Box sets » Welfare trends report

Welfare trends report - January 2019

Measures of reported disability prevalence are often subject to great variability and this can make drawing precise conclusions from them difficult. In this box, we considered two examples where measures varied. The first related to differences between Labour Force Survey and General Health Survey measures of working-age disability prevalence and the second to large changes in the former. We discussed differences in methodology and changes in attitudes as possible drivers.
Public financial support for disabled people extends beyond the extra-costs disability benefits and includes several other welfare payments. This box gave an overview of some of the most important interactions between disability and other benefits that provide support for disabled people.
Our ability to forecast accurately is heavily dependent on the quality of the data we can use. In this box we explained how statistical and expenditure data could be distorted by problems in delivery of the benefit, and the difficulties this creates in identifying emerging trends in the data.
There are several possible approaches to forecasting benefit spending. In this box we outlined the key issues the modelling of disability benefits needed to address, the three approaches we used to forecast spending, and the strengths and limitations of each, concluding that a combination of approaches was better than reliance on any single one.

Welfare trends report - January 2018

Work allowances and UC forecast revisions
In our 2018 Welfare trends report, Chapter 3 looked at the design of universal credit, including how the 'work allowances' within it have been changed since the policy was first factored into our forecasts. This box looked at how our estimates of the net cost or saving from UC relative to the legacy benefits have evolved over time, and the important part played by Government decisions to reduce the UC work allowances.
In our 2018 Welfare trends report, Chapter 3 looked at the design of universal credit, including the role to be played by UC work coaches setting conditions and applying sanctions to encourage claimants to seek and progress in work. This box outlined the role, responsibilities and renumeration of work coaches, as set out in DWP's candidate information pack for applicants for the role.
In our 2018 Welfare trends report, Chapter 4 described how we model the effects of universal credit on spending. This draws heavily on two models owned and operated by DWP - the Policy simulation model (PSM) and the Integrated forecasting model (INFORM). PSM is a static micro-simulation model that uses Family Resources Survey (FRS) data to analyse policy changes. This box detailed how the FRS is used in PSM and some of the issues that raises for our UC forecast.
In our 2018 Welfare trends report, Chapter 4 described how we model the effects of universal credit on spending. This draws heavily on two models owned and operated by DWP - the Policy simulation model (PSM) and the Integrated forecasting model (INFORM). INFORM is a dynamic micro-simulation model that uses DWP administrative data to forecast the caseloads for different benefits. This box described how INFORM is used in our UC forecast and some of its limitations.

Welfare trends report - October 2014

Cohort effects in the age structure of the population
In our 2014 Welfare trends report, Chapter 3 reviewed the drivers of welfare spending. In this box we showed how the age structure of the population in England and Wales had evolved between the census years of 1951, 1981 and 2011. The post-war and 1960s baby-boom generations affect the dependency ratio in different ways over time, as these larger cohorts move from childhood to working adulthood and into retirement.

Economy categories: Labour market, Population and migration

Cross-cutting categories: Demographics

The cyclicality of spending on benefits and tax credits
In our 2014 Welfare trends report, Chapter 4 reviewed the overall trends in welfare spending. In this box, we considered how responsive welfare spending is to the economic cycle by estimating the elasticity of benefits and tax credits spending as a share of GDP with respect to changes in the output gap (the difference between actual GDP and an estimate of its potential or underlying level). We found that the most counter-cyclical benefits have caseloads closely associated with the economic cycle whereas mildly counter-cyclical benefits are likely to only exhibit cyclicality due to spending varying less than GDP, thereby producing a denominator effect.
In our 2014 Welfare trends report, Chapter 4 reviewed the overall trends in welfare spending. In this box we discussed the latest Universal Credit (UC) forecast at the time of writing. This forecast assumed UC would roll-out slowly during 2014-15 and 2015-16 before accelerating in 2016-17 and 2017-18, by which time 5.8 million people were expected to be receiving the benefit. We highlighted that the roll out of UC had already been subject to previous delays and that the decision to produce a top-down UC forecast helped mitigate the uncertainties caused by delivery challenges. We then decomposed the marginal UC forecast into its constituent gross savings and gross costs forecasts.
The ratio of JSA claimants to LFS unemployed
In our 2014 Welfare trends report, Chapter 8 considered spending on unemployed people. This box compared outturn data on unemployment and claimants of unemployment benefits to the levels implied by our March 2014 forecast. As the economy performed better than anticipated in our March 2014 forecast, the ratio of claimants of unemployed benefits to the Labour Force Survey (LFS) measure of unemployment deviated from our projections. This was largely due to a drop in the rate of inflows into unemployment benefits and a rise in the rate of outflows from unemployment benefits, though an increase in the number of people looking for jobs but not claiming unemployment benefits may have increased LFS unemployment and so been a contributing factor.