1 2008 Value and Optimal Savings and Consumption Given Stimulus
This is the example vignette for function: snw_v08_jaeemk from the PrjOptiSNW Package. This is simialr to snw_evuvw20_jaeemk, but for the 2008 Bush stimulus. snw_v08p08_jaeemk already solved for optimal policy and value functions in 2008, given expected unemployment shock in 2009. In this function, given some stimulus amount, we use snw_a4chk_wrk_bisec_vec to compute the updated optimal V and C in 2008 given the stimulus amount, based on the alues for V and C without stimulus computed by snw_v08p08_jaeemk.
Note that snw_a4chk_wrk_bisec_vec computes the adjustment in the savings state that would be equivalent to the increase in stimulus amount (which is not a state variable) to current resources, this is faster than resolving 2008 optimal V and C at specific stimulus check amount levels.
Note snw_evuvw20_jaeemk has EVUVW, but here, we only have V08, because in the 2020 problem, households receive checks ex-post of the COVID MIT shocks in 2020 and the EVUVW is the weighted average in V between the MIT unemployed and non-shock employed state. In 2008, however, there are no shocks yet, the state-space is the same as normal, the only difference is that households might receive stimulus checks from Bush. The Bush stimulus is provided ex-ante of the shock realization. The 2009 shocks due to the great recession is not a MIT shock, but expected shock. The effect of the 2009 shock on consumption, savings is solved by snw_v08p08_jaeemk. The expectation over shock, in another word, for the snw_v08_jaeemk is already included in EV’ in 2008 for 2009.
1.1 Solve 2008 Value and Policy Function with SNW_V08p08_JAEEMK
Solve for the Value and Policy functions in 2008 given expected unemployment shock that is specific to age and education group in 2009, no stimulus amounts.
First, set various parameters
% 1. Paramters
% Parameters
mp_more_inputs = containers.Map('KeyType','char', 'ValueType','any');
mp_more_inputs('fl_ss_non_college') = 0.225;
mp_more_inputs('fl_ss_college') = 0.271;
fl_p50_hh_income_07 = 54831;
mp_more_inputs('fl_scaleconvertor') = fl_p50_hh_income_07;
% st_param_group = 'default_small';
% st_param_group = 'default_dense';
st_param_group = 'default_docdense';
mp_params = snw_mp_param(st_param_group, false, 'tauchen', false, 8, 8, mp_more_inputs);
mp_params('beta') = 0.95;
% Control parameters
mp_controls = snw_mp_control('default_test');
mp_controls('bl_print_a4chk') = false;
mp_controls('bl_print_a4chk_verbose') = false;
mp_controls('bl_print_vfi') = false;
mp_controls('bl_print_vfi_verbose') = false;
mp_controls('bl_print_ds') = false;
mp_controls('bl_print_ds_verbose') = false;
mp_controls('bl_print_v08p08_jaeemk') = false;
mp_controls('bl_print_v08p08_jaeemk_verbose') = false;
mp_controls('bl_print_v08_jaeemk') = true;
mp_controls('bl_print_v08_jaeemk_verbose') = true;Second, solve the steady-state problem, same as employed results in 2009.
% 2. Solve value steady state (2009 employed)
[V_VFI_ss, ap_VFI_ss, cons_VFI_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=530.633
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;
V_emp_2009 = V_VFI_ss;
% Solve for probability mass, needed for pre-compute
[Phi_true] = snw_ds_main_vec(mp_params, mp_controls, ap_VFI_ss, cons_VFI_ss, mp_valpol_more_ss);
Completed SNW_DS_MAIN_VEC;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=1271.649
% Solve for household head and spouse income and sum to total income.
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;Third, solve the unemployment problem in 2009. With 2009 specific unemployment parameters calibrated and found from data. Using \(b\) calibrated by snw_calibrate_2009_b.
% 3. Solve value unemployed 2009
% Set Unemployment Related Variables
mp_params('xi') = 0.532;
% Calibrated by snw_calibrate_2009_b
mp_params('b') = 0.37992;
mp_params('a2_covidyr') = mp_params('a2_greatrecession_2009');
mp_params('TR') = 100/fl_p50_hh_income_07; % Value of a stimulus check (can receive multiple checks). TO DO: Update with alternative values;
[V_unemp_2009] = snw_vfi_main_bisec_vec(mp_params, mp_controls, V_VFI_ss);
Completed SNW_VFI_MAIN_BISEC_VEC 1 Period Unemp Shock;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=326.1213Fourth, solve the 2008 problem, with 2008-specific value and policy functions.
% 4. Value and Optimal choice in 2009
[V_2008, ap_2008, cons_2008, ev_empshk_2009] = ...
snw_v08p08_jaeemk(mp_params, mp_controls, V_emp_2009, V_unemp_2009);
Completed SNW_VFI_MAIN_BISEC_VEC 1 Period Unemp Shock;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=516.0952
Completed SNW_V08P08_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time=522.6257
% 5. pre-compute
cl_st_precompute_list = {'a', ...
'inc', 'inc_unemp', 'spouse_inc', 'spouse_inc_unemp', 'ref_earn_wageind_grid'};
mp_controls('bl_print_precompute_verbose') = false;
[mp_precompute_res] = snw_hh_precompute(mp_params, mp_controls, cl_st_precompute_list, ap_VFI_ss, Phi_true);
Wage quintile cutoffs=0.4645 0.71528 1.0335 1.5632
SNW_HH_PRECOMPUTE: Finished Age Group:1 of 82, time-this-age:3.2746
SNW_HH_PRECOMPUTE: Finished Age Group:2 of 82, time-this-age:3.2852
SNW_HH_PRECOMPUTE: Finished Age Group:3 of 82, time-this-age:3.3869
SNW_HH_PRECOMPUTE: Finished Age Group:4 of 82, time-this-age:3.4589
SNW_HH_PRECOMPUTE: Finished Age Group:5 of 82, time-this-age:3.254
SNW_HH_PRECOMPUTE: Finished Age Group:6 of 82, time-this-age:3.3465
SNW_HH_PRECOMPUTE: Finished Age Group:7 of 82, time-this-age:3.4109
SNW_HH_PRECOMPUTE: Finished Age Group:8 of 82, time-this-age:3.4221
SNW_HH_PRECOMPUTE: Finished Age Group:9 of 82, time-this-age:3.4152
SNW_HH_PRECOMPUTE: Finished Age Group:10 of 82, time-this-age:3.4065
SNW_HH_PRECOMPUTE: Finished Age Group:11 of 82, time-this-age:3.1869
SNW_HH_PRECOMPUTE: Finished Age Group:12 of 82, time-this-age:3.3572
SNW_HH_PRECOMPUTE: Finished Age Group:13 of 82, time-this-age:3.4748
SNW_HH_PRECOMPUTE: Finished Age Group:14 of 82, time-this-age:3.3824
SNW_HH_PRECOMPUTE: Finished Age Group:15 of 82, time-this-age:3.4573
SNW_HH_PRECOMPUTE: Finished Age Group:16 of 82, time-this-age:3.3233
SNW_HH_PRECOMPUTE: Finished Age Group:17 of 82, time-this-age:3.3965
SNW_HH_PRECOMPUTE: Finished Age Group:18 of 82, time-this-age:3.3613
SNW_HH_PRECOMPUTE: Finished Age Group:19 of 82, time-this-age:3.3402
SNW_HH_PRECOMPUTE: Finished Age Group:20 of 82, time-this-age:3.479
SNW_HH_PRECOMPUTE: Finished Age Group:21 of 82, time-this-age:3.2899
SNW_HH_PRECOMPUTE: Finished Age Group:22 of 82, time-this-age:3.3222
SNW_HH_PRECOMPUTE: Finished Age Group:23 of 82, time-this-age:3.3025
SNW_HH_PRECOMPUTE: Finished Age Group:24 of 82, time-this-age:3.3682
SNW_HH_PRECOMPUTE: Finished Age Group:25 of 82, time-this-age:3.3555
SNW_HH_PRECOMPUTE: Finished Age Group:26 of 82, time-this-age:3.3701
SNW_HH_PRECOMPUTE: Finished Age Group:27 of 82, time-this-age:3.5453
SNW_HH_PRECOMPUTE: Finished Age Group:28 of 82, time-this-age:3.2218
SNW_HH_PRECOMPUTE: Finished Age Group:29 of 82, time-this-age:3.3947
SNW_HH_PRECOMPUTE: Finished Age Group:30 of 82, time-this-age:3.3024
SNW_HH_PRECOMPUTE: Finished Age Group:31 of 82, time-this-age:3.3699
SNW_HH_PRECOMPUTE: Finished Age Group:32 of 82, time-this-age:3.4931
SNW_HH_PRECOMPUTE: Finished Age Group:33 of 82, time-this-age:3.3584
SNW_HH_PRECOMPUTE: Finished Age Group:34 of 82, time-this-age:3.4127
SNW_HH_PRECOMPUTE: Finished Age Group:35 of 82, time-this-age:3.4113
SNW_HH_PRECOMPUTE: Finished Age Group:36 of 82, time-this-age:3.2095
SNW_HH_PRECOMPUTE: Finished Age Group:37 of 82, time-this-age:3.4244
SNW_HH_PRECOMPUTE: Finished Age Group:38 of 82, time-this-age:3.5012
SNW_HH_PRECOMPUTE: Finished Age Group:39 of 82, time-this-age:3.2675
SNW_HH_PRECOMPUTE: Finished Age Group:40 of 82, time-this-age:3.2625
SNW_HH_PRECOMPUTE: Finished Age Group:41 of 82, time-this-age:3.4011
SNW_HH_PRECOMPUTE: Finished Age Group:42 of 82, time-this-age:3.2533
SNW_HH_PRECOMPUTE: Finished Age Group:43 of 82, time-this-age:3.5132
SNW_HH_PRECOMPUTE: Finished Age Group:44 of 82, time-this-age:3.4771
SNW_HH_PRECOMPUTE: Finished Age Group:45 of 82, time-this-age:3.3133
SNW_HH_PRECOMPUTE: Finished Age Group:46 of 82, time-this-age:3.4673
SNW_HH_PRECOMPUTE: Finished Age Group:47 of 82, time-this-age:3.1794
SNW_HH_PRECOMPUTE: Finished Age Group:48 of 82, time-this-age:3.1958
SNW_HH_PRECOMPUTE: Finished Age Group:49 of 82, time-this-age:3.4545
SNW_HH_PRECOMPUTE: Finished Age Group:50 of 82, time-this-age:3.355
SNW_HH_PRECOMPUTE: Finished Age Group:51 of 82, time-this-age:3.2059
SNW_HH_PRECOMPUTE: Finished Age Group:52 of 82, time-this-age:3.2882
SNW_HH_PRECOMPUTE: Finished Age Group:53 of 82, time-this-age:3.3772
SNW_HH_PRECOMPUTE: Finished Age Group:54 of 82, time-this-age:3.3279
SNW_HH_PRECOMPUTE: Finished Age Group:55 of 82, time-this-age:3.5412
SNW_HH_PRECOMPUTE: Finished Age Group:56 of 82, time-this-age:3.504
SNW_HH_PRECOMPUTE: Finished Age Group:57 of 82, time-this-age:3.4961
SNW_HH_PRECOMPUTE: Finished Age Group:58 of 82, time-this-age:3.3629
SNW_HH_PRECOMPUTE: Finished Age Group:59 of 82, time-this-age:3.4105
SNW_HH_PRECOMPUTE: Finished Age Group:60 of 82, time-this-age:3.3755
SNW_HH_PRECOMPUTE: Finished Age Group:61 of 82, time-this-age:3.4102
SNW_HH_PRECOMPUTE: Finished Age Group:62 of 82, time-this-age:3.4844
SNW_HH_PRECOMPUTE: Finished Age Group:63 of 82, time-this-age:3.3864
SNW_HH_PRECOMPUTE: Finished Age Group:64 of 82, time-this-age:3.6674
SNW_HH_PRECOMPUTE: Finished Age Group:65 of 82, time-this-age:3.3549
SNW_HH_PRECOMPUTE: Finished Age Group:66 of 82, time-this-age:3.3543
SNW_HH_PRECOMPUTE: Finished Age Group:67 of 82, time-this-age:3.3775
SNW_HH_PRECOMPUTE: Finished Age Group:68 of 82, time-this-age:3.3574
SNW_HH_PRECOMPUTE: Finished Age Group:69 of 82, time-this-age:3.3706
SNW_HH_PRECOMPUTE: Finished Age Group:70 of 82, time-this-age:3.4841
SNW_HH_PRECOMPUTE: Finished Age Group:71 of 82, time-this-age:3.361
SNW_HH_PRECOMPUTE: Finished Age Group:72 of 82, time-this-age:3.3692
SNW_HH_PRECOMPUTE: Finished Age Group:73 of 82, time-this-age:3.4296
SNW_HH_PRECOMPUTE: Finished Age Group:74 of 82, time-this-age:3.4363
SNW_HH_PRECOMPUTE: Finished Age Group:75 of 82, time-this-age:3.391
SNW_HH_PRECOMPUTE: Finished Age Group:76 of 82, time-this-age:3.4927
SNW_HH_PRECOMPUTE: Finished Age Group:77 of 82, time-this-age:3.4073
SNW_HH_PRECOMPUTE: Finished Age Group:78 of 82, time-this-age:3.3416
SNW_HH_PRECOMPUTE: Finished Age Group:79 of 82, time-this-age:3.4042
SNW_HH_PRECOMPUTE: Finished Age Group:80 of 82, time-this-age:3.4844
SNW_HH_PRECOMPUTE: Finished Age Group:81 of 82, time-this-age:3.4572
SNW_HH_PRECOMPUTE: Finished Age Group:82 of 82, time-this-age:3.2411
SNW_HH_PRECOMPUTE: Finished Age Group:83 of 82, time-this-age:3.3428
Completed SNW_HH_PRECOMPUTE;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=284.951.2 Solve for 2008 Value and Consumption with 0 and 2 Checks
Now we use the snw_v08_jaeemk function, which takes as inputs the 2008-specific value and policy we have already found, to compute the 2008 value and consumption based on different stimulus amounts via asset-equivalent transformation given stimulus amounts.
