Universal Credit

Universal Credit (UC) is a means-tested benefit payment administered by the Department for Work and Pensions (DWP) in the United Kingdom. It was introduced in 2013 to replace several existing means-tested benefits and tax credits for working-age individuals and families. Universal Credit was introduced to replace the following six legacy benefits:

• Income-based Jobseeker’s Allowance (JSA): Intended for individuals who are actively seeking employment and are on a low income.

• Income-related Employment and Support Allowance (ESA): Designed for individuals who are unable to work due to illness or disability.

• Income Support: Generally for people on a low income who do not qualify for JSA or ESA.

• Child Tax Credit (CTC): Aimed at families or individuals responsible for children.

• Working Tax Credits: Geared toward individuals or families who are on a low income and are working.

• Housing Benefit for working-age claimants: Helps people with the cost of their rent if they are on a low income.

Universal Credit parameters can be found in policyengine_uk/parameters/gov/dwp/universal_credit and logic in policyengine_uk/variables/dwp/universal_credit.py.

Legislation

The legal framework for Universal Credit is primarily defined in the legislation provided on the legislation.gov.uk website （The Universal Credit Regulations 2013).

Universal Credit Rate Changes

Universal Credit consists of various components or elements, each providing financial support for different circumstances. Some of the key components include:

• Standard allowance

• Child amounts

• Disabled child additions

• Limited Capability for Work amount

• Limited Capability for Work and Work-Related Activity amount

• Carer amount

The table below shows some of the rates covered by PolicyEngine-UK.

Show code cell source Hide code cell source

from policyengine_uk.system import system

parameters = system.parameters

carer_amount = parameters.gov.dwp.universal_credit.elements.carer.amount
child_amount = parameters.gov.dwp.universal_credit.elements.child.amount
disabled_amount = parameters.gov.dwp.universal_credit.elements.disabled.amount
disabled_child_amount = (
    parameters.gov.dwp.universal_credit.elements.child.disabled.amount
)
higher_amount = (
    parameters.gov.dwp.universal_credit.elements.child.first.higher_amount
)

elements = [
    carer_amount,
    child_amount,
    disabled_amount,
    disabled_child_amount,
    higher_amount,
]  # [...]

dates = [
    "2016-01-01",
    "2017-01-01",
    "2018-01-01",
    "2019-01-01",
    "2020-01-01",
    "2021-01-01",
    "2022-01-01",
    "2023-01-01",
    "2024-01-01",
]
names = ["Carer", "Child", "Disabled", "Disabled child", "First child"]

import pandas as pd

df = pd.DataFrame()

for date in dates:
    for element, name in zip(elements, names):
        # Append to a dataframe: row = date, column = element, value = amount
        new_row = {"date": date, "element": name, "amount": element(date)}
        # Append row to the dataframe
        df = pd.concat([df, pd.DataFrame([new_row])])


# merge element cells
pivot_df = df.pivot(index="date", columns="element", values="amount")
pivot_df.fillna("")

element	Carer	Child	Disabled	Disabled child	First child
date
2016-01-01	150.39	231.67	315.60	126.11	277.08
2017-01-01	150.39	231.67	315.60	126.11	277.08
2018-01-01	151.89	231.67	318.76	126.11	277.08
2019-01-01	156.45	231.67	328.32	126.11	277.08
2020-01-01	160.20	231.67	336.20	126.11	277.08
2021-01-01	162.92	235.85	341.92	128.25	281.25
2022-01-01	163.73	237.08	343.63	128.89	282.50
2023-01-01	168.81	244.58	354.28	132.89	290.00
2024-01-01	185.86	269.58	390.06	146.31	315.00

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import plotly.express as px
from policyengine_core.charts import format_fig

fig = px.line(
    df,
    title="Universal Credit elements over time",
    x="date",
    y="amount",
    color="element",
).update_layout(
    yaxis_range=[0, 500],
    yaxis_tickformat=",.0f",
    yaxis_tickprefix="£",
    yaxis_title="Amount(£m)",
    xaxis_title="Year",
    legend_title="Element",
)

fig = format_fig(fig)
fig