.4863924 -4.67 0.000 -3.225139 -1.31656 ------------------------------------------------------------------------------ 2. Explanation of Constructed Variables logearnings = log(earnings) the ‘l’ prefixes on the independent variables indicate natural logs. For example, ls = log(s), ltenure = log(tenure), etc1. 3. Interpretation Conditional on the other characteristics that influence earnings, ‘catgov’, i.e., the indicator variable for whether an individual is employed by the government sector or not has a negative but insignificant coefficient. Therefore, there is no evidence that working for the government leads to systematically different mean hourly earnings compared to not working for the government sector. ...

Weight in 2002 however, has a negative impact on hourly earnings. Similarly, being a female leads to a systematically lower mean wage, as does being employed in a major US region. The final model was chosen on the basis of the following aspects. First, the R-squared and adjusted R-squared were found to be highest under this specification. Note from the table above the R-squared is approximately 0.36 implying that 36 percent of the variability in the natural logarithm of earnings is explained by the variability of the included independent variables. For a cross-sectional model, this is a pretty decent performance. Secondly, the included variables are the only ones that had statistically significant impacts. Finally, hours and tenure are likely to depend on hourly earnings – higher

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