FIrst, obtain V and C with zero stimulus.
% Call Function
welf_checks = 0;
[V_2008_check0, C_2008_check0] = snw_v08_jaeemk(...
welf_checks, mp_params, mp_controls, V_2008, cons_2008, mp_precompute_res);
Solve for V_2008_check for 0 stimulus checks
Completed SNW_A4CHK_WRK_BISEC_VEC;SNW_MP_PARAM=st_biden_or_trump_undefined;welf_checks=0;TR=0.0018238;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=70.6533
Completed SNW_V08_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;timeEUEC=3e-05
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ __________
C_2008_check 1 1 6 4.37e+07 83 5.265e+05 2.3277e+08 5.3267 8.4419 1.5848 0.036202 141.62
V_2008_check 2 2 6 4.37e+07 83 5.265e+05 -6.6426e+08 -15.201 21.85 -1.4375 -508.1 -0.0071773
xxx TABLE:C_2008_check xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.036218 0.036736 0.038184 0.042735 0.048545 12.256 12.541 12.835 13.136 13.136
r2 0.036271 0.036736 0.038385 0.043404 0.049852 12.491 12.778 13.072 13.374 13.374
r3 0.036717 0.037251 0.039845 0.044907 0.051515 12.744 13.032 13.327 13.628 13.628
r4 0.038144 0.038678 0.041269 0.046371 0.053128 12.989 13.277 13.573 13.872 13.872
r5 0.039534 0.040068 0.042653 0.047793 0.054687 13.224 13.513 13.809 14.105 14.105
r79 0.2016 0.20214 0.20586 0.21598 0.23568 35.82 37.367 39.414 41.705 44.029
r80 0.2016 0.20214 0.20586 0.21598 0.23568 40.755 42.955 45.289 47.95 50.773
r81 0.2016 0.20214 0.20586 0.21598 0.23568 48.912 52.041 55.022 57.919 60.242
r82 0.2016 0.20214 0.20586 0.21598 0.23568 66.719 69.201 72.373 77.005 80.728
r83 0.2016 0.20214 0.20586 0.21598 0.23568 116.83 122.65 128.67 134.89 141.3
xxx TABLE:V_2008_check xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -295.66 -295.26 -292.66 -286.62 -277.22 -4.3615 -4.2673 -4.1741 -4.0822 -3.9911
r2 -286.11 -285.71 -283.12 -277.16 -268.03 -4.2548 -4.1641 -4.0744 -3.9858 -3.8979
r3 -276.49 -276.09 -273.59 -267.84 -259.11 -4.1461 -4.0589 -3.9727 -3.8874 -3.8028
r4 -266.77 -266.41 -264.08 -258.7 -250.49 -4.0342 -3.9507 -3.868 -3.7862 -3.7045
r5 -257.99 -257.65 -255.48 -250.43 -242.69 -3.9287 -3.8485 -3.769 -3.6903 -3.6114
r79 -13.356 -13.343 -13.253 -13.025 -12.638 -0.22088 -0.21055 -0.20083 -0.1917 -0.18313
r80 -12.025 -12.012 -11.923 -11.695 -11.308 -0.16977 -0.1618 -0.15428 -0.14721 -0.14056
r81 -10.382 -10.369 -10.28 -10.052 -9.6651 -0.11711 -0.11162 -0.10645 -0.10156 -0.096943
r82 -8.1742 -8.1611 -8.0716 -7.844 -7.457 -0.065329 -0.062239 -0.059357 -0.056632 -0.054053
r83 -4.9602 -4.9471 -4.8576 -4.6301 -4.2431 -0.020966 -0.019971 -0.019037 -0.01816 -0.017335
% Call FunctionSecond, obtain V and C with two stimulus checks.
welf_checks = 2;
[V_2008_check2, C_2008_check2] = snw_v08_jaeemk(...
welf_checks, mp_params, mp_controls, V_2008, cons_2008, mp_precompute_res);
Solve for V_2008_check for 2 stimulus checks
Completed SNW_A4CHK_WRK_BISEC_VEC;SNW_MP_PARAM=st_biden_or_trump_undefined;welf_checks=2;TR=0.0018238;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;time cost=71.7044
Completed SNW_V08_JAEEMK;SNW_MP_PARAM=default_docdense;SNW_MP_CONTROL=default_test;timeEUEC=2.16e-05
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
CONTAINER NAME: mp_outcomes ND Array (Matrix etc)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
i idx ndim numel rowN colN sum mean std coefvari min max
_ ___ ____ ________ ____ _________ ___________ _______ ______ ________ ________ __________
C_2008_check 1 1 6 4.37e+07 83 5.265e+05 2.328e+08 5.3273 8.442 1.5847 0.037917 141.63
V_2008_check 2 2 6 4.37e+07 83 5.265e+05 -6.6365e+08 -15.187 21.798 -1.4353 -502.51 -0.0071771
xxx TABLE:C_2008_check xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
________ ________ ________ ________ ________ _______ _______ _______ _______ _______
r1 0.037941 0.038148 0.039819 0.043807 0.049244 12.256 12.541 12.835 13.136 13.136
r2 0.038108 0.038344 0.040188 0.044594 0.050571 12.492 12.778 13.073 13.374 13.374
r3 0.03941 0.039781 0.041664 0.046126 0.052244 12.745 13.032 13.327 13.628 13.628
r4 0.040834 0.041205 0.043102 0.047618 0.053867 12.989 13.278 13.573 13.872 13.872
r5 0.04222 0.042589 0.0445 0.049065 0.055435 13.224 13.513 13.809 14.105 14.105
r79 0.20525 0.20579 0.20951 0.21963 0.23776 35.821 37.368 39.415 41.707 44.03
r80 0.20525 0.20579 0.20951 0.21963 0.23776 40.756 42.957 45.29 47.952 50.774
r81 0.20525 0.20579 0.20951 0.21963 0.2378 48.914 52.043 55.024 57.92 60.244
r82 0.20525 0.20579 0.20951 0.21963 0.23814 66.72 69.203 72.375 77.007 80.731
r83 0.20525 0.20579 0.20951 0.21963 0.23933 116.84 122.66 128.68 134.89 141.31
xxx TABLE:V_2008_check xxxxxxxxxxxxxxxxxx
c1 c2 c3 c4 c5 c526496 c526497 c526498 c526499 c526500
_______ _______ _______ _______ _______ _________ _________ _________ _________ _________
r1 -293.09 -292.72 -290.49 -284.88 -275.86 -4.3615 -4.2672 -4.1741 -4.0821 -3.9911
r2 -283.55 -283.18 -280.98 -275.47 -266.73 -4.2548 -4.164 -4.0743 -3.9857 -3.8979
r3 -274.01 -273.65 -271.52 -266.23 -257.88 -4.146 -4.0589 -3.9726 -3.8874 -3.8028
r4 -264.47 -264.13 -262.14 -257.19 -249.33 -4.0341 -3.9506 -3.8679 -3.7861 -3.7045
r5 -255.84 -255.53 -253.66 -249 -241.59 -3.9286 -3.8484 -3.769 -3.6903 -3.6113
r79 -13.268 -13.255 -13.171 -12.954 -12.578 -0.22088 -0.21054 -0.20082 -0.1917 -0.18312
r80 -11.937 -11.924 -11.841 -11.623 -11.248 -0.16976 -0.16179 -0.15428 -0.1472 -0.14055
r81 -10.294 -10.281 -10.198 -9.9804 -9.6057 -0.11711 -0.11162 -0.10644 -0.10156 -0.096941
r82 -8.0862 -8.0735 -7.9895 -7.7724 -7.3981 -0.065327 -0.062237 -0.059355 -0.056631 -0.054052
r83 -4.8723 -4.8595 -4.7755 -4.5584 -4.1854 -0.020965 -0.01997 -0.019036 -0.018159 -0.017335Differences between Checks in Expected Value and Expected Consumption
mn_V_U_gain_check = V_2008_check2 - V_2008_check0;
mn_MPC_U_gain_share_check = (C_2008_check2 - C_2008_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 = 18:100;
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=0.0018238 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 1.0061 0.90365 0.8116 0.72825 0.65329 0.58628 0.5266 0.47353 0.42638 0.3845 0.3473 0.31429 0.28498 0.25896 0.23588 0.2154 0.19724 0.18115 0.16689 0.15426 0.14309 0.13321 0.1245 0.11681 0.11002 0.10403 0.098763 0.09412 0.09001 0.086404 0.08322 0.080389 0.077887 0.075677 0.073684 0.071941 0.070406 0.06901 0.067811 0.066737 0.065776 0.06493 0.064176 0.063505 0.062898 0.062366 0.06189 0.061454 0.061076 0.060734 0.06042 0.060146 0.0599 0.059669 0.059467 0.05929 0.059119 0.058972 0.058838 0.058709 0.058604 0.058495 0.058407 0.058325 0.058246 0.058178 0.058111 0.058055 0.057999 0.057952 0.057909 0.057868 0.057831 0.057797 0.057766 0.057737 0.057712 0.057688 0.057668 0.057651 0.057635 0.75253 0.68104 0.61644 0.55735 0.50379 0.45562 0.41248 0.37393 0.33947 0.30867 0.28115 0.25654 0.23454 0.21486 0.19726 0.18153 0.16746 0.15489 0.14367 0.13365 0.12471 0.11676 0.10968 0.1034 0.097807 0.092841 0.088442 0.084545 0.08107 0.078001 0.075274 0.07282 0.070651 0.068708 0.066956 0.065416 0.064037 0.062792 0.061706 0.060734 0.059859 0.059078 0.058389 0.057765 0.057196 0.056704 0.056253 0.055841 0.055485 0.055159 0.054859 0.054595 0.05436 0.054138 0.05394 0.053768 0.053604 0.05346 0.053329 0.053203 0.053098 0.052993 0.052906 0.052826 0.052747 0.052681 0.052614 0.052558 0.052503 0.052456 0.052412 0.052372 0.052335 0.052301 0.052271 0.052241 0.052217 0.052193 0.052173 0.052155 0.052139 0.5966 0.53715 0.48371 0.435 0.39108 0.35187 0.31706 0.28621 0.25887 0.23464 0.21316 0.1941 0.17718 0.16214 0.14878 0.13689 0.12632 0.11691 0.10854 0.1011 0.09449 0.088625 0.083422 0.078819 0.074729 0.071099 0.067891 0.065049 0.062507 0.060262 0.058261 0.056455 0.054845 0.053402 0.052085 0.050918 0.049875 0.048913 0.048073 0.047317 0.046623 0.046002 0.04545 0.04494 0.044474 0.044063 0.043686 0.043337 0.04303 0.042749 0.042486 0.042253 0.042044 0.041844 0.041664 0.041509 0.041357 0.041221 0.041101 0.040981 0.040883 0.040781 0.040697 0.040621 0.040543 0.04048 0.040413 0.040361 0.040305 0.040261 0.040217 0.040178 0.040142 0.040108 0.040078 0.040049 0.040024 0.040001 0.03998 0.039964 0.039947 0.55952 0.50149 0.44952 0.40228 0.3598 0.32199 0.28853 0.25897 0.23288 0.20985 0.18951 0.17154 0.15566 0.14162 0.12921 0.11822 0.1085 0.099902 0.092296 0.085571 0.079631 0.074391 0.069776 0.065717 0.062134 0.058973 0.056197 0.053751 0.051577 0.049676 0.047987 0.046467 0.04512 0.043911 0.042805 0.041832 0.040966 0.040165 0.039469 0.038843 0.038267 0.037752 0.037291 0.036863 0.036469 0.036123 0.035803 0.035506 0.035244 0.035002 0.034775 0.034572 0.03439 0.034214 0.034056 0.033917 0.033781 0.033661 0.033552 0.033443 0.033354 0.03326 0.033183 0.033114 0.033041 0.032982 0.03292 0.032871 0.032818 0.032776 0.032735 0.032698 0.032663 0.032631 0.032602 0.032574 0.032551 0.032528 0.032509 0.032493 0.032477 0.54805 0.49025 0.43853 0.39156 0.34935 0.31182 0.27863 0.24935 0.22352 0.20075 0.18067 0.16296 0.14733 0.13353 0.12135 0.11058 0.10108 0.092692 0.085291 0.078762 0.073014 0.067958 0.063518 0.059625 0.056205 0.053197 0.050568 0.048266 0.046223 0.044444 0.04287 0.041458 0.040214 0.039106 0.038096 0.03721 0.036426 0.035699 0.035075 0.034516 0.033999 0.033541 0.033134 0.032756 0.03241 0.032108 0.031827 0.031565 0.031336 0.031125 0.030928 0.030753 0.030595 0.03044 0.030301 0.030183 0.030065 0.029961 0.029864 0.029769 0.029693 0.029612 0.029544 0.029483 0.02942 0.02937 0.029314 0.029271 0.029224 0.029188 0.029152 0.029118 0.029088 0.02906 0.029034 0.029009 0.028989 0.028969 0.028951 0.028938 0.028924
