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@jdebacker
jdebacker committed Jul 9, 2024
1 parent eda9ef2 commit 33e5206
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10 changes: 4 additions & 6 deletions ogcore/pensions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -539,11 +539,6 @@ def delta_ret(r, Y, p):
surv_rates = 1 - p.mort_rates_SS
dir_delta_s_empty = np.zeros(p.S - p.retire + 1)
g_dir_value = g_dir(r, Y, p.g_y, p.g_n, p.dir_growth_rate)
print("G dir value type = ", type(p.S))
print("G dir value type = ", type(p.retire))
print("G dir value type = ", type(surv_rates))
print("G dir value type = ", type(g_dir_value))
print("G dir value type = ", type(dir_delta_s_empty))
dir_delta = delta_ret_loop(
p.S, p.retire, surv_rates, g_dir_value, dir_delta_s_empty
)
Expand Down Expand Up @@ -577,8 +572,11 @@ def deriv_PS_loop(w, e, S, S_ret, per_rmn, d_theta, vpoint, factor):


@numba.jit
def deriv_NDC_loop(w, e, per_rmn, S, S_ret, tau_p, g_ndc_value, delta_ret_value, d_theta):
def deriv_NDC_loop(
w, e, per_rmn, S, S_ret, tau_p, g_ndc_value, delta_ret_value, d_theta
):
for s in range((S - per_rmn), S_ret):
print("TESTING", tau_p, delta_ret_value, g_ndc_value)
d_theta[s - (S - per_rmn)] = (
tau_p
* w[s - (S - per_rmn)]
Expand Down
304 changes: 162 additions & 142 deletions tests/test_pensions.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -376,166 +376,186 @@ def test_deriv_NDC(args, d_NDC_expected):
assert np.allclose(d_NDC, d_NDC_expected)


# ################pension benefit: DB############
# p = Specifications()
# p.pension_system = 'DB'
# p.S = 7
# p.retire = 4
# p.last_career_yrs = 3
# p.yr_contr = p.retire
# p.rep_rate_py = 0.2
# p.g_y = np.ones(p.T) * 0.03
# w_db = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
# e_db = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
# n_db = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
# Y = None
# j = 1
# factor = 2
# omegas = None
# lambdas = 1
# pension_expected_db = [0, 0, 0, 0, 0.337864778, 0.327879365, 0.318189065]
# args_pb = (w_db, e_db, n_db, r, Y, lambdas, j, factor)

# ################pension benefit: NDC############
# p2 = Specifications()
# p2.pension_system = 'NDC'
# p2.ndc_growth_rate = 'LR GDP'
# p2.dir_growth_rate = 'r'
# p2.S = 4
# p2.retire = 2
# w_ndc = np.array([1.2, 1.1, 1.21, 1])
# e_ndc = np.array([1.1, 1.11, 0.9, 0.87])
# n_ndc = np.array([0.4, 0.45, 0.4, 0.3])
# Y = None
# j = 1
# p2.g_y = 0.03
# p2.g_n_SS = 0.0
# r = 0.03
# factor = 2
# p2.tau_p = 0.3
# p2.k_ret = 0.4615
# p2.mort_rates_SS = np.array([0.01, 0.05, 0.3, 0.4, 1])
# omegas = None
# lambdas = 1
# pension_expected_ndc = [0, 0, 0.279756794, 0.271488732]
# args_ndc = (w_ndc, e_ndc, n_ndc, r, Y, lambdas, j, factor)

# ################pension benefit: PS############
# p3 = Specifications()
# p3.pension_system = 'PS'
# p3.S = 7
# p3.S_ret = 4
# w_ppb = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
# e_ppb = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
# n_ppb = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
# omegas = (1/p3.S) * np.ones(p3.S)
# p3.omega_SS = omegas
# p3.vpoint = 0.4
# factor = 2
# Y = None
# lambdas = 1
# j_ind = 1
# p3.g_y = np.ones(p3.T) * 0.03
# pension_expected_ppb = [0, 0, 0, 0, 0.004164689, 0.004041603, 0.003922156]
# args_ps = (w_db, e_db, n_db, r, Y, lambdas, j_ind, factor)

