Economic assumptions

Consumer price index¶

We apply CPI to these variables:

• Reported benefits, apart from the reported council tax reduction under Council tax below– carers_allowance_reported, child_benefit_reported, child_tax_credit_reported, esa_contrib_reported, esa_income_reported, housing_benefit_reported, iidb_reported, incapacity_benefit_reported, income_support_reported, jsa_contrib_reported, jsa_income_reported, maternity_allowance_reported, pension_credit_reported, sda_reported, state_pension_reported, universal_credit_reported, winter_fuel_allowance_reported, working_tax_credit_reported

• All consumption categories EXCEPT energy– alcohol_and_tobacco_consumption, clothing_and_footwear_consumption, communication_consumption, education_consumption, food_and_non_alcoholic_beverages_consumption, health_consumption, household_furnishings_consumption, housing_water_and_electricity_consumption, miscellaneous_consumption, recreation_consumption, restaurants_and_hotels_consumption, transport_consumption

domestic_energy_consumption, electricity_consumption and gas_consumption are deliberately excluded. The data build calibrates them to NEED mean kWh and converts to pounds at Ofgem Q2 2026 unit rates, so the stored values already carry FY26/27 price levels; uprating them from the dataset’s data year would re-apply price changes that are already included. See PolicyEngine/policyengine-uk#1867. A dataset that declares its energy price year instead projects them with household electricity and gas price indices (gov.economic_assumptions.energy_prices): ONS CPI prices to the latest outturn, then the OBR’s utilities inflation forecast, holding VAT on electricity at 5% so the VAT formulas apply any change in the rate. See the economic assumptions README. liquid_fuel_consumption and solid_fuel_consumption (heating oil, LPG, coal and wood) are not covered by Ofgem’s price cap, so they are CPI-uprated from the data year like other LCFS spending.

• Other variables– afcs_reported, bsp_reported, bus_fare_spending, bus_fare_spending_reported, childcare_expenses, company_car_capital_contributions, company_car_list_price, company_car_private_use_payments, free_school_fruit_veg, free_school_meals, free_school_milk, local_bus_uncapped_single_fare, maintenance_expenses, statutory_maternity_pay, statutory_paternity_pay, statutory_sick_pay, state_pension, war_widows_pension_reported, widowed_parents_allowance_reported

bsp_reported stays nominally flat when a dataset explicitly supplies both war_widows_pension_reported and widowed_parents_allowance_reported in the same source year, including all-zero columns. Datasets with either column missing and household situations retain CPI uprating. See the bereavement-benefit split guide.

Road fuel volume¶

We use HMRC road-fuel clearances and OBR-implied forecast volumes for petrol and diesel spending, because PolicyEngine converts these spending fields into litres by dividing by pump prices.

We apply this to: diesel_spending, petrol_spending

The pump prices that turn spending into litres (household.consumption.fuel.prices.petrol and .diesel) are DESNZ calendar-year averages (Quarterly Energy Prices tables 4.1.1 and 4.1.2), the same basis Microcosm UK uses to price household fuel spending. The spending growth rates (petrol_spending_litre_proxy, diesel_spending_litre_proxy) are derived from those prices, so that weighted litres follow the volume path above: (1 + growth) = (1 + volume growth) × price_t / price_t−1 ÷ (1 + population growth). Whenever the prices change, re-derive the growth rates; a test checks that they match.

Benefit uprating (September CPI)¶

Most benefit rates rise each April by the CPI 12-month rate for the September before, as in the annual review under Social Security Administration Act 1992 s150 (written statement HCWS1101: 3.8% for April 2026). Published September figures come from ONS series D7G7 (3.5% for September 2026, which sets April 2027); later years use the OBR’s September CPI forecast and, after the EFO horizon, calendar-year CPI. Each figure is rounded to 0.1 percentage points, and a fall in prices leaves rates unchanged. Parameters with uprating: gov.benefit_uprating_cpi rise by these rates after their last published value.

Average earnings¶

We use average earnings for these variables: defined_benefit_pension_input_amount, employee_pension_contributions, employer_pension_contributions, employment_income, employment_income_before_lsr, pension_contributions_via_bonus_sacrifice, pension_contributions_via_salary_sacrifice, pension_contributions_via_salary_sacrifice_pre_2015, personal_pension_contributions, student_loan_repayments

The map leaves out employee_pension_contributions_reported, so we hold a stored value flat to 2030 and grow it with average earnings from 2031.

Newly assigned student loan plans¶

Dataset projection holds ages fixed, so a person of a given age represents a later university intake in each projected year. When projection adds a loan to a tertiary-educated person without one, it uses current household region as a proxy for the funding nation, because the dataset does not record the funding agency. This proxy cannot identify people who moved between nations.

