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Discussion of:The unequal gains from product innovations: evidence from the
US retail sectorby Xavier Jaravel
andPrice selection
by Carlos Carvalho and Oleksiy Kryvtsov
Günter W. BeckECB conference on “Understanding inflation: lessons from the past,
lessons for the future?21 September 2017
Micro prices and inflation dynamics 1 / 22
Introduction
• I have the great pleasure to discuss two papers that provide veryinteresting new insights into the dynamics of inflation.=⇒ It was a very enriching and enjoyable exercise to study the papersand prepare this discussion!
• Apart from being very well done, the two papers have in common thatthey make diligent use of disaggregate micro price data to addresstheir respective questions of interest.=⇒ In my discussion, I will primarily look at the papers taking a “dataperspective”.
Micro prices and inflation dynamics 2 / 22
Overview
1 Micro price data sets employed: major features
2 Inflation inequality
3 Price selection and inflation dynamics
Micro prices and inflation dynamics 3 / 22
Micro price data sets
Table of contents
1 Micro price data sets employed: major features
2 Inflation inequality
3 Price selection and inflation dynamics
Micro prices and inflation dynamics 4 / 22
Micro price data sets
Micro price data sets: major featuresDesirable features of micro price data to be used for the analysis of price setting
High level of disaggregation Broad coverage Det. inf. on consumersCONSUMER PANEL AND RETAIL SCANNER DATA PRODUCT HIERARCHY
10 Departments
~1,100 Product Modules
~3.6 million UPC Codes
~125 Product Groups
~175,000 Brands
Nielsen Defined
Manufacturer Defined
Dry Grocery
Gum
Sugar-free Chewing Gum
Wrigley’s Extra
Size, Flavor, Form, etc.
8
Example
Micro price data sets
Micro price data sets: major featuresDesirable features of micro price data sets for the analysis of price setting
High level of disaggregation Broad coverage Det. inf. on purchasersCONSUMER PANEL AND RETAIL SCANNER DATA PRODUCT HIERARCHY
10 Departments
~1,100 Product Modules
~3.6 million UPC Codes
~125 Product Groups
~175,000 Brands
Nielsen Defined
Manufacturer Defined
Dry Grocery
Gum
Sugar-free Chewing Gum
Wrigley’s Extra
Size, Flavor, Form, etc.
8
Example
Demographic data(such as income,education, householdsize, age, ...)Geographic dataQuantities bought...
=⇒ “Trilemma” of micro price data set choice: No existing price data set satisfies allthree features
Micro prices and inflation dynamics 5 / 22Source: Kilts data center presentatoin Source: Eurostat
=⇒ “Trilemma” of micro price data set choice: No existing price data set satisfies allthree features.
Micro prices and inflation dynamics 5 / 22
Micro price data sets
Micro price data sets: major featuresMicro price data sets: a crude characterizationDisaggr. CPI data Retailer scanner data Consumer panel data
Level of disaggr. Up to “item level” UPC UPC
Inf. on goods? Crude Comprehensive inf. Comprehensive inf.
Geogr. disaggr. Sel. areas ZIP code ZIP code
Inf. on seller - Yes Yes
Inf. on buyer - - Yes
Coverage Comprehensive FMCG, partlyelectronic goods FMCG
Length/frequ. Long, monthly Last two decades,weekly
Last two decades,undated
Further issuesNot all retailersare willing to
share their data
Reporting errors(see Einav et al., 2010)
Micro prices and inflation dynamics 6 / 22
Inflation inequality
Table of contents
1 Micro price data sets employed: major features
2 Inflation inequality
3 Price selection and inflation dynamics
Micro prices and inflation dynamics 7 / 22
Inflation inequality
Inflation inequality
• The paper examines the implications of economic growth and risingincome inequality on innovation in product markets and itsconsequences for inflation rates experienced by households withdifferent incomes.
• Major steps of the analysis and their findings:• First, heterogeneities in inflation and varieties consumed across
household income groups are documented at several levels ofaggregation.
• Example result (inflation of continued goods): Figure 1B:
Figure 1: In�ation for Continued Products across Income Groups
Panel A: Nested CES Exact Price Index
1.2
1.4
1.6
1.8
2
Nes
ted
CES
Infla
tion
Rat
e, 2
004-
2015
(Ann
ualiz
ed, %
)
Below$30,000
$30,000 to $100,000 Above$100,000
Household Income
Panel B: Stability of In�ation Di�erence across Price Indices
.4
.45
.5
.55
.6
.65
.7
.75
.8
.85
Diff
eren
ce B
etw
een
Ann
ualiz
ed A
vera
ge In
flatio
nR
ates
(pp)
for H
ouse
hold
s w
ith In
com
e B
elow
$30k
and
Hou
shol
ds w
ith In
com
e A
bove
$10
0k
CES Idea
l for U
PC-Stor
e
Paasc
he
Geometr
ic Paa
sche
Tornqv
ist
Geometr
ic La
spey
resW
alsh
CES Idea
l
Fisher
Marsha
ll-Edg
ewort
h
Lasp
eyres
CES Idea
l for U
PC-Loca
l Mark
et
Panel C: Nested-CES Exact Price Index across Age-Income Groups
0
.5
1
1.5
2
2.5
Aver
age
Infla
tion
Rat
e, 2
004-
2015
(%)
Age 45 and below Age 45 to 64 Age 65 and above
$25k
and b
elow
$25k
to $5
0k
$50k
to $7
0k
$70k
to $1
00k
$100
k and
abov
e
$25k
and b
elow
$25k
to $5
0k
$50k
to $7
0k
$70k
to $1
00k
$100
k and
abov
e
$25k
and b
elow
$25k
to $5
0k
$50k
to $7
0k
$70k
to $1
00k
$100
k and
abov
e
Notes: Panels A to C report the average in�ation rate for various household groups from 2004 to 2015. In any given year, the sampleincludes all barcodes observed in the current and previous year. The price indices are described in Sections 3.2 and 3.3. AppendixTables C4, C5, C6, C7 and C15 show the robustness of these results.
