1 2019 Full States EV and EC of One Check
This is the example vignette for function: snw_evuvw19_jaeemk from the PrjOptiSNW Package. 2019 integrated over VU and VW, given optimal savings choices, unemployment shocks and various expectations.
1.1 Test SNW_EVUVW19_JAEEMK Defaults
Call the function with defaults.
clear all;
st_solu_type = 'bisec_vec';
% Solve the VFI Problem and get Value Function
mp_params = snw_mp_param('default_docdense');
mp_params('beta') = 0.95;
% mp_params = snw_mp_param('default_dense');
mp_controls = snw_mp_control('default_test');
% set Unemployment Related Variables
mp_params('a2_covidyr') = mp_params('a2_covidyr_manna_heaven');
% mp_params('a2_covidyr') = mp_params('a2_covidyr_tax_fully_pay');
% Solve for Unemployment Values
mp_controls('bl_print_vfi') = false;
mp_controls('bl_print_vfi_verbose') = true;
mp_controls('bl_print_ds') = false;
mp_controls('bl_print_ds_verbose') = false;
mp_controls('bl_print_precompute') = false;
mp_controls('bl_print_precompute_verbose') = false;
mp_controls('bl_print_a4chk') = false;
mp_controls('bl_print_a4chk_verbose') = false;
mp_controls('bl_print_evuvw20_jaeemk') = false;
mp_controls('bl_print_evuvw20_jaeemk_verbose') = false;
% Solve the Model to get V working and unemployed
[V_ss,ap_ss,cons_ss,mp_valpol_more_ss] = snw_vfi_main_bisec_vec(mp_params, mp_controls);
Completed SNW_VFI_MAIN_BISEC_VEC;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=488.7831
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ ______
V_VFI 1 1 6 4.37e+07 83 5.265e+05 -1.2728e+08 -2.9126 20.655 -7.0915 -485.24 16.447
ap_VFI 2 2 6 4.37e+07 83 5.265e+05 1.3962e+09 31.95 36.423 1.14 0 160.21
cons_VFI 3 3 6 4.37e+07 83 5.265e+05 2.3374e+08 5.3487 8.4439 1.5787 0.036717 141.66
xxx TABLE:V_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -274.81 -274.42 -271.94 -266.29 -257.26 14.439 14.533 14.626 14.718 14.809
r2 -265.29 -264.9 -262.43 -256.84 -248.12 14.494 14.585 14.674 14.763 14.85
r3 -255.77 -255.38 -252.93 -247.53 -239.24 14.55 14.636 14.723 14.808 14.892
r4 -246.16 -245.8 -243.52 -238.46 -230.68 14.606 14.689 14.772 14.853 14.935
r5 -237.48 -237.14 -235.01 -230.26 -222.92 14.654 14.734 14.813 14.891 14.97
r79 -9.6662 -9.655 -9.5783 -9.3823 -9.0457 2.4698 2.4801 2.4898 2.4989 2.5075
r80 -8.7031 -8.6919 -8.6152 -8.4192 -8.0826 2.253 2.261 2.2685 2.2755 2.2822
r81 -7.5138 -7.5026 -7.4258 -7.2298 -6.8933 1.9749 1.9803 1.9855 1.9904 1.995
r82 -5.9155 -5.9043 -5.8275 -5.6315 -5.295 1.582 1.5851 1.588 1.5907 1.5933
r83 -3.5892 -3.578 -3.5012 -3.3052 -2.9687 0.97904 0.98004 0.98097 0.98185 0.98267
xxx TABLE:ap_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
__ __ __________ _________ ________ _______ _______ _______ _______ _______
r1 0 0 0.00051498 0.0066578 0.021589 112.13 117.66 123.39 129.3 135.72
r2 0 0 0.00051498 0.0057684 0.020245 112.16 117.7 123.42 129.34 135.75
r3 0 0 0.00020768 0.0041456 0.018539 112.19 117.72 123.45 129.36 135.77
r4 0 0 0.00010346 0.0041199 0.018307 112.85 118.38 124.11 130.02 136.44
r5 0 0 5.2907e-06 0.0041199 0.018091 113.53 119.06 124.78 130.7 137.11
r79 0 0 0 0 0 81.091 85.373 89.342 93.265 97.358
r80 0 0 0 0 0 76.137 79.759 83.442 86.995 90.589
r81 0 0 0 0 0 67.958 70.652 73.689 77.006 81.091
r82 0 0 0 0 0 50.126 53.467 56.319 57.902 60.587
r83 0 0 0 0 0 0 0 0 0 0
xxx TABLE:cons_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.036717 0.037251 0.040477 0.044486 0.049324 12.272 12.557 12.851 13.152 13.152
r2 0.036717 0.037251 0.040477 0.045375 0.050668 12.508 12.794 13.089 13.391 13.391
r3 0.036717 0.037251 0.040784 0.046998 0.052374 12.762 13.05 13.345 13.646 13.646
r4 0.038144 0.038678 0.042314 0.048449 0.054031 13.008 13.297 13.593 13.891 13.891
r5 0.039534 0.040068 0.043802 0.049839 0.055635 13.245 13.534 13.83 14.125 14.125
r79 0.2179 0.21844 0.22216 0.23228 0.25197 35.858 37.4 39.448 41.74 44.062
r80 0.2179 0.21844 0.22216 0.23228 0.25197 40.785 42.986 45.321 47.983 50.804
r81 0.2179 0.21844 0.22216 0.23228 0.25197 48.942 52.071 55.052 57.95 60.279
r82 0.2179 0.21844 0.22216 0.23228 0.25197 66.755 69.238 72.404 77.036 80.765
r83 0.2179 0.21844 0.22216 0.23228 0.25197 116.87 122.69 128.71 134.92 141.34
inc_VFI = mp_valpol_more_ss('inc_VFI');
spouse_inc_VFI = mp_valpol_more_ss('spouse_inc_VFI');
total_inc_VFI = inc_VFI + spouse_inc_VFI;
% Solve employment, same as 2020, except with possible change in tax
mp_params('xi') = 1;
mp_params('b') = 0;
[V_emp_2020,~,cons_emp_2020,~] = snw_vfi_main_bisec_vec(mp_params, mp_controls, V_ss);
Completed SNW_VFI_MAIN_BISEC_VEC 1 Period Unemp Shock;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=482.1886
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ ______
V_VFI 1 1 6 4.37e+07 83 5.265e+05 -1.2728e+08 -2.9126 20.655 -7.0915 -485.24 16.447
ap_VFI 2 2 6 4.37e+07 83 5.265e+05 1.3962e+09 31.95 36.423 1.14 0 160.21
cons_VFI 3 3 6 4.37e+07 83 5.265e+05 2.3374e+08 5.3487 8.4439 1.5787 0.036717 141.66
xxx TABLE:V_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -274.81 -274.42 -271.94 -266.29 -257.26 14.439 14.533 14.626 14.718 14.809
r2 -265.29 -264.9 -262.43 -256.84 -248.12 14.494 14.585 14.674 14.763 14.85
r3 -255.77 -255.38 -252.93 -247.53 -239.24 14.55 14.636 14.723 14.808 14.892
r4 -246.16 -245.8 -243.52 -238.46 -230.68 14.606 14.689 14.772 14.853 14.935
r5 -237.48 -237.14 -235.01 -230.26 -222.92 14.654 14.734 14.813 14.891 14.97
r79 -9.6662 -9.655 -9.5783 -9.3823 -9.0457 2.4698 2.4801 2.4898 2.4989 2.5075
r80 -8.7031 -8.6919 -8.6152 -8.4192 -8.0826 2.253 2.261 2.2685 2.2755 2.2822
r81 -7.5138 -7.5026 -7.4258 -7.2298 -6.8933 1.9749 1.9803 1.9855 1.9904 1.995
r82 -5.9155 -5.9043 -5.8275 -5.6315 -5.295 1.582 1.5851 1.588 1.5907 1.5933
r83 -3.5892 -3.578 -3.5012 -3.3052 -2.9687 0.97904 0.98004 0.98097 0.98185 0.98267
xxx TABLE:ap_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
__ __ __________ _________ ________ _______ _______ _______ _______ _______
r1 0 0 0.00051498 0.0066578 0.021589 112.13 117.66 123.39 129.3 135.72
r2 0 0 0.00051498 0.0057684 0.020245 112.16 117.7 123.42 129.34 135.75
r3 0 0 0.00020768 0.0041456 0.018539 112.19 117.72 123.45 129.36 135.77
r4 0 0 0.00010346 0.0041199 0.018307 112.85 118.38 124.11 130.02 136.44
r5 0 0 5.2907e-06 0.0041199 0.018091 113.53 119.06 124.78 130.7 137.11
r79 0 0 0 0 0 81.091 85.373 89.342 93.265 97.358
r80 0 0 0 0 0 76.137 79.759 83.442 86.995 90.589
r81 0 0 0 0 0 67.958 70.652 73.689 77.006 81.091
r82 0 0 0 0 0 50.126 53.467 56.319 57.902 60.587
r83 0 0 0 0 0 0 0 0 0 0
xxx TABLE:cons_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.036717 0.037251 0.040477 0.044486 0.049324 12.272 12.557 12.851 13.152 13.152
r2 0.036717 0.037251 0.040477 0.045375 0.050668 12.508 12.794 13.089 13.391 13.391
r3 0.036717 0.037251 0.040784 0.046998 0.052374 12.762 13.05 13.345 13.646 13.646
r4 0.038144 0.038678 0.042314 0.048449 0.054031 13.008 13.297 13.593 13.891 13.891
r5 0.039534 0.040068 0.043802 0.049839 0.055635 13.245 13.534 13.83 14.125 14.125
r79 0.2179 0.21844 0.22216 0.23228 0.25197 35.858 37.4 39.448 41.74 44.062
r80 0.2179 0.21844 0.22216 0.23228 0.25197 40.785 42.986 45.321 47.983 50.804
r81 0.2179 0.21844 0.22216 0.23228 0.25197 48.942 52.071 55.052 57.95 60.279
r82 0.2179 0.21844 0.22216 0.23228 0.25197 66.755 69.238 72.404 77.036 80.765
r83 0.2179 0.21844 0.22216 0.23228 0.25197 116.87 122.69 128.71 134.92 141.34
% Solve unemployment, different income than under ss due to income losses
mp_params('xi') = 0.50;
mp_params('b') = 0.50;
[V_unemp_2020,~,cons_unemp_2020,~] = snw_vfi_main_bisec_vec(mp_params, mp_controls, V_ss);
Completed SNW_VFI_MAIN_BISEC_VEC 1 Period Unemp Shock;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=484.2447
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ ______
V_VFI 1 1 6 4.37e+07 83 5.265e+05 -1.3655e+08 -3.1246 21.031 -6.7306 -505 16.431
ap_VFI 2 2 6 4.37e+07 83 5.265e+05 1.3778e+09 31.529 36.357 1.1531 0 151.79
cons_VFI 3 3 6 4.37e+07 83 5.265e+05 2.3211e+08 5.3114 8.4428 1.5895 0.027723 141.08
xxx TABLE:V_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -283.64 -282.96 -278.82 -271.25 -260.97 14.399 14.494 14.589 14.682 14.774
r2 -274.13 -273.45 -269.31 -261.74 -251.67 14.454 14.546 14.637 14.727 14.816
r3 -264.61 -263.93 -259.79 -252.24 -242.65 14.51 14.598 14.686 14.773 14.858
r4 -254.66 -254.03 -250.17 -243.06 -234.04 14.565 14.65 14.734 14.817 14.899
r5 -245.67 -245.08 -241.48 -234.76 -226.24 14.611 14.693 14.774 14.854 14.933
r79 -9.6662 -9.655 -9.5783 -9.3823 -9.0457 2.4688 2.4792 2.489 2.4982 2.5068
r80 -8.7031 -8.6919 -8.6152 -8.4192 -8.0826 2.2523 2.2603 2.2679 2.275 2.2817
r81 -7.5138 -7.5026 -7.4258 -7.2298 -6.8933 1.9743 1.9799 1.9851 1.99 1.9947
r82 -5.9155 -5.9043 -5.8275 -5.6315 -5.295 1.5817 1.5848 1.5878 1.5905 1.5931
r83 -3.5892 -3.578 -3.5012 -3.3052 -2.9687 0.97895 0.97995 0.98089 0.98178 0.98261
xxx TABLE:ap_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
__ __ __ __________ ________ _______ _______ _______ _______ _______
r1 0 0 0 0.0011815 0.013905 109.97 115.52 121.25 127.17 133.28
r2 0 0 0 0.00090277 0.013905 109.94 115.49 121.22 127.14 133.25
r3 0 0 0 0.00051498 0.013905 109.9 115.44 121.17 127.09 133.2
r4 0 0 0 0.00051498 0.013905 110.33 115.88 121.6 127.52 133.64
r5 0 0 0 0.00048777 0.013905 110.78 116.32 122.05 127.97 134.09
r79 0 0 0 0 0 80.977 84.854 88.823 92.746 96.839
r80 0 0 0 0 0 75.625 79.248 82.93 86.483 90.439
r81 0 0 0 0 0 67.452 70.146 73.182 76.669 81.091
r82 0 0 0 0 0 50.126 53.467 55.817 57.4 60.587
r83 0 0 0 0 0 0 0 0 0 0
xxx TABLE:cons_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.027723 0.028258 0.031999 0.040974 0.048028 11.996 12.272 12.557 12.851 13.152
r2 0.027723 0.028258 0.031999 0.041253 0.048028 12.23 12.508 12.794 13.089 13.391
r3 0.027723 0.028258 0.031999 0.041641 0.048028 12.483 12.762 13.05 13.345 13.646
r4 0.028805 0.029339 0.033081 0.042722 0.049108 12.728 13.008 13.297 13.593 13.891
r5 0.029859 0.030394 0.034135 0.043802 0.050161 12.963 13.245 13.534 13.83 14.125
r79 0.2179 0.21844 0.22216 0.23228 0.25197 35.453 37.4 39.448 41.74 44.062
r80 0.2179 0.21844 0.22216 0.23228 0.25197 40.785 42.986 45.321 47.983 50.442
r81 0.2179 0.21844 0.22216 0.23228 0.25197 48.942 52.071 55.052 57.78 59.773
r82 0.2179 0.21844 0.22216 0.23228 0.25197 66.254 68.736 72.404 77.036 80.263
r83 0.2179 0.21844 0.22216 0.23228 0.25197 116.37 122.19 128.21 134.43 140.84
[Phi_true] = snw_ds_main(mp_params, mp_controls, ap_ss, cons_emp_2020, mp_valpol_more_ss);
Completed SNW_DS_MAIN;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=1911.1684
% Get Matrixes
cl_st_precompute_list = {'a', ...
'inc', 'inc_unemp', 'spouse_inc', 'spouse_inc_unemp', 'ref_earn_wageind_grid',...
'ap_idx_lower_ss', 'ap_idx_higher_ss', 'ap_idx_lower_weight_ss'};
mp_controls('bl_print_precompute_verbose') = false;
[mp_precompute_res] = snw_hh_precompute(mp_params, mp_controls, cl_st_precompute_list, ap_ss, Phi_true);
Wage quintile cutoffs=0.4645 0.71528 1.0335 1.5632
Completed SNW_HH_PRECOMPUTE;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=358.2151.2 Solve for 2019 Evuvw With 0 and 2 Checks
% Call Function
welf_checks = 0;
[ev19_jaeemk_check0, ec19_jaeemk_check0, ev20_jaeemk_check0, ec20_jaeemk_check0] = snw_evuvw19_jaeemk(...
welf_checks, st_solu_type, mp_params, mp_controls, ...