The official repayment-plan rules give SAAS borrowers Plan 4 and Student Finance Northern Ireland borrowers Plan 1. For undergraduate loans, England uses Plan 1 before the 2012 intake, Plan 2 for 2012–2022, and Plan 5 from 2023; Wales uses Plan 1 before 2012 and Plan 2 from 2012. New assignments follow these routes; an unknown region leaves the person without an assigned loan. New holders from the 2023 and later intakes are added only in England: Plan 5 is the only plan that did not exist in the base year, while the other nations’ plans for those intakes are already in the data. Plan 1 loans first paid before September 2006 (the 2005 and earlier intakes) are written off at 65, later ones about 29 years after the course started. Take-up stays at a fixed 40% probability with a draw keyed on person ID. These routes and the write-off ages decide only which new loans we assign. Existing holders keep their recorded plan and repayment, because the data hold no course or repayment dates that could establish a new plan or a cancellation.

Lagged average earnings¶

We lag earnings growth by one year and use that to uprate housing service charges, including ground rent (housing_service_charges).

Per capita GDP¶

We derive these rates as calendar-year nominal GDP growth from the OBR’s October 2026 EFO divided by population growth: (1 + GDP growth) / (1 + population growth) − 1.

We use per capita GDP for these variables: benunit_reported_capital, cash_isa, cash_isa_balance, consumer_debt, corporate_wealth, directly_held_shares, dividend_income, gross_financial_wealth, household_lifetime_isa_balance, income_not_otherwise_charged, individual_savings_account_dividend_income, lifetime_isa_balance, lump_sum_income, maintenance_income, miscellaneous_income, mortgage_capital_repayment, mortgage_debt, net_financial_wealth, non_residential_property_purchased, non_residential_property_secured_debt, non_residential_property_value, other_residential_property_secured_debt, owned_company_capital, owned_company_holding_value, owned_company_trade_assets, owned_land, pension_credit_reported_capital, pension_income, private_pension_wealth, private_transfer_income, property_income, property_rental_income, rent_paid_as_boarder, rent_paid_as_lodger, savings, spousal_maintenance_income, stocks_and_shares_isa, stocks_and_shares_isa_balance, sublet_income, uc_reported_capital, unit_and_investment_trusts

Capital gains¶

By default we grow capital gains at the per capita GDP rates above. A simulation can supply a versioned projection of aggregate gains instead, and we convert it to factors for each record using population growth. See the capital gains projection guide.

We apply this to: capital_gains, capital_gains_badr, capital_gains_before_response, capital_gains_carried_interest, capital_gains_residential_property

Savings income¶

When we project a dataset to later years (up to 2032-33), we grow savings income by the path the dataset says its savings income was calibrated to. A dataset that declares the obr_savings_income_2022_2025 projection grows it by the OBR’s savings income forecast per person from 2022-23 to 2025-26 and by per capita GDP in other years. A dataset that declares nothing grows it by per capita GDP in every year, as before. The OBR’s series is the “Savings income” row of its receipts table “Self-employed income streams: growth rates” (October 2026 EFO, Table 4.5, from 2024-25; earlier years from the March 2026 and October 2024 EFOs), divided by population growth.

From 2023-24 to 2025-26 the OBR’s savings income rises 35.8%, where per capita GDP and population growth would give 10.2%. After 2025-26 these variables follow per capita GDP under either rule.

The declaration matters because a dataset’s savings income is fitted to targets its builder moved forward along some path. Growing it by a different path would miss those targets. Datasets store the declaration in the H5 root attribute policyengine_uk.savings_income_projection.

We apply this to non_cash_isa_cash_interest, other_investment_income and savings_interest_income, and to ISA interest in datasets whose ISA income contract projects it additively. Datasets with the legacy gross contract grow gross interest, ISA interest included, and hold the ISA part flat. Inputs given directly to a calculation, and years after the dataset projection, use the index each variable declares: household interest income (ONS D.41) for interest and per capita GDP for other investment income.

Council tax¶

We project each country’s council tax receipts, net of council tax reduction, with the OBR’s figures (October 2026 EFO, Expenditure Table 5.1) from 2025-26, and official outturns before then: MHCLG’s council tax requirement for England, the Scottish Government’s net amount billed and the Welsh Government’s net collectable debit.

Receipts are the Band D level times the tax base, the number of Band D equivalent dwellings, so they grow with the number of homes as well as with bills. Household weights grow with population, so each household’s council_tax grows by its country’s receipts growth divided by population growth: (1 + receipts growth) / (1 + population growth) − 1. Each country’s weighted total then grows with its receipts. Northern Ireland has no council tax (it has domestic rates), so any council_tax a dataset gives its households is held flat. A person’s council_tax_benefit_reported grows by the same factor as their household’s council_tax, so a reported reduction keeps its share of the bill.

