def test_top_share():
'''
Test of top share calculation
'''
ineq = Inequality(dist, pop_weights, ability_weights, S, J)
top_share = ineq.top_share(0.05)
assert np.allclose(top_share, 0.285714286)
def wealth_moments_table(base_ss, base_params, table_format=None, path=None):
'''
Creates table with moments of the wealth distribution from the model
and SCF data.
Args:
base_ss (dictionary): SS output from baseline run
base_params (OG-USA Specifications class): baseline parameters
object
table_format (string): format to return table in: 'csv', 'tex',
'excel', 'json', if None, a DataFrame is returned
path (string): path to save table to
Returns:
table (various): table in DataFrame or string format or `None`
if saved to disk
'''
table_dict = {
'Moment': [
'Share 0-25%', 'Share 25-50%', 'Share 50-70%', 'Share 70-80%',
'Share 80-90%', 'Share 90-99%', 'Share 99-100%',
'Gini Coefficient', 'var(ln(Wealth))'
],
'Data': [],
'Model': []
}
base_ineq = Inequality(base_ss['bssmat_splus1'], base_params.omega_SS,
base_params.lambdas, base_params.S, base_params.J)
base_values = [
1 - base_ineq.top_share(0.75),
base_ineq.top_share(0.75) - base_ineq.top_share(0.5),
base_ineq.top_share(0.5) - base_ineq.top_share(0.3),
base_ineq.top_share(0.3) - base_ineq.top_share(0.2),
base_ineq.top_share(0.2) - base_ineq.top_share(0.1),
base_ineq.top_share(0.1) - base_ineq.top_share(0.01),
base_ineq.top_share(0.01),
base_ineq.gini(),
base_ineq.var_of_logs()
]
table_dict['Model'].extend(base_values)
# get moments from Survey of Consumer Finances data
scf = wealth.get_wealth_data()
table_dict['Data'] = wealth.compute_wealth_moments(scf,
base_params.lambdas)
# Make df with dict so can use pandas functions
table_df = pd.DataFrame.from_dict(table_dict)
table = save_return_table(table_df, table_format, path, precision=3)
return table
def calc_moments(ss_output, p):
"""
This function calculates moments from the SS output that correspond
to the data moments used for estimation.
Args:
ss_output = dictionary, variables from SS of model
p (OG-USA Specifications object): model parameters
Returns:
model_moments (array-like): Array of model moments
"""
# Create Inequality object
wealth_ineq = Inequality(ss_output["bssmat_splus1"], p.omega_SS, p.lambdas,
p.S, p.J)
# wealth moments
# moments are: bottom 25% of wealth, next 25% share of wealth
# (25-50 pctile), next 20% share of wealth (50-70 pctile),
# next 10% share (70-80 pctile), next 10% share (80-90 pctile),
# next 9% share (90-99 pctile), top 1% share,
# gini coefficient, variance of log wealth
model_moments = np.array([
1 - wealth_ineq.top_share(0.75),
wealth_ineq.top_share(0.75) - wealth_ineq.top_share(0.50),
wealth_ineq.top_share(0.50) - wealth_ineq.top_share(0.30),
wealth_ineq.top_share(0.30) - wealth_ineq.top_share(0.20),
wealth_ineq.top_share(0.20) - wealth_ineq.top_share(0.10),
wealth_ineq.top_share(0.10) - wealth_ineq.top_share(0.01),
wealth_ineq.top_share(0.01),
wealth_ineq.gini(),
wealth_ineq.var_of_logs(),
])
return model_moments
def inequality_plot(
base_tpi, base_params, reform_tpi=None, reform_params=None,
var='c_path', ineq_measure='gini', pctiles=None, plot_type='levels',
num_years_to_plot=50, start_year=DEFAULT_START_YEAR,
vertical_line_years=None, plot_title=None, path=None):
'''
Plot measures of inequality over the time path.