V_emp_2020, cons_emp_2020, V_unemp_2020, cons_unemp_2020, mp_precompute_res);
Completed SNW_A4CHK_WRK_BISEC_VEC;welf_checks=0;TR=0.0017225;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=68.836
Completed SNW_A4CHK_UNEMP_BISEC_VEC;welf_checks=0;TR=0.0017225;xi=0.5;b=0.5;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=69.8961
Completed SNW_EVUVW20_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;timeEUEC=8.2469
Completed SNW_EVUVW19_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=5058.1482
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ __________ ____ _________ ___________ _______ ______ ________ ________ ______
ec19_jaeemk 1 1 6 4.3173e+07 82 5.265e+05 1.9762e+08 4.5774 5.3272 1.1638 0.036494 64.569
ec20_jaeemk 2 2 6 4.37e+07 83 5.265e+05 2.3357e+08 5.3448 8.4447 1.58 0.033462 141.66
ev19_jaeemk 3 3 6 4.3173e+07 82 5.265e+05 -1.2119e+08 -2.8072 20.003 -7.1255 -454.58 16.358
ev20_jaeemk 4 4 6 4.37e+07 83 5.265e+05 -1.2937e+08 -2.9604 20.785 -7.021 -492.39 16.445
xxx TABLE:ec19_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.036494 0.036494 0.037029 0.041925 0.048857 12.024 12.296 12.576 12.864 13.138
r2 0.036494 0.036494 0.037029 0.041745 0.049665 12.268 12.541 12.822 13.111 13.382
r3 0.037912 0.037912 0.038127 0.041994 0.050655 12.503 12.777 13.06 13.349 13.618
r4 0.039293 0.039293 0.039401 0.043382 0.052052 12.761 13.036 13.319 13.607 13.842
r5 0.040635 0.040635 0.04064 0.044725 0.053494 13.01 13.286 13.569 13.855 14.055
r78 0.2179 0.2179 0.2179 0.2179 0.2179 27.797 28.793 29.808 30.995 32.447
r79 0.2179 0.2179 0.2179 0.2179 0.2179 30.454 31.684 32.756 33.984 35.551
r80 0.2179 0.2179 0.2179 0.2179 0.2179 33.715 35.537 37.399 38.98 40.213
r81 0.2179 0.2179 0.2179 0.2179 0.2179 40.14 41.425 43.212 45.644 48.477
r82 0.2179 0.2179 0.2179 0.2179 0.2179 52.118 55.559 58.496 60.127 62.893
xxx TABLE:ec20_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.033462 0.033996 0.037408 0.043215 0.048855 12.249 12.534 12.827 13.127 13.152
r2 0.033462 0.033996 0.037408 0.043883 0.049712 12.485 12.771 13.065 13.366 13.391
r3 0.033462 0.033996 0.037604 0.045059 0.050801 12.739 13.026 13.321 13.621 13.646
r4 0.034763 0.035298 0.038972 0.046376 0.052249 12.985 13.273 13.568 13.866 13.891
r5 0.036032 0.036566 0.040303 0.047654 0.053654 13.221 13.51 13.805 14.101 14.125
r79 0.2179 0.21844 0.22216 0.23228 0.25197 35.858 37.4 39.448 41.74 44.062
r80 0.2179 0.21844 0.22216 0.23228 0.25197 40.785 42.986 45.321 47.983 50.804
r81 0.2179 0.21844 0.22216 0.23228 0.25197 48.942 52.071 55.052 57.95 60.279
r82 0.2179 0.21844 0.22216 0.23228 0.25197 66.755 69.238 72.404 77.036 80.765
r83 0.2179 0.21844 0.22216 0.23228 0.25197 116.87 122.69 128.71 134.92 141.34
xxx TABLE:ev19_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -264.92 -264.92 -264.47 -260.13 -252.27 14.364 14.458 14.551 14.643 14.728
r2 -254.9 -254.9 -254.45 -250.7 -243.18 14.418 14.509 14.599 14.688 14.769
r3 -244.75 -244.75 -244.59 -241.74 -234.51 14.473 14.56 14.646 14.732 14.81
r4 -235.6 -235.6 -235.52 -232.8 -226.09 14.529 14.612 14.695 14.777 14.844
r5 -227.32 -227.32 -227.32 -224.7 -218.45 14.576 14.656 14.736 14.814 14.87
r78 -9.6725 -9.6725 -9.6725 -9.6725 -9.6725 2.4176 2.4297 2.441 2.4518 2.4621
r79 -8.7092 -8.7092 -8.7092 -8.7092 -8.7092 2.2043 2.215 2.2241 2.2322 2.2401
r80 -7.5196 -7.5196 -7.5196 -7.5196 -7.5196 1.9308 1.938 1.9447 1.9507 1.9562
r81 -5.9209 -5.9209 -5.9209 -5.9209 -5.9209 1.5463 1.55 1.5539 1.558 1.5624
r82 -3.5937 -3.5937 -3.5937 -3.5937 -3.5937 0.95581 0.95855 0.96061 0.96167 0.96338
xxx TABLE:ev20_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -278.01 -277.51 -274.43 -268.08 -258.6 14.436 14.53 14.623 14.715 14.806
r2 -268.49 -268 -264.92 -258.61 -249.41 14.491 14.582 14.671 14.76 14.847
r3 -258.97 -258.47 -255.41 -249.23 -240.47 14.546 14.633 14.719 14.805 14.889
r4 -249.24 -248.78 -245.93 -240.13 -231.89 14.602 14.686 14.768 14.85 14.932
r5 -240.44 -240.01 -237.35 -231.89 -224.12 14.65 14.73 14.81 14.888 14.967
r79 -9.6662 -9.655 -9.5783 -9.3823 -9.0457 2.4698 2.4801 2.4898 2.4989 2.5075
r80 -8.7031 -8.6919 -8.6152 -8.4192 -8.0826 2.253 2.261 2.2685 2.2755 2.2822
r81 -7.5138 -7.5026 -7.4258 -7.2298 -6.8933 1.9749 1.9803 1.9855 1.9904 1.995
r82 -5.9155 -5.9043 -5.8275 -5.6315 -5.295 1.582 1.5851 1.588 1.5907 1.5933
r83 -3.5892 -3.578 -3.5012 -3.3052 -2.9687 0.97904 0.98004 0.98097 0.98185 0.98267
% Call Function
welf_checks = 2;
[ev19_jaeemk_check2, ec19_jaeemk_check2, ev20_jaeemk_check2, ec20_jaeemk_check2] = snw_evuvw19_jaeemk(...
welf_checks, st_solu_type, mp_params, mp_controls, ...
V_emp_2020, cons_emp_2020, V_unemp_2020, cons_unemp_2020, mp_precompute_res);
Completed SNW_A4CHK_WRK_BISEC_VEC;welf_checks=2;TR=0.0017225;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=69.6483
Completed SNW_A4CHK_UNEMP_BISEC_VEC;welf_checks=2;TR=0.0017225;xi=0.5;b=0.5;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=69.632
Completed SNW_EVUVW20_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;timeEUEC=7.8042
Completed SNW_EVUVW19_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=4864.3569
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ __________ ____ _________ ___________ _______ ______ ________ ________ ______
ec19_jaeemk 1 1 6 4.3173e+07 82 5.265e+05 1.9765e+08 4.578 5.3272 1.1636 0.039577 64.573
ec20_jaeemk 2 2 6 4.37e+07 83 5.265e+05 2.3359e+08 5.3454 8.4448 1.5798 0.036651 141.67
ev19_jaeemk 3 3 6 4.3173e+07 82 5.265e+05 -1.2064e+08 -2.7944 19.95 -7.1392 -449 16.358
ev20_jaeemk 4 4 6 4.37e+07 83 5.265e+05 -1.2878e+08 -2.947 20.729 -7.0341 -485.95 16.445
xxx TABLE:ec19_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.039586 0.039586 0.040055 0.043746 0.049764 12.024 12.296 12.576 12.865 13.138
r2 0.039751 0.039751 0.040251 0.043953 0.050609 12.268 12.541 12.822 13.111 13.382
r3 0.041192 0.041192 0.041395 0.044454 0.051633 12.503 12.777 13.06 13.349 13.618
r4 0.042594 0.042594 0.042696 0.045859 0.053051 12.761 13.036 13.319 13.607 13.843
r5 0.043937 0.043937 0.043942 0.04725 0.054513 13.01 13.286 13.569 13.855 14.055
r78 0.22135 0.22135 0.22135 0.22135 0.22135 27.797 28.794 29.809 30.996 32.448
r79 0.22135 0.22135 0.22135 0.22135 0.22135 30.455 31.685 32.757 33.985 35.552
r80 0.22135 0.22135 0.22135 0.22135 0.22135 33.717 35.539 37.401 38.981 40.214
r81 0.22135 0.22135 0.22135 0.22135 0.22135 40.142 41.427 43.215 45.646 48.479
r82 0.22135 0.22135 0.22135 0.22135 0.22135 52.121 55.563 58.5 60.13 62.897
xxx TABLE:ec20_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.036651 0.037139 0.039378 0.044197 0.049529 12.25 12.534 12.827 13.127 13.152
r2 0.036651 0.037139 0.039605 0.044899 0.050459 12.485 12.771 13.065 13.366 13.391
r3 0.036804 0.037319 0.040134 0.046059 0.051622 12.739 13.026 13.321 13.621 13.646
r4 0.038157 0.038682 0.041485 0.0474 0.053101 12.985 13.273 13.568 13.867 13.891
r5 0.039474 0.040008 0.042798 0.048699 0.054535 13.221 13.51 13.805 14.101 14.125
r79 0.22135 0.22188 0.22561 0.23572 0.25394 35.859 37.401 39.449 41.741 44.063
r80 0.22135 0.22188 0.22561 0.23572 0.25397 40.787 42.988 45.322 47.984 50.805
r81 0.22135 0.22188 0.22561 0.23572 0.25434 48.944 52.073 55.054 57.951 60.28
r82 0.22135 0.22188 0.22561 0.23572 0.25469 66.757 69.24 72.406 77.038 80.767
r83 0.22135 0.22188 0.22561 0.23572 0.25541 116.87 122.69 128.71 134.93 141.34
xxx TABLE:ev19_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -262.28 -262.28 -261.88 -258.26 -250.89 14.364 14.458 14.551 14.643 14.728
r2 -252.28 -252.28 -251.88 -248.84 -241.85 14.418 14.509 14.599 14.688 14.769
r3 -242.33 -242.33 -242.18 -239.92 -233.24 14.473 14.56 14.646 14.732 14.81
r4 -233.33 -233.33 -233.27 -231.09 -224.88 14.529 14.612 14.695 14.777 14.844
r5 -225.21 -225.21 -225.2 -223.1 -217.31 14.576 14.656 14.736 14.814 14.87
r78 -9.6013 -9.6013 -9.6013 -9.6013 -9.6013 2.4176 2.4297 2.441 2.4518 2.4621
r79 -8.6379 -8.6379 -8.6379 -8.6379 -8.6379 2.2043 2.215 2.2241 2.2322 2.2401
r80 -7.4483 -7.4483 -7.4483 -7.4483 -7.4483 1.9308 1.938 1.9447 1.9507 1.9563
r81 -5.8497 -5.8497 -5.8497 -5.8497 -5.8497 1.5463 1.55 1.5539 1.558 1.5624
r82 -3.5225 -3.5225 -3.5225 -3.5225 -3.5225 0.95582 0.95855 0.96061 0.96167 0.96339
xxx TABLE:ev20_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _______ _______ _______ _______ _______
r1 -275.12 -274.68 -272.28 -266.43 -257.32 14.436 14.53 14.623 14.715 14.806
r2 -265.6 -265.16 -262.78 -257.01 -248.18 14.491 14.582 14.671 14.76 14.847
r3 -256.09 -255.66 -253.32 -247.71 -239.31 14.546 14.633 14.72 14.805 14.889
r4 -246.56 -246.15 -243.96 -238.69 -230.79 14.603 14.686 14.769 14.85 14.932
r5 -237.94 -237.56 -235.5 -230.54 -223.08 14.65 14.73 14.81 14.888 14.967
r79 -9.595 -9.584 -9.5115 -9.3234 -8.9958 2.4698 2.4801 2.4898 2.4989 2.5075
r80 -8.6319 -8.6209 -8.5484 -8.3603 -8.0332 2.253 2.261 2.2685 2.2756 2.2822
r81 -7.4426 -7.4316 -7.3591 -7.171 -6.8443 1.9749 1.9803 1.9855 1.9904 1.995
r82 -5.8443 -5.8333 -5.7608 -5.5727 -5.2463 1.582 1.5851 1.588 1.5907 1.5933
r83 -3.518 -3.507 -3.4345 -3.2464 -2.9207 0.97904 0.98004 0.98097 0.98185 0.98267Differences between Checks in Expected Value and Expected Consumption
mn_V_U_gain_check = ev19_jaeemk_check2 - ev19_jaeemk_check0;
mn_MPC_U_gain_share_check = (ec19_jaeemk_check2 - ec19_jaeemk_check0)./(welf_checks*mp_params('TR'));1.3 Param Results Define Frames
Define the matrix dimensions names and dimension vector values. Policy and Value Functions share the same ND dimensional structure.
% Grids:
age_grid = 18:99;
agrid = mp_params('agrid')';
eta_H_grid = mp_params('eta_H_grid')';
eta_S_grid = mp_params('eta_S_grid')';
ar_st_eta_HS_grid = string(cellstr([num2str(eta_H_grid', 'hz=%3.2f;'), num2str(eta_S_grid', 'wz=%3.2f')]));
edu_grid = [0,1];
marry_grid = [0,1];
kids_grid = (1:1:mp_params('n_kidsgrid'))';
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
cl_mp_datasetdesc = {};
cl_mp_datasetdesc{1} = containers.Map({'name', 'labval'}, {'age', age_grid});
cl_mp_datasetdesc{2} = containers.Map({'name', 'labval'}, {'savings', agrid});
cl_mp_datasetdesc{3} = containers.Map({'name', 'labval'}, {'eta', 1:length(eta_H_grid)});
cl_mp_datasetdesc{4} = containers.Map({'name', 'labval'}, {'edu', edu_grid});
cl_mp_datasetdesc{5} = containers.Map({'name', 'labval'}, {'marry', marry_grid});
cl_mp_datasetdesc{6} = containers.Map({'name', 'labval'}, {'kids', kids_grid});1.4 Analyze Difference in V and C with Check
The difference between V and V with Check, marginal utility gain given the check.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
mp_support_graph('cl_st_xtitle') = {'Savings States, a'};
mp_support_graph('st_legend_loc') = 'eastoutside';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('it_legend_select') = 21; % how many shock legends to show
mp_support_graph('cl_colors') = 'jet';MEAN(MN_V_GAIN_CHECK(A,Z))
Tabulate value and policies along savings and shocks:
% Set
ar_permute = [1,4,5,6,3,2];
% Value Function
