diff --git a/.gitignore b/.gitignore index 9a741bc4..5c1ab238 100644 --- a/.gitignore +++ b/.gitignore @@ -19,3 +19,6 @@ **/_build !policyengine_uk_data/storage/*.csv **/version.json + +# Hypothesis example database (property-based tests) +.hypothesis/ diff --git a/changelog.d/ctr-gross-council-tax.fixed.md b/changelog.d/ctr-gross-council-tax.fixed.md new file mode 100644 index 00000000..dc72e8a1 --- /dev/null +++ b/changelog.d/ctr-gross-council-tax.fixed.md @@ -0,0 +1,2 @@ +- Build `council_tax` as the bill after discounts and before council tax reduction, as policyengine-uk expects: add reported CTR (CTREBAMT) back to the FRS CTANNUAL, which is net of it, impute missing bills from non-recipients only, and in Scotland net off water and sewerage using the 2024-25 gross charges (uk-data#496). +- Stop imputing reported council tax reduction onto SPI-synthetic rows; set it to zero there (uk-data#497). diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py index e440d486..5c55603e 100644 --- a/policyengine_uk_data/datasets/frs.py +++ b/policyengine_uk_data/datasets/frs.py @@ -85,6 +85,16 @@ FRS_APPROVED_TRAINING_CODES = tuple(range(1, 10)) UNKNOWN_QUALIFYING_EDUCATION_OR_TRAINING_ENTRY_AGE = 1000 +SCOTLAND_GVTREGNO = 12 +# From FRS 2024-25, CWATAMT1 and CSEWAMT1 are DWP derived variables, the +# "Weeklyised gross annual dom. water/sew. charge on bill" (DV summary +# 2024-25). Earlier releases carry interview answers under the same names, +# populated for only about half of Scottish households. +FIRST_SURVEY_YEAR_WITH_GROSS_SCOTTISH_WATER_CHARGES = 2024 +# Scottish Water Charges Reduction Scheme: at most a 35% reduction in water +# and sewerage charges for households receiving council tax reduction. +SCOTTISH_WATER_CHARGES_MAXIMUM_REDUCTION = 0.35 + @lru_cache(maxsize=None) def load_legacy_jobseeker_max_annual_hours(year: int) -> int: @@ -525,6 +535,121 @@ def split_reported_education_grants( FRS_RELEASE_FOLDER_PATTERN = re.compile(r"^frs_(\d{4})_(\d{2})$") +def derive_council_tax(household: pd.DataFrame, year: int) -> np.ndarray: + """Annual council tax bill after discounts and before council tax reduction. + + policyengine-uk's ``council_tax`` is the gross liability that its council + tax reduction (CTR) formulas reduce, so it must not already be net of CTR. + The FRS CTANNUAL is net of it: the 2024-25 DV summary labels it "Annual + CT amount after discounts/reduction", derived from inputs including CTREB + and CTREBAMT, and the interview question it replaced asked for the amount + payable "after deducting any discounts or reduction". In England and + Wales in 2022-23 to 2024-25, recipients' CTANNUAL plus CTREBAMT (weekly) + x 365.25/7 matches the mean bill of non-recipients in the same region, + band and single-adult cell. So: + + - A household reporting a reduction (CTREB = 1) gets CTANNUAL plus its + annualised CTREBAMT, including when CTANNUAL is 0 (a full reduction). + If CTREBAMT is missing or not positive, the reduction is unknown and + the bill is the larger of CTANNUAL and the imputed cell mean. + - A missing or negative CTANNUAL is imputed as the mean bill of + non-recipients with a positive bill in the same (region, band, + single-adult) cell, or 0 if the cell has none (Northern Ireland, which + has no council tax, always has none). + - Other households keep CTANNUAL. + + In Scotland CTANNUAL also includes water and sewerage charges, which are + not council tax and which CTR does not cover, so they are netted off + first. (They belong in ``water_and_sewerage_charges``, which is still + zero for Scotland in 2024-25: uk-data#467.) From 2024-25 the netting uses + the gross charges CWATAMT1 and CSEWAMT1, as CSEWAMT is blank that year. + Measured on the 2024-25 release: + + - Non-recipients: netting the full gross charges leaves a status-discount + (25%) bill at 0.75 of the undiscounted bill in every band, so DWP's + derivation applies the status discount