1 2019 (Biden/Trump Checks) Full States EV and EC of Two Checks
This is the example vignette for function: snw_evuvw19_jaeemk_foc from the PrjOptiSNW Package. 2019 integrated over VU and VW, given optimal savings choices, unemployment shocks and various expectations.
Given 2020 JAEEMK (age, endogenous savings, education, income shock, marital status, kids count), what is the expected value for the planner given 2020 JAEEMK and transition between 2019 to 2020 JAEEMK given some stimulus check assignment based on 2019 information? (Stimulus amount set by WELF_CHECKS). This is similar to snw_evuvw19_jaeemk, except the solution here, under snw_evuvw19_jaeemk_foc, relies on First Order Conditions, and are hence faster.
Despite the name, this function supports solving the 2019 looking into 2020 as well as the 2007 looking into 2008 problems. The idea is that the planner only has information from 2019 and from 2007, and must allocate using those information. Stimulus, however, is given in 2020 and in 2008. So the planner needs to consider expected values in consumption or welfare given the transition probabilities of states in 2007 to 2008 and in 2019 to 2020. The snw_evuvw19_jmky file then aggregates the full state-space results to just JMKY state-space, which is the extend of information available to the planner.
1.1 Test SNW_EVUVW19_JAEEMK Defaults for 2019
Call the function with defaults parameters.
clear all;
% Solution types
st_biden_or_trump = 'bidenchk';
st_solu_type = 'bisec_vec';
% Solve the VFI Problem and get Value Function
mp_params = snw_mp_param('default_docdense');
% mp_params = snw_mp_param('default_dense');
mp_params('beta') = 0.95;
mp_params('st_biden_or_trump') = st_biden_or_trump;
% 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=519.5191
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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
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i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ __________
V_VFI 1 1 6 4.37e+07 83 5.265e+05 -6.6619e+08 -15.245 21.865 -1.4343 -504.39 -0.0071775
ap_VFI 2 2 6 4.37e+07 83 5.265e+05 1.3967e+09 31.962 36.426 1.1397 0 160.24
cons_VFI 3 3 6 4.37e+07 83 5.265e+05 2.3276e+08 5.3263 8.4413 1.5848 0.036717 141.61
xxx TABLE:V_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -293.96 -293.57 -291.09 -285.44 -276.41 -4.3584 -4.2643 -4.1713 -4.0795 -3.9885
r2 -284.42 -284.03 -281.55 -275.97 -267.24 -4.2519 -4.1612 -4.0717 -3.9832 -3.8955
r3 -274.87 -274.48 -272.03 -266.62 -258.33 -4.1429 -4.0559 -3.9698 -3.8847 -3.8002
r4 -265.22 -264.86 -262.58 -257.53 -249.74 -4.0309 -3.9475 -3.8649 -3.7833 -3.7017
r5 -256.51 -256.17 -254.04 -249.3 -241.96 -3.9252 -3.8452 -3.7659 -3.6873 -3.6084
r79 -13.642 -13.628 -13.535 -13.298 -12.896 -0.22092 -0.21058 -0.20086 -0.19173 -0.18315
r80 -12.283 -12.269 -12.176 -11.939 -11.537 -0.16979 -0.16182 -0.1543 -0.14722 -0.14057
r81 -10.605 -10.591 -10.498 -10.261 -9.8589 -0.11712 -0.11163 -0.10646 -0.10157 -0.09695
r82 -8.3494 -8.3358 -8.2424 -8.0055 -7.6035 -0.065333 -0.062242 -0.05936 -0.056635 -0.054056
r83 -5.0665 -5.0529 -4.9595 -4.7226 -4.3206 -0.020968 -0.019972 -0.019038 -0.018161 -0.017336
xxx TABLE:ap_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
__ __ __________ _________ ________ _______ _______ _______ _______ _______
r1 0 0 0.00051498 0.0066578 0.021589 112.13 117.67 123.4 129.31 135.72
r2 0 0 0.00051498 0.0057684 0.020245 112.17 117.71 123.43 129.34 135.76
r3 0 0 0.00020768 0.0041456 0.018539 112.2 117.73 123.45 129.37 135.78
r4 0 0 0.00010346 0.0041199 0.018307 112.86 118.39 124.11 130.03 136.44
r5 0 0 5.2907e-06 0.0041199 0.018091 113.53 119.07 124.79 130.71 137.12
r79 0 0 0 0 0 81.091 85.364 89.335 93.258 97.348
r80 0 0 0 0 0 76.124 79.747 83.431 86.986 90.578
r81 0 0 0 0 0 67.945 70.639 73.673 76.991 81.091
r82 0 0 0 0 0 50.126 53.467 56.302 57.884 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 12.265 12.55 12.844 13.145 13.145
r2 0.036717 0.037251 0.040477 0.045375 0.050668 12.501 12.787 13.082 13.383 13.383
r3 0.036717 0.037251 0.040784 0.046998 0.052374 12.755 13.042 13.337 13.638 13.638
r4 0.038144 0.038678 0.042314 0.048449 0.054031 13 13.289 13.584 13.883 13.883
r5 0.039534 0.040068 0.043802 0.049839 0.055635 13.236 13.525 13.821 14.116 14.116
r79 0.19737 0.19791 0.20163 0.21175 0.23145 35.811 37.362 39.409 41.7 44.025
r80 0.19737 0.19791 0.20163 0.21175 0.23145 40.752 42.953 45.286 47.946 50.769
r81 0.19737 0.19791 0.20163 0.21175 0.23145 48.909 52.039 55.022 57.919 60.234
r82 0.19737 0.19791 0.20163 0.21175 0.23145 66.71 69.193 72.375 77.007 80.72
r83 0.19737 0.19791 0.20163 0.21175 0.23145 116.82 122.65 128.66 134.88 141.29
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=512.4091
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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
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i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ __________
V_VFI 1 1 6 4.37e+07 83 5.265e+05 -6.6619e+08 -15.245 21.865 -1.4343 -504.39 -0.0071775
ap_VFI 2 2 6 4.37e+07 83 5.265e+05 1.3967e+09 31.962 36.426 1.1397 0 160.24
cons_VFI 3 3 6 4.37e+07 83 5.265e+05 2.3276e+08 5.3263 8.4413 1.5848 0.036717 141.61
xxx TABLE:V_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -293.96 -293.57 -291.09 -285.44 -276.41 -4.3584 -4.2643 -4.1713 -4.0795 -3.9885
r2 -284.42 -284.03 -281.55 -275.97 -267.24 -4.2519 -4.1612 -4.0717 -3.9832 -3.8955
r3 -274.87 -274.48 -272.03 -266.62 -258.33 -4.1429 -4.0559 -3.9698 -3.8847 -3.8002
r4 -265.22 -264.86 -262.58 -257.53 -249.74 -4.0309 -3.9475 -3.8649 -3.7833 -3.7017
r5 -256.51 -256.17 -254.04 -249.3 -241.96 -3.9252 -3.8452 -3.7659 -3.6873 -3.6084
r79 -13.642 -13.628 -13.535 -13.298 -12.896 -0.22092 -0.21058 -0.20086 -0.19173 -0.18315
r80 -12.283 -12.269 -12.176 -11.939 -11.537 -0.16979 -0.16182 -0.1543 -0.14722 -0.14057
r81 -10.605 -10.591 -10.498 -10.261 -9.8589 -0.11712 -0.11163 -0.10646 -0.10157 -0.09695
r82 -8.3494 -8.3358 -8.2424 -8.0055 -7.6035 -0.065333 -0.062242 -0.05936 -0.056635 -0.054056
r83 -5.0665 -5.0529 -4.9595 -4.7226 -4.3206 -0.020968 -0.019972 -0.019038 -0.018161 -0.017336
xxx TABLE:ap_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
__ __ __________ _________ ________ _______ _______ _______ _______ _______
r1 0 0 0.00051498 0.0066578 0.021589 112.13 117.67 123.4 129.31 135.72
r2 0 0 0.00051498 0.0057684 0.020245 112.17 117.71 123.43 129.34 135.76
r3 0 0 0.00020768 0.0041456 0.018539 112.2 117.73 123.45 129.37 135.78
r4 0 0 0.00010346 0.0041199 0.018307 112.86 118.39 124.11 130.03 136.44
r5 0 0 5.2907e-06 0.0041199 0.018091 113.53 119.07 124.79 130.71 137.12
r79 0 0 0 0 0 81.091 85.364 89.335 93.258 97.348
r80 0 0 0 0 0 76.124 79.747 83.431 86.986 90.578
r81 0 0 0 0 0 67.945 70.639 73.673 76.991 81.091
r82 0 0 0 0 0 50.126 53.467 56.302 57.884 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 12.265 12.55 12.844 13.145 13.145
r2 0.036717 0.037251 0.040477 0.045375 0.050668 12.501 12.787 13.082 13.383 13.383
r3 0.036717 0.037251 0.040784 0.046998 0.052374 12.755 13.042 13.337 13.638 13.638
r4 0.038144 0.038678 0.042314 0.048449 0.054031 13 13.289 13.584 13.883 13.883
r5 0.039534 0.040068 0.043802 0.049839 0.055635 13.236 13.525 13.821 14.116 14.116
r79 0.19737 0.19791 0.20163 0.21175 0.23145 35.811 37.362 39.409 41.7 44.025
r80 0.19737 0.19791 0.20163 0.21175 0.23145 40.752 42.953 45.286 47.946 50.769
r81 0.19737 0.19791 0.20163 0.21175 0.23145 48.909 52.039 55.022 57.919 60.234
r82 0.19737 0.19791 0.20163 0.21175 0.23145 66.71 69.193 72.375 77.007 80.72
r83 0.19737 0.19791 0.20163 0.21175 0.23145 116.82 122.65 128.66 134.88 141.29
% 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=510.0916
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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 -6.7567e+08 -15.462 22.251 -1.4391 -524.15 -0.0071775
ap_VFI 2 2 6 4.37e+07 83 5.265e+05 1.3783e+09 31.541 36.36 1.1528 0 151.82
cons_VFI 3 3 6 4.37e+07 83 5.265e+05 2.3114e+08 5.2893 8.4402 1.5957 0.027723 141.04
xxx TABLE:V_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -302.8 -302.11 -297.97 -290.4 -280.12 -4.3991 -4.3032 -4.2086 -4.1151 -4.023
r2 -293.25 -292.57 -288.43 -280.86 -270.8 -4.2921 -4.1998 -4.1086 -4.0185 -3.9297
r3 -283.7 -283.02 -278.88 -271.34 -261.75 -4.1826 -4.094 -4.0063 -3.9196 -3.834
r4 -273.72 -273.09 -269.23 -262.13 -253.1 -4.0721 -3.987 -3.9028 -3.8196 -3.7373
r5 -264.7 -264.11 -260.51 -253.79 -245.27 -3.9679 -3.8861 -3.8051 -3.725 -3.6456
r79 -13.642 -13.628 -13.535 -13.298 -12.896 -0.22191 -0.21148 -0.20167 -0.19245 -0.1838
r80 -12.283 -12.269 -12.176 -11.939 -11.537 -0.17053 -0.16249 -0.1549 -0.14776 -0.14105
r81 -10.605 -10.591 -10.498 -10.261 -9.8589 -0.11764 -0.11208 -0.10686 -0.10194 -0.097291
r82 -8.3494 -8.3358 -8.2424 -8.0055 -7.6035 -0.065608 -0.062497 -0.059592 -0.056839 -0.054243
r83 -5.0665 -5.0529 -4.9595 -4.7226 -4.3206 -0.021056 -0.020052 -0.019111 -0.018227 -0.017397
xxx TABLE:ap_VFI xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
__ __ __ __________ ________ _______ _______ _______ _______ _______
r1 0 0 0 0.0011815 0.013905 109.98 115.52 121.26 127.18 133.29
r2 0 0 0 0.00090277 0.013905 109.95 115.49 121.22 127.14 133.26
r3 0 0 0 0.00051498 0.013905 109.9 115.45 121.18 127.1 133.21
r4 0 0 0 0.00051498 0.013905 110.34 115.88 121.61 127.53 133.65
r5 0 0 0 0.00048777 0.013905 110.79 116.33 122.06 127.98 134.1
r79 0 0 0 0 0 80.974 84.852 88.823 92.746 96.836
r80 0 0 0 0 0 75.619 79.241 82.926 86.481 90.439
r81 0 0 0 0 0 67.445 70.139 73.173 76.669 81.091
r82 0 0 0 0 0 50.126 53.467 55.806 57.389 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 11.989 12.265 12.55 12.844 13.145
r2 0.027723 0.028258 0.031999 0.041253 0.048028 12.223 12.501 12.787 13.082 13.383
r3 0.027723 0.028258 0.031999 0.041641 0.048028 12.476 12.755 13.042 13.337 13.638
r4 0.028805 0.029339 0.033081 0.042722 0.049108 12.72 13 13.289 13.584 13.883
r5 0.029859 0.030394 0.034135 0.043802 0.050161 12.955 13.236 13.525 13.821 14.116
r79 0.19737 0.19791 0.20163 0.21175 0.23145 35.417 37.362 39.409 41.7 44.025
r80 0.19737 0.19791 0.20163 0.21175 0.23145 40.752 42.953 45.286 47.946 50.402
r81 0.19737 0.19791 0.20163 0.21175 0.23145 48.909 52.039 55.022 57.741 59.734
r82 0.19737 0.19791 0.20163 0.21175 0.23145 66.215 68.697 72.375 77.007 80.224
r83 0.19737 0.19791 0.20163 0.21175 0.23145 116.33 122.15 128.17 134.39 140.8
[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=1473.2344
% Get Matrixes
cl_st_precompute_list = {'a', ...
