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Federal Reserve Bank of Chicago

Inflation at the Household Level
Greg Kaplan and Sam Schulhofer-Wohl

August, 2017
WP 2017-13
Working papers are not edited, and all opinions and errors are the
responsibility of the author(s). The views expressed do not necessarily
reflect the views of the Federal Reserve Bank of Chicago or the Federal
Reserve System.

*

Inflation at the Household Level∗
Greg Kaplan and Sam Schulhofer-Wohl
Revised August 2017

ABSTRACT
We use scanner data to estimate inflation rates at the household level. Households’ inflation rates
have an annual interquartile range of 6.2 to 9.0 percentage points. Most of the heterogeneity comes
not from variation in broadly defined consumption bundles but from variation in prices paid for the
same types of goods. Lower-income households experience higher inflation, but most cross-sectional
variation is uncorrelated with observables. Households’ deviations from aggregate inflation exhibit
only slightly negative serial correlation. Almost all variability in a household’s inflation rate comes
from variability in household-level prices relative to average prices, not from variability in aggregate
inflation.
Keywords: Inflation; Heterogeneity
JEL classification: D12, D30, E31

∗

Kaplan: University of Chicago and National Bureau of Economic Research (gkaplan@uchicago.edu).
Schulhofer-Wohl: Federal Reserve Bank of Chicago (sam@frbchi.org). We thank Cristian Alonso and Emily
Moschini for research assistance, Joan Gieseke for editorial assistance, and our discussants Yuriy Gorodnichenko, Bart Hobijn, Peter Klenow, and Sarah Lein as well as the editor, associate editor, referee, and
numerous conference and seminar participants for helpful comments. An earlier version of this paper was
completed while Schulhofer-Wohl was employed at the Federal Reserve Bank of Minneapolis. The views
expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Chicago,
the Federal Reserve Bank of Minneapolis, or the Federal Reserve System.

1. Introduction
One major objective of monetary policy in most countries is to control the inflation
rate. Policymakers typically measure inflation with an aggregate price index, such as, in
the United States, the Bureau of Economic Analysis’ price index for personal consumption
expenditures (Federal Open Market Committee, 2016). But an aggregate price index is based
on an aggregate consumption bundle and on average prices for various goods and services.
It does not necessarily correspond to the consumption bundle, prices, or inflation rate experienced by any given household. To know how changes in monetary policy and inflation will
affect households’ economic choices and well-being, we need to know how inflation behaves
at the household level.
We take a first step toward an understanding of heterogeneity in inflation rates by
using scanner data on households’ purchases to characterize inflation rates at the household
level. Inflation rates can vary across households because households buy different bundles
of goods or because households pay different prices for the same goods. Previous research
on inflation heterogeneity has focused exclusively on variation in consumption bundles and
assumed that all households pay the average price for each broadly defined category of good.
By employing data from the Kilts-Nielsen Consumer Panel (KNCP), a dataset that records
the prices, quantities and specific goods purchased in 500 million transactions by about 50,000
U.S. households from 2004 through 2013, we can also consider variation in prices paid and
in the mix of goods within broad categories.1 These new sources of variation are crucial to
our results. We find that inflation at the household level is remarkably dispersed, with an
interquartile range of 6.2 to 9.0 basis points annually, about five times larger than the amount
of variation found in previous work. Almost two-thirds of the variation we measure comes
from differences in prices paid for identical goods, and about one-third from differences in the
mix of goods within broad categories; only 7 percent of the variation arises from differences
in consumption bundles defined by broad categories.
Despite the massive degree of heterogeneity, the entire distribution of household-level
1

Argente and Lee (2016) and Jaravel (2016) use the KNCP data to estimate inflation rates as a function
of income but assume that all households in a given broad range of incomes (such as all households earning
more than $100,000 per year) have the same consumption bundle and pay the same price for each good.

