def ev_single_meet_test(setup,V,sown,female,t,skip_mar=False,trim_lvl=0.000001):
# computes expected value of single person meeting a partner
# this creates potential partners and integrates over them
# this also removes unlikely combinations of future z and partner's
# characteristics so we have to do less bargaining
nexo = setup.pars['nexo_t'][t]
ns = sown.size
iexo = np.arange(nexo)
iassets_c = np.arange(ns)
# this just says that grid position of couple = grid position of single fem
res_m = v_mar_igrid(setup,t,V,iassets_c,iexo,
female=female,marriage=True)
res_c = v_mar_igrid(setup,t,V,iassets_c,iexo,
female=female,marriage=False)
(vfoutm,vmoutm), nprm, decm, thtm = res_m['Values'], res_m['NBS'], res_m['Decision'], res_m['theta']
(vfoutc, vmoutc), nprc, decc, thtc = res_c['Values'], res_c['NBS'], res_c['Decision'], res_c['theta']
i_mar =((nprm>=nprc) & (nprm>0)) # ((vfoutm>vfoutc) & (vmoutm>vfoutc) & (nprm>0))#
print('worked!')
def graphs(mdl, ai, zfi, zmi, psii, ti, thi):
# Import Value Funcrtion previously saved on File
#with open('name_model.pkl', 'rb') as file:
# (Packed,dec) = pickle.load(file)
Packed = mdl.V
setup = mdl.setup
dec = mdl.decisions
################################################
# Unpack Stuff to Make it easier to Visualize
################################################
T = setup.pars['Tret']
agrid = setup.agrid_c
agrids = setup.agrid_s
psig = setup.exogrid.psi_t[ti]
vtoutf = np.zeros([T, len(agrids), len(psig)])
thetf = np.zeros([T, len(agrids), len(psig)])
thetf_c = np.zeros([T, len(agrids), len(psig)])
vtoutm = np.zeros([T, len(agrids), len(psig)])
vtoutf_c = np.zeros([T, len(agrids), len(psig)])
vtoutm_c = np.zeros([T, len(agrids), len(psig)])
inds = np.zeros(len(psig))
# TODO: vectorize this part too (I do not understand what exactly it does...)
# Here I get the renegotiated values
for t in range(T):
npsi = setup.pars['n_psi_t'][t + 1]
inds = setup.all_indices(t + 1, (zfi * np.ones(npsi, dtype=np.int16),
zmi * np.ones(npsi, dtype=np.int16),
np.arange(npsi, dtype=np.int16)))[0]
# cohabitation
ai_a = ai * np.ones_like(
setup.agrid_s,
dtype=np.int32) # these are assets of potential partner
resc = v_mar_igrid(setup,
t + 1,
Packed[t],
ai_a,
inds,
female=True,
marriage=False)
(vf_c, vm_c), nbs_c, decc, tht_c = resc['Values'], resc['NBS'], resc[
'Decision'], resc['theta']
tcv = setup.thetagrid_fine[tht_c]
tcv[tht_c == -1] = None
# marriage
resm = v_mar_igrid(setup,
t,
Packed[t],
ai_a,
inds,
female=True,
marriage=True)
(vf_m, vm_m), nbs_m, decm, tht_m = resm['Values'], resm['NBS'], resm[
'Decision'], resm['theta']
tcm = setup.thetagrid_fine[tht_m]
tcm[tht_m == -1] = None
#Cohabitation-Marriage Choice
i_mar = (nbs_m >= nbs_c)
vout_ft = i_mar * vf_m + (1.0 - i_mar) * vf_c
thet_ft = i_mar * tcm + (1.0 - i_mar) * tcv
vout_mt = i_mar * vm_m + (1.0 - i_mar) * vm_c
vtoutf[t, :, :] = vout_ft
vtoutf_c[t, :, :] = vf_c
thetf[t, :, :] = thet_ft
thetf_c[t, :, :] = tcv
vtoutm[t, :, :] = vout_mt
vtoutm_c[t, :, :] = vm_c
#Renegotiated Value
nex = setup.all_indices(max(ti - 1, 0), (zfi, zmi, psii))[0]
inde = setup.theta_orig_on_fine[thi]
