def test_compute_gen_imbalance():
size_generation = 1
cohort = create_neutral_profiles_cohort(population=size_generation)
simulation = Simulation()
simulation.discount_rate = 0
simulation.growth_rate = 0
simulation.cohorts = cohort
simulation.create_present_values('tax')
gen_imbalance = simulation.compute_gen_imbalance(typ='tax')
# print gen_imbalance
assert gen_imbalance == -5000.0 / (2 * 199.0)
def test_compute_gen_imbalance():
size_generation = 1
cohort = create_neutral_profiles_cohort(population = size_generation)
simulation = Simulation()
simulation.discount_rate = 0
simulation.growth_rate = 0
simulation.cohorts = cohort
simulation.create_present_values('tax')
gen_imbalance = simulation.compute_gen_imbalance(typ = 'tax')
# print gen_imbalance
assert gen_imbalance[0] == -5000.0/(2*199.0), gen_imbalance[1] == -5000.0/(2*199.0)
def test_compute_gen_imbalance():
size_generation = 1
cohort = create_neutral_profiles_cohort(population = size_generation)
simulation = Simulation()
simulation.discount_rate = 0
simulation.growth_rate = 0
simulation.cohorts = cohort
simulation.create_present_values('tax')
print simulation.aggregate_pv.tail(20).to_string()
print simulation.cohorts.head(20).to_string()
gen_imbalance = simulation.compute_gen_imbalance(typ = 'tax', to_return='difference')
# print gen_imbalance, -5000.0/(2*199.0)
assert gen_imbalance == -5000.0/(2*199.0)
def test():
country = "france"
population_filename = os.path.join(SRC_PATH, 'countries', country,
'sources', 'data_fr', 'proj_pop_insee',
'proj_pop.h5')
profiles_filename = os.path.join(SRC_PATH, 'countries', country, 'sources',
'data_fr', 'profiles.h5')
CBonnet_results = os.path.join(SRC_PATH, 'countries', country, 'sources',
'Carole_Bonnet', 'theoretical_results.xls')
simulation = Simulation()
population_scenario = "projpop0760_FECbasESPbasMIGbas"
simulation.load_population(population_filename, population_scenario)
simulation.load_profiles(profiles_filename)
xls = ExcelFile(CBonnet_results)
"""
Hypothesis set #1 :
actualization rate r = 3%
growth rate g = 1%
net_gov_wealth = -3217.7e+09 (unit : Franc Français (FF) of 1996)
"""
#Setting parameters
year_length = 200
r = 0.03
g = 0.01
net_gov_wealth = -3217.7e+09
net_gov_spendings = 0
simulation.set_population_projection(year_length=year_length,
method="exp_growth")
simulation.set_tax_projection(method="per_capita", rate=g)
simulation.set_growth_rate(g)
simulation.set_discount_rate(r)
simulation.create_cohorts()
simulation.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(net_gov_spendings)
#Calculating net transfers
#Net_transfers = tax paid to the state minus money recieved from the state
#TODO: transform this in a method
simulation.cohorts['total_taxes'] = 0
simulation.cohorts['total_payments'] = 0
taxes_list = ['tva', 'tipp', 'cot', 'irpp', 'impot', 'property']
payments_list = ['chomage', 'retraite', 'revsoc', 'maladie', 'educ']
simulation.cohorts.compute_net_transfers(name='net_transfers',
taxes_list=taxes_list,
payments_list=payments_list)
"""
Reproducing the table 2 : Comptes générationnels par âge et sexe (Compte central)
"""
#Generating generationnal accounts
simulation.create_present_values(typ='net_transfers')
print "PER CAPITA PV"
print simulation.percapita_pv.xs(0, level='age').head()
print simulation.percapita_pv.xs((0, 2007), level=['sex', 'year']).head()
# Calculating the Intertemporal Public Liability
ipl = simulation.compute_ipl(typ='net_transfers')
print "------------------------------------"
print "IPL =", ipl
print "share of the GDP : ", ipl / 8050.6e+09 * 100, "%"