'inc', 'inc_unemp', 'spouse_inc', 'spouse_inc_unemp', 'ref_earn_wageind_grid',...
'ar_z_ctr_amz'};
% 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=279.41351.2 Solve for 2019 Evuvw With 0 and 2 Checks
Solve for 0 and 2 checks, by finding the increase to asset state-space that is equivalent to the check increase, so that the problem can be solved without increasing the state-space.
% Call Function
welf_checks = 0;
[ev19_jaeemk_check0, ec19_jaeemk_check0, ev20_jaeemk_check0, ec20_jaeemk_check0] = snw_evuvw19_jaeemk_foc(...
welf_checks, st_solu_type, mp_params, mp_controls, ...
V_emp_2020, ap_ss, cons_emp_2020, V_unemp_2020, cons_unemp_2020, mp_precompute_res);
Completed SNW_A4CHK_WRK_BISEC_VEC;SNW_MP_PARAM=bidenchk;welf_checks=0;TR=0.0015999;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=70.3965
Completed SNW_A4CHK_UNEMP_BISEC_VEC;welf_checks=0;TR=0.0015999;xi=0.5;b=0.5;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=69.7891
Completed SNW_EVUVW20_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;timeEUEC=8.3561
Completed SNW_EVUVW19_JAEEMK_FOC;st_biden_or_trump=bidenchk;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=13.292
----------------------------------------
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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 1.9673e+08 4.5568 5.3248 1.1685 0.036494 64.526
ec20_jaeemk 2 2 6 4.37e+07 83 5.265e+05 2.3259e+08 5.3225 8.4421 1.5861 0.033462 141.61
ev19_jaeemk 3 3 6 4.3173e+07 82 5.265e+05 -6.4971e+08 -15.049 21.201 -1.4088 -473.7 -0.01597
ev20_jaeemk 4 4 6 4.37e+07 83 5.265e+05 -6.683e+08 -15.293 21.999 -1.4385 -511.54 -0.0071775
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 12.017 12.289 12.569 12.857 13.165
r2 0.036494 0.036494 0.037029 0.041745 0.049665 12.261 12.534 12.815 13.104 13.411
r3 0.037912 0.037912 0.038127 0.041994 0.050655 12.495 12.77 13.052 13.341 13.647
r4 0.039293 0.039293 0.039401 0.043382 0.052052 12.753 13.028 13.311 13.599 13.902
r5 0.040635 0.040635 0.04064 0.044725 0.053494 13.002 13.278 13.56 13.846 14.145
r78 0.19737 0.19737 0.19737 0.19737 0.19791 27.77 28.769 29.78 30.961 32.408
r79 0.19737 0.19737 0.19737 0.19737 0.19737 30.426 31.659 32.732 33.953 35.512
r80 0.19737 0.19737 0.19737 0.19737 0.19737 33.678 35.498 37.364 38.952 40.187
r81 0.19737 0.19737 0.19737 0.19737 0.19737 40.112 41.394 43.173 45.605 48.455
r82 0.19737 0.19737 0.19737 0.19737 0.19737 52.096 55.537 58.457 60.087 62.871
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 12.242 12.527 12.819 13.12 13.145
r2 0.033462 0.033996 0.037408 0.043883 0.049712 12.478 12.763 13.057 13.358 13.383
r3 0.033462 0.033996 0.037604 0.045059 0.050801 12.731 13.018 13.313 13.613 13.638
r4 0.034763 0.035298 0.038972 0.046376 0.052249 12.977 13.265 13.56 13.858 13.883
r5 0.036032 0.036566 0.040303 0.047654 0.053654 13.213 13.501 13.796 14.092 14.116
r79 0.19737 0.19791 0.20163 0.21175 0.23145 35.811 37.362 39.409 41.7 44.025
r80 0.19737 0.19791 0.20163 0.21175 0.23145 40.752 42.953 45.286 47.946 50.769
r81 0.19737 0.19791 0.20163 0.21175 0.23145 48.909 52.039 55.022 57.919 60.234
r82 0.19737 0.19791 0.20163 0.21175 0.23145 66.71 69.193 72.375 77.007 80.72
r83 0.19737 0.19791 0.20163 0.21175 0.23145 116.82 122.65 128.66 134.88 141.29
xxx TABLE:ev19_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -284.04 -284.04 -283.59 -279.25 -271.39 -4.3825 -4.288 -4.1947 -4.1025 -4.0066
r2 -273.99 -273.99 -273.54 -269.79 -262.27 -4.2743 -4.1834 -4.0935 -4.0047 -3.9122
r3 -263.81 -263.81 -263.65 -260.8 -253.57 -4.1639 -4.0766 -3.9902 -3.9047 -3.8157
r4 -254.62 -254.62 -254.55 -251.83 -245.12 -4.05 -3.9664 -3.8835 -3.8015 -3.7155
r5 -246.32 -246.32 -246.31 -243.7 -237.45 -3.9424 -3.8621 -3.7826 -3.7036 -3.6205
r78 -13.649 -13.649 -13.649 -13.649 -13.635 -0.27318 -0.26108 -0.24974 -0.23894 -0.22858
r79 -12.29 -12.29 -12.29 -12.29 -12.29 -0.21858 -0.20783 -0.19878 -0.19059 -0.18275
r80 -10.611 -10.611 -10.611 -10.611 -10.611 -0.16128 -0.15409 -0.14735 -0.14136 -0.13575
r81 -8.3555 -8.3555 -8.3555 -8.3555 -8.3555 -0.10114 -0.097402 -0.093444 -0.089432 -0.084914
r82 -5.0715 -5.0715 -5.0715 -5.0715 -5.0715 -0.044205 -0.041465 -0.039418 -0.038353 -0.036628
xxx TABLE:ev20_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -297.16 -296.66 -293.58 -287.23 -277.75 -4.3618 -4.2675 -4.1744 -4.0824 -3.9914
r2 -287.62 -287.12 -284.04 -277.74 -268.53 -4.2552 -4.1645 -4.0747 -3.9861 -3.8983
r3 -278.06 -277.57 -274.51 -268.33 -259.57 -4.1462 -4.059 -3.9728 -3.8876 -3.803
r4 -268.3 -267.84 -264.99 -259.19 -250.96 -4.0343 -3.9508 -3.8681 -3.7863 -3.7047
r5 -259.47 -259.05 -256.38 -250.92 -243.16 -3.9288 -3.8486 -3.7691 -3.6904 -3.6115
r79 -13.642 -13.628 -13.535 -13.298 -12.896 -0.22092 -0.21058 -0.20086 -0.19173 -0.18315
r80 -12.283 -12.269 -12.176 -11.939 -11.537 -0.16979 -0.16182 -0.1543 -0.14722 -0.14057
r81 -10.605 -10.591 -10.498 -10.261 -9.8589 -0.11712 -0.11163 -0.10646 -0.10157 -0.09695
r82 -8.3494 -8.3358 -8.2424 -8.0055 -7.6035 -0.065333 -0.062242 -0.05936 -0.056635 -0.054056
r83 -5.0665 -5.0529 -4.9595 -4.7226 -4.3206 -0.020968 -0.019972 -0.019038 -0.018161 -0.017336
% Call Function
welf_checks = 2;
[ev19_jaeemk_check2, ec19_jaeemk_check2, ev20_jaeemk_check2, ec20_jaeemk_check2] = snw_evuvw19_jaeemk_foc(...
welf_checks, st_solu_type, mp_params, mp_controls, ...
V_emp_2020, ap_ss, cons_emp_2020, V_unemp_2020, cons_unemp_2020, mp_precompute_res);
Completed SNW_A4CHK_WRK_BISEC_VEC;SNW_MP_PARAM=bidenchk;welf_checks=2;TR=0.0015999;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=71.415
Completed SNW_A4CHK_UNEMP_BISEC_VEC;welf_checks=2;TR=0.0015999;xi=0.5;b=0.5;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=71.216
Completed SNW_EVUVW20_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;timeEUEC=7.9626
Completed SNW_EVUVW19_JAEEMK_FOC;st_biden_or_trump=bidenchk;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=13.4174
----------------------------------------
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 1.9675e+08 4.5574 5.3248 1.1684 0.039362 64.529
ec20_jaeemk 2 2 6 4.37e+07 83 5.265e+05 2.3261e+08 5.323 8.4422 1.586 0.036428 141.62
ev19_jaeemk 3 3 6 4.3173e+07 82 5.265e+05 -6.4916e+08 -15.036 21.153 -1.4068 -468.51 -0.015969
ev20_jaeemk 4 4 6 4.37e+07 83 5.265e+05 -6.6772e+08 -15.28 21.948 -1.4364 -505.55 -0.0071773
xxx TABLE:ec19_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.039371 0.039371 0.03984 0.043617 0.0497 12.017 12.289 12.569 12.858 13.165
r2 0.039522 0.039522 0.040022 0.043796 0.050541 12.261 12.534 12.815 13.104 13.411
r3 0.040961 0.040961 0.041164 0.044279 0.051563 12.496 12.77 13.052 13.342 13.647
r4 0.042361 0.042361 0.042463 0.045683 0.05298 12.753 13.028 13.311 13.599 13.902
r5 0.043704 0.043704 0.043709 0.04707 0.054441 13.002 13.278 13.561 13.846 14.145
r78 0.20057 0.20057 0.20057 0.20057 0.20111 27.771 28.77 29.781 30.962 32.409
r79 0.20057 0.20057 0.20057 0.20057 0.20057 30.427 31.66 32.733 33.955 35.514
r80 0.20057 0.20057 0.20057 0.20057 0.20057 33.68 35.5 37.365 38.953 40.188
r81 0.20057 0.20057 0.20057 0.20057 0.20057 40.114 41.396 43.176 45.607 48.456
r82 0.20057 0.20057 0.20057 0.20057 0.20057 52.099 55.54 58.46 60.09 62.874
xxx TABLE:ec20_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.036428 0.036915 0.039238 0.044128 0.049481 12.243 12.527 12.82 13.12 13.145
r2 0.036428 0.036915 0.039449 0.044827 0.050406 12.478 12.763 13.057 13.358 13.383
r3 0.036567 0.037083 0.039954 0.045988 0.051564 12.732 13.018 13.313 13.613 13.638
r4 0.037916 0.038441 0.041306 0.047327 0.053041 12.977 13.265 13.56 13.858 13.883
r5 0.03923 0.039763 0.04262 0.048625 0.054472 13.213 13.502 13.797 14.092 14.116
r79 0.20057 0.20111 0.20483 0.21495 0.23328 35.812 37.363 39.41 41.702 44.026
r80 0.20057 0.20111 0.20483 0.21495 0.23328 40.753 42.954 45.288 47.948 50.77
r81 0.20057 0.20111 0.20483 0.21495 0.23328 48.911 52.041 55.024 57.92 60.235
r82 0.20057 0.20111 0.20483 0.21495 0.23352 66.711 69.194 72.378 77.009 80.722
r83 0.20057 0.20111 0.20483 0.21495 0.23465 116.83 122.65 128.67 134.88 141.3
xxx TABLE:ev19_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -281.59 -281.59 -281.18 -277.52 -270.11 -4.3824 -4.288 -4.1946 -4.1024 -4.0066
r2 -271.55 -271.55 -271.16 -268.06 -261.04 -4.2742 -4.1833 -4.0934 -4.0046 -3.9121
r3 -261.56 -261.56 -261.41 -259.11 -252.39 -4.1639 -4.0766 -3.9902 -3.9047 -3.8156
r4 -252.52 -252.52 -252.45 -250.24 -244 -4.0499 -3.9663 -3.8835 -3.8014 -3.7155
r5 -244.35 -244.35 -244.35 -242.21 -236.39 -3.9423 -3.8621 -3.7825 -3.7036 -3.6204
r78 -13.568 -13.568 -13.568 -13.568 -13.555 -0.27317 -0.26107 -0.24974 -0.23893 -0.22857
r79 -12.209 -12.209 -12.209 -12.209 -12.209 -0.21857 -0.20783 -0.19877 -0.19059 -0.18274
r80 -10.531 -10.531 -10.531 -10.531 -10.531 -0.16127 -0.15408 -0.14735 -0.14135 -0.13575
r81 -8.2749 -8.2749 -8.2749 -8.2749 -8.2749 -0.10114 -0.097398 -0.09344 -0.089428 -0.084911
r82 -4.991 -4.991 -4.991 -4.991 -4.991 -0.044202 -0.041463 -0.039416 -0.038351 -0.036626
xxx TABLE:ev20_jaeemk xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -294.47 -294.03 -291.58 -285.7 -276.56 -4.3618 -4.2675 -4.1743 -4.0824 -3.9914
r2 -284.93 -284.49 -282.06 -276.25 -267.39 -4.2552 -4.1644 -4.0747 -3.9861 -3.8983
r3 -275.39 -274.95 -272.56 -266.91 -258.48 -4.1462 -4.059 -3.9728 -3.8875 -3.803
r4 -265.81 -265.4 -263.16 -257.86 -249.93 -4.0343 -3.9507 -3.8681 -3.7862 -3.7046
r5 -257.15 -256.77 -254.66 -249.67 -242.18 -3.9287 -3.8486 -3.7691 -3.6904 -3.6115
