recent improvements and current challenges in the bls consumer price index (cpi) program
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Recent improvements and current challenges in the BLS Consumer Price Index (CPI) Program. Presentation to the Council of Professional Associations on Federal Statistics (COPAFS) December 3, 2010 Mike Horrigan Associate Commissioner Office of Prices and Living Conditions. Outline. - PowerPoint PPT PresentationTRANSCRIPT
Recent improvements and current challenges in the BLS Consumer Price
Index (CPI) Program
Presentation to theCouncil of Professional Associations
on Federal Statistics (COPAFS)
December 3, 2010
Mike HorriganAssociate Commissioner
Office of Prices and Living Conditions1
2
Outline CPI Modernization Effort
Housing Geography
Redesign of the CPI estimation system Redesign of the Consumer Expenditure
(CE) Survey The Telephone Point of Purchase Survey
(TPOPS)
3
Outline CPI-W and Social Security CPIs for demographic groups (elderly) Measuring cost of living differentials by
geography (place-to-place comparisons) Research on corporate and scanner data Improving preliminary estimates of the
Chained CPI-U
CPI MODERNIZATION EFFORT
CPI Modernization Goal is to move the remaining
components of periodic CPI Revision (Housing and Geographic Sampling) to a continuous process
Housing (Rent Sample) will be converted first – beginning in 2010
Geographic sampling will follow after incorporating 2010 Decennial results
5
Housing Three Phases
Augmentation (2010 – 2011)– Increase sample of renters from 35,000 to 50,000
Replacement (2012 – 2015)– Replace original 35,000 unit sample
Continuous Updating (2016 -- )– Annually replace 1/6th of the full rent sample
6
Geographic Two phases
2010 Decennial sample– Use traditional approach where a new sample is
drawn based on the decennial Census– Expectation that 67% are overlap with existing
design– Remaining 33% replace existing areas that were
not reselected – New areas are initiated into CPI over 4-6 years• TPOPS; CE; new Rent samples needed for new
areas
7
Geographic
Post 2010 Decennial– Select new geographic areas from American
Community Survey and Decennial Census– Rotate new areas into CPI design on an ongoing
basis• TPOPS; CE and Rent sample required for each
new area
8
REDESIGN OF THE CPI ESTIMATION SYSTEM
New Estimation System Oldest existing CPI production system
Outdated computer environment Rigid structures and fixed data tables limit
alternative item structures and formula New system to take advantage of new
computing environment Flexibility with respect to Item structures and
index estimation formula Support researchers and index experimentation
10
REDESIGN OF THE CONSUMER EXPENDITURE
(CE) SURVEY
Why redesign the CE? Original design of the surveys - 1980s Improvements in data collection such as
CAPI, improved diary forms Response rates a concern Surveys are burdensome Nature of how members of consuming
units spend money has changed Concerns over underreporting of
expenditures12
The CE redesign process Survey Redesign Panel
Data capture forum
AAPOR panel on record use
Data users’ forum
Household survey producers workshop13
The CE redesign process Methodology workshop
Two independent contracts to outside survey houses to provide redesign options
CNSTAT consensus panel
14
The CE redesign philosophy FY 2011 CE budget initiative
Marginal changes and thinking outside the box
Needs of the CPI Cost weights Item selection TPOPS
15
The CE redesign philosophy Interview of the future
Role of technology in data collection
Role of administrative data
16
The CE redesign philosophy Cognitive methods to reduce burden
and improve the quality of the estimates Role of incentives Proxy reporting Use of global questions and matrix methods Questionnaire order effects Structured vs conversational interviewing Recall period
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TELEPHONE POINT-OF-PURCHASE SURVEY
(TPOPS)
TPOPS Outlet Sample drawn from Telephone
Point of Purchase Survey Response rate is declining as households are
better at screening unwanted calls Potential biased results because “Cell Phone
only” households are excluded– Latest estimates are 17-25% of all households– Younger households are even more likely to be
cell phone only
19
TPOPS Outlet sample frame replacement
focuses on 3 approaches
Short run– Add Cell phone households to TPOPS– Planned start summer 2011
20
TPOPS Long run
– Examine use of administrative and corporate data as an alternative, examples:• Census of Retail Trade• Medical Expenditure Panel Survey
– Examine alternative survey approaches to replace TPOPS:• Incorporate into Consumer Expenditure
Survey• Mixed mode surveys for targeted
demographics using mail, personal visit, and internet collection
21
CPI-W, CPI-E, AND SOCIAL SECURITY
Social Security COLAs based on the CPI-W
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Year Jul Aug Sep Average
Percent change from
previous peak
2007 203.700 203.199 203.889 203.5962008 216.304 215.247 214.935 215.495 5.8%2009 210.526 211.156 211.322 211.001 -2.1%2010 213.898 214.205 214.306 214.136 -0.6%
CPI relative importances by population, for selected expenditure groups, December 2009 (based on 2007-2008
Consumer Expenditure Survey weights).