# test_data = [(args_pb, pension_expected_db),
# (args_ndc, pension_expected_ndc),
# (args_ps, pension_expected_ppb)]

# @pytest.mark.parametrize('classes,args,pension_expected', test_data,
# ids=['DB', 'NDC', 'PS'])
# def test_pension_benefit(classes, args, pension_expected):
# '''
# Test of pensions.get_pension_benefit
# '''
# w, e, n, r, Y, lambdas, j_ind, factor = args

# pension = pensions.pension_amount(
# w, n, theta, t, j, shift, method, e, p)
# assert (np.allclose(pension, pension_expected))



################pension benefit: DB############
demographics_ndc = deepcopy(demographics)
households_pb = deepcopy(households)
pensions_db = deepcopy(pensions_class)
pensions_db.pension_system = 'DB'
households_pb.S = 7
households_pb.S_ret = 4
w_db = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
e_db = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
n_db = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
pensions_db.last_career_yrs = 3
pensions_db.yr_contr = S_ret
pensions_db.rep_rate_py = 0.2
Y = None
j_ind = 1
firms.g_y = 0.03
factor = 2
omegas = None
lambdas = 1
pension_expected_db = [0, 0, 0, 0, 0.337864778, 0.327879365, 0.318189065]
classes_pb = (demographics_ndc, households_pb, firms, pensions_db)
args_pb = (w_db, e_db, n_db, r, Y, lambdas, j_ind, factor)

################pension benefit: NDC############
households_ndc = deepcopy(households)
pensions_ndc = deepcopy(pensions_class)
pensions_ndc.pension_system = 'NDC'
pensions_ndc.ndc_growth_rate = 'LR GDP'
pensions_ndc.dir_growth_rate = 'r'
households_ndc.S = 4
households_ndc.S_ret = 2
w_ndc = np.array([1.2, 1.1, 1.21, 1])
e_ndc = np.array([1.1, 1.11, 0.9, 0.87])
n_ndc = np.array([0.4, 0.45, 0.4, 0.3])
Y = None
j_ind = 1
firms.g_y = 0.03
demographics_ndc.g_n_SS = 0.0
r = 0.03
factor = 2
pensions_ndc.tau_p = 0.3
pensions_ndc.k_ret = 0.4615
demographics_ndc.mort_rates_SS = np.array([0.01, 0.05, 0.3, 0.4, 1])
omegas = None
lambdas = 1
pension_expected_ndc = [0, 0, 0.279756794, 0.271488732]
classes_ndc = (demographics_ndc, households_ndc, firms, pensions_ndc)
args_ndc = (w_ndc, e_ndc, n_ndc, r, Y, lambdas, j_ind, factor)

################pension benefit: PS############
demographics_ppb = deepcopy(demographics)
households_ppb = deepcopy(households)
pensions_ppb = deepcopy(pensions_class)
pensions_ppb.pension_system = 'PS'
households_ppb.S = 7
households_ppb.S_ret = 4
w_ppb = np.array([1.2, 1.1, 1.21, 1.0, 1.01, 0.99, 0.8])
e_ppb = np.array([1.1, 1.11, 0.9, 0.87, 0.87, 0.7, 0.6])
n_ppb = np.array([0.4, 0.45, 0.4, 0.42, 0.3, 0.2, 0.2])
omegas = (1/households_ppb.S) * np.ones(households_ppb.S)
demographics_ppb.omega_SS = omegas
pensions_ppb.vpoint = 0.4
factor = 2
Y = None
lambdas = 1
j_ind = 1
firms.g_y = 0.03
pension_expected_ppb = [0, 0, 0, 0, 0.004164689, 0.004041603, 0.003922156]
classes_ppb = (demographics_ppb, households_ppb, firms, pensions_ppb)
args_ppb = (w_db, e_db, n_db, r, Y, lambdas, j_ind, factor)

test_data = [(classes_pb, args_pb, pension_expected_db),
(classes_ndc, args_ndc, pension_expected_ndc),
(classes_ppb, args_ppb, pension_expected_ppb)]