14
Micro prices and inflation dynamics 8 / 22
Inflation inequality
Inflation inequality
• Major steps and findings (... continued):• Decomposing observed inflation differences shows that they are mostly
due to the purchase of different UPC-level goods within a modulerather than different expenditures pattern across modules/categories.=⇒ Existing studies such as Hobijn und Lagakos (2005) fail to findthese effects since they employ category-level prices (and expenditureshares that differ across household income groups).
• Employing national trends in age-income group dynamics the authormoreover shows that the changes in demand related to economic growthand rising inequality cause the supply changes in goods marketsdocumented above.
• Finally, a theoretical model is presented that is capable to replicate theempirical findings is presented.
Micro prices and inflation dynamics 9 / 22
Inflation inequality
Inflation inequality
• An aspect that the author might want to consider is how his empiricalfindings are related to the role that the purchasing behavior of differentincome groups and relative price developments with respect to nationalbrands and private-label goods play.
• For a variety of reasons, national brands are generally more expensivethan private-label goods, i.e., belong to the class of premium goods.
• One of the reasons for the price gap is attributed to the phenomenonof “double marginalization".
• To illustrate this point we refer to Hong und Li (2017).
Micro prices and inflation dynamics 10 / 22
Inflation inequality
Inflation inequality
• Hong und Li (2017) develop a model of the vertical (and horizontal)market structure of the retail sector.
• In the vertically non-integrated setup (national brand supply), bothmanufacturing and retailing firms act as monopolistic price setters, theequilibrium retail price is given by:
pi =εi
εi − 1
(θr
i +µi
µi − 1 [c + θmi ]
). (1)
• In the vertically integrated setup (private-label goods supply), theequilibrium price is on the other hand given by:
pi =εVI
εVI − 1 (θri + c + θm
i ) . (2)
Micro prices and inflation dynamics 11 / 22
Inflation inequality
Inflation inequality
• To fit into the story outlined in the current paper we must have that• higher-income households tend to spend relatively more on national
brands and• inflation rates must have been lower for national brands than for private
label goods?
• Is this the case?
Micro prices and inflation dynamics 12 / 22
Inflation inequality
Inflation inequality: national brand/private label price gaps
Copyright © 2013 Information Resources, Inc. (IRI). All rights reserved. IRIworldwide.com
PRIVATE LABEL AND NATIONAL BRANDS: PAVING THE PATH TO GROWTH TOGETHER TIMES & TRENDS
18% 20%
11% 12%
31% 32%
34% 31%
6% 5%
2010 2013
50%>
30-49%
20-29%
10-19%
<10%
The price gap between private label and national brand solutions is diminishing; today nearly one-
third of private label products offer savings of less than 20% versus their national brand
counterpart.
PRIVATE LABEL AVERAGE PRICE DISCOUNT VS. BRANDED
TOTAL U.S.*- MULTI-OUTLET + CONVENIENCE
Exhibit 9
Source: IRI Market Advantage™; 52 weeks ending 9/8/2013 and same period 2010; among the top 100 categories, based on private label dollar sales MULOC
private label products are, in their
own right, true brands, and a
growing number of these lines
have their own dedicated teams
that focus on all stages of
marketing, from product
innovation to distribution.
Balancing the assortment of
private and national brand
offerings at the category level,
and then constantly measuring
price gap to ensure consumers
are getting the value they expect,
is absolutely critical. Achieving
and maintaining this delicate
balance will require a very
granular approach, for the value
equation looks different for each
and every consumer.
Source: 1NY Times October 1, 2013
11
Optimize Pricing to Balance
the Value Equation
On average, private label
solutions offer consumers savings
of 22% versus national brand
solutions. The price gap,
however, has been on the decline
for several years (SEE EXHIBIT
9). Today, only 5% of private
label categories provide savings
of more than 50% to consumers.
A key driver of the shrinking
private label price gap is the
proliferation of premium-tier
private label solutions,
particularly across edibles.