# if ti = 0 it creates an object that was not used for the solutions,
# as V in v_ren_new is the next period value function. ti-1 should be here.
if len(agrids) > 1:
V_ren_c = v_ren_uni(
setup, Packed[ti], False, ti - 1,
return_extra=True)[1]['Values'][0][np.arange(agrid.size), nex,
inde]
v_ren_mar = v_ren_uni if setup.div_costs.unilateral_divorce else v_ren_bil
V_ren_m = v_ren_mar(
setup, Packed[ti], True, ti - 1,
return_extra=True)[1]['Values'][0][np.arange(agrid.size), nex,
inde]
else:
V_ren_c = v_ren_uni(
setup, Packed[ti], False, ti - 1,
return_extra=True)[1]['Values'][0][np.arange(agrid.size), nex,
inde]
v_ren_mar = v_ren_uni if setup.div_costs.unilateral_divorce else v_ren_bil
V_ren_m = v_ren_mar(
setup, Packed[ti], True, ti - 1,
return_extra=True)[1]['Values'][0][np.arange(agrid.size), nex,
inde]
#Divorced Women and Men
# this thing assembles values of divorce / separation
vals = [{
'Couple, M':
v_ren_mar(setup, Packed[t], True, t - 1, return_vdiv_only=True),
'Couple, C':
v_ren_uni(setup, Packed[t], False, t - 1, return_vdiv_only=True),
} for t in range(T)]
Vf_div = v_reshape(setup, 'Couple, M', 'Value of Divorce, female', vals,
T)[..., 0, :]
Vm_div = v_reshape(setup, 'Couple, M', 'Value of Divorce, male', vals,
T)[..., 0, :]
# I take index 0 as ipsi does not matter for this
#Single Women
Vfs, cfs, sfs = [
v_reshape(setup, 'Female, single', f, Packed, T)
for f in ['V', 'c', 's']
]
Vms, cms, sms = [
v_reshape(setup, 'Male, single', f, Packed, T)
for f in ['V', 'c', 's']
]
#Couples: Marriage+Cohabitation
ithetam_R = v_reshape(setup, 'Couple, M', 'thetas', dec, T - 1)
thetam_R = setup.thetagrid_fine[ithetam_R]
thetam_R[ithetam_R == -1] = None
ithetac_R = v_reshape(setup, 'Couple, C', 'thetas', dec, T - 1)
thetac_R = setup.thetagrid_fine[ithetac_R]
thetac_R[ithetac_R == -1] = None
Vm, Vmm, Vfm, cm, sm, flsm = [
v_reshape(setup, 'Couple, M', f, Packed, T)
for f in ['V', 'VM', 'VF', 'c', 's', 'fls']
]
Vc, Vmc, Vfc, cc, sc, flsc = [
v_reshape(setup, 'Couple, C', f, Packed, T)
for f in ['V', 'VM', 'VF', 'c', 's', 'fls']
]
#########################################
# Additional Variables needed for graphs
#########################################
#Account for Single-Marriage and Single-Cohabit thresholds
trem = np.array([100.0])
trec = np.array([100.0])
for i in reversed(range(len(psig))):
if (not np.isnan(thetf[ti, ai, i])):
trem = psig[i]
if (not np.isnan(thetf_c[ti, ai, i])):
trec = psig[i]
tre = min(trem, trec)
if tre > 50.0:
tre = max(psig)
#####################################
################################
## Actually Construct graphs
################################
####################################
#Save the graphs
pdf = matplotlib.backends.backend_pdf.PdfPages("policy_graphs.pdf")
##########################################
# Value Functions wrt Love
##########################################
fig = plt.figure()
f1 = fig.add_subplot(2, 1, 1)
plt.plot(psig,
Vm[ai, zfi, zmi, 0:len(psig), thi, ti],
'k',
markersize=6,
label='Couple Marriage')
#plt.plot(psig, Vc[ai,zfi,zmi,0:len(psig),thi,ti],'k',markersize=6, label='Couple Cohabitation')
plt.plot(psig,
Vmm[ai, zfi, zmi, 0:len(psig), thi, ti],
'bo',
markersize=6,
label='Man, Marriage')
plt.plot(psig,
Vmc[ai, zfi, zmi, 0:len(psig), thi, ti],
'b',
linewidth=0.4,
label='Man, Cohabitation')
plt.plot(psig,
Vfc[ai, zfi, zmi, 0:len(psig), thi, ti],
'r',
linewidth=0.4,
label='Women, Cohabitation')
plt.plot(psig,
Vfm[ai, zfi, zmi, 0:len(psig), thi, ti],
'r*',
markersize=6,
label='Women, Marriage')
plt.axvline(x=tre,
color='b',
linestyle='--',
label='Treshold Single-Couple')
