Ejemplo n.º 1
0
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
Ejemplo n.º 2
0
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
Ejemplo n.º 3
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()
Ejemplo n.º 4
0
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()
Ejemplo n.º 5
0
def generate_simulation():
    
    print 'Entering the comparison function'
    
    simulation = Simulation()
    population_scenario = "projpop0760_FECbasESPbasMIGbas"
    population_scenario_alt = "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']
    truncated_pop = concat([corrected_pop.iloc[0:101, :], corrected_pop.iloc[1111:1212,:]])
    
    simulation.population = concat([corrected_pop, simulation.population])
    simulation.population_alt = 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="aggregate", 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[[x>=2027 for x in simulation.cohorts_alt.index.get_level_values(2)], 'cot'] *= (1+0.1)
#     simulation.cohorts_alt.loc[[x>=2027 for x in simulation.cohorts_alt.index.get_level_values(2)], 'retraite'] *= (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)
    
    
    return simulation
Ejemplo n.º 6
0
def generate_simulation():

    print 'Entering the comparison function'

    simulation = Simulation()
    population_scenario = "projpop0760_FECbasESPbasMIGbas"
    population_scenario_alt = "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']
    truncated_pop = concat(
        [corrected_pop.iloc[0:101, :], corrected_pop.iloc[1111:1212, :]])

    simulation.population = concat([corrected_pop, simulation.population])
    simulation.population_alt = 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[[x>=2075 for x in simulation.cohorts_alt.index.get_level_values(2)], '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)

    return simulation
Ejemplo n.º 7
0
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')
Ejemplo n.º 8
0
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')
Ejemplo n.º 9
0
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
Ejemplo n.º 10
0
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()
Ejemplo n.º 11
0
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()