r79 -13.561 -13.548 -13.46 -13.233 -12.841 -0.22091 -0.21058 -0.20085 -0.19173 -0.18315
r80 -12.203 -12.189 -12.101 -11.874 -11.482 -0.16978 -0.16181 -0.1543 -0.14722 -0.14057
r81 -10.524 -10.511 -10.423 -10.196 -9.8048 -0.11712 -0.11163 -0.10645 -0.10157 -0.096948
r82 -8.2689 -8.2556 -8.1674 -7.9402 -7.5499 -0.065331 -0.062241 -0.059358 -0.056634 -0.054054
r83 -4.9861 -4.9727 -4.8846 -4.6573 -4.2682 -0.020967 -0.019972 -0.019038 -0.01816 -0.017336Differences 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=0.0015999 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 0.56295 0.52208 0.47698 0.43181 0.38933 0.3507 0.31612 0.28529 0.25787 0.23347 0.21177 0.19245 0.17527 0.15998 0.14636 0.13425 0.12346 0.11385 0.1053 0.097679 0.090873 0.084802 0.079356 0.074493 0.070139 0.066256 0.062786 0.059699 0.056954 0.054491 0.052283 0.050304 0.04854 0.04697 0.04559 0.044361 0.04326 0.042291 0.04142 0.040649 0.039966 0.039354 0.038806 0.038312 0.037869 0.037469 0.037108 0.036785 0.03649 0.036223 0.035983 0.035763 0.035564 0.035384 0.035221 0.035071 0.034936 0.034812 0.034699 0.034597 0.034502 0.034417 0.034338 0.034265 0.034199 0.034138 0.034083 0.034032 0.033985 0.033942 0.033903 0.033867 0.033834 0.033804 0.033776 0.033752 0.033729 0.033708 0.03369 0.033674 0.033661 0.56295 0.52208 0.47698 0.43181 0.38933 0.3507 0.31612 0.28529 0.25787 0.23347 0.21177 0.19245 0.17527 0.15998 0.14636 0.13425 0.12346 0.11385 0.1053 0.097679 0.090873 0.084802 0.079356 0.074493 0.070139 0.066255 0.062783 0.059688 0.056937 0.054469 0.052258 0.050286 0.048519 0.046956 0.045577 0.044343 0.04325 0.042278 0.041409 0.040639 0.039955 0.039346 0.038798 0.038304 0.037863 0.037462 0.037103 0.03678 0.036485 0.036219 0.035978 0.035759 0.03556 0.035381 0.035218 0.035068 0.034933 0.03481 0.034697 0.034595 0.034501 0.034415 0.034336 0.034264 0.034198 0.034137 0.034082 0.034031 0.033984 0.033941 0.033902 0.033866 0.033833 0.033803 0.033776 0.033751 0.033728 0.033708 0.03369 0.033674 0.033661 0.53405 0.49483 0.45177 0.40879 0.36846 0.33188 0.2992 0.27012 0.2443 0.22137 0.20099 0.18289 0.16679 0.15247 0.13973 0.12838 0.11828 0.10928 0.10127 0.094118 0.087726 0.082008 0.076868 0.072269 0.068154 0.064483 0.061193 0.058266 0.055659 0.053318 0.051221 0.049342 0.047661 0.046165 0.044839 0.043655 0.042598 0.04166 0.040819 0.040072 0.039408 0.038814 0.038278 0.037797 0.037364 0.036973 0.03662 0.036303 0.036014 0.035752 0.035515 0.035299 0.035103 0.034926 0.034765 0.034618 0.034485 0.034363 0.034251 0.034151 0.034058 0.033973 0.033895 0.033823 0.033759 0.033699 0.033644 0.033594 0.033548 0.033506 0.033467 0.033432 0.033399 0.033369 0.033342 0.033318 0.033295 0.033275 0.033257 0.033241 0.033228 0.50159 0.46355 0.42168 0.37988 0.34072 0.30528 0.27369 0.24567 0.22087 0.19892 0.17949 0.1623 0.14709 0.13362 0.1217 0.11114 0.1018 0.093522 0.086196 0.079704 0.073935 0.068817 0.064248 0.060191 0.056581 0.053384 0.050543 0.048031 0.045813 0.043836 0.042078 0.040515 0.039124 0.037892 0.036807 0.03584 0.034978 0.034215 0.03353 0.03292 0.032378 0.03189 0.03145 0.031053 0.030696 0.03037 0.030075 0.029808 0.029564 0.029341 0.029139 0.028953 0.028784 0.028629 0.028488 0.028358 0.028239 0.02813 0.02803 0.027938 0.027853 0.027776 0.027705 0.027638 0.027578 0.027522 0.027472 0.027424 0.027381 0.027341 0.027304 0.027271 0.02724 0.027211 0.027185 0.027162 0.02714 0.027121 0.027103 0.027088 0.027076 0.49153 0.45367 0.41201 0.37043 0.3315 0.29629 0.26492 0.23713 0.21255 0.19082 0.17161 0.15462 0.13961 0.12634 0.11461 0.10424 0.095081 0.086982 0.079825 0.073497 0.067886 0.06292 0.058498 0.054582 0.051108 0.04804 0.045322 0.042927 0.040822 0.038952 0.037297 0.035831 0.034532 0.033386 0.032382 0.031492 0.030702 0.030006 0.029385 0.028833 0.028346 0.02791 0.027518 0.027165 0.026848 0.02656 0.026301 0.026067 0.025852 0.025658 0.025481 0.025319 0.025171 0.025037 0.024914 0.024801 0.024698 0.024603 0.024516 0.024437 0.024363 0.024296 0.024234 0.024176 0.024124 0.024075 0.02403 0.023989 0.023951 0.023916 0.023884 0.023854 0.023827 0.023802 0.023779 0.023758 0.023739 0.023722 0.023707 0.023694 0.023682
2 0.00051498 0.56295 0.52208 0.47698 0.43181 0.38933 0.3507 0.31612 0.28529 0.25787 0.23347 0.21176 0.19245 0.17526 0.15997 0.14635 0.13423 0.12345 0.11385 0.1053 0.097676 0.090868 0.084794 0.079348 0.074483 0.07013 0.066248 0.062778 0.059692 0.056947 0.054486 0.052279 0.0503 0.048537 0.046967 0.045587 0.044358 0.043258 0.042289 0.041418 0.040648 0.039964 0.039353 0.038805 0.038311 0.037868 0.037468 0.037108 0.036784 0.03649 0.036223 0.035982 0.035763 0.035564 0.035384 0.035221 0.035071 0.034936 0.034812 0.034699 0.034597 0.034502 0.034417 0.034338 0.034265 0.034199 0.034138 0.034083 0.034032 0.033985 0.033942 0.033903 0.033867 0.033834 0.033804 0.033776 0.033752 0.033729 0.033708 0.03369 0.033674 0.033661 0.56295 0.52208 0.47698 0.43181 0.38933 0.3507 0.31612 0.28529 0.25787 0.23347 0.21176 0.19245 0.17526 0.15997 0.14635 0.13423 0.12345 0.11385 0.1053 0.097676 0.090868 0.084794 0.079348 0.074483 0.07013 0.066247 0.062775 0.059681 0.05693 0.054464 0.052253 0.050282 0.048516 0.046953 0.045575 0.044341 0.043248 0.042276 0.041407 0.040638 0.039954 0.039345 0.038797 0.038304 0.037862 0.037462 0.037102 0.036779 0.036485 0.036218 0.035978 0.035759 0.03556 0.035381 0.035218 0.035068 0.034933 0.03481 0.034697 0.034595 0.034501 0.034415 0.034336 0.034264 0.034198 0.034137 0.034082 0.034031 0.033984 0.033941 0.033902 0.033866 0.033833 0.033803 0.033776 0.033751 0.033728 0.033708 0.03369 0.033674 0.033661 0.53391 0.4947 0.45165 0.40867 0.36836 0.33178 0.2991 0.27003 0.24421 0.22128 0.20091 0.1828 0.16671 0.1524 0.13967 0.12832 0.11822 0.10923 0.10122 0.09408 0.087691 0.081974 0.076836 0.072236 0.068123 0.064453 0.061166 0.058241 0.055636 0.053298 0.051203 0.049325 0.047645 0.046151 0.044826 0.043643 0.042587 0.041649 0.040808 0.040061 0.039398 0.038804 0.038269 0.037788 0.037356 0.036964 0.036612 0.036294 0.036005 0.035743 0.035507 0.035291 0.035095 0.034918 0.034757 0.03461 0.034477 0.034355 0.034243 0.034143 0.03405 0.033965 0.033887 0.033816 0.033751 0.033691 0.033637 0.033586 0.03354 0.033498 0.033459 0.033424 0.033391 0.033362 0.033334 0.03331 0.033287 0.033267 0.033249 0.033233 0.03322 0.50158 0.46354 0.42167 0.37988 0.34072 0.30527 0.27368 0.24567 0.22086 0.19891 0.17948 0.16229 0.14707 0.1336 0.12168 0.11113 0.10178 0.093511 0.086187 0.079696 0.073926 0.068805 0.064235 0.060177 0.056569 0.053372 0.05053 0.04802 0.045803 0.043827 0.04207 0.040508 0.039117 0.037886 0.036801 0.035835 0.034973 0.034209 0.033525 0.032915 0.032373 0.031886 0.031446 0.031049 0.030692 0.030366 0.030071 0.029805 0.02956 0.029338 0.029135 0.02895 0.02878 0.028626 0.028485 0.028354 0.028236 0.028127 0.028026 0.027935 0.02785 0.027773 0.027702 0.027635 0.027575 0.027519 0.027468 0.027421 0.027378 0.027338 0.027301 0.027268 0.027237 0.027208 0.027182 0.027159 0.027137 0.027118 0.0271 0.027085 0.027073 0.49153 0.45367 0.412 0.37043 0.3315 0.29629 0.26492 0.23713 0.21255 0.19082 0.1716 0.15462 0.1396 0.12633 0.1146 0.10423 0.095071 0.086975 0.079821 0.073493 0.067881 0.062913 0.058489 0.054572 0.051099 0.048032 0.045313 0.04292 0.040815 0.038947 0.037292 0.035827 0.034528 0.033383 0.03238 0.03149 0.0307 0.030004 0.029383 0.028832 0.028345 0.027909 0.027516 0.027164 0.026847 0.026559 0.0263 0.026066 0.025852 0.025657 0.02548 0.025319 0.025171 0.025037 0.024914 0.024801 0.024698 0.024603 0.024516 0.024437 0.024363 0.024296 0.024233 0.024176 0.024123 0.024074 0.02403 0.023988 0.023951 0.023916 0.023883 0.023854 0.023827 0.023802 0.023779 0.023758 0.023739 0.023722 0.023707 0.023693 0.023682
3 0.0041199 0.56193 0.52054 0.475 0.42987 0.38768 0.34937 0.31503 0.2844 0.25712 0.23284 0.21124 0.19202 0.17491 0.15967 0.1461 0.13402 0.12326 0.11368 0.10514 0.097521 0.09072 0.084655 0.07922 0.074372 0.070032 0.066165 0.062706 0.059635 0.056895 0.054441 0.052238 0.050261 0.048504 0.046937 0.045562 0.044335 0.043238 0.042272 0.041404 0.040636 0.039954 0.039343 0.038797 0.038304 0.037862 0.037463 0.037103 0.03678 0.036485 0.036219 0.035979 0.03576 0.035561 0.035382 0.035218 0.035069 0.034934 0.03481 0.034697 0.034595 0.0345 0.034415 0.034336 0.034263 0.034198 0.034137 0.034081 0.03403 0.033984 0.033941 0.033901 0.033866 0.033833 0.033803 0.033775 0.03375 0.033727 0.033707 0.033689 0.033673 0.03366 0.56193 0.52054 0.475 0.42987 0.38768 0.34937 0.31503 0.2844 0.25712 0.23284 0.21124 0.19202 0.17491 0.15967 0.1461 0.13402 0.12326 0.11368 0.10514 0.097521 0.09072 0.084655 0.07922 0.074372 0.070032 0.066164 0.062702 0.059623 0.056877 0.054417 0.052213 0.050244 0.048481 0.046926 0.045547 0.044317 0.043228 0.042258 0.041393 0.040625 0.039943 0.039335 0.038788 0.038297 0.037856 0.037456 0.037097 0.036775 0.03648 0.036215 0.035974 0.035756 0.035557 0.035378 0.035215 0.035066 0.034931 0.034808 0.034695 0.034593 0.034499 0.034413 0.034334 0.034262 0.034196 0.034136 0.03408 0.034029 0.033983 0.03394 0.033901 0.033865 0.033832 0.033802 0.033775 0.03375 0.033727 0.033707 0.033688 0.033673 0.033659 0.53202 0.49235 0.44891 0.40601 0.36603 0.3298 0.29738 0.26852 0.24287 0.22008 0.19985 0.18186 0.16588 0.15166 0.13901 0.12775 0.11772 0.10878 0.1008 0.093679 0.08732 0.081636 0.076534 0.071968 0.067878 0.064226 0.060956 0.058056 0.055466 0.053147 0.051067 0.049197 0.047525 0.046041 0.044724 0.043549 0.042498 0.041565 0.04073 0.039986 0.039326 0.038734 0.038201 0.037722 0.037291 0.036901 0.036549 0.036233 0.035944 0.035683 0.035447 0.035232 