#plt.axvline(x=treb, color='b', linestyle='--', label='Tresh Bilateral')
plt.xlabel('Love')
plt.ylabel('Utility')
#plt.title('Utility Divorce costs: men=0.5, women=0.5')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=3,
fontsize='x-small')
##########################################
# FLS wrt Love
##########################################
fig = plt.figure()
f2 = fig.add_subplot(2, 1, 1)
graphc = [None] * len(psig)
graphm = [None] * len(psig)
for i in range(len(psig)):
graphc[i] = setup.ls_levels[flsc[ai, zfi, zmi, i, thi, ti]]
graphm[i] = setup.ls_levels[flsm[ai, zfi, zmi, i, thi, ti]]
plt.plot(psig, graphm, 'k', markersize=6, label='Marriage')
plt.plot(psig, graphc, 'r*', markersize=6, label='Cohabitation')
plt.axvline(x=tre,
color='b',
linestyle='--',
label='Treshold Single-Couple')
#plt.axvline(x=treb, color='b', linestyle='--', label='Tresh Bilateral')
plt.xlabel('Love')
plt.ylabel('FLS')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=3,
fontsize='x-small')
##########################################
# FLS wrt female earnings
##########################################
fig = plt.figure()
f3 = fig.add_subplot(2, 1, 1)
graphc = [None] * setup.pars['n_zf_t'][ti]
graphm = [None] * setup.pars['n_zf_t'][ti]
for i in range(setup.pars['n_zf_t'][ti]):
graphc[i] = setup.ls_levels[flsc[ai, i, zmi, psii, thi, ti]]
graphm[i] = setup.ls_levels[flsm[ai, i, zmi, psii, thi, ti]]
plt.plot(range(setup.pars['n_zf_t'][ti]),
graphm,
'k',
markersize=6,
label='Marriage')
plt.plot(range(setup.pars['n_zf_t'][ti]),
graphc,
'r*',
markersize=6,
label='Cohabitation')
#plt.axvline(x=treb, color='b', linestyle='--', label='Tresh Bilateral')
plt.xlabel('Female Productivity')
plt.ylabel('FLS')
#plt.title('Utility Divorce costs: men=0.5, women=0.5')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=2,
fontsize='x-small')
##########################################
# FLS wrt theta
##########################################
fig = plt.figure()
f4 = fig.add_subplot(2, 1, 1)
graphc = [None] * len(setup.thetagrid)
graphm = [None] * len(setup.thetagrid)
for i in range(len(setup.thetagrid)):
graphc[i] = setup.ls_levels[flsc[ai, zfi, zmi, psii, i, ti]]
graphm[i] = setup.ls_levels[flsm[ai, zfi, zmi, psii, i, ti]]
plt.plot(setup.thetagrid, graphm, 'k', markersize=6, label='Marriage')
plt.plot(setup.thetagrid, graphc, 'r*', markersize=6, label='Cohabitation')
#plt.axvline(x=treb, color='b', linestyle='--', label='Tresh Bilateral')
plt.xlabel('Theta')
plt.ylabel('FLS')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=2,
fontsize='x-small')
##########################################
# Surplues of Marriage wrt Cohabitation, Love grid
##########################################
#Generate Marriage Surplus wrt cohabitation + Value Functions
surpM = [None] * len(psig)
surpW = [None] * len(psig)
for i in range(len(psig)):
surpM[i] = max(
Vmm[ai, zfi, zmi, i, thi, ti] - Vmc[ai, zfi, zmi, i, thi, ti], 0.0)
surpW[i] = max(
Vfm[ai, zfi, zmi, i, thi, ti] - Vfc[ai, zfi, zmi, i, thi, ti], 0.0)
#Graph for the Surplus
zero = np.array([0.0] * psig)
fig = plt.figure()
f5 = fig.add_subplot(2, 1, 1)
plt.plot(psig, zero, 'k', linewidth=1)
plt.plot(psig, surpM, 'b', linewidth=1.5, label='Man')
plt.plot(psig, surpW, 'r', linewidth=1.5, label='Women')
plt.axvline(x=tre,
color='b',
linestyle='--',
label='Treshold Single-Couple')
plt.ylim(-0.1 * max(max(surpM), max(surpW), max(surpM), max(surpW)),
1.1 * max(max(surpM), max(surpW)))
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=3,
fontsize='x-small')
plt.xlabel('Love')
plt.ylabel('Marriage Surplus wrt Cohab.')
