decoding the low inflation conundrum with online and ... · decoding the low inflation conundrum...
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Pym Manopimoke Vorada Limjaroenrat
Akarapat Charoenpanich Chonnakan Rittinon
Decoding the Low Inflation Conundrum with Online and Offline Price Data
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-3
0
3
6
9
12
15
1987 1992 1997 2002 2007 2012 2017
Perc
ent
Ch
ange
fro
m a
Yea
r A
go (
Perc
ent)
Recent Evolution of Worldwide Inflation Rates
Thailand
Low for Long
-3
0
3
6
9
12
15
1987 1992 1997 2002 2007 2012 2017
Perc
ent
Ch
ange
fro
m a
Yea
r A
go (
Perc
ent)
Recent Evolution of Worldwide Inflation Rates
United States Asia 6 Euro 3
Average Thai Inflation
1990-2000 4.97
2000-2010 2.46
2010-2018 1.73
2015-2018 0.03
Note: Inflation is year-on-year changes in the headline consumer price index. Asia 6 is the average of inflation in China, Indonesia, Korea, Malaysia, the Philippines and Singapore. Euro 3 is the average of inflation in France, Germany and the United Kingdom.
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Weak Relationship with the Real Economy
0.1
0.2
0.3
0.4
0.5
1999 2001 2003 2005 2007 2009 2011 2013 2015 2017
Re
gre
ssio
n C
oe
ffic
ien
t
Relationship between Inflation and Output Gap
Note: Plotted is the time-varying slope of an estimated Phillips curve with time-varying volatility. We use quarter-on-quarter headline CPI inflation and Bank of Thailand output gap estimates, calculated as the difference between actual and potential output.
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“Too low inflation is puzzling and worrisome”- Charles Evans, President of the Federal Reserve Bank of Chicago, 2014.
“I have a question mark, and it leads me to recommend vigilance with regard to inflation”- Robert Parry, President of the Federal Reserve Bank of San Francisco, 1997.
“We don’t know what’s going on with inflation”- Stanley Fischer, Vice Chairman of the Federal Reserve, 2017.
“[t]he biggest surprise in the US economy
this year has been inflation”- Janet Yellen, Chair of the Federal Reserve, 2017.
“Inflation is a little bit below target, and it’s kind of a mystery”- Jerome Powell, Chair of the Federal Reserve, 2018.
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Research Question
What drives inflation?What explains inflation in the past?
Use a bottoms-up approach based on various econometric techniques and datasets
How will the future of inflation evolve?
Get a granular yet holistic view of inflation
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Understanding Inflation
with Offline Data
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-4
-2
0
2
4
6
8
10
12
1995 2000 2005 2010 2015
Perc
ent
Thai Inflation Dynamics
Mean = 2.4Var = 4.8
Mean = 1.7Var = 2.6
Mean = 5.1Var = 8.9
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Inflation Decomposition
Trend vs Cycle
• Use 10 sectoral inflation series over 1995Q1 – 2018Q2
Common Trend
SectoralTrend
SectoralCycle
Common Cycle
Pure Idiosyncratic
Relative
Sector (% Share in CPI)
Raw Food (15.7)
Food in Core (20.5)
Clothing (2.9)
Housing excl. Gas (19.0)
Healthcare (6.2)
Transport excl. Fuel (16.5)
Recreation & Education (6.1)
Tobacco & Alcohol (1.4)
Gas & Electricity (4.2)
Fuel (7.6)
CORE
Note: Percentage share in CPI calculated according to 2015 expenditure share weights.
Pure vs Relative vs Idiosyncratic
• Use 179 goods-level inflation series over 2001Q2 – 2018Q2
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Trend vs Cycle
-15
-10
-5
0
5
10
15
2000 2005 2010 2015
Inflation (Percent)
CPI Core
-15
-10
-5
0
5
10
15
2000 2005 2010 2015
Inflation (Percent)
Trend
Quarter Core Trend
2017Q3 0.78 0.54
2017Q4 0.85 0.57
2018Q1 0.49 0.62
2018Q2 0.89 0.78
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Variation of Inflation
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2
4
6
8
10
12
1995 2000 2005 2010 2015
Per
cen
t
Variation of Inflation Cycle
Common Cycle Sectoral Cycle
0
0.5
1
1.5
2
2.5
3
3.5
4
1995 2000 2005 2010 2015
Per
cen
t
Variation of Inflation Trend
Common Trend Sectoral Trend
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Sectoral Influence towards Trend
Note: The solid line is the approximate weights for the estimated trend and the dashed line is its 2011 expenditure share weight.