print "------------------------------------"
#Calculating the generational imbalance
gen_imbalance = simulation.compute_gen_imbalance(typ='net_transfers')
print "----------------------------------"
print "imbalance : [n_1=", gen_imbalance[0], ", n_1-n_0=", gen_imbalance[
1], ", n_1/n_0=", gen_imbalance[2], "]"
print "----------------------------------"
#Creating age classes
cohorts_age_class = AccountingCohorts(
simulation.percapita_pv.create_age_class(step=5))
cohorts_age_class._types = [
u'tva', u'tipp', u'cot', u'irpp', u'impot', u'property', u'chomage',
u'retraite', u'revsoc', u'maladie', u'educ', u'net_transfers'
]
age_class_pv_fe = cohorts_age_class.xs((1, 2007), level=['sex', 'year'])
age_class_pv_ma = cohorts_age_class.xs((0, 2007), level=['sex', 'year'])
print "AGE CLASS PV"
print age_class_pv_fe.head()
print age_class_pv_ma.head()
#Plotting
age_class_pv = cohorts_age_class.xs(2007,
level="year").unstack(level="sex")
age_class_pv = age_class_pv['net_transfers']
age_class_pv.columns = ['men', 'women']
# age_class_pv['total'] = age_class_pv_ma['net_transfers'] + age_class_pv_fe['net_transfers']
# age_class_pv['total'] *= 1.0/2.0
age_class_theory = xls.parse('Feuil1', index_col=0)
age_class_pv['men_CBonnet'] = age_class_theory['men_Cbonnet']
age_class_pv['women_CBonnet'] = age_class_theory['women_Cbonnet']
age_class_pv.plot(style='--')
plt.legend()
plt.axhline(linewidth=2, color='black')
plt.show()
def test():
print "Entering the simulation of C. Bonnet"
simulation = Simulation()
population_scenario = "projpop0760_FECbasESPbasMIGbas"
simulation.load_population(population_filename, population_scenario)
# Adding missing population data between 1996 and 2007 :
store_pop = HDFStore(os.path.join(SRC_PATH, "countries", country, "sources", "Carole_Bonnet", "pop_1996_2006.h5"))
corrected_pop = store_pop["population"]
print simulation.population.head().to_string()
print corrected_pop.head().to_string()
print " longueurs des inputs"
print "prévisions insee", len(simulation.population), "population corrigée", len(corrected_pop)
simulation.population = concat([corrected_pop, simulation.population])
print " longueur après combinaison", len(simulation.population)
# Loading profiles :
simulation.load_profiles(profiles_filename)
xls = ExcelFile(CBonnet_results)
"""
Hypothesis set #1 :
actualization rate r = 3%
growth rate g = 1%
net_gov_wealth = -3217.7e+09 (unit : Franc Français (FRF) of 1996)
non ventilated government spendings in 1996 : 1094e+09 FRF
"""
# Setting parameters :
year_length = 250
simulation.year_length = year_length
r = 0.03
g = 0.01
n = 0.00
net_gov_wealth = -3217.7e09
year_gov_spending = (1094) * 1e09
# avg_gov_spendings = 0
# # List w/ the economic affairs
# spending_list = [241861, 246856, 245483, 251110, 261752, 271019,
# 286330, 290499, 301556, 315994, 315979, 332317,
# 343392, 352239, 356353, 356858]
# count = 0
# for spent in spending_list:
# year_gov_spending = spent*1e+06*((1+g)/(1+r))**count*6.55957
# print year_gov_spending
# net_gov_spendings += year_gov_spending
# avg_gov_spendings += year_gov_spending
# count += 1
# avg_gov_spendings /= (count)
# print 'avg_gov_spendings = ', avg_gov_spendings
# Loading simulation's parameters :
simulation.set_population_projection(year_length=year_length, method="stable")
simulation.set_tax_projection(method="per_capita", rate=g)
simulation.set_growth_rate(g)
simulation.set_discount_rate(r)
simulation.set_population_growth_rate(n)
simulation.create_cohorts()