0.035036 0.03486 0.034699 0.034552 0.034419 0.034297 0.034186 0.034085 0.033992 0.033908 0.03383 0.033759 0.033694 0.033634 0.03358 0.033529 0.033483 0.033441 0.033402 0.033367 0.033335 0.033305 0.033278 0.033253 0.033231 0.03321 0.033192 0.033177 0.033164 0.5005 0.46194 0.41963 0.37788 0.33902 0.30389 0.27255 0.24472 0.22007 0.19824 0.17892 0.16182 0.14668 0.13327 0.12139 0.11087 0.10156 0.093308 0.085997 0.079508 0.073746 0.068635 0.064078 0.060037 0.056442 0.053261 0.050432 0.047937 0.045726 0.043758 0.042007 0.040446 0.03906 0.037835 0.036752 0.035789 0.03493 0.03417 0.033489 0.032881 0.032341 0.031855 0.031416 0.031021 0.030664 0.030339 0.030045 0.029779 0.029535 0.029313 0.029111 0.028926 0.028756 0.028602 0.028461 0.028331 0.028213 0.028104 0.028003 0.027912 0.027827 0.02775 0.027679 0.027613 0.027553 0.027497 0.027446 0.027398 0.027355 0.027315 0.027279 0.027245 0.027214 0.027186 0.02716 0.027136 0.027115 0.027095 0.027078 0.027063 0.02705 0.4905 0.45212 0.41002 0.36848 0.32985 0.29495 0.26383 0.23623 0.2118 0.19019 0.17108 0.15419 0.13925 0.12603 0.11435 0.10402 0.09488 0.086803 0.079661 0.073335 0.06773 0.062771 0.058359 0.054459 0.050998 0.047946 0.04524 0.04286 0.040762 0.038901 0.037252 0.035788 0.034494 0.033354 0.032352 0.031465 0.030679 0.029986 0.029367 0.028818 0.028333 0.027898 0.027507 0.027156 0.02684 0.026552 0.026293 0.02606 0.025846 0.025652 0.025475 0.025314 0.025166 0.025032 0.02491 0.024797 0.024694 0.024599 0.024512 0.024433 0.024359 0.024292 0.02423 0.024172 0.02412 0.024071 0.024027 0.023985 0.023947 0.023912 0.02388 0.023851 0.023823 0.023798 0.023776 0.023755 0.023736 0.023719 0.023703 0.02369 0.023679
4 0.013905 0.46781 0.4349 0.40085 0.36671 0.33426 0.30434 0.27716 0.25264 0.23064 0.21092 0.19318 0.17719 0.16279 0.14983 0.13816 0.12762 0.11808 0.10946 0.1017 0.094707 0.088438 0.082794 0.077711 0.073143 0.069038 0.065348 0.062037 0.059083 0.056429 0.054044 0.051903 0.049969 0.048247 0.04671 0.045355 0.044142 0.043062 0.042103 0.041244 0.040484 0.039808 0.039202 0.038661 0.038172 0.037732 0.037335 0.036977 0.036656 0.036363 0.036098 0.035859 0.035641 0.035443 0.035264 0.035102 0.034953 0.034818 0.034695 0.034582 0.03448 0.034386 0.034301 0.034222 0.034149 0.034084 0.034023 0.033968 0.033917 0.03387 0.033827 0.033788 0.033752 0.033719 0.033689 0.033662 0.033637 0.033614 0.033594 0.033575 0.03356 0.033546 0.46781 0.4349 0.40085 0.36671 0.33426 0.30434 0.27716 0.25264 0.23064 0.21092 0.19318 0.17719 0.16279 0.14983 0.13816 0.12762 0.11808 0.10946 0.1017 0.094707 0.088438 0.082794 0.077711 0.073143 0.069037 0.065346 0.06203 0.059069 0.056407 0.054021 0.05188 0.049954 0.048223 0.046701 0.045337 0.044126 0.043051 0.04209 0.041235 0.040473 0.039798 0.039195 0.038652 0.038164 0.037726 0.037328 0.036972 0.036651 0.036358 0.036094 0.035855 0.035638 0.03544 0.035261 0.035099 0.03495 0.034815 0.034692 0.03458 0.034478 0.034384 0.034299 0.03422 0.034148 0.034082 0.034022 0.033966 0.033916 0.033869 0.033826 0.033787 0.033751 0.033719 0.033689 0.033661 0.033636 0.033614 0.033593 0.033575 0.033559 0.033546 0.43586 0.40478 0.37294 0.34114 0.31101 0.28327 0.25811 0.23545 0.21515 0.19698 0.18065 0.16594 0.1527 0.1408 0.13008 0.12039 0.11163 0.10371 0.096571 0.09015 0.084376 0.079172 0.074475 0.070242 0.066437 0.063004 0.059918 0.057154 0.05467 0.052441 0.050438 0.048626 0.047008 0.045569 0.044281 0.043134 0.042107 0.04119 0.04037 0.039638 0.038989 0.038405 0.03788 0.037407 0.03698 0.036594 0.036246 0.035932 0.035646 0.035387 0.035152 0.034939 0.034744 0.034569 0.034409 0.034262 0.03413 0.034009 0.033898 0.033797 0.033705 0.033621 0.033543 0.033472 0.033407 0.033347 0.033293 0.033243 0.033197 0.033155 0.033116 0.033081 0.033048 0.033019 0.032992 0.032967 0.032945 0.032924 0.032906 0.032891 0.032878 0.40623 0.37614 0.34533 0.31457 0.28546 0.25872 0.23454 0.21283 0.19346 0.17619 0.16074 0.14688 0.13445 0.12332 0.11335 0.10437 0.096287 0.089 0.082466 0.076608 0.07138 0.066692 0.062489 0.05873 0.055372 0.05237 0.049691 0.047316 0.045193 0.043299 0.041612 0.040094 0.038742 0.037549 0.036484 0.035538 0.034694 0.033943 0.033271 0.032671 0.032138 0.031657 0.031223 0.030832 0.030477 0.030155 0.029863 0.029598 0.029356 0.029135 0.028935 0.028751 0.028582 0.028429 0.028288 0.028159 0.028041 0.027932 0.027832 0.027741 0.027657 0.02758 0.027509 0.027443 0.027383 0.027327 0.027276 0.027229 0.027186 0.027146 0.027109 0.027076 0.027045 0.027017 0.026991 0.026967 0.026946 0.026926 0.026909 0.026894 0.026881 0.39637 0.36647 0.33585 0.30531 0.27642 0.24991 0.22595 0.20446 0.18531 0.16825 0.15301 0.13935 0.12712 0.11618 0.1064 0.097603 0.089694 0.082579 0.076211 0.070514 0.06544 0.060902 0.056842 0.053221 0.049996 0.047121 0.044563 0.042303 0.040292 0.038504 0.036918 0.035496 0.034235 0.033126 0.032142 0.031271 0.030499 0.029815 0.029205 0.028663 0.028184 0.027754 0.027367 0.027019 0.026705 0.026421 0.026164 0.025932 0.025719 0.025526 0.025351 0.025191 0.025044 0.024911 0.024789 0.024676 0.024573 0.024479 0.024392 0.024313 0.02424 0.024173 0.024111 0.024053 0.024001 0.023952 0.023908 0.023866 0.023829 0.023794 0.023762 0.023732 0.023705 0.02368 0.023657 0.023636 0.023617 0.0236 0.023585 0.023572 0.023561
5 0.032959 0.36797 0.34609 0.32201 0.29764 0.27416 0.25219 0.23194 0.21339 0.19647 0.18106 0.16704 0.15429 0.1427 0.13217 0.12263 0.114 0.1062 0.099117 0.092688 0.086852 0.081557 0.076782 0.072449 0.068526 0.06497 0.061745 0.058839 0.056202 0.053819 0.051667 0.049711 0.047951 0.046357 0.044945 0.04368 0.042541 0.041535 0.040621 0.039803 0.039075 0.038422 0.037837 0.03731 0.036835 0.036406 0.036016 0.035666 0.03535 0.035061 0.0348 0.034564 0.034348 0.034152 0.033975 0.033813 0.033666 0.033532 0.033409 0.033298 0.033196 0.033102 0.033017 0.032939 0.032867 0.032801 0.032741 0.032685 0.032635 0.032588 0.032545 0.032506 0.032471 0.032438 0.032408 0.03238 0.032356 0.032333 0.032312 0.032294 0.032278 0.032265 0.36797 0.34609 0.32201 0.29764 0.27416 0.25219 0.23194 0.21339 0.19647 0.18106 0.16704 0.15429 0.1427 0.13217 0.12263 0.114 0.1062 0.099117 0.092688 0.086852 0.081557 0.076782 0.072449 0.068526 0.064969 0.061739 0.058825 0.056179 0.053796 0.051643 0.049696 0.047926 0.046344 0.04493 0.043661 0.042531 0.04152 0.040608 0.039794 0.039063 0.038412 0.037829 0.037301 0.036828 0.036398 0.03601 0.03566 0.035344 0.035057 0.034796 0.034559 0.034344 0.034149 0.033972 0.03381 0.033663 0.033529 0.033407 0.033296 0.033194 0.033101 0.033016 0.032937 0.032865 0.0328 0.032739 0.032684 0.032633 0.032587 0.032545 0.032506 0.03247 0.032437 0.032407 0.03238 0.032355 0.032332 0.032312 0.032294 0.032278 0.032265 0.33227 0.31241 0.29072 0.26886 0.24787 0.22825 0.21017 0.19364 0.17858 0.16487 0.15242 0.14109 0.1308 0.12145 0.11298 0.10531 0.098364 0.092062 0.086332 0.081115 0.076373 0.072083 0.068183 0.064647 0.061434 0.058518 0.055889 0.053502 0.051345 0.049395 0.047616 0.046006 0.044555 0.043252 0.042081 0.041027 0.040083 0.039229 0.038461 0.037771 0.037154 0.036597 0.036094 0.035639 0.035226 0.034852 0.034514 0.034207 0.033928 0.033674 0.033444 0.033234 0.033043 0.032869 0.032711 0.032567 0.032436 0.032316 0.032206 0.032106 0.032014 0.03193 0.031853 0.031782 0.031718 0.031658 0.031604 0.031554 0.031509 0.031467 0.031428 0.031393 0.031361 0.031331 0.031304 0.03128 0.031257 0.031237 0.031219 0.031203 0.03119 0.30608 0.28705 0.26621 0.24522 0.22509 0.20632 0.18907 0.17334 0.15906 0.14612 0.13439 0.12377 0.11416 0.10547 0.097629 0.090575 0.084225 0.078485 0.073291 0.068592 0.064342 0.060527 0.057077 0.053968 0.051162 0.048628 0.046357 0.044305 0.04246 0.040806 0.039306 0.037955 0.036745 0.035665 0.034697 0.033829 0.033054 0.032352 0.03172 0.031153 0.030644 0.030183 0.029765 0.029388 0.029043 0.02873 0.028445 0.028185 0.027948 0.027731 0.027533 0.027352 0.027186 0.027034 0.026895 0.026766 0.02665 0.026542 0.026443 0.026352 0.026268 0.026192 0.026121 0.026055 0.025995 0.02594 0.025889 0.025842 0.025799 0.025759 0.025723 0.025689 0.025658 0.02563 0.025604 0.025581 0.025559 0.02554 0.025523 0.025508 0.025495 0.29651 0.27764 0.257 0.23622 0.2163 0.19774 0.18071 0.16519 0.15112 0.13838 0.12685 0.11643 0.10701 0.098502 0.090846 0.08397 0.077792 0.072218 0.067186 0.062643 0.058543 0.054874 0.051565 0.048591 0.045914 0.043504 0.041352 0.039414 0.037678 0.036127 0.034727 0.03347 0.032348 0.031352 0.030463 0.029668 0.028964 0.028328 0.027757 0.027247 0.026792 0.02638 0.026009 0.025675 0.02537 0.025094 0.024844 0.024616 0.024409 0.024219 0.024047 0.023889 0.023744 0.023612 0.023491 0.023379 0.023278 0.023184 0.023098 0.02302 0.022946 0.02288 0.022818 0.022761 0.022709 0.02266 0.022616 0.022575 0.022537 0.022502 0.02247 0.022441 0.022414 0.022389 0.022366 0.022345 0.022326 0.022309 0.022294 0.022281 0.02227
6 0.064373 0.29936 0.28324 0.26551 0.24731 0.22949 0.21256 0.19668 0.18198 0.16845 0.15601 0.14461 0.13419 0.12468 0.116 0.10809 0.10089 0.094314 0.088347 0.082929 0.078028 0.073572 0.069541 0.065869 0.062524 0.059482 0.056711 0.054182 0.051879 0.049796 0.04789 0.046161 0.044577 0.043151 0.04186 0.040692 0.039652 0.038704 0.03785 0.037084 0.036396 0.035775 0.035219 0.034714 0.034258 0.033844 0.033468 0.03313 0.032821 0.032541 0.032286 0.032054 0.031842 0.031651 0.031475 0.031316 0.031171 0.031038 0.030916 0.030806 0.030705 0.030612 0.030528 0.030449 0.030378 0.030313 0.030252 0.030198 0.030147 0.030101 0.030058 0.030019 0.029984 0.029951 0.029921 0.029894 0.029869 0.029847 0.029826 0.029808 0.029792 0.029779 0.29639 0.28051 0.26311 0.24527 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=0.0015999 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