st_title = ['MEAN(MN_V_U_GAIN_CHECK(A,Z)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_v = ff_summ_nd_array(st_title, mn_V_U_gain_check, true, ["mean"], 4, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_V_U_GAIN_CHECK(A,Z)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group savings mean_eta_1 mean_eta_2 mean_eta_3 mean_eta_4 mean_eta_5 mean_eta_6 mean_eta_7 mean_eta_8 mean_eta_9 mean_eta_10 mean_eta_11 mean_eta_12 mean_eta_13 mean_eta_14 mean_eta_15 mean_eta_16 mean_eta_17 mean_eta_18 mean_eta_19 mean_eta_20 mean_eta_21 mean_eta_22 mean_eta_23 mean_eta_24 mean_eta_25 mean_eta_26 mean_eta_27 mean_eta_28 mean_eta_29 mean_eta_30 mean_eta_31 mean_eta_32 mean_eta_33 mean_eta_34 mean_eta_35 mean_eta_36 mean_eta_37 mean_eta_38 mean_eta_39 mean_eta_40 mean_eta_41 mean_eta_42 mean_eta_43 mean_eta_44 mean_eta_45 mean_eta_46 mean_eta_47 mean_eta_48 mean_eta_49 mean_eta_50 mean_eta_51 mean_eta_52 mean_eta_53 mean_eta_54 mean_eta_55 mean_eta_56 mean_eta_57 mean_eta_58 mean_eta_59 mean_eta_60 mean_eta_61 mean_eta_62 mean_eta_63 mean_eta_64 mean_eta_65 mean_eta_66 mean_eta_67 mean_eta_68 mean_eta_69 mean_eta_70 mean_eta_71 mean_eta_72 mean_eta_73 mean_eta_74 mean_eta_75 mean_eta_76 mean_eta_77 mean_eta_78 mean_eta_79 mean_eta_80 mean_eta_81 mean_eta_82 mean_eta_83 mean_eta_84 mean_eta_85 mean_eta_86 mean_eta_87 mean_eta_88 mean_eta_89 mean_eta_90 mean_eta_91 mean_eta_92 mean_eta_93 mean_eta_94 mean_eta_95 mean_eta_96 mean_eta_97 mean_eta_98 mean_eta_99 mean_eta_100 mean_eta_101 mean_eta_102 mean_eta_103 mean_eta_104 mean_eta_105 mean_eta_106 mean_eta_107 mean_eta_108 mean_eta_109 mean_eta_110 mean_eta_111 mean_eta_112 mean_eta_113 mean_eta_114 mean_eta_115 mean_eta_116 mean_eta_117 mean_eta_118 mean_eta_119 mean_eta_120 mean_eta_121 mean_eta_122 mean_eta_123 mean_eta_124 mean_eta_125 mean_eta_126 mean_eta_127 mean_eta_128 mean_eta_129 mean_eta_130 mean_eta_131 mean_eta_132 mean_eta_133 mean_eta_134 mean_eta_135 mean_eta_136 mean_eta_137 mean_eta_138 mean_eta_139 mean_eta_140 mean_eta_141 mean_eta_142 mean_eta_143 mean_eta_144 mean_eta_145 mean_eta_146 mean_eta_147 mean_eta_148 mean_eta_149 mean_eta_150 mean_eta_151 mean_eta_152 mean_eta_153 mean_eta_154 mean_eta_155 mean_eta_156 mean_eta_157 mean_eta_158 mean_eta_159 mean_eta_160 mean_eta_161 mean_eta_162 mean_eta_163 mean_eta_164 mean_eta_165 mean_eta_166 mean_eta_167 mean_eta_168 mean_eta_169 mean_eta_170 mean_eta_171 mean_eta_172 mean_eta_173 mean_eta_174 mean_eta_175 mean_eta_176 mean_eta_177 mean_eta_178 mean_eta_179 mean_eta_180 mean_eta_181 mean_eta_182 mean_eta_183 mean_eta_184 mean_eta_185 mean_eta_186 mean_eta_187 mean_eta_188 mean_eta_189 mean_eta_190 mean_eta_191 mean_eta_192 mean_eta_193 mean_eta_194 mean_eta_195 mean_eta_196 mean_eta_197 mean_eta_198 mean_eta_199 mean_eta_200 mean_eta_201 mean_eta_202 mean_eta_203 mean_eta_204 mean_eta_205 mean_eta_206 mean_eta_207 mean_eta_208 mean_eta_209 mean_eta_210 mean_eta_211 mean_eta_212 mean_eta_213 mean_eta_214 mean_eta_215 mean_eta_216 mean_eta_217 mean_eta_218 mean_eta_219 mean_eta_220 mean_eta_221 mean_eta_222 mean_eta_223 mean_eta_224 mean_eta_225 mean_eta_226 mean_eta_227 mean_eta_228 mean_eta_229 mean_eta_230 mean_eta_231 mean_eta_232 mean_eta_233 mean_eta_234 mean_eta_235 mean_eta_236 mean_eta_237 mean_eta_238 mean_eta_239 mean_eta_240 mean_eta_241 mean_eta_242 mean_eta_243 mean_eta_244 mean_eta_245 mean_eta_246 mean_eta_247 mean_eta_248 mean_eta_249 mean_eta_250 mean_eta_251 mean_eta_252 mean_eta_253 mean_eta_254 mean_eta_255 mean_eta_256 mean_eta_257 mean_eta_258 mean_eta_259 mean_eta_260 mean_eta_261 mean_eta_262 mean_eta_263 mean_eta_264 mean_eta_265 mean_eta_266 mean_eta_267 mean_eta_268 mean_eta_269 mean_eta_270 mean_eta_271 mean_eta_272 mean_eta_273 mean_eta_274 mean_eta_275 mean_eta_276 mean_eta_277 mean_eta_278 mean_eta_279 mean_eta_280 mean_eta_281 mean_eta_282 mean_eta_283 mean_eta_284 mean_eta_285 mean_eta_286 mean_eta_287 mean_eta_288 mean_eta_289 mean_eta_290 mean_eta_291 mean_eta_292 mean_eta_293 mean_eta_294 mean_eta_295 mean_eta_296 mean_eta_297 mean_eta_298 mean_eta_299 mean_eta_300 mean_eta_301 mean_eta_302 mean_eta_303 mean_eta_304 mean_eta_305 mean_eta_306 mean_eta_307 mean_eta_308 mean_eta_309 mean_eta_310 mean_eta_311 mean_eta_312 mean_eta_313 mean_eta_314 mean_eta_315 mean_eta_316 mean_eta_317 mean_eta_318 mean_eta_319 mean_eta_320 mean_eta_321 mean_eta_322 mean_eta_323 mean_eta_324 mean_eta_325 mean_eta_326 mean_eta_327 mean_eta_328 mean_eta_329 mean_eta_330 mean_eta_331 mean_eta_332 mean_eta_333 mean_eta_334 mean_eta_335 mean_eta_336 mean_eta_337 mean_eta_338 mean_eta_339 mean_eta_340 mean_eta_341 mean_eta_342 mean_eta_343 mean_eta_344 mean_eta_345 mean_eta_346 mean_eta_347 mean_eta_348 mean_eta_349 mean_eta_350 mean_eta_351 mean_eta_352 mean_eta_353 mean_eta_354 mean_eta_355 mean_eta_356 mean_eta_357 mean_eta_358 mean_eta_359 mean_eta_360 mean_eta_361 mean_eta_362 mean_eta_363 mean_eta_364 mean_eta_365 mean_eta_366 mean_eta_367 mean_eta_368 mean_eta_369 mean_eta_370 mean_eta_371 mean_eta_372 mean_eta_373 mean_eta_374 mean_eta_375 mean_eta_376 mean_eta_377 mean_eta_378 mean_eta_379 mean_eta_380 mean_eta_381 mean_eta_382 mean_eta_383 mean_eta_384 mean_eta_385 mean_eta_386 mean_eta_387 mean_eta_388 mean_eta_389 mean_eta_390 mean_eta_391 mean_eta_392 mean_eta_393 mean_eta_394 mean_eta_395 mean_eta_396 mean_eta_397 mean_eta_398 mean_eta_399 mean_eta_400 mean_eta_401 mean_eta_402 mean_eta_403 mean_eta_404 mean_eta_405
_____ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________
1 0 0.5996 0.55567 0.50718 0.45861 0.41293 0.3714 0.33421 0.30106 0.27156 0.24532 0.22197 0.2012 0.18271 0.16626 0.15161 0.13857 0.12696 0.11662 0.10742 0.099215 0.091906 0.08538 0.079541 0.074304 0.069608 0.065406 0.061654 0.058316 0.055346 0.052696 0.050316 0.048187 0.046287 0.044604 0.043113 0.041801 0.040628 0.039583 0.038661 0.037838 0.037111 0.036463 0.035881 0.03536 0.034891 0.03447 0.034091 0.033749 0.033441 0.033161 0.032908 0.032679 0.032471 0.032284 0.032113 0.031957 0.031817 0.031688 0.031571 0.031465 0.031366 0.031277 0.031196 0.03112 0.031052 0.030989 0.030931 0.030878 0.03083 0.030785 0.030744 0.030707 0.030673 0.030642 0.030613 0.030587 0.030563 0.030542 0.030523 0.030506 0.030493 0.5996 0.55567 0.50718 0.45861 0.41293 0.3714 0.33421 0.30106 0.27156 0.24532 0.22197 0.2012 0.18271 0.16626 0.15161 0.13857 0.12696 0.11662 0.10742 0.099215 0.091906 0.08538 0.079541 0.074304 0.069608 0.065406 0.061653 0.058314 0.055338 0.052683 0.050297 0.048165 0.04627 0.044585 0.043102 0.041787 0.040613 0.039574 0.038649 0.037829 0.037101 0.036454 0.035875 0.035352 0.034885 0.034464 0.034085 0.033744 0.033436 0.033157 0.032904 0.032675 0.032468 0.032281 0.03211 0.031955 0.031814 0.031686 0.031569 0.031462 0.031365 0.031276 0.031194 0.031119 0.031051 0.030988 0.03093 0.030877 0.030829 0.030784 0.030744 0.030706 0.030672 0.030641 0.030613 0.030587 0.030563 0.030542 0.030523 0.030506 0.030492 0.56866 0.5265 0.4802 0.43399 0.39063 0.3513 0.31615 0.28488 0.25711 0.23244 0.21053 0.19105 0.17374 0.15834 0.14463 0.13242 0.12154 0.11186 0.10324 0.09555 0.088683 0.082541 0.077032 0.072086 0.067648 0.063672 0.060118 0.056946 0.054121 0.051598 0.049326 0.047298 0.045489 0.043878 0.042452 0.041184 0.040055 0.039048 0.038153 0.037357 0.03665 0.03602 0.035453 0.034944 0.034487 0.034074 0.033703 0.033368 0.033065 0.032791 0.032542 0.032316 0.032113 0.031928 0.03176 0.031607 0.031468 0.031341 0.031225 0.031121 0.031024 0.030936 0.030856 0.030781 0.030714 0.030652 0.030595 0.030543 0.030495 0.030451 0.030411 0.030374 0.030341 0.03031 0.030281 0.030256 0.030232 0.030211 0.030192 0.030176 0.030163 0.53474 0.49384 0.44883 0.4039 0.36179 0.32368 0.28971 0.25958 0.23291 0.2093 0.1884 0.16991 0.15354 0.13905 0.12623 0.11487 0.10481 0.095907 0.088019 0.081033 0.07484 0.069341 0.064446 0.060079 0.056184 0.05272 0.049646 0.046927 0.044522 0.042391 0.040489 0.038801 0.037305 0.035981 0.034814 0.033782 0.032863 0.032045 0.03132 0.030673 0.030097 0.029583 0.029119 0.028701 0.028324 0.027982 0.027674 0.027393 0.027138 0.026906 0.026695 0.026502 0.026326 0.026165 0.026018 0.025883 0.02576 0.025647 0.025543 0.025448 0.02536 0.02528 0.025206 0.025138 0.025075 0.025017 0.024965 0.024916 0.024871 0.024829 0.024791 0.024756 0.024724 0.024695 0.024668 0.024643 0.02462 0.0246 0.024582 0.024566 0.024553 0.52429 0.4836 0.43881 0.39411 0.35225 0.31439 0.28066 0.25078 0.22434 0.20097 0.18031 0.16204 0.14589 0.13161 0.11899 0.10784 0.097977 0.089261 0.081555 0.074746 0.068725 0.063389 0.058652 0.054437 0.050688 0.047364 0.044423 0.041832 0.039547 0.037531 0.035739 0.034156 0.03276 0.031529 0.03045 0.029499 0.028657 0.027912 0.027255 0.02667 0.026153 0.025694 0.025281 0.02491 0.024577 0.024275 0.024004 0.023759 0.023536 0.023334 0.023149 0.022981 0.022828 0.022688 0.022561 0.022444 0.022337 0.022239 0.022149 0.022067 0.021991 0.021921 0.021857 0.021797 0.021743 0.021692 0.021646 0.021603 0.021564 0.021528 0.021494 0.021463 0.021435 0.021409 0.021385 0.021364 0.021344 0.021326 0.02131 0.021296 0.021285
2 0.00051498 0.5996 0.55567 0.50718 0.45861 0.41293 0.3714 0.33421 0.30106 0.27156 0.24532 0.22197 0.20119 0.1827 0.16625 0.1516 0.13856 0.12695 0.11662 0.10741 0.099214 0.091904 0.085375 0.079534 0.074295 0.0696 0.065398 0.061646 0.058309 0.05534 0.052691 0.050311 0.048183 0.046283 0.044601 0.043111 0.041798 0.040625 0.039581 0.038659 0.037837 0.03711 0.036462 0.03588 0.035359 0.034891 0.034469 0.03409 0.033749 0.03344 0.033161 0.032908 0.032679 0.032471 0.032284 0.032113 0.031957 0.031817 0.031688 0.031571 0.031464 0.031366 0.031277 0.031195 0.03112 0.031052 0.030989 0.030931 0.030878 0.03083 0.030785 0.030744 0.030707 0.030673 0.030642 0.030613 0.030587 0.030563 0.030542 0.030523 0.030506 0.030493 0.5996 0.55567 0.50718 0.45861 0.41293 0.3714 0.33421 0.30106 0.27156 0.24532 0.22197 0.20119 0.1827 0.16625 0.1516 0.13856 0.12695 0.11662 0.10741 0.099214 0.091904 0.085375 0.079534 0.074295 0.0696 0.065398 0.061645 0.058306 0.055331 0.052677 0.050293 0.048161 0.046267 0.044581 0.0431 0.041784 0.04061 0.039572 0.038647 0.037827 0.0371 0.036453 0.035873 0.035351 0.034884 0.034463 0.034084 0.033744 0.033435 0.033156 0.032904 0.032675 0.032468 0.03228 0.03211 0.031955 0.031814 0.031686 0.031568 0.031462 0.031365 0.031275 0.031194 0.031119 0.031051 0.030988 0.03093 0.030877 0.030829 0.030784 0.030744 0.030706 0.030672 0.030641 0.030612 0.030587 0.030563 0.030542 0.030523 0.030506 0.030492 0.56852 0.52636 0.48008 0.43387 0.39052 0.3512 0.31605 0.28478 0.25701 0.23235 0.21044 0.19097 0.17366 0.15826 0.14456 0.13236 0.12149 0.11181 0.1032 0.095512 0.088651 0.082509 0.077003 0.072054 0.067618 0.063643 0.060092 0.056922 0.0541 0.051579 0.04931 0.047282 0.045475 0.043866 0.042441 0.041174 0.040044 0.039038 0.038144 0.037349 0.036642 0.036012 0.035446 0.034937 0.034479 0.034067 0.033696 0.033361 0.033058 0.032784 0.032536 0.03231 0.032106 0.031921 0.031753 0.0316 0.031461 0.031335 0.031219 0.031114 0.031018 0.03093 0.03085 0.030775 0.030708 0.030646 0.030589 0.030537 0.030489 0.030445 0.030405 0.030368 0.030335 0.030304 0.030275 0.03025 0.030226 0.030205 0.030186 0.03017 0.030157 0.53473 0.49383 0.44883 0.40389 0.36179 0.32368 0.2897 0.25958 0.2329 0.20929 0.18839 0.1699 0.15353 0.13904 0.12621 0.11485 0.1048 0.095895 0.08801 0.081026 0.074834 0.069332 0.064435 0.060066 0.056172 0.052708 0.049634 0.046916 0.044512 0.042382 0.040481 0.038794 0.037298 0.035975 0.034809 0.033777 0.032858 0.032041 0.031315 0.030668 0.030092 0.029579 0.029115 0.028697 0.02832 0.027979 0.02767 0.02739 0.027135 0.026903 0.026692 0.026498 0.026323 0.026162 0.026015 0.02588 0.025757 0.025644 0.02554 0.025445 0.025358 0.025277 0.025203 0.025135 0.025073 0.025015 0.024962 0.024913 0.024868 0.024826 0.024788 0.024753 0.024721 0.024692 0.024665 0.02464 0.024618 0.024597 0.024579 0.024564 0.024551 0.52429 0.48359 0.43881 0.39411 0.35225 0.31439 0.28066 0.25078 0.22434 0.20097 0.1803 0.16203 0.14588 0.1316 0.11898 0.10783 0.097968 0.089254 0.081551 0.074744 0.068723 0.063384 0.058645 0.054428 0.050679 0.047356 0.044415 0.041824 0.03954 0.037526 0.035735 0.034152 0.032756 0.031526 0.030447 0.029497 0.028655 0.02791 0.027252 0.026669 0.026151 0.025693 0.025279 0.024909 0.024576 0.024275 0.024004 0.023758 0.023535 0.023333 0.023148 0.02298 0.022827 0.022688 0.022561 0.022444 0.022337 0.022239 0.022149 0.022067 0.021991 0.021921 0.021856 0.021797 0.021743 0.021692 0.021646 0.021603 0.021564 0.021527 0.021494 0.021463 0.021435 0.021409 0.021385 0.021363 0.021343 0.021326 0.02131 0.021296 0.021284
