problems in measuring uk inflation

PROBLEMS IN MEASURING UK INFLATIONPresentation to Occupational Pensioners’

Alliance3rd November 2011

Jill LeylandVice President, Royal Statistical Society

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

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

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”

RSS Getstats campaign• 1 0 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

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

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

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

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

15 years of the two price indices

90

100

110

120

130

140

150

160

Apr-96 Apr-98 Apr-00 Apr-02 Apr-04 Apr-06 Apr-08 Apr-10

Apr 1996 = 100

Consumer price index (CPI)

Retail price index (RPI)

Source: Office for National Statistics (ONS)

Impact on pensionsTwo 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 £5,672B is 6% better off• In 2006: A gets £5,778; B £6,438B is 11% better off• In 2011: A gets £6,778; B £7,680B is 13% better off

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

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.

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

Calculation differences; when constructing a price index1) 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...

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

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

The impact of the formula effect

NB: rough and ready adjustment

95

100

105

110

115

120

125

Dec-04 Sep-05 Jun-06 Mar-07 Dec-07 Sep-08 Jun-09 Mar-10 Dec-10 Sep-11

CPI, RPI and RPI adjusted, (Dec 2004 = 100)

CPIRPIRPI adj

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

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

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.

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

Regression enables a “best fit” to be calculated

• Does not have to be linear, can be other mathematical functions

• Often multi-dimensional y = coo + c1x1 + c2x2 + c3x3 + ...etc

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

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

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)