_____ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ 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____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________
1 0 0.81275 0.81268 0.81262 0.81251 0.81229 0.81192 0.81144 0.81092 0.81047 0.81017 0.81011 0.81038 0.81108 0.81226 0.8138 0.81538 0.81659 0.81719 0.81719 0.81644 0.8146 0.812 0.80816 0.80271 0.7965 0.78949 0.78174 0.77388 0.76593 0.75842 0.75085 0.74253 0.73442 0.72645 0.71873 0.71053 0.70201 0.69347 0.68462 0.67566 0.66662 0.6575 0.64821 0.63854 0.62851 0.61843 0.60845 0.59832 0.58817 0.57847 0.56919 0.5599 0.55094 0.54232 0.53372 0.52562 0.51752 0.50969 0.50202 0.49472 0.48741 0.47969 0.47305 0.46662 0.46003 0.45369 0.44762 0.4418 0.4366 0.43148 0.42698 0.42304 0.41952 0.41633 0.41373 0.41122 0.40872 0.40648 0.40414 0.40197 0.40025 0.81275 0.81268 0.81262 0.81251 0.81229 0.81192 0.81144 0.81092 0.81047 0.81017 0.81011 0.81038 0.81108 0.81226 0.8138 0.81538 0.81659 0.81719 0.81719 0.81644 0.8146 0.812 0.80816 0.80271 0.7965 0.78956 0.78171 0.77356 0.76638 0.75853 0.75031 0.7424 0.73429 0.72624 0.71837 0.71005 0.7018 0.69315 0.68441 0.67558 0.66638 0.6573 0.64805 0.63821 0.62826 0.61826 0.60811 0.59789 0.58792 0.57832 0.56882 0.55945 0.55057 0.5419 0.53322 0.52518 0.51687 0.50916 0.50147 0.49415 0.48651 0.47913 0.4725 0.46579 0.45929 0.45273 0.44673 0.44107 0.4357 0.43078 0.42653 0.42253 0.41906 0.41613 0.41338 0.41099 0.40859 0.40636 0.4039 0.40193 0.40025 0.80883 0.80769 0.80686 0.80639 0.80624 0.80633 0.80656 0.80696 0.80743 0.80791 0.80845 0.80901 0.8097 0.81046 0.8112 0.81171 0.81166 0.81133 0.81064 0.8092 0.80655 0.80302 0.79854 0.79309 0.7877 0.78216 0.77604 0.76953 0.76229 0.75422 0.74556 0.737 0.72876 0.72071 0.71272 0.70468 0.69641 0.68788 0.67904 0.66988 0.6606 0.65119 0.64159 0.63164 0.6216 0.61152 0.60151 0.59151 0.58146 0.57141 0.56156 0.55179 0.54221 0.53318 0.52427 0.51592 0.50766 0.4998 0.49212 0.48466 0.47708 0.4696 0.46285 0.45658 0.4505 0.44461 0.43948 0.43489 0.43067 0.4265 0.42272 0.41956 0.41678 0.41423 0.41178 0.40945 0.40705 0.40474 0.40279 0.40092 0.39955 0.62457 0.62348 0.62258 0.62195 0.6216 0.62145 0.62142 0.62142 0.62144 0.62146 0.62147 0.62147 0.6214 0.62123 0.62089 0.62032 0.6194 0.61805 0.61621 0.61361 0.6099 0.60561 0.60055 0.59467 0.58896 0.58321 0.57706 0.57071 0.56411 0.55728 0.55015 0.54311 0.53596 0.52903 0.52239 0.51552 0.50852 0.50139 0.49412 0.48654 0.47878 0.47101 0.46327 0.45545 0.44767 0.43996 0.43242 0.42506 0.41783 0.4109 0.40424 0.39773 0.39163 0.386 0.38048 0.37564 0.37084 0.36661 0.36258 0.35889 0.35529 0.3518 0.34888 0.34631 0.34381 0.34152 0.33945 0.33755 0.33573 0.33382 0.33205 0.33035 0.32877 0.32729 0.32597 0.32482 0.32359 0.3224 0.32135 0.32039 0.31971 0.56794 0.56709 0.56636 0.56586 0.5656 0.56554 0.56558 0.56569 0.56582 0.56597 0.56614 0.5663 0.56642 0.56646 0.56635 0.56601 0.56533 0.56421 0.5626 0.56022 0.55672 0.55263 0.54778 0.5421 0.53659 0.53103 0.52509 0.51893 0.51253 0.50594 0.49909 0.49238 0.4856 0.47905 0.47279 0.46631 0.45971 0.453 0.44615 0.43902 0.43173 0.42445 0.41717 0.40981 0.40253 0.39534 0.38834 0.38154 0.37483 0.36846 0.36238 0.35646 0.35093 0.34589 0.34102 0.33682 0.33265 0.32907 0.32569 0.32266 0.31971 0.31686 0.31456 0.31259 0.31065 0.30888 0.30735 0.30599 0.30472 0.30335 0.30209 0.30087 0.2998 0.29877 0.29782 0.29705 0.29623 0.29545 0.29477 0.29417 0.29379
2 0.00051498 0.81275 0.81268 0.81262 0.81251 0.81229 0.81192 0.81144 0.81092 0.81047 0.81017 0.8101 0.81037 0.81107 0.81225 0.8138 0.81538 0.81659 0.81719 0.81717 0.81641 0.81453 0.81197 0.80811 0.80266 0.79652 0.78949 0.78169 0.7738 0.76579 0.75831 0.75073 0.74238 0.73432 0.72638 0.71874 0.71049 0.70202 0.69341 0.68454 0.67555 0.66659 0.65743 0.64821 0.63847 0.62845 0.61838 0.60841 0.59824 0.58804 0.57837 0.5691 0.55982 0.55089 0.54228 0.53369 0.52561 0.51749 0.50966 0.50196 0.49467 0.48736 0.47966 0.47305 0.46662 0.46001 0.45366 0.44762 0.44178 0.43658 0.43148 0.42697 0.42303 0.41951 0.41633 0.41373 0.41122 0.40871 0.40648 0.40413 0.40196 0.40024 0.81275 0.81268 0.81262 0.81251 0.81229 0.81192 0.81144 0.81092 0.81047 0.81017 0.8101 0.81037 0.81107 0.81225 0.8138 0.81538 0.81659 0.81719 0.81717 0.81641 0.81453 0.81197 0.80811 0.80266 0.79652 0.78956 0.78166 0.77349 0.76627 0.7584 0.75017 0.74226 0.73419 0.72618 0.71837 0.71002 0.70181 0.69308 0.68434 0.67546 0.66635 0.65723 0.64803 0.63814 0.62819 0.61821 0.60808 0.59781 0.58779 0.57821 0.56872 0.55937 0.55052 0.54188 0.53318 0.52517 0.51684 0.50912 0.50141 0.4941 0.48646 0.4791 0.4725 0.46578 0.45927 0.45271 0.44672 0.44106 0.43568 0.43076 0.42651 0.42251 0.41905 0.41612 0.41338 0.41098 0.40858 0.40635 0.40389 0.40193 0.40025 0.80853 0.80736 0.80652 0.80605 0.8059 0.80598 0.80621 0.80661 0.80706 0.80753 0.80805 0.80862 0.80931 0.81008 0.81081 0.81133 0.81129 0.81094 0.81022 0.80877 0.80614 0.80266 0.79817 0.79271 0.78739 0.78172 0.77557 0.76906 0.76182 0.75383 0.74517 0.73653 0.72832 0.72038 0.71251 0.70445 0.69624 0.68765 0.67881 0.66963 0.66045 0.651 0.64144 0.63143 0.6214 0.61132 0.60133 0.59132 0.58126 0.57124 0.56139 0.55159 0.54203 0.53303 0.52413 0.51581 0.50754 0.49965 0.49194 0.48449 0.47691 0.46946 0.46271 0.45644 0.45037 0.44449 0.43936 0.43477 0.43055 0.42639 0.4226 0.41946 0.41668 0.41413 0.41168 0.40934 0.40694 0.40463 0.40269 0.40082 0.39945 0.62452 0.62343 0.62253 0.6219 0.62155 0.6214 0.62137 0.62138 0.62139 0.6214 0.62142 0.62141 0.62135 0.62118 0.62084 0.62027 0.61936 0.61801 0.61615 0.61354 0.6098 0.60554 0.60045 0.59458 0.58894 0.58316 0.57696 0.57059 0.56394 0.55712 0.54998 0.54293 0.53583 0.52893 0.52236 0.51544 0.5085 0.5013 0.49402 0.48639 0.47871 0.47091 0.46322 0.45535 0.44758 0.43987 0.43234 0.42495 0.41767 0.41076 0.40411 0.39762 0.39154 0.38593 0.38043 0.3756 0.37079 0.36654 0.3625 0.35881 0.35522 0.35175 0.34886 0.34627 0.34378 0.34148 0.33941 0.33751 0.33569 0.33378 0.33201 0.33031 0.32873 0.32726 0.32594 0.32479 0.32356 0.32237 0.32132 0.32036 0.31968 0.56794 0.56709 0.56636 0.56586 0.5656 0.56553 0.56558 0.56568 0.56581 0.56597 0.56613 0.56629 0.56641 0.56645 0.56634 0.566 0.56533 0.56422 0.56259 0.56019 0.55665 0.55259 0.54772 0.54204 0.53661 0.53103 0.52504 0.51885 0.5124 0.50581 0.49896 0.49224 0.48551 0.47898 0.4728 0.46627 0.45972 0.45294 0.44608 0.43891 0.4317 0.42439 0.41716 0.40975 0.40247 0.39529 0.3883 0.38146 0.37471 0.36835 0.36228 0.35638 0.35087 0.34586 0.34099 0.33681 0.33263 0.32903 0.32565 0.32262 0.31966 0.31684 0.31457 0.31258 0.31064 0.30887 0.30734 0.30598 0.30471 0.30334 0.30208 0.30086 0.2998 0.29877 0.29781 0.29705 0.29623 0.29545 0.29477 0.29416 0.29378
3 0.0041199 0.81254 0.81219 0.8118 0.8115 0.81123 0.81087 0.81042 0.80995 0.80955 0.80931 0.80932 0.80965 0.81041 0.81164 0.81321 0.8148 0.81602 0.81661 0.81654 0.81558 0.81357 0.81103 0.80699 0.80169 0.79564 0.78842 0.78044 0.77261 0.76462 0.75732 0.74988 0.74162 0.73378 0.72609 0.71831 0.71005 0.70142 0.69271 0.6838 0.67489 0.66603 0.65679 0.64751 0.63778 0.62783 0.61756 0.6074 0.59705 0.58699 0.57736 0.56813 0.55917 0.55013 0.54173 0.53328 0.52524 0.51714 0.50931 0.50156 0.49438 0.48689 0.47935 0.47294 0.46647 0.45983 0.45347 0.44747 0.44158 0.43634 0.43131 0.42679 0.42289 0.41935 0.4162 0.41362 0.4111 0.40859 0.40636 0.40401 0.40186 0.40019 0.81254 0.81219 0.8118 0.8115 0.81123 0.81087 0.81042 0.80995 0.80955 0.80931 0.80932 0.80965 0.81041 0.81164 0.81321 0.8148 0.81602 0.81661 0.81654 0.81558 0.81357 0.81103 0.80699 0.80169 0.79566 0.78849 0.78031 0.7723 0.76523 0.75728 0.74932 0.7415 0.73367 0.72586 0.71793 0.70963 0.7012 0.69236 0.68367 0.67483 0.66577 0.6566 0.64727 0.63747 0.62756 0.61738 0.60701 0.59668 0.58674 0.57718 0.56779 0.55869 0.54974 0.54135 0.53275 0.5248 0.51652 0.5088 0.50105 0.49374 0.48598 0.47876 0.47228 0.46559 0.45905 0.45254 0.44648 0.44088 0.4355 0.43057 0.4263 0.42229 0.41887 0.41598 0.41325 0.41087 0.40848 0.40623 0.40378 0.40181 0.40015 0.80567 0.80414 0.80293 0.80224 0.80201 0.80208 0.80227 0.80269 0.80311 0.80359 0.80414 0.8048 0.80553 0.8064 0.80712 0.8076 0.80765 0.8074 0.80659 0.80498 0.8024 0.79904 0.79452 0.78928 0.784 0.77784 0.77121 0.76471 0.75783 0.75029 0.74207 0.73343 0.72499 0.71718 0.70965 0.70185 0.69363 0.68505 0.67637 0.6673 0.65822 0.64879 0.63912 0.62915 0.61905 0.60874 0.5985 0.58848 0.57865 0.56889 0.55911 0.54957 0.54 0.53132 0.52259 0.51446 0.50613 0.49811 0.49045 0.48298 0.47523 0.46798 0.4614 0.45514 0.44903 0.44322 0.43816 0.43355 0.4293 0.42517 0.42141 0.41833 0.41554 0.41303 0.41057 0.40823 0.4058 0.40352 0.4016 0.39977 0.39837 0.62394 0.62258 0.62135 0.62053 0.62013 0.62 0.62001 0.62006 0.62013 0.62021 0.62031 0.62037 0.62037 0.62024 0.61994 0.61938 0.61848 0.61713 0.61521 0.6124 0.60854 0.60429 0.59904 0.59331 0.58777 0.5818 0.57543 0.5691 0.56257 0.55579 0.54889 0.5419 0.53504 0.52836 0.52169 0.51478 0.50766 0.50033 0.49308 0.48546 0.47787 0.47002 0.46224 0.45444 0.44673 0.43882 0.43108 0.42357 0.4164 0.4095 0.40293 0.39671 0.39054 0.38517 0.37978 0.37503 0.37027 0.36597 0.36195 0.3583 0.35455 0.35124 0.34852 0.34591 0.34344 0.34116 0.33908 0.33717 0.3353 0.33344 0.33165 0.32996 0.3284 0.32696 0.32564 0.32449 0.32327 0.32207 0.32104 0.32007 0.31941 0.5677 0.56658 0.56552 0.56483 0.56452 0.56446 0.56455 0.5647 0.56488 0.56509 0.56533 0.56556 0.56574 0.56582 0.56574 0.56541 0.56474 0.56362 0.56194 0.55934 0.55568 0.55164 0.54659 0.54106 0.53571 0.52994 0.52377 0.51763 0.51129 0.50475 0.49813 0.49147 0.48498 0.47868 0.47238 0.46586 0.45914 0.45223 0.4454 0.43824 0.43113 0.42376 0.41643 0.40909 0.40187 0.39448 0.38729 0.38032 0.37367 0.36733 0.36134 0.3557 0.35011 0.34533 0.34058 0.33646 0.33234 0.32868 0.32532 0.32232 0.31921 0.31654 0.31445 0.31243 0.31051 0.30875 0.30721 0.30585 0.30452 0.3032 0.30192 0.30071 0.29966 0.29866 0.29771 0.29694 0.29613 0.29534 0.29467 0.29406 0.29369
