def test_comparison():
population_dataframe = create_testing_population_dataframe(year_start=2001,
year_end=2261,
population=2)
profiles_dataframe = create_constant_profiles_dataframe(
population_dataframe, tax=1)
r = 0.00
g = 0.00
n = 0.00
simulation = Simulation()
simulation.population = population_dataframe
simulation.population_alt = population_dataframe
simulation.profiles = profiles_dataframe
net_gov_wealth = -10
net_gov_spending = 0
taxes_list = ['tax']
payments_list = ['sub']
simulation.set_population_projection(year_length=simulation.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.set_population_growth_rate(n)
simulation.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(net_gov_spending, default=True)
simulation.create_cohorts()
simulation.create_present_values(typ='tax')
simulation.cohorts_alt = simulation.cohorts
simulation.cohorts_alt.loc[
[x == 2102 for x in simulation.cohorts_alt.index.get_level_values(2)],
'tax'] = (-100)
simulation.create_present_values(typ='tax', default=False)
ipl_base = simulation.compute_ipl(typ='tax')
ipl_alt = simulation.compute_ipl(typ='tax', default=False, precision=False)
#Saving the decomposed ipl:
to_save = simulation.break_down_ipl(typ='tax', default=False, threshold=60)
# to_save = age_class_pv
xls = "C:/Users/Utilisateur/Documents/GitHub/ga/src/countries/france/sources/Carole_Bonnet/choc_test_alt.xlsx"
to_save.to_excel(xls, 'ipl')
print ipl_base
print ipl_alt
assert ipl_base == -105232
def test_comparison():
population_dataframe = create_testing_population_dataframe(year_start=2001, year_end=2261, population=2)
profiles_dataframe = create_constant_profiles_dataframe(population_dataframe, tax=1)
r = 0.00
g = 0.00
n = 0.00
simulation = Simulation()
simulation.population = population_dataframe
simulation.population_alt = population_dataframe
simulation.profiles = profiles_dataframe
net_gov_wealth = -10
net_gov_spending = 0
taxes_list = ['tax']
payments_list = ['sub']
simulation.set_population_projection(year_length=simulation.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.set_population_growth_rate(n)
simulation.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(net_gov_spending, default=True)
simulation.create_cohorts()
simulation.create_present_values(typ='tax')
simulation.cohorts_alt = simulation.cohorts
simulation.cohorts_alt.loc[[x==2102 for x in simulation.cohorts_alt.index.get_level_values(2)], 'tax'] = (-100)
simulation.create_present_values(typ='tax', default=False)
ipl_base = simulation.compute_ipl(typ='tax')
ipl_alt = simulation.compute_ipl(typ='tax', default=False, precision=False)
#Saving the decomposed ipl:
to_save = simulation.break_down_ipl(typ='tax', default=False, threshold=60)
# to_save = age_class_pv
xls = os.path.join(SRC_PATH, 'test_comparison.xlsx')
to_save.to_excel(xls, 'ipl')
print ipl_base
print ipl_alt
assert ipl_base == -105232
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 transition():
levels = ['haut', 'bas']
taxes_list = ['tva', 'tipp', 'cot', 'irpp', 'impot', 'property']
payments_list = ['chomage', 'retraite', 'revsoc', 'maladie', 'educ']
year_length = 250
year_min = 1996
year_max = year_min+year_length-1
arrays=arange(year_min, year_min+60)
record = DataFrame(index=arrays)
simulation = Simulation()
for param1 in levels:
for param2 in levels:
population_scenario = "projpop0760_FEC"+param1+"ESP"+param2+"MIG"+param1
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']
simulation.population = concat([corrected_pop, simulation.population])
simulation.load_profiles(profiles_filename)
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
# 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.set_gov_wealth(net_gov_wealth)
simulation.set_gov_spendings(year_gov_spending, default=True, compute=True)
record[population_scenario] = NaN
col_name2 = population_scenario+'_precision'
record[col_name2] = NaN
simulation.create_cohorts()
simulation.cohorts.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values(typ='net_transfers')
for year in range(year_min, year_min+60):
#On tente de tronquer la df au fil du temps
try:
simulation.aggregate_pv = simulation.aggregate_pv.drop(labels=year-1, level='year')
except:
print 'except path'
pass
simulation.aggregate_pv = AccountingCohorts(simulation.aggregate_pv)