##########################################
# Value Function and Assets
##########################################
fig = plt.figure()
f6 = fig.add_subplot(2, 1, 1)
plt.plot(agrid,
Vmm[0:len(agrid), zfi, zmi, psii, thi, ti],
'bo',
markersize=6,
markevery=5,
label='Man, Marriage')
plt.plot(agrid,
Vmc[0:len(agrid), zfi, zmi, psii, thi, ti],
'b',
linewidth=0.4,
label='Man, Cohabitation')
plt.plot(agrid,
Vfc[0:len(agrid), zfi, zmi, psii, thi, ti],
'r',
linewidth=0.4,
label='Women, Cohabitation')
plt.plot(agrid,
Vfm[0:len(agrid), zfi, zmi, psii, thi, ti],
'r*',
markersize=6,
markevery=5,
label='Women, Marriage')
#plt.axvline(x=treb, color='b', linestyle='--', label='Tresh Bilateral')
plt.ylabel('Utility')
plt.xlabel('Assets')
#plt.title('Utility Divorce costs: men=0.5, women=0.5')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=4,
fontsize='x-small')
##########################################
# Surplues of Marriage wrt Cohabitation, Asset
##########################################
#Generate Marriage Surplus wrt cohabitation + Value Functions
surpM = [None] * len(agrid)
surpW = [None] * len(agrid)
for i in range(len(agrid)):
surpM[i] = max(
Vmm[i, zfi, zmi, psii, thi, ti] - Vmc[i, zfi, zmi, psii, thi, ti],
0.0)
surpW[i] = max(
Vfm[i, zfi, zmi, psii, thi, ti] - Vfc[i, zfi, zmi, psii, thi, ti],
0.0)
#Graph for the Surplus
zero = np.array([0.0] * agrid)
fig = plt.figure()
f7 = fig.add_subplot(2, 1, 1)
plt.plot(agrid, zero, 'k', linewidth=1)
plt.plot(agrid, surpM, 'b', linewidth=1.5, label='Man')
plt.plot(agrid, surpW, 'r', linewidth=1.5, label='Women')
#plt.axvline(x=treb, color='b', label='Tresh Bilateral')
#plt.axvline(x=treu, color='r', linestyle='--', label='Tresh Unilateral')
plt.ylim(
-0.1 * max(max(surpM), max(surpW), max(surpM), max(surpW)) - 0.0001,
1.1 * max(max(surpM), max(surpW)) + 0.0001)
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=3,
fontsize='x-small')
plt.xlabel('Assets')
plt.ylabel('Marriage Surplus wrt Cohab.')
##########################################
# Vf and ASSETS
##########################################
fig = plt.figure()
f8 = fig.add_subplot(2, 1, 1)
#plt.plot(agrid, Vm[0:len(agrid),zfi,zmi,psii,thi,ti],'bo',markersize=4, label='Before Ren M')
#plt.plot(agrid, Vc[0:len(agrid),zfi,zmi,psii,thi,ti],'r*',markersize=2,label='Before Ren C')
plt.plot(agrid, V_ren_c, 'y', markersize=4, label='After Ren C')
plt.plot(agrid,
V_ren_m,
'k',
linestyle='--',
markersize=4,
label='After Ren M')
#plt.plot(agrid, Vm_div[0:len(agrid),zfi,zmi,ti],'b',markersize=2, label='Male Divorce')
plt.plot(agrid,
setup.thetagrid[thi] * Vf_div[0:len(agrid), zfi, zmi, ti] +
(1 - setup.thetagrid[thi]) * Vm_div[0:len(agrid), zfi, zmi, ti],
'r',
linestyle='--',
markersize=2,
label='Female Divorce')
plt.ylabel('Utility')
plt.xlabel('Assets')
#plt.title('Utility Divorce costs: men=0.5, women=0.5')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=3,
fontsize='x-small')
##########################################
# Consumption and Assets
##########################################
fig = plt.figure()
f9 = fig.add_subplot(2, 1, 1)
plt.plot(agrid,
cm[0:len(agrid), zfi, zmi, psii, thi, ti],
'k',
markevery=1,
label='Marriage')
plt.plot(agrid,
cc[0:len(agrid), zfi, zmi, psii, thi, ti],
'r',
linestyle='--',
markevery=1,
label='Cohabitation')
#plt.plot(agrid, sc[0:len(agrid),zfi,zmi,psii,thi,ti],'k',linewidth=2.0,linestyle='--', label='Cohabitation')
#plt.plot(agrids, cms[0:len(agrids),zmi,ti],'b',linewidth=2.0,label='Men, Single')
#plt.plot(agrids, cfs[0:len(agrids),zfi,ti],'r',linewidth=2.0,linestyle='--', label='Women, Single')
#plt.axvline(x=treb, color='b', linestyle='--', label='Tresh Bilateral')
plt.ylabel('Consumption')
plt.xlabel('Assets')
#plt.title('Utility Divorce costs: men=0.5, women=0.5')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=2,
fontsize='x-small')
##########################################
# Savings and Assets
##########################################
