The regression below was estimated using the

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The regression below was estimated using the BEAUTY.dta dataset discussed in class on Tuesday. Use this to answer the next five questions. Recall the the looks variables are defined as follows: looks1 = 1 if homely, 0 otherwise looks2 = 1 if quite plain, 0 otherwise looks3 = 1 if average, 0 otherwise looks4 = 1 if good looking, 0 otherwise looks5 = 1 if strikingly beautiful/handsome, 0 otherwise . reg lwage educ exper expersq looks1 looks2 looks3 looks4 looks5 note: looks5 omitted because of collinearity Source | SS df MS Number of obs = 1260 -------------+------------------------------ F( 7, 1252) = 56.01 Model | 106.116516 7 15.1595023 Prob > F = 0.0000 Residual | 338.863456 1252 .270657712 R-squared = 0.2385 -------------+------------------------------ Adj R-squared = 0.2342 Total | 444.979972 1259 .353439215 Root MSE = .52025 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0682993 .0058026 11.77 0.000 .0569154 .0796833 exper | .0484757 .0047766 10.15 0.000 .0391048 .0578467 expersq | -.0006987 .0001074 -6.50 0.000 -.0009094 -.000488 looks1 | -.4653829 .1889268 -2.46 0.014 -.836031 -.0947348 looks2 | -.3025159 .1275879 -2.37 0.018 -.5528256 -.0522061 looks3 | -.1358712 .1213901 -1.12 0.263 -.3740217 .1022793 looks4 | -.1556256 .1225351 -1.27 0.204 -.3960224 .0847711 looks5 | 0 (omitted) _cons | .4113932 .145361 2.83 0.005 .1262153 .6965712 6. What is the expected change in earnings [ Δ ln( wage )] associated with 1 additional year of experience for someone who has no experience (that is, exper = 0)?
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