3 0.0041199 0.59858 0.55412 0.50516 0.45663 0.41125 0.37004 0.33309 0.30014 0.27079 0.24467 0.22143 0.20075 0.18233 0.16594 0.15134 0.13834 0.12676 0.11644 0.10725 0.099065 0.091768 0.085239 0.079418 0.074187 0.069502 0.065313 0.061577 0.058249 0.05529 0.052645 0.05027 0.048146 0.046249 0.044571 0.043084 0.041775 0.040604 0.039565 0.038645 0.037825 0.037099 0.036452 0.035872 0.035353 0.034885 0.034465 0.034086 0.033745 0.033436 0.033157 0.032905 0.032676 0.032469 0.032282 0.032111 0.031956 0.031815 0.031687 0.031569 0.031463 0.031365 0.031276 0.031195 0.031119 0.031051 0.030988 0.030931 0.030878 0.030829 0.030785 0.030744 0.030706 0.030672 0.030641 0.030612 0.030586 0.030563 0.030541 0.030522 0.030506 0.030492 0.59858 0.55412 0.50516 0.45663 0.41125 0.37004 0.33309 0.30014 0.27079 0.24467 0.22143 0.20075 0.18233 0.16594 0.15134 0.13834 0.12676 0.11644 0.10725 0.099065 0.091768 0.085239 0.079418 0.074187 0.069502 0.065313 0.061576 0.058246 0.05528 0.052631 0.05025 0.048124 0.046233 0.044551 0.043075 0.04176 0.04059 0.039554 0.038633 0.037815 0.037089 0.036443 0.035865 0.035345 0.034878 0.034458 0.03408 0.03374 0.033431 0.033153 0.032901 0.032672 0.032465 0.032278 0.032108 0.031953 0.031813 0.031684 0.031567 0.031461 0.031363 0.031274 0.031193 0.031118 0.03105 0.030987 0.030929 0.030877 0.030828 0.030784 0.030743 0.030706 0.030672 0.03064 0.030612 0.030586 0.030562 0.030541 0.030522 0.030506 0.030492 0.56657 0.52396 0.47726 0.43113 0.38811 0.34914 0.31427 0.28322 0.25562 0.23111 0.20933 0.18999 0.17278 0.15748 0.14388 0.13176 0.12097 0.11135 0.10276 0.095114 0.08829 0.082177 0.076715 0.071791 0.067374 0.063418 0.059887 0.056738 0.05394 0.051432 0.049179 0.047165 0.045362 0.043763 0.042349 0.041089 0.039965 0.038965 0.038076 0.037284 0.03658 0.035953 0.035389 0.034882 0.034426 0.034015 0.033645 0.033311 0.033009 0.032735 0.032487 0.032262 0.032059 0.031875 0.031707 0.031554 0.031415 0.031289 0.031173 0.031069 0.030972 0.030884 0.030804 0.03073 0.030663 0.0306 0.030544 0.030492 0.030444 0.0304 0.03036 0.030323 0.03029 0.030259 0.03023 0.030205 0.030181 0.03016 0.030141 0.030125 0.030112 0.53365 0.49222 0.44675 0.40185 0.36005 0.32226 0.28854 0.2586 0.23208 0.2086 0.18781 0.16941 0.15312 0.13869 0.12591 0.11459 0.10457 0.095686 0.087819 0.080846 0.074667 0.069166 0.06429 0.059929 0.056046 0.052596 0.049539 0.04683 0.044438 0.042313 0.040417 0.038736 0.037242 0.035923 0.034762 0.033732 0.032817 0.032003 0.03128 0.030636 0.030061 0.029549 0.029087 0.02867 0.028295 0.027954 0.027646 0.027367 0.027112 0.02688 0.026669 0.026476 0.026301 0.02614 0.025994 0.025859 0.025736 0.025623 0.025519 0.025425 0.025337 0.025257 0.025183 0.025114 0.025052 0.024994 0.024942 0.024893 0.024848 0.024806 0.024768 0.024733 0.024701 0.024672 0.024645 0.02462 0.024598 0.024577 0.024559 0.024544 0.02453 0.52327 0.48204 0.43678 0.39212 0.35056 0.31302 0.27954 0.24985 0.22357 0.20032 0.17976 0.16158 0.14551 0.13129 0.11872 0.1076 0.097767 0.089075 0.08139 0.074593 0.068584 0.063246 0.058526 0.054316 0.050579 0.047268 0.044343 0.041761 0.039488 0.037479 0.035692 0.034115 0.032721 0.031495 0.03042 0.029473 0.028633 0.027892 0.027237 0.026655 0.02614 0.025682 0.02527 0.024901 0.024569 0.024268 0.023997 0.023753 0.02353 0.023328 0.023144 0.022976 0.022823 0.022684 0.022557 0.02244 0.022334 0.022236 0.022146 0.022064 0.021988 0.021918 0.021854 0.021794 0.02174 0.021689 0.021643 0.0216 0.021561 0.021525 0.021491 0.021461 0.021432 0.021406 0.021383 0.021361 0.021341 0.021323 0.021307 0.021293 0.021282
4 0.013905 0.49775 0.46232 0.42566 0.38891 0.35398 0.32177 0.29251 0.26611 0.24243 0.2212 0.2021 0.18489 0.16939 0.15544 0.14287 0.13153 0.12126 0.11199 0.10363 0.096127 0.089391 0.083335 0.077862 0.072936 0.068492 0.064493 0.060913 0.057706 0.054849 0.052276 0.049961 0.047884 0.046025 0.044376 0.042914 0.041622 0.040462 0.039439 0.038524 0.037712 0.036993 0.036352 0.035777 0.03526 0.034796 0.034378 0.034001 0.033662 0.033355 0.033078 0.032827 0.032599 0.032393 0.032206 0.032036 0.031881 0.031741 0.031613 0.031496 0.03139 0.031292 0.031203 0.031122 0.031047 0.030979 0.030916 0.030858 0.030805 0.030757 0.030712 0.030672 0.030634 0.0306 0.030569 0.03054 0.030515 0.030491 0.030469 0.03045 0.030434 0.03042 0.49775 0.46232 0.42566 0.38891 0.35398 0.32177 0.29251 0.26611 0.24243 0.2212 0.2021 0.18489 0.16939 0.15544 0.14287 0.13153 0.12126 0.11199 0.10363 0.096127 0.089391 0.083335 0.077862 0.072936 0.068492 0.064492 0.060912 0.057701 0.054837 0.052259 0.04994 0.047866 0.04601 0.044356 0.042905 0.041605 0.040451 0.039427 0.038512 0.037703 0.036983 0.036343 0.035769 0.035252 0.03479 0.034371 0.033995 0.033658 0.03335 0.033073 0.032823 0.032595 0.032389 0.032203 0.032033 0.031878 0.031738 0.03161 0.031494 0.031388 0.03129 0.031202 0.03112 0.031045 0.030977 0.030914 0.030857 0.030804 0.030756 0.030712 0.030671 0.030634 0.0306 0.030569 0.03054 0.030514 0.03049 0.030469 0.03045 0.030434 0.03042 0.46358 0.43013 0.39585 0.36161 0.32917 0.29931 0.27223 0.24782 0.22597 0.20641 0.18883 0.173 0.15875 0.14593 0.13439 0.12396 0.11453 0.10601 0.098344 0.091448 0.085251 0.079672 0.074614 0.070055 0.06593 0.062209 0.058865 0.055864 0.053183 0.050768 0.048597 0.04665 0.044903 0.043349 0.041971 0.040738 0.039638 0.038658 0.037784 0.037005 0.036312 0.035694 0.035138 0.034637 0.034187 0.03378 0.033413 0.033083 0.032783 0.032511 0.032266 0.032042 0.03184 0.031656 0.031489 0.031337 0.031199 0.031073 0.030958 0.030854 0.030758 0.03067 0.03059 0.030516 0.030449 0.030387 0.03033 0.030278 0.030231 0.030187 0.030147 0.03011 0.030076 0.030046 0.030017 0.029992 0.029968 0.029947 0.029928 0.029912 0.029898 0.43265 0.40027 0.36709 0.33398 0.30264 0.27385 0.24782 0.22445 0.2036 0.185 0.16837 0.15344 0.14007 0.12808 0.11734 0.10768 0.098979 0.091138 0.08411 0.077823 0.072208 0.067182 0.062656 0.058603 0.054963 0.051705 0.048805 0.04622 0.04393 0.041881 0.040048 0.038419 0.036962 0.035671 0.034535 0.033522 0.03262 0.03182 0.031105 0.030468 0.029901 0.029395 0.028937 0.028524 0.028152 0.027814 0.027508 0.027231 0.026978 0.026748 0.026539 0.026347 0.026172 0.026012 0.025866 0.025732 0.025609 0.025497 0.025393 0.025299 0.025212 0.025132 0.025058 0.024989 0.024928 0.02487 0.024817 0.024768 0.024723 0.024682 0.024644 0.024609 0.024577 0.024548 0.024521 0.024496 0.024473 0.024453 0.024435 0.02442 0.024406 0.42242 0.39023 0.35727 0.32439 0.29328 0.26474 0.23895 0.21582 0.1952 0.17683 0.16042 0.14572 0.13255 0.12078 0.11024 0.10078 0.092265 0.084609 0.07776 0.071647 0.066198 0.061333 0.056962 0.053058 0.049562 0.046441 0.043672 0.041212 0.039041 0.037106 0.035382 0.033855 0.032497 0.031298 0.030248 0.029316 0.02849 0.027762 0.027114 0.026539 0.02603 0.025578 0.02517 0.024804 0.024476 0.024177 0.023909 0.023666 0.023445 0.023244 0.023061 0.022894 0.022742 0.022604 0.022478 0.022361 0.022255 0.022157 0.022067 0.021986 0.02191 0.02184 0.021776 0.021716 0.021663 0.021612 0.021566 0.021523 0.021484 0.021448 0.021414 0.021384 0.021355 0.021329 0.021306 0.021284 0.021264 0.021246 0.02123 0.021217 0.021205
5 0.032959 0.39069 0.36715 0.34122 0.31498 0.28971 0.26606 0.24426 0.22429 0.20608 0.18949 0.1744 0.16067 0.14819 0.13685 0.12658 0.1173 0.1089 0.10129 0.094386 0.08812 0.082436 0.077277 0.072596 0.06836 0.064508 0.061022 0.057866 0.055026 0.052458 0.050135 0.048033 0.04613 0.044426 0.042898 0.041546 0.040334 0.039245 0.038278 0.037405 0.036629 0.035937 0.035315 0.034758 0.034254 0.033801 0.033392 0.033022 0.03269 0.032388 0.032114 0.031867 0.031641 0.031437 0.031252 0.031083 0.030929 0.03079 0.030663 0.030547 0.030441 0.030344 0.030256 0.030174 0.030099 0.030032 0.029969 0.029912 0.029859 0.029811 0.029766 0.029725 0.029688 0.029654 0.029623 0.029595 0.029569 0.029545 0.029524 0.029505 0.029488 0.029475 0.39069 0.36715 0.34122 0.31498 0.28971 0.26606 0.24426 0.22429 0.20608 0.18949 0.1744 0.16067 0.14819 0.13685 0.12658 0.1173 0.1089 0.10129 0.094386 0.08812 0.082436 0.077277 0.072596 0.06836 0.064508 0.061021 0.057862 0.055015 0.052441 0.050113 0.048012 0.046116 0.044403 0.042888 0.04153 0.040317 0.039235 0.038265 0.037395 0.036618 0.035926 0.035307 0.034749 0.034246 0.033795 0.033385 0.033017 0.032685 0.032383 0.032109 0.031862 0.031637 0.031433 0.031249 0.031081 0.030927 0.030788 0.030661 0.030544 0.030439 0.030342 0.030254 0.030173 0.030098 0.03003 0.029967 0.02991 0.029858 0.02981 0.029765 0.029725 0.029688 0.029654 0.029623 0.029594 0.029568 0.029545 0.029523 0.029504 0.029488 0.029474 0.35259 0.33121 0.30786 0.28433 0.26173 0.24061 0.22115 0.20335 0.18713 0.17238 0.15897 0.14679 0.1357 0.12564 0.11651 0.10826 0.10079 0.094018 0.087868 0.082274 0.077187 0.072553 0.068342 0.064518 0.061039 0.057882 0.055025 0.052447 0.050113 0.048 0.046087 0.044349 0.042785 0.041385 0.040128 0.039002 0.037988 0.03708 0.036261 0.035527 0.034872 0.034282 0.033748 0.033267 0.032832 0.032438 0.032082 0.031761 0.031468 0.031201 0.03096 0.030741 0.030541 0.030361 0.030196 0.030045 0.029909 0.029784 0.02967 0.029566 0.029471 0.029384 0.029304 0.02923 0.029164 0.029102 0.029046 0.028994 0.028947 0.028903 0.028863 0.028826 0.028793 0.028762 0.028734 0.028708 0.028685 0.028664 0.028645 0.028629 0.028615 0.32529 0.30479 0.28236 0.25977 0.23809 0.21788 0.19931 0.18238 0.16701 0.15307 0.14045 0.12902 0.11867 0.10931 0.10087 0.093274 0.086438 0.08027 0.074699 0.069657 0.065099 0.060975 0.057244 0.053885 0.05084 0.048098 0.045626 0.043411 0.041417 0.039621 0.038009 0.036554 0.035249 0.034091 0.033055 0.03213 0.031298 0.030554 0.029883 0.029281 0.028743 0.028256 0.027816 0.027418 0.027057 0.026728 0.02643 0.026159 0.025911 0.025685 0.025479 0.02529 0.025117 0.024959 0.024815 0.024682 0.02456 0.024449 0.024346 0.024252 0.024165 0.024086 0.024012 0.023944 0.023882 0.023825 0.023772 0.023723 0.023679 0.023637 0.0236 0.023565 0.023533 0.023503 0.023476 0.023452 0.02343 0.023409 0.023391 0.023376 0.023363 0.31534 0.29503 0.27281 0.25044 0.22899 0.20901 0.19068 0.17397 0.15882 0.14511 0.1327 0.12148 0.11134 0.10218 0.093936 0.086533 0.079882 0.073894 0.068497 0.063623 0.059228 0.05526 0.051681 0.048468 0.045563 0.042956 0.040612 0.038519 0.036642 0.034958 0.033453 0.032098 0.03089 0.029821 0.02887 0.028024 0.027267 0.026595 0.025989 0.025448 0.024968 0.024534 0.024143 0.023791 0.023473 0.023184 0.022922 0.022685 0.022469 0.022272 0.022092 0.021928 0.021778 0.021641 0.021516 0.0214 0.021295 0.021198 0.021109 0.021028 0.020952 0.020883 0.020819 0.02076 0.020706 0.020656 0.02061 0.020567 0.020528 0.020492 0.020458 0.020428 0.0204 0.020374 0.02035 0.020329 0.020309 0.020291 0.020275 0.020261 0.02025
6 0.064373 0.31749 0.30013 0.28105 0.26146 0.24228 0.22405 0.20695 0.19113 0.17656 0.16317 0.1509 0.13968 0.12944 0.1201 0.11158 0.10384 0.096795 0.090377 0.08453 0.079228 0.074422 0.070064 0.066099 0.062496 0.059201 0.056202 0.053483 0.051007 0.048755 0.046712 0.044843 0.04315 0.041609 0.04023 0.038984 0.03786 0.036858 0.035947 0.035129 0.034398 0.03374 0.03315 0.032616 0.032134 0.031699 0.031302 0.030946 0.030622 0.030328 0.03006 0.029818 0.029597 0.029395 0.029213 0.029047 0.028896 0.028758 0.028631 0.028517 0.028412 0.028315 0.028228 0.028147 0.028072 0.028005 0.027942 0.027885 0.027833 0.027785 0.027741 0.0277 0.027663 0.027629 0.027598 0.02757 0.027544 0.02752 0.027499 0.02748 0.027464 0.02745 0.3143 0.2972 0.27848 0.25927 0...
% Consumption
st_title = ['MEAN(MN_MPC_U_GAIN_CHECK(A,Z)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_c = ff_summ_nd_array(st_title, mn_MPC_U_gain_share_check, true, ["mean"], 4, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_MPC_U_GAIN_CHECK(A,Z)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group savings mean_eta_1 mean_eta_2 mean_eta_3 mean_eta_4 mean_eta_5 mean_eta_6 mean_eta_7 mean_eta_8 mean_eta_9 mean_eta_10 mean_eta_11 mean_eta_12 mean_eta_13 mean_eta_14 mean_eta_15 mean_eta_16 mean_eta_17 mean_eta_18 mean_eta_19 mean_eta_20 mean_eta_21 mean_eta_22 mean_eta_23 mean_eta_24 mean_eta_25 mean_eta_26 mean_eta_27 mean_eta_28 mean_eta_29 mean_eta_30 mean_eta_31 mean_eta_32 mean_eta_33 mean_eta_34 mean_eta_35 mean_eta_36 mean_eta_37 mean_eta_38 mean_eta_39 mean_eta_40 mean_eta_41 mean_eta_42 mean_eta_43 mean_eta_44 mean_eta_45 mean_eta_46 mean_eta_47 mean_eta_48 mean_eta_49 mean_eta_50 mean_eta_51 mean_eta_52 mean_eta_53 mean_eta_54 mean_eta_55 mean_eta_56 mean_eta_57 mean_eta_58 mean_eta_59 mean_eta_60 mean_eta_61 mean_eta_62 mean_eta_63 mean_eta_64 mean_eta_65 mean_eta_66 mean_eta_67 mean_eta_68 mean_eta_69 mean_eta_70 mean_eta_71 mean_eta_72 mean_eta_73 mean_eta_74 mean_eta_75 mean_eta_76 mean_eta_77 mean_eta_78 mean_eta_79 mean_eta_80 mean_eta_81 mean_eta_82 mean_eta_83 mean_eta_84 mean_eta_85 mean_eta_86 mean_eta_87 mean_eta_88 mean_eta_89 mean_eta_90 mean_eta_91 mean_eta_92 mean_eta_93 mean_eta_94 mean_eta_95 mean_eta_96 mean_eta_97 mean_eta_98 mean_eta_99 mean_eta_100 mean_eta_101 mean_eta_102 mean_eta_103 mean_eta_104 mean_eta_105 mean_eta_106 mean_eta_107 mean_eta_108 mean_eta_109 mean_eta_110 mean_eta_111 mean_eta_112 mean_eta_113 mean_eta_114 mean_eta_115 mean_eta_116 mean_eta_117 mean_eta_118 mean_eta_119 mean_eta_120 mean_eta_121 mean_eta_122 mean_eta_123 mean_eta_124 mean_eta_125 mean_eta_126 mean_eta_127 mean_eta_128 mean_eta_129 mean_eta_130 mean_eta_131 mean_eta_132 mean_eta_133 mean_eta_134 mean_eta_135 mean_eta_136 mean_eta_137 mean_eta_138 mean_eta_139 mean_eta_140 mean_eta_141 mean_eta_142 mean_eta_143 mean_eta_144 mean_eta_145 mean_eta_146 mean_eta_147 mean_eta_148 mean_eta_149 mean_eta_150 mean_eta_151 mean_eta_152 mean_eta_153 mean_eta_154 mean_eta_155 mean_eta_156 mean_eta_157 mean_eta_158 mean_eta_159 mean_eta_160 mean_eta_161 mean_eta_162 mean_eta_163 mean_eta_164 mean_eta_165 mean_eta_166 mean_eta_167 mean_eta_168 mean_eta_169 mean_eta_170 mean_eta_171 mean_eta_172 mean_eta_173 mean_eta_174 mean_eta_175 mean_eta_176 mean_eta_177 mean_eta_178 mean_eta_179 mean_eta_180 mean_eta_181 mean_eta_182 mean_eta_183 mean_eta_184 mean_eta_185 mean_eta_186 mean_eta_187 mean_eta_188 mean_eta_189 mean_eta_190 mean_eta_191 mean_eta_192 mean_eta_193 mean_eta_194 mean_eta_195 mean_eta_196 mean_eta_197 mean_eta_198 mean_eta_199 mean_eta_200 mean_eta_201 mean_eta_202 mean_eta_203 mean_eta_204 mean_eta_205 mean_eta_206 mean_eta_207 mean_eta_208 mean_eta_209 mean_eta_210 