The labor market in the US is one of the most complex and dynamic markets in the world. As we enter the “real world” it is important four our careers and to be informed votors.

• Data Overview
• Population Gender
• Gender and Income
• Education by Gender
• Earnings by Education
• Employment Earnings
• Employment by Sex and Age

Using Data from IPUMS (Integrated Public Use Microdata Series) we have representative data of the US population.

Are there underlying factors that are driving the differences in income? We already saw that education does not explain the differences in income. Leaving the workforce also impacts earnings.

Oneway (individual) effect Within Model

Call: plm(formula = incwage ~ educ_num + sex + uhrswork, data = SmallDF_FE, model = “within”, index = c(“statefip”))

Unbalanced Panel: n = 51, T = 482-30087, N = 256540

Residuals: Min. 1st Qu. Median 3rd Qu. Max. -174284.2 -29999.8 -9631.7 13407.7 748384.0

Coefficients: Estimate Std. Error t-value Pr(>|t|)
educ_num 10609.169 68.676 154.481 < 2.2e-16 sexmale 15759.469 270.836 58.188 < 2.2e-16 uhrswork 1623.007 10.492 154.683 < 2.2e-16 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Total Sum of Squares: 1.4287e+15 Residual Sum of Squares: 1.1555e+15 R-Squared: 0.19123 Adj. R-Squared: 0.19106 F-statistic: 20214.7 on 3 and 256486 DF, p-value: < 2.22e-16

\[ \begin{align*} \text{Income}_i = & \ \beta_0 + \sum_{j=1}^{k-1} \beta_{j} \cdot \text{Field}_j \\ & + \beta_{\text{sex}} \cdot \text{Male}_i \\ & + \sum_{j=1}^{k-1} \beta_{j, \text{sex}} \cdot (\text{Field}_j \cdot \text{Male}_i) \\ & + \epsilon_i \end{align*} \]

\[ \begin{align*} \text{Income}_i = & \ \beta_0 + \beta_1 \cdot \text{Business}_i + \beta_2 \cdot \text{Education}_i \\ & + \beta_3 \cdot \text{Engineering}_i + \beta_4 \cdot \text{Health}_i \\ & + \beta_5 \cdot \text{Social Sciences}_i + \beta_{\text{sex}} \cdot \text{Male}_i \\ & + \beta_{1, \text{sex}} \cdot (\text{Business}_i \cdot \text{Male}_i) \\ & + \beta_{2, \text{sex}} \cdot (\text{Education}_i \cdot \text{Male}_i) \\ & + \beta_{3, \text{sex}} \cdot (\text{Engineering}_i \cdot \text{Male}_i) \\ & + \beta_{4, \text{sex}} \cdot (\text{Health}_i \cdot \text{Male}_i) \\ & + \beta_{5, \text{sex}} \cdot (\text{Social Sciences}_i \cdot \text{Male}_i) \\ & + \epsilon_i \end{align*} \]

\[ \text{Interaction Term: } \beta_3 (\text{Gender} \times \text{Education}) + \beta_4 \text{Age} + \epsilon_2 \]

## 
## \begin{table}[!htbp] \centering 
##   \caption{Regression Results: Predicting Hours Worked} 
##   \label{tab:hours_work_regression} 
## \begin{tabular}{@{\extracolsep{5pt}}lc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  & \multicolumn{1}{c}{\textit{Dependent variable:}} \\ 
## \cline{2-2} 
## \\[-1.8ex] & Hours Worked per Week \\ 
## \hline \\[-1.8ex] 
##  Education Level & 0.000 \\ 
##   & (0.000) \\ 
##   Female & 0.000 \\ 
##   & (0.000) \\ 
##   Family Income & 0.000 \\ 
##   & (0.000) \\ 
##   Age & 0.000 \\ 
##   & (0.000) \\ 
##   Employed & 0.000 \\ 
##   & (0.000) \\ 
##   Constant & 0.000 \\ 
##   & (0.000) \\ 
##  \hline \\[-1.8ex] 
## Observations & 416,189 \\ 
## Residual Std. Error & 0.000 (df = 416183) \\ 
## \hline 
## \hline \\[-1.8ex] 
## \textit{Note:}  & \multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
## \end{tabular} 
## \end{table}

• Transit Times
• Poverty
• Degree Field
• English & Citizenship
• Race
• Marriage
• State Variations
• Within occupation trends