simulation.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(year_gov_spending, default=True, compute=True)
# Calculating net transfers :
# Net_transfers = tax paid to the state minus money recieved from the state
taxes_list = ["tva", "tipp", "cot", "irpp", "impot", "property"]
payments_list = ["chomage", "retraite", "revsoc", "maladie", "educ"]
simulation.cohorts.compute_net_transfers(name="net_transfers", taxes_list=taxes_list, payments_list=payments_list)
"""
Reproducing the table 2 : Comptes générationnels par âge et sexe (Compte central)
"""
# Generating generationnal accounts :
year = 1996
simulation.create_present_values(typ="net_transfers")
print "PER CAPITA PV"
print simulation.percapita_pv.xs(0, level="age").head(10)
print simulation.percapita_pv.xs((0, year), level=["sex", "year"]).head(10)
# Calculating the Intertemporal Public Liability
ipl = simulation.compute_ipl(typ="net_transfers")
print "------------------------------------"
print "IPL =", ipl
print "share of the GDP : ", ipl / 8050.6e09 * 100, "%"
print "------------------------------------"
# Calculating the generational imbalance
gen_imbalance = simulation.compute_gen_imbalance(typ="net_transfers")
print "----------------------------------"
print "[n_1/n_0=", gen_imbalance, "]"
print "----------------------------------"
# Creating age classes
cohorts_age_class = simulation.create_age_class(typ="net_transfers", step=5)
cohorts_age_class._types = [
u"tva",
u"tipp",
u"cot",
u"irpp",
u"impot",
u"property",
u"chomage",
u"retraite",
u"revsoc",
u"maladie",
u"educ",
u"net_transfers",
]
age_class_pv_fe = cohorts_age_class.xs((1, year), level=["sex", "year"])
age_class_pv_ma = cohorts_age_class.xs((0, year), level=["sex", "year"])
print "AGE CLASS PV"
print age_class_pv_fe.head()
print age_class_pv_ma.head()
age_class_pv = concat([age_class_pv_fe, age_class_pv_ma], axis=1)
print age_class_pv
age_class_pv.to_excel(str(xls_adress) + "\calibration.xlsx", "compte_generation")
# Plotting
age_class_pv = cohorts_age_class.xs(year, level="year").unstack(level="sex")
age_class_pv = age_class_pv["net_transfers"]
age_class_pv.columns = ["men", "women"]
# age_class_pv['total'] = age_class_pv_ma['net_transfers'] + age_class_pv_fe['net_transfers']
# age_class_pv['total'] *= 1.0/2.0
age_class_theory = xls.parse("Feuil1", index_col=0)
age_class_pv["men_CBonnet"] = age_class_theory["men_Cbonnet"]
age_class_pv["women_CBonnet"] = age_class_theory["women_Cbonnet"]
age_class_pv.plot(style="--")
plt.legend()
plt.axhline(linewidth=2, color="black")
plt.show()
def test():
print 'Entering the simulation of C. Bonnet'
simulation = Simulation()
population_scenario = "projpop0760_FECbasESPbasMIGbas"
simulation.load_population(population_filename, population_scenario)
# Adding missing population data between 1996 and 2007 :
store_pop = HDFStore(os.path.join(SRC_PATH, 'countries', country, 'sources',
'Carole_Bonnet', 'pop_1996_2006.h5'))
corrected_pop = store_pop['population']
print simulation.population.head().to_string()
print corrected_pop.head().to_string()
print ' longueurs des inputs'
print 'prévisions insee', len(simulation.population), 'population corrigée', len(corrected_pop)
simulation.population = concat([corrected_pop, simulation.population])
print ' longueur après combinaison',len(simulation.population)
#Loading profiles :
simulation.load_profiles(profiles_filename)
xls = ExcelFile(CBonnet_results)
"""
Hypothesis set #1 :
actualization rate r = 3%
growth rate g = 1%
net_gov_wealth = -3217.7e+09 (unit : Franc Français (FRF) of 1996)