inflation shifts in parallel with aggregate inflation, and the central tendency of household-level
inflation closely tracks aggregate inflation.2 Households with low incomes, more household
members, or older household heads experience higher inflation on average, whereas those in
the Midwest and West experience lower inflation. The cumulative differences in inflation
rates across income groups, in particular, are striking: Over the nine years from the third
quarter of 2004 through the third quarter of 2013, average inflation cumulates to 33 percent
for households with incomes below $20,000 but to just 25 percent for households with incomes
above $100,000. The negative correlation of inflation with income implies that inequality in
real incomes is rising faster than inequality in nominal incomes. Nonetheless, the differences
in inflation rates across demographic groups are small relative to the total variance of the
inflation distribution, and observable household characteristics have little power overall to
predict household inflation rates.
The household-level inflation rates display reasonable substitution patterns in the aggregate. Households substitute on average toward lower-priced goods, and they spend more
when they face low relative prices. The average substitution bias that results from measuring
inflation with initial-period consumption bundles is comparable in magnitude to what has
been estimated for aggregate indexes such as the Consumer Price Index (CPI). However, as
with inflation rates themselves, substitution patterns display a great deal of heterogeneity.
Many households are observed to substitute toward higher-priced goods, and demographics
have little power to explain differences in substitution.
We also explore the evolution of households’ inflation rates over time. Deviations
from mean inflation exhibit only slightly negative serial correlation within each household
over time. As a result, inflation rates measured over time periods longer than a year are also
very heterogeneous, and the difference between a household’s price level and the aggregate
price level is quite persistent. We use our estimates to provide a simple characterization of
the stochastic process for deviations of household-level inflation from aggregate inflation. Together, the low serial correlation and high cross-sectional variation of household-level inflation
2

Neither of these results is mechanical. If the rate of increase of the prices that a particular household
pays is correlated with the household’s consumption bundle, then the mean of household inflation rates need
not match the aggregate inflation rate, and the distribution need not shift along with the aggregate.

2

imply that variation in aggregate inflation is almost irrelevant for variation in a household’s
inflation rate. In a benchmark calculation, 91 percent of the variance of a household’s inflation rate comes from variation in the particular prices the household faces, and just 9 percent
from variation in the aggregate inflation rate.
Previous research on household-level heterogeneity in inflation in the United States,
such as Michael (1979), Hobijn and Lagakos (2005), and Hobijn et al. (2009), has largely used
microdata from the Consumer Expenditure Survey (CEX) to measure household-specific
consumption bundles, then constructed household-level inflation rates by applying these
household-specific consumption bundles to published indexes of average prices for relatively
broad categories of goods, known as item strata. Such an analysis assumes both that all households pay the same price for a given good (for example, a 20-ounce can of Dole pineapple
chunks) and that all households purchase the same mix of goods within each item stratum (for
example, the same mix of 20-ounce cans of Dole pineapple, other size cans of Dole pineapple,
other brands of pineapple, and other fruits within the category “canned fruits”). Relative to
this literature, our key innovation is to use the detailed price and barcode data in the KNCP
to also measure variation in the prices that households pay and the goods they choose within
item strata. We observe the barcode of each good purchased and can thus account for both
variation in the price of a specific good and variation across households in the mix of goods
purchased within item strata. We thus build on the findings of Kaplan and Menzio (2015),
who use the KNCP data to characterize variation over time and space in the prices at which
particular goods are sold. However, because the KNCP focuses on goods sold in retail outlets
— a universe that includes about 30 percent of household consumption (Kilts Center, 2013a)
— we are unable, unlike the CEX-based literature, to measure the impact of heterogeneity
in consumption of other goods and services. In particular, we cannot measure the impact of
differences in spending on education, health care, and gasoline, which Hobijn and Lagakos
(2005) found were important sources of inequality in inflation rates. Nonetheless, when we
treat the data similarly to previous research by imposing common prices on all households, we
measure a similar amount of inflation heterogeneity as in previous papers whose calculations
encompassed a broader universe of goods and services.
Our findings are also related to the growing literature on households’ and small firms’
3