2 0.00051498 0.99612 0.89566 0.80504 0.72289 0.64888 0.58264 0.52359 0.47104 0.42432 0.38278 0.34588 0.31309 0.28396 0.25809 0.23512 0.21474 0.19665 0.18062 0.16641 0.15383 0.14269 0.13285 0.12417 0.11651 0.10974 0.10378 0.098525 0.093898 0.089802 0.086205 0.083027 0.080202 0.077704 0.075497 0.073506 0.071766 0.070232 0.068839 0.067641 0.066569 0.065609 0.064764 0.064011 0.06334 0.062733 0.062202 0.061726 0.06129 0.060913 0.060571 0.060257 0.059983 0.059738 0.059507 0.059304 0.059127 0.058957 0.058809 0.058675 0.058547 0.058441 0.058333 0.058245 0.058162 0.058083 0.058016 0.057949 0.057892 0.057837 0.05779 0.057746 0.057706 0.057669 0.057634 0.057604 0.057574 0.05755 0.057526 0.057506 0.057488 0.057473 0.74572 0.67571 0.61209 0.55382 0.5009 0.45324 0.41051 0.37229 0.33811 0.30754 0.2802 0.25574 0.23385 0.21426 0.19673 0.18105 0.16703 0.15449 0.1433 0.1333 0.1244 0.11646 0.10941 0.10315 0.097574 0.092624 0.08824 0.084357 0.080893 0.077831 0.075109 0.07266 0.070493 0.068552 0.066802 0.065264 0.063887 0.062644 0.061559 0.060588 0.059715 0.058934 0.058246 0.057622 0.057054 0.056562 0.056111 0.055699 0.055344 0.055018 0.054718 0.054454 0.054219 0.053997 0.053799 0.053627 0.053464 0.05332 0.053188 0.053063 0.052958 0.052853 0.052766 0.052685 0.052607 0.05254 0.052473 0.052418 0.052362 0.052315 0.052272 0.052231 0.052195 0.05216 0.05213 0.0521 0.052076 0.052052 0.052032 0.052015 0.051999 0.59101 0.5329 0.48032 0.43232 0.38894 0.35016 0.31568 0.28509 0.25797 0.23392 0.21257 0.19361 0.17677 0.16179 0.14846 0.1366 0.12605 0.11667 0.10832 0.10089 0.094289 0.088437 0.083248 0.078657 0.074577 0.070958 0.067761 0.064927 0.062393 0.060152 0.058153 0.05635 0.054741 0.053298 0.051982 0.050815 0.049773 0.048812 0.047973 0.047217 0.046524 0.045904 0.045352 0.044842 0.044376 0.043966 0.043589 0.04324 0.042933 0.042652 0.042389 0.042156 0.041947 0.041747 0.041568 0.041412 0.04126 0.041125 0.041004 0.040884 0.040786 0.040685 0.0406 0.040524 0.040446 0.040384 0.040317 0.040265 0.040208 0.040165 0.040121 0.040081 0.040045 0.040011 0.039982 0.039952 0.039928 0.039904 0.039884 0.039867 0.039851 0.55402 0.49732 0.44621 0.39968 0.35774 0.32035 0.28721 0.25792 0.23204 0.20918 0.18897 0.1711 0.15529 0.14131 0.12893 0.11797 0.10827 0.099688 0.092096 0.085382 0.079454 0.074227 0.069624 0.065575 0.062003 0.058852 0.056086 0.053648 0.051482 0.049584 0.047898 0.04638 0.045034 0.043825 0.042719 0.041747 0.040881 0.040082 0.039387 0.038761 0.038185 0.037671 0.037209 0.036782 0.036388 0.036042 0.035722 0.035425 0.035164 0.034922 0.034694 0.034492 0.03431 0.034134 0.033976 0.033837 0.033701 0.033581 0.033472 0.033362 0.033274 0.033179 0.033103 0.033033 0.032961 0.032902 0.03284 0.032791 0.032738 0.032696 0.032655 0.032618 0.032583 0.032551 0.032522 0.032494 0.032471 0.032448 0.032428 0.032412 0.032397 0.54255 0.48609 0.43523 0.38895 0.34729 0.31018 0.27732 0.2483 0.22268 0.20008 0.18013 0.16252 0.14696 0.13321 0.12107 0.11034 0.10085 0.092481 0.085093 0.078576 0.07284 0.067796 0.063368 0.059486 0.056076 0.053079 0.05046 0.048166 0.04613 0.044355 0.042784 0.041374 0.04013 0.039021 0.038012 0.037127 0.036344 0.035617 0.034994 0.034435 0.033919 0.033461 0.033055 0.032677 0.032331 0.032029 0.031748 0.031486 0.031258 0.031046 0.03085 0.030675 0.030516 0.030362 0.030222 0.030104 0.029987 0.029882 0.029786 0.02969 0.029615 0.029533 0.029465 0.029404 0.029341 0.029291 0.029235 0.029192 0.029145 0.029109 0.029073 0.02904 0.029009 0.028982 0.028956 0.028931 0.028911 0.02889 0.028873 0.028859 0.028845
3 0.0041199 0.84723 0.77113 0.6992 0.63256 0.57176 0.51677 0.46727 0.42282 0.38296 0.34726 0.3153 0.28671 0.26116 0.23835 0.21799 0.19982 0.18363 0.1692 0.15635 0.14491 0.13476 0.12574 0.11776 0.11067 0.10438 0.098788 0.093838 0.089433 0.085532 0.082073 0.078989 0.076271 0.07385 0.071705 0.069793 0.068118 0.066631 0.065292 0.064128 0.063084 0.062143 0.06132 0.060579 0.05992 0.059324 0.058802 0.058331 0.057902 0.05753 0.057191 0.056881 0.056608 0.056366 0.056137 0.055936 0.05576 0.055591 0.055444 0.05531 0.055183 0.055077 0.054969 0.054882 0.0548 0.054721 0.054653 0.054587 0.054531 0.054475 0.054428 0.054384 0.054344 0.054307 0.054273 0.054243 0.054213 0.054188 0.054165 0.054144 0.054127 0.054111 0.65202 0.59768 0.54533 0.49628 0.45122 0.41029 0.37333 0.34001 0.31002 0.28304 0.25878 0.23697 0.21738 0.19978 0.18397 0.16979 0.15706 0.14564 0.13541 0.12624 0.11804 0.11072 0.10418 0.098344 0.093136 0.088467 0.084311 0.080589 0.07727 0.074306 0.071644 0.069276 0.06717 0.065271 0.063592 0.062102 0.060769 0.05957 0.058513 0.057569 0.05671 0.055948 0.055272 0.054656 0.054098 0.053613 0.053168 0.052762 0.052411 0.052088 0.051791 0.051528 0.051296 0.051076 0.05088 0.050708 0.050546 0.050403 0.050272 0.050147 0.050042 0.049938 0.049851 0.049771 0.049693 0.049626 0.04956 0.049504 0.049448 0.049402 0.049358 0.049318 0.049281 0.049247 0.049217 0.049187 0.049163 0.049139 0.049119 0.049101 0.049085 0.52051 0.47565 0.43214 0.39135 0.35403 0.32035 0.29016 0.26317 0.23907 0.21756 0.19837 0.18124 0.16597 0.15235 0.14019 0.12934 0.11967 0.11103 0.10332 0.096439 0.090314 0.08486 0.080015 0.075693 0.071842 0.068392 0.065319 0.062562 0.060099 0.057891 0.055899 0.054132 0.052543 0.051111 0.049835 0.048695 0.047676 0.046742 0.045919 0.045177 0.044489 0.043881 0.043332 0.042827 0.042367 0.041959 0.041585 0.041239 0.040934 0.040655 0.040395 0.040161 0.039954 0.039756 0.039577 0.039421 0.03927 0.039135 0.039015 0.038895 0.038797 0.038696 0.038612 0.038536 0.038458 0.038396 0.038329 0.038277 0.038221 0.038177 0.038133 0.038094 0.038058 0.038024 0.037994 0.037965 0.03794 0.037917 0.037896 0.03788 0.037863 0.48532 0.44177 0.39962 0.36022 0.32425 0.29188 0.26295 0.23718 0.21426 0.19388 0.17577 0.15968 0.14539 0.13271 0.12145 0.11145 0.10258 0.094701 0.087709 0.081509 0.076022 0.071164 0.066881 0.06308 0.059719 0.056719 0.054065 0.051694 0.049591 0.047721 0.046034 0.044547 0.043214 0.042012 0.040944 0.039996 0.03915 0.038375 0.037694 0.03708 0.036507 0.036003 0.035543 0.035118 0.034729 0.034385 0.034068 0.033773 0.033513 0.033272 0.033046 0.032843 0.032663 0.032488 0.03233 0.032192 0.032056 0.031937 0.031827 0.031719 0.03163 0.031536 0.031459 0.03139 0.031318 0.031259 0.031196 0.031148 0.031095 0.031053 0.031011 0.030975 0.03094 0.030908 0.030879 0.030851 0.030828 0.030805 0.030786 0.03077 0.030754 0.47396 0.43063 0.38873 0.34959 0.31389 0.2818 0.25315 0.22765 0.20498 0.18486 0.16701 0.15117 0.13713 0.12468 0.11365 0.10388 0.095223 0.087557 0.080768 0.074765 0.069468 0.064792 0.060682 0.057047 0.053846 0.050998 0.048491 0.04626 0.044285 0.042536 0.040963 0.039584 0.038354 0.037252 0.036279 0.035418 0.034653 0.033951 0.033341 0.032793 0.032279 0.031831 0.031427 0.031051 0.03071 0.030409 0.030131 0.029872 0.029644 0.029434 0.029238 0.029063 0.028906 0.028752 0.028614 0.028496 0.028378 0.028274 0.028178 0.028083 0.028007 0.027926 0.027858 0.027797 0.027734 0.027684 0.027628 0.027586 0.027539 0.027502 0.027466 0.027433 0.027403 0.027375 0.027349 0.027324 0.027304 0.027284 0.027266 0.027253 0.027239
4 0.013905 0.64775 0.59855 0.55009 0.50372 0.46037 0.42041 0.38387 0.35061 0.32039 0.29298 0.26816 0.2457 0.2254 0.20707 0.19054 0.17564 0.16222 0.15016 0.13932 0.12961 0.12091 0.11314 0.10617 0.099963 0.094402 0.08945 0.085034 0.081084 0.077583 0.074467 0.071672 0.069196 0.066996 0.065002 0.063242 0.061685 0.060263 0.059013 0.057901 0.056898 0.056001 0.055209 0.054499 0.053862 0.053292 0.052789 0.05233 0.051915 0.051551 0.051222 0.050918 0.050649 0.050413 0.050188 0.04999 0.049815 0.049649 0.049505 0.04937 0.049246 0.04914 0.049034 0.048948 0.048865 0.048788 0.04872 0.048654 0.048598 0.048542 0.048496 0.048451 0.048411 0.048375 0.04834 0.04831 0.048281 0.048256 0.048233 0.048212 0.048195 0.048179 0.52427 0.48679 0.44914 0.41272 0.37849 0.34685 0.3179 0.29152 0.26753 0.24575 0.22599 0.20807 0.19183 0.17712 0.1638 0.15176 0.14087 0.13104 0.12217 0.11418 0.107 0.10054 0.094735 0.089529 0.084841 0.080648 0.076893 0.073515 0.070505 0.06781 0.065372 0.063209 0.061275 0.059505 0.057953 0.056551 0.055279 0.054151 0.053134 0.052225 0.0514 0.050666 0.050016 0.049416 0.048887 0.048416 0.047982 0.04759 0.047244 0.04693 0.046639 0.046379 0.046152 0.045936 0.045745 0.045574 0.045412 0.045272 0.04514 0.045019 0.044914 0.04481 0.044725 0.044644 0.044567 0.044501 0.044434 0.044379 0.044324 0.044278 0.044233 0.044193 0.044156 0.044122 0.044092 0.044063 0.044038 0.044015 0.043995 0.043978 0.043961 0.42889 0.39749 0.36556 0.33453 0.3054 0.2786 0.25423 0.23217 0.21227 0.19432 0.17816 0.1636 0.15051 0.13872 0.12812 0.11859 0.11002 0.10232 0.095415 0.089226 0.083682 0.078728 0.074283 0.07031 0.066739 0.063552 0.060698 0.058128 0.055837 0.053784 0.051924 0.050269 0.048786 0.047422 0.046217 0.045128 0.044128 0.043233 0.042429 0.041699 0.041028 0.040435 0.039897 0.0394 0.038958 0.038557 0.038189 0.037851 0.037548 0.037274 0.037016 0.036784 0.03658 0.036384 0.036208 0.036052 0.035901 0.035769 0.035647 0.03553 0.035432 0.035331 0.035249 0.035172 