Expenditure group CPI-U CPI-W CPI-EAll items 100.00 100.00 100.00 Food and beverages 14.80 16.43 12.35 Food at home 7.80 8.90 7.16 Food away from home 5.94 6.43 4.37 Alcoholic beverages 1.06 1.09 0.82 Housing 41.96 39.75 47.08 Shelter 33.29 30.17 36.55 Rent of primary residence 5.97 8.48 3.77 Owners’ equivalent rent 25.21 20.96 31.52 Fuel oil 0.19 0.16 0.28
CPI relative importances by population, for selected expenditure groups, December 2009 (based on 2007-2008
Consumer Expenditure Survey weights).
Apparel 3.70 3.79 2.65 Transportation 16.69 18.65 14.22 Motor fuel 4.53 5.78 3.36 Medical care 6.51 5.26 11.07 Medical care commodities 1.61 1.30 2.95 Medical care services 4.90 3.96 8.12 Recreation 6.44 6.03 5.53 Education and communication
6.43 6.18 3.91
College tuition and fees 1.49 0.96 0.55 Other goods and services 3.48 3.92 3.19 Tobacco and smoking products
0.87 1.40 0.59
Annualized CPI increases using third quarter averages*
CPI population
1982-1993
1993-2007
2007-2008
2008-2009
2009-2010
1982-2010
CPI-E 4.0 2.8 5.1 -1.4 1.0 3.1
CPI-U 3.6 2.6 5.3 -1.6 1.2 2.9
CPI-W 3.4 2.6 5.8 -2.1 1.5 2.8
*For 1982 CPI-E, December figure is used rather than the third quarter average
Building Block
CPI-U CPI-W CPI-E
A. Scope Urban Consumer Urban Wage Earner and Clerical Worker
Reference person or spouse age 62 plus
Includes Older Americans / retirees / social security recipients
Does not include retirees - Includes non-social security recipients
- Does not include surviving spouses less than 62 or their minor children
- Does not include elderly living with families (children) where the head and spouse are less than 62
B. Geography Designed for the urban consumer
Uses geographic sample for the CPI-U
Uses geographic sample for the CPI-U
Not designed specifically for the urban wage earner and clerical worker
Not designed specifically to reflect where Older Americans live
Building Block
CPI-U CPI-W CPI-E
C. Weighting Based on 76,000 interviews
Based on 22,500 interviews - results to higher variances
Based on 19,400 interviews - results in higher variances
Weights on medical and shelter significantly higher; education and food and beverages lower than CPI-U population
D. Retail Outlets Designed for the urban consumer
Uses the retail outlet frame for the CPI-U; The sample is not designed to reflect where the urban wage earner and clerical worker shops
Uses the retail outlet frame for the CPI-U; The sample is not designed to reflect where Older Americans shop
Building Block
CPI-U CPI-W CPI-E
E. Market Basket
Designed to represent the market basket of purchases of the urban consumers
Designed to represent the market basket of purchases of the urban wage and clerical consumers
Designed to represent the market basket of purchases of the urban older consumers
F. Collecting the right price
Designed to collect the out of pocket expenses paid by the urban consumer, including taxes and inclusive of discounts.
Uses the prices collected for items selected to represent the purchasing patterns of the CPI-U. The items and prices may not be representative of the purchasing patterns of the urban wage and clerical worker.
For any item category (apples), the retail stores cannot report reliably the revenue they get for different types of apples they sell to urban wage earners and clerical workers.
Uses the prices collected for items selected to represent the purchasing patterns of the CPI-U. The items and prices may not be representative of the purchasing patterns of the urban older consumers
For any item category (apples), the retail stores cannot report reliably the revenue they get for different types of apples they sell to Older Americans.
Senior citizen discount rates may be far more prevalent for the Older American population than the urban population as a whole.
G. Repricing Item substitution, quality change and monthly production requirements are of highest quality
Same comments applies Same comment applies.
ALTERNATIVE APPROACHES TO
ESTIMATING CPI’S FOR DEMOGRAPHIC GROUPS – EXAMPLE: THE ELDERLY
Estimating Price Indexes for demographic groups – the case of the
elderly
Price indexes for a demographic group, for example, the elderly, require information on: Where they make consumer purchases -
TPOPS What they purchase – CE and TPOPS The ability to select items for pricing at each
outlet – disaggregation
The disaggregation process is a key step Selecting apples for pricing requires getting
information on relative sales revenue by type of apple – from the store manager
31
Estimating Price Indexes for demographic groups – the case of the
elderly
Asking a store manager to estimate revenue by type of product across all customers is difficult enough; Asking them to estimate revenue by type of
product for sales to a particular demographic group (say the elderly) is far more problematic.
