Research and analysis

Low income poverty projections for children, FYE 2026 to FYE 2031, July 2026

Published 9 July 2026

Summary

This analysis provides the government’s current best estimate of the future low income poverty trends for children based on the policy position and economic assumptions after Spring Statement 2026.

It is estimated that there will be 3.6 million children in relative low income after housing costs (AHC) in the final year of parliament (financial year ending (FYE) 2030). This is projected to represent a reduction of 400,000 children in relative low income after housing costs across the Parliament (between FYE 2025 and FYE 2030).

Projections of low income poverty trends are not a forecast, they are a projection under a specific set of assumptions and are subject to a high degree of uncertainty. They should only be used to provide an indication of future trends.

Please note that the projections have been stitched to updated Households Below Average Income (HBAI) statistics which now use an improved approach to using administrative data in place of Family Resources Survey (FRS) survey responses.

Methodology

The Department for Work and Pensions’ Policy Simulation Model (PSM) is used to estimate future trends in low income poverty. The PSM is a static microsimulation model^{[footnote 1]} based on a snapshot of the UK population from the Family Resources Survey (FRS), currently for the financial years ending (FYE) 2022, 2023 and 2024. It uses caseload forecasts alongside benefit rules and economic assumptions to simulate results such as poverty levels for each year, currently up to and including FYE 2031, for the United Kingdom.

Calibration to benefit caseload data means the PSM better models benefit income compared to survey estimates, as published in Households Below Average Income (HBAI), because the FRS underreports benefit receipt. This means that the modelled estimates of poverty levels can systematically differ from survey data. We therefore apply the changes to poverty levels year on year produced by the PSM to the most recent HBAI data to estimate the poverty levels. This better reflects the estimated low income poverty trends but means that any sampling and measurement error in the FYE 2025 HBAI data will affect the projected levels.

Note that since the previous publication of poverty projections, HBAI data for FYE 2025 has been released which included a methodology update to improve the quality of the statistics by linking the survey data to administrative data. This data has not yet been incorporated into the PSM. Until this work is completed later this year, we have stitched the change between years projected by the model onto the FYE 2025 official estimates, to produce poverty projections over time. We have also updated the absolute poverty line to be based on FYE 2025 data, rather than FYE 2011, in line with the changes made to HBAI. The methodological improvement to the HBAI statistics has led to a change in the poverty level, which is reflected in the projections.

To project future low income poverty trends, the base PSM methodology has been modified to better model the impact of changing caseloads as a result of the Move to Universal Credit. This involves changing the grossing regime so that working-age income-related benefit caseload forecasts are held at their base level (FYE 2024 outturn) and adjusting the caseloads at the sample level using standard Move to Universal Credit (UC) assumptions and take-up adjustments to match the overall UC forecast. This ensures alignment with the UC caseload forecast but prevents the grossing regime from artificially distorting the income distribution and ensures the projections are sensitive to economic and policy factors.

The methodology used to produce these projections has been developed in consultation with an external Expert Advisory Group.

This projections analysis uses a model consistent with the policy position immediately after Spring Statement 2026 and Office for Budget Responsibility’s (OBR’s) economic and caseload forecasts in March 2026.

The figures in this publication are not directly comparable to previously published poverty projections as they are stitched to HBAI data from FYE 2025 which uses a different methodology than the HBAI estimates previously used for stitching (FYE 2024).

Poverty projections are not a forecast, they are an estimate of future trends in low-income poverty levels under a very specific set of assumptions. The model relies on multiple assumptions about the economy and benefit caseloads which are subject to a high degree of uncertainty. Routine updates at each fiscal event, for example to the OBR economic assumptions, can materially change projected poverty rates, even without policy changes.

Projections should therefore be interpreted as the best currently available estimate rather than a precise forecast of future poverty rates.

Poverty projections are presented for four measures of poverty: both relative and absolute low income and both before and after housing costs.

Read further information on the methodology behind the low income measures and in the Household below average income series: quality and methodology information report.

Estimates are rounded to the nearest 100,000 children and the nearest whole percentage point for consistency with published HBAI low income poverty rates and due to uncertainties inherent in the modelling approach.

Figures for FYE 2026 are based on projections because the most recent available HBAI data is for FYE 2025.

Low income projections for children

It is estimated that there will be 3.6 million children in relative low income after housing costs in the final year of parliament (FYE 2030). This is projected to represent a reduction of 400,000 children in relative low income after housing costs between FYE 2025 and FYE 2030. This is expected to be the largest reduction in the number of children in relative low income AHC over a Parliament since comparable records began in the 1990s ^{[footnote 2]}.