0.035095 0.035033 0.034966 0.034915 0.034858 0.034815 0.034769 0.034732 0.034695 0.034662 0.034632 0.034603 0.034578 0.034555 0.034534 0.034518 0.034501 0.39687 0.36661 0.33588 0.30606 0.27812 0.25249 0.22925 0.2083 0.18945 0.17252 0.15733 0.14372 0.13152 0.12059 0.11081 0.10206 0.09423 0.087231 0.080983 0.075416 0.070457 0.066053 0.062123 0.058636 0.055517 0.05275 0.050286 0.048079 0.04613 0.044391 0.042818 0.041429 0.040186 0.039038 0.038033 0.037126 0.036293 0.035548 0.03488 0.034271 0.03371 0.033214 0.032762 0.032342 0.031968 0.03163 0.031316 0.031028 0.030769 0.030532 0.030308 0.030106 0.029928 0.029755 0.029599 0.029461 0.029325 0.029207 0.029095 0.02899 0.028901 0.028807 0.028732 0.028661 0.02859 0.028531 0.02847 0.028421 0.028368 0.028327 0.028284 0.028248 0.028213 0.028181 0.028152 0.028125 0.028101 0.028079 0.028059 0.028043 0.028028 0.38573 0.35569 0.32519 0.29563 0.26796 0.2426 0.21963 0.19893 0.18035 0.16368 0.14875 0.13538 0.12342 0.11273 0.10317 0.094636 0.087017 0.080226 0.074178 0.068804 0.064032 0.059805 0.056044 0.052719 0.049754 0.047137 0.044817 0.042744 0.04092 0.039301 0.03784 0.036558 0.035417 0.034369 0.033455 0.032635 0.031879 0.031209 0.030609 0.030066 0.029564 0.029125 0.028728 0.028357 0.02803 0.027734 0.027459 0.027206 0.026978 0.026773 0.026578 0.026403 0.026249 0.026096 0.02596 0.025842 0.025724 0.025622 0.025523 0.025431 0.025355 0.025274 0.025207 0.025145 0.025083 0.025033 0.024978 0.024935 0.024888 0.024852 0.024815 0.024783 0.024752 0.024725 0.024698 0.024674 0.024654 0.024634 0.024616 0.024603 0.024589
5 0.032959 0.46258 0.43381 0.40406 0.37453 0.34616 0.31948 0.29469 0.2718 0.25076 0.23144 0.21375 0.19756 0.18275 0.16923 0.1569 0.14566 0.13543 0.12615 0.11772 0.11009 0.10318 0.096934 0.091294 0.086201 0.081621 0.077486 0.073763 0.070432 0.067432 0.064725 0.062308 0.060139 0.058173 0.056415 0.054855 0.053443 0.052166 0.051042 0.050026 0.049102 0.048287 0.047549 0.046887 0.046284 0.045749 0.04527 0.044829 0.044441 0.04409 0.043771 0.043483 0.043223 0.042989 0.042776 0.042583 0.042408 0.042246 0.042108 0.041971 0.041853 0.041748 0.041643 0.041558 0.041475 0.0414 0.041333 0.041268 0.041212 0.041157 0.041111 0.041065 0.041026 0.040988 0.040955 0.040924 0.040896 0.040871 0.040848 0.040827 0.040811 0.040794 0.39828 0.37437 0.34919 0.32397 0.29967 0.27681 0.2556 0.23606 0.21813 0.20171 0.18669 0.17296 0.16041 0.14895 0.1385 0.12896 0.12028 0.11239 0.10522 0.098708 0.092804 0.087452 0.082608 0.07822 0.074266 0.070686 0.067452 0.064545 0.061917 0.059531 0.057397 0.055476 0.053717 0.052155 0.050748 0.049473 0.048321 0.047292 0.046367 0.045521 0.04476 0.044085 0.043467 0.042899 0.042402 0.041947 0.04153 0.041164 0.040827 0.040522 0.040248 0.039997 0.039771 0.039565 0.039379 0.039208 0.03905 0.038914 0.038781 0.038666 0.038562 0.03846 0.038374 0.038293 0.038219 0.038153 0.038087 0.038033 0.037977 0.037931 0.037885 0.037847 0.037809 0.037776 0.037746 0.037717 0.037692 0.037669 0.037649 0.037632 0.037616 0.33953 0.31877 0.2966 0.27427 0.25273 0.23253 0.21386 0.19675 0.18116 0.16695 0.15404 0.14232 0.13166 0.122 0.11323 0.10528 0.098081 0.091572 0.08569 0.08038 0.075586 0.071258 0.067357 0.063835 0.060672 0.057813 0.055232 0.052918 0.050824 0.048927 0.047229 0.045697 0.044293 0.04304 0.041911 0.040885 0.039945 0.039109 0.038353 0.037649 0.03702 0.036454 0.035931 0.03545 0.035023 0.034631 0.034269 0.033947 0.033649 0.033379 0.033132 0.032904 0.032699 0.03251 0.032337 0.03218 0.032031 0.031903 0.031778 0.031666 0.03157 0.031469 0.031387 0.031308 0.031235 0.031172 0.031107 0.031055 0.030999 0.030956 0.030909 0.030873 0.030835 0.030803 0.030772 0.030744 0.030719 0.030697 0.030676 0.03066 0.030643 0.31203 0.29219 0.27099 0.24964 0.22908 0.20983 0.1921 0.1759 0.16118 0.14783 0.13574 0.12481 0.11492 0.10599 0.097917 0.090636 0.084074 0.078171 0.072866 0.068101 0.063825 0.059987 0.056547 0.053456 0.050697 0.048213 0.045984 0.043999 0.042211 0.040594 0.039157 0.037863 0.036675 0.03562 0.034675 0.033818 0.033034 0.032336 0.031706 0.031115 0.03059 0.030115 0.029673 0.029265 0.028903 0.02857 0.02826 0.027985 0.027728 0.027493 0.027278 0.02708 0.0269 0.026732 0.026579 0.026439 0.026305 0.02619 0.026075 0.025974 0.025886 0.025792 0.025718 0.025645 0.025577 0.025518 0.025457 0.025408 0.025355 0.025315 0.025271 0.025235 0.0252 0.025169 0.02514 0.025113 0.025089 0.025068 0.025048 0.025032 0.025016 0.30127 0.28163 0.26066 0.23957 0.21927 0.20028 0.1828 0.16687 0.1524 0.1393 0.12745 0.11675 0.10709 0.098378 0.090523 0.083456 0.0771 0.071397 0.066284 0.061704 0.057604 0.053934 0.050656 0.047721 0.045112 0.042773 0.040681 0.038825 0.03716 0.03566 0.034331 0.033145 0.032057 0.0311 0.030243 0.029468 0.028758 0.028134 0.027573 0.027045 0.02658 0.026161 0.025771 0.025412 0.025097 0.024803 0.024532 0.024291 0.024066 0.023862 0.023676 0.023504 0.023348 0.0232 0.023067 0.022946 0.022831 0.022731 0.02263 0.022541 0.022466 0.022385 0.022319 0.022254 0.022196 0.022146 0.022091 0.022048 0.022002 0.021966 0.021927 0.021896 0.021865 0.021838 0.021811 0.021788 0.021767 0.021748 0.02173 0.021717 0.021703
6 0.064373 0.33044 0.31319 0.29451 0.27537 0.25656 0.23856 0.22161 0.2058 0.19113 0.17758 0.16508 0.15357 0.14299 0.13327 0.12435 0.11617 0.10868 0.10183 0.095556 0.089827 0.084597 0.079826 0.075481 0.071525 0.067926 0.064664 0.0617 0.059003 0.056558 0.054348 0.05234 0.050509 0.048861 0.04738 0.046022 0.044793 0.043698 0.042703 0.041791 0.040979 0.040241 0.03958 0.038971 0.038426 0.037942 0.037492 0.037083 0.036725 0.036392 0.036085 0.035819 0.035571 0.035342 0.03514 0.034958 0.034784 0.03463 0.034495 0.034364 0.03425 0.034145 0.034044 0.03396 0.033873 0.033804 0.033737 0.033674 0.033618 0.033564 0.033518 0.033472 0.033433 0.033395 0.033362 0.033331 0.033304 0.033279 0.033256 0.033236 0.033219 0.033203 0.30329 0.28759 0.27036 0.25261 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=0.0018238 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 0.96418 0.92776 0.90436 0.88966 0.87994 0.87411 0.86951 0.8656 0.86219 0.85964 0.85793 0.85812 0.85959 0.86235 0.86684 0.87021 0.87308 0.87504 0.87757 0.88309 0.89051 0.89688 0.90277 0.90436 0.90529 0.89705 0.87795 0.85341 0.82046 0.80184 0.79237 0.78762 0.79976 0.82235 0.84126 0.8409 0.84306 0.82281 0.80365 0.78542 0.77225 0.76938 0.76581 0.76234 0.75864 0.75558 0.74873 0.73596 0.72801 0.72041 0.70878 0.70647 0.69428 0.68158 0.67551 0.6683 0.65607 0.64253 0.63126 0.61947 0.61005 0.5953 0.57647 0.56726 0.54929 0.54089 0.53034 0.52024 0.5107 0.5079 0.50183 0.50223 0.49787 0.49136 0.48563 0.48358 0.48229 0.48755 0.47394 0.46532 0.47709 0.96418 0.92776 0.90436 0.88967 0.87994 0.87411 0.86951 0.8656 0.86219 0.85964 0.85793 0.85812 0.85959 0.86235 0.86684 0.87021 0.87308 0.87504 0.87757 0.88309 0.89051 0.89688 0.90277 0.90436 0.90529 0.89681 0.8764 0.85111 0.81934 0.80131 0.79168 0.78752 0.79562 0.82288 0.83451 0.83988 0.83675 0.81979 0.80089 0.77962 0.76847 0.76114 0.75666 0.7561 0.75435 0.74867 0.74085 0.73002 0.72153 0.71173 0.70064 0.70293 0.68847 0.6787 0.6626 0.65859 0.64876 0.63805 0.61701 0.59969 0.58815 0.57582 0.55837 0.54801 0.53285 0.53387 0.52467 0.51935 0.51464 0.50371 0.50406 0.50105 0.49322 0.48965 0.48657 0.47721 0.47823 0.48536 0.47432 0.47193 0.46926 0.78177 0.74256 0.71707 0.7014 0.69147 0.68592 0.68162 0.67735 0.67492 0.67187 0.66961 0.67065 0.67089 0.67215 0.67592 0.67717 0.67717 0.67737 0.67487 0.67863 0.68291 0.68418 0.69037 0.68871 0.69148 0.67809 0.65637 0.62607 0.59331 0.57351 0.56353 0.5568 0.56996 0.59816 0.61376 0.61654 0.6162 0.60089 0.58734 0.57129 0.56795 0.56661 0.56372 0.56108 0.55597 0.55411 0.55087 0.54248 0.5395 0.53654 0.53005 0.52803 0.51871 0.51032 0.50873 0.50305 0.49901 0.50024 0.48384 0.48053 0.47479 0.47085 0.46341 0.46668 0.45797 0.45373 0.44993 0.44673 0.44296 0.43373 0.43869 0.43013 0.43128 0.42689 0.42523 0.42192 0.42623 0.42408 0.41612 0.41441 0.41536 0.5803 0.54839 0.5194 0.50539 0.49746 0.49407 0.48807 0.48313 0.47793 0.47696 0.47632 0.47365 0.47702 0.479 0.48027 0.48393 0.48708 0.48857 0.48996 0.49779 0.50698 0.51403 0.52285 0.52484 0.5268 0.51862 0.50377 0.47888 0.44076 0.41992 0.41196 0.40682 0.42342 0.44532 0.45718 0.46197 0.45097 0.44235 0.41283 0.39482 0.39442 0.3974 0.39967 0.40685 0.40502 0.4014 0.39462 0.38561 0.38927 0.38352 0.37538 0.37939 0.37373 0.36514 0.3676 0.36355 0.35978 0.35852 0.35348 0.35061 0.35 0.34792 0.34522 0.35168 0.33552 0.34094 0.33633 0.33834 0.33512 0.32692 0.33449 0.32436 0.32585 0.3288 0.32062 0.32498 0.32343 0.3317 0.32103 0.31914 0.31902 0.53727 0.4973 0.47542 0.46722 0.45354 0.44835 0.44221 0.43991 0.43945 0.43428 0.43266 0.43061 0.43182 0.43097 0.43668 0.44053 0.45154 0.44209 0.45351 0.45849 0.46086 0.46731 0.47416 0.47338 0.47966 0.46937 0.44945 0.42447 0.39713 0.37119 0.36949 0.3585 0.38003 0.39762 0.41686 0.41635 0.42258 0.40183 0.39071 0.36352 0.36166 0.35958 0.36207 0.36513 0.369 0.37004 0.35652 0.35199 0.35587 0.34437 0.3406 0.34522 0.34507 0.33569 0.33399 0.33447 0.33308 0.34004 0.32638 0.31563 0.3222 0.32059 0.32142 0.32142 0.30705 0.31866 0.31528 0.31244 0.31044 0.31542 0.3047 0.31029 0.30544 0.30586 0.30622 0.29987 0.30196 0.3138 0.2995 0.29917 0.30434
