def shortDescription(self):
from paramGeneratorModule import ParamGenerator
T_model = ParamGenerator.timing(self)['T_model']
desc = (('[ %s ]' % self.Description) + ('\n \t%-25s= %u' %
('T_model', T_model)) +
('\n \t%-25s= %u' % ('IsLowReturn', self.IsLowReturn)) +
('\n \t%-25s= %7.8f' % ('Beta', self.beta)) +
('\n \t%-25s= %7.8f' % ('Gamma', self.gamma)) +
('\n \t%-25s= %7.8f' % ('Sigma', self.sigma)) +
('\n \t%-25s= %e' % ('Model$', self.modelunit_dollar)))
return desc
def __init__(self, scenario, DIST=None, Market=None, OPTs=None):
if not scenario.isSteady():
raise Exception(
'Unable to generate income distribution moments for transition paths.'
)
# PARAMETERS
pathFinder = PathFinder(scenario)
self.scenario = scenario
save_dir = PathFinder.getCacheDir(scenario)
# Define time constants and grids
timing = ParamGenerator.timing(scenario)
grids = ParamGenerator.grids(scenario)
T_life = timing['T_life'] # Total life years
T_model = timing['T_model'] # Transition path model years
Tmax_work = timing['Tmax_work'] # Largest retirement age
ng = grids['ng'] # num groups
nz = grids['nz'] # num labor productivity shocks
zs = grids['zs'] # shocks grid (by demographic type and age)
nk = grids['nk'] # num asset points
nb = grids['nb'] # num avg. earnings points
# Useful later for a couple of functions
self.kv = grids['kv']
self.karray = np.tile(np.reshape(grids['kv'], [1, nk, 1, 1, 1, 1]),
[nz, 1, nb, T_life, ng, T_model])
self.T_work = Tmax_work
self.T_life = T_life
## DISTRIBUTION AND POLICY FUNCTIONS
# Import households distribution
if DIST is None:
with open(os.path.join(save_dir, 'distribution.pkl'),
'rb') as handle:
s = pickle.load(handle)
DIST = s['DIST']
dist = DIST.flatten(order='F')
if T_model == 1:
DIST = DIST[:, :, :, :, :, np.newaxis]
dist_l = np.zeros((nz, nk, nb, T_life, ng, T_model))
dist_l[0:nz, 0:nk, 0:nb, 0:Tmax_work, 0:ng,
0:T_model] = DIST[0:nz, 0:nk, 0:nb, 0:Tmax_work, 0:ng,
0:T_model] # Working age population
dist_l[0:nz, 0:nk, 0:nb, Tmax_work - 1:T_life, 0:ng,
0:T_model] = 0 # Retired population
dist_l = dist_l.flatten(order='F') / np.sum(dist_l)
# Useful later for a couple of functions
self.DIST = DIST
# Import market variables
if Market is None:
with open(os.path.join(save_dir, 'market.pkl')) as handle:
s = pickle.load(handle)
wages = s['wages']
capsharesAM = s['capsharesAM']
bondDividendRates = s['bondDividendRates']
equityDividendRates = s['equityDividendRates']
else:
wages = Market['wages']
capsharesAM = Market['capsharesAM']
bondDividendRates = Market['bondDividendRates']
equityDividendRates = Market['equityDividendRates']
# Import policy functions
f = lambda X: np.tile(np.reshape(X, [nz, nk, nb, T_life, 1, T_model]),
[1, 1, 1, 1, ng, 1])
if OPTs is None:
with open(os.path.join(save_dir, 'decisions.pkl')) as handle:
s = pickle.load(handle)
s = s['OPTs']
labinc = f(s['LABOR']) * np.tile(
np.reshape(np.transpose(zs, [2, 1, 0]),
[nz, 1, 1, T_life, 1, T_model]),
[1, nk, nb, 1, ng, 1]) * wages
k = f(s['SAVINGS'])
self.ben = f(s['OASI_BENEFITS'])
self.lab = f(s['LABOR'])
self.con = f(s['CONSUMPTION'])
else:
labinc = f(OPTs['LABOR']) * np.tile(
np.reshape(np.transpose(zs, [2, 1, 0]),
[nz, 1, 1, T_life, 1, T_model]),
[1, nk, nb, 1, ng, 1]) * wages
k = f(OPTs['SAVINGS'])
self.ben = f(OPTs['OASI_BENEFITS'])
self.lab = f(OPTs['LABOR'])
self.con = f(OPTs['CONSUMPTION'])
kinc = ((1 - capsharesAM) * bondDividendRates +
capsharesAM * equityDividendRates) * k
totinc = labinc.flatten(order='F') + kinc.flatten(
order='F') + self.ben.flatten(order='F') # Total income
labinc = labinc.flatten(order='F') # Labor income
k = k.flatten(order='F') # Asset holdings for tomorrow (k')
# DATA WEALTH AND INCOME DISTRIBUTIONS
file = pathFinder.getMicrosimInputPath(
'SIM_NetPersonalWealth_distribution')
self.a_distdata = pd.read_csv(file)
self.a_distdata.append([99.9, float('nan'),
1]) # Append last point for graph
file = pathFinder.getMicrosimInputPath(
'SIM_PreTaxLaborInc_distribution')
self.l_distdata = pd.read_csv(file)
self.l_distdata.append([99.9, float('nan'),
1]) # Append last point for graph
# MODEL WEALTH AND INCOME DISTRIBUTIONS
# Compute wealth distribution
self.a_distmodel = get_moments(dist, k)
# Gini and Lorenz curve
(self.a_ginimodel, self.a_lorenz) = gini(dist, k)
# Compute labor income distribution
self.l_distmodel = get_moments(dist_l, labinc)
# Gini and Lorenz curve
(self.l_ginimodel, self.l_lorenz) = gini(dist_l, labinc)
# Compute total income distribution
self.t_distmodel = get_moments(dist, totinc)
# Gini and Lorenz curve
(self.t_ginimodel, self.t_lorenz) = gini(dist, labinc)