4 0.013905 0.74117 0.73709 0.73619 0.73592 0.736 0.73628 0.73677 0.73763 0.7391 0.74131 0.74415 0.74755 0.7515 0.75604 0.76092 0.76564 0.76975 0.77301 0.77537 0.77672 0.77695 0.77671 0.77506 0.77265 0.76846 0.76323 0.75719 0.75084 0.7442 0.73838 0.73168 0.72447 0.71707 0.70965 0.70186 0.69374 0.68537 0.67675 0.66757 0.6588 0.6501 0.64168 0.63298 0.62363 0.6138 0.60405 0.59425 0.5842 0.57482 0.56574 0.55686 0.54838 0.54001 0.5318 0.52383 0.51592 0.50808 0.50061 0.49287 0.48585 0.47831 0.47114 0.46467 0.45827 0.45162 0.44531 0.43938 0.43364 0.42826 0.42313 0.41857 0.41472 0.41124 0.4082 0.40565 0.40312 0.40058 0.39839 0.39602 0.39395 0.39222 0.74117 0.73709 0.73619 0.73592 0.736 0.73628 0.73677 0.73763 0.7391 0.74131 0.74415 0.74755 0.7515 0.75604 0.76092 0.76564 0.76975 0.77301 0.77537 0.77672 0.77695 0.77671 0.77506 0.77265 0.76845 0.76328 0.75688 0.75069 0.74464 0.73804 0.7313 0.72429 0.71699 0.70938 0.7014 0.69341 0.68503 0.67632 0.66744 0.65861 0.64982 0.64151 0.63262 0.6232 0.61355 0.60373 0.59385 0.58393 0.5746 0.56547 0.55659 0.54793 0.53957 0.53123 0.52328 0.51533 0.50756 0.5001 0.4924 0.48514 0.47759 0.47059 0.46392 0.45724 0.45066 0.44446 0.43838 0.43283 0.4274 0.42246 0.41809 0.41407 0.41075 0.40793 0.40532 0.40289 0.40052 0.39818 0.39582 0.39383 0.39222 0.7246 0.71919 0.71743 0.71678 0.71694 0.71764 0.71889 0.72052 0.72278 0.72563 0.72903 0.73281 0.73681 0.74093 0.74499 0.7484 0.75148 0.75397 0.75579 0.75676 0.75652 0.75584 0.75405 0.75204 0.74873 0.74475 0.74015 0.73498 0.72943 0.72347 0.71651 0.7086 0.70038 0.69198 0.68372 0.67566 0.6674 0.65899 0.65036 0.64178 0.63324 0.62481 0.61606 0.60681 0.59722 0.58755 0.57802 0.56844 0.55942 0.55053 0.5416 0.53291 0.52443 0.51608 0.50799 0.49991 0.49188 0.48422 0.47656 0.4692 0.46162 0.45472 0.44825 0.44225 0.43613 0.43053 0.42555 0.4211 0.4168 0.41262 0.40887 0.40591 0.40314 0.40075 0.39823 0.39585 0.39342 0.39119 0.38929 0.38747 0.38613 0.55164 0.54656 0.54482 0.54403 0.544 0.5445 0.54545 0.54684 0.5488 0.55135 0.55428 0.55742 0.56061 0.56381 0.56683 0.56942 0.57142 0.57273 0.57326 0.57277 0.57113 0.56919 0.56633 0.56351 0.55982 0.55586 0.55144 0.54657 0.54136 0.53597 0.53017 0.52399 0.51769 0.51124 0.50462 0.49791 0.49089 0.48371 0.47621 0.46872 0.46131 0.45423 0.44706 0.43966 0.4321 0.42462 0.41737 0.41018 0.40362 0.39726 0.39111 0.3853 0.37982 0.3746 0.36977 0.3651 0.36072 0.35668 0.35271 0.34924 0.34555 0.3425 0.33977 0.33724 0.33477 0.33259 0.33063 0.32879 0.32678 0.32487 0.32304 0.32137 0.31984 0.31848 0.31722 0.31604 0.31482 0.31364 0.31262 0.31166 0.31101 0.49629 0.49143 0.48986 0.48919 0.48925 0.48982 0.49085 0.49232 0.49438 0.49705 0.50012 0.50341 0.50678 0.51017 0.5134 0.5162 0.51843 0.51997 0.52072 0.52044 0.51901 0.51728 0.51462 0.51198 0.50849 0.50471 0.50048 0.49579 0.49078 0.48561 0.48009 0.47423 0.4683 0.46223 0.45598 0.44965 0.44303 0.43626 0.42919 0.42216 0.41523 0.40862 0.4019 0.39496 0.38788 0.38092 0.37422 0.36756 0.36152 0.35571 0.35013 0.34489 0.33999 0.33536 0.33116 0.32712 0.32338 0.31997 0.31666 0.31385 0.31078 0.30837 0.30625 0.30429 0.30237 0.30071 0.29928 0.29798 0.29653 0.29514 0.29382 0.29264 0.29161 0.29067 0.28976 0.28898 0.28816 0.28738 0.28671 0.28611 0.28575
5 0.032959 0.65341 0.6534 0.65431 0.65624 0.65879 0.66152 0.66419 0.66666 0.66891 0.6709 0.67266 0.67423 0.67579 0.67755 0.6796 0.68188 0.68415 0.68585 0.68658 0.68746 0.68777 0.68825 0.6881 0.68661 0.68375 0.67964 0.67492 0.66963 0.66513 0.66044 0.65499 0.64925 0.64345 0.6377 0.63198 0.62611 0.62038 0.61414 0.60793 0.60177 0.59557 0.58923 0.58269 0.57543 0.56814 0.56086 0.55355 0.54603 0.53855 0.53112 0.52373 0.51658 0.50937 0.50203 0.49488 0.48775 0.48061 0.47357 0.46647 0.45943 0.45238 0.44567 0.43919 0.43279 0.42632 0.42024 0.41426 0.40821 0.40284 0.39776 0.39331 0.38953 0.3861 0.38328 0.38067 0.37826 0.37572 0.37346 0.3711 0.36916 0.36741 0.65341 0.6534 0.65431 0.65624 0.65879 0.66152 0.66419 0.66666 0.66891 0.6709 0.67266 0.67423 0.67579 0.67755 0.6796 0.68188 0.68415 0.68585 0.68658 0.68746 0.68777 0.68825 0.6881 0.6866 0.68382 0.67941 0.67469 0.67015 0.66515 0.66001 0.65471 0.64913 0.64322 0.63735 0.63152 0.62581 0.61997 0.61394 0.60777 0.60152 0.59524 0.58899 0.58211 0.57498 0.56781 0.56046 0.55302 0.54565 0.53824 0.5307 0.52332 0.51613 0.50889 0.50146 0.49429 0.48711 0.47997 0.47302 0.46579 0.45868 0.45158 0.44512 0.43839 0.43169 0.42534 0.41915 0.4129 0.40711 0.40191 0.39713 0.39266 0.38901 0.38559 0.38296 0.3804 0.37807 0.37551 0.37315 0.37095 0.36894 0.36737 0.62019 0.61892 0.61912 0.62096 0.62386 0.62731 0.63088 0.6344 0.63779 0.64096 0.64365 0.64588 0.64768 0.64894 0.65027 0.65162 0.65294 0.65411 0.65445 0.65473 0.65448 0.65454 0.65441 0.65344 0.65166 0.64913 0.6461 0.64256 0.63862 0.63409 0.62875 0.62283 0.61662 0.61039 0.60433 0.59827 0.59231 0.58637 0.58053 0.57475 0.56886 0.5626 0.55592 0.5489 0.54182 0.53491 0.52823 0.5215 0.51463 0.50756 0.50037 0.49314 0.4859 0.4785 0.47136 0.46423 0.45722 0.45027 0.44321 0.43636 0.42951 0.42336 0.41728 0.41119 0.40552 0.40029 0.39553 0.39091 0.38656 0.38254 0.3791 0.3763 0.37355 0.37131 0.36877 0.3664 0.36386 0.36167 0.35986 0.3581 0.3568 0.46212 0.46113 0.46121 0.46264 0.46508 0.46806 0.47119 0.47421 0.47696 0.47932 0.48119 0.48252 0.48335 0.48379 0.48401 0.48417 0.48434 0.48411 0.48301 0.48205 0.4805 0.47929 0.47795 0.47606 0.47372 0.47088 0.46775 0.46437 0.46078 0.45682 0.45232 0.44754 0.44274 0.43811 0.43364 0.42918 0.42469 0.42005 0.41529 0.41047 0.40559 0.4006 0.3955 0.39032 0.38522 0.38027 0.3755 0.37081 0.36615 0.36147 0.35685 0.3524 0.34814 0.34376 0.33973 0.33585 0.33221 0.3287 0.3251 0.32191 0.31871 0.31601 0.31342 0.31089 0.30872 0.30666 0.30473 0.3027 0.3007 0.2988 0.29695 0.29549 0.29393 0.29269 0.29144 0.29032 0.289 0.28784 0.28688 0.28593 0.2853 0.40843 0.40765 0.40789 0.40943 0.41194 0.41498 0.41818 0.42127 0.4241 0.42656 0.42854 0.43001 0.43099 0.4316 0.432 0.43236 0.43275 0.43273 0.43185 0.4311 0.42976 0.42874 0.42758 0.42587 0.42371 0.42105 0.41809 0.41488 0.41147 0.40773 0.4035 0.39904 0.39459 0.39034 0.38624 0.38215 0.37806 0.37384 0.36953 0.36517 0.36075 0.35622 0.35156 0.34683 0.3422 0.33776 0.33353 0.32937 0.32521 0.32108 0.31702 0.31314 0.30944 0.30565 0.30224 0.29899 0.29597 0.29309 0.29013 0.28758 0.285 0.28291 0.2809 0.27894 0.27728 0.27575 0.27437 0.27288 0.27143 0.27006 0.26869 0.26771 0.26662 0.26578 0.26489 0.26415 0.26323 0.26245 0.26184 0.26125 0.2609
6 0.064373 0.5566 0.55625 0.55661 0.55771 0.55943 0.56158 0.56413 0.56698 0.5701 0.57357 0.57744 0.58181 0.5867 0.59209 0.59763 0.60273 0.60678 0.61054 0.61366 0.61671 0.61896 0.62051 0.62085 0.61961 0.61671 0.61246 0.60728 0.60284 0.59851 0.59406 0.5893 0.58448 0.57947 0.57436 0.56944 0.56487 0.55997 0.55499 0.55009 0.54528 0.54053 0.53595 0.53081 0.52531 0.51966 0.51373 0.50749 0.50117 0.49483 0.48836 0.48189 0.47546 0.46916 0.4626 0.45604 0.4495 0.44279 0.43619 0.42958 0.42311 0.41643 0.41024 0.4039 0.39759 0.39138 0.38489 0.37876 0.37297 0.36763 0.36278 0.35862 0.35485 0.3516 0.34891 0.34639 0.3438 0.34147 0.33924 0.33674 0.33492 0.33329 0.55569 0.55512 0.55534 0.55654 ...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=0.0015999 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 0.030027 0.029309 0.027141 0.024832 0.022901 0.021272 0.019891 0.018713 0.017702 0.016834 0.016083 0.015432 0.014829 0.01434 0.013913 0.013543 0.013223 0.012943 0.012703 0.012495 0.012319 0.012169 0.01203 0.011926 0.011843 0.011777 0.011728 0.011694 0.011675 0.011669 0.011675 0.011691 0.01174 0.011777 0.011824 0.011879 0.011944 0.012018 0.012101 0.012193 0.012294 0.012405 0.012704 0.012848 0.013007 0.01315 0.0085154 0.013263 0.013157 0.013049 0.012939 0.012827 0.012712 0.012596 0.012478 0.012356 0.012228 0.012098 0.011965 0.011829 0.011689 0.011546 0.011404 0.011258 0.011119 0.010982 0.010838 0.010698 0.010546 0.010403 0.01028 0.010189 0.010124 0.010065 0.010015 0.0099828 0.009948 0.0099106 0.0098247 0.0097034 0.0094359 0.0088823
2 2 0 0.04151 0.04055 0.037518 0.034247 0.031504 0.029182 0.027206 0.025512 0.02405 0.022786 0.021685 0.020724 0.01983 0.019095 0.018445 0.017876 0.017374 0.016931 0.016542 0.016197 0.015897 0.015634 0.015384 0.015186 0.015014 0.014868 0.014743 0.014641 0.014558 0.014495 0.014447 0.014414 0.014429 0.014427 0.014438 0.014463 0.014502 0.014556 0.014623 0.014706 0.014805 0.01492 0.015293 0.015416 0.015494 0.015756 0.0095565 0.015406 0.015268 0.015129 0.014986 0.014843 0.0147 0.014555 0.014406 0.014254 0.014099 0.013942 0.013791 0.01364 0.013485 0.013326 0.013169 0.013012 0.012853 0.012689 0.012536 0.012395 0.012264 0.012143 0.012026 0.011928 0.011837 0.011751 0.01167 0.011603 0.011519 0.011445 0.011367 0.011256 0.011084 0.010706