non ventilated government spendings in 1996 : 1094e+09 FRF
"""
#Setting parameters :
year_length = 250
simulation.year_length = year_length
r = 0.03
g = 0.01
n = 0.00
net_gov_wealth = -3217.7e+09
year_gov_spending = (1094)*1e+09
# avg_gov_spendings = 0
# # List w/ the economic affairs
# spending_list = [241861, 246856, 245483, 251110, 261752, 271019,
# 286330, 290499, 301556, 315994, 315979, 332317,
# 343392, 352239, 356353, 356858]
# count = 0
# for spent in spending_list:
# year_gov_spending = spent*1e+06*((1+g)/(1+r))**count*6.55957
# print year_gov_spending
# net_gov_spendings += year_gov_spending
# avg_gov_spendings += year_gov_spending
# count += 1
# avg_gov_spendings /= (count)
# print 'avg_gov_spendings = ', avg_gov_spendings
# Loading simulation's parameters :
simulation.set_population_projection(year_length=year_length, method="stable")
simulation.set_tax_projection(method="per_capita", rate=g)
simulation.set_growth_rate(g)
simulation.set_discount_rate(r)
simulation.set_population_growth_rate(n)
simulation.create_cohorts()
simulation.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(year_gov_spending, default=True, compute=True)
#Calculating net transfers :
#Net_transfers = tax paid to the state minus money recieved from the state
taxes_list = ['tva', 'tipp', 'cot', 'irpp', 'impot', 'property']
payments_list = ['chomage', 'retraite', 'revsoc', 'maladie', 'educ']
simulation.cohorts.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
"""
Reproducing the table 2 : Comptes générationnels par âge et sexe (Compte central)
"""
#Generating generationnal accounts :
year = 1996
simulation.create_present_values(typ = 'net_transfers')
print "PER CAPITA PV"
print simulation.percapita_pv.xs(0, level = 'age').head(10)
print simulation.percapita_pv.xs((0, year), level = ['sex', 'year']).head(10)
# Calculating the Intertemporal Public Liability
ipl = simulation.compute_ipl(typ = 'net_transfers')
print "------------------------------------"
print "IPL =", ipl
print "share of the GDP : ", ipl/8050.6e+09*100, "%"
print "------------------------------------"
#Calculating the generational imbalance
gen_imbalance = simulation.compute_gen_imbalance(typ = 'net_transfers')
print "----------------------------------"
print "[n_1/n_0=", gen_imbalance,"]"
print "----------------------------------"
#Creating age classes
cohorts_age_class = simulation.create_age_class(typ = 'net_transfers', step = 5)
cohorts_age_class._types = [u'tva', u'tipp', u'cot', u'irpp', u'impot', u'property', u'chomage', u'retraite', u'revsoc', u'maladie', u'educ', u'net_transfers']
age_class_pv_fe = cohorts_age_class.xs((1, year), level = ['sex', 'year'])
age_class_pv_ma = cohorts_age_class.xs((0, year), level = ['sex', 'year'])
print "AGE CLASS PV"
print age_class_pv_fe.head()
print age_class_pv_ma.head()
age_class_pv = concat([age_class_pv_fe, age_class_pv_ma], axis=1)
print age_class_pv
age_class_pv.to_excel(str(xls_adress)+'\calibration.xlsx', 'compte_generation')
#Plotting
age_class_pv = cohorts_age_class.xs(year, level = "year").unstack(level="sex")
age_class_pv = age_class_pv['net_transfers']
age_class_pv.columns = ['men' , 'women']
# age_class_pv['total'] = age_class_pv_ma['net_transfers'] + age_class_pv_fe['net_transfers']
# age_class_pv['total'] *= 1.0/2.0
age_class_theory = xls.parse('Feuil1', index_col = 0)
age_class_pv['men_CBonnet'] = age_class_theory['men_Cbonnet']
age_class_pv['women_CBonnet'] = age_class_theory['women_Cbonnet']
age_class_pv.plot(style = '--') ; plt.legend()
plt.axhline(linewidth=2, color='black')
plt.show()