2 0.00051498 0.95984 0.92012 0.8967 0.8773 0.86664 0.86053 0.8569 0.85376 0.85074 0.84879 0.84814 0.85024 0.85389 0.85863 0.86392 0.86877 0.87283 0.87666 0.88109 0.88852 0.89438 0.90068 0.90699 0.9075 0.90621 0.8946 0.87285 0.84584 0.81417 0.79667 0.79109 0.78679 0.80165 0.82619 0.84334 0.84124 0.84362 0.82222 0.80181 0.78392 0.77182 0.76877 0.76561 0.76189 0.75869 0.75559 0.74817 0.736 0.72788 0.71961 0.70843 0.70589 0.6945 0.68166 0.67586 0.66754 0.65585 0.64222 0.63096 0.61966 0.6102 0.59419 0.57596 0.5676 0.54985 0.54113 0.52955 0.51983 0.51069 0.50802 0.50158 0.50228 0.49793 0.49074 0.4852 0.48328 0.48196 0.48695 0.47359 0.46547 0.47721 0.95984 0.92012 0.8967 0.8773 0.86665 0.86054 0.8569 0.85376 0.85074 0.84879 0.84814 0.85024 0.85389 0.85863 0.86392 0.86877 0.87283 0.87666 0.88109 0.88852 0.89438 0.90068 0.907 0.9075 0.90621 0.89442 0.87127 0.84356 0.81306 0.79605 0.79059 0.78659 0.7976 0.82671 0.83645 0.84057 0.8373 0.8192 0.79904 0.77792 0.76813 0.76051 0.75669 0.75581 0.75432 0.74867 0.74034 0.73016 0.72119 0.71108 0.70038 0.70274 0.6888 0.67909 0.66279 0.65795 0.64792 0.6378 0.61738 0.59996 0.58844 0.57465 0.5578 0.54776 0.53307 0.53384 0.52423 0.5194 0.51482 0.50347 0.50398 0.50127 0.49286 0.48997 0.48607 0.47688 0.47789 0.48503 0.47388 0.47201 0.46946 0.77672 0.73372 0.70825 0.68781 0.67669 0.67073 0.66783 0.6645 0.66238 0.65999 0.65881 0.66217 0.66473 0.66803 0.67273 0.67552 0.67701 0.67904 0.67861 0.68474 0.68679 0.68808 0.69434 0.69193 0.69187 0.67504 0.65129 0.61775 0.58577 0.56736 0.56179 0.55554 0.57119 0.60153 0.61539 0.61647 0.61669 0.59961 0.5857 0.56964 0.56694 0.56541 0.56273 0.55986 0.55496 0.5533 0.54987 0.54255 0.53932 0.53552 0.52948 0.52721 0.5182 0.51028 0.50882 0.50145 0.49789 0.49927 0.48337 0.48077 0.47441 0.46952 0.46208 0.46639 0.4578 0.45288 0.44903 0.44597 0.44217 0.43316 0.43813 0.42957 0.43051 0.42606 0.42432 0.42111 0.42527 0.42324 0.41544 0.41348 0.41491 0.57628 0.54092 0.5117 0.49306 0.48428 0.48083 0.47551 0.47111 0.46666 0.46642 0.46651 0.46587 0.4717 0.47528 0.47754 0.48269 0.48694 0.49019 0.49383 0.50356 0.51113 0.51807 0.52718 0.52843 0.52808 0.51647 0.49902 0.47091 0.43464 0.41467 0.41109 0.40643 0.42549 0.44931 0.45922 0.46254 0.45164 0.44209 0.41053 0.3932 0.39396 0.39693 0.3996 0.40682 0.40488 0.40139 0.39418 0.38564 0.38954 0.38337 0.37508 0.37937 0.37431 0.36503 0.36819 0.36325 0.3596 0.35852 0.35378 0.35112 0.35062 0.34738 0.34506 0.3519 0.33601 0.34115 0.33566 0.33838 0.33526 0.32705 0.33456 0.32448 0.3257 0.32891 0.32044 0.32484 0.3233 0.33126 0.32105 0.31916 0.31921 0.53286 0.48965 0.46795 0.45503 0.44046 0.43476 0.42966 0.42813 0.42794 0.42333 0.42277 0.42275 0.42622 0.42714 0.43396 0.43905 0.4514 0.44375 0.45724 0.46415 0.46471 0.471 0.47828 0.47658 0.48062 0.46703 0.4444 0.41693 0.39077 0.36634 0.36839 0.35754 0.38197 0.40152 0.41907 0.41682 0.42332 0.40116 0.38877 0.36196 0.36143 0.35895 0.36212 0.36497 0.36896 0.37 0.35592 0.35254 0.35601 0.34358 0.34054 0.34506 0.34512 0.33578 0.33436 0.33368 0.33274 0.33959 0.32634 0.31603 0.32228 0.32016 0.32118 0.32129 0.30759 0.31866 0.31484 0.31221 0.31053 0.31541 0.30468 0.3104 0.30551 0.30596 0.30612 0.29946 0.30168 0.31372 0.29931 0.29923 0.30444
3 0.0041199 0.87755 0.8775 0.87564 0.87396 0.87313 0.87278 0.87267 0.87266 0.87254 0.87199 0.8709 0.86894 0.86622 0.86277 0.85882 0.85471 0.85116 0.84816 0.84502 0.8393 0.83139 0.82238 0.8115 0.80362 0.79577 0.79259 0.79354 0.80193 0.80871 0.81964 0.83688 0.84596 0.84726 0.84567 0.82651 0.80363 0.79168 0.7699 0.76458 0.76011 0.761 0.75975 0.75973 0.75435 0.74787 0.73871 0.7338 0.72229 0.71555 0.70714 0.70144 0.69346 0.68233 0.67625 0.66522 0.65562 0.64256 0.63355 0.6194 0.608 0.59724 0.57857 0.56329 0.55468 0.53683 0.5254 0.5131 0.50282 0.50253 0.49631 0.49353 0.49493 0.48678 0.48673 0.47468 0.47647 0.47206 0.47421 0.46164 0.45378 0.46604 0.87755 0.8775 0.87564 0.87396 0.87314 0.87278 0.87267 0.87266 0.87254 0.87199 0.8709 0.86894 0.86622 0.86277 0.85882 0.85471 0.85116 0.84816 0.84502 0.8393 0.83139 0.82238 0.8115 0.80362 0.79553 0.79252 0.79211 0.8006 0.80784 0.81886 0.83746 0.84419 0.84502 0.84292 0.82087 0.8039 0.78398 0.76609 0.75942 0.75463 0.75439 0.75435 0.75157 0.75096 0.74055 0.73217 0.72434 0.71469 0.70753 0.69738 0.69258 0.68446 0.67507 0.66458 0.64823 0.64289 0.63239 0.62507 0.6115 0.58879 0.57709 0.55513 0.54314 0.5381 0.52884 0.51826 0.51135 0.50661 0.50301 0.49112 0.49269 0.48914 0.48065 0.48628 0.47434 0.46853 0.47022 0.47276 0.46958 0.45731 0.45875 0.67684 0.67466 0.67216 0.6705 0.66993 0.66971 0.66962 0.67022 0.66987 0.66943 0.66984 0.66799 0.66431 0.66127 0.65786 0.65356 0.65026 0.64518 0.64167 0.63752 0.62601 0.61617 0.60196 0.59376 0.58419 0.57706 0.57736 0.58373 0.59064 0.60064 0.61653 0.62437 0.62604 0.62213 0.60754 0.58902 0.57444 0.55865 0.55078 0.5518 0.55254 0.54931 0.55278 0.54859 0.54011 0.53185 0.52656 0.52085 0.52113 0.5175 0.51053 0.51128 0.49703 0.49567 0.48808 0.48528 0.47946 0.4778 0.46937 0.47348 0.45654 0.45191 0.4447 0.44703 0.44257 0.43544 0.43326 0.43076 0.43321 0.41858 0.41973 0.42373 0.41309 0.41334 0.40598 0.40728 0.40871 0.40359 0.40197 0.3912 0.39594 0.49317 0.49388 0.49067 0.48988 0.49084 0.49073 0.48989 0.48887 0.48997 0.48846 0.48616 0.4844 0.48212 0.47494 0.47286 0.46751 0.4645 0.45945 0.45973 0.45533 0.44913 0.44107 0.4316 0.4255 0.41673 0.41308 0.41573 0.42062 0.42579 0.43554 0.45614 0.46653 0.47112 0.46704 0.43937 0.42543 0.40301 0.38816 0.37042 0.37062 0.38151 0.3902 0.40082 0.4 0.39307 0.38227 0.37698 0.37167 0.37355 0.3674 0.36513 0.36159 0.36114 0.35823 0.35089 0.34959 0.34756 0.3476 0.34515 0.3415 0.34021 0.33452 0.33564 0.34043 0.32867 0.3257 0.3238 0.32536 0.32794 0.31796 0.31875 0.31756 0.31364 0.31976 0.31137 0.31325 0.31307 0.31573 0.31319 0.30306 0.30913 0.44595 0.44599 0.44786 0.44782 0.44563 0.44813 0.44499 0.44624 0.44829 0.44519 0.44287 0.43944 0.43806 0.43057 0.4288 0.42418 0.42788 0.41477 0.42099 0.41182 0.40104 0.3915 0.38076 0.37145 0.3699 0.36303 0.36387 0.37174 0.38251 0.39147 0.41245 0.4166 0.42726 0.41644 0.4047 0.37828 0.37289 0.34849 0.34981 0.34072 0.34593 0.3501 0.35847 0.35846 0.3569 0.35276 0.33807 0.33826 0.34135 0.33129 0.33223 0.33146 0.32828 0.32577 0.32288 0.321 0.3191 0.32671 0.31293 0.30887 0.30887 0.3061 0.31139 0.31004 0.30134 0.30314 0.30201 0.29802 0.30005 0.30442 0.29233 0.3023 0.29449 0.30012 0.29751 0.289 0.29127 0.29976 0.28919 0.28401 0.29136
4 0.013905 0.79847 0.79688 0.79546 0.79569 0.79653 0.79773 0.79913 0.80063 0.80217 0.80367 0.80514 0.80656 0.80791 0.80923 0.81034 0.81125 0.81164 0.81124 0.80953 0.80793 0.8064 0.80492 0.80379 0.80021 0.79738 0.7915 0.78643 0.7763 0.76652 0.75843 0.74522 0.73973 0.72988 0.72907 0.72947 0.73334 0.74182 0.74121 0.74333 0.74183 0.73268 0.72792 0.7135 0.7063 0.69581 0.68826 0.68128 0.67466 0.66981 0.65937 0.65231 0.64677 0.639 0.62987 0.61935 0.61056 0.59638 0.58589 0.57603 0.56187 0.55039 0.53301 0.52059 0.49732 0.4919 0.47619 0.47267 0.45892 0.4604 0.45717 0.45182 0.45689 0.44634 0.44351 0.43753 0.43631 0.42563 0.42319 0.42455 0.42156 0.42308 0.79846 0.79687 0.79544 0.79567 0.79653 0.79774 0.79913 0.80063 0.80217 0.80367 0.80514 0.80656 0.80791 0.80923 0.81034 0.81125 0.81164 0.81124 0.80953 0.80793 0.8064 0.80492 0.80379 0.80009 0.79708 0.79019 0.78483 0.77428 0.76644 0.75629 0.74584 0.73416 0.73012 0.72319 0.72739 0.73119 0.73673 0.73689 0.73858 0.73454 0.73061 0.71864 0.70844 0.69894 0.68705 0.68247 0.6731 0.66414 0.66125 0.65116 0.64178 0.63356 0.62529 0.6156 0.60176 0.59311 0.58088 0.56836 0.55436 0.54049 0.52579 0.51209 0.50053 0.4888 0.48262 0.47527 0.46976 0.4585 0.45458 0.45483 0.45446 0.44836 0.44539 0.44597 0.43661 0.42771 0.43242 0.42394 0.42722 0.4195 0.42361 0.57795 0.57569 0.57377 0.57378 0.57446 0.57593 0.57748 0.5791 0.58136 0.58364 0.5851 0.58653 0.58866 0.59041 0.59183 0.59308 0.59265 0.59324 0.59398 0.59194 0.59136 0.5894 0.58901 0.58697 0.5826 0.57742 0.57047 0.56084 0.55171 0.54068 0.52773 0.51853 0.51013 0.504 0.50578 0.50902 0.5156 0.51873 0.51873 0.51529 0.50965 0.49886 0.48761 0.48105 0.47559 0.47133 0.46736 0.46484 0.46046 0.45324 0.44952 0.44377 0.44152 0.43719 0.42906 0.426 0.42259 0.41585 0.41495 0.40861 0.40419 0.40238 0.39228 0.38112 0.38642 0.38183 0.37967 0.3737 0.37229 0.36893 0.36805 0.3713 0.35529 0.35885 0.35295 0.35616 0.35306 0.34391 0.3504 0.33846 0.34165 0.41338 0.41256 0.41311 0.41468 0.41572 0.4156 0.41633 0.41924 0.41997 0.42091 0.42208 0.42305 0.42272 0.42364 0.42247 0.42468 0.42634 0.42898 0.42907 0.4295 0.42839 0.42483 0.4219 0.41633 0.41297 0.4093 0.40577 0.39197 0.38298 0.37597 0.36383 0.35819 0.35058 0.34493 0.34709 0.35091 0.35049 0.35063 0.35127 0.36023 0.363 0.36472 0.3578 0.34516 0.33844 0.32977 0.32699 0.33122 0.32593 0.32171 0.32142 0.31742 0.31637 0.3113 0.30843 0.30431 0.30252 0.30522 0.30469 0.29872 0.29865 0.30018 0.29289 0.28416 0.28798 0.28379 0.28111 0.27981 0.28431 0.27856 0.27582 0.28416 0.27464 0.27629 0.27237 0.27307 0.27212 0.26749 0.27309 0.26477 0.2715 0.35995 0.36027 0.36861 0.36146 0.36649 0.36755 0.36939 0.36795 0.3705 0.37282 0.37141 0.37108 0.37502 0.37356 0.37781 0.37833 0.37914 0.37445 0.37912 0.37203 0.372 0.37036 0.37034 0.36617 0.36607 0.35709 0.35009 0.34226 0.33424 0.32778 0.31279 0.30746 0.30705 0.29344 0.3091 0.304 0.32297 0.3166 0.32207 0.32507 0.31733 0.31209 0.31103 0.30107 0.29963 0.29586 0.28867 0.28866 0.29344 0.28226 0.28057 0.28389 0.28262 0.27783 0.28014 0.27394 0.27133 0.26972 0.26498 0.26256 0.26592 0.26451 0.26214 0.25744 0.25415 0.25542 0.25827 0.24882 0.24991 0.25851 0.25121 0.2569 0.24908 0.25019 0.25032 0.24534 0.24625 0.2488 0.24414 0.23808 0.24739
