diff --git a/changelog.d/frs-uc-gainful-self-employment.fixed.md b/changelog.d/frs-uc-gainful-self-employment.fixed.md new file mode 100644 index 00000000..34363c66 --- /dev/null +++ b/changelog.d/frs-uc-gainful-self-employment.fixed.md @@ -0,0 +1 @@ +Set policyengine-uk's Universal Credit input uc_is_in_gainful_self_employment from the FRS, as a survey proxy for UC Regs 2013 reg 64 that overrides the model's income-based default: true for every adult whose main job (EMPSTATI) is self-employment, including traders who break even or make a loss, and for anyone whose self-employment profit is above their employment income (ADM H4034); false otherwise. The SPI copy re-derives it from its own imputed incomes. diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py index e440d486..135c3e0c 100644 --- a/policyengine_uk_data/datasets/frs.py +++ b/policyengine_uk_data/datasets/frs.py @@ -53,6 +53,10 @@ EmploymentStatus.LONG_TERM_DISABLED.name, EmploymentStatus.SHORT_TERM_DISABLED.name, ) +SELF_EMPLOYED_STATUSES = ( + EmploymentStatus.FT_SELF_EMPLOYED.name, + EmploymentStatus.PT_SELF_EMPLOYED.name, +) FORMULA_MODELED_EDUCATION_GRANT_VARIABLES = ( "childcare_grant", "parents_learning_allowance", @@ -392,6 +396,39 @@ def derive_is_parent_from_frs_microdata( return is_adult_record & has_dependent_children +def derive_uc_is_in_gainful_self_employment( + employment_status, self_employment_income, employment_income +) -> np.ndarray: + """Whether each person is in gainful self-employment for Universal Credit. + + UC Regs 2013 reg 64(a) asks whether the person carries on a trade as their + main employment. DWP's Advice for Decision Making starts from hours + (H4031) but lets earnings outweigh them: someone who works more hours as + an employee but earns more from self-employment is likely to be gainfully + self-employed (H4034). So the flag is true for: + + - a self-employed main job (FRS EMPSTATI, the job the respondent names as + their dominant activity, else the one with more hours), whatever its + profit: a trade can make a loss or break even and still be carried on + in expectation of profit (ADM H4013, H4054, H4503); + - a side trade whose profit is above the person's employment income. + + policyengine-uk's default reads any self-employment income other than + zero as gainful self-employment, and none as none. + + The flag is fixed from survey-year (or SPI-imputed) incomes. Uprating + reprices the two incomes by different indices, so in a projected year a + flagged side trade can earn less than the job, and the reverse; the flag + does not follow. + """ + self_employed_main_job = np.isin( + np.asarray(employment_status, dtype=object), SELF_EMPLOYED_STATUSES + ) + profit = np.asarray(self_employment_income, dtype=float) + pay = np.asarray(employment_income, dtype=float) + return self_employed_main_job | ((profit > 0) & (profit > pay)) + + def _as_non_negative_array(values) -> np.ndarray: values = np.asarray(values, dtype=float) return np.maximum(np.nan_to_num(values, nan=0.0), 0.0) @@ -1045,6 +1082,15 @@ def determine_education_level(fted_val, typeed2_val, age_val): ) * WEEKS_IN_YEAR pe_person["self_employment_income"] = np.maximum(0, person.seincam2) * WEEKS_IN_YEAR + # policyengine-uk releases without this input skip the column and keep + # their formula. + pe_person["uc_is_in_gainful_self_employment"] = ( + derive_uc_is_in_gainful_self_employment( + pe_person.employment_status, + pe_person.self_employment_income, + pe_person.employment_income, + ) + ) INVERTED_BASIC_RATE = 1.25 diff --git a/policyengine_uk_data/datasets/imputations/income.py b/policyengine_uk_data/datasets/imputations/income.py index feafcb27..98fe14a5 100644 --- a/policyengine_uk_data/datasets/imputations/income.py +++ b/policyengine_uk_data/datasets/imputations/income.py @@ -292,6 +292,23 @@ def impute_income(dataset: UKSingleYearDataset) -> UKSingleYearDataset: target_dataset=zero_weight_copy, ) + # The copy keeps its FRS donor's employment status but now has SPI + # incomes, so derive its gainful self-employment flag again from the + # copy's own values. + if "uc_is_in_gainful_self_employment" in zero_weight_copy.person.columns: + from policyengine_uk_data.datasets.frs import ( + derive_uc_is_in_gainful_self_employment, + ) + + person = zero_weight_copy.person + person["uc_is_in_gainful_self_employment"] = ( + derive_uc_is_in_gainful_self_employment( + person.employment_status, + person.self_employment_income, + person.employment_income, + ) + ) + dataset = impute_over_incomes( dataset, model, diff --git a/policyengine_uk_data/tests/test_uc_gainful_self_employment.py b/policyengine_uk_data/tests/test_uc_gainful_self_employment.py new file mode 100644 index 00000000..48766685 --- /dev/null +++ b/policyengine_uk_data/tests/test_uc_gainful_self_employment.py @@ -0,0 +1,233 @@ +import numpy as np +import pandas as pd +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st +from policyengine_uk.variables.household.income.employment_status import ( + EmploymentStatus, +) + +from policyengine_uk_data.datasets.frs import ( + SELF_EMPLOYED_STATUSES, + derive_uc_is_in_gainful_self_employment, +) +from policyengine_uk_data.tests.test_imputation_source_flags import ( + _FakeDataset, + _stack_without_remapping, +) + +STATUSES = [status.name for status in EmploymentStatus] +GAINFUL = "uc_is_in_gainful_self_employment" +incomes = st.floats(0, 1e6, allow_nan=False) + + +def derive(status, profit, pay): + return derive_uc_is_in_gainful_self_employment([status], [profit], [pay])[0] + + +def oracle(status, profit, pay): + # Written out per person, independently of the vectorised helper. + if status == "FT_SELF_EMPLOYED" or status == "PT_SELF_EMPLOYED": + return True + return profit > 0 and profit > pay + + +def test_self_employed_statuses_are_model_enum_members(): + assert set(SELF_EMPLOYED_STATUSES) <= set(STATUSES) + + +@pytest.mark.parametrize("status", STATUSES) +@pytest.mark.parametrize("profit", [0.0, 5_000.0]) +def test_every_status_without_a_side_trade_that_out_earns_pay(status, profit): + # Pay at or above profit: only the main-job status decides. + expected = status in ("FT_SELF_EMPLOYED", "PT_SELF_EMPLOYED") + assert derive(status, profit, 10_000.0) == expected + + +@pytest.mark.parametrize("profit", [0.0, 1.0, 20_000.0]) +def test_self_employed_main_job_is_gainful_whatever_the_profit(profit): + # A loss reaches this build floored at zero, so zero covers losses too. + for status in SELF_EMPLOYED_STATUSES: + assert derive(status, profit, 0.0) + assert derive(status, profit, 50_000.0) + + +def test_side_trade_counts_only_when_it_out_earns_pay(): + # ADM H4034 example 1 (Jos): more employed hours, more self-employed pay. + assert derive("PT_EMPLOYED", 140 * 52, 80 * 52) + # ADM H4035 example 2 (Ann): the job earns more. + assert not derive("PT_EMPLOYED", 40 * 52, 49.6 * 52) + assert not derive("FT_EMPLOYED", 10_000.0, 10_000.0) + assert not derive("FT_EMPLOYED", 0.0, 0.0) + assert derive("UNEMPLOYED", 1.0, 0.0) + + +@pytest.mark.parametrize("status", STATUSES) +@pytest.mark.parametrize( + "profit, pay", + [(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0), (2.0, 1.0), (1.0, 2.0)], +) +def test_truth_table_against_oracle(status, profit, pay): + assert derive(status, profit, pay) == oracle(status, profit, pay) + + +@settings(max_examples=500, deadline=None) +@given(st.sampled_from(STATUSES), incomes, incomes) +def test_invariants(status, profit, pay): + gainful = derive(status, profit, pay) + # Every self-employed main job is gainful. + if status in SELF_EMPLOYED_STATUSES: + assert gainful + # Nobody else is gainful without a profit above their pay. + if gainful: + assert status in SELF_EMPLOYED_STATUSES or profit > max(pay, 0) + # More profit never removes the flag; more pay never adds it. + assert derive(status, profit * 2 + 1, pay) >= gainful + assert derive(status, profit, pay * 2 + 1) <= gainful + + +@settings(max_examples=100, deadline=None) +@given(st.lists(st.tuples(st.sampled_from(STATUSES), incomes, incomes), max_size=40)) +def test_vectorised_matches_elementwise(rows): + statuses = [r[0] for r in rows] + profits = [r[1] for r in rows] + pays = [r[2] for r in rows] + result = derive_uc_is_in_gainful_self_employment(statuses, profits, pays) + assert result.dtype == bool + assert result.tolist() == [oracle(*r) for r in rows] + np.testing.assert_array_equal( + derive_uc_is_in_gainful_self_employment( + pd.Series(statuses, dtype="category"), + pd.Series(profits, dtype=float), + pd.Series(pays, dtype=float), + ), + result, + ) + + +def _frs_like_dataset(statuses, profits, pays): + n = len(statuses) + person = pd.DataFrame( + { + "person_id": np.arange(1, n + 1), + "person_household_id": np.arange(1, n + 1), + "person_benunit_id": np.arange(1, n + 