Data: Pitfalls & Opportunities
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1 BRANDEIS INTERNATIONAL BUSINESS SCHOOL Data: Pitfalls & Opportunities Stephen G Cecchetti
2 Know your data I. Pitfalls II. Opportunities 2
3 Business Cycles: Chart 1 3
4 Business Cycles: Chart
5 Business Cycles: Which predicts better? Recessions since 1950: 10 Number predicted by Series 1: 6 Number predicted by Series 2: 15 5
6 Business Cycles: Which predicts better? Recessions since 1950: 10 Number predicted by Series 1: 6 If series 1 forecasts a downturn, it happens every time. Number predicted by Series 2: 15 If series 2 forecasts a downturn, it happens two out of three times What are these series? 6
7 Business Cycles: Which predicts better? Recessions since 1950: 10 Number predicted by Series 1: 6 Number predicted by Series 2: 15 What are these series? #1 is the number of sunspot #2 is the S&P 500 7
8 Business Cycles: Which predicts better? Recessions since 1950: 10 Number predicted by Series 1: 6 Number predicted by Series 2: 15 What are these series? #1 is the number of sunspot #2 is the S&P 500 8
9 Signal versus Noise: Seasonality How big a change is meaningful? 9
10 Seasonality in GDP 10
11 Seasonality in GDP 11
12 Signal versus Noise: Seasonality How big a change is meaningful? 12
13 Revisions: GDP Growth 1990 Third Quarter 13
14 Revisions: GDP Growth 1990 Third Quarter 14
15 Signal versus Noise: Revisions Revisions: Tax data Benchmark surveys 15
16 Signal versus Noise: Revisions Revisions: Tax data Benchmark surveys Leads people to prefer employment to output because revisions tend to be smaller & faster 16
17 Data construction Do the data mean what you think? Example: Owner-occupied housing Before 1983: House prices mortgage interest rates property taxes insurance maintenance costs 17
18 Interest Rates and Inflation 18
19 Data construction Do the data mean what you think? Example: Owner-occupied housing Before 1983: House prices mortgage interest rates property taxes insurance maintenance costs Implication: Interest rates Inflation! 19
20 Data construction Do the data mean what you think? Example: Owner-occupied housing Before 1983: House prices mortgage interest rates property taxes insurance maintenance costs Implication: Interest rates Inflation! After 1983: rental equivalence 20
21 Interest Rates and Inflation 21
22 Data construction Do the data mean what you think? Example: Owner-occupied housing Before 1983: House prices mortgage interest rates property taxes insurance maintenance costs Implication: Interest rates Inflation! After 1983: rental equivalence 22
23 Pitfalls 1. Beware correlations 2. Seasonality is huge 3. Revisions can be big 4. Know how your data are constructed 23
24 Billion Prices Project 24
25 Billion Prices Project 25
26 Growth of noncash payments 26
27 Value of card payments 27
28 Value of card payments 28
29 Payments and Credit Using technology in low-income countries M-Pesa: Money transfer using mobile phones (Started in Kenya in 2007, 17 mn users by 2012) AADHAAR: Unique biometric ID with bank account (Started in India in 2016, has nearly 1 bn registrants) 29
30 Payments and Credit Using technology in low-income countries M-Pesa: Money transfer using mobile phones (Started in Kenya in 2007, 17 mn users by 2012) AADHAAR: Unique biometric ID with bank account (Started in India in 2016, has nearly 1 bn registrants) Measure non-market activity Create credit records 30
31 Payments and Credit Using technology in low-income countries M-Pesa: Money transfer using mobile phones (Started in Kenya in 2007, 17 mn users by 2012) AADHAAR: Unique biometric ID with bank account (Started in India in 2016, has nearly 1 bn registrants) Measure non-market activity Create credit records 31
32 Prices and Productivity Information Technology 2013 iphone 5s 1975 Cray-1 32
33 Prices and Productivity Information Technology iphone to scale = 1000 times power (just the graphics card!) 100,000 times price = (that s a 35+% annual deflation) 2013 iphone 5s 1975 Cray-1 33
34 Prices and Productivity Information Technology Consumption Less than 1% If IT inflation overstatement 10 p.p. Overall inflation 0.1 pp too high 34
35 Prices and Productivity Information Technology Consumption Less than 1% If IT inflation overstatement 10 p.p. Overall inflation 0.1 pp too high Health Care 35
36 Value of Health Care 36
37 Prices and Productivity Information Technology Consumption Less than 1% If IT inflation overstatement 10 p.p. Overall inflation 0.1 pp too high Health Care 17% of consumption! Quality adjustment is very difficult Digital medical records improve measurement. 37
38 Health Care Price Index 38
39 Prices and Productivity Information Technology Consumption Less than 1% If IT inflation overstatement 10 p.p. Overall inflation 0.1 pp too high Health Care 17% of consumption! Quality adjustment is very difficult Digital medical records improve measurement. Are we overestimating inflation in health care? 39
40 Prices and Productivity Information Technology Investment & productivity 16% is IT hardware & software Investment = 16.1% of GDP If IT inflation overstatement 10 p.p. Investment understated 1.6 pp GDP & productivity understated 0.256pp!! 40
41 Prices and Productivity Prices: IT is less than 1% of consumption Health care is 17% of consumption Quality adjustment is difficult Are we overestimating inflation in health care? Productivity: 16% is IT hardware & software Investment = 16.1% of GDP If IT inflation overstatement 10 p.p. Investment understated 1.6 pp GDP & productivity understated 0.256pp!! 41
42 Prices and Productivity Prices: IT is less than 1% of consumption Health care is 17% of consumption Quality adjustment is difficult Are we overestimating inflation in health care? Productivity: 16% is IT hardware & software Investment = 16.1% of GDP If IT inflation overstatement 10 p.p. Investment understated 1.6 pp GDP & productivity understated 0.256pp!! 42
43 Pitfalls 1. Beware correlations 2. Seasonality is huge 3. Revisions can be big 4. Know how your data are constructed Opportunities 1. Verifying official statistics 2. Improve consumption data 3. Track payments & credit in low-income countries 4. Improve price and productivity measurement 43
44 BRANDEIS INTERNATIONAL BUSINESS SCHOOL Data: Pitfalls & Opportunities Stephen G Cecchetti
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