mean_eta_211 mean_eta_212 mean_eta_213 mean_eta_214 mean_eta_215 mean_eta_216 mean_eta_217 mean_eta_218 mean_eta_219 mean_eta_220 mean_eta_221 mean_eta_222 mean_eta_223 mean_eta_224 mean_eta_225 mean_eta_226 mean_eta_227 mean_eta_228 mean_eta_229 mean_eta_230 mean_eta_231 mean_eta_232 mean_eta_233 mean_eta_234 mean_eta_235 mean_eta_236 mean_eta_237 mean_eta_238 mean_eta_239 mean_eta_240 mean_eta_241 mean_eta_242 mean_eta_243 mean_eta_244 mean_eta_245 mean_eta_246 mean_eta_247 mean_eta_248 mean_eta_249 mean_eta_250 mean_eta_251 mean_eta_252 mean_eta_253 mean_eta_254 mean_eta_255 mean_eta_256 mean_eta_257 mean_eta_258 mean_eta_259 mean_eta_260 mean_eta_261 mean_eta_262 mean_eta_263 mean_eta_264 mean_eta_265 mean_eta_266 mean_eta_267 mean_eta_268 mean_eta_269 mean_eta_270 mean_eta_271 mean_eta_272 mean_eta_273 mean_eta_274 mean_eta_275 mean_eta_276 mean_eta_277 mean_eta_278 mean_eta_279 mean_eta_280 mean_eta_281 mean_eta_282 mean_eta_283 mean_eta_284 mean_eta_285 mean_eta_286 mean_eta_287 mean_eta_288 mean_eta_289 mean_eta_290 mean_eta_291 mean_eta_292 mean_eta_293 mean_eta_294 mean_eta_295 mean_eta_296 mean_eta_297 mean_eta_298 mean_eta_299 mean_eta_300 mean_eta_301 mean_eta_302 mean_eta_303 mean_eta_304 mean_eta_305 mean_eta_306 mean_eta_307 mean_eta_308 mean_eta_309 mean_eta_310 mean_eta_311 mean_eta_312 mean_eta_313 mean_eta_314 mean_eta_315 mean_eta_316 mean_eta_317 mean_eta_318 mean_eta_319 mean_eta_320 mean_eta_321 mean_eta_322 mean_eta_323 mean_eta_324 mean_eta_325 mean_eta_326 mean_eta_327 mean_eta_328 mean_eta_329 mean_eta_330 mean_eta_331 mean_eta_332 mean_eta_333 mean_eta_334 mean_eta_335 mean_eta_336 mean_eta_337 mean_eta_338 mean_eta_339 mean_eta_340 mean_eta_341 mean_eta_342 mean_eta_343 mean_eta_344 mean_eta_345 mean_eta_346 mean_eta_347 mean_eta_348 mean_eta_349 mean_eta_350 mean_eta_351 mean_eta_352 mean_eta_353 mean_eta_354 mean_eta_355 mean_eta_356 mean_eta_357 mean_eta_358 mean_eta_359 mean_eta_360 mean_eta_361 mean_eta_362 mean_eta_363 mean_eta_364 mean_eta_365 mean_eta_366 mean_eta_367 mean_eta_368 mean_eta_369 mean_eta_370 mean_eta_371 mean_eta_372 mean_eta_373 mean_eta_374 mean_eta_375 mean_eta_376 mean_eta_377 mean_eta_378 mean_eta_379 mean_eta_380 mean_eta_381 mean_eta_382 mean_eta_383 mean_eta_384 mean_eta_385 mean_eta_386 mean_eta_387 mean_eta_388 mean_eta_389 mean_eta_390 mean_eta_391 mean_eta_392 mean_eta_393 mean_eta_394 mean_eta_395 mean_eta_396 mean_eta_397 mean_eta_398 mean_eta_399 mean_eta_400 mean_eta_401 mean_eta_402 mean_eta_403 mean_eta_404 mean_eta_405
_____ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________
1 0 0.81458 0.81464 0.81469 0.81468 0.81453 0.81424 0.81382 0.81335 0.8129 0.81254 0.81238 0.81256 0.81324 0.8145 0.81626 0.81821 0.81993 0.82116 0.82186 0.82209 0.82162 0.82012 0.81704 0.8128 0.80645 0.79968 0.7918 0.78398 0.77641 0.76858 0.76071 0.75299 0.7443 0.73635 0.72853 0.72067 0.71195 0.70314 0.69412 0.68504 0.67559 0.66627 0.65674 0.6463 0.63562 0.62498 0.61445 0.60441 0.5942 0.58411 0.57436 0.56484 0.55554 0.54653 0.53836 0.52971 0.52171 0.51365 0.5059 0.49854 0.49069 0.48308 0.4761 0.46937 0.46272 0.45661 0.45052 0.44481 0.43959 0.43463 0.43016 0.42595 0.4221 0.41886 0.41629 0.41368 0.41116 0.4087 0.4065 0.40451 0.40274 0.81458 0.81464 0.81469 0.81468 0.81453 0.81424 0.81382 0.81335 0.8129 0.81254 0.81238 0.81256 0.81324 0.8145 0.81626 0.81821 0.81993 0.82116 0.82186 0.82209 0.82162 0.82012 0.81704 0.8128 0.80645 0.79968 0.79193 0.78407 0.77607 0.7688 0.76105 0.75233 0.74419 0.73635 0.72844 0.7202 0.71163 0.7028 0.69375 0.68475 0.67516 0.66593 0.65625 0.6457 0.63545 0.62492 0.61432 0.60411 0.59358 0.58363 0.57369 0.56403 0.55487 0.54617 0.53752 0.52922 0.52113 0.51309 0.5053 0.49753 0.48998 0.48233 0.47537 0.4686 0.46217 0.4559 0.44973 0.44411 0.43869 0.43399 0.4297 0.42535 0.42167 0.41867 0.41594 0.41344 0.41098 0.40855 0.40627 0.40436 0.40259 0.81104 0.80991 0.80908 0.80861 0.80846 0.80854 0.80876 0.80908 0.80951 0.81014 0.81077 0.81143 0.81204 0.81282 0.81358 0.81435 0.81499 0.81544 0.81548 0.81483 0.81348 0.81099 0.80769 0.80336 0.79781 0.79279 0.78684 0.78042 0.7733 0.76536 0.75687 0.74812 0.73996 0.73171 0.72339 0.71508 0.7064 0.69743 0.68808 0.67863 0.66881 0.65914 0.64934 0.63905 0.62876 0.61812 0.60747 0.59719 0.58667 0.57636 0.5662 0.55628 0.54652 0.53711 0.52837 0.51959 0.51118 0.50282 0.49493 0.4875 0.48021 0.47259 0.46575 0.45941 0.45334 0.44741 0.44218 0.43741 0.43309 0.42907 0.42551 0.42215 0.41926 0.41661 0.41409 0.41169 0.40928 0.40688 0.40498 0.40306 0.40164 0.62729 0.62617 0.62524 0.62457 0.6242 0.62404 0.624 0.62402 0.62405 0.62408 0.62409 0.62408 0.62403 0.6239 0.62369 0.62334 0.62281 0.622 0.62082 0.61914 0.61668 0.61337 0.60898 0.60425 0.59836 0.59282 0.58657 0.5803 0.57383 0.56697 0.5599 0.55246 0.54512 0.53793 0.53085 0.52384 0.5165 0.5091 0.50154 0.49385 0.48566 0.47769 0.46973 0.46152 0.45349 0.44523 0.43708 0.42952 0.42194 0.41486 0.40791 0.40128 0.39497 0.38901 0.3837 0.37845 0.37359 0.36894 0.36484 0.36115 0.35781 0.3543 0.35135 0.3488 0.34626 0.34387 0.3417 0.33981 0.33797 0.33625 0.33462 0.33275 0.33103 0.32945 0.32815 0.32692 0.32569 0.32432 0.32329 0.32233 0.32168 0.56876 0.5679 0.56716 0.56665 0.56638 0.56632 0.56636 0.56646 0.5666 0.56675 0.56693 0.56709 0.56724 0.56732 0.56732 0.56719 0.56688 0.56629 0.56531 0.56382 0.56156 0.55844 0.55424 0.54971 0.54402 0.53868 0.53264 0.5266 0.52036 0.51376 0.50699 0.49989 0.49292 0.48613 0.47945 0.47285 0.46593 0.45895 0.45182 0.44457 0.43688 0.42941 0.42194 0.41422 0.4067 0.39897 0.39136 0.38434 0.3773 0.37077 0.36442 0.35841 0.3527 0.34736 0.34269 0.33809 0.33388 0.3299 0.32646 0.32344 0.32075 0.31788 0.31556 0.31361 0.31166 0.30981 0.30819 0.30684 0.30555 0.30436 0.30325 0.30187 0.30065 0.29953 0.29861 0.29775 0.29693 0.29599 0.29532 0.29471 0.29435
2 0.00051498 0.81458 0.81464 0.81469 0.81468 0.81453 0.81424 0.81382 0.81335 0.8129 0.81254 0.81238 0.81255 0.81323 0.8145 0.81626 0.81821 0.81993 0.82116 0.82186 0.82209 0.82157 0.82011 0.81703 0.81277 0.80641 0.79968 0.7918 0.7839 0.7763 0.76844 0.76061 0.75284 0.74419 0.7363 0.72851 0.72069 0.71192 0.70308 0.69404 0.68494 0.67555 0.6662 0.65671 0.64625 0.63553 0.62492 0.61438 0.6043 0.59409 0.58401 0.57424 0.56475 0.55548 0.54649 0.53833 0.52968 0.52167 0.51362 0.50587 0.49851 0.49065 0.48304 0.47609 0.46936 0.46271 0.45661 0.45052 0.4448 0.43958 0.43463 0.43016 0.42593 0.42209 0.41885 0.41629 0.41368 0.41115 0.4087 0.4065 0.4045 0.40274 0.81458 0.81464 0.81469 0.81468 0.81453 0.81424 0.81382 0.81335 0.8129 0.81254 0.81238 0.81255 0.81323 0.8145 0.81626 0.81821 0.81993 0.82116 0.82186 0.82209 0.82157 0.82011 0.81703 0.81277 0.80641 0.79968 0.79193 0.78399 0.77595 0.76867 0.76091 0.75218 0.74408 0.73631 0.72842 0.72022 0.7116 0.70275 0.69368 0.68464 0.67512 0.66586 0.65621 0.64565 0.63537 0.62486 0.61426 0.60399 0.59348 0.58354 0.57357 0.56395 0.55482 0.54612 0.53748 0.52919 0.52108 0.51305 0.50526 0.4975 0.48993 0.4823 0.47535 0.46859 0.46216 0.45588 0.44972 0.4441 0.43868 0.43397 0.42969 0.42534 0.42166 0.41867 0.41594 0.41343 0.41097 0.40855 0.40627 0.40436 0.40259 0.81075 0.80959 0.80875 0.80828 0.80813 0.8082 0.80842 0.80874 0.80915 0.80978 0.81039 0.81106 0.81164 0.81243 0.81319 0.81398 0.81465 0.81507 0.81507 0.81446 0.8131 0.81066 0.80739 0.803 0.79744 0.79239 0.78644 0.77997 0.77288 0.76496 0.75651 0.74768 0.73951 0.73141 0.72316 0.71492 0.70619 0.69721 0.68786 0.67839 0.66866 0.65894 0.64918 0.63888 0.62855 0.61792 0.60727 0.59696 0.58649 0.57621 0.566 0.55608 0.54634 0.53696 0.52823 0.51948 0.51105 0.50269 0.49479 0.48737 0.48005 0.47244 0.46561 0.45927 0.45322 0.44729 0.44208 0.4373 0.43299 0.42896 0.4254 0.42205 0.41916 0.41651 0.414 0.41159 0.40919 0.40679 0.40489 0.40297 0.40156 0.62723 0.62612 0.62519 0.62452 0.62415 0.62399 0.62395 0.62397 0.62401 0.62403 0.62404 0.62403 0.62398 0.62385 0.62364 0.6233 0.62277 0.62196 0.62078 0.61909 0.61658 0.61331 0.60893 0.60418 0.59828 0.59278 0.58653 0.58018 0.57367 0.56679 0.55974 0.55227 0.54497 0.53784 0.53079 0.52383 0.51644 0.50901 0.50143 0.4937 0.48559 0.47759 0.46966 0.46144 0.45338 0.44515 0.43698 0.42937 0.42181 0.41473 0.40776 0.40117 0.39488 0.38894 0.38364 0.3784 0.37351 0.36889 0.36478 0.3611 0.35775 0.35423 0.3513 0.34876 0.34622 0.34384 0.34166 0.33978 0.33794 0.33622 0.33459 0.33271 0.33099 0.32942 0.32812 0.32689 0.32565 0.32429 0.32326 0.3223 0.32166 0.56876 0.5679 0.56716 0.56664 0.56638 0.56631 0.56636 0.56646 0.56659 0.56675 0.56692 0.56709 0.56723 0.56731 0.56731 0.56719 0.56688 0.56629 0.56531 0.56381 0.56151 0.55843 0.55424 0.54968 0.54398 0.53868 0.53264 0.52652 0.52024 0.51362 0.50687 0.49974 0.49281 0.48608 0.47943 0.47288 0.4659 0.4589 0.45174 0.44447 0.43683 0.42934 0.42191 0.41417 0.40662 0.39892 0.39129 0.38423 0.37721 0.37067 0.3643 0.35832 0.35265 0.34732 0.34266 0.33807 0.33384 0.32988 0.32643 0.32341 0.32072 0.31785 0.31555 0.3136 0.31165 0.30981 0.30818 0.30683 0.30554 0.30435 0.30324 0.30186 0.30065 0.29953 0.29861 0.29775 0.29693 0.29598 0.29531 0.29471 0.29435
3 0.0041199 0.81443 0.81422 0.81395 0.81376 0.81357 0.81328 0.8129 0.81248 0.81207 0.81176 0.81167 0.81191 0.81265 0.81395 0.81575 0.81771 0.81944 0.82069 0.82141 0.82155 0.82103 0.81934 0.81629 0.81181 0.80546 0.79852 0.79065 0.78277 0.77519 0.76742 0.75976 0.75219 0.7437 0.73584 0.72826 0.7203 0.7114 0.70232 0.69336 0.68434 0.67497 0.66564 0.65596 0.64564 0.63475 0.62413 0.61349 0.60309 0.59314 0.58307 0.57333 0.56379 0.55479 0.54608 0.53785 0.52945 0.52129 0.51338 0.50566 0.49829 0.49031 0.48284 0.47598 0.46916 0.46264 0.45652 0.45043 0.44468 0.43944 0.43454 0.43007 0.42579 0.42198 0.41877 0.41622 0.41361 0.41105 0.40863 0.40642 0.40444 0.40269 0.81443 0.81422 0.81395 0.81376 0.81357 0.81328 0.8129 0.81248 0.81207 0.81176 0.81167 0.81191 0.81265 0.81395 0.81575 0.81771 0.81944 0.82069 0.82141 0.82155 0.82103 0.81934 0.81629 0.81181 0.80546 0.79852 0.79078 0.78281 0.77483 0.76782 0.75984 0.75156 0.74362 0.73586 0.72812 0.71978 0.71108 0.70201 0.69306 0.684 0.67453 0.66529 0.65537 0.64501 0.6346 0.62405 0.61333 0.60279 0.59253 0.5826 0.57275 0.56304 0.55414 0.54569 0.53703 0.52895 0.52066 0.51272 0.505 0.49728 0.48958 0.48212 0.47522 0.46839 0.46202 0.45573 0.44957 0.44396 0.43861 0.43382 0.42954 0.42515 0.42153 0.4186 0.41587 0.41337 0.41089 0.40848 0.40621 0.40429 0.40256 0.80795 0.80644 0.80525 0.80457 0.80433 0.80437 0.80457 0.80489 0.80533 0.80596 0.80662 0.80732 0.80797 0.80875 0.80956 0.81043 0.81117 0.81162 0.81153 0.81094 0.80973 0.80733 0.80413 0.79957 0.794 0.78845 0.78218 0.77573 0.76893 0.76146 0.75322 0.74463 0.7361 0.72799 0.7204 0.7123 0.70364 0.69456 0.68548 0.67613 0.66644 0.65683 0.64689 0.63671 0.62607 0.61539 0.60455 0.59406 0.58402 0.574 0.5638 0.55383 0.5444 0.5354 0.52668 0.51826 0.50958 0.50122 0.49343 0.48598 0.47851 0.47112 0.46433 0.45794 0.45195 0.4461 0.44097 0.43614 0.43187 0.42779 0.42428 0.42097 0.41809 0.41548 0.41297 0.41055 0.40809 0.40575 0.40386 0.40194 0.40055 0.6267 0.62533 0.62408 0.62324 0.62283 0.62268 0.62268 0.62275 0.62283 0.62292 0.623 0.62306 0.62306 0.62298 0.6228 0.62248 0.62196 0.62118 0.62001 0.61824 0.61574 0.61224 0.60788 0.60292 0.59703 0.59132 0.58508 0.57876 0.57228 0.56552 0.55852 0.55136 0.54421 0.53712 0.53025 0.52317 0.51569 0.50801 0.50055 0.49282 0.48473 0.47674 0.46867 0.46061 0.45236 0.44409 0.4358 0.42794 0.42064 0.41356 0.40665 0.40001 0.39397 0.3883 0.38294 0.37797 0.37288 0.36841 0.36436 0.36066 0.35721 0.35384 0.351 0.34837 0.3459 0.34355 0.34138 0.33947 0.33765 0.3359 0.33428 0.33238 0.3307 0.32915 0.32786 0.32664 0.32538 0.32404 0.32302 0.32206 0.32142 0.56859 0.56746 0.5664 0.56571 0.5654 0.56534 0.56542 0.56557 0.56575 0.56596 0.5662 0.56643 0.56663 0.56676 0.56679 0.56668 0.56638 0.56581 0.56484 0.56326 0.56096 0.55765 0.55348 0.54872 0.54302 0.53751 0.53148 0.52537 0.51912 0.51263 0.50592 0.4991 0.49232 0.48562 0.47916 0.47248 0.46542 0.45816 0.45113 0.44386 0.43625 0.42877 0.42119 0.4136 0.40586 0.39812 0.39037 0.38305 0.37628 0.36975 0.36343 0.35741 0.35198 0.34692 0.3422 0.33787 0.33345 0.32963 0.32624 0.32321 0.32041 0.31768 0.31547 0.31343 0.31154 0.30973 0.30811 0.30674 0.30546 0.30425 0.30314 0.30174 0.30056 0.29947 0.29855 0.2977 0.29685 0.29592 0.29526 0.29465 0.2943
