where formula_2 is the error term. In the model, "female" = 1 when the person is a female and "female" = 0 when the person is male. formula_3 can be interpreted as the difference in wages between females and males, holding education constant. Thus, δ0 helps to determine whether there is a discrimination in wages between males and females. For example, if δ0>0 (positive coefficient), then women earn a higher wage than men (keeping other factors constant). The coefficients attached to the dummy variables are called differential intercept coefficients. The model can be depicted graphically as an intercept shift between females and males. In the figure, the case δ0<0 is shown (wherein men earn a higher wage than women).