fig = plt.figure()
f10 = fig.add_subplot(2, 1, 1)
plt.plot(agrid,
sm[0:len(agrid), zfi, zmi, psii, thi, ti],
'ko',
markersize=6,
markevery=1,
label='Marriage')
plt.plot(agrid,
sc[0:len(agrid), zfi, zmi, psii, thi, ti],
'r*',
markersize=6,
markevery=1,
label='Cohabitation')
#plt.plot(agrid, agrid,'k',linewidth=1.0,linestyle='--')
#plt.plot(agrids, sms[0:len(agrids),zmi,ti],'b',linewidth=2.0,label='Men, Single')
#plt.plot(agrids, sfs[0:len(agrids),zfi,ti],'r',linewidth=2.0,linestyle='--', label='Women, Single')
#plt.axvline(x=treb, color='b', linestyle='--', label='Tresh Bilateral')
plt.ylabel('Savings')
plt.xlabel('Assets')
#plt.title('Utility Divorce costs: men=0.5, women=0.5')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=2,
fontsize='x-small')
#print(111,cms[0:len(agrid),zmi,ti])
##########################################
# Value Function and Pareto Weights
##########################################
fig = plt.figure()
f11 = fig.add_subplot(2, 1, 1)
plt.plot(setup.thetagrid,
Vmm[ai, zfi, zmi, psii, 0:len(setup.thetagrid), ti],
'bo',
markersize=6,
label='Man, Marriage')
plt.plot(setup.thetagrid,
Vmc[ai, zfi, zmi, psii, 0:len(setup.thetagrid), ti],
'b',
linewidth=0.4,
label='Man, Cohabitation')
plt.plot(setup.thetagrid,
Vfc[ai, zfi, zmi, psii, 0:len(setup.thetagrid), ti],
'r',
linewidth=0.4,
label='Women, Cohabitation')
plt.plot(setup.thetagrid,
Vfm[ai, zfi, zmi, psii, 0:len(setup.thetagrid), ti],
'r*',
markersize=6,
label='Women, Marriage')
#plt.axvline(x=treb, color='b', linestyle='--', label='Tresh Bilateral')
plt.ylabel('Utility')
plt.xlabel('Pareto Weight-Women')
#plt.title('Utility Divorce costs: men=0.5, women=0.5')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=4,
fontsize='x-small')
###########################################
# Value of Marriage-Cohabitation over time
###########################################
#Generate Marriage Surplus wrt cohabitation + Value Functions
surpM = [None] * T
surpW = [None] * T
for i in range(T):
surpM[i] = max(
Vmm[ai, zfi, zmi, psii, thi, i] - Vmc[ai, zfi, zmi, psii, thi, i],
0.0)
surpW[i] = max(
Vfm[ai, zfi, zmi, psii, thi, i] - Vfc[ai, zfi, zmi, psii, thi, i],
0.0)
#Graph for the Surplus
zero = np.array([0.0] * np.array(range(T)))
fig = plt.figure()
f12 = fig.add_subplot(2, 1, 1)
plt.plot(np.array(range(T)), zero, 'k', linewidth=1)
plt.plot(np.array(range(T)), surpM, 'b', linewidth=1.5, label='Man')
plt.plot(np.array(range(T)), surpW, 'r', linewidth=1.5, label='Women')
#plt.axvline(x=treb, color='b', label='Tresh Bilateral')
#plt.axvline(x=treu, color='r', linestyle='--', label='Tresh Unilateral')
plt.ylim(
-0.1 * max(max(surpM), max(surpW),
max(surpM) - 0.0001, max(surpW)),
1.1 * max(max(surpM), max(surpW)) + 0.0001)
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=3,
fontsize='x-small')
plt.xlabel('Time')
plt.ylabel('Marriage Surplus wrt Cohab.')
##########################################
# Rebargained Surplus
##########################################
#Generate Marriage Surplus wrt cohabitation + Value Functions
surpM = [None] * len(psig)
surpW = [None] * len(psig)
for i in range(len(psig)):
surpM[i] = max(vtoutm[ti, ai, i] - vtoutm_c[ti, ai, i], 0.0)
surpW[i] = max(vtoutf[ti, ai, i] - vtoutf_c[ti, ai, i], 0.0)
#Graph for the Surplus
zero = np.array([0.0] * psig)
fig = plt.figure()
f13 = fig.add_subplot(2, 1, 1)
plt.plot(psig, zero, 'k', linewidth=1)
plt.plot(psig, surpM, 'b', linewidth=1.5, label='Man')
plt.plot(psig, surpW, 'r', linewidth=1.5, label='Women')
plt.axvline(x=tre,
color='b',
linestyle='--',
label='Treshold Single-Couple')
#plt.axvline(x=treb, color='b', label='Tresh Bilateral')
#plt.axvline(x=treu, color='r', linestyle='--', label='Tresh Unilateral')
plt.ylim(-0.1 * max(max(surpM), max(surpW), max(surpM), max(surpW)),
1.1 * max(max(surpM), max(surpW)))
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=3,
fontsize='x-small')
plt.xlabel('Love')
plt.ylabel('Marriage Surplus wrt Cohab.')
