2. Mike is interested in knowing how Party afliation (0: Democratic Party, 1: Republican Party), gender (0: Female, 1: Male), and years in the congress would affect the public approval of one's proposal submitted to the congress {a 1-5 index, with 5 being the highest approval). Mike collects all data about congressmen and their proposals submitted in 2019. a. Mikes nd that not all congressmen submit proposals in 2019. Mike then decides to use the Heckman Selection Model. Why does Mike need to use this model instead of simple OLS regression? What is the problem? ( lOpoints) b. The table below is the results of Heckman Selection Model. Can you list all variables that Mike choose as the factors that decides whether one would submit proposals or not? (5 points) c. Can you explain the effect ofpany afliation on the public approval of proposals? Whether it is a signicant eect? If it is, at which confidence level it is signicant? (10 points} d. Can you explain the effect of Gender in proposal submissions? (5 points) DV: Resme Reggnse Main Regression Party Afliation 006??" (0.0234) Male 0.0265 (0.0698) Years in the congress 0.0924 (0.144} Constant 2.45411\"l 50.02373! Selection: Submission Male -0.3?6*" (0.135} Years in the congress 0.433 (0.231} Constant 0.70?" 50.132} Observations 63? m p