Unpredictable Earnings: The volatility of pay packets and its impact on living standards.pptx

Most people are used to receiving regular monthly pay cheques, hopefully with the occasional bonus and an annual rise. But while this is often taken for granted, for other workers the size and timing of their pay cheques are far more volatile – with knock on effects on their ability to pay bills, save, plan ahead and smooth their living standards over time. But with Brits notoriously adverse to talking about pay, the scale of earnings volatility across the country is unknown.

How many workers are subject to volatile earnings, and how much does the timing and size vary from pay cheque to pay cheque? Who is most likely to experience earnings volatility, and in which industries is it most prevalent? What are the wider living standards consequences for families? And what can policy makers do to mitigate the impacts of unpredictable earnings?

1. March 25 @resfoundation Unpredictable earnings The volatility of pay packets and its impact on living standards Sarah O’Connor, Columnist and Associate Editor at the Financial Times Sope Otulana, Head of Research at Nest Insight Joanne Cairns, Head of Research and Policy at USDAW Nye Cominetti, Principal Economist at the Resolution Foundation Chair: Ruth Curtice, Chief Executive of the Resolution Foundation

2. 2 Tax data lets us dive deeper into workers’ earnings @resfoundation The dataset: a 1% sample of employees (and all their payslips) in PAYE system, 2014 to 2019 250,000 employees, 27m payslips Link to survey data ( ASHE ) in April for more info. about workers & employers Benefits: big sample; measured at maximum frequency Limitations: no self-employment or other income; no household info.

3. 3 Mean of absolute arc percentage annual change in real weekly earnings among 20-59-year-olds: UK Notes: Latest data points are 2023 (LFS), 2022 (BHPS/UKHLS), 2020 (ASHE/NESPD). Earnings are adjusted for CPI inflation. Arc-percentage change uses average of both periods as denominator – similar to normal percentage change for low values. Source: Analysis of ISER, British Household Panel Survey; ISER, UK Household Longitudinal Study; ONS, Five-Quarter Longitudinal Labour Force Survey; ONS, Annual Survey of Hours and Earnings / New Earnings Survey Panel. @resfoundation Long-term: average volatility at annual frequency flat/falling

4. 4 Mean of absolute arc percentage annual change in real weekly earnings among 20-59-year-olds: UK Drivers of lower ‘labour market’ volatility since 1990s: Lower rates of entry to and exit from work Lower use of temporary contracts Notes: Latest data points are 2023 (LFS), 2022 (BHPS/UKHLS), 2020 (ASHE/NESPD). Earnings are adjusted for CPI inflation. Arc-percentage change uses average of both periods as denominator – similar to normal percentage change for low values. Source: Analysis of ISER, British Household Panel Survey; ISER, UK Household Longitudinal Study; ONS, Five-Quarter Longitudinal Labour Force Survey; ONS, Annual Survey of Hours and Earnings / New Earnings Survey Panel. @resfoundation Long-term: average volatility at annual frequency flat/falling

5. 5 Distribution of the arc percentage change in real monthly earnings compared to the previous month among 20-59-year-olds working in both months: UK, 2014-15 to 2018-19 Notes: Earnings are deflated using CPIH. Results are pooled across all the months in dataset. For this figure, counts are based on arc-percentage change rounded to the nearest percentage point, which means ‘zero change’ in fact relates to arc-percentage changes between -0.5% and 0.5%, and ‘1-10% change’ in fact means changes between 0.5% and 10.5%, and equivalently for the other categories. Arc-percentage change uses average of both periods as denominator – similar to normal percentage change for low values. Source: Analysis of HMRC PAYE dataset. @resfoundation Turning to the payslip data: earnings are same as previous month only in 4-in-10 months Quiz answer: The average absolute change on the previous month is …

6. 6 Stylised examples of earnings to illustrate ‘trajectory’ categories Definitions of trajectory categories: Extremely stable: all months within 5% of annual average Highly stable: all months within 10% of annual average Small blip: 10-25% from annual average Large blip: 25%+ from annual average Erratic: 4+ months where pay 25%+ from average These are artificial series drawn for illustration and do not represent actual data. @resfoundation Looking at separate months misses deep volatility faced by some: multiple large changes within year

7. 7 Proportion of employees aged 20-59 and working all months in the year, by category of within-year earnings trajectory: UK, 2014-15 to 2018-19 Notes: Results are pooled across financial years. Analysis is based on real earnings, deflated using CPIH. Source: Analysis of HMRC PAYE dataset. @resfoundation Looking at separate months misses deep volatility faced by some: multiple large changes within year

8. 8 Proportion of employees aged 20-59 and working all months in the year, by category of within-year earnings trajectory: UK, 2014-15 to 2018-19 Notes: Results are pooled across financial years. ‘Stable’ here includes the ‘Extremely stable’ and ‘highly stable’ categories as defined earlier. ‘Blips’ includes categories involving 1-2 small or large blips. Analysis is based on real earnings, deflated using CPIH. Source: Analysis of HMRC PAYE dataset and HMRC-ASHE PAYE dataset. @resfoundation ‘Erratic’ pay is most common among workers who are young, low-paid, in temporary jobs, working multiple jobs

9. 9 Proportion of employees on a zero-hours contract (2018-2023), and average arc percentage change in real monthly earnings compared to the previous month among 20-59-year-olds (working both months): UK Notes: Industries not labelled are Manufacturing, Real estate, Professional services, Vehicle sales & repair, ICT, and 'Other' services. Size of bubble indicates the total employees in industry. Earnings are deflated using CPIH. Earnings volatility data is pooled across all months in dataset and zero-hours contract data is pooled across all LFS quarters where the variables are available across 2018-2023. Source: Analysis of HMRC-ASHE PAYE dataset; ONS, Labour Force Survey @resfoundation Volatile pay is associated with use of zero-hours contracts

10. 10 Average arc percentage change in real monthly earnings, finance and insurance versus the rest of economy (workers working in both months): UK, 2014-15 to 2018-19 Notes: Earnings are deflated using CPIH. Arc-percentage change uses average of both periods as denominator – similar to normal percentage change for low values. Source: Analysis of HMRC-ASHE PAYE data. @resfoundation Not all volatility is bad: earnings volatility in some sectors driven by big bonuses

11. 11 Average arc percentage change in real monthly earnings, finance and insurance versus the rest of economy (workers working in both months): UK, 2014-15 to 2018-19 Every March, bonuses comprise more than half (55 per cent) of average earnings in Finance & insurance, versus 8 per cent for other workers Notes: Earnings are deflated using CPIH. Arc-percentage change uses average of both periods as denominator – similar to normal percentage change for low values. Source: Analysis of HMRC-ASHE PAYE data. @resfoundation Not all volatility is bad: earnings volatility in some sectors driven by big bonuses

12. 12 What can Government and employers do? @resfoundation Government: ZHC reforms will help workers experiencing high volatility Strengthen statutory sick pay, don’t just extend coverage Universal Credit may amplify volatility, but solutions hard Help low-paid workers build financial buffers Employers: Pay staff at the frequency that most suits them

13. March 25 @resfoundation Unpredictable earnings The volatility of pay packets and its impact on living standards Sarah O’Connor, Columnist and Associate Editor at the Financial Times Sope Otulana, Head of Research at Nest Insight Joanne Cairns, Head of Research and Policy at USDAW Nye Cominetti, Principal Economist at the Resolution Foundation Chair: Ruth Curtice, Chief Executive of the Resolution Foundation