inflation expectations. Binder (2017) and Kumar et al. (2015) find that households in the
United States and small firms in New Zealand, respectively, do not have well-anchored inflation expectations and are poorly informed about central bank policies and aggregate inflation
dynamics. One possible explanation is that if aggregate inflation is only a minor determinant
of household- or firm-level inflation, then households and firms may rationally choose to be
inattentive (Reis, 2006; Sims, 2003) to aggregate inflation and policies that determine it.
More broadly, the weak link between aggregate inflation and household-level inflation may
help explain the well-known long and variable lags in the impact of monetary policy on the
economy: If household-level inflation is only loosely related to aggregate inflation, it may be
difficult for households to detect changes in aggregate inflation, and hence households may
react weakly or at least slowly to aggregate inflation.
Since the seminal work by Lucas (1972) and Lucas (1975), macroeconomics has had a
long tradition of modeling monetary non-neutrality as arising because agents (either households or firms) have imperfect information about whether the changes they observe in nominal variables reflect economy-wide or agent-specific shocks. King (1982), for example, shows
that when different agents have different information about monetary and demand shocks,
monetary policy can affect real activity by changing how agents infer the underlying shocks
from the signals they receive. More recent descendants of this literature include Angeletos
and La’O (2009) and Nimark (2008). For more than forty years, this literature has remained essentially purely theoretical, partly because attempts to quantify the strength of the
imperfect-information mechanism require some way of disciplining the amount of information
about aggregate price changes contained in individual observations of prices. Our estimates
of the stochastic process relating household-level inflation to aggregate inflation could, in
principle, be used as a first step toward empirical quantification of these models. Although
we do not take this step in this paper, our central finding — that almost all of the variation in
household-level inflation is disconnected from movements in aggregate inflation — supports
the idea that the informational frictions embedded in these models could be substantial.
The paper proceeds as follows. Section 2 describes the data and how we construct
household-level inflation rates. Section 3 characterizes cross-sectional properties of the inflation distribution. Section 4 characterizes time-series properties of household-level inflation.
4

Section 5 concludes.

2. Data and Estimation
This section describes the KNCP data and how we use the data to calculate inflation
rates.
A. Data
The KNCP tracks the shopping behavior of approximately 50,000 U.S. households
over the period 2004 to 2013. Households in the panel provide information about each of
their shopping trips using a Universal Product Code (UPC) (i.e., barcode) scanning device
provided by Nielsen. Panelists use the device to enter details about each of their shopping
trips, including the date and store where the purchases were made, and then scan the barcode
of each purchased good and enter the number of units purchased. The price of the good is
recorded in one of two ways, depending on the store where the purchase took place. If the
good was purchased at a store that Nielsen covers, the price is set automatically to the average
price of the good at the store during the week when the purchase was made. If the good was
purchased at a store that Nielsen does not cover, the price is directly entered by the panelist.
Panelists are also asked to record whether the good was purchased using one of four types
of deals: (i) store feature, (ii) store coupon, (iii) manufacturer coupon, or (iv) other deal. If
the deal involved a coupon, the panelist is prompted to input its value. We do not strip the
effect of coupons or other deals out of prices, nor do we attempt to distinguish between sale
and non-sale prices. Our interest is in understanding the prices that a household actually
pays, including the consequences of choosing to take advantage of sales or not.
Households in the KNCP are drawn from 76 geographically dispersed markets, known
as Scantrack markets, each of which roughly corresponds to a Metropolitan Statistical Area
(MSA). Demographic data on household members are collected at the time of entry into the
panel and are then updated annually through a written survey during the fourth quarter of
each year. The collected information includes age, education, marital status, employment,
type of residence, and race. For further details on the KNCP, see Kaplan and Menzio (2015).3
3

The KNCP has become an increasingly commonly used dataset for studies of prices and expenditure.
Examples of recent studies that have used the KNCP include Einav, Leibtag, and Nevo (2010), Handbury

5

We use bootstrap standard errors to account approximately for the sampling design of
the KNCP. The KNCP sample is stratified across 61 geographic areas, some of which include
multiple Scantrack markets. Nielsen replenishes the sample weekly (Kilts Center, 2013b), but
even at a quarterly frequency, there are quarters when too few new households join the sample
in some geographic strata for us to be able to resample from these households. We therefore
treat the sample as if it is replenished at an annual frequency. We resample households within
groups defined by geographic stratum and the year in which the household first appears in the
data. As recommended by Rao, Wu, and Yue (1992), we ensure that the bootstrap is unbiased
by resampling N − 1 households with replacement from a group containing N households.
When we resample a household, we include all quarterly observations from that household
in our bootstrap sample. Thus, our bootstrap accounts both for the geographic stratification
of the original sample and for serial correlation over time within households, for example
because of particular households’ unique purchasing patterns. However, because we do not
have access to all details of Nielsen’s sampling and weighting procedure, our bootstrap is
only approximate. In particular, we do not know whether new households are chosen purely
at random or based on observable characteristics. In addition, we cannot recompute the
sampling weights in each bootstrap sample because we do not have Nielsen’s formula for
computing the weights.
B. Calculating Household Inflation Rates With Household-Level Prices
A price index measures the weighted average rate of change of some set of prices,
weighted by some consumption bundle. Aggregate price indexes use the national average
mix of consumption to define the consumption bundle. To construct household-level price
indexes, we must define household-level consumption bundles and choose a time period over
which to measure the change in prices.
We can measure the change between two dates in a household’s price for some good
only if the household buys the good on both dates. On any given day, most households do
not buy most goods — even goods that they buy relatively frequently. Therefore, although
the KNCP data record the date of each purchase, we aggregate each household’s data to a
(2013) and Bronnenberg et al. (2015).

