Individuals might work part or full time. Define a mincer wage equation that is a function of experienc, education and other individual characteristics.
First define parameters.
% Experience COEF_beta_psi_0 = 1.8884; COEF_beta_psi_k = 0.0767; COEF_beta_psi_kk = -0.2683; % Education COEF_beta_psi_e0 = 0.0465; COEF_beta_w_e_1 = 0.1432; COEF_beta_w_e_2 = 0.1435; COEF_beta_w_e_3 = 0.2806; % for part time COEF_beta_w_part_0 = -0.0082; COEF_beta_w_part_1 = -0.4863;
Second define the log wage equation. This wage equation is a function of the parameters defined above, and also Education (ED), experience (EX) and the wage shcok (EPS_w).
% Log of wage f_log_psi = @(ED, EX) ... COEF_beta_psi_0 ... + COEF_beta_psi_k.*EX ... + COEF_beta_psi_kk.*(EX.^2/100) ... + COEF_beta_psi_e0.*(ED - 12) ... + COEF_beta_w_e_1.*(ED == 12) ... + COEF_beta_w_e_2.*(ED > 12).*(ED < 16) ... + COEF_beta_w_e_3.*(ED >= 16);
Third, define wage, which might differ depending on work status as well as schooling status. D_e is schooling or not, which can take values of 0 or 1. D_k is work status, which can take values or 0, 0.5 (part-time work) and 1 (full-time work).
% Per hour wage considering part time, part time wage differ if also schooling f_hr_wage = @(D_e, D_k, ED, EX) ... exp(f_log_psi(ED, EX)).*exp((D_k==0.5).*(COEF_beta_w_part_0 + COEF_beta_w_part_1.*D_e)); % Total wage f_wage = @(D_e, D_k, ED, EX) ... f_hr_wage(D_e, D_k, ED, EX).*(2080.*(D_k == 1) + 1040.*(D_k == 0.5) + 0.*(D_k == 0));
Fourth, test the wage equation by calling it with different work and schooling choices, along with different education, experience, and shock levels.
% no experience, education, not school, not work disp(['f_wage(0,0,0,0,0)=' num2str(f_wage(0,0,0,0))]); f_wage(0,0,0,0,0)=0 % no experience, education, part-time disp(['f_wage(0,0.5,0,0,0)=' num2str(f_wage(0,0.5,0,0))]); f_wage(0,0.5,0,0,0)=3901.7326 % no experience, education, full-time disp(['f_wage(0,1,0,0,0)=' num2str(f_wage(0,1,0,0))]); f_wage(0,1,0,0,0)=7867.7167