5 0.032959 0.71392 0.71064 0.71031 0.71109 0.71248 0.71436 0.71665 0.71918 0.72191 0.72464 0.72731 0.72983 0.73221 0.73447 0.73657 0.73818 0.73915 0.73983 0.74017 0.74096 0.7408 0.74023 0.7396 0.7388 0.73722 0.73639 0.7327 0.72939 0.72855 0.72449 0.72325 0.71678 0.7154 0.70692 0.69653 0.69384 0.67918 0.67258 0.66818 0.66186 0.66046 0.65848 0.65511 0.65645 0.64852 0.64359 0.63775 0.62801 0.62134 0.61663 0.60754 0.59693 0.58986 0.57878 0.56894 0.56029 0.55436 0.53984 0.52234 0.50558 0.49429 0.47959 0.46508 0.45312 0.44141 0.43524 0.42831 0.42417 0.41683 0.40963 0.41026 0.40028 0.39644 0.39856 0.39858 0.3871 0.37988 0.37756 0.38005 0.3789 0.37704 0.70357 0.70038 0.7007 0.70234 0.70473 0.70764 0.7108 0.7141 0.71739 0.72064 0.72378 0.72676 0.7295 0.73218 0.73465 0.73651 0.73767 0.73856 0.73913 0.74008 0.74002 0.73956 0.73879 0.73781 0.73551 0.73409 0.73072 0.72848 0.72605 0.7237 0.71763 0.71572 0.71046 0.70211 0.69409 0.68618 0.67375 0.66971 0.65983 0.65501 0.65531 0.64793 0.65046 0.64564 0.63924 0.63546 0.62889 0.61897 0.61102 0.60407 0.59413 0.58297 0.57551 0.56263 0.55005 0.53622 0.52622 0.51196 0.49515 0.47958 0.46704 0.45669 0.4514 0.44443 0.43864 0.43048 0.42349 0.41887 0.41325 0.40853 0.40467 0.40316 0.39565 0.39353 0.39056 0.39011 0.38394 0.3726 0.38036 0.38044 0.37854 0.47406 0.47069 0.47026 0.47095 0.47234 0.47419 0.47658 0.47913 0.48147 0.48385 0.48613 0.48842 0.49037 0.49198 0.49341 0.49455 0.49552 0.49578 0.49451 0.49405 0.49273 0.4922 0.49077 0.48846 0.48581 0.48332 0.47983 0.4763 0.47338 0.46891 0.46565 0.46197 0.45857 0.45227 0.44632 0.44074 0.43305 0.42611 0.42164 0.42171 0.41998 0.42075 0.423 0.42257 0.41865 0.41523 0.41119 0.40369 0.39861 0.39568 0.38905 0.38394 0.38202 0.37718 0.3731 0.36746 0.36621 0.35862 0.35277 0.34487 0.34253 0.3392 0.33676 0.33716 0.32581 0.32241 0.31713 0.31246 0.30773 0.31092 0.30606 0.29922 0.29979 0.30194 0.2956 0.29972 0.29305 0.286 0.29031 0.29231 0.28352 0.33342 0.33199 0.3321 0.33277 0.33387 0.33584 0.33731 0.33759 0.3385 0.33928 0.34008 0.34232 0.34611 0.34967 0.35465 0.35795 0.3597 0.35968 0.35906 0.35708 0.35406 0.35171 0.35077 0.35175 0.35263 0.35264 0.34748 0.34619 0.34486 0.3405 0.33823 0.33338 0.32854 0.31914 0.30946 0.29729 0.28895 0.28383 0.28477 0.29064 0.29452 0.2958 0.29088 0.29104 0.28735 0.28798 0.28778 0.28828 0.28128 0.27895 0.27768 0.27163 0.2695 0.26762 0.2648 0.26242 0.26336 0.26124 0.2557 0.25331 0.25497 0.24891 0.24543 0.24972 0.24403 0.24364 0.2364 0.23664 0.23521 0.23394 0.23212 0.23468 0.22979 0.23132 0.22927 0.22555 0.2257 0.21832 0.22694 0.2242 0.22135 0.27706 0.28272 0.28512 0.28052 0.28668 0.28591 0.28869 0.28668 0.29211 0.29668 0.29445 0.29731 0.29981 0.30446 0.3042 0.3063 0.30587 0.30888 0.31002 0.30668 0.3095 0.30793 0.31042 0.30808 0.30473 0.30537 0.30102 0.29691 0.29377 0.29965 0.28973 0.29662 0.29042 0.27985 0.27789 0.26884 0.2644 0.25562 0.24633 0.25411 0.24604 0.25134 0.26028 0.25726 0.25561 0.25429 0.25263 0.25056 0.25098 0.24057 0.23805 0.24476 0.24108 0.23725 0.23259 0.23093 0.23051 0.22461 0.2174 0.2221 0.22041 0.22447 0.22063 0.2171 0.21544 0.2176 0.20931 0.21288 0.20747 0.20778 0.21159 0.20461 0.20688 0.20504 0.21087 0.20231 0.2062 0.20485 0.19814 0.19921 0.2021
6 0.064373 0.63466 0.6344 0.63533 0.63678 0.63877 0.64105 0.64326 0.64563 0.64798 0.65039 0.65296 0.65566 0.65809 0.66051 0.6628 0.66473 0.66696 0.66909 0.67123 0.67321 0.67462 0.67632 0.67666 0.67781 0.67713 0.67614 0.6766 0.67614 0.67433 0.67277 0.67091 0.66999 0.66506 0.66189 0.66166 0.65399 0.65062 0.64726 0.6398 0.63333 0.62748 0.61713 0.61279 0.60313 0.59754 0.59467 0.58767 0.58203 0.57631 0.56799 0.56073 0.55229 0.54368 0.53564 0.52275 0.51009 0.49632 0.48555 0.47347 0.46104 0.44393 0.43007 0.41918 0.41499 0.40911 0.40293 0.39314 0.3898 0.38434 0.37824 0.37541 0.36919 0.36987 0.36524 0.35981 0.35693 0.3596 0.35119 0.35277 0.34209 0.34471 0.56931 0.56902 0.57056 0.57321 ...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, 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 = V_2008_check2 - V_2008_check0;
mn_C_W_gain_check_use = C_2008_check2 - C_2008_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=0.0018238 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 mean_age_100
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 1 0 0.03116 0.030004 0.028268 0.025846 0.023825 0.022124 0.020684 0.019458 0.018407 0.017504 0.016724 0.01605 0.015464 0.014956 0.014514 0.014129 0.013796 0.013507 0.013257 0.013042 0.012858 0.012703 0.012572 0.012465 0.012378 0.012309 0.012259 0.012223 0.012203 0.012196 0.012201 0.012218 0.012245 0.012282 0.012328 0.012382 0.012444 0.012515 0.012592 0.012677 0.012767 0.012863 0.012964 0.013069 0.013186 0.013327 0.013161 0.014149 0.014017 0.013883 0.013745 0.013605 0.01346 0.013312 0.013162 0.013008 0.012848 0.012682 0.01251 0.012333 0.012152 0.011964 0.011771 0.011572 0.011368 0.011162 0.010953 0.010744 0.010535 0.010328 0.010125 0.0099264 0.0097344 0.0095496 0.0093712 0.0091998 0.0090345 0.0088742 0.0087105 0.0085273 0.0082832 0.0078767 0.0069706
2 2 0 0.042925 0.041372 0.038951 0.035539 0.032682 0.03027 0.028218 0.026462 0.024949 0.023639 0.0225 0.021507 0.020638 0.019876 0.019206 0.018616 0.018097 0.01764 0.017238 0.016885 0.016576 0.016305 0.01607 0.015867 0.015693 0.015545 0.015422 0.01532 0.015239 0.015178 0.015134 0.015106 0.015094 0.015096 0.015112 0.015141 0.015184 0.01524 0.01531 0.015393 0.015491 0.015604 0.015732 0.015877 0.016044 0.016251 0.016177 0.016978 0.016831 0.016683 0.016533 0.016381 0.016226 0.016069 0.01591 0.015749 0.015583 0.015412 0.015237 0.015058 0.014875 0.014687 0.014494 0.014297 0.014095 0.013891 0.013687 0.013483 0.013281 0.013084 0.012891 0.012704 0.012523 0.012348 0.012178 0.012014 0.011854 0.011698 0.011538 0.011359 0.011127 0.01075 0.0098579
3 3 0 0.050042 0.048477 0.045918 0.041935 0.038603 0.035792 0.033403 0.031359 0.029599 0.028075 0.026751 0.025596 0.024584 0.023696 0.022915 0.022227 0.021621 0.021086 0.020614 0.020198 0.019833 0.019512 0.019232 0.018989 0.018779 0.018599 0.018447 0.018321 0.018218 0.018138 0.018079 0.018039 0.018018 0.018015 0.018028 0.018059 0.018106 0.018172 0.018256 0.01836 0.018483 0.018627 0.018788 0.018964 0.019151 0.019341 0.019004 0.020049 0.019886 0.019721 0.019556 0.019389 0.01922 0.019049 0.018877 0.018704 0.018527 0.018345 0.018158 0.017967 0.017772 0.017572 0.017366 0.017155 0.01694 0.016722 0.016505 0.016289 0.016076 0.015867 0.015662 0.015461 0.015265 0.015074 0.014886 0.014702 0.014521 0.014343 0.014163 0.013968 0.01372 0.013293 0.012073
4 4 0 0.056814 0.055153 0.05234 0.047822 0.044043 0.040856 0.038147 0.03583 0.033836 0.032109 0.030608 0.029298 0.028152 0.027145 0.026259 0.025478 0.024789 0.024181 0.023645 0.023172 0.022755 0.02239 0.02207 0.021792 0.021552 0.021346 0.021172 0.021027 0.020909 0.020817 0.020749 0.020704 0.02068 0.020677 0.020694 0.020732 0.020791 0.020871 0.020974 0.021099 0.021247 0.021416 0.021601 0.021793 0.021979 0.022139 0.021657 0.022997 0.022821 0.022644 0.022467 0.022288 0.022106 0.021923 0.021739 0.021555 0.021365 0.02117 0.020969 0.020764 0.020553 0.020337 0.020113 0.019883 0.019649 0.019411 0.019173 0.018934 0.018699 0.018466 0.018236 0.018011 0.017789 0.017571 0.017356 0.017144 0.016934 0.016729 0.016522 0.0163 0.016004 0.015445 0.013941
5 5 0 0.06224 0.060592 0.057683 0.052755 0.048636 0.045166 0.04222 0.039702 0.037537 0.035665 0.034039 0.032621 0.031381 0.030293 0.029337 0.028496 0.027754 0.027101 0.026525 0.026018 0.025573 0.025182 0.024841 0.024546 0.024291 0.024073 0.02389 0.023739 0.023618 0.023524 0.023458 0.023416 0.023399 0.023404 0.023432 0.023484 0.023558 0.023657 0.02378 0.023927 0.024097 0.024285 0.024482 0.024678 0.024856 0.025002 0.024377 0.026082 0.025888 0.025693 0.025498 0.025301 0.025102 0.0249 0.024698 0.024495 0.024286 0.024069 0.023845 0.023615 0.023379 0.023134 0.022881 0.022619 0.022351 0.022078 0.021803 0.021527 0.021253 0.02098 0.02071 0.020445 0.020182 0.019924 0.01967 0.019419 0.019173 0.018932 0.018689 0.01842 0.018044 0.017351 0.015587
6 1 1 0.0089468 0.0085141 0.0080936 0.0073219 0.006674 0.0061254 0.0056585 0.005259 0.0049185 0.0046273 0.0043773 0.0041623 0.0039774 0.0038191 0.0036838 0.0035676 0.0034681 0.0033839 0.0033133 0.0032551 0.0032083 0.0031718 0.0031448 0.0031265 0.0031165 0.0031142 0.0031192 0.0031312 0.0031499 0.0031753 0.003207 0.0032452 0.0032898 0.0033408 0.0033985 0.0034629 0.0035347 0.0036143 0.0037028 0.0038018 0.0039133 0.0040402 0.0041865 0.0043601 0.0045762 0.0048674 0.0052768 0.0067394 0.0068018 0.00686 0.0069203 0.0069756 0.007034 0.007089 0.0071382 0.0071781 0.0072101 0.0072287 0.0072439 0.0072595 0.0072709 0.00727 0.0072525 0.0072286 0.0071962 0.0071578 0.0071092 0.0070587 0.0070126 0.0069549 0.0068873 0.0068195 0.0067512 0.0066867 0.0066194 0.0065466 0.0064658 0.0063781 0.0062883 0.0061766 0.0060001 0.0057134 0.0050696