4 0.013905 0.74362 0.73968 0.73889 0.73872 0.73889 0.73924 0.73979 0.7407 0.74217 0.74431 0.74706 0.75037 0.75429 0.75893 0.76404 0.76913 0.77373 0.77755 0.78069 0.78323 0.78499 0.78546 0.78507 0.78267 0.77886 0.7736 0.76775 0.76141 0.75513 0.74838 0.74201 0.73481 0.7273 0.71983 0.7123 0.70429 0.69566 0.6869 0.67761 0.66851 0.65945 0.65057 0.64147 0.63154 0.62099 0.61059 0.60042 0.59065 0.5811 0.57167 0.56222 0.55342 0.54493 0.53664 0.52875 0.52043 0.51284 0.50518 0.4976 0.49043 0.48243 0.47507 0.46819 0.46139 0.45504 0.44904 0.44281 0.4371 0.43185 0.427 0.42239 0.4181 0.41441 0.4113 0.40877 0.40617 0.40355 0.40118 0.39896 0.39701 0.39527 0.74362 0.73968 0.73889 0.73872 0.73889 0.73924 0.73979 0.7407 0.74217 0.74431 0.74706 0.75037 0.75429 0.75893 0.76404 0.76913 0.77373 0.77755 0.78069 0.78323 0.78499 0.78546 0.78507 0.78267 0.77886 0.77373 0.76788 0.7612 0.75484 0.74898 0.74169 0.73443 0.72718 0.71984 0.71199 0.70372 0.69536 0.68666 0.67735 0.66808 0.65909 0.65025 0.6408 0.63088 0.62075 0.6104 0.60016 0.59035 0.58054 0.57103 0.56161 0.55283 0.54437 0.53615 0.52796 0.51998 0.51221 0.50444 0.49691 0.48942 0.48168 0.47442 0.4675 0.46053 0.45434 0.44805 0.44203 0.43627 0.43106 0.42623 0.42186 0.41744 0.41392 0.41105 0.40846 0.40595 0.40344 0.40103 0.39877 0.39684 0.39516 0.72802 0.72264 0.7209 0.72026 0.7204 0.7211 0.72226 0.72394 0.72634 0.7293 0.73267 0.73634 0.74027 0.74441 0.74865 0.75263 0.75623 0.75923 0.76169 0.76386 0.76504 0.76533 0.76484 0.76275 0.75995 0.75616 0.75181 0.74682 0.7412 0.73497 0.72796 0.72006 0.71164 0.70325 0.6948 0.68639 0.67797 0.66926 0.66026 0.65124 0.6423 0.63353 0.62452 0.61503 0.60502 0.59473 0.58461 0.57495 0.56557 0.55637 0.54705 0.53839 0.52978 0.5213 0.513 0.50468 0.4964 0.48846 0.48088 0.4735 0.4661 0.45902 0.45228 0.44606 0.44018 0.43453 0.42936 0.4247 0.42051 0.41639 0.41285 0.40968 0.40681 0.40432 0.40183 0.39933 0.39686 0.39456 0.39269 0.3908 0.38946 0.55492 0.54982 0.54806 0.54726 0.5472 0.5477 0.54865 0.55006 0.55203 0.55457 0.55749 0.56063 0.56384 0.5671 0.57025 0.57307 0.57542 0.57723 0.57848 0.5791 0.57889 0.57754 0.57585 0.57298 0.56964 0.56562 0.5614 0.55664 0.55141 0.54581 0.53985 0.53355 0.527 0.52039 0.51356 0.50651 0.49936 0.49196 0.48415 0.47631 0.46857 0.46103 0.45352 0.44585 0.43791 0.42984 0.42205 0.41486 0.40805 0.4015 0.395 0.38919 0.38358 0.37825 0.37325 0.36839 0.36381 0.35961 0.35574 0.35228 0.34876 0.34561 0.34277 0.34008 0.33774 0.33552 0.33334 0.33143 0.32963 0.32782 0.32617 0.32427 0.32265 0.32118 0.31993 0.31871 0.31744 0.3161 0.31512 0.31415 0.31353 0.49773 0.49288 0.4913 0.49063 0.49068 0.49125 0.49227 0.49374 0.4958 0.49846 0.50154 0.50484 0.50823 0.51168 0.51503 0.51806 0.52063 0.52263 0.52407 0.5249 0.52488 0.52372 0.52222 0.51954 0.51638 0.51255 0.50854 0.50397 0.49897 0.49362 0.48796 0.48198 0.47581 0.46959 0.46316 0.45652 0.44978 0.4428 0.43542 0.42805 0.42078 0.41374 0.40671 0.39952 0.39208 0.38454 0.37729 0.37063 0.36434 0.35834 0.35242 0.34722 0.34223 0.3375 0.33313 0.32892 0.32499 0.32144 0.31824 0.31543 0.31256 0.31004 0.30781 0.30571 0.30393 0.30225 0.30062 0.29926 0.29799 0.29671 0.29558 0.29416 0.29304 0.29201 0.29112 0.29029 0.28941 0.28848 0.28785 0.28723 0.2869
5 0.032959 0.66095 0.66107 0.6621 0.66413 0.66676 0.66957 0.6723 0.67482 0.67706 0.679 0.68067 0.68216 0.68372 0.68559 0.6879 0.69058 0.69337 0.69601 0.6983 0.70037 0.70149 0.70264 0.70211 0.7011 0.69828 0.6943 0.68945 0.68452 0.67944 0.67449 0.66944 0.66329 0.6573 0.65162 0.64608 0.64006 0.6339 0.6277 0.62131 0.6149 0.60843 0.60191 0.59484 0.58701 0.57918 0.57171 0.56443 0.55722 0.54952 0.54176 0.53417 0.52644 0.51909 0.51172 0.50467 0.49729 0.49039 0.48346 0.47639 0.46897 0.46171 0.45453 0.44754 0.44106 0.43474 0.42878 0.42277 0.41688 0.41152 0.40666 0.40192 0.39786 0.39427 0.39141 0.38886 0.38634 0.38362 0.38133 0.37913 0.37725 0.37552 0.66095 0.66107 0.6621 0.66413 0.66676 0.66957 0.6723 0.67482 0.67706 0.679 0.68067 0.68216 0.68372 0.68559 0.6879 0.69058 0.69337 0.69601 0.6983 0.70037 0.70149 0.70264 0.70211 0.7011 0.69833 0.69443 0.68924 0.68416 0.67995 0.67457 0.6689 0.6631 0.65733 0.65149 0.64557 0.63965 0.63359 0.62738 0.62091 0.61432 0.60798 0.60139 0.59414 0.58652 0.57895 0.57145 0.56405 0.55648 0.54885 0.54116 0.5333 0.5259 0.51865 0.51097 0.50389 0.49667 0.48967 0.48282 0.4754 0.46805 0.46098 0.45368 0.44652 0.44011 0.43402 0.4278 0.42157 0.41583 0.41075 0.40591 0.40117 0.39722 0.39373 0.39112 0.38857 0.38618 0.38338 0.38115 0.37896 0.37702 0.37538 0.62858 0.62733 0.62756 0.62937 0.63228 0.6358 0.6395 0.64305 0.64628 0.64937 0.65205 0.65432 0.65624 0.65791 0.65946 0.66113 0.66289 0.66489 0.6667 0.66821 0.66894 0.66956 0.66898 0.66855 0.66699 0.66462 0.66155 0.65786 0.65383 0.64926 0.64394 0.63787 0.63149 0.62501 0.61862 0.61227 0.60591 0.59967 0.59348 0.58742 0.58133 0.57493 0.56805 0.56069 0.55331 0.54616 0.53931 0.53244 0.52539 0.51821 0.51092 0.50348 0.49608 0.48846 0.48121 0.47387 0.46673 0.45965 0.45268 0.44569 0.43892 0.43241 0.4261 0.42025 0.41453 0.40906 0.40426 0.39957 0.39527 0.39135 0.3878 0.38495 0.38211 0.37985 0.37725 0.37482 0.37219 0.37007 0.36824 0.3664 0.36513 0.47036 0.46934 0.46941 0.47083 0.47325 0.47623 0.47938 0.48241 0.48516 0.48753 0.4894 0.49075 0.49161 0.49213 0.49249 0.49291 0.49348 0.49411 0.49451 0.49466 0.49382 0.49317 0.49135 0.48988 0.48754 0.48482 0.48162 0.47814 0.47447 0.4704 0.4658 0.46079 0.45569 0.45072 0.44593 0.44117 0.43633 0.43144 0.42643 0.42138 0.41627 0.41108 0.40571 0.40017 0.39478 0.38962 0.38475 0.38003 0.37527 0.37051 0.3658 0.36115 0.35668 0.35216 0.34811 0.34403 0.34029 0.33675 0.33331 0.32995 0.32685 0.32394 0.32118 0.31886 0.31661 0.31438 0.3124 0.3104 0.30853 0.30672 0.30478 0.3032 0.30161 0.30034 0.29906 0.2979 0.29645 0.29533 0.29434 0.29339 0.29279 0.41495 0.41416 0.41439 0.41593 0.41844 0.42148 0.42468 0.42776 0.4306 0.43306 0.43505 0.43654 0.43756 0.43825 0.4388 0.43941 0.44018 0.44099 0.44159 0.44194 0.44128 0.44082 0.43917 0.43787 0.43571 0.43316 0.43015 0.42687 0.42341 0.41959 0.41527 0.41058 0.40585 0.40127 0.39688 0.39252 0.38808 0.38362 0.37907 0.37447 0.36985 0.36513 0.36022 0.35516 0.35026 0.34561 0.34128 0.33708 0.33283 0.32861 0.32448 0.32043 0.31656 0.31263 0.30921 0.30578 0.30268 0.29978 0.297 0.29428 0.29181 0.28951 0.28734 0.28558 0.28387 0.28218 0.28077 0.27931 0.27797 0.2767 0.27524 0.27415 0.27304 0.27217 0.27125 0.27047 0.26942 0.26867 0.26804 0.26744 0.26712
6 0.064373 0.56543 0.56521 0.56569 0.56689 0.56869 0.57092 0.57354 0.57643 0.57956 0.58297 0.58676 0.59109 0.59604 0.60164 0.6077 0.61387 0.61945 0.62392 0.62725 0.63005 0.63241 0.63438 0.6349 0.63391 0.63103 0.6267 0.62193 0.61666 0.61251 0.60806 0.60312 0.59798 0.5931 0.58836 0.58347 0.57863 0.57394 0.569 0.56386 0.55889 0.55404 0.54924 0.54368 0.53775 0.53176 0.52573 0.51967 0.51332 0.5068 0.50001 0.4932 0.48646 0.47991 0.47349 0.46681 0.46047 0.45384 0.44721 0.44062 0.4335 0.42684 0.42027 0.41355 0.40703 0.40104 0.39477 0.38872 0.38302 0.37763 0.37268 0.36832 0.36439 0.36102 0.35841 0.35585 0.35326 0.35061 0.34841 0.34614 0.3443 0.34264 0.56452 0.56409 0.56442 0.56572 ...Graph Mean Values:
st_title = ['MEAN(MN\_V\_U\_GAIN\_CHECK(A,Z)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_V\_U\_GAIN\_CHECK(a,z))'};
ff_graph_grid((tb_az_v{1:end, 3:end})', ar_st_eta_HS_grid, agrid, mp_support_graph);
Graph Mean Consumption (MPC: Share of Check Consumed):
st_title = ['MEAN(MN\_MPC\_U\_GAIN\_CHECK(A,Z)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_MPC\_U\_GAIN\_CHECK(a,z))'};
ff_graph_grid((tb_az_c{1:end, 3:end})', ar_st_eta_HS_grid, agrid, mp_support_graph);
1.5 Analyze Marginal Value and MPC over Y(a,eta), Conditional On Kids, Marry, Age, Education
Income is generated by savings and shocks, what are the income levels generated by all the shock and savings points conditional on kids, marital status, age and educational levels. Plot on the Y axis MPC, and plot on the X axis income levels, use colors to first distinguish between different a levels, then use colors to distinguish between different eta levles.
Set Up date, Select Age 37vn
, unmarried, no kids, lower education:
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
% 38 year old, unmarried, no kids, lower educated
% Only Household Head Shock Matters so select up to 'n_eta_H_grid'
mn_total_inc_jemk = total_inc_VFI(19,:,1:mp_params('n_eta_H_grid'),1,1,1);
mn_V_W_gain_check_use = ev19_jaeemk_check2 - ev19_jaeemk_check0;
mn_C_W_gain_check_use = ec19_jaeemk_check2 - ec19_jaeemk_check0;Select Age, Education, Marital, Kids Count:s
% Selections
it_age = 21; % +18
it_marital = 1; % 1 = unmarried
it_kids = 1; % 1 = kids is zero
it_educ = 1; % 1 = lower education
% Select: NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
mn_C_W_gain_check_jemk = mn_C_W_gain_check_use(it_age, :, 1:mp_params('n_eta_H_grid'), it_educ, it_marital, it_kids);
mn_V_W_gain_check_jemk = mn_V_W_gain_check_use(it_age, :, 1:mp_params('n_eta_H_grid'), it_educ, it_marital, it_kids);
% Reshape, so shock is the first dim, a is the second
mt_total_inc_jemk = permute(mn_total_inc_jemk,[3,2,1]);
mt_C_W_gain_check_jemk = permute(mn_C_W_gain_check_jemk,[3,2,1]);
mt_C_W_gain_check_jemk(mt_C_W_gain_check_jemk<=1e-10) = 1e-10;
mt_V_W_gain_check_jemk = permute(mn_V_W_gain_check_jemk,[3,2,1]);
mt_V_W_gain_check_jemk(mt_V_W_gain_check_jemk<=1e-10) = 1e-10;
% Generate meshed a and shock grid
[mt_eta_H, mt_a] = ndgrid(eta_H_grid(1:mp_params('n_eta_H_grid')), agrid);1.6 Marginal Value Gains, Color as Shock, Conditional on Age, Marital, Kids, and Education
How do shocks and a impact marginal value. First plot one asset level, variation comes only from increasingly higher shocks:
figure();
it_a = 1;
scatter((mt_total_inc_jemk(:,it_a)), (mt_V_W_gain_check_jemk(:,it_a)), 100);
title({'MN\_V\_W\_GAIN\_CHECK(Y(A, eta)), Lowest A, J38M0E0K0', ...
'Each Circle is A Different Shock Level'});
xlabel('Y(a,eta) = Spouse + Household head Income Joint');
ylabel('MN\_V\_W\_GAIN\_CHECK(A, eta)');
grid on;
grid minor;
figure();
it_shock = 1;
scatter(log(mt_total_inc_jemk(:,it_a)), log(mt_V_W_gain_check_jemk(:,it_a)), 100);
title({'MN\_V\_W\_GAIN\_CHECK(Y(A, eta)), Lowest A, J38M0E0K0', ...
'Each Circle is A Different Shock Level'});
xlabel(' of Y(a,eta) = Spouse + Household head Income Joint');
ylabel(' of MN\_V\_W\_GAIN\_CHECK(A, eta)');
grid on;
grid minor;
Plot all asset levels:
figure();
scatter((mt_total_inc_jemk(:)), (mt_V_W_gain_check_jemk(:)), 100, mt_a(:));
title({'(MN\_V\_W\_GAIN\_CHECK(Y,eta)), All A (Savings) Levels, J38M0E0K0', ...
'Color Represent different A Savings State, Circle-Group=Shock'});
xlabel('income(a,eps)');
ylabel('MN\_V\_W\_GAIN\_CHECK(EM,J)');
grid on;
grid minor;
figure();
scatter((mt_total_inc_jemk(:)), log(mt_V_W_gain_check_jemk(:)), 100, mt_a(:));
title({'(MN\_V\_W\_GAIN\_CHECK(Y,eta)), All A (Savings) Levels, J38M0E0K0', ...
'Color Represent different A Savings State, Circle-Group=Shock'});
xlabel('income(a,eps)');
ylabel('log of (MN\_V\_W\_GAIN\_CHECK(EM,J))');
xlim([0,7]);
grid on;
grid minor;
1.7 Marginal Consumption Gains, Color as Shock, Conditional on Age, Marital, Kids, and Education
How do shocks and a impact marginal value. First plot one asset level, variation comes only from increasingly higher shocks:
figure();
it_a = 50;
scatter(log(mt_total_inc_jemk(:,it_a)), mt_C_W_gain_check_jemk(:,it_a), 100);
title({'MN\_C\_W\_GAIN\_CHECK(Y(A, eta)), Given A Savings Level, J38M0E0K0', ...
'Each Circle is A Different shock Level'});
xlabel('Y(a,eta) = Spouse + Household head Income Joint');
ylabel('MN\_C\_W\_GAIN\_CHECK(A, eta)');
grid on;
grid minor;
Plot all asset levels:
figure();
scatter((mt_total_inc_jemk(:)), (mt_C_W_gain_check_jemk(:)), 100, mt_a(:));
title({'(MN\_C\_W\_GAIN\_CHECK(Y,eta)), All A (Savings) Levels, J38M0E0K0', ...
'Color Represent different A Savings State, Circle-Group=Shock'});
xlabel('income(a,eps)');
ylabel('MN\_C\_W\_GAIN\_CHECK(EM,J)');
grid on;
grid minor;
figure();
scatter(log(mt_total_inc_jemk(:)), log(mt_C_W_gain_check_jemk(:)), 100, mt_a(:));
title({'(MN\_C\_W\_GAIN\_CHECK(Y,eta)), All A (Savings) Levels, J38M0E0K0', ...
'Color Represent different A Savings State, Circle-Group=Shock'});
xlabel('log of income(a,eps)');
ylabel('log of (MN\_V\_W\_GAIN\_CHECK(EM,J))');
grid on;
grid minor;
1.8 Analyze Kids and Marriage and Age
Aggregating over education, savings, and shocks, what are the differential effects of Marriage and Age.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
ar_row_grid = [...
"k0M0", "K1M0", "K2M0", "K3M0", "K4M0", ...
"k0M1", "K1M1", "K2M1", "K3M1", "K4M1"];
mp_support_graph('cl_st_xtitle') = {'Age'};
mp_support_graph('st_legend_loc') = 'best';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('st_rounding') = '6.2f'; % format shock legend
mp_support_graph('cl_scatter_shapes') = {...
'o', 'd' ,'s', 'x', '*', ...
'o', 'd', 's', 'x', '*'};
mp_support_graph('cl_colors') = {...
'red', 'red', 'red', 'red', 'red'...