3 3 0 0.048832 0.047813 0.043942 0.040179 0.037025 0.034356 0.032083 0.030135 0.028454 0.027 0.025735 0.024629 0.023596 0.02275 0.022001 0.021345 0.020766 0.020254 0.019805 0.019406 0.019058 0.018753 0.018461 0.01823 0.01803 0.01786 0.017713 0.017593 0.017496 0.017422 0.017366 0.017326 0.017346 0.017346 0.01736 0.017392 0.017442 0.01751 0.017598 0.017705 0.017834 0.017984 0.018458 0.018611 0.018707 0.018914 0.011051 0.018353 0.018204 0.018056 0.017908 0.01776 0.017613 0.017467 0.017321 0.017175 0.017026 0.016875 0.016721 0.016567 0.01641 0.016244 0.016078 0.015919 0.015772 0.015646 0.015535 0.015419 0.01532 0.015234 0.015126 0.015014 0.014911 0.014816 0.014721 0.014639 0.014546 0.014462 0.0144 0.014334 0.014253 0.013897
4 4 0 0.055554 0.054443 0.050039 0.045784 0.042216 0.039197 0.036627 0.034424 0.032522 0.030878 0.029446 0.028194 0.027023 0.026065 0.025217 0.024474 0.023818 0.023238 0.022728 0.022276 0.021881 0.021535 0.021204 0.020941 0.020715 0.020522 0.020356 0.020221 0.020112 0.02003 0.019969 0.019927 0.019955 0.019959 0.019981 0.020025 0.020089 0.020176 0.020286 0.02042 0.020577 0.020758 0.021313 0.021496 0.021588 0.021738 0.012533 0.021155 0.021 0.020837 0.020676 0.020513 0.020354 0.020196 0.020038 0.019881 0.019722 0.019561 0.0194 0.01924 0.019077 0.018915 0.018756 0.018611 0.018483 0.018357 0.018239 0.018127 0.018043 0.01797 0.017887 0.017835 0.017764 0.017671 0.017587 0.017496 0.0174 0.017292 0.017196 0.017109 0.016937 0.016458
5 5 0 0.0609 0.05977 0.054981 0.05038 0.046522 0.043258 0.040479 0.0381 0.036047 0.034273 0.032728 0.03138 0.030117 0.029086 0.028175 0.027377 0.026674 0.026053 0.025508 0.025026 0.024606 0.024239 0.023887 0.023611 0.023373 0.023172 0.023001 0.022864 0.022755 0.022676 0.022619 0.022584 0.022628 0.022645 0.022682 0.022743 0.022829 0.022938 0.023073 0.023234 0.023418 0.023623 0.024246 0.024462 0.024569 0.024665 0.01422 0.024149 0.023983 0.023818 0.023653 0.023486 0.023315 0.023144 0.022977 0.022807 0.022625 0.022438 0.022251 0.02206 0.021862 0.021667 0.021476 0.021302 0.021153 0.021028 0.020913 0.020809 0.020702 0.020603 0.020536 0.020477 0.020419 0.020389 0.020297 0.02021 0.020126 0.020012 0.019885 0.019715 0.019424 0.018754
6 1 1 0.0055093 0.0051081 0.0046334 0.0041967 0.0038272 0.0035138 0.0032482 0.0030214 0.002826 0.0026604 0.0025186 0.0023967 0.0022868 0.0021977 0.0021213 0.0020577 0.0020043 0.0019595 0.0019237 0.0018952 0.001874 0.0018594 0.0018487 0.0018455 0.0018477 0.0018549 0.0018667 0.0018836 0.0019052 0.0019316 0.0019623 0.0019973 0.0020407 0.0020862 0.0021361 0.0021914 0.002253 0.0023216 0.0023978 0.0024828 0.0025778 0.0026854 0.0028403 0.0029916 0.0031738 0.0034122 0.0033529 0.0049955 0.0050694 0.0051431 0.0052194 0.0052916 0.0053661 0.0054301 0.0054901 0.0055402 0.0055848 0.0056356 0.0056789 0.005721 0.0057542 0.0057711 0.0057766 0.0057866 0.0057926 0.0057911 0.0057977 0.0058043 0.0057973 0.0057917 0.0058024 0.00581 0.0058129 0.0058139 0.0058105 0.005795 0.0057756 0.0057569 0.0057168 0.005644 0.0055043 0.0050876
7 2 1 0.0077846 0.0072287 0.0065562 0.0059314 0.0054057 0.004957 0.0045732 0.0042424 0.0039584 0.0037147 0.0035041 0.003322 0.0031569 0.0030215 0.0029039 0.0028037 0.0027179 0.0026444 0.002583 0.0025316 0.0024904 0.0024581 0.0024311 0.0024142 0.0024044 0.0024013 0.002404 0.0024132 0.0024284 0.0024499 0.0024766 0.0025086 0.0025513 0.002596 0.0026462 0.0027026 0.0027658 0.002837 0.0029167 0.0030065 0.0031076 0.0032217 0.0033927 0.0035514 0.003745 0.0039929 0.0037261 0.0054827 0.0055588 0.0056381 0.0057147 0.005787 0.00586 0.0059272 0.0059894 0.0060446 0.0060929 0.0061416 0.0061851 0.0062243 0.0062588 0.0062874 0.0063067 0.0063146 0.0063284 0.0063427 0.0063524 0.006363 0.0063708 0.0063786 0.0063844 0.0063834 0.0063722 0.0063611 0.0063558 0.0063449 0.0063382 0.0063362 0.0063158 0.0062538 0.0060994 0.0056831
8 3 1 0.0094266 0.008771 0.0079723 0.0072201 0.0065859 0.00605 0.005592 0.0051974 0.0048558 0.0045641 0.0043107 0.0040899 0.0038899 0.0037262 0.0035841 0.0034632 0.0033592 0.0032699 0.003195 0.0031321 0.0030814 0.0030412 0.0030073 0.0029859 0.0029729 0.0029679 0.0029698 0.0029795 0.0029963 0.0030205 0.0030508 0.003087 0.0031366 0.0031881 0.0032462 0.0033118 0.0033848 0.0034658 0.0035564 0.0036584 0.0037726 0.0039016 0.0040981 0.0042665 0.0044662 0.004721 0.0041592 0.0060341 0.0061194 0.006206 0.0062961 0.0063795 0.006452 0.0065282 0.006603 0.0066724 0.0067375 0.0067958 0.0068507 0.0068955 0.0069368 0.0069739 0.0070164 0.0070534 0.0070811 0.0071109 0.0071379 0.0071608 0.007185 0.0072089 0.0072279 0.0072449 0.0072526 0.0072443 0.0072346 0.0072389 0.0072434 0.0072504 0.0072508 0.0072066 0.0070563 0.0066186
9 4 1 0.011763 0.010976 0.009988 0.0090498 0.0082597 0.0075823 0.0070052 0.0065149 0.0060924 0.0057284 0.0054115 0.0051359 0.004885 0.0046786 0.0044983 0.0043428 0.0042088 0.0040934 0.0039962 0.0039138 0.0038467 0.0037926 0.0037461 0.0037148 0.003694 0.0036828 0.0036801 0.0036868 0.0037019 0.0037259 0.0037571 0.0037956 0.0038503 0.003907 0.0039714 0.0040451 0.0041281 0.0042207 0.0043237 0.0044382 0.0045642 0.0047035 0.0049227 0.0051019 0.0053003 0.0055752 0.0046227 0.006588 0.0066854 0.0067809 0.0068835 0.006983 0.007066 0.0071472 0.0072346 0.0073127 0.0073934 0.007465 0.0075307 0.0075818 0.0076364 0.0076853 0.0077356 0.0077884 0.007826 0.0078662 0.0079038 0.0079391 0.0079714 0.0080066 0.0080392 0.008063 0.0080851 0.0080883 0.0080829 0.0080879 0.0081008 0.0081148 0.0081216 0.0080842 0.0079255 0.0074239
10 5 1 0.014764 0.013879 0.012683 0.011539 0.010569 0.0097415 0.0090357 0.0084328 0.0079076 0.0074528 0.0070597 0.0067197 0.0064092 0.0061527 0.0059289 0.0057377 0.0055731 0.0054304 0.0053093 0.0052059 0.0051214 0.005053 0.0049936 0.0049536 0.0049266 0.0049116 0.004907 0.0049141 0.0049317 0.0049606 0.004998 0.0050437 0.0051103 0.0051785 0.0052556 0.0053432 0.0054414 0.0055508 0.0056714 0.0058042 0.0059502 0.0061109 0.0063731 0.0065676 0.006767 0.0070405 0.0054025 0.0076466 0.0077504 0.0078575 0.0079642 0.0080715 0.0081735 0.0082656 0.0083622 0.0084476 0.0085347 0.00862 0.0087037 0.0087772 0.0088411 0.0088981 0.0089496 0.0090014 0.009053 0.0091078 0.0091614 0.0091995 0.0092334 0.0092628 0.0092881 0.0092978 0.0093159 0.0093294 0.009333 0.009338 0.0093573 0.0093794 0.0093858 0.0093361 0.0091418 0.0085346
% 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=0.0015999 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 0.08445 0.099691 0.10757 0.10468 0.10247 0.09965 0.09741 0.095527 0.093623 0.091892 0.090154 0.088739 0.087311 0.086115 0.08499 0.084006 0.083079 0.082282 0.081537 0.080836 0.080248 0.079709 0.079059 0.078607 0.078174 0.077796 0.07737 0.077125 0.076846 0.076677 0.076527 0.076343 0.076503 0.076557 0.076673 0.076787 0.077086 0.077425 0.07791 0.078485 0.079407 0.080457 0.0824 0.084365 0.087481 0.094952 0.092305 0.12621 0.12944 0.1328 0.13505 0.13787 0.14154 0.14469 0.14767 0.15085 0.15426 0.15798 0.16194 0.16617 0.17076 0.17607 0.18199 0.18846 0.19394 0.20045 0.20762 0.21545 0.22386 0.2339 0.24389 0.25578 0.26982 0.28582 0.30005 0.32039 0.34043 0.37145 0.40929 0.47417 0.60078 0.99996
2 2 0 0.096015 0.11123 0.12103 0.11789 0.11535 0.11281 0.11077 0.10899 0.10711 0.10568 0.10438 0.10311 0.10202 0.101 0.10009 0.099404 0.09885 0.098204 0.097716 0.097131 0.09667 0.096213 0.095684 0.095194 0.094792 0.094384 0.093861 0.093426 0.092982 0.092606 0.092296 0.091971 0.091678 0.091357 0.091281 0.091236 0.091198 0.091167 0.091323 0.091562 0.092204 0.092943 0.094372 0.095504 0.097988 0.10505 0.10427 0.15293 0.15565 0.15849 0.16071 0.1634 0.16662 0.1695 0.17224 0.1752 0.17841 0.18199 0.18627 0.19092 0.19585 0.20018 0.2056 0.21146 0.21672 0.22241 0.22925 0.23669 0.24403 0.25367 0.26367 0.27493 0.28735 0.30222 0.3168 0.3348 0.35646 0.38633 0.42569 0.4917 0.61445 0.99996
3 3 0 0.10769 0.12614 0.13451 0.13081 0.12755 0.1248 0.12226 0.11997 0.11796 0.1162 0.11449 0.11318 0.11175 0.11058 0.10963 0.10875 0.10812 0.10743 0.10696 0.10643 0.1061 0.10554 0.10506 0.10487 0.10446 0.10405 0.10378 0.10342 0.10302 0.10277 0.10254 0.10222 0.10188 0.10156 0.10142 0.10129 0.10124 0.10123 0.10132 0.10152 0.10189 0.10252 0.10375 0.10468 0.10704 0.11382 0.11521 0.16666 0.16925 0.17192 0.17472 0.17772 0.18089 0.18417 0.18762 0.19128 0.1952 0.19939 0.20382 0.20802 0.21258 0.21667 0.22209 0.22818 0.23498 0.24258 0.25104 0.25918 0.26726 0.27739 0.28859 0.30033 0.31121 0.32554 0.34177 0.35709 0.37991 0.40687 0.44606 0.50672 0.63028 0.99996
4 4 0 0.11389 0.13321 0.14167 0.1377 0.13399 0.13108 0.1279 0.12577 0.1234 0.12129 0.11966 0.11804 0.11644 0.11524 0.11427 0.11339 0.1127 0.11197 0.11136 0.11085 0.11043 0.11026 0.10959 0.10936 0.10923 0.10876 0.10843 0.10821 0.10796 0.10765 0.10735 0.10703 0.10684 0.10656 0.10628 0.10608 0.10589 0.1058 0.10571 0.10589 0.1061 0.10647 0.10761 0.10852 0.11095 0.11782 0.11998 0.16955 0.1721 0.1747 0.17748 0.18043 0.18357 0.18689 0.19038 0.1941 0.19806 0.20231 0.20649 0.21004 0.21434 0.21947 0.22529 0.232 0.23958 0.24783 0.25651 0.26376 0.27385 0.28438 0.29566 0.30646 0.31782 0.33224 0.34695 0.36233 0.38262 0.40867 0.44565 0.5032 0.62212 0.99996
5 5 0 0.1198 0.14051 0.14851 0.144 0.13992 0.13604 0.13275 0.12962 0.12684 0.12463 0.12268 0.12082 0.11872 0.11755 0.11619 0.11504 0.11416 0.11325 0.11268 0.11207 0.11155 0.11113 0.11057 0.11048 0.11015 0.10975 0.10947 0.10941 0.10915 0.10887 0.10858 0.10832 0.10826 0.1081 0.10781 0.10767 0.1074 0.10711 0.10704 0.10718 0.10728 0.10777 0.10877 0.10981 0.11236 0.11948 0.1218 0.16892 0.17132 0.17379 0.17644 0.17928 0.18224 0.18533 0.18867 0.19228 0.19608 0.20018 0.20346 0.20694 0.21151 0.21669 0.22274 0.22969 0.23742 0.24574 0.25379 0.26171 0.27175 0.2824 0.29396 0.30453 0.31606 0.32999 0.34461 0.35924 0.37934 0.40265 0.43714 0.49296 0.61567 0.99996