Safeway, for instance, recently
extended the Safeway Selects
line by adding new pizzas with a
hand-stretched dough—a product
that was inspired by a team trip
to a trade show in Italy. And,
Stop & Shop is offering a growing
selection within its Nature’s
Promise natural and organic line,
as well as the Simply Enjoy
premium snack line. Juan
DePaoli, who oversees private-
label brands for Ahold USA,
states, “These are gourmet
products of exceptional tastes,
discovered and crafted by
foodies.1”
These product line expansions,
and the motivators behind the
moves, demonstrate a very real
trend in private label. Today’s
Avg. PL Discount:
24% 22%
Micro prices and inflation dynamics 13 / 22
Inflation inequality
Inflation inequality
Micro prices and inflation dynamics 14 / 22
Price selection and inflation dynamics
Table of contents
1 Micro price data sets employed: major features
2 Inflation inequality
3 Price selection and inflation dynamics
Micro prices and inflation dynamics 15 / 22
Price selection and inflation dynamics
Price selection
• The paper quantifies the contribution of price selection to the variationin inflation.
• Price selection occurs when after an aggregate shock those firmsadjust their prices whose current (preset) prices are furthest away fromthe (not observable) optimal price, p∗
t .
• Implications: The stronger the extent of price selection, the quicker theaggregate price level responds to an aggregate nominal shock and thesmaller are the real effects of such a shock, see Caplin und Spulber(1987) and Golosov und Lucas (2007).
• Knowing the extent of price selection present in the data can help todiscriminate between price-setting models.
Micro prices and inflation dynamics 16 / 22
Price selection and inflation dynamics
Price selection
• Major idea underlying the - model-free - measure of price selection:When price selection is present, prices that are adjusted are notrepresentative of the population of prices
• To examine the contribution of price selection to variation in inflationrates, regressions of the following form a run:
Pprect = βDPct + controls + error (3)
• This is done for different levels of aggregation.
Micro prices and inflation dynamics 17 / 22
Price selection and inflation dynamics
Price selectionSelected results
U.K. Canada U.S. U.K. Canada U.S.Category level
Regular prices Prices incl. sales−0.385∗∗∗ −0.172∗∗∗ −0.259∗∗∗ −0.359∗∗∗ −0.255∗∗∗ −0.217∗∗∗
Aggregate levelRegular prices Prices incl. sales
−0.198∗∗∗ −0.011 0.060∗ −0.394∗∗∗ −0.041 −0.140∗∗∗
=⇒ The degree of price selection is sizeable at the category level and declines withaggregation.=⇒ Price selection is more pronounced in categories where price changes are lessfrequent, larger in absolute magnitude, or in months with larger inflation deviations.=⇒ There exist pronounced differences across countries.=⇒ Are these differences structural or can they be traced to differences in the dataused?
Micro prices and inflation dynamics 18 / 22
Price selection and inflation dynamics
Price selection
• One aspect that could matter is that the geographical samplingprocess that statistical offices use might influence results (see, e.g., thestudy by Handbury und Weinstein, 2015).=⇒ FitzGerald und Shoemaker (2013) show that, for the U.S. biasesbetween official price indices and price indices computed using Nielsenscanner data at the city level are large but are not sizeable at thenational level.
• The level of aggregation at the “goods level” might matter: Do the705 categories employed by the Statistical Office of Canada correspondto a higher level of aggregation?
Micro prices and inflation dynamics 19 / 22
Price selection and inflation dynamics
Price selection
• The authors exclude private label goods from the IRI data sample.• Hong und Li (2017) have shown that the pass-through of a marginal
cost shocks tend to be larger for private-label goods than for nationalbrands.
• The reasons are that the absence of double marginalization in this casereduces markups.
• Thus, in response to a cost shock, the desired price changes less fornational brand goods than for private-label goods.
• Given identical price change costs the degree of self-selection shouldthus be higher for private label goods.=⇒ Including private-label goods and conducting the exercise at thegoods-level might increase the extent of price selection.
Micro prices and inflation dynamics 20 / 22
References
References I
Caplin, Andrew S. und Daniel F. Spulber (1987). “Menu costs and theneutrality of money”. In: The Quarterly Journal of Economics 102.4,S. 703–725.
Einav, Liran, Ephraim Leibtag und Aviv Nevo (2010). “Recordingdiscrepancies in Nielsen homescan data. Are they present and do theymatter?” In: Quantitative Marketing and Economics 2, S. 207–239.
FitzGerald, Jenny und Owen Shoemaker (2013). “Evaluating the ConsumerPrice Index Using Nielsen‚Äôs Scanner Data”. Bureau of Labor Statistics.
Golosov, Mikhail und Robert E. Lucas Jr. (2007). “Menu costs and Phillipscurves”. In: The Journal of Political Economy 115.2, S. 171–199.
Micro prices and inflation dynamics 21 / 22
References
References II
Handbury, Jessie und David E. Weinstein (2015). “Goods prices andavailability in cities”. In: The Review of Economic Studies 82.1,S. 258–296.
Hobijn, Bart und David Lagakos (2005). Inflation inequality in the UnitedStates. London [u.a.]
Hong, Gee Hee und Nicholas Li (2017). “Market structure and costpass-through in retail”. In: Review of Economics and Statistics 99.1,S. 151–166.
Micro prices and inflation dynamics 22 / 22
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