##########################################
# Initial thetas
##########################################
#Generate Marriage Surplus wrt cohabitation + Value Functions
surpM = [None] * len(psig)
surpW = [None] * len(psig)
for i in range(len(psig)):
surpM[i] = max(vtoutm[ti, ai, i] - vtoutm_c[ti, ai, i], 0.0)
surpW[i] = max(vtoutf[ti, ai, i] - vtoutf_c[ti, ai, i], 0.0)
#Graph for the Surplus
zero = np.array([0.0] * psig)
fig = plt.figure()
f14 = fig.add_subplot(2, 1, 1)
plt.plot(psig, zero, 'k', linewidth=1)
plt.plot(psig,
thetf[ti, ai, 0:len(psig)],
'b',
linewidth=1.5,
label='Theta Marriage')
plt.plot(psig,
thetf_c[ti, ai, 0:len(psig)],
'r',
linestyle='--',
linewidth=1.5,
label='Theta Cohabitation')
plt.axvline(x=tre,
color='k',
linestyle='--',
label='Treshold Single-Couple')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=3,
fontsize='x-small')
plt.xlabel('Love')
plt.ylabel('Theta')
##########################################
# Renegotiated Thetas-Possible Split
##########################################
zero = np.array([0.0] * psig)
fig = plt.figure()
f15 = fig.add_subplot(2, 1, 1)
plt.plot(psig, zero, 'k', linewidth=1)
plt.plot(psig,
thetam_R[ai, zfi, zmi, 0:len(psig), 0, ti],
'b',
linewidth=1.5,
label='Theta Marriage')
plt.plot(psig,
thetac_R[ai, zfi, zmi, 0:len(psig), 0, ti],
'r',
linestyle='--',
linewidth=1.5,
label='Theta Cohabitation')
for j in range(0, len(setup.thetagrid_fine), 10):
plt.plot(psig,
thetam_R[ai, zfi, zmi, 0:len(psig), j, ti],
'b',
linewidth=1.5)
plt.plot(psig,
thetac_R[ai, zfi, zmi, 0:len(psig), j, ti],
'r',
linestyle='--',
linewidth=1.5)
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=2,
fontsize='x-small')
plt.xlabel('Love')
plt.ylabel('Theta')
##########################################
# Thetas and Assets
##########################################
zero = np.array([0.0] * psig)
fig = plt.figure()
f16 = fig.add_subplot(2, 1, 1)
#plt.plot(psig, zero,'k',linewidth=1)
plt.plot(setup.thetagrid,
sm[ai, zfi, zmi, psii, 0:len(setup.thetagrid), ti],
'b',
linewidth=1.5,
label='Savings Marriage')
#plt.plot(setup.thetagrid, sc[ai,zfi,zmi,psii,ti,0:len(setup.thetagrid)],'r', linestyle='--',linewidth=1.5, label='Savings Cohabitation')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=1,
fontsize='x-small')
plt.xlabel('Theta')
plt.ylabel('Assets')
##########################################
# Assets and Theta
##########################################
zero = np.array([0.0] * psig)
fig = plt.figure()
f17 = fig.add_subplot(2, 1, 1)
#plt.plot(psig, zero,'k',linewidth=1)
plt.plot(agrid,
thetam_R[0:len(agrid), zfi, zmi, psii, 0, ti],
'b',
linewidth=1.5,
label='Theta Marriage')
plt.plot(agrid,
thetac_R[0:len(agrid), zfi, zmi, psii, 0, ti],
'r',
linestyle='--',
linewidth=1.5,
label='Theta Cohabitation')
for j in range(0, len(setup.thetagrid_fine), 10):
plt.plot(agrid,
thetam_R[0:len(agrid), zfi, zmi, psii, j, ti],
'b',
linewidth=1.5)
plt.plot(agrid,
thetac_R[0:len(agrid), zfi, zmi, psii, j, ti],
'r',
linestyle='--',
linewidth=1.5)
#plt.plot(setup.thetagrid, sc[ai,zfi,zmi,psii,ti,0:len(setup.thetagrid)],'r', linestyle='--',linewidth=1.5, label='Savings Cohabitation')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.3),
fancybox=True,
shadow=True,
ncol=2,
fontsize='x-small')
plt.xlabel('Assets')
plt.ylabel('Thetas')
##########################################
