outsiders -0.138 -0.135 0.231 0.242 -0.006 -0.153 -0.145 (-2.06) (-2.00) (0.68) (0.71) (-0.31) (-1.32) (-1.24) % Busy directors -0.056 -0.048 0.076 0.040 -0.007 -0.010 -0.219 -0.192 (-1.09) (-0.95) (0.35) (0.19) (-0.68) (-1.99) (-1.77) Industy f.e. Yes N 1340 1284 1300 1161 Adj-R squared 10.9% 10.6% 35.6% 35.8% 45.1% 45.0% 23.7% 23.3%

Association between predicted compensation from dyadic constraint and future performance Dependent Variables: Stock Return MTB ROA Sales growth Intercept 0.019 0.040 -0.731 -0.763 -0.098 -0.115 -0.191 -0.140 (0.29) (0.66) (-1.66) (-1.72) (-2.20) (-2.66) (-0.99) (-0.77) Lag Return -0.023 -0.021 (-0.74) (-0.70) Lag MTB 0.558 (15.83) (15.88) Lag ROA 0.509 (13.51) (13.55) Lag 3-year Growth 0.287 0.286 (8.34) Mean Predicted SN Comp 0.037 0.032 0.000 0.075 (2.37) (0.44) (0.10) (2.73) Sum Predicted SN Comp 0.011 0.014 0.002 0.020 (2.84) (0.69) (1.56) (2.65) Firm size 0.001 0.058 0.061 0.015 0.022 (-0.07) (0.12) (1.74) (1.79) (7.82) (8.32) (1.54) (1.55) (5.27) (5.30) Board size -0.044 -0.065 -0.113 -0.133 -0.027 0.039 -0.002 (-1.33) (-2.05) (-0.69) (-0.80) (-2.6) (-2.65) (0.63) (-0.02) % Outsiders -0.127 -0.124 0.241 0.250 -0.005 -0.119 (-1.87) (-1.83) (0.72) (0.74) (-0.27) (-0.10) (-1.00) (-0.96) % Busy directors 0.025 0.027 0.197 0.212 -0.012 -0.006 -0.063 -0.068 (0.54) (0.56) (0.94) (1.01) (-0.83) (-0.44) (-0.66) Industy f.e. Yes N 1340 1284 1300 1161 Adj-R squared 10.5% 10.7% 35.5% 45.0% 45.1% 23.6% 23.7%

Including stock option compensation   CEO CFO Executive Chairman Non-Executive Dependent variable: Total compensation Closeness 2109.54 957.91 1317.83 -150.03 -75.14 (5.09) (3.79) (4.65) (-1.29) (-4.15) Dyadic constraint -673.00 -468.91 -235.77 116.89 23.55 (-3.98) (-3.20) (-1.31) (5.26) (7.83) Dependent variable: % Long-term incentives 0.83 0.68 0.71 (6.02) (4.79) (4.45) -0.20 -0.25 -0.16 (-4.20) (-3.82) (-1.88) Dependent Variable: Stock Return MTB ROA Sales growth Mean Predicted Compensation (closeness) 0.034 0.038 0.000 0.090 (1.98) (0.53) (0.11) (2.83) Mean Predicted Compensation (dyadic constraint) 0.010 0.017 0.001 0.021 (2.16) (0.85) (2.50) Sum Predicted Compensation (closeness) 0.008 0.016 0.015 (2.13) (1.64) (0.47) (2.43) Sum Predicted Compensation (dyadic constraint) 0.003 0.006 (2.32) (0.29) (1.61) (2.64)

Implications Better connected executive (non-executive) directors get higher (lower) compensation Compensation that directors receive for the resources and monitoring they provide is associated with higher future stock market performance and sales growth Market for directors works reasonably well without any regulatory intervention. Strict rules and guidelines about director selection and pay might distort the current equilibrium