Does the Beauty Premium Effect Exist: Empirical Evidence from Executive's Excess Compensation
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摘要: 以2009年~2016年中国创业板上市公司为研究对象, 实证检验高管超额薪酬是否存在美貌溢价效应, 研究发现美貌溢价效应确实存在, 当总经理容貌好于董事长时, 高管容貌垂直对对高管超额薪酬的美貌溢价起到负向调节作用, 相反则不显著。进一步按照董事长容貌进行分组, 研究证实高管容貌垂直对对总经理超额薪酬美貌溢价效应的调节作用存在着差异性, 并发现私有企业、传统行业以及东中西部地区均存在着显著的高管超额薪酬美貌溢价效应。本文的研究结论为高管超额薪酬研究提供了一个有趣的研究视角。Abstract: Taking the listed companies on the GEM of China from 2009 to 2016 as the research object, this paper empirically tests whether there is a beauty premium effect in the excess compensation of executives. The results indicates that there is a beauty premium effect in executive's excess compensation; when the general manager's appearance superior to that of the chairman, vertical executive's appearance has a negative moderating effect on the beauty premium of executive's excess compensation, on the contrary, it is not significant. This paper also validates the differences in the regulatory effect of the executive's vertical pair of appearance on chairperson's beauty premium effect according to the chairperson's appearance grouping, and there exists significant beauty premium effect of the executive's excess compensation in different enterprises, industries and regions. The findings provide an interesting perspective for the future research on executive's excess compensation.
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表 1 变量定义
变量符号 变量名称 变量说明 EXCOMP 总经理超额薪酬 总经理薪酬总额的对数减去总经理期望薪酬 FS 总经理容貌 总经理面部得分 DV 高管容貌垂直对 董事长面部得分减总经理面部得分的值 AQI 环境空气质量 综合考虑SO2、NO2、PM10、PM2.5、CO、O3等六项污染物的污染程度,环境空气质量综合指数数值越大表明综合污染程度越重。 SEXCOMP 总经理超额薪酬替代变量 总经理超额薪酬与前三名高管薪酬总额对数的比值 ROE 净资产收益率 净利润与净资产之比 SIZE 公司规模 年初总资产账面值之自然对数 NPR 企业性质 1为国有企业,0为私有企业 IOS 总资产增长率 企业年末总资产的增长额同年初资产总额之比 ZONE 区域 1为东部地区,0为中西部地区 Boardsize 董事会规模 董事总人数 MB 企业成长性 公司净资产市值与账面价值比,即市账比 YEAR 年度 年度虚拟变量 IND 行业分类 国泰安数据库证监会行业分类2012年版 表 2 各变量描述性统计
变量 N 均值 标准差 最小值 中位数 最大值 EXCOMP 415 0.410 0.350 0 0.340 2.200 FS 415 45.33 10.36 19 46 76 DV 415 -1.180 13.75 -37 -2 31 SEXCOMP 415 0.030 0.020 0 0.020 0.130 ROE 415 0.070 0.190 -3.240 0.080 0.280 SIZE 415 21.10 0.750 19.53 20.96 23.89 NPR 415 0.080 0.280 0 0 1 IOS 415 0.550 0.990 -0.440 0.170 10.74 ZONE 415 0.830 0.380 0 1 1 Boardsize 415 8.200 1.470 5 9 14 MB 415 4.060 2.870 0.740 3.340 19.30 YEAR 415 2013 1.980 2009 2014 2016 IND 415 2.080 2.340 1 1 13 表 3 Pearson相关性系数表
表 4 总经理容貌与其超额薪酬关系及高管容貌垂直对调节作用的回归结果
