DiscFac = DiscFacDstns[e].X[b]
AgentCount = int(
np.floor(AgentCountTotal * EducShares[e] *
DiscFacDstns[e].pmf[b]))
ThisType = deepcopy(BaseTypeList[e])
ThisType.AgentCount = AgentCount
ThisType.DiscFac = DiscFac
ThisType.seed = n
TypeList.append(ThisType)
n += 1
base_dict['Agents'] = TypeList
# Make a figure to show unemployment probabilities by demographics
if True:
t0 = time()
makePandemicShockProbsFigure(BaseTypeList, 'Deep',
**deep_pandemic_changes)
t1 = time()
print(
'Making unemployment probability by demographics (long pandemic) figure took '
+ mystr(t1 - t0) + ' seconds.')
t0 = time()
makePandemicShockProbsFigure(BaseTypeList, 'Basic', **pandemic_changes)
t1 = time()
print('Making unemployment probability by demographics figure took ' +
mystr(t1 - t0) + ' seconds.')
# Solve and simulate each type to get to the initial distribution of states
# and then prepare for new counterfactual simulations
t0 = time()
baseline_commands = [
def run_web(spec_name):
# Keep parameters in check as they are changed by reloading them
import Parameters
reload(Parameters)
from Parameters import T_sim, init_dropout, init_highschool, init_college, EducShares, DiscFacDstns,\
AgentCountTotal, base_dict, stimulus_changes, pandemic_changes, AggregationFactor
from GiveItAwayModel import GiveItAwayNowType
from GiveItAwayTools import runExperiment, makePandemicShockProbsFigure
t0 = time()
data = dict()
mystr = lambda x: '{:.2f}'.format(x)
figs_dir = '../../Figures/' + spec_name + '/'
# debug
#print(pandemic_changes['DeepA1'], base_dict['Lspell_real'], init_dropout['uPfac_big'])
# # Make baseline types
DropoutType = GiveItAwayNowType(**init_dropout)
HighschoolType = GiveItAwayNowType(**init_highschool)
CollegeType = GiveItAwayNowType(**init_college)
BaseTypeList = [DropoutType, HighschoolType, CollegeType]
# Fill in the Markov income distribution for each base type
IncomeDstn_unemp = DiscreteDistribution(
np.array([1.0]),
[np.array([1.0]), np.array([DropoutType.IncUnemp])])
IncomeDstn_big = []
for ThisType in BaseTypeList:
for t in range(ThisType.T_cycle):
if t < ThisType.T_retire:
IncomeDstn_big.append([
ThisType.IncomeDstn[t], IncomeDstn_unemp, IncomeDstn_unemp,
ThisType.IncomeDstn[t], IncomeDstn_unemp, IncomeDstn_unemp
])
ThisType.IncomeDstn[t] = [
ThisType.IncomeDstn[t], IncomeDstn_unemp
]
else:
IncomeDstn_big.append(6 * [ThisType.IncomeDstn[t]])
ThisType.IncomeDstn[t] = 2 * [ThisType.IncomeDstn[t]]
ThisType.IncomeDstn_big = IncomeDstn_big
# Make the overall list of types
TypeList = []
n = 0
for b in range(DiscFacDstns[0].X.size):
for e in range(3):
DiscFac = DiscFacDstns[e].X[b]
AgentCount = int(
np.floor(AgentCountTotal * EducShares[e] *
DiscFacDstns[e].pmf[b]))
ThisType = deepcopy(BaseTypeList[e])
ThisType.AgentCount = AgentCount
ThisType.DiscFac = DiscFac
ThisType.seed = n
TypeList.append(ThisType)
n += 1
base_dict['Agents'] = TypeList
# Make a figure to show unemployment probabilities by demographics
pandemic_changes['show_fig'] = False
data['fig6'] = makePandemicShockProbsFigure(BaseTypeList, 'TRASH',
**pandemic_changes)
del pandemic_changes['show_fig']
# Solve and simulate each type to get to the initial distribution of states
# and then prepare for new counterfactual simulations
baseline_commands = [
'solve()', 'initializeSim()', 'simulate()', 'saveState()',
'switchToCounterfactualMode()', 'makeAlternateShockHistories()'
]
multiThreadCommands(TypeList, baseline_commands)
# Define dictionaries to be used in counterfactual scenarios
stim_dict = base_dict.copy()
stim_dict.update(**stimulus_changes)
pan_dict = base_dict.copy()
pan_dict.update(**pandemic_changes)
both_dict = pan_dict.copy()
both_dict.update(**stimulus_changes)
# Run the baseline consumption level
C_base, X_base, Z_base, cAll_base, Weight_base, Mrkv_base, U_base, ltAll_base, LT_by_inc_base = runExperiment(
**base_dict)
# Get consumption when the pandemic hits (no stim)
C_pan, X_pan, Z_pan, cAll_pan, Weight_pan, Mrkv_pan, U_pan, ltAll_pan, LT_by_inc_pan = runExperiment(
**pan_dict)
# Get consumption when the pandemic hits and there's a stimulus
C_both, X_both, Z_both, cAll_both, Weight_both, Mrkv_both, U_both, ltAll_both, LT_by_inc_both = runExperiment(
**both_dict)
# Calculate baseline consumption for those who *would* be in each Markov state in the pandemic
X_alt = np.zeros((4, T_sim))
X_alt[0, :] = np.sum(cAll_base * Weight_base * Mrkv_pan[0, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[0, :], axis=1)
X_alt[1, :] = np.sum(cAll_base * Weight_base * Mrkv_pan[1, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[1, :], axis=1)
X_alt[2, :] = np.sum(cAll_base * Weight_base * Mrkv_pan[2, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[2, :], axis=1)