'blue', 'blue', 'blue', 'blue', 'blue'};MEAN(VAL(KM,J)), MEAN(AP(KM,J)), MEAN(C(KM,J))
Tabulate value and policies:
% Set
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
ar_permute = [2,3,4,1,6,5];
% Value Function
st_title = ['MEAN(MN_V_U_GAIN_CHECK(KM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_v = ff_summ_nd_array(st_title, mn_V_U_gain_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_V_U_GAIN_CHECK(KM,J)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group kids marry mean_age_18 mean_age_19 mean_age_20 mean_age_21 mean_age_22 mean_age_23 mean_age_24 mean_age_25 mean_age_26 mean_age_27 mean_age_28 mean_age_29 mean_age_30 mean_age_31 mean_age_32 mean_age_33 mean_age_34 mean_age_35 mean_age_36 mean_age_37 mean_age_38 mean_age_39 mean_age_40 mean_age_41 mean_age_42 mean_age_43 mean_age_44 mean_age_45 mean_age_46 mean_age_47 mean_age_48 mean_age_49 mean_age_50 mean_age_51 mean_age_52 mean_age_53 mean_age_54 mean_age_55 mean_age_56 mean_age_57 mean_age_58 mean_age_59 mean_age_60 mean_age_61 mean_age_62 mean_age_63 mean_age_64 mean_age_65 mean_age_66 mean_age_67 mean_age_68 mean_age_69 mean_age_70 mean_age_71 mean_age_72 mean_age_73 mean_age_74 mean_age_75 mean_age_76 mean_age_77 mean_age_78 mean_age_79 mean_age_80 mean_age_81 mean_age_82 mean_age_83 mean_age_84 mean_age_85 mean_age_86 mean_age_87 mean_age_88 mean_age_89 mean_age_90 mean_age_91 mean_age_92 mean_age_93 mean_age_94 mean_age_95 mean_age_96 mean_age_97 mean_age_98 mean_age_99
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________
1 1 0 0.032313 0.031538 0.029203 0.026718 0.024639 0.022886 0.021399 0.020131 0.019042 0.018107 0.017297 0.016595 0.015946 0.015418 0.014957 0.014557 0.01421 0.013907 0.013646 0.01342 0.013227 0.013063 0.01291 0.012795 0.012701 0.012626 0.012568 0.012526 0.012499 0.012487 0.012486 0.012494 0.012539 0.012568 0.012606 0.012653 0.012707 0.012769 0.012837 0.012912 0.012991 0.013074 0.013351 0.013445 0.013527 0.01356 0.0080537 0.012192 0.012095 0.011996 0.011895 0.011791 0.011686 0.011579 0.01147 0.011357 0.01124 0.01112 0.010996 0.01087 0.010742 0.010613 0.010481 0.010352 0.010223 0.01009 0.0099585 0.0098177 0.0096806 0.009556 0.0094572 0.0093824 0.0093193 0.0092633 0.0092274 0.0091886 0.0091543 0.0090898 0.0090195 0.0088951 0.0086505 0.0081801
2 2 0 0.044673 0.043637 0.04037 0.036851 0.033899 0.0314 0.029272 0.027449 0.025875 0.024514 0.023328 0.022293 0.021329 0.020537 0.019836 0.019222 0.01868 0.018201 0.01778 0.017407 0.017081 0.016795 0.016524 0.016306 0.016118 0.015957 0.015818 0.015703 0.015608 0.015534 0.015475 0.015431 0.015438 0.015426 0.015427 0.015441 0.015468 0.015509 0.015561 0.015626 0.015702 0.01579 0.016145 0.016206 0.016204 0.016368 0.0090549 0.014163 0.014035 0.013907 0.013776 0.013642 0.013506 0.013371 0.013235 0.013101 0.012966 0.012829 0.01269 0.012552 0.012412 0.01227 0.012116 0.011954 0.011798 0.01165 0.011513 0.011391 0.011272 0.011163 0.011071 0.010972 0.010887 0.010801 0.010733 0.010653 0.010607 0.010524 0.010442 0.010339 0.010206 0.0098274
3 3 0 0.052552 0.051453 0.047283 0.043234 0.03984 0.036967 0.034521 0.032424 0.030614 0.029049 0.027686 0.026496 0.025383 0.024471 0.023664 0.022956 0.022332 0.021779 0.021293 0.020862 0.020485 0.020154 0.019838 0.019586 0.019367 0.01918 0.019018 0.018884 0.018774 0.018689 0.018622 0.018571 0.018585 0.018574 0.018579 0.018601 0.01864 0.018696 0.018769 0.018861 0.018969 0.019092 0.019551 0.019641 0.019649 0.019733 0.010488 0.016871 0.016735 0.016602 0.016467 0.016332 0.016199 0.016068 0.015935 0.015798 0.01566 0.01552 0.015376 0.015228 0.015077 0.014925 0.014777 0.014642 0.014523 0.014409 0.0143 0.014202 0.014098 0.013985 0.013874 0.013776 0.013683 0.013601 0.013517 0.013428 0.013351 0.013293 0.013279 0.013266 0.013174 0.012794
4 4 0 0.059787 0.058589 0.053843 0.049266 0.045426 0.042177 0.039411 0.03704 0.034993 0.033223 0.031681 0.030333 0.029073 0.02804 0.027126 0.026325 0.025618 0.024992 0.024441 0.023953 0.023525 0.02315 0.022791 0.022506 0.022259 0.022047 0.021865 0.021715 0.021593 0.021499 0.021426 0.021373 0.021395 0.02139 0.021403 0.021437 0.021492 0.021568 0.021665 0.021783 0.02192 0.022073 0.022615 0.022727 0.022715 0.022717 0.011905 0.019435 0.019287 0.01914 0.018994 0.018849 0.018704 0.018562 0.01842 0.018276 0.018132 0.017987 0.017842 0.017694 0.017543 0.017397 0.017262 0.017129 0.017007 0.016891 0.016787 0.016702 0.016611 0.016541 0.016478 0.016385 0.016293 0.016214 0.016127 0.016046 0.015944 0.015859 0.015808 0.015749 0.015661 0.015236
5 5 0 0.06554 0.064322 0.059162 0.054211 0.05006 0.046547 0.043558 0.040997 0.038787 0.036878 0.035214 0.033763 0.032402 0.031292 0.03031 0.02945 0.028693 0.028022 0.027434 0.026914 0.02646 0.026062 0.025681 0.02538 0.025122 0.024901 0.024713 0.024561 0.024439 0.024348 0.02428 0.024235 0.024274 0.024282 0.024311 0.024364 0.02444 0.024539 0.024661 0.024805 0.024967 0.025141 0.025749 0.025883 0.025864 0.025777 0.013513 0.022202 0.02205 0.021894 0.021737 0.02158 0.021425 0.021266 0.021104 0.02094 0.020773 0.020603 0.020432 0.020258 0.02008 0.019909 0.01975 0.01961 0.019484 0.01937 0.019257 0.01915 0.019051 0.018981 0.018906 0.018853 0.018799 0.018707 0.018622 0.01854 0.018451 0.018342 0.018236 0.018128 0.01789 0.017352
6 1 1 0.0059295 0.0054975 0.0049865 0.0045164 0.0041185 0.0037811 0.0034951 0.0032509 0.0030403 0.0028619 0.0027092 0.0025777 0.0024592 0.0023631 0.0022806 0.0022118 0.0021541 0.0021056 0.0020667 0.0020356 0.0020124 0.0019962 0.0019842 0.0019802 0.0019819 0.001989 0.0020009 0.0020181 0.0020403 0.0020677 0.0020994 0.0021356 0.0021806 0.0022275 0.002279 0.0023358 0.0023991 0.0024692 0.0025469 0.002633 0.0027287 0.0028361 0.0029924 0.0031403 0.0033133 0.0035382 0.0033559 0.0048868 0.0049555 0.0050249 0.0050932 0.0051566 0.0052239 0.0052821 0.0053325 0.0053772 0.0054178 0.0054656 0.0055025 0.0055417 0.0055661 0.0055714 0.0055766 0.0055848 0.0055866 0.0055828 0.0055909 0.0055897 0.0055802 0.0055799 0.0055846 0.005587 0.005584 0.0055785 0.0055686 0.0055465 0.005534 0.0055121 0.0054756 0.0054111 0.0052785 0.0048759
7 2 1 0.0083787 0.0077803 0.0070563 0.0063837 0.0058177 0.0053346 0.0049215 0.0045653 0.0042594 0.0039969 0.0037701 0.0035738 0.0033959 0.0032499 0.003123 0.003015 0.0029223 0.0028429 0.0027764 0.0027207 0.002676 0.0026407 0.0026111 0.0025923 0.0025812 0.0025771 0.0025792 0.0025882 0.0026035 0.0026254 0.0026529 0.0026857 0.0027299 0.0027761 0.0028278 0.0028857 0.0029506 0.0030234 0.0031047 0.0031958 0.0032977 0.0034116 0.0035844 0.0037396 0.0039252 0.0041555 0.0037261 0.005356 0.005427 0.0054979 0.0055671 0.0056372 0.0057036 0.005765 0.0058187 0.0058658 0.0059104 0.0059542 0.0059937 0.0060268 0.0060574 0.0060756 0.0060853 0.0060948 0.0061094 0.0061181 0.0061275 0.0061318 0.0061352 0.0061383 0.0061336 0.0061265 0.0061148 0.0060998 0.0060923 0.0060826 0.0060857 0.0060774 0.0060512 0.0059971 0.0058397 0.00546
8 3 1 0.010146 0.0094404 0.0085806 0.0077709 0.0070881 0.0065113 0.0060182 0.0055932 0.0052255 0.0049112 0.0046384 0.0044005 0.004185 0.0040086 0.0038553 0.0037249 0.0036126 0.0035162 0.0034352 0.0033671 0.0033121 0.0032683 0.0032313 0.0032076 0.003193 0.0031869 0.0031881 0.0031976 0.0032146 0.0032394 0.0032706 0.003308 0.0033595 0.0034129 0.003473 0.0035407 0.0036159 0.003699 0.0037917 0.0038954 0.0040109 0.0041402 0.0043394 0.0045039 0.0046938 0.0049307 0.0041572 0.0058931 0.0059712 0.00605 0.0061303 0.0062066 0.0062794 0.0063507 0.0064177 0.0064749 0.0065314 0.0065858 0.0066377 0.0066778 0.0067124 0.00675 0.0067855 0.0068116 0.0068339 0.0068648 0.006886 0.0069039 0.0069216 0.0069383 0.0069512 0.0069579 0.0069543 0.006941 0.0069339 0.0069365 0.0069492 0.0069674 0.006956 0.006906 0.0067662 0.0063388
9 4 1 0.012661 0.011814 0.01075 0.0097404 0.0088898 0.0081607 0.0075394 0.0070115 0.0065566 0.0061646 0.0058234 0.0055265 0.0052562 0.0050339 0.0048395 0.0046719 0.0045273 0.0044028 0.0042977 0.0042086 0.0041359 0.0040772 0.0040265 0.0039922 0.0039691 0.0039563 0.0039524 0.0039586 0.0039738 0.0039983 0.0040304 0.0040701 0.0041271 0.0041859 0.0042525 0.0043287 0.0044144 0.0045096 0.0046151 0.0047318 0.0048592 0.0049987 0.0052213 0.005396 0.0055825 0.005837 0.0046187 0.0064369 0.006524 0.0066117 0.0067044 0.0067902 0.0068716 0.0069534 0.0070299 0.0071018 0.0071677 0.0072313 0.0072928 0.007347 0.007391 0.0074364 0.0074843 0.0075248 0.007553 0.0075921 0.0076289 0.0076561 0.0076833 0.0077104 0.0077338 0.0077506 0.0077569 0.0077533 0.0077452 0.0077535 0.0077723 0.0077969 0.0077983 0.0077501 0.0076016 0.0071128
10 5 1 0.015891 0.014939 0.013651 0.012419 0.011375 0.010485 0.009725 0.009076 0.0085106 0.0080209 0.0075976 0.0072315 0.0068971 0.0066208 0.0063796 0.0061735 0.0059959 0.0058421 0.0057113 0.0055996 0.0055081 0.005434 0.0053694 0.0053257 0.0052959 0.0052789 0.0052729 0.0052795 0.0052972 0.0053268 0.0053655 0.0054128 0.0054823 0.0055532 0.0056332 0.005724 0.0058255 0.0059381 0.0060617 0.0061969 0.0063445 0.0065054 0.0067728 0.0069619 0.0071444 0.0073918 0.0053909 0.0074542 0.0075532 0.0076512 0.0077516 0.007845 0.0079358 0.0080263 0.0081149 0.008196 0.0082692 0.0083428 0.0084152 0.0084831 0.0085324 0.008581 0.0086376 0.0086872 0.0087308 0.0087773 0.0088184 0.0088483 0.0088787 0.0088992 0.0089127 0.0089213 0.0089369 0.0089446 0.0089414 0.0089531 0.008974 0.0089929 0.0090006 0.0089464 0.0087516 0.0081814
% Consumption Function
st_title = ['MEAN(MN_MPC_U_GAIN_CHECK(KM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_c = ff_summ_nd_array(st_title, mn_MPC_U_gain_share_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_MPC_U_GAIN_CHECK(KM,J)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group kids marry mean_age_18 mean_age_19 mean_age_20 mean_age_21 mean_age_22 mean_age_23 mean_age_24 mean_age_25 mean_age_26 mean_age_27 mean_age_28 mean_age_29 mean_age_30 mean_age_31 mean_age_32 mean_age_33 mean_age_34 mean_age_35 mean_age_36 mean_age_37 mean_age_38 mean_age_39 mean_age_40 mean_age_41 mean_age_42 mean_age_43 mean_age_44 mean_age_45 mean_age_46 mean_age_47 mean_age_48 mean_age_49 mean_age_50 mean_age_51 mean_age_52 mean_age_53 mean_age_54 mean_age_55 mean_age_56 mean_age_57 mean_age_58 mean_age_59 mean_age_60 mean_age_61 mean_age_62 mean_age_63 mean_age_64 mean_age_65 mean_age_66 mean_age_67 mean_age_68 mean_age_69 mean_age_70 mean_age_71 mean_age_72 mean_age_73 mean_age_74 mean_age_75 mean_age_76 mean_age_77 mean_age_78 mean_age_79 mean_age_80 mean_age_81 mean_age_82 mean_age_83 mean_age_84 mean_age_85 mean_age_86 mean_age_87 mean_age_88 mean_age_89 mean_age_90 mean_age_91 mean_age_92 mean_age_93 mean_age_94 mean_age_95 mean_age_96 mean_age_97 mean_age_98 mean_age_99
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________
1 1 0 0.084565 0.099857 0.10794 0.10516 0.10247 0.099966 0.097687 0.095619 0.093715 0.091999 0.090521 0.089251 0.08762 0.086574 0.085345 0.084429 0.083543 0.082735 0.082077 0.081419 0.080821 0.080321 0.079759 0.07929 0.078878 0.078521 0.078188 0.077967 0.077738 0.077623 0.077473 0.077381 0.077545 0.07772 0.077848 0.078083 0.078407 0.078866 0.0794 0.080066 0.081085 0.08213 0.084414 0.086491 0.089894 0.098463 0.095468 0.12954 0.13231 0.13551 0.13833 0.14086 0.1435 0.14632 0.14926 0.15242 0.15585 0.15951 0.16342 0.16772 0.17271 0.17819 0.18424 0.19073 0.19608 0.2024 0.20975 0.21748 0.22641 0.23706 0.24736 0.25916 0.27307 0.28931 0.30317 0.32318 0.34255 0.37347 0.41068 0.47513 0.60185 0.99997
2 2 0 0.096126 0.11139 0.12136 0.11819 0.11553 0.11307 0.11099 0.10909 0.10735 0.10599 0.10482 0.10349 0.10235 0.10157 0.10068 0.10003 0.099447 0.098865 0.098298 0.097831 0.097436 0.097017 0.096428 0.096211 0.095723 0.095391 0.095003 0.094534 0.094238 0.093867 0.093569 0.093386 0.093113 0.092895 0.092951 0.092873 0.092774 0.09284 0.093222 0.093573 0.094217 0.094792 0.096519 0.097968 0.10074 0.10855 0.10768 0.15653 0.15892 0.16167 0.16425 0.16665 0.16905 0.17166 0.17448 0.17785 0.18162 0.18569 0.19008 0.19461 0.19933 0.20368 0.20846 0.21349 0.21841 0.22431 0.23129 0.23914 0.2469 0.2569 0.26736 0.27839 0.29061 0.30549 0.31983 0.33813 0.35953 0.38925 0.42856 0.49362 0.61584 0.99997
3 3 0 0.1078 0.12631 0.13473 0.13138 0.12786 0.12514 0.12252 0.12034 0.11821 0.11656 0.11498 0.11356 0.11218 0.11118 0.1102 0.10944 0.10871 0.10817 0.10765 0.1071 0.1067 0.1064 0.10606 0.10563 0.10549 0.10526 0.10482 0.10461 0.10437 0.1041 0.10392 0.10373 0.10345 0.10322 0.1032 0.10315 0.10303 0.10317 0.10344 0.10382 0.10419 0.10464 0.10584 0.1072 0.10992 0.11763 0.11866 0.16982 0.17247 0.17523 0.17808 0.18105 0.18419 0.1875 0.19096 0.19457 0.19846 0.20261 0.20693 0.21095 0.21545 0.21959 0.22507 0.23132 0.23826 0.24582 0.25424 0.26237 0.27028 0.28051 0.29158 0.30319 0.31391 0.32812 0.34385 0.35896 0.38195 0.40951 0.44941 0.51011 0.63243 0.99996
4 4 0 0.114 0.13339 0.14178 0.13811 0.13446 0.13124 0.12835 0.12587 0.1235 0.1218 0.11989 0.11853 0.11686 0.11568 0.11474 0.11384 0.11315 0.11249 0.11213 0.11151 0.11125 0.1108 0.1106 0.11032 0.10995 0.10994 0.10962 0.1093 0.10914 0.10903 0.10878 0.10848 0.10844 0.10822 0.10806 0.10798 0.10789 0.10787 0.10785 0.10794 0.10824 0.10877 0.10974 0.11099 0.11378 0.12191 0.12347 0.17235 0.17496 0.17768 0.18053 0.18354 0.18674 0.19011 0.19362 0.19735 0.20133 0.20552 0.20958 0.21297 0.21725 0.22256 0.22875 0.23571 0.24336 0.25159 0.2603 0.26746 0.27709 0.28739 0.29854 0.30916 0.32051 0.33461 0.34861 0.36369 0.38414 0.41095 0.44862 0.50625 0.6245 0.99997
5 5 0 0.11992 0.14069 0.14855 0.14469 0.14011 0.13651 0.13295 0.13002 0.12745 0.12513 0.12293 0.12126 0.11927 0.11787 0.11665 0.11561 0.11469 0.11389 0.11338 0.11261 0.11215 0.11168 0.11159 0.11114 0.11088 0.1109 0.11048 0.11029 0.11027 0.11019 0.10992 0.10969 0.10973 0.10965 0.10948 0.10943 0.10931 0.10902 0.10886 0.10899 0.10927 0.10978 0.11089 0.11222 0.11506 0.12354 0.12528 0.17109 0.17358 0.17615 0.17879 0.18165 0.18477 0.18802 0.1914 0.195 0.19886 0.2029 0.20605 0.20959 0.21441 0.22 0.22639 0.23355 0.24117 0.24947 0.25754 0.26539 0.27543 0.28608 0.29717 0.30731 0.31871 0.33242 0.3466 0.36067 0.38057 0.40417 0.43911 0.49558 0.61799 0.99997
