Consider the following regression estimates (XM3): Linear regression Number of obs = 1, 260 F (2, 1257) 8. 22 Prob > F 0. 0003 R-squared = 0 . 0125 Root MSE = . 59124 Robust 1wage Coef. Std. Err. t P>It| [95% Conf. Interval] belavg -. 2087896 . 0515497 -4.05 0 . 000 -. 3099225 -. 1076566 abvavg - . 0454376 . 0376333 -1 . 21 0 . 228 -. 1192687 . 0283935 cons 1 . 698296 . 0219465 77. 38 0 . 000 1 . 65524 1 . 741351 where . /wage is the natural logarithm of wage, . belavg = 1 if the person's looks are rated as below average, and . abvavg = 1 if the person's looks are rated as above average. Interpret the belavg coefficient