# imbalance = simulation.compute_gen_imbalance(typ='net_transfers')
ipl = simulation.compute_ipl(typ='net_transfers')
# Calcul du résidut de l'IPL pour vérifier la convergence
#(on se place tard dans la projection)
precision_df = simulation.aggregate_pv
print precision_df.head().to_string()
year_min_ = array(list(precision_df.index.get_level_values(2))).min()
year_max_ = array(list(precision_df.index.get_level_values(2))).max() - 1
# age_min = array(list(self.index.get_level_values(0))).min()
age_max_ = array(list(precision_df.index.get_level_values(0))).max()
print 'CALIBRATION CHECK : ', year_min_, year_max_
past_gen_dataframe = precision_df.xs(year_min_, level = 'year')
past_gen_dataframe = past_gen_dataframe.cumsum()
past_gen_transfer = past_gen_dataframe.get_value((age_max_, 1), 'net_transfers')
# print ' past_gen_transfer = ', past_gen_transfer
future_gen_dataframe = precision_df.xs(0, level = 'age')
future_gen_dataframe = future_gen_dataframe.cumsum()
future_gen_transfer = future_gen_dataframe.get_value((1, year_max_), 'net_transfers')
# print ' future_gen_transfer =', future_gen_transfer
#Note : do not forget to eliminate values counted twice
last_ipl = past_gen_transfer + future_gen_transfer + net_gov_wealth - simulation.net_gov_spendings - past_gen_dataframe.get_value((0, 0), 'net_transfers')
last_ipl = -last_ipl
print last_ipl, ipl
precision = (ipl - last_ipl)/ipl
print 'precision = ', precision
record.loc[year, population_scenario] = ipl
record.loc[year, col_name2] = precision
print record.head().to_string()
xls = "C:/Users/Utilisateur/Documents/GitHub/ga/src/countries/france/sources/Carole_Bonnet/"+'ipl_evolution'+'.xlsx'
print record.head(30).to_string()
record.to_excel(xls, 'ipl')
def test_comparison():
print 'Entering the comparison function'
simulation = Simulation()
population_scenario_alt = "projpop0760_FECbasESPbasMIGbas"
population_scenario = "projpop0760_FECbasESPbasMIGbas"
simulation.load_population(population_filename, population_scenario)
simulation.load_population(population_filename, population_scenario_alt, default=False)
# 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']
simulation.population = concat([corrected_pop, simulation.population])
simulation.population_alt = concat([corrected_pop.iloc[0:101, :], corrected_pop.iloc[1111:1212,:]]) #concat([corrected_pop, simulation.population_alt])
#Loading profiles :
simulation.load_profiles(profiles_filename)
year_length = 250
simulation.set_year_length(nb_year=year_length)
"""
Default Hypothesis set :
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
"""
r = 0.03
g = 0.01
n = 0.00
pi = 0.01
net_gov_wealth = -3217.7e+09
year_gov_spending = (1094)*1e+09
taxes_list = ['tva', 'tipp', 'cot', 'irpp', 'impot', 'property']
payments_list = ['chomage', 'retraite', 'revsoc', 'maladie', 'educ']
simulation.set_population_projection(year_length=simulation.year_length, method="exp_growth")
simulation.set_tax_projection(method="desynchronized", rate=g, inflation_rate=pi, typ=taxes_list, payments_list=payments_list)
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)
simulation.cohorts.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values('net_transfers', default=True)
"""
Alternate Hypothesis set :
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
"""
r_alt = 0.03
g_alt = 0.01
n_alt = 0.00
pi_alt = 0.01
net_gov_wealth_alt = -3217.7e+09
year_gov_spending_alt = (1094)*1e+09
simulation.set_tax_projection(method="desynchronized", rate=g_alt, inflation_rate=pi_alt, typ=taxes_list, payments_list=payments_list)
simulation.set_growth_rate(g_alt, default=False)
simulation.set_discount_rate(r_alt, default=False)
simulation.set_population_growth_rate(n_alt, default=False)
simulation.create_cohorts(default=False)
simulation.set_gov_wealth(net_gov_wealth_alt, default=False)
simulation.set_gov_spendings(year_gov_spending_alt, default=False, compute=True)
#simulation.cohorts_alt.loc[(0,0,2014):, 'cot'] *= (1+0.1)
simulation.cohorts_alt.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values('net_transfers', default=False)
#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, 1996), level = ['sex', 'year'])
cohorts_age_class_alt = simulation.create_age_class(typ = 'net_transfers', step = 5, default=False)