6

quarterly frequency to increase the number of goods that a household is observed to buy in
multiple time periods. If a household buys the same product (defined by barcode) more than
once in a quarter, we set the household’s quarterly price for that product to the volumeweighted average of prices that the household paid.
Many prices exhibit marked seasonality. It would be virtually impossible to seasonally
adjust the household-level price indexes because we do not observe a long time series for each
household. Therefore, we remove seasonality in price changes by constructing price indexes
at an annual frequency, comparing prices paid in quarter t and quarter t + 4. Some residual
seasonality may remain if consumption bundles change seasonally, but in practice we observe
little seasonality in the annual price indexes we compute. To prevent mismeasured prices
from distorting our estimates, we exclude a product from the calculation for a particular
household at date t if the product’s price for that household increases or decreases by a factor
of more than three between t and t + 4.
Two commonly used price indexes are the Laspeyres index, which weights price changes
between two dates by the consumption bundle at the initial date, and the Paasche index,
which weights price changes by the consumption bundle at the final date. We compute both
of these.
When we calculate a household’s inflation rate between quarters t and t + 4, we consider only goods (defined by barcodes) that the household bought in both of those quarters.
To reduce sampling error, we restrict the sample to households with at least five matched
barcodes in the two quarters. (On average across all dates in the sample, 77 percent of households that make any purchase in quarter t also make some purchase in quarter t + 4, and 72
percent buy at least five matched barcodes whose prices change by a factor no greater than
three.) Thus, let qijt be the quantity of good j bought by household i in quarter t, and let
pijt be the price paid. The Laspeyres and Paasche inflation rates for household i between t
and t + 4 are then

P
L
πit,t+4

j : qijt ,
qij,t+4 >0

= P

pij,t+4 qijt

j : qijt ,
qij,t+4 >0

7

pijt qijt

(1)

and

P
P
πit,t+4

j : qijt ,
qij,t+4 >0

pij,t+4 qij,t+4

= P

j : qijt ,
qij,t+4 >0

,

pijt qij,t+4

(2)

respectively. We also compute the Fisher index, which is the geometric mean of Laspeyres
and Paasche:
F
πit,t+4

q
L
P
πit,t+4
.
= πit,t+4

(3)

C. Calculating Household Inflation Rates With Aggregate Prices
We compute three sets of household-level inflation indexes with prices defined at a more
aggregated level. To make these indexes comparable with the indexes using household-level
prices, we continue to consider only UPCs that the household bought in each of two quarters
one year apart. Thus, the indexes with more aggregated prices use the identical consumption
bundle as the indexes with household-level prices, but a different vector of prices. Although
we could drop the restriction to repeatedly purchased UPCs when we use more aggregated
prices, we choose not to do so in the interest of comparability.
First, we compute inflation indexes that assign to every household the average price
for each barcode. Cross-sectional variation in this index comes only from variation in which
barcodes each household buys, not from variation in the price changes for particular barcodes.
The Laspeyres index at the household level with barcode-average prices is
P
L,BC
πit,t+4

j : qijt ,
qij,t+4 >0

= P

p̄j,t+4 qijt

j : qijt ,
qij,t+4 >0

p̄jt qijt

,

(4)

where p̄jt is the volume-weighted average price for barcode j in quarter t. The Paasche
P,BC
F,BC
and Fisher indexes with barcode-average prices, πi,t,t+4
and πi,t,t+4
, are defined analogously.

