PROBLEMS IN MEASURING UK INFLATION

1. PROBLEMS IN MEASURING UK INFLATION Presentation to Occupational Pensioners’ Alliance 3rd November 2011 Jill Leyland Vice President, Royal Statistical Society

2. Outline of presentation • About the RSS • The issues with the Consumer and Retail Price indices • What needs to be done • Hedonic regressions in price indices

3. About the RSS • Only learned society in UK representing statistics and statisticians • Founded 1834 • 6,000+ members, around one quarter overseas • Anyone with an interest in statistics can join – you do not have to be a professional statistician

4. The Statistics User Forum • Organisation for statistics users • Auspices of RSS, funds from UK Statistics Authority and ESRC • Currently for professional statistics users but intention to develop into forum for all including “citizen users”

5. RSS Getstats campaign • 10 year statistical literacy campaign • Numbers are everywhere. Getstats aims to give everyone the skills and confidence to use numbers well • Launched on 20th October 2010 • www.getstats.org

6. Measuring inflation often controversial – in any country • People have different spending patterns • so different inflation experiences • Notice price rises more than price falls • Conceptual problems • Calculation issues • no single “right” way of compiling index numbers and • different approaches give different results

7. In the UK • Two different “consumer” price indices, each with some variants: • Consumer price index (CPI)– originally called the Harmonised Index of Consumer Prices (HICP). First published 15 years ago in 1996 • Retail price index (RPI)– long standing and familiar. First published in 1950s; also earlier versions

8. Why two? • Different EU countries calculated price indices in different ways • Need for “harmonised” indices for EU and eurozone purposes • CPI therefore developed on harmonised definitions • UK obliged by EU law to calculate and publish CPI • Not obliged to use it for any particular purpose

9. UK government decided • In 2003 to use Harmonised Index of Consumer Prices as Bank of England’s target • Renamed it the CPI (unusual step) • 2010 June budget decided to uprate benefits, tax credits, public sector pensions by CPI rather than RPI from April 2011 • From 2012 tax bands to be linked to CPI • Private sector pensions depends on scheme rules but some influenced by govt changes

10. 15 years of the two price indices Source: Office for National Statistics (ONS)

11. Impact on pensions Two people, A and B , retired 15 years ago in 1996 on pension of £5,000. A’s pension is uprated annually by CPI, B by RPI • In 2001: A gets £5,341; B £5,672 B is 6% better off • In 2006: A gets £5,778; B £6,438 B is 11% better off • In 2011: A gets £6,778; B £7,680 B is 13% better off

12. Why the difference? • The items included in the index • How they are calculated – “formula effect” • Difference in items included normally makes RPI grow faster than CPI but not always • But formula effect always makes RPI grow faster

13. Differences in items included • CPI excludes some owner occupier housing costs including mortgage interest • Also council tax, Vehicle Excise Duty, TV licenses, Trade Union dues • Includes UK spending by overseas residents • Insurance premiums lower weight in CPI • Plans to include some measure of house ownership but designed for macroeconomic purposes. Not entirely suitable for “cost of living” measurement.

14. Which means... • CPI coverage in RSS view therefore not appropriate for wage negotiations, pensions and benefits uplift etc • But CPI covers all population; RPI excludes the richest, some low-income pensioner, and those living in institutions plus some items relevant to those people • Neither ideal for pensions uplift, wage negotiations etc

15. Calculation differences; when constructing a price index 1) Decide: • what items to include and their weight in the overall basket • base period (set as 100) 2) For each item: • collect prices from different shops etc every month • calculate “average” % increase (or decrease) between base period and current month (calculation step 1) 3) Calculate weighted “average” percentage increase (decrease) for ALL items – (calculation step 2) But...

16. How to calculate the “average”? Many methods – choice a matter of judgement. Most common for indices: • Arithmetic mean (two variations): add up the n items and divide by n • Geometric mean: Multiply the n items together and take nth root of the result Key point: assumption about degree to which people switch to lower priced items

17. Formula effect occurs in stage 1 • CPI : geometric mean – assumes some switching to lower priced items • RPI uses arithmetic mean – assumes no switching to lower price items, probably overstates inflation • Formula effect made average difference of 0.5 to 0.6 percentage points on annual inflation rate (much higher than in other countries) • Now formula effect around one percentage point

18. The impact of the formula effect NB: rough and ready adjustment

19. Are they fit for purpose? • CPI fine for macroeconomic purposes (eg inflation targeting, international comparisons) • But coverage not suitable for other uses including wage negotiations, pensions/benefits uplift etc • But RPI not perfect. Better coverage but excludes part of population; could well overstate inflation

20. How did we get here? • Fundamental problem: overly dominant influence of central government on official statistics generally • This is changing but slowly • For RPI and CPI meant macroeconomic needs dominant • Initial “user group” meeting on November 18 – could be breakthrough

21. What needs to be done • An index reflecting typical household budget – probably a family of indices for different household groups (eg pensions uprating, wage negotiations etc). Based on expressed needs of users. • To eliminate the formula effect: proper assessment of extent to which consumers switch to lower priced items; appropriate index treatment for different items selected. Some work now underway.

22. Hedonic regression • If new product comes onto market at higher price but with improved quality, how much of the rise is due to improved quality and how much a “true” rise? • Generally dealt with by looking at similar products with unchanged specification • But this does not work well when rapid and frequent quality changes eg computers

23. Regression enables a “best fit” to be calculated • Does not have to be linear, can be other mathematical functions • Often multi-dimensional y = co+ c1x1 + c2x2 + c3x3 + ...etc

24. For hedonic methods • “Hedonic” as based on utility, value or pleasure items gives customer • Approach looks at characteristics - eg for computers: memory, processing speed, monitor size etc • Using regression, develops a “predicted price” in any period as function of these characteristics

25. In Hedonic methods (2) • When quality change occurs, take ratio of predicted price for new item to predicted price for old item and apply this to actual price in previous period • Used in UK for computers, digital cameras, pre-paid mobile phones • Other countries vary: can be used for clothing (to deal with fashion changes), or household rents

26. Advantages and disadvantages • Can provide solution when traditional methods fail • Requires a lot of information • More opaque as a method, raises suspicion in some minds • Conclusion – useful when used sparingly (case in UK)