def test_unaf():
e = 2
ea = 0
rev_smic_chef = 1.5
rev_smic_part = 1.5
temps_garde ="classique" # alternee_pension_non_decl', 'alternee_pension_decl
uc_parameters = {'alpha' : 0, 'beta' : .5, 'gamma' : 1}
print get_unaf(e,ea,a=1)
disabled = None
opt_pension_unaf = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters,
criterium = "unaf2", disabled=disabled)
print "unaf2 : ", opt_pension_unaf
def optimal_pension(criterium, disable_api = False):
e = 2
ea = 0
rev_smic_chef = 3.5
rev_smic_part = 0
temps_garde ="alternee_pension_non_decl" # 'classique', 'alternee_pension_decl
uc_parameters = {'alpha' : 0.4, 'beta' : .7, 'gamma' : 1.4}
disabled = []
if disable_api:
disabled += ['api']
if criterium == "unaf":
print get_unaf(e,ea)
opt_pension_nv = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters,
criterium = "nivvie", disabled=disabled)
print "nivvie : ", opt_pension_nv
opt_pension_unaf = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters,
criterium = "unaf", disabled=disabled)
print "unaf : ", opt_pension_unaf
opt_pension = max(opt_pension_nv, opt_pension_unaf)
else:
opt_pension = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters,
criterium=criterium, disabled=disabled)
asf = get_asf(e+ea, year=YEAR)
print 'opt_pension :', opt_pension
print 'asf :', asf
if opt_pension >= asf:
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = opt_pension, disabled = disabled + ['asf'] )
else:
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = 0, disabled=disabled)
print df.to_string()
return df
def compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters=None, criterium=None, disabled=None):
"""
Return optimal pension according to different criteria
Parameters
----------
e : number of kids
ea : number of teenagers
rev_smic_chef : revenue of the non custodian parent (in SMIC)
rev_smic_part : revenue of the custodian parent (in SMIC)
temps_garde : type of custody
uc_parameters : parameters to compute the household consumption units
criterium : the normative criteria used to compute the optimal pension
disabled : the disabled presattions of the socio-fiscal model
Returns
-------
optimal_pension : the optimal pension
"""
EPS = 1e-3
from scipy.optimize import fixed_point, fsolve
from scripts.unaf import get_unaf
dep_decent_unaf = get_unaf(e, ea)
dep_decent_unaf2 = get_unaf(e, ea, a=1)
# Define a useful function
def func_optimal_pension(pension):
if disabled is None:
dis = ["asf"]
elif "asf" not in disabled:
dis = disabled + ['asf']
df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters=uc_parameters, pension=pension, disabled=dis)
print df.to_string()
df = df.set_index([u"ménage"])
private_cost_after = ( df.get_value(u"chef", u"prise en charge privée de l'enfant") +
df.get_value(u"part", u"prise en charge privée de l'enfant") )
revdisp_chef = df.get_value(u"chef", u"revdisp")
revdisp_part = df.get_value(u"part", u"revdisp")
total_cost_after_chef = df.get_value(u"chef", u"dépense totale pour enfants")
total_cost_after_part = df.get_value(u"part", u"dépense totale pour enfants")
total_cost_before = df.get_value(u"couple", u"dépense totale pour enfants")
public_cost_after_chef = df.get_value(u"chef", u"prise en charge publique de l'enfant")
public_cost_after_part = df.get_value(u"part", u"prise en charge publique de l'enfant")
nivvie_chef_after = df.get_value(u"chef", u"nivvie")
nivvie_part_after = df.get_value(u"part", u"nivvie")
nivvie_chef_before = df.get_value(u"chef", u"nivvie")/df.get_value(u"chef", u"nivvie_loss")
nivvie_part_before = df.get_value(u"part", u"nivvie")/df.get_value(u"part", u"nivvie_loss")
# opt_pension = private_cost_after*(revdisp_chef + pension)/(revdisp_chef+revdisp_part) - total_cost_after_chef + public_cost_after_chef
private_cost_after_chef = df.get_value(u"chef", u"prise en charge privée de l'enfant")
private_cost_after_part = df.get_value(u"part", u"prise en charge privée de l'enfant")
private_cost_before = df.get_value(u"couple", u"prise en charge privée de l'enfant")
if criterium == "revdisp":
opt_pension = private_cost_after*revdisp_chef/(revdisp_chef+revdisp_part)-private_cost_after_chef
elif criterium == "nivvie":
opt_pension = private_cost_after*nivvie_chef_after/(nivvie_chef_after+nivvie_part_after)-private_cost_after_chef
elif criterium == "revdisp_pyc":
opt_pension = private_cost_after*(revdisp_chef+pension)/(revdisp_chef+revdisp_part)-private_cost_after_chef
elif criterium == "jacquot":
alpha = uc_parameters['alpha']
beta = uc_parameters['beta']
gamma = uc_parameters['gamma']
A = (.3*e + .5*ea)/(1 + .3*e + .5*ea)
B = (alpha*gamma*(.3*e + .5*ea))/(1 + alpha*gamma*(.3*e + .5*ea))
C = 1 + .3*e + .5*ea
D = 1 + alpha*gamma*(.3*e + .5*ea)
opt_pension = ((A-B)*revdisp_chef*revdisp_part)/((revdisp_chef/C) + (revdisp_part/D))
elif criterium == "share_private_cost_before_chef":
opt_pension = - private_cost_after_chef + private_cost_before*nivvie_chef_before/(nivvie_chef_before+nivvie_part_before)
elif criterium == "share_private_cost_before_part":
opt_pension = private_cost_after_part - private_cost_before*nivvie_part_before/(nivvie_chef_before+nivvie_part_before)
elif criterium == "share_private_cost_before_chef_nivvie_after":
opt_pension = - private_cost_after_chef + private_cost_before*nivvie_chef_after/(nivvie_chef_after+nivvie_part_after)
elif criterium == "share_private_cost_before_chef_nivvie_after_coef":
opt_pension = - private_cost_after_chef + 1.4*private_cost_before*nivvie_chef_after/(nivvie_chef_after+nivvie_part_after)
elif criterium == "share_private_cost_before_part_nivvie_after":
opt_pension = private_cost_after_part - private_cost_before*nivvie_part_after/(nivvie_chef_after+nivvie_part_after)
elif criterium == "same_total_cost":
return total_cost_after_chef+total_cost_after_part-total_cost_before
elif criterium == "unaf":
return total_cost_after_part - dep_decent_unaf
elif criterium == "unaf2":
return revdisp_part - dep_decent_unaf2
return opt_pension
if criterium == "same_total_cost" or criterium in ["unaf", "unaf2"]:
res = fsolve(func_optimal_pension, 10000, xtol = EPS)
print res
optimal_pension = res[0]
else:
try :
optimal_pension = fixed_point(func_optimal_pension, 0, xtol = EPS, maxiter = 10)
except RuntimeError:
optimal_pension = 10000
return optimal_pension