By comparing the indexes with household-level prices and the indexes with barcode-average
prices, we can measure how much cross-sectional variation in household inflation rates comes
from differences in which barcodes each household buys, and how much from differences in
price changes for the same barcodes.
We next compute household-level inflation indexes that, similarly to the previous
literature, account only for heterogeneity in broadly defined consumption bundles and not
8

for heterogeneity in prices or in the selection of specific goods within each broad category of
goods. By matching every purchase to the CPI price for the corresponding item stratum —
for example, by matching Dole canned pineapples to the canned fruit index — and assigning
to each purchase the average inflation rate for that item stratum, we can remove variation
in prices for specific goods and variation in the mix of goods within item strata, leaving
differences in how households spread their consumption across item strata as the only source
of heterogeneity. We have two possible sources for average inflation rates at the stratum
level: the prices observed in the KNCP and the stratum price indexes published by the BLS
for the various strata that make up the CPI. Let k(j) be the CPI category corresponding to
be the CPI sub-index for item stratum k at date t. For each good j
barcode j, and let pCPI
kt
such that qijt > 0 and qij,t+4 > 0, we define household i’s consumption share of good j at the
initial date as
pijt qijt
X
.
pijt qijt

sLijt,t+4 =

(5)

j : qi`t ,
qij,t+4 >0

The Laspeyres index at the household level with CPI prices is
L,CPI
πit,t+4
=

X

sLijt,t+4

j : qijt ,
qij,t+4 >0

pCPI
k(j),t+4
pCPI
k(j),t

,

(6)

F,CPI
P,CPI
and πi,t,t+4
, are defined analoand the Paasche and Fisher indexes with CPI prices, πi,t,t+4

gously.4 To produce a similar index with stratum-average prices from the KNCP, we must
CPI
first produce the analog to pCPI
with the KNCP data. The Laspeyres version of this
k,t+4 /pkt

stratum-level inflation rate is
X

L,S
πkt,t+4
=

qijt p̄j,t+4

i,j : j∈k
qijt ,
qij,t+4 >0

X

.

(7)

qijt p̄j,t

i,j : j∈k
qijt ,
qij,t+4 >0

4

We calculate the quarterly value of the CPI as the average of the monthly values for the three months in
the quarter.

9

Then the Laspeyres inflation rate at the household level with stratum-average prices is
L,S
πit,t+4
=

X

L,S
,
sLikt,t+4 πkt,t+4

(8)

k

where sLikt,t+4 is the share of stratum k in household i’s total spending at date t. The Paasche
and Fisher indexes with stratum-average prices are defined similarly.
D. Comparing Household-Level Inflation Rates and Aggregate Inflation
Our household-level inflation rates are not directly comparable to published aggregate
inflation rates because our data cover only a subset of goods. We construct an aggregate
inflation rate that is comparable to our household-level indexes by measuring the aggregate
consumption bundle in our data and using this bundle to aggregate the CPI item stratum price
indexes. (We cannot make a similar comparison to the Bureau of Economic Analysis’ personal
consumption expenditure index because that program does not provide prices for detailed
types of goods.) We use the Laspeyres index for this comparison because the aggregate CPI
is a Laspeyres aggregate of item stratum prices (Bureau of Labor Statistics, 2015). The
aggregate expenditure share of good j for this index is the good’s share in total spending,
counting only spending that is included in our index because it represents a household buying
the same good at both dates:
X

pijt qijt

i : qijt ,
qij,t+4 >0

sLjt,t+4 = X X

,

(9)

pCPI
k(j),t+4
L
sjt,t+4 CPI .
pk(j),t

(10)

pi`t qi`t

i : qi`t ,
qi`,t+4 >0

`

and our aggregate inflation index is
L,CPI
πt,t+4

=

X
j

L,CPI
The aggregate index πt,t+4
is a version of the CPI that is based on the same set of goods

as our household-level price indexes. The price data in it are all from the CPI, and the

10

consumption bundle is the aggregate of the bundles used to construct our household-level
price indexes.5
L,CPI
The aggregate index πt,t+4
can be rewritten as a weighted average of household-level

indexes with CPI prices:
X
L,CPI
πt,t+4
=

L,CPI
xit,t+4 πit,t+4

i

X

,

(11)

xit,t+4

i

where the weights xit,t+4 are each household i’s expenditure at date t on goods included in
the household-level price index with household-level prices:
xit,t+4 =

X

pijt qijt .