7 2 1 0.012008 0.011426 0.01086 0.0098202 0.0089462 0.0082092 0.0075836 0.0070476 0.0065863 0.006188 0.0058427 0.0055446 0.0052878 0.0050659 0.0048739 0.0047083 0.0045659 0.0044441 0.0043406 0.0042537 0.0041817 0.0041233 0.0040774 0.004043 0.0040192 0.0040053 0.0040008 0.0040052 0.0040182 0.0040396 0.0040691 0.0041065 0.0041518 0.0042049 0.0042661 0.0043356 0.0044142 0.0045025 0.0046012 0.0047115 0.0048357 0.004977 0.0051386 0.0053271 0.0055553 0.0058561 0.0062689 0.0076866 0.0077571 0.0078198 0.0078901 0.007954 0.0080204 0.0080702 0.0081208 0.008161 0.0082003 0.0082328 0.0082624 0.0082856 0.008295 0.0082901 0.0082744 0.0082624 0.0082429 0.0082123 0.0081782 0.0081397 0.0080948 0.0080426 0.0079869 0.0079247 0.0078731 0.0078163 0.0077465 0.0076701 0.0075913 0.0075162 0.0074322 0.00732 0.007155 0.0068766 0.0062012
8 3 1 0.014485 0.01381 0.013144 0.011882 0.010826 0.0099375 0.0091808 0.0085351 0.0079843 0.0075114 0.0071026 0.0067472 0.0064384 0.0061702 0.0059377 0.0057366 0.0055634 0.0054152 0.0052889 0.0051823 0.0050935 0.005021 0.0049634 0.0049195 0.0048883 0.004869 0.0048608 0.0048632 0.0048758 0.0048982 0.00493 0.004971 0.005021 0.0050801 0.0051484 0.0052267 0.0053153 0.0054148 0.0055256 0.005649 0.0057862 0.0059393 0.0061128 0.0063154 0.006557 0.006867 0.0072585 0.0085546 0.0086292 0.0087065 0.008778 0.0088568 0.0089314 0.0090002 0.0090526 0.0091058 0.0091499 0.0091905 0.0092246 0.009262 0.0092835 0.0092853 0.0092769 0.0092691 0.0092567 0.0092325 0.0092039 0.0091689 0.0091311 0.0090843 0.0090319 0.0089744 0.0089137 0.0088616 0.0087984 0.0087205 0.0086388 0.0085555 0.008472 0.008363 0.0081879 0.007882 0.007093
9 4 1 0.017392 0.016611 0.015824 0.014325 0.013061 0.011988 0.011078 0.010303 0.0096395 0.009066 0.0085698 0.0081419 0.0077726 0.0074529 0.007175 0.0069333 0.0067239 0.0065432 0.0063885 0.0062571 0.0061468 0.0060558 0.0059825 0.0059256 0.0058837 0.005856 0.0058415 0.0058396 0.0058496 0.005871 0.0059035 0.0059466 0.0060001 0.0060638 0.0061374 0.0062216 0.0063168 0.006424 0.0065448 0.0066814 0.0068349 0.0070057 0.0071953 0.0074088 0.0076578 0.0079681 0.0083212 0.0094019 0.009486 0.0095725 0.0096519 0.0097412 0.0098254 0.0099086 0.0099708 0.010033 0.010084 0.010133 0.010177 0.010223 0.010255 0.010265 0.010262 0.010259 0.010252 0.010234 0.010209 0.010178 0.010144 0.010099 0.010047 0.0099886 0.0099242 0.009869 0.0098047 0.0097274 0.0096395 0.0095495 0.0094587 0.0093448 0.009151 0.0087953 0.0079063
10 5 1 0.021156 0.020287 0.019385 0.017573 0.016058 0.014774 0.013691 0.012766 0.011972 0.011287 0.010691 0.010172 0.0097213 0.00933 0.0089903 0.0086957 0.0084413 0.0082219 0.0080335 0.0078731 0.0077381 0.0076264 0.0075359 0.0074649 0.007412 0.0073761 0.0073559 0.0073508 0.0073598 0.0073826 0.0074185 0.007467 0.0075277 0.0076004 0.0076854 0.0077833 0.0078942 0.0080189 0.0081587 0.008314 0.008484 0.0086681 0.0088666 0.0090841 0.0093274 0.0096216 0.0099312 0.010756 0.010862 0.010956 0.011042 0.011131 0.011211 0.011294 0.01137 0.011454 0.011525 0.011576 0.011613 0.011648 0.011682 0.011704 0.011713 0.011717 0.011715 0.011698 0.011664 0.011624 0.011578 0.011539 0.011491 0.01143 0.011351 0.011266 0.01119 0.011108 0.011011 0.010911 0.010799 0.010663 0.010452 0.010036 0.008985
% 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=0.0018238 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 mean_age_100
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 1 0 0.071209 0.076265 0.082919 0.080484 0.079666 0.078062 0.076584 0.074349 0.073766 0.072176 0.071811 0.070312 0.069403 0.068715 0.067464 0.067787 0.066739 0.066735 0.066299 0.065041 0.065353 0.065876 0.064978 0.064663 0.065115 0.064574 0.064926 0.064574 0.064009 0.065787 0.064436 0.064971 0.065391 0.065737 0.06589 0.066325 0.066596 0.06807 0.068085 0.069542 0.069514 0.071506 0.071793 0.07435 0.077938 0.080499 0.090702 0.1129 0.11542 0.11827 0.12121 0.12311 0.12561 0.1291 0.13186 0.13482 0.13794 0.14134 0.14503 0.14894 0.15315 0.15781 0.16303 0.16866 0.17499 0.17929 0.18625 0.19339 0.20034 0.20829 0.2177 0.22555 0.23775 0.25119 0.26414 0.28041 0.30106 0.32185 0.34848 0.39086 0.45727 0.59128 0.99997
2 2 0 0.08025 0.085444 0.092048 0.090298 0.089234 0.088128 0.086304 0.084717 0.084401 0.08284 0.082871 0.08148 0.081412 0.080447 0.080786 0.080765 0.079254 0.080544 0.079741 0.079758 0.080427 0.079512 0.080119 0.080133 0.079251 0.080673 0.079602 0.080812 0.080688 0.08102 0.081936 0.081659 0.081533 0.0826 0.082318 0.082778 0.084259 0.0842 0.086111 0.086514 0.087272 0.08721 0.089862 0.091755 0.094161 0.097153 0.10608 0.14585 0.14812 0.15041 0.15279 0.15534 0.15802 0.16078 0.16368 0.16678 0.17013 0.17382 0.1779 0.18227 0.18689 0.19067 0.19487 0.20008 0.20552 0.21172 0.21902 0.22725 0.23445 0.2433 0.25369 0.26516 0.27577 0.28792 0.30329 0.31597 0.33669 0.35671 0.38271 0.42742 0.49493 0.62274 0.99997
3 3 0 0.087972 0.095508 0.10343 0.10119 0.099548 0.097144 0.095155 0.094156 0.092209 0.092147 0.090388 0.090084 0.089217 0.089026 0.087424 0.087444 0.087767 0.086858 0.08707 0.087008 0.086549 0.087225 0.086756 0.086733 0.087251 0.086032 0.087158 0.086935 0.086348 0.087782 0.08716 0.087503 0.087777 0.088516 0.08877 0.088369 0.089467 0.089344 0.090573 0.091324 0.092312 0.092947 0.093907 0.094791 0.097087 0.1011 0.11276 0.15135 0.15364 0.15616 0.15865 0.16132 0.16412 0.16708 0.17024 0.17359 0.17717 0.1809 0.18494 0.18903 0.1913 0.19595 0.2007 0.20624 0.21257 0.21995 0.22803 0.23465 0.24328 0.25298 0.26323 0.27444 0.28341 0.29654 0.31145 0.32381 0.34271 0.36108 0.38753 0.42837 0.49351 0.61517 0.99997
4 4 0 0.092255 0.099859 0.10923 0.10651 0.10388 0.10207 0.09988 0.098028 0.096548 0.096059 0.093819 0.093364 0.092869 0.092216 0.091467 0.090854 0.090552 0.09008 0.09007 0.089524 0.090584 0.09009 0.089534 0.089833 0.089938 0.089834 0.090277 0.089987 0.090305 0.090602 0.090492 0.090885 0.09056 0.091162 0.09067 0.092102 0.091928 0.093313 0.092913 0.093836 0.094089 0.095313 0.096271 0.098866 0.1005 0.10253 0.11491 0.15358 0.15576 0.15805 0.16057 0.16307 0.16576 0.1686 0.17169 0.17494 0.17833 0.18199 0.18593 0.188 0.19211 0.19648 0.20145 0.20729 0.21386 0.22103 0.22893 0.23413 0.24393 0.25353 0.26387 0.27413 0.28409 0.29673 0.31107 0.3231 0.34152 0.35786 0.38201 0.42008 0.48122 0.60222 0.99997
5 5 0 0.09665 0.1041 0.11456 0.11152 0.10814 0.10532 0.10426 0.10182 0.099276 0.098218 0.096549 0.096552 0.094473 0.093246 0.093105 0.092266 0.092005 0.090558 0.09162 0.090677 0.090597 0.090081 0.090808 0.089995 0.090315 0.090637 0.090543 0.090719 0.090798 0.090772 0.090754 0.090589 0.091198 0.091159 0.091577 0.092413 0.092352 0.0926 0.09253 0.093942 0.095162 0.095494 0.096967 0.099014 0.1014 0.10273 0.11591 0.15176 0.15407 0.15653 0.15906 0.16166 0.16431 0.16713 0.17015 0.17325 0.17652 0.18008 0.18342 0.18532 0.18949 0.19362 0.19828 0.20377 0.21012 0.21726 0.22524 0.23044 0.23965 0.24884 0.25908 0.27034 0.27911 0.29161 0.3058 0.31723 0.33517 0.35133 0.3743 0.40961 0.47042 0.59728 0.99997
6 1 1 0.101 0.10439 0.10978 0.10855 0.10822 0.10834 0.10808 0.10797 0.10797 0.10699 0.10569 0.10409 0.10276 0.10124 0.10046 0.10105 0.10148 0.10145 0.10081 0.10054 0.099313 0.098618 0.098388 0.097698 0.096917 0.096574 0.09559 0.095036 0.094169 0.093253 0.092917 0.092161 0.091778 0.091247 0.090457 0.089897 0.089695 0.088804 0.087854 0.087606 0.087364 0.087647 0.087476 0.087871 0.088429 0.090211 0.092309 0.12147 0.12459 0.12556 0.12804 0.13305 0.13573 0.13965 0.14277 0.13794 0.13808 0.13814 0.14977 0.14779 0.15709 0.15864 0.16005 0.16432 0.1729 0.17714 0.18148 0.18992 0.20194 0.20381 0.21254 0.22249 0.23646 0.25521 0.2602 0.27263 0.28533 0.3074 0.34922 0.38087 0.44986 0.59137 0.99998
7 2 1 0.10297 0.10717 0.11242 0.11166 0.10915 0.10816 0.1077 0.10784 0.10748 0.10781 0.109 0.10885 0.10815 0.1073 0.10704 0.10593 0.10548 0.10536 0.10499 0.10462 0.10446 0.10378 0.1032 0.10288 0.10238 0.10199 0.10185 0.10161 0.10129 0.10113 0.10061 0.099413 0.099608 0.099262 0.099041 0.098265 0.098513 0.098505 0.097963 0.097605 0.097333 0.097527 0.09728 0.09624 0.096784 0.098868 0.10153 0.13378 0.13742 0.13588 0.14371 0.14564 0.14165 0.14081 0.14542 0.15213 0.15616 0.15522 0.16248 0.16564 0.16375 0.1671 0.17581 0.18613 0.18458 0.19289 0.1984 0.20989 0.21386 0.22327 0.23174 0.24359 0.26237 0.26504 0.27072 0.28873 0.30704 0.3409 0.3624 0.39507 0.47439 0.60562 0.99998
8 3 1 0.10827 0.11355 0.11922 0.11713 0.11645 0.11572 0.11512 0.11271 0.11143 0.111 0.11066 0.11031 0.10952 0.10982 0.11044 0.11037 0.11029 0.11089 0.11042 0.11054 0.10985 0.11004 0.10931 0.10874 0.10816 0.1084 0.10772 0.10778 0.1069 0.10683 0.10646 0.10518 0.10458 0.10404 0.10397 0.10345 0.10285 0.102 0.1014 0.10051 0.099521 0.099353 0.099995 0.10122 0.10114 0.10422 0.10771 0.13809 0.1423 0.14295 0.15098 0.14951 0.158 0.14745 0.1515 0.15169 0.16013 0.16442 0.16546 0.17027 0.17083 0.17184 0.17582 0.18703 0.19882 0.1982 0.20551 0.2146 0.22475 0.22912 0.23782 0.2454 0.26322 0.27782 0.28251 0.29147 0.30951 0.3399 0.37556 0.39991 0.46756 0.60035 0.99998