X_alt[3, :] = np.sum(cAll_base * Weight_base * Mrkv_pan[3, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[3, :], axis=1)
# Calculate [baseline] labor and transfer income for those who *would* be in each Markov state in the pandemic
LT_base = np.zeros((4, T_sim))
LT_base[0, :] = np.sum(ltAll_base * Weight_base * Mrkv_pan[0, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[0, :],
axis=1)
LT_base[1, :] = np.sum(ltAll_base * Weight_base * Mrkv_pan[1, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[1, :],
axis=1)
LT_base[2, :] = np.sum(ltAll_base * Weight_base * Mrkv_pan[2, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[2, :],
axis=1)
LT_base[3, :] = np.sum(ltAll_base * Weight_base * Mrkv_pan[3, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[3, :],
axis=1)
LT_base_all = np.sum(ltAll_base * Weight_base, axis=1) / np.sum(
Weight_base, axis=1)
# Calculate [pandemic] labor and transfer income for those who *would* be in each Markov state in the pandemic
LT_pan = np.zeros((4, T_sim))
LT_pan[0, :] = np.sum(ltAll_pan * Weight_base * Mrkv_pan[0, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[0, :],
axis=1)
LT_pan[1, :] = np.sum(ltAll_pan * Weight_base * Mrkv_pan[1, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[1, :],
axis=1)
LT_pan[2, :] = np.sum(ltAll_pan * Weight_base * Mrkv_pan[2, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[2, :],
axis=1)
LT_pan[3, :] = np.sum(ltAll_pan * Weight_base * Mrkv_pan[3, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[3, :],
axis=1)
LT_pan_all = np.sum(ltAll_pan * Weight_base, axis=1) / np.sum(Weight_base,
axis=1)
# Calculate [pandemic with stimulus] labor and transfer income for those who *would* be in each Markov state in the pandemic
LT_both = np.zeros((4, T_sim))
LT_both[0, :] = np.sum(ltAll_both * Weight_base * Mrkv_pan[0, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[0, :],
axis=1)
LT_both[1, :] = np.sum(ltAll_both * Weight_base * Mrkv_pan[1, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[1, :],
axis=1)
LT_both[2, :] = np.sum(ltAll_both * Weight_base * Mrkv_pan[2, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[2, :],
axis=1)
LT_both[3, :] = np.sum(ltAll_both * Weight_base * Mrkv_pan[3, :],
axis=1) / np.sum(Weight_base * Mrkv_pan[3, :],
axis=1)
LT_both_all = np.sum(ltAll_both * Weight_base, axis=1) / np.sum(
Weight_base, axis=1)
# Plot the unemployment rate over time in baseline and pandemic
data["fig1"] = [U_base * 100, U_pan * 100]
# Plot aggregate consumption under baseline, pandemic, and pandemic + CARES Act
data["fig2"] = [
C_base * AggregationFactor,
C_pan * AggregationFactor,
C_both * AggregationFactor,
]
# Plot aggregate income under baseline, pandemic, and pandemic + CARES Act
data["fig3"] = [
LT_base_all * AggregationFactor,
LT_pan_all * AggregationFactor,
LT_both_all * AggregationFactor,
]
# Plot average consumption by initial employment state after pandemic
data["fig4"] = [
X_both[0, :] * 1000,
X_both[1, :] * 1000,
X_both[2, :] * 1000,
X_pan[0, :] * 1000,
X_pan[1, :] * 1000,
X_pan[2, :] * 1000,
X_alt[0, :] * 1000,
X_alt[1, :] * 1000,
X_alt[2, :] * 1000,
]
# Plot average labor income plus transfers by initial employment state after pandemic
data["fig5"] = [
LT_both[0, :] * 1000,
LT_both[1, :] * 1000,
LT_both[2, :] * 1000,
LT_pan[0, :] * 1000,
LT_pan[1, :] * 1000,
LT_pan[2, :] * 1000,
LT_base[0, :] * 1000,
LT_base[1, :] * 1000,
LT_base[2, :] * 1000,
]
t1 = time()
print('Running the specification called ' + spec_name + ' took ' +
mystr(t1 - t0) + ' seconds.')
with open(f"../../Data/Dashboard/data_{spec_name}.pickle", "wb") as f:
pickle.dump(data, f)
for e in range(3):
DiscFac = DiscFacDstns[e].X[b]
AgentCount = int(
np.floor(AgentCountTotal * EducShares[e] *
DiscFacDstns[e].pmf[b]))
ThisType = deepcopy(BaseTypeList[e])
ThisType.AgentCount = AgentCount
ThisType.DiscFac = DiscFac
ThisType.seed = n
TypeList.append(ThisType)
n += 1
base_dict['Agents'] = TypeList
# Make a figure to show unemployment probabilities by demographics
pandemic_changes['for_mini'] = True
makePandemicShockProbsFigure(BaseTypeList, spec_name, **pandemic_changes)
del pandemic_changes['for_mini']
# Solve and simulate each type to get to the initial distribution of states
# and then prepare for new counterfactual simulations
baseline_commands = [
'solve()', 'initializeSim()', 'simulate()', 'saveState()',
'switchToCounterfactualMode()', 'makeAlternateShockHistories()'
]
multiThreadCommands(TypeList, baseline_commands)
# Define dictionaries to be used in counterfactual scenarios
stim_dict = base_dict.copy()
stim_dict.update(**stimulus_changes)
pan_dict = base_dict.copy()
pan_dict.update(**pandemic_changes)