def get_data():
"""Generate key datasets for UBI reforms.
Returns:
DataFrame: Baseline DataFrame with core variables.
DataFrame: UBI tax reform DataFrame with core variables.
float: Yearly revenue raised by the UBI tax reform.
"""
baseline = Microsimulation()
baseline_df = baseline.df(
[var for var in BASELINE_COLS if var != "is_disabled_for_ubi"],
map_to="household")
reform_no_ubi = ubi_reform(0, 0, 0, 0, pd.Series([0] * 12, index=REGIONS))
reform_no_ubi_sim = Microsimulation(reform_no_ubi)
reform_base_df = reform_no_ubi_sim.df(BASELINE_COLS, map_to="household")
budget = (baseline.calc("net_income", map_to="household").sum() -
reform_no_ubi_sim.calc("net_income", map_to="household").sum())
baseline_df_pd = pd.DataFrame(baseline_df)
baseline_df_pd["household_weight"] = baseline_df.weights
reform_base_df_pd = pd.DataFrame(reform_base_df)
reform_base_df_pd["household_weight"] = reform_base_df.weights
return baseline_df_pd, reform_base_df_pd, budget
def impute_incomes(dataset: Dataset = FRS, year: int = 2022) -> MicroDataFrame:
"""Imputation of high incomes from the SPI.
Args:
dataset (type): The dataset to clone.
year (int): The year to clone.
Returns:
Dict[str, ArrayLike]: The mapping from the original dataset to the cloned dataset.
"""
from openfisca_uk import Microsimulation
# Most recent SPI used - if it's before the FRS year then data will be uprated
# automatically by OpenFisca-UK
spi = Microsimulation(dataset=SPI)
frs = Microsimulation(
dataset=dataset,
year=year,
)
regions = spi.calc("region").unique()
spi_df = spi.df(PREDICTORS + IMPUTATIONS)
frs_df = frs.df(PREDICTORS)
frs_df.region = frs_df.region.map(
{name: float(i)
for i, name in enumerate(regions)})
spi_df.region = spi_df.region.map(
{name: float(i)
for i, name in enumerate(regions)})
return si.rf_impute(
x_train=spi_df.drop(IMPUTATIONS, axis=1),
y_train=spi_df[IMPUTATIONS],
x_new=frs_df,
verbose=True,
)
"in_poverty_bhc",
"in_deep_poverty_bhc",
]
BASELINE_HH_COLS = ["household_weight", "people", "region"]
# Extract these for baseline too.
REFORM_HH_COLS = [
"household_net_income",
"equiv_household_net_income",
"poverty_gap_bhc",
"poverty_gap_ahc",
]
p_base = mdf.MicroDataFrame(
baseline_sim.df(BASELINE_PERSON_COLS + REFORM_PERSON_COLS,
map_to="person"),
weights="household_weight",
)
p_base.rename(
dict(zip(REFORM_PERSON_COLS, [i + "_base" for i in REFORM_PERSON_COLS])),
axis=1,
inplace=True,
)
hh_base = mdf.MicroDataFrame(
baseline_sim.df(BASELINE_HH_COLS + REFORM_HH_COLS, map_to="household"),
weights="household_weight",
)
hh_base.rename(
dict(zip(REFORM_HH_COLS, [i + "_base" for i in REFORM_HH_COLS])),
axis=1,