解释变量 被解释变量:EXCOMP (1) (2) (3) (4) (5) (6) (7) 全样本 DV>0 DV < 0 DV>0 DV>0 DV < 0 DV < 0 FS 0.005*** 0.013*** 0.007** 0.112*** 0.117*** 0.050* 0.066** (2.79) (3.43) (2.20) (3.51) (3.68) (1.74) (2.22) DV 0.027 0.032 -0.021 -0.019 (0.80) (0.94) (-0.68) (-0.60) FS×DV 0.044 -0.059* (1.37) (-1.87) ROE 0.005 0.261 -0.039 0.255 0.258 -0.048 -0.066 (0.06) (1.26) (-0.40) (1.23) (1.24) (-0.48) (-0.65) SIZE 0.101*** 0.138*** 0.132*** 0.136*** 0.127** 0.136*** 0.136*** (3.40) (2.85) (3.06) (2.81) (2.59) (3.12) (3.14) NPR 0.027 0.073 -0.027 0.044 0.031 -0.018 -0.012 (0.41) (0.86) (-0.20) (0.47) (0.33) (-0.14) (-0.09) IOS -0.016 -0.031 -0.003 -0.030 -0.030 -0.003 -0.004 (-0.84) (-0.95) (-0.11) (-0.89) (-0.89) (-0.12) (-0.16) ZONE 0.123** 0.160** 0.087 0.143* 0.136* 0.086 0.102 (2.54) (2.14) (1.26) (1.84) (1.75) (1.25) (1.48) Boardsize 0.003 -0.011 0.021 -0.010 -0.006 0.021 0.023 (0.25) (-0.55) (1.22) (-0.50) (-0.31) (1.23) (1.33) MB 0.022*** 0.043*** 0.007 0.043*** 0.042*** 0.008 0.009 (2.86) (3.15) (0.70) (3.10) (3.04) (0.81) (0.93) YEAR 控制 控制 控制 控制 控制 控制 控制 IND 控制 控制 控制 控制 控制 控制 控制 Constant -1.547** -2.306** -3.151*** -2.259** -1.426 -2.906*** -2.943*** (-2.14) (-2.12) (-3.11) (-2.22) (-1.29) (-2.87) (-2.93) Observations 415 179 231 179 179 231 231 Prob>F 0.000 2 0.002 0.008 0.003 0.002 0.011 0.006 Adj.R2 0.082 0.135 0.087 0.133 0.138 0.085 0.096 说明:*p < 0.1、**p < 0.05、***p < 0.01,括号内为T值。下表同。 表 5 2SLS回归结果
被解释变量 FS EXCOMP 解释变量 第一阶段 第二阶段 AQI -0.077***(-3.48) FS 0.014*(1.65) ROE 12.111(1.04) 1.035**(2.40) SIZE 1.097(1.05) 0.031(0.80) NPR -8.578***(-5.70) 0.149(1.44) IOS 2.559(1.64) -0.023(-0.44) ZONE 1.859(1.16) 0.085*(1.96) MB 0.170(0.55) -0.0001(-0.02) YEAR 控制 控制 IND 控制 控制 Constant 41.382*(1.76) -1.611**(-1.97) Observations 278 278 Prob>F 0.000 0 0.000 0 R-squared 0.293 0.078 表 6 稳健性检验
解释变量 被解释变量:SEXCOMP (1) (2) (3) (4) (5) (6) (7) 全样本 DV>0 DV < 0 DV>0 DV>0 DV < 0 DV < 0 FS 0.005*** 0.013*** 0.007** 0.112*** 0.117*** 0.050* 0.067** (2.79) (3.43) (2.20) (3.51) (3.68) (1.74) (2.24) DV 0.027 0.032 -0.021 -0.018 (0.80) (0.94) (-0.68) (-0.59) FS×DV 0.044 -0.0603* (1.37) (-1.91) ROE 0.005 0.261 -0.039 0.255 0.258 -0.048 -0.066 (0.06) (1.26) (-0.40) (1.23) (1.24) (-0.48) (-0.66) SIZE 0.101*** 0.138*** 0.132*** 0.136*** 0.127** 0.136*** 0.136*** (3.40) (2.85) (3.06) (2.81) (2.59) (3.12) (3.14) NPR 0.027 0.073 -0.027 0.044 0.031 -0.018 -0.012 (0.41) (0.86) (-0.20) (0.47) (0.33) (-0.14) (-0.09) IOS -0.016 -0.031 -0.003 -0.030 -0.030 -0.003 -0.004 (-0.84) (-0.95) (-0.11) (-0.89) (-0.89) (-0.12) (-0.16) ZONE 0.123** 0.160** 0.087 0.143* 0.136* 0.086 0.103 (2.54) (2.14) (1.26) (1.84) (1.75) (1.25) (1.48) Boardsize 0.003 -0.011 0.021 -0.010 -0.006 0.021 0.023 (0.25) (-0.55) (1.22) (-0.50) (-0.31) (1.23) (1.34) MB 0.022*** 0.043*** 0.007 0.043*** 0.042*** 0.008 0.009 (2.86) (3.15) (0.70) (3.10) (3.04) (0.81) (0.94) YEAR 控制 控制 控制 控制 控制 控制 控制 IND 控制 控制 控制 控制 控制 控制 控制 Constant -1.547** -2.306** -3.151*** -2.259** -1.426 -2.906*** -2.943*** (-2.14) (-2.12) (-3.11) (-2.22) (-1.29) (-2.87) (-2.93) Observations 415 179 231 179 179 231 231 Prob>F 0.000 2 0.002 0.008 0.003 0.002 0.002 0.011 Adj.R2 0.082 0.135 0.087 0.133 0.138 0.138 0.085 表 7 按董事长容貌分组的高管容貌垂直对调节作用