1), + "employment_status": statuses, + "employment_income": pays, + "self_employment_income": profits, + "savings_interest_income": 0.0, + "dividend_income": 0.0, + "private_pension_income": 0.0, + "property_income": 0.0, + } + ) + person[GAINFUL] = derive_uc_is_in_gainful_self_employment( + person.employment_status, + person.self_employment_income, + person.employment_income, + ) + household = pd.DataFrame( + { + "household_id": np.arange(1, n + 1), + "household_weight": 1.0, + "region": "LONDON", + } + ) + return _FakeDataset(person=person, household=household) + + +@settings(max_examples=50, deadline=None) +@given( + st.lists( + st.tuples(st.sampled_from(STATUSES), incomes, incomes, incomes, incomes), + min_size=1, + max_size=10, + ) +) +def test_spi_copy_flag_follows_its_own_imputed_incomes(rows): + from policyengine_uk_data.datasets import disability_benefits + from policyengine_uk_data.datasets.imputations import frs_only + from policyengine_uk_data.datasets.imputations import income as income_module + + imputed_profit = [r[3] for r in rows] + imputed_pay = [r[4] for r in rows] + + def impute_over_incomes(dataset, _model, output_variables): + dataset = dataset.copy() + if "self_employment_income" in output_variables: + dataset.person["self_employment_income"] = imputed_profit + dataset.person["employment_income"] = imputed_pay + return dataset + + with pytest.MonkeyPatch.context() as m: + m.setattr(income_module, "create_income_model", lambda: object()) + m.setattr(income_module, "subsample_dataset", lambda d, _n: d.copy()) + m.setattr(income_module, "impute_over_incomes", impute_over_incomes) + m.setattr( + frs_only, + "impute_frs_only_variables", + lambda train_dataset, target_dataset: target_dataset, + ) + m.setattr( + disability_benefits, + "strip_internal_disability_reported_amounts", + lambda dataset: dataset, + ) + m.setattr(income_module, "stack_datasets", _stack_without_remapping) + result = income_module.impute_income( + _frs_like_dataset( + [r[0] for r in rows], [r[1] for r in rows], [r[2] for r in rows] + ) + ) + + person = result.person + n = len(rows) + assert len(person) == 2 * n + # The SPI copy kept the donors' statuses and took the imputed incomes. + assert person.self_employment_income.iloc[n:].tolist() == imputed_profit + assert person[GAINFUL].tolist() == [ + oracle(*values) + for values in zip( + person.employment_status, + person.self_employment_income, + person.employment_income, + ) + ] + + +def test_frs_only_stage_leaves_the_rule_inputs_alone(): + # The second-stage QRF rewrites these columns on the SPI copy after the + # flag's inputs are set; none of them may be an input to the flag. + from policyengine_uk_data.datasets.imputations.frs_only import ( + FRS_ONLY_PERSON_VARIABLES, + ) + + rule_columns = { + GAINFUL, + "employment_status", + "employment_income", + "self_employment_income", + } + assert rule_columns.isdisjoint(FRS_ONLY_PERSON_VARIABLES) + + +@pytest.mark.parametrize("fixture", ["frs", "enhanced_frs"]) +def test_built_dataset_flag(fixture, request): + # Skips only when no built dataset exists; a build without the column fails. + dataset = request.getfixturevalue(fixture) + person = dataset.person + assert GAINFUL in person.columns, f"{fixture} lacks {GAINFUL}: rebuild it" + gainful = person[GAINFUL].to_numpy(dtype=bool) + self_employed = np.isin(person.employment_status, SELF_EMPLOYED_STATUSES) + assert gainful[self_employed].all() + profit = person.self_employment_income.to_numpy() + assert (profit[gainful & ~self_employed] > 0).all() + if fixture == "frs": + # Later enhanced-FRS stages reprice incomes, so the exact rule holds on + # the base build only. + np.testing.assert_array_equal( + gainful, + derive_uc_is_in_gainful_self_employment( + person.employment_status, profit, person.employment_income + ), + ) diff --git a/pyproject.toml b/pyproject.toml index beff7f1a..58de5b1d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,7 @@ dependencies = [ dev = [ "ruff>=0.9.0", "pytest", + "hypothesis", "torch", "l0-python>=0.4.0", "tables", diff --git a/uv.lock b/uv.lock index a3e9f44d..c301c6c9 100644 --- a/uv.lock +++ b/uv.lock @@ -577,6 +577,75 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/35/f4/124858007ddf3c61e9b144107304c9152fa80b5b6c168da07d86fe583cc1/huggingface_hub-1.1.5-py3-none-any.whl", hash = "sha256:e88ecc129011f37b868586bbcfae6c56868cae80cd56a79d61575426a3aa0d7d", size = 516000, 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