to council tax only. The + discount is therefore not applied to the charges here. + - Recipients: the recipient identity above holds (1.00 overall, 0.98-1.02 + across bands and discount groups) when 65% of the gross charges are + netted, and falls to 0.83 when all of them are. This matches a flat reduction at the Water + Charges Reduction Scheme's 35% maximum, so recipients' charges are + netted at 65% of gross. + + Earlier releases keep the previous netting of CSEWAMT plus CWATAMTD, the + discounted charges. A table without a CTREB column has no recipients, so + it gets the previous CTANNUAL-based bill. + + Args: + household: Raw FRS household table with lower-case column names, one + row per household. + year: FRS survey year (2024 for FRS 2024-25). + + Returns: + Annual council tax per household, in the order of ``household``. + """ + + def column(name: str) -> pd.Series: + if name in household.columns: + return pd.to_numeric(household[name], errors="coerce") + return pd.Series(np.nan, index=household.index) + + ctannual = column("ctannual") + region = column("gvtregno") + band = column("ctband") + single_adult = column("adulth") == 1 + reports_reduction = column("ctreb") == 1 + weekly_reduction = column("ctrebamt") + reduction_known = reports_reduction & (weekly_reduction > 0) + + in_scotland = region == SCOTLAND_GVTREGNO + if year >= FIRST_SURVEY_YEAR_WITH_GROSS_SCOTTISH_WATER_CHARGES and { + "cwatamt1", + "csewamt1", + }.issubset(household.columns): + gross_weekly_charges = column("cwatamt1").clip(lower=0).fillna(0) + column( + "csewamt1" + ).clip(lower=0).fillna(0) + share_in_bill = np.where( + reports_reduction, 1 - SCOTTISH_WATER_CHARGES_MAXIMUM_REDUCTION, 1 + ) + weekly_charges = gross_weekly_charges * share_in_bill + else: + weekly_charges = column("csewamt").clip(lower=0).fillna(0) + column( + "cwatamtd" + ).clip(lower=0).fillna(0) + scottish_charges = np.where(in_scotland, weekly_charges * WEEKS_IN_YEAR, 0) + council_tax_only = (ctannual - scottish_charges).clip(lower=0) + + # Cell means from non-recipients only: a recipient's CTANNUAL is net of + # its reduction and would pull the imputed gross bill down. + in_pool = (ctannual > 0) & ~reports_reduction + cell_keys = [region, band, single_adult] + cell_mean = ( + council_tax_only[in_pool] + .groupby([key[in_pool] for key in cell_keys], dropna=False) + .mean() + ) + imputed = ( + cell_mean.reindex(pd.MultiIndex.from_arrays(cell_keys)).fillna(0).to_numpy() + ) + + missing_bill = ctannual.isna() | (ctannual < 0) + council_tax = np.select( + [ + missing_bill, + reports_reduction & ~reduction_known, + reduction_known, + ], + [ + imputed, + np.maximum(council_tax_only, imputed), + council_tax_only + weekly_reduction * WEEKS_IN_YEAR, + ], + default=council_tax_only, + ) + return np.nan_to_num(council_tax, nan=0.0) + + def survey_year_from_frs_folder_name(raw_frs_folder) -> int | None: """Survey year encoded in an FRS release folder name (``frs_2024_25`` -> 2024). @@ -931,68 +1056,9 @@ def determine_education_level(fted_val, typeed2_val, age_val): household.typeacc, 1, range(1, 8), ACCOMMODATIONS ).values - # Impute Council Tax - - # In Scotland, council tax bills are collected together with Scottish - # Water and sewerage charges, and the FRS CTANNUAL variable includes - # them. Net them off (they are weekly variables; CTANNUAL is annual) so - # council_tax is tax only: the water charges are already captured - # separately in water_and_sewerage_charges, so leaving them in both - # double-counts them and overstates Scottish council tax by roughly - # £500 per household (~25% of the Scottish total). - SCOTLAND_GVTREGNO = 12 - scottish_water_annual = pd.Series( - np.where( - household.gvtregno == SCOTLAND_GVTREGNO, - ( - np.maximum(household.csewamt.fillna(0), 0) - + np.maximum(household.cwatamtd.fillna(0), 0) - ) - * (365.25 / 7), - 0, - ), - index=household.index, - ) - ctannual_tax_only = np.maximum(household.ctannual - scottish_water_annual, 0) - - # Only ~25% of household report Council Tax bills - use - # these to build a model to impute missing values - CT_valid = household.ctannual > 0 - - # Find the mean reported Council Tax bill for a given - # (region, CT band, is-single-person-household) triplet - region = household.gvtregno[CT_valid] - band = household.ctband[CT_valid] - single_person = (household.adulth == 1)[CT_valid] - ctannual = ctannual_tax_only[CT_valid] - - # Build the table - ct_mean = ctannual.groupby([region, band, single_person], dropna=False).mean() - ct_mean = ct_mean.replace(-1, ct_mean.mean()) - - # For every household consult the table to find the imputed - # Council Tax bill - pairs = household.set_index( - [household.gvtregno, household.ctband, (household.adulth == 1)] - ) - hh_CT_mean = pd.Series(index=pairs.index) - has_mean = pairs.index.isin(ct_mean.index) - hh_CT_mean[has_mean] = ct_mean[pairs.index[has_mean]].values - hh_CT_mean[~has_mean] = 0 - ct_imputed = hh_CT_mean - - # For households which originally reported Council Tax, - # use the reported value. Otherwise, use the imputed value - council_tax = pd.Series( - np.where( - # 2018 FRS uses blanks for missing values, 2019 FRS - # uses -1 for missing values - (household.ctannual < 0) | household.ctannual.isna(), - np.maximum(ct_imputed, 0).values, - ctannual_tax_only, - ) - ) - pe_household["council_tax"] = council_tax.fillna(0) + # Council tax: the bill after discounts and before council tax + # reduction, with Scottish water and sewerage charges netted off. + pe_household["council_tax"] = derive_council_tax(household, year) BANDS = ["A", "B", "C", "D", "E", "F", "G", "H", "I"] # Band 1 is the most common pe_household["council_tax_band"] = ( diff --git a/policyengine_uk_data/datasets/imputations/frs_only.py b/policyengine_uk_data/datasets/imputations/frs_only.py index 242fbf24..3669ddfe 100644 --- a/policyengine_uk_data/datasets/imputations/frs_only.py +++ b/policyengine_uk_data/datasets/imputations/frs_only.py @@ -95,13 +95,30 @@ "incapacity_benefit_reported", "maternity_allowance_reported", "winter_fuel_allowance_reported", - "council_tax_benefit_reported", "jsa_contrib_reported", "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", ] +# FRS-only person variables set to zero on SPI-donor rows instead of being +# imputed. +# +# ``council_tax_benefit_reported`` is the household's reported council tax +# reduction (FRS CTREBAMT), which the FRS build puts on the household +# reference person only. A person-level QRF on personal incomes cannot +# reproduce that. It put amounts on two or three people in some households. +# On the 2024-25 build, imputed receipt also rose with household income on +# these rows, while reported receipt in the FRS falls steeply with it. +# policyengine-uk does not treat the amount as noise: any positive value +# makes the benefit unit claim CTR (``would_claim_council_tax_reduction``), +# and where it has no CTR scheme for the household, +# ``council_tax_benefit`` is the reported amount itself. At zero, CTR on +# these rows comes only from the model's own eligibility and take-up. +SPI_DONOR_ZEROED_PERSON_VARIABLES = [ + "council_tax_benefit_reported", +] + def _one_hot_encode(df: pd.DataFrame, columns: list[str]) -> pd.DataFrame: """Return ``df`` with object-typed ``columns`` one-hot encoded. @@ -177,12 +194,17 @@ def impute_frs_only_variables( to predict values for every row of ``target_dataset``; predictions replace the existing (donor-leaked) values in ``FRS_ONLY_PERSON_VARIABLES`` only. Variables absent from either - frame are skipped silently. + frame are skipped silently. ``SPI_DONOR_ZEROED_PERSON_VARIABLES`` + are set to zero on ``target_dataset`` rather than imputed. """ from policyengine_uk_data.utils.qrf import QRF target_dataset = target_dataset.copy() + for column in SPI_DONOR_ZEROED_PERSON_VARIABLES: + if column in