(12)

j : qijt ,
qij,t+4 >0

L,CPI
Because πt,t+4
weights households according to their spending, it is what Prais (1959) called

a plutocratic price index. The published CPI is likewise a plutocratic index because it defines
the consumption bundle based on each good’s expenditure share in aggregate spending. One
can alternatively construct democratic aggregate indexes that weight households equally, but
because our goal here is to compare household-level indexes with an analog to the published
CPI, we focus on the plutocratic index.
The online appendix gives details on the distribution of spending across types of goods
in the KNCP. About 61 percent of spending in the KNCP is on food and beverages, a
share that rises to 74 percent in the matched purchases that we use to measure household
inflation. By contrast, food and beverages have only a 15 percent weight in the published
CPI. But despite the heavy weight of food in the KNCP, many other types of purchases are
represented, including housekeeping supplies, pet products, and personal care items. Housing
and transportation, on the other hand, get much less weight in our data than in the CPI.
Apparel is measured in the KNCP but gets zero weight in our household inflation rates
because we observe no purchases of matched apparel barcodes in consecutive periods.
Figure 1 compares the aggregate index for the KNCP universe, π L,CPI , with the pub5

Another, minor difference between our index and the published CPI is that our index is chain weighted,
with new base-period weights defined at each date t, whereas the CPI uses a fixed base period.

11

lished CPI and several CPI sub-indexes. The dates on the horizontal axis correspond to the
initial quarter of the one-year period over which the inflation rate is calculated. The aggregate index for the KNCP data behaves similarly to the overall CPI but lags it somewhat, as
does the CPI sub-index for food at home. Unsurprisingly, given the large share of food at
home in the KNCP data, our index moves closely with the CPI for food at home, though our
index is somewhat less volatile. The overweighting of food offsets the absence of energy in
our data, so that our index’s volatility is similar to that of the overall CPI and substantially
greater than the volatility of the CPI excluding energy. Over the period we study, our aggregate inflation rate averages 2.6 percent with a standard deviation of 1.9 percentage points,
compared with a mean of 2.4 percent and standard deviation of 1.5 percentage points for the
published CPI, and a mean of 2.6 percent and standard deviation of 2.4 percentage points for
the food-at-home sub-index. Our index is precisely estimated; the bootstrap standard errors
average 2 basis points.
Both when we compute the aggregate consumption bundle and when we compute
household price indexes with CPI prices or barcode-level prices, we use only those specific
goods — defined by barcodes — that each household purchases at both dates. Thus, in all
of our indexes, we measure inflation for the subset of goods that households buy repeatedly.
This inflation rate may differ from an inflation rate that includes goods bought less frequently,
but we have no way to compute the latter rate at the household level.
The foregoing analysis considers only differences in the categories of goods covered in
the KNCP versus the CPI, applying the same CPI prices to both datasets. (Recall that we
use CPI prices to compute the KNCP aggregate inflation index, π L,CPI .) However, when we
compute household-level inflation rates, we will use price data from the KNCP. In principle,
the prices recorded in the KNCP could differ from those recorded in the CPI for similar goods.
To assess this concern, we map each barcode in the KNCP to a CPI item stratum and compute
inflation rates in the KNCP for each item stratum.6 Figure 2 shows two ways of comparing
the resulting item stratum inflation rates in the KNCP with the corresponding published item
stratum inflation rates for the CPI. In the upper panel, we show the distribution across item
6

We compute these inflation rates using the Laspeyres index and the volume-weighted average observed
price for each barcode at each date.

12

strata of the difference between the KNCP inflation rate and the published CPI inflation rate.
The mean and median differences are small, and in most quarters, the discrepancy is no more
than 1 percentage point for roughly half of the item strata. However, some item strata have
substantially larger discrepancies. The lower panel of the figure aggregates the item strata
inflation rates from the KNCP and the CPI to produce aggregate inflation rates, weighting
each item stratum by total expenditure on that stratum in the KNCP. The aggregate inflation
rate using KNCP prices is almost identical to that using CPI prices. Thus, on average, the
prices recorded in the KNCP reflect the same trends as the prices recorded in the CPI.

3. The Cross-Sectional Distribution of Inflation Rates
Figure 3 shows the distributions of household-level inflation rates for a typical period,
the fourth quarter of 2004 to the fourth quarter of 2005, calculated with Laspeyres indexes.
In each panel, a kernel density estimate of the distribution of household-level inflation rates
with household-level prices is superimposed on estimates of the distributions of the householdlevel rates with barcode-average prices, with stratum-average prices, and with CPI prices.
Household-level inflation rates with household-level prices are remarkably dispersed. For
legibility, the plots run from -5 percent to 10 percent, but nearly 20 percent of households fall
outside these bounds. (The full distribution is shown in the web appendix.) The interquartile
range of annual inflation rates is 6.7 percentage points, and the difference between the 10th
and 90th percentiles is 14.8 percentage points. The smallest observed inflation rate with
household-level prices was −43 percent, the 1st percentile was -15 percent, the 99th percentile
was 23 percent, and and the largest was 102 percent. The distributions of the Fisher and
Paasche indexes are similar and are shown in the web appendix.
The vast majority of the cross-sectional variation in household-level price indexes
comes from cross-sectional dispersion in prices paid for goods within an item stratum, not
variation in the allocation of expenditure to item strata. The variance of the index with
CPI prices is 2.5 percent of the variance of the index with household-level prices,7 and the
interquartile range is just 0.95 percentage point. The inflation rates with stratum-average
7

