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Measuring Social Transfers in Kind in the UK

This study examines the measurement and impact of social transfers in kind (STIK) in the UK, including education services, healthcare (NHS) services, and public transport subsidies. It explores the impact of STIK on income distribution and poverty estimates, and discusses the challenges and implications of including STIK in income statistics.

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Measuring Social Transfers in Kind in the UK

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  1. Measuring Social Transfers in Kind in the UK Richard Tonkin, James Lewis & Nathan Thomas 2nd - 4th December 2013 Email: richard.tonkin@ons.gsi.gov.uk Twitter: @richt2

  2. Overview • Introduction • Why measure STIK? • UK poverty statistics • Measuring STIK in the UK: • Education services • Healthcare (NHS) services • Public transport subsidies • Impact of STIK on income distribution • Impact of STIK on poverty estimates • Conclusions

  3. Introduction • Canberra Group Handbook (2011): • “Social Transfers in Kind (STIK) are … goods and services provided by government and non-profit institutions that benefit individuals but are provided free or at subsidised prices.” • Accounting for STIK important for cross-country comparisons of income distribution/poverty • Not included in operational definition of disposable income due to practical challenges

  4. Introduction • Analysis of STIK included in “Effects of Taxes & Benefits on Household Income” (ETB) • ETB also includes analysis of indirect taxes (including VAT & fuel/alcohol duties) • In-kind benefits included in ETB: • Education services • Health services (NHS) • Rail/bus subsidies • Housing subsidies • Free school meals

  5. Introduction • ETB based on UK Household Budget Survey • Living Costs and Food Survey (LCF) • Annual survey of 5,000+ households • CAPI interview plus 2-week expenditure diary • Main UK poverty statistics produced from different source • Family Resources Survey (FRS) • Larger sample (20,000 households) • Not possible to estimate STIK or indirect taxes

  6. Measuring education services • ‘Actual consumption’ approach applied • Use admin data on cost per pupil/student for different types of schools, nurseries, universities, etc. • Value attributed to those recorded in LCF as receiving each kind of state education • No benefit allocated for pupils attending private schools/receiving home schooling

  7. Measuring health services (NHS) • ‘Insurance value’ approach applied • Use data on cost of providing various types of healthcare: e.g. hospital inpatient/outpatient; GP consultations; pharmaceutical services, etc. • Each individual allocated value based on data on average use by age/sex • No adjustment made for use of private healthcare • Not feasible from data • Argument that all individuals benefit from existence of public healthcare services

  8. Measuring public transport subsidies • Subsidies allocated to households based on expenditure data from LCF • Rail subsidy calculated separately for those living in London/South East, reflecting higher levels of subsidy for London transport • Allowances made for use of rail travel by business sector and tourists • Bus travel calculate in similar way, but with additional benefit allocated for individuals holding concessionary bus pass

  9. Impact of STIK on income distribution • STIK by equivalised disposable income quintile, 2011/12:

  10. Impact of STIK on income distribution • Original, disposable, adjusted disposable & final income by income quintile, 2011/12: 14 x 3.5 x 5 x

  11. Impact of STIK on poverty statistics • STIK not included in published UK poverty statistics • Standard equivalisation scales designed for cash income • Don’t take into account varying needs for e.g. education/health services • Simplified Needs Adjusted (SNA) scale (Aaberge et al., 2013):

  12. Impact of STIK on poverty statistics • Relative at-risk-of-poverty rates, 2011/12: • Rates based on percentage of population below 60% of median equivalised household income

  13. Impact of STIK on poverty statistics • Relative at-risk-of-poverty rates by age group, 2011/12:

  14. Impact of STIK on poverty statistics • Relative at-risk-of-poverty rates by household type, 2011/12:

  15. Conclusions • Poverty statistics including STIK very beneficial for international comparisons • For within-country comparisons, measurement of STIK important for analysis on income redistribution • Appropriateness for income poverty less clear: Should in-kind income be treated as comparable to cash income? • Selection of appropriate equivalisation scale vital • Outcome of analysis dependent on other choices/ assumptions, including: • Which STIK included in analysis • Actual consumption vs. insurance value vs. flat rate • Allocation of benefit at household vs. individual level

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