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Only a minimum income can ensure support for everyone

The government support schemes designed to help those impacted by the economic shutdown are not available to millions who need them


The government support schemes designed to help those impacted by the economic shutdown are not available to everyone who needs them. Our analysis has found that women, young people, non-white workers, and renters are at particularly high risk of missing out on support.

Millions of workers in sectors shutdown by the government, and millions more in sectors outside of the public sector but not identified as key’ by the government are likely to have been impacted by the economic slowdown. Without government support, these workers’ jobs and livelihoods are at risk. But while many are protected under the government’s job retention and self-employed income protection schemes, some are at high risk of falling through the gaps.

Building on previous NEF work, our analysis looked initially at the 1.6 million people who were at the highest risk of both losing work and missing out on the government’s job retention scheme and self-employed income protection scheme. These are people who work in an industry that has been shutdown by government guidance (such as restaurants and non-food retail) and who also meet one of the following characteristics: fixed-term contracts, zero-hour contracts, those who were already underemployed by their employer and the self-employed (who are not eligible for additional support until June).

Our analysis found that, out of the 1.6 million people at very high risk of both losing work and missing out on the government’s schemes:

  • Young people (16 – 24 years old) were 2.3 times as likely as other age groups (130% higher risk) to be in this highest risk group.
  • Older workers (50 – 69 years old) were 1.6 times as likely as other age groups (60% higher risk).
  • Non-white workers were 1.5 times as likely as white workers (50% higher risk).
  • Women workers were 1.3 times as likely as men (30% higher risk).

Social renters were 1.1 times as likely (10% higher risk) as people living in other housing types. In addition, we also looked at the 4.0 million people who were at next highest risk of losing work and falling through the gaps in the government support schemes, because they were in insecure employment and were outside of occupations considered to be key workers. For this group, the pattern was different depending on whether people were employed or self-employed. For employees, young people (130% higher risk), older workers (50% higher risk), non-white workers (40% higher risk), women (20% higher risk) and private (40%) and social (30%) renters were all at higher risk compared with other population groups in those categories. Meanwhile for the self-employed, men (40% higher risk) and white workers (30%) were among those most likely to be at risk.

Separate income analysis, which was possible for employees but not the self-employed, also showed that those with hourly pay below the real living wage were 1.7times more likely (70% higher risk) to lose work and fall through the gaps in support than those earning above this threshold. Our full results are set out in the table below.

Likelihood of different workers being at risk of losing work and missing out on government protection schemes



Risk ratios — very high risk group (total 1.6 million workers)

Risk ratios — high risk group (total 4.0 million workers)

Group

Subgroup

Employees

Self-employed

Combined (employees and self-employed)

Employees

Self-employed

Sex

Male

0.7

0.5

0.8

0.9

1.4


Female

1.4

2.0

1.3

1.2

0.7

Age

16 — 24

8.3

1.6

2.3

2.3

0.8


25 — 34

0.6

1.0

0.7

1.2

1.0


35 — 49

0.2

1.0

0.5

0.4

1.0


50 — 69

0.7

0.9

1.6

1.5

1.0

Tenure

Homeowner

0.8

0.9

0.9

0.7

1.0


Private renter

1.2

1.0

1.0

1.4

1.0


Social renter

1.3

1.5

1.1

1.3

0.9

Ethnicity

White

0.9

0.6

0.7

0.7

1.3


Non-white

1.2

1.6

1.5

1.4

0.8

Income

Above living wage

0.1

-

-

0.6

-


Below living wage

8.3

-

-

1.7

-

Source: NEF analysis of the Q4 2019 Labour Force Survey.
NB: The comparator group for each respective risk ratio is everyone outside of a given subgroup. Ratios are estimated independently from one another and do not control for other demographic characteristics outside of a given sub-group. We do not present combined findings for employees and self-employed within the larger at risk’ group (4.0 million) because of divergent patterns of risk across different worker characteristics. Risk ratios based on incomes are also not reported for the self-employed due to a lack of reliable data.

Those who lose work and cannot rely on government support schemes will have to fall back onto universal credit (UC). But support from universal credit is also weaker for many of these disadvantaged groups. The main payment for under-25s is set at just under £79/​week, 16% lower than the equivalent payment for older people. And many migrants will have more limited eligibility to claim unemployment support.

Our research also showed that many of these same groups are also more likely to be on lower than average earnings, and are therefore less likely to have accumulated meaningful savings. Over 700,000 employees who were in one of the two risk groups earned less than the living wage, and within this number, more than 430,000 of them were working in sectors that have been directly shut down.

NEF’s recent work set out a proposal for a non-conditional and non-means tested (at the point of access) Minimum Income Guarantee (MIG) of £221 per week per working-age adult, including young people. This would offer support to a wider group of people and mean faster payment (via the UC advanced payment system) than the current government support schemes do.

The MIG scheme would provide support for those who do not qualify for the two government support schemes as well as those that will have to wait for support, such as the self-employed. As with the government’s existing two schemes, the MIG scheme would be capped at £2,500 a month. This means that, while anyone can apply, if a claimant’s monthly income rises above the £2,500 threshold, they would have to pay back the difference in the 2020/​21 tax year. Based on NEF modelling using data from the Department for Work and Pensions and Office for National Statistics, the MIG is estimated to cost between £13 and £20 billion, less than half the estimated costs of the government’s current protection schemes.

Notes

  1. Based on analysis of the Labour Force Survey, NEF economists identified all workers that fitted the characteristics for insecure work prior to the economy shutdown, which we defined as those on zero-hours contracts, those who were already underemployed by their employer (which were defined as those working less hours than they wanted or working variable hours), and those on fixed-term contracts. See https://neweconomics.org/2020/04/up-to‑5 – 6‑million-people-are-at-high-risk-of-losing-work-and-falling-through-the-cracks-in-new-government-support-systems for further details.
  2. The estimate for workers most at risk is based on a new analysis of the ONS Labour Force Survey, ONS. (2020). Labour Force Survey (Q4 2019) (the latest available). When defining​‘key workers’ in the dataset, we follow the approach of Farquharson, Ch., Rasul, I. and Sibieta, L. (2020). Key workers: key facts and questions. IFS, and use the same occupational (SOC-based) definitions of key workers’. But to conduct the analysis of job insecurity among those outside the key’ occupations — the non-key’ workers — we also exclude all public sector workers, given that they are much more likely to be reallocated rather than lose employment.
  3. In defining shutdown industries’ we follow the industry (SIC) based methodology of Joyce, R. and Xu, X. (2020). Sector shutdowns during the coronavirus crisis: which workers are most exposed? IFS. Both key’ and shutdown industry’ classification types are inherently difficult to define precisely therefore should not be seen as definitive. Please see the cited briefings for more details about the approaches and their caveats.
  4. Demographic characteristics are based on self-reported data in the Labour Force Survey. Note that we are unable to further disaggregate ethnicity, or report on those at very high risk due to very small sample sizes in the underlying data. Sex is taken from a question in the Labour Force Survey to which respondent can choose male or female. It is not clear whether this can differ from sex as identified on someone’s birth certificate. The only available responses are female or male. There are no questions on gender or gender identity.

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