Employer National Insurance incidence

Employer National Insurance reforms can change wages, tax receipts and household income through the model’s incidence assumptions. The parameters under gov.contrib.policyengine.employer_ni default to:

These values are adjustable model assumptions. No empirical estimate is claimed for the defaults, and this page does not select a preferred incidence assumption.

Wage adjustment and supplied income¶

The wage channel compares the baseline and reform employer-NI rate, secondary threshold and employer-pension exemption. For its eligible earnings branch, it solves for pay holding modeled employer cost fixed, then multiplies the pay change by employee_incidence. At 1, the full computed adjustment applies in the same model year. The formula returns zero without a baseline, without a relevant parameter change, at employee incidence zero, or for prior pay below the baseline secondary threshold. This describes the implemented calculation, including its threshold guard; it does not assert a general equilibrium response for every worker.

employment_income adds this adjustment to employment_income_before_lsr and other earnings components. An input actually stored in the final employment_income holder overrides its formula. The UK Simulation constructor moves situation inputs named employment_income to employment_income_before_lsr. Consequently, putting that field in a constructor situation can still allow the wage response. The example below demonstrates an override with set_input after construction and before calculation. Dataset and other input routes should be checked for which earnings holder they populate.

The wage formula, earnings components and constructor remapping provide the implementation details.

Consumer and capital channels¶

Consumer and capital incidence apply to the remaining change in modeled employer cost after the wage response. Each is zero when its own parameter is zero or employee incidence is one. Otherwise, the formulas use person weights and allocate that change using household consumption or corporate wealth per person. Their weighted aggregate exposure must be nonzero; they also return zero when the simulation has no dataset attribute.

An attribute can be present with dataset=None, so that guard alone is not a promise that every household calculation has zero residual incidence. The example below has no representative dataset or consumption/wealth exposures and returns zero for both residual channels. It cannot show their population effects.

Setting employee_incidence to zero alone leaves the consumer and capital parameters at 0.5. Set all three parameters to zero to disable these three employer-NI incidence channels. This does not disable unrelated labor-supply or other behavioral responses.

The response amounts enter household_tax; those same variables are not added to gov_tax. See the consumer and capital allocation formulas.

Secondary Class 1 only¶

All three channels cover secondary Class 1 contributions. ni_employer also holds Class 1A contributions on benefits in kind (ni_class_1a), but employer_cost, baseline_employer_cost and adjusted_employer_cost leave them out, as they leave out the benefits themselves. No household bears Class 1A or a change in it, whether the change comes from the benefits (as with a rise in the company car appropriate percentages) or from the employer rate, which Class 1A follows (SSCBA 1992 s. 10(5)). Such a reform raises gov_tax by its Class 1A change on top of what households pay. Passing Class 1A through these channels is #2752.

Measures and aggregation¶

Report these quantities separately:

• Gross employer-NI liability or its reform change: ni_employer, a person amount.

• Net government balance: gov_balance = gov_tax - gov_spending, a household amount. A positive reform change improves the modeled balance. Wage changes can affect income tax, employee NI and benefits, so this is not a gross employer-NI yield.

• Household incidence here: the change in nominal household_net_income, defined as household_market_income + household_benefits - household_tax - pension_contributions. A negative change is a household loss under this measure.

The example sums each output over one person or one household, as appropriate. Population results require the matching person or household weights. Do not sum household values repeated on person records as if they were distinct households. These measures do not by themselves establish a representative budget costing.

Worked household example¶

For model year 2026, consider one 40-year-old London employee with £40,000 of pre-response earnings, no pension contributions and no other supplied income. Raise the employer rate from 15% to 16%, holding the other parameters fixed. Current main includes the annual secondary-threshold correction: £5,000 rather than the old rounded-weekly equivalent of £4,992. Each comparison below applies the same incidence assumptions to its baseline and reform.

Annual changes in pounds, rounded to two decimal places:

At fixed pay, the extra liability is 0.01 × (40,000 − 5,000) = £350. With full wage incidence, the baseline employer cost is £45,250. Solving pay + 0.16 × (pay − 5,000) = 45,250 gives pay of approximately £39,698.28. The income-tax and employee-NI changes partly offset the wage fall. The last three rows coincide in this example; they do not imply identical population incidence.

Run this snippet from a checkout’s supported Python environment:

from policyengine_core.reforms import Reform
from policyengine_uk import Simulation

YEAR = 2026
SPAN = "2026-01-01.2026-12-31"
INCIDENCE = "gov.contrib.policyengine.employer_ni."
RATE = "gov.hmrc.national_insurance.class_1.rates.employer"
VARIABLES = [
    "ni_employer",
    "income_tax",
    "ni_employee",
    "employment_income",
    "gov_balance",
    "household_net_income",
]


def result(employee, consumer, capital, direct_pay=False):
    pay_input = "employment_income" if direct_pay else "employment_income_before_lsr"
    situation = {
        "people": {"worker": {"age": {YEAR: 40}, pay_input: {YEAR: 40_000}}},
        "benunits": {"family": {"members": ["worker"]}},
        "households": {"home": {"members": ["worker"], "region": {YEAR: "LONDON"}}},
    }
    assumptions = {
        INCIDENCE + "employee_incidence": {SPAN: employee},
        INCIDENCE + "consumer_incidence": {SPAN: consumer},
        INCIDENCE + "capital_incidence": {SPAN: capital},
    }
    baseline = Simulation(
        situation=situation,
        reform=Reform.from_dict(assumptions, country_id="uk"),
    )
    reform = Simulation(
        situation=situation,
        reform=Reform.from_dict({**assumptions, RATE: {SPAN: 0.16}}, country_id="uk"),
    )
    if direct_pay:
        # UK construction moves situation earnings to the pre-response input.
        # Explicitly supply the final earnings holder after construction.
        baseline.set_input("employment_income", YEAR, [40_000])
        reform.set_input("employment_income", YEAR, [40_000])
    before = {v: float(baseline.calculate(v, YEAR).sum()) for v in VARIABLES}
    after = {v: float(reform.calculate(v, YEAR).sum()) for v in VARIABLES}
    return {
        "baseline": before,
        "reform": after,
        "delta": {v: after[v] - before[v] for v in VARIABLES},
        "residual_channels": {
            v: float(reform.calculate(v, YEAR).sum())
            for v in (
                "employer_ni_response_consumer_incidence",
                "employer_ni_response_capital_incidence",
            )
        },
    }


rows = {
    "Employee 1; consumer/capital 0.5": result(1, 0.5, 0.5),
    "Employee 0; consumer/capital 0.5": result(0, 0.5, 0.5),
    "All three zero": result(0, 0, 0),
    "Pay set after construction; employee 1": result(1, 0.5, 0.5, direct_pay=True),
}
for label, row in rows.items():
    print(label, {v: round(value, 2) for v, value in row["delta"].items()})

The residual-channel outputs are retained in rows for inspection. No private microdata, representative population estimate or empirical calibration is used.

Comparing an official costing¶

Match the definition and horizon of the specific official figure before comparing it with a model result. For example, the OBR’s October 2024 Economic and fiscal outlook reported £25.7 billion of employer-NICs revenue in 2029–30 before indirect economic effects (overview paragraph 1.7). Its paragraph 2.14 assumed about 60% of the cost would pass to employees in the short term, with more adjustment later through prices and nominal wages. These are dated forecast assumptions, not evidence for the model’s defaults. Other official headlines can use different definitions.