9 4 1 0.10932 0.11394 0.12031 0.11884 0.11867 0.11712 0.11609 0.11555 0.11515 0.11377 0.11284 0.1117 0.11109 0.11056 0.10997 0.1094 0.10937 0.1096 0.10879 0.10974 0.10894 0.10923 0.10921 0.10883 0.10917 0.10834 0.10808 0.10775 0.10757 0.10693 0.10628 0.10623 0.10505 0.10487 0.10397 0.10404 0.10314 0.10324 0.10334 0.10428 0.10489 0.10454 0.10426 0.10514 0.10555 0.10626 0.11244 0.14112 0.14131 0.1464 0.14894 0.15323 0.15945 0.15138 0.15009 0.15468 0.16247 0.16603 0.16756 0.17442 0.17745 0.17345 0.17908 0.19347 0.19975 0.20074 0.20841 0.21773 0.22667 0.22924 0.24014 0.2494 0.26349 0.28022 0.28541 0.29288 0.3105 0.33334 0.37341 0.39947 0.45403 0.59374 0.99998
10 5 1 0.11555 0.12093 0.1289 0.12462 0.12328 0.12196 0.12068 0.11824 0.11673 0.11572 0.11507 0.11406 0.11298 0.11231 0.11222 0.11132 0.11089 0.11085 0.11097 0.1102 0.11082 0.10993 0.10986 0.10965 0.10981 0.10893 0.1094 0.10855 0.10846 0.10791 0.10792 0.10722 0.1065 0.10594 0.10569 0.10466 0.10457 0.10412 0.10377 0.10477 0.10455 0.10327 0.10394 0.10448 0.10679 0.10937 0.11427 0.14819 0.15525 0.14391 0.14942 0.1476 0.156 0.15904 0.15742 0.1631 0.16485 0.1611 0.16492 0.17776 0.18179 0.17871 0.18647 0.19187 0.19993 0.2018 0.21022 0.21796 0.22542 0.24167 0.24324 0.24906 0.25532 0.27037 0.29442 0.29644 0.31069 0.32912 0.35735 0.4075 0.45609 0.58685 0.99998 Graph Mean Values:
st_title = ['MEAN(MN\_V\_U\_GAIN\_CHECK(KM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_V\_U\_GAIN\_CHECK(KM,J))'};
ff_graph_grid((tb_az_v{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Consumption (MPC: Share of Check Consumed):
st_title = ['MEAN(MN\_MPC\_U\_GAIN\_CHECK(KM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_MPC\_U\_GAIN\_CHECK(KM,J))'};
ff_graph_grid((tb_az_c{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
1.9 Analyze Education and Marriage
Aggregating over education, savings, and shocks, what are the differential effects of Marriage and Age.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
ar_row_grid = ["E0M0", "E1M0", "E0M1", "E1M1"];
mp_support_graph('cl_st_xtitle') = {'Age'};
mp_support_graph('st_legend_loc') = 'best';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('st_rounding') = '6.2f'; % format shock legend
mp_support_graph('cl_scatter_shapes') = {'*', 'p', '*','p' };
mp_support_graph('cl_colors') = {'red', 'red', 'blue', 'blue'};MEAN(VAL(EM,J)), MEAN(AP(EM,J)), MEAN(C(EM,J))
Tabulate value and policies:
% Set
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
ar_permute = [2,3,6,1,4,5];
% Value Function
st_title = ['MEAN(MN_V_U_GAIN_CHECK(EM,J)), welf_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR'))];
tb_az_v = ff_summ_nd_array(st_title, mn_V_U_gain_check, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(MN_V_U_GAIN_CHECK(EM,J)), welf_checks=2, TR=0.0018238 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 mean_age_100
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 0 0 0.050249 0.049057 0.04709 0.044412 0.042032 0.039911 0.038016 0.03632 0.034797 0.033428 0.032194 0.031082 0.030077 0.02917 0.02835 0.027609 0.026938 0.026333 0.025786 0.025293 0.02485 0.024453 0.024098 0.023783 0.023504 0.02326 0.023049 0.022868 0.022717 0.022595 0.022499 0.022429 0.022384 0.022362 0.022364 0.022389 0.022437 0.022509 0.022603 0.022721 0.022859 0.023015 0.023181 0.023348 0.0235 0.023608 0.022868 0.02289 0.022705 0.02252 0.022333 0.022143 0.02195 0.021755 0.021559 0.02136 0.021155 0.020944 0.020726 0.020504 0.020276 0.02004 0.019797 0.019547 0.019292 0.019033 0.018772 0.018511 0.018253 0.017998 0.017747 0.017501 0.017261 0.017027 0.016798 0.016575 0.016357 0.016144 0.015927 0.015688 0.01537 0.014807 0.013373
2 1 0 0.047023 0.045182 0.042174 0.037147 0.033084 0.029772 0.027053 0.024804 0.022934 0.021369 0.020055 0.018947 0.01801 0.017216 0.016542 0.01597 0.015484 0.015073 0.014726 0.014433 0.014188 0.013984 0.013817 0.013681 0.013573 0.013489 0.013427 0.013383 0.013357 0.013346 0.013349 0.013365 0.013391 0.013427 0.013474 0.01353 0.013596 0.013673 0.013761 0.013862 0.013975 0.014103 0.014246 0.014405 0.014587 0.014815 0.014882 0.017212 0.017072 0.01693 0.016787 0.016642 0.016495 0.016346 0.016196 0.016044 0.015888 0.015727 0.015561 0.015391 0.015217 0.015038 0.014853 0.014663 0.014469 0.014273 0.014076 0.013879 0.013684 0.013492 0.013303 0.013118 0.012937 0.01276 0.012586 0.012416 0.01225 0.012087 0.011922 0.011741 0.011501 0.011079 0.0099988
3 0 1 0.015742 0.015072 0.014412 0.01336 0.012437 0.011628 0.010923 0.010302 0.0097541 0.0092682 0.0088371 0.008457 0.008122 0.0078267 0.0075662 0.0073367 0.0071354 0.0069595 0.0068068 0.0066755 0.0065639 0.0064705 0.0063942 0.0063339 0.0062886 0.0062575 0.0062399 0.0062354 0.0062434 0.0062637 0.006296 0.0063399 0.0063955 0.0064624 0.0065411 0.006632 0.0067358 0.0068531 0.0069855 0.0071348 0.0073023 0.0074899 0.0076993 0.0079372 0.0082153 0.0085691 0.0089763 0.0099633 0.010055 0.010139 0.010223 0.010309 0.010389 0.010465 0.010531 0.010601 0.010654 0.010691 0.010722 0.010764 0.010789 0.010792 0.010784 0.010776 0.010754 0.010717 0.01068 0.010639 0.010593 0.010534 0.010465 0.01039 0.010315 0.010246 0.01017 0.010081 0.009981 0.0098766 0.0097673 0.0096322 0.0094222 0.0090488 0.0081063
4 1 1 0.013853 0.013188 0.012511 0.011009 0.0097884 0.0087852 0.0079538 0.0072624 0.0066863 0.0062038 0.0057964 0.0054504 0.005157 0.0049085 0.0046981 0.0045199 0.0043697 0.0042438 0.0041391 0.004053 0.0039835 0.0039288 0.0038874 0.0038579 0.0038393 0.0038307 0.0038314 0.0038406 0.0038579 0.0038829 0.0039153 0.0039546 0.0040007 0.0040536 0.0041132 0.00418 0.0042543 0.0043367 0.0044277 0.0045283 0.0046393 0.0047623 0.0049006 0.005061 0.0052542 0.005503 0.0058464 0.007292 0.0073596 0.0074266 0.0074894 0.0075547 0.0076196 0.0076798 0.0077305 0.007772 0.0078139 0.0078536 0.0078864 0.0079071 0.0079258 0.0079336 0.0079275 0.0079178 0.0079113 0.0078962 0.0078659 0.0078293 0.0077915 0.0077537 0.007712 0.0076652 0.0076105 0.0075542 0.0074941 0.0074278 0.0073575 0.0072874 0.0072128 0.0071145 0.0069563 0.0066726 0.0059957
% 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=0.0018238 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 mean_age_100
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 0 0 0.07766 0.081478 0.084248 0.083711 0.083393 0.082988 0.083255 0.08261 0.082233 0.082344 0.082587 0.081951 0.082045 0.082147 0.0818 0.082635 0.081981 0.082159 0.082276 0.082398 0.082341 0.082792 0.082655 0.081885 0.082681 0.082898 0.082602 0.082556 0.08293 0.083444 0.082815 0.083155 0.083567 0.08371 0.08362 0.083965 0.084696 0.085061 0.085661 0.087213 0.087718 0.08833 0.08985 0.092113 0.094271 0.097205 0.10877 0.14176 0.14397 0.14634 0.14876 0.15127 0.15406 0.15718 0.16018 0.16332 0.16664 0.17023 0.17402 0.17731 0.18125 0.18557 0.19025 0.19562 0.20168 0.20799 0.21562 0.22211 0.23052 0.23952 0.24954 0.25984 0.2702 0.28314 0.29756 0.31068 0.33046 0.34899 0.37423 0.41424 0.4782 0.60491 0.99997
2 1 0 0.093675 0.10299 0.11663 0.11229 0.10879 0.1053 0.10162 0.098616 0.096247 0.094232 0.091589 0.090766 0.088905 0.087313 0.086299 0.085011 0.084545 0.083751 0.083644 0.082405 0.083064 0.082322 0.082223 0.082658 0.082067 0.081802 0.082401 0.082655 0.081929 0.082941 0.083096 0.083088 0.083016 0.08396 0.084069 0.084829 0.085145 0.08595 0.086424 0.086851 0.087622 0.088659 0.08967 0.091397 0.094164 0.0964 0.10738 0.14442 0.14683 0.14942 0.15215 0.15453 0.15706 0.1599 0.16287 0.16603 0.16939 0.17302 0.17687 0.18011 0.18393 0.18825 0.19308 0.19879 0.20516 0.2117 0.21937 0.22584 0.23414 0.24325 0.25349 0.264 0.27385 0.28646 0.30074 0.31353 0.33241 0.35054 0.37579 0.4163 0.48074 0.60657 0.99997
3 0 1 0.099571 0.10294 0.10634 0.10623 0.10602 0.10588 0.10646 0.10699 0.10755 0.10752 0.10744 0.10681 0.10647 0.10613 0.10612 0.10601 0.10612 0.10646 0.10627 0.10628 0.10612 0.10575 0.10567 0.10519 0.10513 0.10451 0.10427 0.10397 0.10338 0.10293 0.10251 0.10165 0.10108 0.10053 0.099936 0.099418 0.09891 0.098562 0.098339 0.098486 0.098576 0.098565 0.098835 0.099087 0.10019 0.10199 0.10605 0.13711 0.13844 0.13711 0.14335 0.14161 0.14563 0.14528 0.1498 0.15186 0.15147 0.1524 0.16012 0.16747 0.1665 0.16867 0.17521 0.1826 0.18308 0.1921 0.20047 0.2121 0.21582 0.22169 0.22836 0.23903 0.25871 0.26877 0.27913 0.28728 0.30234 0.32669 0.36007 0.39571 0.46172 0.5955 0.99998
4 1 1 0.11528 0.12105 0.12991 0.12609 0.12429 0.12264 0.12061 0.11794 0.11596 0.1146 0.11386 0.11279 0.11133 0.11036 0.10993 0.10922 0.10888 0.1088 0.10812 0.10798 0.10724 0.10689 0.10632 0.10593 0.10544 0.10518 0.10478 0.10432 0.10398 0.10349 0.10317 0.10243 0.10192 0.10162 0.10132 0.1007 0.1006 0.10011 0.099391 0.099422 0.098885 0.098367 0.098347 0.098893 0.09929 0.10158 0.10525 0.13595 0.1419 0.14077 0.14509 0.15 0.1547 0.15005 0.14908 0.15196 0.1612 0.16157 0.16396 0.16688 0.17387 0.17122 0.17567 0.18654 0.19932 0.19621 0.20114 0.20795 0.22124 0.22915 0.23783 0.24494 0.25364 0.27069 0.27818 0.28959 0.30688 0.33358 0.3671 0.39741 0.45905 0.59567 0.99998 Graph Mean Values:
st_title = ['MEAN(MN\_V\_U\_GAIN\_CHECK(EM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_V\_U\_GAIN\_CHECK(EM,J))'};
ff_graph_grid((tb_az_v{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Consumption (MPC: Share of Check Consumed):
st_title = ['MEAN(MN\_MPC\_U\_GAIN\_CHECK(EM,J)), welf\_checks=' num2str(welf_checks) ', TR=' num2str(mp_params('TR')) ''];
mp_support_graph('cl_st_graph_title') = {st_title};
mp_support_graph('cl_st_ytitle') = {'MEAN(MN\_MPC\_U\_GAIN\_CHECK(EM,J))'};
ff_graph_grid((tb_az_c{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