@pytest.mark.parametrize('classes,args,pension_expected', test_data,
ids=['DB', 'NDC', 'PS'])
def test_pension_benefit(classes, args, pension_expected):
'''
Test of pensions.get_pension_benefit
'''

demographics_ndc, households, firms, pensions = classes
w, e, n, r, Y, lambdas, j_ind, factor = args

pension = pensions.pension_benefit(
demographics_ndc, households, firms, w, e, n, r,
Y, lambdas, j_ind, factor)
assert (np.allclose(pension, pension_expected))
############SS or complete lifetimes############
households_pl1 = deepcopy(households)
pensions_pl1 = deepcopy(pensions_class)
households_pl1.S = 3
households_pl1.S_ret = 2
per_rmn = households_pl1.S
p = Specifications()
p.S = 3
p.retire = 2
per_rmn = p.S
p.g_n_SS = 0.0
p.g_y = np.ones(p.T) * 0.03
p.ndc_growth_rate = "LR GDP"
p.dir_growth_rate = "r"
w = np.array([1.2, 1.1, 1.21])
e = np.array([1.1, 1.11, 0.9])
firms.g_y = 0.03
demographics.g_n_SS = 0.0
pensions_pl1.ndc_growth_rate = 'LR GDP'
pensions_pl1.dir_growth_rate = 'r'
r = 0.02
pensions_pl1.tau_p = 0.3
pensions_pl1.k_ret = 0.4615
pensions_pl1.delta_ret = 1.857010214
pensions_pl1.g_ndc = firms.g_y + demographics.g_n_SS
demographics.mort_rates_SS = np.array([0.1, 0.2, 0.4, 1.0])
r = np.ones(p.T) * 0.02
p.tau_p = 0.3
p.k_ret = 0.4615
delta_ret = 1.857010214
g_ndc = p.g_y[-1] + p.g_n_SS
p.mort_rates_SS = np.array([0.1, 0.2, 0.4, 1.0])
deriv_NDC_loop_expected1 = np.array([0.757437326, 0.680222841, 0.0])
d_theta_empty = np.zeros_like(w)

#args3 = (pensions_pl1, households_pl1, w, e, per_rmn)
args3 = (w, e, per_rmn, households_pl1.S, households_pl1.S_ret, firms.g_y,
pensions_pl1.tau_p, pensions_pl1.g_ndc, pensions_pl1.delta_ret,
d_theta_empty)
args3 = (w, e, per_rmn, g_ndc, delta_ret, d_theta_empty, p)

################Incomplete lifetimes#################
households_pl2 = deepcopy(households)
pensions_pl2 = deepcopy(pensions_class)
households_pl2.S = 4
households_pl2.S_ret = 2
p2 = Specifications()
p2.S = 4
p2.retire = 2
per_rmn = 3
#w = np.array([1.2, 1.1, 1.21, 1.25])
#e = np.array([1.1, 1.11, 0.9, 1.0])
w = np.array([1.1, 1.21, 1.25])
e = np.array([1.11, 0.9, 1.0])
firms.g_y = 0.04
demographics.g_n_SS = 0.0
pensions_pl2.ndc_growth_rate = 'LR GDP'
pensions_pl2.dir_growth_rate = 'r'
#r = np.array([0.05, 0.03, 0.04, 0.03])
w2 = np.array([1.1, 1.21, 1.25])
e2 = np.array([1.11, 0.9, 1.0])
p2.g_y = np.ones(p.T) * 0.04
p2.g_n_SS = 0.0
p2.ndc_growth_rate = "LR GDP"
p2.dir_growth_rate = "r"
r = np.array([0.03, 0.04, 0.03])
pensions_pl2.tau_p = 0.3
pensions_pl2.k_ret = 0.4615
pensions_pl2.delta_ret = 1.083288196
pensions_pl2.g_ndc = firms.g_y + demographics.g_n_SS
#demographics.mort_rates_SS = np.array([0.1, 0.2, 0.4, 1.0])
demographics.mort_rates_SS = np.array([0.2, 0.4, 1.0])
p2.tau_p = 0.3
p2.k_ret = 0.4615
delta_ret2 = 1.083288196
g_ndc2 = p2.g_y[-1] + p2.g_n_SS
p2.mort_rates_SS = np.array([0.2, 0.4, 1.0])
deriv_NDC_loop_expected2 = np.array([0.396808466, 0.0, 0.0])
#d_theta_empty = np.zeros_like(w)
d_theta_empty = np.zeros(per_rmn)
args4 = (w, e, per_rmn, households_pl2.S, households_pl2.S_ret, firms.g_y,
pensions_pl2.tau_p, pensions_pl2.g_ndc, pensions_pl2.delta_ret,
d_theta_empty)
args4 = (w2, e2, per_rmn2, g_ndc2, delta_ret2, d_theta_empty, p2)