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Cross-sectional Decomposition
0 10 20 30 40 50 60 70
Energy
Food
Percent
Relationship with Relative Component
64%
23%
-20
-15
-10
-5
0
5
10
15
20
2002 2005 2008 2011 2014 2017
Pe
rce
nt
Inflation (demeaned)
CPI (demeaned) Pure
-20
-15
-10
-5
0
5
10
15
20
2002 2005 2008 2011 2014 2017
Pe
rce
nt
Inflation (demeaned)
Relative
-20
-15
-10
-5
0
5
10
15
20
2002 2005 2008 2011 2014 2017
Pe
rce
nt
Inflation (demeaned)
Idiosyncratic
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Importance of Inflation Components
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10
20
30
40
50
60
70
Median Good CPI
Pe
rce
nt
Fraction of Variability Explained
Pure Relative Idiosyncratic
Note: Calculated based on the average squared canonical coherence measure at all frequencies.
0
10
20
30
40
50
60
70
80
90
100
0 30 60 90 120 150
Pe
rce
nt
Fraction of Variability Explained at the Goods Level
Pure Relative Idiosyncratic
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Inflation and the Real Economy
Note: Calculated based on the average squared canonical coherence measure at business cycle frequencies.
0
0.1
0.2
0.3
0.4
0.5
0.6
Inflation Relative Pure
Correlation between GDP and Inflation Components
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Understanding Inflation with Online Data
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Why study online prices?
• Unique and rich source of information
2.0 2.22.6
2.8
2557 2558 2559 2560E
Value of E-Commerce in Thailand
Source: ETDA, Unit: Trillion THB
• Different market structure than offline
• Helps understand a new and rapidly growing segment of retail
• Potential impact on aggregate inflation dynamics
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0 5 10 15 20 25 30 35 40 45 50
Household consumption goods
Household Electronics
Books
Computers
Chlidren Products
Cameras
Phones
Health and Beauty
Clothing and Jewelry
Percent
Categories of Products on Priceza
21
134
27
0 20 40 60 80 100 120 140 160
Platform
Small
Large
Count
Retail Outlet Types
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Data Sample
ProductsSellers
ProductLife Span
(days)All 11,920 70 252
Outlet typeLarge 2,765 5 265Platform 9,006 99 244Small 5,543 4 281
Sample Period July 2015 – June 2018 Baht
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Our Focus
• Behavior of price changes Dynamic Pricing Strategy –Duration– Size–Direction
• Cross-sectional Characteristics Price Competition– Synchronization–Dispersion
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Behavior of Price Changes
• 52 percent of price changes are price decreases• Duration and size are positively correlated • Prices of more expensive products are more rigid• Products with more sellers change more frequently
Average Size Change (%)Increase Decrease
All 8.8 8.9Large 11.5 11.3Platform 8.74 8.97Small 7.54 7.39
Average Duration (days)
75 days
All 75Large 37Platform 68Small 104
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Price Synchronization
Average Price Synchronization (%)
Daily Monthly
All 2.79 31.0
Platform 17.0 42.2
Median Price Synchronization (%)
Daily Monthly
All 0 0
Platform 0 33.3Note: Synchronization = (A-1)/(B-1) x 100, A>0, B>1 with A as the number of sellers that changes their prices for product i at time t and B as the total number of sellers.
Baht
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Price Dispersion
• Large and small stores consistently charge more than marketplace vendors
• Sellers tend to remain expensive or cheap (46% of all sellers never change ranking)
• Prices of more expensive products are less disperse
• Prices of products with more sellers are less disperse
Average Price Dispersion (%)Range Gap
All 41.9 17.9Platform 47.1 20.6Note: Range = (max-min)/min x 100, Gap = (min2-min1)/min1 x 100
2
2.5
3
Jan-17 Apr-17 Jul-17 Oct-17
Qu
arti
le
Average Relative Ranking of Stores
Large Platform Small
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Online vs Offline Prices
Monthly Inflation (%)
Online Offline
Refrigerators -0.18 -1.01
Fans 0.38 -0.28
Microwaves 2.69 0.01
Air conditioners -3.70 -0.32
Phones -13.9 -1.08
Televisions -13.5 -0.51
Washing machines 0.47 -0.55
Irons 1.93 -0.77
Watches 1.80 -0.91
Duration (days)
Online Offline
86 205
58 274
66 192
64 167
47 212
49 231
71 226
55 178
81 270
Size Change (%)
Online Offline
2.26 5.88
2.80 7.75
2.40 20.5
2.39 23.8
2.34 12.5
3.09 16.2
2.45 8.93
3.65 9.71
1.48 16.4
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Takeaways
• Understanding inflation requires a granular perspective– Information excluded from core can help measure trend– Noisy price fluctuations can mask out important economic relationships
• Inflation is well under control– The underlying rate of inflation remains low– The recent persistent decline in inflation comes from sector-specific shocks
• Inflation is evolving and will continue to evolve– Structural changes in 2000 and 2010– Need a better understanding about the drivers of online prices