Analysing the economy using electronic fund transfers

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Analysing the economy using electronic fund
transfers

• Brian PinkGovernment StatisticianStatistics New
  Zealandbrian_pink_at_stats.govt.nz

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(No Transcript)
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Retail Sales Indicators
(bill)
(bill)
4.5
4.5
4.0
4.0
SNZ Retail Survey
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
ETSL EFTPOS transactions
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
1995
1996
1997
1998
1999
2000
2001
2002
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EFTPOS in New Zealand

• ETSL set up by 5 of the major banks (manage over
  80 of all EFTPOS transactions)
• Over 65 of all retail transactions are done by
  EFTPOS
• High coverage of footwear (82), hardware (81),
  clothing (75) and food (66)
• Low coverage of appliances sales (30) and motor
  vehicle sales (1)
• Growth in numbers of merchants with terminals has
  tailed off in last 2 years

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The ETSL database

• Merchant database
• merchant ID,
• geographic location
• industry

• Transaction database
• merchant ID,
• card number,
• value of transaction,
• date,
• time,
• transaction type,
• value of cash out

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Estimates of monthly changein retail sales
Percent
40
30
EFTPOS
20
SNZ
10
0
-10
-20
-30
1999
2001
2002
2000
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Difference between EFTPOS andSNZ monthly change
estimates
Percent
20
15
10
5
0
-5
-15
-20
1995
1996
1997
1998
1999
2000
2001
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Using the ETSL data for official measures

• Very timely
• Scope for regional and industry analysis
• No extra load imposed on the merchants
• Coverage is patchy .... although actual bias
  seems small
• Vulnerable to variations in ETSLs market share,
• and quality of their industry coding

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SNZ plans

• To investigate the development of
• A timely (monthly) indicator series based on
  EFTPOS data
• Which is combined with a quarterly survey
  (derived from GST data and some direct surveying)
• reduce compliance load
• improve timeliness
• quality of quarterly GDP protected by
  comprehensive coverage of the quarterly

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Other potential information products

• Retail Market Information and Modelling
• Consumer Spend Profiling
• Marketing Spend Effectiveness
• Store Information
• Predictive Modelling
• Mail Order and Telesales Market Monitoring

• Tourism Market Information
• Card Issuing and Acquiring Market measurement
• EFTPOS TerminalMarket Information
• Loyalty Programs
• Internet Growth

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ExampleMeasuring the impact of special events
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Issues for ETSL in managing the information

• Privacy Legislation
• Commercial Sensitivity
• Security
• Data Ownership

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Trading day effects in monthly data

• Number of trading days per month can influence
  the behaviour of retail trade data
• It is a predictable factor and removing its
  effect can shed light on underlying trends and
  other characteristics of the time series
• It is difficult to get a good handle on what the
  trading factors should be from the aggregated
  monthly series
• However, EFTPOS data lets us see them . . .
  directly

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Total Daily EFTPOS By Month
80,000,000
70,000,000
60,000,000
50,000,000
Total EFTPOS ()
40,000,000
30,000,000
20,000,000
10,000,000
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
Day of the month
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Total Daily EFTPOS By Week
80,000,000
70,000,000
60,000,000
50,000,000
Total EFTPOS ()
40,000,000
30,000,000
20,000,000
10,000,000
0
M
T
W
T
F
S
S
Day
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Concluding comments

• Electronic transaction databases have the
  potential to
• help us unravel trading day effects on
  monthlysurvey data
• radically altering how we measure the retail
  sector
• Frequency and timeliness of reporting
• Compliance costs
• Processing Costs