Use the regression to answer the following questions: a. Discuss the role of the

Question

Use the regression to answer the following questions:

a. Discuss the role of the interaction variable in regression 3.

b. Discuss the nonlinearity of regression 4.

c. Discuss the use of regional indicator variables in regression 4.  

The Return to Education and the Gender Gap Dependent variable: logarithm of Hourly Earnings. Regressoir Years of education 0.1035** (0.0009) 0.1050** (0.0009) 0.1001** (0.0011) 0.432* (0.024) 0.0121** (0.0017) 0.1052** (0.0012) Female 0.263** (0.004) 0.451** Female x Years of education Potential experience Potential experience (0.024) 0.0134** (0.0017) 0.0145* (0.0012) -0.000202 * * (0.000019) Midwest 0.095** (0.006) 0.092** (0.006) 0.025* (0.007) 1.592** (0.023) South est Intercept 1.533** (0.012) 1.629** (0.012) 1.697** (0.016) 0.208 0.258 0.258 0.267 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 fo women and 0 for men. Midwest, South, and West are indicator variables denoting the region of the United States in which the worker lives: For example, Midwest equals 1 if the worker lives in the Midwest and equals 0 otherwise (the omitted region is Northeast). Standard errors are reported in parentheses below the estimated coefficients. Individual coefficients are statistically significant at the *5% or ** 1% significance level

Explanation / Answer

a) The interaction in the dummy tells that if the year of education for female increases by one year than hourly earnings for female will increase by 1.21%

b) Non linearity in regression looks at how hourly earning increases as experience increases i.e. whether it increases at an increasing rate or adecreasing rate( This we judge by looking at the signs.)

c) Regional indicator variables look at how the hourly earnings differs region wise

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