# Put graphs together
##########################################
#show()
for fig in range(1,
plt.gcf().number +
1): ## will open an empty extra figure :(
pdf.savefig(fig)
pdf.close()
matplotlib.pyplot.close("all")
def ev_single_meet(setup,V,sown,female,t,skip_mar=False,trim_lvl=0.000001,dec_c=None):
# computes expected value of single person meeting a partner
# this creates potential partners and integrates over them
# this also removes unlikely combinations of future z and partner's
# characteristics so we have to do less bargaining
nexo = setup.pars['nexo_t'][t]
ns = sown.size
p_mat = setup.part_mats['Female, single'][t].T if female else setup.part_mats['Male, single'][t].T
p_mat = p_mat.astype(setup.dtype,copy=False)
V_next = np.ones((ns,nexo),dtype=setup.dtype)*(-1e10)
inds = np.where( np.any(p_mat>0,axis=1 ) )[0]
EV = setup.dtype(0.0)
i_assets_c, p_assets_c = setup.i_a_mat[female], setup.prob_a_mat[female]
npart = i_assets_c.shape[1]
matches = setup.matches['Female, single'][t] if female else setup.matches['Male, single'][t]
dec = np.zeros(matches['iexo'].shape,dtype=np.bool)
morc = np.zeros(matches['iexo'].shape,dtype=np.bool)
tht = -1*np.ones(matches['iexo'].shape,dtype=np.int32)
iconv = matches['iconv']
for i in range(npart):
if not skip_mar:
# try marriage
res_m = v_mar_igrid(setup,t,V,i_assets_c[:,i],inds,
female=female,marriage=True)
res_c = v_mar_igrid(setup,t,V,i_assets_c[:,i],inds,
female=female,marriage=False)
else:
# try marriage
res_m = v_no_mar(setup,t,V,i_assets_c[:,i],inds,
female=female,marriage=True)
res_c = v_no_mar(setup,t,V,i_assets_c[:,i],inds,
female=female,marriage=False)
(vfoutm,vmoutm), nprm, decm, thtm = res_m['Values'], res_m['NBS'], res_m['Decision'], res_m['theta']
# try cohabitation
(vfoutc, vmoutc), nprc, decc, thtc = res_c['Values'], res_c['NBS'], res_c['Decision'], res_c['theta']
# choice is made based on Nash Surplus value
i_mar =(nprm>=nprc) #((vfoutm>vfoutc) & (vmoutm>vfoutc))#
if female:
vout = i_mar*vfoutm + (~i_mar)*vfoutc
else:
vout = i_mar*vmoutm + (~i_mar)*vmoutc
assert vout.dtype == setup.dtype
# #New couple decision decisions
# # if dec_c is not None:
# #coh=((i_mar==False) & ((i_mar*decm + (~i_mar)*decc)==True))
# coh=((i_mar*decm + (~i_mar)*decc)==True)
# if np.any(coh):
# #Get index of iexo complete grid
# mask=np.in1d(np.linspace(0,len(V_next[0,:])-1,len(V_next[0,:]-1)),inds)
# index_1=np.array(mask[None,...,None]+dec_c['Cohabitation preferred to Marriage']*0,dtype=bool)
# i_mar[coh]=(~dec_c['Cohabitation preferred to Marriage'][index_1])
# #
dec[:,:,iconv[:,i]] = (i_mar*decm + (~i_mar)*decc)[:,None,:]
tht[:,:,iconv[:,i]] = (i_mar*thtm + (~i_mar)*thtc)[:,None,:]
morc[:,:,iconv[:,i]] = i_mar[:,None,:]
V_next[:,inds] = vout
EV += (p_assets_c[:,i][:,None])*np.dot(V_next,p_mat)
assert EV.dtype == setup.dtype
mout = matches.copy()
mout['Decision'] = dec
mout['M or C'] = morc
mout['theta'] = tht
return EV, mout
def mar_graphs(mdl,t=1):
setup = mdl.setup
V = mdl.V
from marriage import v_mar_igrid
from gridvec import VecOnGrid
agrid_c = mdl.setup.agrid_c
agrid_s = mdl.setup.agrid_s
icouple = VecOnGrid(agrid_c,2*agrid_s).i
npsi = setup.pars['n_psi_t'][t]
psig = setup.exogrid.psi_t[t]
nzf = setup.pars['n_zf_t'][t]
zfg = setup.exogrid.zf_t[t]
zmg = setup.exogrid.zm_t[t]
izm = 2
wm = setup.pars['m_wage_trend'][t] + zmg[izm]