cohorts_age_class_alt._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_alt = cohorts_age_class_alt.xs((1, 1996), level = ['sex', 'year'])
print "AGE CLASS PV"
print age_class_pv_fe.head(20)
print age_class_pv_fe_alt.head(20)
# Calculating the Intertemporal Public Liability
ipl = simulation.compute_ipl(typ = 'net_transfers')
ipl_alt = simulation.compute_ipl(typ = 'net_transfers', default = False)
print "------------------------------------"
print "IPL par défaut =", ipl
print "IPL alternatif =", ipl_alt
print "share of the GDP : ", ipl/8050.6e+09*100, "%"
print "-alternative share-", ipl_alt/8050.6e+09*100, "%"
print "------------------------------------"
print "INTERNAL CHECKS :"
print simulation.net_gov_spendings, simulation.net_gov_spendings_alt
print simulation.net_gov_wealth, simulation.net_gov_wealth_alt
#Plotting
age_class_pv = cohorts_age_class.xs(1996, level = "year").unstack(level="sex")
age_class_pv = age_class_pv['net_transfers']
age_class_pv.columns = ['men' , 'women']
age_class_pv_alt = cohorts_age_class_alt.xs(1996, level = "year").unstack(level="sex")
age_class_pv_alt = age_class_pv_alt['net_transfers']
age_class_pv_alt.columns = ['men_alt' , 'women_alt']
age_class_pv['men_alt'] = age_class_pv_alt['men_alt'] ; age_class_pv['women_alt'] = age_class_pv_alt['women_alt']
age_class_pv.plot(style = '--') ; plt.legend()
# age_class_pv_alt.plot(style = '--') ; plt.legend()
plt.axhline(linewidth=2, color='black')
plt.show()
# #Plotting profiles :
# profiles_to_plot = simulation.cohorts.xs(90, level = "age").unstack(level="sex")
# profiles_to_plot = profiles_to_plot['net_transfers']
# profiles_to_plot.columns = ['men' , 'women']
#
# profiles_to_plot_alt = simulation.cohorts_alt.xs(90, level = "age").unstack(level="sex")
# profiles_to_plot_alt = profiles_to_plot_alt['net_transfers']
# profiles_to_plot_alt.columns = ['men_alt' , 'women_alt']
#
# profiles_to_plot['men_alt'] = profiles_to_plot_alt['men_alt'] ; profiles_to_plot['women_alt'] = profiles_to_plot_alt['women_alt']
# profiles_to_plot.plot(style = '--') ; plt.legend()
# # age_class_pv_alt.plot(style = '--') ; plt.legend()
# plt.axhline(linewidth=2, color='black')
# cohorts_age_class.xs(2020, level = "year").unstack(level="sex").plot(subplots=True)
# plt.show()
#Saving the decomposed ipl:
to_save = simulation.break_down_ipl(typ='net_transfers', default=False, threshold=60)
# to_save = age_class_pv
xls = "C:/Users/Utilisateur/Documents/GitHub/ga/src/countries/france/sources/Carole_Bonnet/stationnaire_alt.xlsx"
to_save.to_excel(xls, 'ipl')
def simple_scenario():
#Initialisation et entrée des paramètres de base :
simulation = Simulation()
year_length = 300
simulation.set_year_length(nb_year=year_length)
net_gov_wealth = -3217.7e+09
year_gov_spending = (1094)*1e+09
net_gov_wealth_alt = -3217.7e+09
year_gov_spending_alt = (1094)*1e+09
taxes_list = ['tva', 'tipp', 'cot', 'irpp', 'impot', 'property']
payments_list = ['chomage', 'retraite', 'revsoc', 'maladie', 'educ']
simulation.set_population_projection(year_length=simulation.year_length, method="exp_growth")
#On charge la population:
population_scenario = "projpop0760_FECbasESPbasMIGbas"
store_pop = HDFStore(os.path.join(SRC_PATH, 'countries', country, 'sources',
'Carole_Bonnet', 'pop_1996_2006.h5'))
corrected_pop = store_pop['population']
simulation.population = concat([corrected_pop.iloc[0:101, :], corrected_pop.iloc[1111:1212,:]]) #<- la première année TODO: c'est moche
simulation.load_population(population_filename, population_scenario, default=False)
simulation.population_alt = concat([corrected_pop, simulation.population_alt])
simulation.load_profiles(profiles_filename)
r = 0.03
g = 0.01
n = 0.00
r_alt = r
g_alt = g
n_alt = 0.00
#On crée le témoin :
simulation.set_tax_projection(method="aggregate", 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)
simulation.cohorts.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values('net_transfers', default=True)
#On crée le groupe test :
simulation.set_growth_rate(g_alt, default=False)
simulation.set_discount_rate(r_alt, default=False)
simulation.set_population_growth_rate(n_alt, default=False)
simulation.create_cohorts(default=False)
simulation.cohorts_alt.loc[[x>=2015 for x in simulation.cohorts_alt.index.get_level_values(2)], 'retraite'] *= (1/2)