test_data = [
(args3, deriv_NDC_loop_expected1),
(args4, deriv_NDC_loop_expected2),
]

test_data = [(args3, deriv_NDC_loop_expected1),
(args4, deriv_NDC_loop_expected2)]

@pytest.mark.parametrize('args,deriv_NDC_loop_expected', test_data,
ids=['SS', 'incomplete'])
@pytest.mark.parametrize(
"args,deriv_NDC_loop_expected", test_data, ids=["SS", "incomplete"]
)
def test_deriv_NDC_loop(args, deriv_NDC_loop_expected):
"""
Test of the pensions.deriv_NDC_loop() function.
"""
(w, e, per_rmn, households.S, households.S_ret, firms.g_y, pensions_pl2.tau_p,
pensions_pl2.g_ndc, pensions_pl2.delta_ret, d_theta_empty) = args
(w, e, per_rmn, g_ndc_value, delta_ret_value, d_theta, p) = args

print("TESTING", p.tau_p, delta_ret_value, g_ndc_value)
deriv_NDC_loop = pensions.deriv_NDC_loop(
w, e, per_rmn, households.S, households.S_ret, firms.g_y,
pensions_pl2.tau_p, pensions_pl2.g_ndc, pensions_pl2.delta_ret,
d_theta_empty)
w,
e,
per_rmn,
p.S,
p.retire,
p.tau_p,
g_ndc_value,
delta_ret_value,
d_theta,
)

assert np.allclose(deriv_NDC_loop, deriv_NDC_loop_expected)


############SS or complete lifietimes############
p = Specifications()
p.S = 4
p.retire = 2
p.g_y = np.ones(p.T) * 0.04
p.g_n_SS = 0.0
p.ndc_growth_rate = "LR GDP"
p.dir_growth_rate = "r"
r = 0.02
g_ndc_amount = p.g_y[-1] + p.g_n_SS
p.mort_rates_SS = np.array([0.1, 0.2, 0.4, 0.6, 1.0])
dir_delta_s_empty = np.zeros(p.S - p.retire + 1)
dir_delta_ret_expected1 = 1.384615385
surv_rates = np.zeros(p.S - p.retire + 1)
surv_rates = 1 - p.mort_rates_SS
args5 = (surv_rates, g_ndc_amount, dir_delta_s_empty, p)
test_data = [(args5, dir_delta_ret_expected1)]


@pytest.mark.parametrize("args,dir_delta_ret_expected", test_data, ids=["SS"])
def test_delta_ret_loop(args, dir_delta_ret_expected):
"""
Test of the pensions.delta_ret_loop() function.
"""
(surv_rates, g_ndc_value, dir_delta_s_empty, p) = args
dir_delta = pensions.delta_ret_loop(
p.S, p.retire, surv_rates, g_ndc_value, dir_delta_s_empty
)

assert np.allclose(dir_delta, dir_delta_ret_expected)

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