解释变量 被解释变量:EXCOMP QS≤25% 25% < QS≤50% 50%≤QS < 75% QS≥75% FS 0.041 -0.265** 0.112*** 0.117*** 0.112*** 0.117*** 1.402*** 0.689* (0.31) (-2.20) (3.51) (3.68) (3.51) (3.68) (3.27) (2.00) DV 0.061 -0.485*** 0.027 0.032 0.027 0.032 0.878*** -0.030 (0.55) (-3.37) (0.80) (0.94) (0.80) (0.94) (3.19) (-0.11) FS×DV -0.502*** 0.044 0.044 0.572*** (-4.67) (1.37) (1.37) (4.75) ROE -0.405 -2.124 0.255 0.258 0.255 0.258 1.252* 0.146 (-0.23) (-1.57) (1.23) (1.24) (1.23) (1.24) (1.83) (0.27) SIZE 0.186 0.248*** 0.136*** 0.127** 0.136*** 0.127** 0.035 0.097 (1.63) (2.85) (2.81) (2.59) (2.81) (2.59) (0.25) (0.96) NPR -0.143 -0.187 0.044 0.031 0.044 0.031 2.154*** 2.047*** (-0.76) (-1.32) (0.47) (0.33) (0.47) (0.33) (3.32) (4.34) IOS -0.190** -0.080 -0.030 -0.030 -0.030 -0.030 0.149 0.044 (-2.19) (-1.16) (-0.89) (-0.89) (-0.89) (-0.89) (1.47) (0.57) ZONE 0.186 0.149 0.143* 0.136* 0.143* 0.136* 0.415 0.744*** (1.25) (1.33) (1.84) (1.75) (1.84) (1.75) (1.58) (3.67) Boardsize -0.036 0.016 -0.010 -0.006 -0.010 -0.006 -0.155** -0.212*** (-0.90) (0.49) (-0.50) (-0.31) (-0.50) (-0.31) (-2.14) (-3.93) MB 0.057 0.073** 0.043*** 0.042*** 0.043*** 0.042*** 0.145** 0.055 (1.48) (2.52) (3.10) (3.04) (3.10) (3.04) (2.61) (1.23) YEAR 控制 控制 控制 控制 控制 控制 控制 控制 IND 控制 控制 控制 控制 控制 控制 控制 控制 Constant -1.942 -4.768** -2.259** -1.426 -2.259** -1.426 -2.689 -3.719 (-0.87) (-2.68) (-2.22) (-1.29) (-2.22) (-1.29) (-0.89) (-1.67) Observations 46 46 179 179 179 179 43 43 Prob>F 0.527 0.009 0.003 0.002 0.003 0.002 0.017 0.0001 Adj.R2 -0.017 0.425 0.133 0.138 0.133 0.138 0.399 0.684 表 8 不同企业性质、行业、地区回归结果
解释变量 被解释变量:EXCOMP 国有 私有 高科技 传统 东部 中西部 FS 0.005 0.005** 0.004 0.004* 0.004* 0.011*** (0.46) (2.42) (1.45) (1.87) (1.93) (2.72) ROE 3.128** -0.008 -0.015 0.287 -0.021 0.736 (2.68) (-0.09) (-0.19) (1.36) (-0.21) (0.94) SIZE -0.112 0.097*** -0.086* 0.123*** 0.123*** -0.005 (-0.75) (3.13) (-1.81) (3.35) (3.67) (-0.08) NPR 0.154 -0.041 0.008 0.107 (0.83) (-0.57) (0.10) (1.22) IOS 0.106 -0.023 -0.019 -0.019 -0.013 -0.046 (1.26) (-1.10) (-0.75) (-0.73) (-0.58) (-0.84) ZONE -0.028 0.136** 0.092 0.086 (-0.17) (2.58) (0.76) (1.56) Boardsize 0.104** -0.003 -0.004 0.025 0.004 -0.006 (2.12) (-0.24) (-0.21) (1.56) (0.24) (-0.27) MB -0.015 0.022*** -0.011 0.031*** 0.027*** -0.005 (-0.27) (2.74) (-1.00) (2.63) (2.85) (-0.34) YEAR 控制 控制 控制 控制 控制 控制 IND 控制 控制 控制 控制 Constant 1.699 -2.469*** 1.773* -2.655*** -3.018*** 0.154 (0.61) (-3.24) (1.86) (-3.49) (-3.63) (0.11) Observations 35 380 115 300 344 71 Prob>F 0.114 0.000 3 0.407 0.000 0.005 0.106 Adj.R2 0.275 0.083 0.007 0.111 0.062 0.140 -
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