wzf = np.exp( setup.pars['f_wage_trend'][t] + zfg) / np.exp(wm)
'''
print(psig)
izf = 3
izm = 2
inds_ = (izf*np.ones(npsi,dtype=np.int16),izm*np.ones(npsi,dtype=np.int16),np.arange(npsi,dtype=np.int16))
inds = mdl.setup.all_indices(t,inds_)[0]
'''
inds = mdl.setup.all_indices(t)[0]
iac = np.searchsorted(agrid_c,2.0*wm)
ias = np.searchsorted(agrid_s,1.0*wm)
results = list()
for upp in [False,True]:
uloss = 0.0 #setup.pars['disutil_shotgun'] if upp else 0.0
res = v_mar_igrid(setup,t,V[t],icouple,inds,female=True,giveabirth=upp,
unplanned_pregnancy=upp,
uloss_fem=0.0,uloss_mal=0.0,
uloss_fem_single=uloss,uloss_mal_single=uloss,
return_all=True)
res_r = mdl.x_reshape(res['theta'][...,None],t).squeeze(axis=-1)
tht_r = setup.thetagrid_fine[res_r]
#tht_v[res['theta'] < 0] = None
tht_all = ma.masked_where(res_r<0,tht_r)
tht_pick = tht_all[iac,:,izm,:]
print(tht_pick.shape)
fig, ax = plt.subplots()
cs = ax.contourf(zfg,psig,tht_pick.T,cmap='Blues',vmin=0.0,vmax=0.8)
cb = fig.colorbar(cs)
cb.set_label(r'Resulting female bargaining power ($\theta$)')
plt.xlabel('Female productivity')
plt.ylabel(r'Love shock at match ($\psi$)',labelpad=-3.0)
plt.title('Bargaining: {}'.format('Unplanned Pregnancy (No Stigma)' if upp else 'Regular Match'))
results.append(res)
#ax.grid(True)
ax.set_xticks(zfg)
ax.set_yticks(psig)#np.arange(-4,5))
plt.savefig(('barg_upp.pdf' if upp else 'barg_reg_match.pdf'))
result_noupp, result_upp = results
izf = 3
izm = 3
ipsi = 9
fig, axs = plt.subplots(1,2)
fig2, axs2 = plt.subplots()
for n, upp, res in zip([0,1],[False,True],[result_noupp,result_upp]):
Vfm,Vmm,Vfs,Vms,gamma = res['ins']
Vfm0, Vmm0 = [mdl.x_reshape(x,t) for x in [Vfm,Vmm]]
Vfs0,Vms0 = [mdl.x_reshape(x[...,None],t) for x in [Vfs,Vms]]
if not upp:
norm_f = Vfs0[iac,izf,izm,ipsi]
norm_m = Vms0[iac,izf,izm,ipsi]
vfm_tht = Vfm0[iac,izf,izm,ipsi,:] - norm_f
vmm_tht = Vmm0[iac,izf,izm,ipsi,:] - norm_m
vfs_tht = Vfs0[iac,izf,izm,ipsi]*np.ones(vfm_tht.size) - norm_f
vms_tht = Vms0[iac,izf,izm,ipsi]*np.ones(vmm_tht.size) - norm_m
tht_fine = setup.thetagrid_fine
l = 'unplanned pregnancy' if upp else 'regular match'
c = 'o' if upp else 'x'
axs[0].plot(tht_fine,vfm_tht,'{}-k'.format(c),label='Agree, {}'.format(l),markevery=25)
axs[0].plot(tht_fine,vfs_tht,'{}--k'.format(c),label='Disagree, {}'.format(l),markevery=25)
axs[1].plot(tht_fine,vmm_tht,'{}-k'.format(c),label='Agree, {}'.format(l),markevery=25)
axs[1].plot(tht_fine,vms_tht,'{}--k'.format(c),label='Disagree, {}'.format(l),markevery=25)
axs[1].set_title('Male')
axs[0].set_title('Female')
axs2.plot(tht_fine,vfm_tht-vfs_tht,'{}-k'.format(c),label='F, {}'.format(l),markevery=25)
axs2.plot(tht_fine,vmm_tht-vms_tht,'{}--k'.format(c),label='M, {}'.format(l),markevery=25)
if not upp: axs2.plot(tht_fine,np.zeros_like(tht_fine),':k'.format(c),markevery=25)
axs[0].set_ylabel(r'Value functions (normalized)')
[ax.set_xlabel(r'Bargaining power ($\theta$)') for ax in axs]
axs[1].legend(bbox_to_anchor=(-1.4, -0.175),loc='upper left',ncol=2)
fig.suptitle('Change in values b/c of unplanned pregnancy',fontsize=16)
fig.subplots_adjust(bottom=+0.25)
fig.savefig('change_upp.pdf',bbox='tight',pad_inches=0.5)
fig2.suptitle('Surplus over disagreement',fontsize=16)
axs2.legend(ncol=1)
axs2.set_xlim(0.0,1.0)
#axs[0].set_ylim(-50,10)
#axs[1].set_ylim(-50,10)