Domestic rates¶

A Northern Ireland household’s rates bill equals its home’s capital value times the sum of the regional rate and its council’s district rate. Domestic capital values stay at the 1 January 2005 valuation list, so the bill grows with those two rates. We grow domestic_rates by their combined growth across the 11 councils, weighted by the domestic rates households paid to each council the year before. From 2027-28 we hold the 2026-27 growth, assuming the domestic regional rate rises 5% a year, as the Finance Minister’s draft Budget proposed to 2028-29, and district rates keep their 2026-27 growth. Households outside Northern Ireland have no domestic rates.

Water and sewerage¶

We project water_and_sewerage_charges by country. Households in England and Wales follow the average household bill from Ofwat. Households in Scotland follow Scottish Water’s charges, which Scottish Water sets within limits from the Water Industry Commission for Scotland. Both series end at 2029-30 and keep that rate in later years. Northern Ireland has no domestic water charges, so any a dataset gives its households stay flat. These country routes run to the end of the dataset projection in 2032. From 2033 policyengine-core grows every household’s charges by the Ofwat index the variable declares, in Scotland and Northern Ireland too. That index grows to 2074, the last year any growth series covers, so charges hold from 2075.

House prices¶

We use the OBR’s house price growth (October 2026 EFO, economy Table 1.16).

We apply this to: additional_residential_property_purchased, main_residence_value, other_residential_property_value

Mortgage interest¶

We use the OBR’s mortgage interest rate index growth rates. The OBR’s October 2026 EFO stops forecasting mortgage interest payments after 2029, so for 2030 we use its November 2025 forecast, and we hold that rate in later years.

We apply this to: mortgage_interest_repayment, property_finance_costs, property_finance_costs_brought_forward

Private pension index¶

We use RPI year-on-year change from the previous year, capped at 5%.

We apply this to: private_pension_income

Rent¶

We project rent by tenure. Each household that rents privately follows the ONS Price Index of Private Rents for its region to 2025 and then the OBR’s UK private rents forecast (October 2026 EFO, economy Table 1.7). With no published regional forecast, every region takes the UK rate from 2026, and a household with no recorded region takes the UK rate in every year.

Every other household follows social rent growth. To 2025 we use the Regulator of Social Housing’s stock-average general needs rent growth in England. The OBR publishes no social rent forecast, so from 2026 we apply the England social rent cap: CPI inflation in the previous September plus 1 percentage point.

Both series run to the end of the dataset projection in 2032, and we apply each series’ 2031 rate again in 2032. From 2033 each household’s rent holds at its 2032 value, because the variable declares no uprating index.

Per capita mixed income¶

We derive these rates as calendar-year mixed income growth from the OBR’s October 2026 EFO (Table 1.12) divided by population growth, in the same way.

We apply this to: owned_company_income_share, self_employment_gross_receipts, self_employment_income, trading_loss

Gambling duty bases¶

We grow each gambling duty’s base per person with its own series, and get each year’s base by dividing the duty’s receipts by the rate the model charges that year. We take receipts from HMRC’s outturn for 2025-26 and from the OBR’s October 2026 EFO (receipts Table 4.10) from 2026-27, and divide the base’s growth by population growth: (1 + base growth) / (1 + population growth) − 1. Growing the base rather than receipts lets the model apply its own rate changes once. See the gambling duties guide.

We apply each series to its duty’s bases:

• General Betting Duty: betting_exchange_commission, financial_spread_betting_net_losses, horse_and_dog_pool_betting_net_losses, land_based_fixed_odds_betting_net_losses, other_spread_betting_net_losses, remote_other_betting_net_losses, remote_uk_horseracing_betting_net_losses

• Pool Betting Duty: pool_betting_net_receipts

• Remote Gaming Duty: remote_gaming_net_losses

• Machine Games Duty: machine_games_higher_rate_net_takings, machine_games_lower_rate_net_takings, machine_games_standard_rate_higher_stake_net_takings, machine_games_standard_rate_net_takings

• Gaming Duty: land_based_casino_net_losses

• Bingo Duty: bingo_net_receipts

• Lottery Duty: national_lottery_ticket_spending

Rail usage¶

We use passenger journeys in Great Britain from the Office of Rail and Road (Table 1220), and hold journeys flat from 2026-27, with no official forecast to follow. Household weights grow with population, so each household’s rail_usage grows by journey growth divided by population growth: (1 + journey growth) / (1 + population growth) − 1. The weighted total then follows the journeys.

Population¶

We use the OBR’s October 2026 population forecast to 2031-32, read from its economy tables (Table 1.1 real GDP over Table 1.5 real GDP per person, at Q2), and after that the population growth in the OBR’s long-term economic determinants for the March 2026 EFO, which follows the principal projection of the ONS 2024-based national population projections. On the March 2026 EFO the same reading of the tables reproduces the determinants’ forecast years, so the October determinants, once published, should change only the later years.

We apply this to: household_weight