Table 1a: Relative low income AHC projections for children (UK)

	HBAI FYE 2025	Projection FYE 2026	Projection FYE 2027	Projection FYE 2028	Projection FYE 2029	Projection FYE 2030	Projection FYE 2031
Number	4.0 million	4.1 million	3.7 million	3.7 million	3.6 million	3.6 million	3.6 million
Proportion	27%	28%	25%	25%	25%	25%	25%

Table 1b: Projected change in relative low income AHC levels for children over the Parliament (FYE 2025 to FYE 2030, UK)

Relative AHC, children
Difference between FYE 2025 and FYE 2030 (number)	-400,000
Difference between FYE 2025 and FYE 2030 (percentage point)	-2ppt

Table 2a: Relative low income Before Housing Costs (BHC) projections for children (UK)

	HBAI FYE 2025	Projection FYE 2026	Projection FYE 2027	Projection FYE 2028	Projection FYE 2029	Projection FYE 2030	Projection FYE 2031
Number	3.0 million	3.1 million	2.7 million	2.6 million	2.6 million	2.6 million	2.6 million
Proportion	21%	21%	18%	18%	18%	18%	18%

Table 2b: Projected change in relative low income BHC levels for children over the Parliament (FYE 2025 to FYE 2030, UK)

Relative BHC, children
Difference between FYE 2025 and FYE 2030 (number)	-400,000
Difference between FYE 2025 and FYE 2030 (percentage point)	-2ppt

Table 3a: Absolute low income AHC projections for children (UK)

	HBAI FYE 2025	Projection FYE 2026	Projection FYE 2027	Projection FYE 2028	Projection FYE 2029	Projection FYE 2030	Projection FYE 2031
Number	4.0 million	4.1 million	3.6 million	3.5 million	3.5 million	3.5 million	3.4 million
Proportion	27%	28%	24%	24%	24%	24%	24%

Table 3b: Projected change in absolute low income AHC levels for children over the Parliament (FYE 2025 to FYE 2030, UK)

Absolute AHC, children
Difference between FYE 2025 and FYE 2030 (number)	-600,000
Difference between FYE 2025 and FYE 2030 (percentage point)	-3ppt

Table 4a: Absolute low income BHC projections for children (UK)

	HBAI FYE 2025	Projection FYE 2026	Projection FYE 2027	Projection FYE 2028	Projection FYE 2029	Projection FYE 2030	Projection FYE 2031
Number	3.0 million	3.0 million	2.5 million	2.5 million	2.5 million	2.4 million	2.4 million
Proportion	21%	20%	17%	17%	17%	17%	17%

Table 4b: Projected change in absolute low income BHC levels for children over the Parliament (FYE 2025 to FYE 2030, UK)

Absolute BHC, children
Difference between FYE 2025 and FYE 2030 (number)	-600,000
Difference between FYE 2025 and FYE 2030 (percentage point)	-4ppt

Statement of compliance with the Code of Practice for Statistics

The Code of Practice for Statistics (the Code) is built around 3 main concepts, or pillars: 

• trustworthiness – is about having confidence in the people and organisations that publish statistics 
• quality – is about using data and methods that produce statistics  
• value – is about publishing statistics that support society’s needs 

The following explains how we have applied the pillars of the Code in a proportionate way.

Trustworthiness

The figures were created to understand how low income poverty levels are estimated to change over time. They are being published to give equal access to all those with an interest in them.

Quality

The data that underpins this information is taken from DWP’s Policy Simulation Model which includes caseload forecasts taken from DWP and HM Revenue & Customs (HMRC) data.

This projections analysis uses a model consistent with the policy position immediately after Spring Statement 2026 and OBR’s economic and caseload forecasts in March 2026.

The methodology used to produce these projections has been developed in consultation with an external Expert Advisory Group.

Value

Releasing this information serves the public interest in understanding the projected low income poverty trends over time, particularly now that the HBAI poverty statistics have undergone a methodology improvement which led to changes in the estimate of poverty in the UK.

The figures also help reduce the administrative burden of answering Parliamentary questions, Freedom of Information requests and other forms of ad hoc enquiry and serves the public.

Further information and feedback

Contact DWP Press Office if you have any questions or feedback.

1. A static microsimulation model is a modelling approach which uses micro-level observations, in this case from survey data, to simulate other states of the world including future states. ↩

2. Using HBAI data from the financial years of the Parliaments starting from May 1997, the largest reduction in child poverty over a Parliament was under the 1997 to 2001 Labour Government, which oversaw a reduction of 300,000 children in relative low income after housing costs (between FYE 1998 and FYE 2002). ↩