simulation.set_gov_wealth(net_gov_wealth_alt, default=False)
simulation.set_gov_spendings(year_gov_spending_alt, default=False, compute=True)
simulation.cohorts_alt.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values('net_transfers', default=False)
#Calcul de l'IPL et de sa décomposition
ipl_base = simulation.compute_ipl(typ='net_transfers')
ipl_alt = simulation.compute_ipl(typ='net_transfers', default=False, precision=False)
tmp = simulation.cohorts.loc[:, ['net_transfers', 'pop', 'dsct']]
tmp['running_transfers'] = tmp['net_transfers']
tmp['net_transfers'] *= tmp['dsct']
tmp_2 = simulation.cohorts_alt.loc[:, ['net_transfers', 'pop', 'dsct']]
tmp_2['running_transfers'] = tmp_2['net_transfers']
tmp_2['net_transfers'] *= tmp_2['dsct']
xls = "C:/Users/Utilisateur/Documents/GitHub/ga/src/countries/france/sources/Output_folder/"
for year in range(1996, 2007):
flux_df = AccountingCohorts(tmp).extract_generation(year=year, typ='net_transfers', age=0)
flux_df = flux_df.xs(0, level='sex')
print year
flux_df_alt = AccountingCohorts(tmp_2).extract_generation(year=year, typ='net_transfers', age=0)
flux_df_alt = flux_df_alt.xs(0, level='sex')
flux_df[year] = flux_df_alt['net_transfers']
flux_df.to_excel(str(xls)+str(year)+'_.xlsx', 'flux')
print ipl_base, ipl_alt
def compute_elasticities():
print 'Entering the comparison function'
simulation = Simulation()
year_length = 200
simulation.set_year_length(nb_year=year_length)
net_gov_wealth = -3217.7e+09
year_gov_spending = (1094)*1e+09
net_gov_wealth_alt = -3217.7e+09
year_gov_spending_alt = (1094)*1e+09
taxes_list = ['tva', 'tipp', 'cot', 'irpp', 'impot', 'property']
payments_list = ['chomage', 'retraite', 'revsoc', 'maladie', 'educ']
simulation.set_population_projection(year_length=simulation.year_length, method="exp_growth")
epsilon = 0.5
levels = ['haut', 'cent', 'bas']
record = DataFrame(index=levels)
print record
param1 = 'haut'
param2 = 'haut'
param3 = 'cent'
population_scenario = "projpop0760_FEC"+param2+"ESP"+param2+"MIG"+param2
population_scenario_alt = 'projpop0760_FEC'+param1+'ESP'+param2+'MIG'+param3
simulation.load_population(population_filename, population_scenario)
simulation.load_population(population_filename, population_scenario_alt, default=False)
# 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']
simulation.population = concat([corrected_pop, simulation.population])
simulation.population_alt = concat([corrected_pop, simulation.population_alt])
print ' longueur après combinaison',len(simulation.population)
#Loading profiles :
simulation.load_profiles(profiles_filename)
r = 0.03
g = 0.01
n = 0.00
pi = g
r_alt = r
g_alt = g
n_alt = 0.00
pi_alt = g_alt
simulation.set_tax_projection(method="desynchronized", rate=g, inflation_rate=pi, typ=taxes_list, payments_list=payments_list)
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)
simulation.cohorts.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values('net_transfers', default=True)
simulation.set_tax_projection(method="desynchronized", rate=g_alt, inflation_rate=pi_alt, typ=taxes_list, payments_list=payments_list)
simulation.set_growth_rate(g_alt, default=False)
simulation.set_discount_rate(r_alt, default=False)
simulation.set_population_growth_rate(n_alt, default=False)
simulation.create_cohorts(default=False)
simulation.set_gov_wealth(net_gov_wealth_alt, default=False)
simulation.set_gov_spendings(year_gov_spending_alt, default=False, compute=True)
simulation.cohorts_alt.compute_net_transfers(name = 'net_transfers', taxes_list = taxes_list, payments_list = payments_list)
simulation.create_present_values('net_transfers', default=False)
# Calculating the Intertemporal Public Liability
ipl = simulation.compute_ipl(typ = 'net_transfers')
ipl_alt = simulation.compute_ipl(typ = 'net_transfers', default = False)
print 'IPL = ', ipl
print 'IPL_alt= ', ipl_alt
#Elasticities
print "COMPUTING ELASTICITIES"
print '------------------------'
ipl_derivative = (ipl_alt - ipl)/epsilon
print ' semi élasticité of IPL:', ipl_derivative/ipl
print 'Valeur de q :'
print (1+g)/(1+r)
col_name = 'MIG'+param3
record[col_name] = NaN
record.loc[param1, col_name] = ipl_derivative/ipl
print record.to_string()
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()