Need to know which items you are selecting in advance of walking into the store (based on probability principles) with a minimum of further dissaggregation need in the outlet. 32
Estimating Price Indexes for demographic groups – the case of the
elderly
Possible role of the redesigned CE survey CE has demographics Ask for outlet information Ask for item purchase information
33
RESEARCH ON CORPORATE AND SCANNER DATA
Corporate Data
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Product (Vendor)Collection Disagg Weight
BenchmarkChecklist Evaluation
CPI Sample Benchmark
Biases New Indexes
Homescan (Nielsen)
x x x xPackaged Goods -Food & Sundries (Nielsen)
x x x x x
Academic database (IRI Symphony)
x x x x x
Random Weight – Bakery, produce, etc. (Perishables Group)
x x x x x
Corporate Data
36
Product (Vendor)Collection Disagg Weight
BenchmarkChecklist Evaluation
CPI Sample Benchmark
Biases New Indexes
Electronics & Appliance data (NPD & screen scraping)
x x x
New & Used Vehicles (JD Powers)
x x
Women’s apparel(Retail chain)
x xMEPS (AHRQ) x
Data Collection Vendors may be a source of alternative
data, eliminating the burden on respondents and freeing economic assistants to focus on collecting hard-to-gather prices.
37
Checklist Evaluation Disaggregation uses a checklist
designed to identify the price determining characteristics of each item. Compare the characteristics on the checklists
with the characteristics found in other data sources.
38
Disaggregation Research Disaggregation is the process of
randomly selecting the specific item whose price will be collected over time. Test the efficacy of disaggregation by examining
the distribution of items in the CPI against other the distributions found in other data sources
Test the effect of “volume seller” selection as an alternative to disaggregation
39
Cost Weight Benchmark The cost weights used in the CPI are
derived from the interview and diary sections of the Consumer Expenditure Survey. Compare the distribution of expenditures in the
CE against the distribution found in other data sources
40
CPI Sample Benchmark The CPI sample is the result of
disaggregation in the outlets using checklists designed by the Commodity Analysts. Compare the distribution of items selected for
pricing using the CPI methodology to the distribution of items reported in the other data sources.
41
Biases Various authors have identified potential
biases in the CPI Small sample bias New and disappearing goods bias Substitution bias Weekday/weekend bias
42
New Indexes New data offers the potential to explore
new formulas or create new indexes: Demographic Indexes (for food, at least) New quality adjustment methods Experimental disease based indexes
43
MEASURING COST-OF-LIVING DIFFERENTIALS
(PLACE-TO-PLACE COMPARISONS)
IMPROVING PRELIMINARY ESTIMATES OF THE
CHAINED CPI-U
46
The CPI and Substitution Bias
The CPI-U (Consumer Price Index for All Urban Consumers) is the “headline” CPI
It is a Lowe or “Modified Laspeyres” index, and does not reflect consumer substitution across item categories
BLS considers the C-CPI-U (Chained CPI-U) to be a closer approximation to a cost-of-living index
47
The Chained CPI-U Uses a superlative Törnqvist formula
requiring current spending as well as current price data
Preliminary C-CPI-U values are published monthly in “real time,” but only become final with a 1-2 year lag
In most years the preliminary indexes have underestimated the final C-CPI-U Final indexes have been closer to the
headline CPI-U than the preliminary indexes were
48
BLS Preliminary C-CPI-Us Based on a geometric mean formula BLS is evaluating three different
methods for potential use in generating the preliminary indexes
Objective is to enhance the usefulness of the C-CPI-U by reducing revisions
49
Method 1. CES Model Changes the formula used in the
preliminary indexes Headline CPI-U implicitly allows for no
substitution; elasticity =0 Overstates final C-CPI-U
Preliminary C-CPI-U indexes assume =1 Understate final C-CPI-U
Constant-elasticity-of-substitution (CES) model can be used to estimate a substitution parameter 0 < < 1
50
Method 2. Predicted Expenditures
Changes the expenditure weights used in the preliminary indexes
Headline CPI-U implicitly assumes quantities purchased in a given item category are constant over time
Preliminary C-CPI-U indexes assume that spending shares remain constant
Seasonal adjustment (X-12 ARIMA) models can be used to predict the expenditure weights
51
Method 3. Time Series Models
Uses historical data on final and first preliminary estimates
Regression and Vector Autoregressive Moving Average (VARMA) models can be used to estimate the relationships
Final index estimates can be projected given the initial preliminary starting points, using Kalman Filter methods
52
Upcoming C-CPI-U Revisions
In February 2011 BLS will release final Chained CPI indexes for 2009
The first revision of the preliminary indexes for 2010 will also be released
BLS plans to use the results of the three methods under evaluation in the determination of the preliminary indexes for 2010 and 2011