6 1 1 0.096558 0.10433 0.1066 0.10427 0.1019 0.099557 0.098102 0.097522 0.097081 0.095566 0.093428 0.091874 0.092459 0.093417 0.091898 0.089768 0.088045 0.087173 0.08656 0.08601 0.085105 0.084154 0.084782 0.08461 0.084097 0.083204 0.082998 0.082193 0.081829 0.081319 0.081086 0.080815 0.080745 0.079415 0.077751 0.077194 0.078398 0.079215 0.079316 0.079516 0.079809 0.080296 0.082 0.082377 0.082501 0.086278 0.087444 0.11208 0.11365 0.11716 0.13057 0.12351 0.12456 0.12351 0.11613 0.1158 0.11867 0.13422 0.1397 0.1504 0.15155 0.1456 0.14216 0.16137 0.17041 0.16873 0.18875 0.19714 0.2047 0.21959 0.22874 0.23434 0.25244 0.26681 0.27985 0.29245 0.31665 0.34687 0.38617 0.45644 0.58838 0.99997
7 2 1 0.10023 0.10921 0.11152 0.10928 0.10824 0.10482 0.10232 0.10056 0.09941 0.09927 0.098584 0.09889 0.098775 0.097406 0.09579 0.094708 0.094126 0.09402 0.094432 0.094323 0.093865 0.093326 0.093042 0.092616 0.09213 0.091751 0.091018 0.090857 0.090299 0.089769 0.089684 0.089652 0.089479 0.089204 0.088447 0.087546 0.087124 0.08708 0.086926 0.086883 0.086875 0.087163 0.088422 0.089466 0.091391 0.094293 0.09618 0.11992 0.12075 0.12604 0.12685 0.12207 0.12832 0.12933 0.13387 0.13354 0.13299 0.14116 0.14514 0.15625 0.15432 0.15871 0.16181 0.17404 0.18445 0.18313 0.19864 0.20685 0.21785 0.2297 0.23933 0.24686 0.26217 0.27244 0.28881 0.30234 0.32432 0.35531 0.39677 0.46372 0.59673 0.99997
8 3 1 0.10587 0.11747 0.1188 0.11732 0.11596 0.11315 0.11096 0.10893 0.10726 0.10515 0.10317 0.10255 0.10132 0.10082 0.10008 0.098567 0.098437 0.09897 0.099784 0.099628 0.099134 0.09871 0.098012 0.097524 0.097391 0.097105 0.096767 0.096315 0.095716 0.095608 0.09528 0.095098 0.094663 0.094396 0.094057 0.09363 0.093408 0.093298 0.0935 0.09368 0.09413 0.094661 0.095748 0.096683 0.097761 0.10176 0.10338 0.12959 0.12617 0.13269 0.12959 0.12596 0.12763 0.13462 0.14222 0.14651 0.14802 0.15023 0.14898 0.1581 0.16054 0.16735 0.18014 0.18844 0.19862 0.2033 0.20918 0.21787 0.23169 0.24062 0.24929 0.2627 0.27431 0.28021 0.2995 0.31657 0.33714 0.36768 0.40878 0.47211 0.60366 0.99997
9 4 1 0.11202 0.12194 0.12444 0.1225 0.11996 0.11812 0.11595 0.1145 0.11218 0.11012 0.10841 0.10715 0.10583 0.10401 0.10198 0.1013 0.10076 0.1015 0.1015 0.10093 0.10047 0.10012 0.099886 0.09956 0.099327 0.099145 0.098947 0.09855 0.098183 0.097772 0.097575 0.097613 0.097758 0.097703 0.097616 0.097518 0.097348 0.097092 0.097481 0.09793 0.098201 0.098494 0.099439 0.10061 0.1021 0.10706 0.10741 0.13587 0.12993 0.13641 0.13698 0.133 0.12733 0.13602 0.1419 0.14867 0.15587 0.15543 0.15205 0.15807 0.16822 0.16813 0.17991 0.19475 0.20332 0.2111 0.21437 0.22285 0.23438 0.24594 0.25326 0.26817 0.27842 0.28684 0.30135 0.3198 0.34381 0.37132 0.41039 0.4715 0.60523 0.99997
10 5 1 0.12325 0.13304 0.13672 0.13148 0.12849 0.12885 0.12336 0.12119 0.11968 0.11776 0.11609 0.11385 0.11243 0.11093 0.11004 0.10894 0.10802 0.10772 0.10762 0.10669 0.1063 0.10591 0.10507 0.10464 0.10412 0.10363 0.10344 0.10265 0.10212 0.1017 0.10117 0.10033 0.10047 0.10037 0.10033 0.10004 0.099694 0.099752 0.099979 0.1005 0.10104 0.10147 0.1026 0.10487 0.10655 0.11225 0.1116 0.13006 0.12966 0.1363 0.13835 0.14372 0.1413 0.14415 0.14403 0.14868 0.15812 0.15755 0.16005 0.1675 0.1753 0.17933 0.18721 0.19714 0.2052 0.21528 0.22102 0.22701 0.23763 0.24899 0.2571 0.26842 0.28056 0.29193 0.29951 0.31944 0.3424 0.36854 0.40866 0.47058 0.60244 0.99997 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=0.0015999 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 0.04847 0.04767 0.045314 0.042735 0.040445 0.0384 0.036575 0.03494 0.03347 0.03215 0.030963 0.029888 0.028844 0.027974 0.027184 0.026473 0.02583 0.02525 0.024727 0.024251 0.023827 0.023447 0.023077 0.022776 0.022511 0.022278 0.022077 0.021905 0.021761 0.021645 0.021554 0.021485 0.021492 0.021478 0.021484 0.021513 0.021568 0.021646 0.021749 0.021876 0.022027 0.0222 0.022762 0.022946 0.02303 0.023106 0.013015 0.02112 0.020957 0.020793 0.020627 0.020459 0.020294 0.020127 0.01996 0.01979 0.019614 0.019434 0.019255 0.019074 0.018887 0.018698 0.018511 0.01833 0.018164 0.018007 0.017861 0.017725 0.017596 0.017476 0.017358 0.017266 0.017192 0.017122 0.017031 0.016944 0.016859 0.016771 0.016665 0.016509 0.016255 0.015688
2 1 0 0.04626 0.045083 0.040135 0.035433 0.031622 0.028506 0.02594 0.023813 0.02204 0.020558 0.019308 0.018255 0.017314 0.01656 0.015917 0.015373 0.014912 0.014517 0.014187 0.013909 0.013677 0.013484 0.013309 0.013181 0.01308 0.013001 0.01294 0.0129 0.012877 0.012872 0.012876 0.012892 0.012947 0.012984 0.013031 0.013088 0.013155 0.013233 0.013323 0.013427 0.013544 0.013676 0.014043 0.014187 0.014316 0.014583 0.0093356 0.015811 0.015687 0.015563 0.015438 0.015312 0.015184 0.015057 0.014928 0.014799 0.014666 0.014531 0.014397 0.014261 0.014122 0.013982 0.013842 0.013711 0.013588 0.013473 0.013363 0.013255 0.013154 0.013065 0.012984 0.012911 0.012831 0.012754 0.012685 0.012628 0.012556 0.012478 0.012405 0.012338 0.012199 0.011791
3 0 1 0.010726 0.010058 0.0092963 0.0085699 0.0079386 0.0073873 0.0069068 0.0064864 0.0061151 0.0057902 0.0055051 0.0052527 0.0050203 0.0048273 0.0046577 0.0045111 0.0043835 0.0042736 0.0041793 0.0040985 0.0040326 0.0039795 0.0039334 0.003903 0.0038829 0.0038727 0.0038719 0.0038801 0.0038972 0.0039231 0.0039575 0.0039989 0.004058 0.0041201 0.0041902 0.00427 0.0043598 0.0044607 0.0045731 0.0046981 0.0048372 0.0049921 0.0052317 0.0054317 0.0056559 0.0059524 0.0050037 0.0071342 0.0072325 0.0073311 0.00743 0.0075311 0.0076251 0.0077121 0.0078041 0.0078827 0.0079502 0.0080159 0.0080893 0.0081529 0.0082034 0.0082428 0.0082783 0.0083092 0.0083402 0.008376 0.0084091 0.0084337 0.0084522 0.0084679 0.0084881 0.0085056 0.0085212 0.0085223 0.0085153 0.0085096 0.0085017 0.0084969 0.008487 0.0084319 0.0082566 0.007676
4 1 1 0.0089734 0.0083275 0.0074368 0.0066048 0.0059203 0.0053506 0.004875 0.0044772 0.004141 0.003858 0.0036168 0.003413 0.0032309 0.0030834 0.0029569 0.0028509 0.0027618 0.0026855 0.0026235 0.0025729 0.0025329 0.0025022 0.0024772 0.0024626 0.0024553 0.0024547 0.0024591 0.0024708 0.0024882 0.0025123 0.0025405 0.002574 0.0026177 0.0026622 0.0027121 0.0027676 0.0028294 0.0028976 0.0029733 0.0030579 0.0031518 0.0032571 0.0034191 0.0035599 0.003725 0.0039443 0.0035017 0.0051646 0.0052409 0.0053192 0.0054012 0.0054739 0.0055419 0.0056072 0.0056676 0.0057243 0.0057872 0.0058473 0.0058903 0.0059271 0.0059675 0.0060035 0.0060356 0.0060686 0.0060922 0.0061115 0.0061322 0.006153 0.0061709 0.0061915 0.0062087 0.006214 0.0062143 0.0062125 0.0062115 0.0062123 0.0062244 0.0062381 0.0062294 0.006178 0.0060344 0.0056631
% 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=0.0015999 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 0.092366 0.10262 0.1087 0.10789 0.10721 0.10648 0.10595 0.10548 0.10482 0.1045 0.10413 0.10373 0.10319 0.1029 0.10259 0.10231 0.10215 0.10186 0.10168 0.10129 0.10105 0.10083 0.10037 0.10017 0.099856 0.099493 0.099122 0.098772 0.098544 0.098168 0.097873 0.097473 0.0973 0.097134 0.097019 0.096902 0.096878 0.096891 0.09701 0.097355 0.097838 0.09857 0.099922 0.10119 0.10376 0.11118 0.11133 0.15488 0.15726 0.15974 0.16223 0.16538 0.16893 0.17215 0.17528 0.17867 0.18235 0.18632 0.19029 0.19423 0.1987 0.20339 0.20908 0.21553 0.22225 0.22938 0.23715 0.24503 0.25362 0.26362 0.27435 0.28571 0.29799 0.31287 0.32782 0.34503 0.36619 0.3937 0.43104 0.49201 0.61519 0.99996
2 1 0 0.11637 0.1417 0.15261 0.14614 0.1405 0.13527 0.13049 0.12647 0.12275 0.11938 0.11641 0.11382 0.11131 0.10929 0.10748 0.10593 0.10462 0.1034 0.10241 0.10163 0.10094 0.10031 0.099617 0.099233 0.098868 0.098406 0.098041 0.097863 0.097441 0.097259 0.097042 0.096879 0.096768 0.096522 0.096366 0.096322 0.096249 0.096199 0.096313 0.096502 0.096913 0.097494 0.098837 0.099966 0.10256 0.10928 0.11009 0.15883 0.16184 0.16494 0.16753 0.1701 0.17301 0.17608 0.17935 0.18281 0.18646 0.19042 0.1945 0.1986 0.20332 0.20824 0.21401 0.22038 0.22681 0.23423 0.24214 0.24969 0.25868 0.26907 0.27995 0.29111 0.30291 0.31746 0.33225 0.34851 0.36932 0.39669 0.43449 0.49549 0.61813 0.99996
3 0 1 0.098134 0.10328 0.1058 0.10505 0.10409 0.10299 0.10242 0.10262 0.10209 0.10093 0.10032 0.10038 0.10043 0.099923 0.099065 0.098389 0.097804 0.097063 0.096795 0.096238 0.095807 0.095208 0.094867 0.094803 0.094436 0.094111 0.09378 0.093171 0.092735 0.092494 0.092193 0.091841 0.091665 0.091361 0.091382 0.091178 0.091734 0.09206 0.09213 0.092555 0.092901 0.093512 0.0944 0.095233 0.095811 0.10076 0.10175 0.12531 0.1192 0.12214 0.12535 0.12678 0.12716 0.13414 0.13897 0.13823 0.1346 0.14417 0.15539 0.16295 0.1591 0.15953 0.16925 0.18128 0.19069 0.19783 0.20618 0.21221 0.22061 0.22971 0.24106 0.25759 0.27272 0.28158 0.2927 0.30583 0.32679 0.35837 0.40095 0.47177 0.59988 0.99997
4 1 1 0.11704 0.13112 0.13343 0.12889 0.12573 0.12281 0.11786 0.11446 0.11215 0.11022 0.10755 0.10535 0.10389 0.10271 0.10085 0.098923 0.097953 0.09869 0.099161 0.098797 0.098142 0.097681 0.097449 0.096775 0.096391 0.095822 0.095488 0.095054 0.094524 0.093972 0.093724 0.093562 0.09358 0.093075 0.091896 0.091193 0.090655 0.090515 0.090751 0.09085 0.091123 0.091322 0.092882 0.094368 0.09631 0.099898 0.10066 0.1257 0.12886 0.1373 0.13959 0.13252 0.1325 0.13291 0.13229 0.13904 0.15087 0.15126 0.14298 0.15318 0.16487 0.16811 0.17124 0.18501 0.19411 0.19478 0.2066 0.21648 0.2299 0.24422 0.25003 0.25461 0.26644 0.27771 0.29491 0.3144 0.33893 0.36552 0.40336 0.46198 0.59869 0.99997 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);
