Final answer:
The coefficient in a regression analysis involving gender and wages would indicate the average difference in pay between men and women after controlling for other variables, likely reflecting the historically documented wage gap where women are paid less than men.
Step-by-step explanation:
The relationship between the coefficient estimates provided by a regression analysis and the average hourly wages of men and women. The regression likely included wage as the dependent variable and gender, along with other potential covariates, as independent variables. If a simple linear regression was used with just gender as the predictor, the coefficient for gender would indicate how much less (or more, theoretically) women earn compared to men, controlling for other variables in the model.
Given the historical context of wage inequality, where women have been shown to earn less than men on average—77 cents to the dollar according to the National Equal Pay Taskforce (2013) and 81 cents to the dollar according to Payscale (2020)—the coefficient for gender in the regression would likely be negative, reflecting this wage gap. Other studies have confirmed this discrepancy, taking into account factors such as occupation, education, work experience, and even the impact of becoming a parent, which can introduce a 'motherhood penalty' for women and a 'fatherhood bonus' for men.
The coefficient reflects systematic differences in pay between men and women after controlling for other factors. If the researcher finds a statistically significant negative coefficient for being a woman, this would support the hypothesis that women are systematically underpaid relative to men.