target_dataset.person.columns: + target_dataset.person[column] = 0.0 + train_person = train_dataset.person target_person = target_dataset.person @@ -203,7 +225,7 @@ def impute_frs_only_variables( if not outputs: logger.warning( "Stage-2 FRS-only imputation: no output variables available; " - "returning target_dataset unchanged." + "returning target_dataset without imputed values." ) return target_dataset diff --git a/policyengine_uk_data/tests/test_council_tax_gross_bill.py b/policyengine_uk_data/tests/test_council_tax_gross_bill.py new file mode 100644 index 00000000..d8105c15 --- /dev/null +++ b/policyengine_uk_data/tests/test_council_tax_gross_bill.py @@ -0,0 +1,259 @@ +"""Council tax is the bill after discounts and before council tax reduction. + +The FRS CTANNUAL is net of the reported reduction (CTREB, CTREBAMT), and in +Scotland it also carries water and sewerage charges. ``derive_council_tax`` +adds the reported reduction back and nets the Scottish charges off. The +fixtures below are synthetic households, not survey records. +""" + +import numpy as np +import pandas as pd +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from policyengine_uk_data.datasets.frs import ( + SCOTLAND_GVTREGNO, + SCOTTISH_WATER_CHARGES_MAXIMUM_REDUCTION, + WEEKS_IN_YEAR, + derive_council_tax, +) + +LONDON = 7 +WALES = 11 +NORTHERN_IRELAND = 13 +BAND_C = 3 +RECIPIENT, NON_RECIPIENT = 1, 2 + + +def _households(rows: list[dict]) -> pd.DataFrame: + """Raw-FRS-shaped household table; unspecified fields are blank.""" + columns = [ + "gvtregno", + "ctband", + "adulth", + "ctannual", + "ctreb", + "ctrebamt", + "cwatamt1", + "csewamt1", + "cwatamtd", + "csewamt", + ] + table = pd.DataFrame(rows).reindex(columns=columns).astype(float) + table.index = pd.RangeIndex(100, 100 + len(table), name="household_id") + return table + + +def _cell(region, ctannual, ctreb=NON_RECIPIENT, ctrebamt=np.nan, adults=2, **kw): + return dict( + gvtregno=region, + ctband=BAND_C, + adulth=adults, + ctannual=ctannual, + ctreb=ctreb, + ctrebamt=ctrebamt, + **kw, + ) + + +def test_full_reduction_recipient_with_zero_bill_gets_gross_bill(): + households = _households( + [_cell(LONDON, 0.0, RECIPIENT, 30.0), _cell(LONDON, 1_600.0)] + ) + council_tax = derive_council_tax(households, 2024) + assert council_tax[0] == pytest.approx(30.0 * WEEKS_IN_YEAR) + + +def test_partial_reduction_recipient_gets_bill_plus_reduction(): + households = _households( + [_cell(WALES, 900.0, RECIPIENT, 12.0), _cell(WALES, 1_700.0)] + ) + council_tax = derive_council_tax(households, 2024) + assert council_tax[0] == pytest.approx(900.0 + 12.0 * WEEKS_IN_YEAR) + + +def test_non_recipients_keep_their_bill_including_zero(): + households = _households( + [ + _cell(LONDON, 1_500.0), + _cell(LONDON, 0.0), # e.g. an exempt dwelling + _cell(WALES, 1_200.0, adults=1), + ] + ) + council_tax = derive_council_tax(households, 2024) + np.testing.assert_allclose(council_tax, [1_500.0, 0.0, 1_200.0]) + + +def test_recipient_with_unknown_reduction_gets_non_recipient_cell_mean(): + households = _households( + [ + _cell(LONDON, 0.0, RECIPIENT, np.nan), + _cell(LONDON, 0.0, RECIPIENT, 0.0), + _cell(LONDON, 1_900.0, RECIPIENT, np.nan), + _cell(LONDON, 1_400.0), + _cell(LONDON, 1_600.0), + ] + ) + council_tax = derive_council_tax(households, 2024) + # The cell mean (1,500), or the reported bill if that is larger: the + # bill before the reduction cannot be below the bill after it. + np.testing.assert_allclose(council_tax[:3], [1_500.0, 1_500.0, 1_900.0]) + + +def test_missing_bills_are_imputed_from_non_recipients_only(): + households = _households( + [ + _cell(LONDON, np.nan), + _cell(LONDON, -1.0), + _cell(LONDON, 1_400.0), + _cell(LONDON, 1_600.0), + # A recipient's net bill must not pull the imputed bill down. + _cell(LONDON, 200.0, RECIPIENT, 25.0), + # A different cell (single adult) must not enter the mean. + _cell(LONDON, 1_125.0, adults=1), + ] + ) + council_tax = derive_council_tax(households, 2024) + np.testing.assert_allclose(council_tax[:2], [1_500.0, 1_500.0]) + + +def test_northern_ireland_has_no_council_tax(): + households = _households([_cell(NORTHERN_IRELAND, np.nan, RECIPIENT, 10.0)]) + assert derive_council_tax(households, 2024)[0] == 0.0 + + +def test_scottish_gross_water_and_sewerage_are_netted_from_2024(): + water, sewerage = 4.0, 5.0 + gross_charges = (water + sewerage) * WEEKS_IN_YEAR + households = _households( + [ + _cell(SCOTLAND_GVTREGNO, 2_000.0, cwatamt1=water, csewamt1=sewerage), + _cell( + SCOTLAND_GVTREGNO, + 900.0, + RECIPIENT, + 10.0, + cwatamt1=water, + csewamt1=sewerage, + ), + ] + ) + council_tax = derive_council_tax(households, 2024) + assert council_tax[0] == pytest.approx(2_000.0 - gross_charges) + # Recipients' CTANNUAL carries the charges after the Water Charges + # Reduction Scheme's maximum 35% reduction. + recipient_charges = gross_charges * (1 - SCOTTISH_WATER_CHARGES_MAXIMUM_REDUCTION) + assert council_tax[1] == pytest.approx( + 900.0 - recipient_charges + 10.0 * WEEKS_IN_YEAR + ) + + +def test_scotland_before_2024_keeps_discounted_charge_netting(): + households = _households( + [ + _cell( + SCOTLAND_GVTREGNO, + 2_000.0, + cwatamtd=3.0, + csewamt=4.0, + cwatamt1=9.0, + csewamt1=9.0, + ) + ] + ) + council_tax = derive_council_tax(households, 2023) + assert council_tax[0] == pytest.approx(2_000.0 - 7.0 * WEEKS_IN_YEAR) + + +def test_charges_are_not_netted_outside_scotland(): + households = _households([_cell(LONDON, 2_000.0, cwatamt1=9.0, csewamt1=9.0)]) + assert derive_council_tax(households, 2024)[0] == pytest.approx(2_000.0) + + +def test_without_reduction_columns_the_bill_is_ctannual(): + households = _households( + [_cell(LONDON, 700.0, RECIPIENT, 20.0), _cell(LONDON, np.nan)] + ).drop(columns=["ctreb", "ctrebamt"]) + council_tax = derive_council_tax(households, 2020) + np.testing.assert_allclose(council_tax, [700.0, 700.0]) + + +# Property-based checks over arbitrary small household tables. + +_amount = st.one_of( + st.none(), + st.just(-1.0), + st.just(0.0), + st.floats(min_value=0.0, max_value=5_000.0), +) +_weekly = st.one_of(st.none(), st.just(0.0), st.floats(min_value=0.0, max_value=100.0)) +_household = st.fixed_dictionaries( + dict( + gvtregno=st.sampled_from([1, LONDON, WALES, SCOTLAND_GVTREGNO, 13]), + ctband=st.one_of(st.none(), st.integers(1, 9)), + adulth=st.integers(1, 4), + ctannual=_amount, + ctreb=st.one_of(st.none(), st.sampled_from([RECIPIENT, NON_RECIPIENT])), + ctrebamt=_weekly, + cwatamt1=_weekly, + csewamt1=_weekly, + cwatamtd=_weekly, + csewamt=_weekly, + ) +) + + +@settings(max_examples=300, deadline=None) +@given( + rows=st.lists(_household, min_size=1, max_size=12), + year=st.sampled_from([2022, 2023, 2024, 2025]), +) +def test_council_tax_invariants(rows, year): + households = _households(rows) + council_tax = derive_council_tax(households, year) + ctannual = households.ctannual.to_numpy() + reduction = households.ctrebamt.to_numpy() + recipient = (households.ctreb == 1).to_numpy() + known_reduction = recipient & (households.ctrebamt > 0).to_numpy() + has_bill = ctannual >= 0 + scotland = (households.gvtregno == SCOTLAND_GVTREGNO).to_numpy() + + # One finite, non-negative bill per household. + assert council_tax.shape == (len(households),) + assert np.isfinite(council_tax).all() and (council_tax >= 0).all() + + # Non-recipients with a bill outside Scotland keep it exactly. + keep = ~recipient & has_bill & ~scotland + np.testing.assert_allclose(council_tax[keep], ctannual[keep]) + + # A known reduction is added back in full, so recipients outside + # Scotland pay CTANNUAL plus it. + add_back = known_reduction & has_bill & ~scotland + np.testing.assert_allclose( + council_tax[add_back], + ctannual[add_back] + reduction[add_back] * WEEKS_IN_YEAR, + ) + + # The bill before the reduction is never below the bill after it. + no_charges = has_bill & recipient & ~scotland + assert (council_tax[no_charges] >= ctannual[no_charges] - 1e-9).all() + + # Recomputing on a subset of rows cannot change the kept bills (the + # cell means only feed missing bills and unknown reductions). + council_tax_keep_only = derive_council_tax(households[keep], year) + np.testing.assert_allclose(council_tax_keep_only, council_tax[keep]) + + +@settings(max_examples=200, deadline=None) +@given(rows=st.lists(_household, min_size=1, max_size=12)) +def test_recipients_do_not_move_the_imputed_bill(rows): + """Changing recipients' bills never changes a non-recipient's result.""" + households = _households(rows) + recipient = (households.ctreb == 1).to_numpy() + changed = households.copy() + changed.loc[recipient, "ctannual"] = 4_321.0 + np.testing.assert_allclose( + derive_council_tax(households, 2024)[~recipient], + derive_council_tax(changed, 2024)[~recipient], + ) diff --git a/policyengine_uk_data/tests/test_frs_only_imputation.py b/policyengine_uk_data/tests/test_frs_only_imputation.py index 1961274e..4ae6abd9 100644 --- a/policyengine_uk_data/tests/test_frs_only_imputation.py +++ b/policyengine_uk_data/tests/test_frs_only_imputation.py @@ -258,3 +258,31 @@ def predict(self, x): assert not result.person["is_severely_disabled_for_benefits"].any() assert (result.person["pip_dl_category"] == "NONE").all() assert (result.person["pip_m_category"] == "NONE").all() + + +def test_spi_donor_rows_get_zero_reported_council_tax_reduction(): + """Reported CTR is zeroed on SPI-donor rows, not imputed (uk-data#497). + + Even when every training person reports CTR, the target rows must end + with none: the stage-2 QRF would otherwise spread a household-level + amount over individual people. + """ + from policyengine_uk_data.datasets.imputations.frs_only import ( + FRS_ONLY_PERSON_VARIABLES, + SPI_DONOR_ZEROED_PERSON_VARIABLES, + impute_frs_only_variables, + ) + + assert "council_tax_benefit_reported" in SPI_DONOR_ZEROED_PERSON_VARIABLES + assert not set(SPI_DONOR_ZEROED_PERSON_VARIABLES) & set(FRS_ONLY_PERSON_VARIABLES) + + train = _fake_dataset(person_rows=400, seed=0) + train.person["council_tax_benefit_reported"] = 1_000.0 + target = _fake_dataset(person_rows=60, seed=1) + target.person["council_tax_benefit_reported"] = 750.0 + + result = impute_frs_only_variables(train_dataset=train, target_dataset=target) + + assert (result.person["council_tax_benefit_reported"] == 0).all() + # The training (FRS) rows keep their reported amounts. + assert (train.person["council_tax_benefit_reported"] == 1_000.0).all() diff --git a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py index 5f1acd85..fc02379b 100644 --- a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py +++ b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py @@ -479,7 +479,8 @@ def fake_read_csv(path, *args, **kwargs): "csewamt": 0, "ctannual": 0, "ctband": 1, - "ctrebamt": 0, + "ctreb": 1, + "ctrebamt": 10, "cwatamtd": 0, "gross4": 0, "gvtregno": 1, @@ -561,3 +562,11 @@ def fake_read_csv(path, *args, **kwargs): ].iloc[0] assert dataset.person["education_grants"].iloc[0] == 100 assert dataset.person["disabled_students_allowance_eligible_expenses"].iloc[0] == 0 + # A full council tax reduction (CTANNUAL 0, CTREBAMT £10 a week) leaves + # the bill before the reduction in council_tax (uk-data#496). + weekly_reduction_annualised = 10 * frs_module.WEEKS_IN_YEAR + assert dataset.household["council_tax"].iloc[0] == weekly_reduction_annualised + assert ( + dataset.person["council_tax_benefit_reported"].iloc[0] + == weekly_reduction_annualised + ) diff --git a/pyproject.toml b/pyproject.toml index beff7f1a..7973343c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,8 +45,9 @@ dev = [ "yaml-changelog>=0.1.7", "itables", "quantile-forest", - "build", "towncrier>=24.8.0", - + "build", + "towncrier>=24.8.0", + "hypothesis>=6.168.3", ] 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