We compute all variances on the subset of observations that fall between the 1st and 99th percentiles of
the distribution of the index with household-level prices, to remove some extreme cases that would inflate
the variance with household-level prices.

13

KNCP prices are slightly more dispersed, with an interquartile range of 1.25 percentage point
and a variance that is 9.8 percent of the variance of the index with household-level prices.
Relative to the CPI prices, stratum-average prices in the KNCP are likely to be a noisy
measure of the true stratum-level price indexes. As a result, the household-level inflation rates
with stratum-average prices are likely to overstate the true amount of variation attributable
to allocation of expenditure across item strata. Therefore, in the remainder of the analysis,
we use CPI prices to assess the amount of variation due to the allocation of expenditure
across item strata.
Differences in the barcodes that households buy within item strata and differences in
the prices that households pay for particular barcodes are both important sources of variation
in household-level price indexes. The variance of the Laspeyres index with barcode-average
prices is 30.6 percent of the variance of the index with household-level prices, implying that
66.9 percent of the variation in the index with household-level prices comes from variation
across households in the prices paid for given barcodes, 30.6 percent from variation in the
choice of barcodes within item strata, and 2.5 percent from variation in the mix of consumption across item strata.
The heterogeneity in inflation rates is not driven by households for which we can
match only a few barcodes across quarters. The distributions for households with at least 25
matched barcodes, shown in the middle panel of Figure 3, are almost as dispersed as those for
the baseline group of households with at least five matched barcodes, shown in the top panel.
Among households with at least 25 matched barcodes, the interquartile range of Laspeyres
inflation rates with household prices is 5.8 percentage points, the difference between the 10th
and 90th percentiles is 12.1 percentage points, the variance of the index with CPI prices is 2.7
percent of the variance of the index with household-level prices, and the variance of the index
with barcode-average prices is 31.0 percent of the variance of the index with household-level
prices.
Spending on matched barcodes is only a minority of most households’ spending. For
the median household, across all quarters, 21 percent of spending is on matched barcodes,
and for three-quarters of households, less than 30 percent of spending is on matched barcodes.
However, the heterogeneity in inflation rates is not driven by households for which especially
14

little spending is on matched barcodes. The distributions of inflation rates for the one-fourth
of households that devote at least 30 percent of their spending to matched barcodes, shown
in the bottom panel of Figure 3, show similar dispersion to those for all households.
Figure 4 examines how the dispersion of household-level inflation rates evolves over
time. The graphs show results calculated from Laspeyres indexes, but graphs based on
Paasche and Fisher indexes, shown in the web appendix, are almost identical. Table 1
summarizes various dispersion measures from all three indexes. The patterns observed in
the fourth quarter of 2004 are quite typical. Household-level inflation rates with householdlevel prices are enormously dispersed, with interquartile ranges of 6.2 to 9.0 percentage points
using the Laspeyres index, and much more dispersed than household-level inflation rates with
barcode-average, stratum-average or CPI prices. The bootstrap standard errors show that
the amount of dispersion is precisely estimated at each date.
On the whole, the inflation inequality we measure with CPI prices is similar to what
has been reported in previous literature that uses a wider universe of goods and services and
imposes CPI prices on all households. For example, Hobijn et al. (2009) found a gap of 1
to 3 percentage points between the 10th and 90th percentiles. But our results show that
assuming all households face the same prices and buy the same mix of goods within CPI item
strata misses most of the heterogeneity in inflation rates. The gap between the 10th and 90th
percentiles is nearly twice as large when we use barcode-average prices, allowing the mix of
goods within item strata to vary across households, as when we use CPI prices. And if we
also allow different households to pay different prices for the same barcode, the gap between
the 10th and 90th percentiles is five times larger than when we use CPI prices.
The bottom panel of Table 1 shows that in most years, the variance of inflation rates
with CPI prices is only a few percent of the variance of inflation rates with household-level
prices. However, in 2008, at the height of the Great Recession, the variance with CPI prices
reached 30 percent of the variance with household-level prices, as the sharp shift in the relative
price of food at home, shown in Figure 1, made heterogeneity in broadly defined consumption
bundles more important. Table 1 also shows that dispersion measured with the Fisher index
is slightly lower than that measured with Laspeyres and Paasche indexes.
The bottom panel of Figure 4 shows how the distribution of household-level Laspeyres
15