6 1 1 0.096646 0.10442 0.10672 0.10443 0.10194 0.099587 0.098422 0.097725 0.097312 0.095943 0.093696 0.092107 0.092674 0.093476 0.091908 0.089987 0.088257 0.087368 0.08712 0.086468 0.085464 0.084656 0.08462 0.085023 0.08447 0.083774 0.083301 0.083156 0.082373 0.082003 0.081679 0.0813 0.081142 0.079605 0.078189 0.078625 0.079524 0.08008 0.080325 0.080683 0.081017 0.081512 0.083754 0.083511 0.084171 0.08813 0.088151 0.11307 0.11451 0.1227 0.12618 0.12016 0.12632 0.12034 0.1143 0.11646 0.12437 0.13846 0.13931 0.15418 0.1456 0.13802 0.15168 0.16578 0.16773 0.18042 0.19546 0.19865 0.20649 0.22167 0.23084 0.23636 0.25457 0.26861 0.27838 0.29507 0.31739 0.34789 0.38242 0.45746 0.59342 0.99998
7 2 1 0.10031 0.1093 0.11166 0.10931 0.10836 0.10496 0.10244 0.10078 0.099513 0.099422 0.098671 0.099219 0.099198 0.097572 0.095963 0.094974 0.094416 0.09412 0.09458 0.094747 0.094191 0.093705 0.09317 0.093002 0.092665 0.092014 0.091839 0.091394 0.090778 0.090822 0.090544 0.089961 0.089941 0.089784 0.088925 0.088171 0.08805 0.087931 0.08784 0.087993 0.088184 0.088432 0.090025 0.090925 0.092683 0.096182 0.097591 0.1204 0.12135 0.12286 0.12513 0.12666 0.13019 0.13407 0.12908 0.13266 0.13495 0.14567 0.14876 0.15415 0.156 0.15924 0.16998 0.1785 0.18313 0.19146 0.20329 0.21033 0.21995 0.22998 0.24136 0.24872 0.26172 0.27433 0.28813 0.30465 0.32709 0.3571 0.39525 0.46529 0.6004 0.99998
8 3 1 0.10594 0.11757 0.11904 0.11731 0.11604 0.11334 0.11105 0.10902 0.10753 0.10534 0.1034 0.1028 0.10157 0.10098 0.10018 0.098702 0.09844 0.099127 0.10016 0.099561 0.09931 0.099246 0.098481 0.097813 0.097593 0.097519 0.097175 0.096677 0.096128 0.096363 0.095783 0.095204 0.094806 0.094847 0.094825 0.094543 0.094387 0.093971 0.094168 0.094679 0.095345 0.095798 0.097113 0.098276 0.098865 0.10388 0.10458 0.12615 0.12676 0.12992 0.12751 0.1296 0.13331 0.1403 0.14327 0.14293 0.14721 0.14976 0.15698 0.15699 0.16235 0.1754 0.18746 0.19185 0.20049 0.20547 0.21138 0.22189 0.234 0.24088 0.25152 0.26444 0.27226 0.28071 0.30113 0.3182 0.34189 0.36995 0.4112 0.47405 0.60393 0.99998
9 4 1 0.11209 0.12204 0.12456 0.12263 0.12006 0.11837 0.11597 0.11472 0.11272 0.11019 0.10858 0.10746 0.10594 0.10436 0.10217 0.10156 0.10111 0.10168 0.1017 0.10106 0.10058 0.1005 0.10038 0.10012 0.099891 0.099646 0.099421 0.098957 0.098657 0.098389 0.09836 0.098369 0.098299 0.09845 0.098508 0.098553 0.098641 0.098263 0.098507 0.098741 0.099477 0.099624 0.10077 0.10219 0.10366 0.10931 0.10742 0.13112 0.1306 0.13452 0.13325 0.13052 0.13416 0.14064 0.14737 0.14727 0.15259 0.15047 0.15986 0.16477 0.16299 0.17329 0.19134 0.19817 0.20551 0.21335 0.2164 0.22488 0.23777 0.2484 0.25565 0.26953 0.27948 0.28619 0.30364 0.32155 0.34611 0.3734 0.41269 0.47341 0.60625 0.99998
10 5 1 0.12333 0.13314 0.13686 0.13165 0.12869 0.12899 0.12346 0.12136 0.11982 0.11797 0.1163 0.11406 0.11255 0.11124 0.11009 0.10933 0.10837 0.10807 0.1079 0.10697 0.10687 0.10608 0.10537 0.10512 0.10468 0.10446 0.10385 0.10322 0.10306 0.10242 0.10149 0.10142 0.10157 0.10165 0.10145 0.10134 0.10123 0.10116 0.1014 0.10171 0.1022 0.1028 0.10404 0.10635 0.10822 0.1146 0.11062 0.13278 0.13398 0.13537 0.14285 0.13785 0.14231 0.14416 0.14991 0.1529 0.15629 0.15486 0.16873 0.17616 0.17404 0.18214 0.19468 0.20062 0.20714 0.21756 0.22301 0.22966 0.24159 0.25125 0.25889 0.27161 0.28175 0.29286 0.30223 0.3213 0.34488 0.37181 0.41094 0.47204 0.60454 0.99998 Graph Mean Values:
st_title = ['MEAN(MN\_V\_U\_GAIN\_CHECK(KM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_V\_U\_GAIN\_CHECK(KM,J))'};
ff_graph_grid((tb_az_v{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Consumption (MPC: Share of Check Consumed):
st_title = ['MEAN(MN\_MPC\_U\_GAIN\_CHECK(KM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_MPC\_U\_GAIN\_CHECK(KM,J))'};
ff_graph_grid((tb_az_c{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
1.9 Analyze Education and Marriage
Aggregating over education, savings, and shocks, what are the differential effects of Marriage and Age.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
ar_row_grid = ["E0M0", "E1M0", "E0M1", "E1M1"];
mp_support_graph('cl_st_xtitle') = {'Age'};
mp_support_graph('st_legend_loc') = 'best';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('st_rounding') = '6.2f'; % format shock legend
mp_support_graph('cl_scatter_shapes') = {'*', 'p', '*','p' };
mp_support_graph('cl_colors') = {'red', 'red', 'blue', 'blue'};MEAN(VAL(EM,J)), MEAN(AP(EM,J)), MEAN(C(EM,J))
Tabulate value and policies:
% Set
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
ar_permute = [2,3,6,1,4,5];
% Value Function
st_title = ['MEAN(MN_V_U_GAIN_CHECK(EM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_v = ff_summ_nd_array(st_title, mn_V_U_gain_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_V_U_GAIN_CHECK(EM,J)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group edu marry mean_age_18 mean_age_19 mean_age_20 mean_age_21 mean_age_22 mean_age_23 mean_age_24 mean_age_25 mean_age_26 mean_age_27 mean_age_28 mean_age_29 mean_age_30 mean_age_31 mean_age_32 mean_age_33 mean_age_34 mean_age_35 mean_age_36 mean_age_37 mean_age_38 mean_age_39 mean_age_40 mean_age_41 mean_age_42 mean_age_43 mean_age_44 mean_age_45 mean_age_46 mean_age_47 mean_age_48 mean_age_49 mean_age_50 mean_age_51 mean_age_52 mean_age_53 mean_age_54 mean_age_55 mean_age_56 mean_age_57 mean_age_58 mean_age_59 mean_age_60 mean_age_61 mean_age_62 mean_age_63 mean_age_64 mean_age_65 mean_age_66 mean_age_67 mean_age_68 mean_age_69 mean_age_70 mean_age_71 mean_age_72 mean_age_73 mean_age_74 mean_age_75 mean_age_76 mean_age_77 mean_age_78 mean_age_79 mean_age_80 mean_age_81 mean_age_82 mean_age_83 mean_age_84 mean_age_85 mean_age_86 mean_age_87 mean_age_88 mean_age_89 mean_age_90 mean_age_91 mean_age_92 mean_age_93 mean_age_94 mean_age_95 mean_age_96 mean_age_97 mean_age_98 mean_age_99
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________
1 0 0 0.052161 0.051299 0.048759 0.045984 0.043519 0.041317 0.039352 0.037593 0.03601 0.034589 0.033309 0.032152 0.031027 0.030089 0.029237 0.02847 0.027777 0.02715 0.026584 0.02607 0.025611 0.025199 0.024797 0.024469 0.024179 0.023924 0.023702 0.023511 0.02335 0.023217 0.023111 0.023028 0.023025 0.022998 0.022991 0.023007 0.023047 0.023111 0.023196 0.023303 0.023427 0.023565 0.02411 0.024213 0.024178 0.024086 0.012277 0.019286 0.019136 0.018988 0.018837 0.018685 0.018533 0.01838 0.018225 0.018068 0.017908 0.017746 0.017583 0.017419 0.017251 0.017083 0.016916 0.016752 0.016603 0.016461 0.016331 0.016206 0.016079 0.015968 0.015872 0.015787 0.015708 0.015615 0.01553 0.015451 0.015383 0.015291 0.015193 0.015074 0.014889 0.01441
2 1 0 0.049785 0.048517 0.043186 0.038128 0.034027 0.030674 0.027912 0.025624 0.023714 0.022119 0.020773 0.01964 0.018626 0.017814 0.01712 0.016534 0.016036 0.01561 0.015254 0.014952 0.014701 0.014491 0.0143 0.01416 0.014048 0.013961 0.013891 0.013844 0.013815 0.013805 0.013804 0.013815 0.013867 0.013899 0.01394 0.013991 0.014052 0.014121 0.014201 0.014292 0.014392 0.014502 0.014854 0.014948 0.015006 0.015176 0.0089293 0.014659 0.014544 0.014428 0.014311 0.014193 0.014075 0.013958 0.013841 0.013721 0.0136 0.013477 0.013352 0.013222 0.01309 0.012962 0.012838 0.012723 0.012611 0.012503 0.012395 0.012299 0.012206 0.012122 0.012043 0.011961 0.011885 0.01182 0.011761 0.011691 0.01162 0.011552 0.01152 0.011477 0.011344 0.010946
3 0 1 0.011544 0.010825 0.010006 0.0092235 0.0085439 0.0079503 0.007433 0.0069803 0.0065805 0.0062306 0.0059235 0.0056516 0.0054012 0.0051932 0.0050103 0.0048522 0.0047144 0.0045957 0.0044938 0.0044063 0.0043348 0.0042771 0.0042268 0.0041932 0.0041708 0.0041588 0.0041569 0.0041645 0.0041816 0.0042078 0.0042431 0.0042856 0.004347 0.0044111 0.0044834 0.0045657 0.0046581 0.0047615 0.0048763 0.0050032 0.0051434 0.0052979 0.0055404 0.0057348 0.0059438 0.0062174 0.0049822 0.0069349 0.0070246 0.0071149 0.0072032 0.0072935 0.0073822 0.0074679 0.007549 0.0076124 0.007667 0.0077321 0.0078016 0.0078538 0.0078924 0.0079288 0.0079611 0.0079852 0.0080095 0.0080428 0.0080701 0.0080864 0.0080988 0.0081108 0.0081196 0.0081305 0.008135 0.0081288 0.0081191 0.0081093 0.0081064 0.0081123 0.008099 0.0080431 0.0078726 0.0073291
4 1 1 0.0096585 0.0089631 0.0080043 0.0071088 0.006372 0.0057587 0.0052467 0.0048184 0.0044564 0.0041517 0.0038919 0.0036724 0.0034762 0.0033173 0.0031809 0.0030667 0.0029705 0.0028881 0.0028211 0.0027664 0.0027229 0.0026895 0.0026622 0.002646 0.0026376 0.0026364 0.0026405 0.0026523 0.0026702 0.0026952 0.0027245 0.0027592 0.0028048 0.0028511 0.0029028 0.0029603 0.0030241 0.0030942 0.0031718 0.003258 0.003353 0.0034588 0.0036237 0.0037618 0.0039199 0.0041239 0.0035174 0.0050759 0.0051477 0.0052194 0.0052955 0.0053608 0.0054236 0.0054831 0.0055365 0.0055939 0.0056516 0.0056998 0.0057352 0.0057768 0.0058113 0.005837 0.0058666 0.0058961 0.005916 0.0059312 0.0059506 0.0059655 0.0059807 0.0059956 0.0060068 0.0060068 0.0060037 0.0059981 0.0059934 0.0059996 0.0060197 0.0060264 0.0060137 0.0059612 0.0058225 0.0054585
% Consumption
st_title = ['MEAN(MN_MPC_U_GAIN_CHECK(EM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_c = ff_summ_nd_array(st_title, mn_MPC_U_gain_share_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_MPC_U_GAIN_CHECK(EM,J)), welf_checks=2, TR=0.0017225 xxxxxxxxxxxxxxxxxxxxxxxxxxx
group edu marry mean_age_18 mean_age_19 mean_age_20 mean_age_21 mean_age_22 mean_age_23 mean_age_24 mean_age_25 mean_age_26 mean_age_27 mean_age_28 mean_age_29 mean_age_30 mean_age_31 mean_age_32 mean_age_33 mean_age_34 mean_age_35 mean_age_36 mean_age_37 mean_age_38 mean_age_39 mean_age_40 mean_age_41 mean_age_42 mean_age_43 mean_age_44 mean_age_45 mean_age_46 mean_age_47 mean_age_48 mean_age_49 mean_age_50 mean_age_51 mean_age_52 mean_age_53 mean_age_54 mean_age_55 mean_age_56 mean_age_57 mean_age_58 mean_age_59 mean_age_60 mean_age_61 mean_age_62 mean_age_63 mean_age_64 mean_age_65 mean_age_66 mean_age_67 mean_age_68 mean_age_69 mean_age_70 mean_age_71 mean_age_72 mean_age_73 mean_age_74 mean_age_75 mean_age_76 mean_age_77 mean_age_78 mean_age_79 mean_age_80 mean_age_81 mean_age_82 mean_age_83 mean_age_84 mean_age_85 mean_age_86 mean_age_87 mean_age_88 mean_age_89 mean_age_90 mean_age_91 mean_age_92 mean_age_93 mean_age_94 mean_age_95 mean_age_96 mean_age_97 mean_age_98 mean_age_99
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________
1 0 0 0.09247 0.10277 0.10886 0.10804 0.10737 0.10669 0.1062 0.10569 0.10512 0.10482 0.10443 0.10407 0.10357 0.10334 0.10301 0.10288 0.10263 0.10245 0.10226 0.10195 0.10179 0.10153 0.10126 0.101 0.10075 0.10052 0.10026 0.099898 0.099619 0.099459 0.099203 0.098886 0.098748 0.098695 0.098681 0.09866 0.098662 0.098761 0.098965 0.099306 0.099906 0.10065 0.10218 0.10362 0.10666 0.11502 0.11465 0.15747 0.15992 0.16297 0.16597 0.16887 0.17171 0.17474 0.17795 0.18139 0.1851 0.18902 0.19295 0.19704 0.20175 0.20673 0.21242 0.2188 0.22517 0.23236 0.24031 0.24788 0.25662 0.26698 0.27786 0.289 0.30106 0.31575 0.33045 0.34739 0.36813 0.3957 0.43323 0.49428 0.61724 0.99997
2 1 0 0.11649 0.14189 0.15288 0.14697 0.1408 0.13569 0.1308 0.12668 0.12297 0.11977 0.11682 0.11437 0.11174 0.10981 0.10804 0.10646 0.10519 0.10401 0.10316 0.10224 0.10155 0.10095 0.10052 0.10004 0.099618 0.09949 0.098981 0.098784 0.098684 0.098467 0.098262 0.098183 0.09816 0.09799 0.097937 0.097949 0.097903 0.097943 0.098144 0.098446 0.098895 0.099389 0.10079 0.10233 0.10509 0.11301 0.11357 0.16227 0.16498 0.16752 0.17002 0.17263 0.17559 0.1787 0.18194 0.18548 0.18935 0.19347 0.19748 0.2013 0.20592 0.21088 0.21674 0.22311 0.22974 0.23708 0.24494 0.25285 0.26183 0.2722 0.28295 0.29388 0.30566 0.32024 0.33438 0.35046 0.37137 0.39925 0.43732 0.498 0.6198 0.99996
3 0 1 0.09821 0.10337 0.10589 0.10515 0.10424 0.1031 0.10254 0.1028 0.10227 0.10103 0.1005 0.10063 0.10055 0.10011 0.099251 0.098615 0.097864 0.097329 0.09723 0.096463 0.096224 0.09552 0.095251 0.095149 0.094825 0.094539 0.094233 0.09375 0.093394 0.093259 0.092631 0.092174 0.092093 0.092204 0.092272 0.092411 0.092848 0.092928 0.093192 0.093811 0.094344 0.094851 0.095892 0.096681 0.097188 0.10296 0.1028 0.12051 0.12131 0.12304 0.12319 0.1271 0.13338 0.1384 0.13808 0.13031 0.13644 0.1531 0.16206 0.15843 0.15693 0.16621 0.17866 0.18103 0.19257 0.20446 0.20946 0.21425 0.22263 0.23171 0.24349 0.25977 0.27233 0.28193 0.29244 0.30685 0.33053 0.36153 0.40376 0.47369 0.60051 0.99998
4 1 1 0.11712 0.13122 0.13364 0.12898 0.12579 0.12299 0.118 0.11464 0.11249 0.11052 0.10776 0.10563 0.10422 0.10294 0.10087 0.099205 0.098375 0.098817 0.099354 0.09906 0.098344 0.098156 0.097555 0.097282 0.096894 0.096427 0.096 0.09561 0.095004 0.09474 0.09451 0.094328 0.094209 0.093531 0.092486 0.09208 0.091887 0.091632 0.091706 0.09171 0.092144 0.092416 0.094389 0.09582 0.097852 0.10188 0.10054 0.12889 0.12957 0.13511 0.13878 0.13082 0.13313 0.1334 0.1355 0.14658 0.14972 0.14258 0.1474 0.16407 0.16346 0.16503 0.1794 0.19294 0.19303 0.19885 0.21036 0.21992 0.23329 0.24516 0.25181 0.25649 0.26758 0.27915 0.29696 0.31746 0.34041 0.36653 0.40124 0.46321 0.60291 0.99998 Graph Mean Values:
st_title = ['MEAN(MN\_V\_U\_GAIN\_CHECK(EM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_V\_U\_GAIN\_CHECK(EM,J))'};
ff_graph_grid((tb_az_v{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Consumption (MPC: Share of Check Consumed):
st_title = ['MEAN(MN\_MPC\_U\_GAIN\_CHECK(EM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_MPC\_U\_GAIN\_CHECK(EM,J))'};
ff_graph_grid((tb_az_c{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