Args:
base_tpi (dictionary): TPI output from baseline run
base_params (OG-USA Specifications class): baseline parameters
object
reform_tpi (dictionary): TPI output from reform run
reform_params (OG-USA Specifications class): reform parameters
object
var(string): name of variable to plot
ineq_measure (string): inequality measure to plot, can be:
'gini': Gini coefficient
'var_of_logs': variance of logs
'pct_ratio': percentile ratio
'top_share': top share of total
pctiles (tuple or None): percentiles for percentile ratios
(numerator, denominator) or percentile for top share (not
required for Gini or var_of_logs)
plot_type (string): type of plot, can be:
'pct_diff': plots percentage difference between baselien
and reform ((reform-base)/base)
'diff': plots difference between baseline and reform
(reform-base)
'levels': plot variables in model units
num_years_to_plot (integer): number of years to include in plot
start_year (integer): year to start plot
vertical_line_years (list): list of integers for years want
vertical lines at
plot_title (string): title for plot
path (string): path to save figure to
Returns:
fig (Matplotlib plot object): plot of inequality measure
'''
assert isinstance(start_year, (int, np.integer))
assert (isinstance(num_years_to_plot, int))
# Make sure both runs cover same time period
if reform_tpi:
assert (base_params.start_year == reform_params.start_year)
assert ineq_measure in ['gini', 'var_of_logs', 'pct_ratio',
'top_share']
if (ineq_measure == 'pct_ratio') | (ineq_measure == 'top_share'):
assert pctiles
year_vec = np.arange(start_year, start_year + num_years_to_plot)
# Check that reform included if doing pct_diff or diff plot
if plot_type == 'pct_diff' or plot_type == 'diff':
assert (reform_tpi is not None)
fig1, ax1 = plt.subplots()
base_values = np.zeros(num_years_to_plot)
for t in range(num_years_to_plot):
idx = (t + start_year) - base_params.start_year
ineq = Inequality(
base_tpi[var][idx, :, :], base_params.omega[idx, :],
base_params.lambdas, base_params.S, base_params.J)
if ineq_measure == 'gini':
base_values[t] = ineq.gini()
ylabel = r'Gini Coefficient'
elif ineq_measure == 'var_of_logs':
base_values[t] = ineq.var_of_logs()
ylabel = r'var(ln(' + VAR_LABELS[var] + r'))'
elif ineq_measure == 'pct_ratio':
base_values[t] = ineq.ratio_pct1_pct2(pctiles[0],
pctiles[1])
ylabel = r'Ratio'
elif ineq_measure == 'top_share':
base_values[t] = ineq.top_share(pctiles)
ylabel = r'Share of Total ' + VAR_LABELS[var]
if reform_tpi:
reform_values = np.zeros_like(base_values)
for t in range(num_years_to_plot):
idx = (t + start_year) - base_params.start_year
ineq = Inequality(
reform_tpi[var][idx, :, :], reform_params.omega[idx, :],
reform_params.lambdas, reform_params.S, reform_params.J)
if ineq_measure == 'gini':
reform_values[t] = ineq.gini()
elif ineq_measure == 'var_of_logs':
reform_values[t] = ineq.var_of_logs()
elif ineq_measure == 'pct_ratio':
reform_values[t] = ineq.ratio_pct1_pct2(pctiles[0],
pctiles[1])
elif ineq_measure == 'top_share':
reform_values[t] = ineq.top_share(pctiles)
if plot_type == 'pct_diff':
plot_var = (reform_values - base_values) / base_values
ylabel = r'Pct. change'
plt.plot(year_vec, plot_var)
elif plot_type == 'diff':
plot_var = reform_values - base_values
ylabel = r'Difference'
plt.plot(year_vec, plot_var)
elif plot_type == 'levels':
plt.plot(year_vec, base_values, label='Baseline')
if reform_tpi:
plt.plot(year_vec, reform_values, label='Reform')
# vertical markers at certain years
if vertical_line_years:
for yr in vertical_line_years:
plt.axvline(x=yr, linewidth=0.5, linestyle='--', color='k')
plt.xlabel(r'Year $t$')
plt.ylabel(ylabel)
if plot_title:
plt.title(plot_title, fontsize=15)
vals = ax1.get_yticks()
if plot_type == 'pct_diff':
ax1.set_yticklabels(['{:,.2%}'.format(x) for x in vals])
plt.xlim((base_params.start_year - 1, base_params.start_year +
num_years_to_plot))
plt.legend(loc=9, bbox_to_anchor=(0.5, -0.15), ncol=2)
if path:
fig_path1 = os.path.join(path)
plt.savefig(fig_path1, bbox_inches="tight")
else:
return fig1
plt.close()
def ineq_table(base_ss,
base_params,
reform_ss=None,
reform_params=None,
var_list=['cssmat'],
table_format=None,
path=None):
'''
Creates table with various inequality measures in the model
steady-state.
Args:
base_ss (dictionary): SS output from baseline run
base_params (OG-USA Specifications class): baseline parameters
object
reform_ss (dictionary): SS output from reform run
reform_params (OG-USA Specifications class): reform parameters
object
var_list (list): names of variable to use in table
table_format (string): format to return table in: 'csv', 'tex',
'excel', 'json', if None, a DataFrame is returned
path (string): path to save table to
Returns:
table (various): table in DataFrame or string format or `None`
if saved to disk
'''
table_dict = {
'Steady-State Variable': [],
'Inequality Measure': [],
'Baseline': []
}
if reform_ss:
table_dict['Reform'] = []
table_dict['% Change'] = []
for i, v in enumerate(var_list):
base_ineq = Inequality(base_ss[v], base_params.omega_SS,
base_params.lambdas, base_params.S,
base_params.J)
if reform_ss:
reform_ineq = Inequality(reform_ss[v], reform_params.omega_SS,
reform_params.lambdas, reform_params.S,
reform_params.J)
table_dict['Steady-State Variable'].extend(
[VAR_LABELS[v], '', '', '', ''])
table_dict['Inequality Measure'].extend([
'Gini Coefficient', 'Var of Logs', '90/10 Ratio', 'Top 10% Share',
'Top 1% Share'
])
base_values = np.array([
base_ineq.gini(),
base_ineq.var_of_logs(),
base_ineq.ratio_pct1_pct2(0.90, 0.10),
base_ineq.top_share(0.1),
base_ineq.top_share(0.01)
])
table_dict['Baseline'].extend(list(base_values))
if reform_ss:
reform_values = np.array([
reform_ineq.gini(),
reform_ineq.var_of_logs(),
reform_ineq.ratio_pct1_pct2(0.90, 0.10),
reform_ineq.top_share(0.1),
reform_ineq.top_share(0.01)
])
table_dict['Reform'].extend(list(reform_values))
table_dict['% Change'].extend(
list(((reform_values - base_values) / base_values) * 100))
# Make df with dict so can use pandas functions
table_df = pd.DataFrame.from_dict(table_dict)
table = save_return_table(table_df, table_format, path, precision=3)
return table