The World Bank asked for a researcher to use data for 2018 from a developing country. The data consists of information on 10000 full-time, full year workers. The highest educational achievement for each worker was either a high school diploma or a bachelors degree. The workers ages ranged from 25 to 40 years. The data set contains information on the region of the country where the person lived, marital status and the number of children. The variables included in the model and dataset are the following:

AWE=average weekly earnings College=binary variable (1 if college, 0 if high school) female=binary variable (1 if female, 0 if male) Age=age (in years) North=binary variable (1 if Region=North, 0 otherwise) East=binary variable (1 if Region=East, 0 otherwise) South=binary variable (1 if Region=South, 0 otherwise) West=binary variable (1 if Region=West, 0 otherwise) Questions 1) Interpret all the coefficients of the variables in the column 2.

2) John is 30 year old male college graduate from South Alanis 40 year old male college graduate from North. Calculate the expected difference in earnings between Alan and John.

Results of Regressions of Average Weekly Earnings on Gender and Education Binary variables Dependent variable: log average weekly earnings (AWE) Regressor 1 2 High school graduate(X1) 0.352 0.373 3 0.371 0.021 0.021 0.021 Male(x2) 0.458 0.021 0.457 0.02 0.451 0.02 Age (X3) 0.011 0.001 0.011 0.001 North (X4) 0.175 0.037 South (X5) 0.103 0.033 East (X6) -0.102 0.043 Intercept 12.84 0.018 12.71 0.049 12.39 0.057 Summary Statistics and Join Tests F-statistic for regional effects=0 SER SSR: Square sum of residuals R^2 1.026 1.023 495615923 0.071 10973 21.87 1.02 432042896 0.0814 10973 0.071 10973 n