inflation rates with household-level prices moves with the aggregate inflation rate π L,CPI
computed for the corresponding universe of goods. The distribution does not exhibit any
noticeable seasonality. The mean and median of the household-level Laspeyres indexes closely
match the aggregate index; thus, the democratic inflation rate in the sense of Prais (1959)
differs little from the plutocratic rate. Moreover, the entire distribution shifts roughly in
parallel with the aggregate index.
Table 2 uses quantile regressions of household-level inflation rates on aggregate inflation and on the median of household-level inflation to measure these shifts. Panel (1) of
the table shows that the lowest quantiles of household inflation move one for one with the
aggregate index. However, higher quantiles move somewhat more than one-for-one with the
aggregate index, with the relationship strongest for the highest quantiles. Thus, the distribution spreads out, especially in the upper half, when aggregate inflation is higher. An increase
of 1 percentage point in the aggregate inflation index raises the gap between the median and
the 90th percentile of household inflation by 0.2 percentage point. This spreading out comes
from two sources. First, as panel (2) of the table shows, higher quantiles move slightly more
than one-for-one with median inflation. Second, median inflation itself moves more than
one-for-one with the aggregate index, as indicated by the slope coefficient for the fifth decile
in panel (1).
Table 3 examines the consequences of inflation inequality for income inequality. The
table shows the cumulative inflation rates for households at different income levels over the
nine-year period for which we have data, as well as some demographic characteristics of
households in the different income groups.8 Because there is substantial attrition in the
dataset, we use a synthetic cohort approach to calculate cumulative inflation rates: We first
compute the average one-year inflation rate in each income group for each year, then cumulate
the average one-year rates to find an average cumulative inflation rate. (The online appendix
illustrates the year-by-year inflation rates for each group.) Over the nine years of data, the
cumulative inflation rate is 8 to 9 percentage points lower for households with incomes above
$100,000, compared with households with incomes below $20,000. If these differences in
8

Household income is measured in nominal terms but is reported in bins, so we cannot group households
by real income in a consistent way over time.

16

inflation rates for goods in the KNCP extended to the universe of goods and services, they
would imply that the difference in real incomes between the top and bottom groups was
growing at a rate of nearly 1 percentage point per year faster than the difference in nominal
incomes. Our results confirm the significant gap in inflation between high- and low-income
households that Argente and Lee (2016) report for the 2004–2010 period.
The lowest-income households are disproportionately likely to have elderly and lesseducated heads and to live in the South, and disproportionately less likely to have children
and to be Asian or Hispanic. But these demographic differences do not explain much of
the difference in inflation rates across income groups. Table 4 examines the association of
household-level inflation rates with a large set of household demographics in a regression
framework. At each date, we compute the difference between each household’s inflation
rate and the aggregate inflation rate for the equivalent universe of goods, π L,CPI . Then we
regress this difference on household characteristics, controlling for time dummies to absorb
any aggregate effects that might be correlated with the sample distribution of household
characteristics. (Although the regression contains multiple observations on most households,
we do not control for household fixed effects because they would absorb the variation in
household characteristics, which are mostly time invariant.) The first two columns compute
household-level inflation rates with household-level prices and the Laspeyres index, estimating
the coefficients first with ordinary least squares regression and then with median regression,
which is robust to outliers in the inflation distribution. The third and fourth columns compute
household-level inflation rates with barcode-average and CPI prices, respectively, estimating
the coefficients with median regression in both cases.
The results with household-level prices show several significant associations between
demographics and household-inflation. Low-income households experience higher inflation,
even after controlling for all other demographics. According to the median regression, the
median annual inflation rate is 0.6 percentage point higher for a household with income
below $20,000, compared with a household with income of at least $100,000, holding other
demographics fixed. The difference is even larger for mean inflation rates, measured with the
OLS regression, and if cumulated over nine years would be nearly as large as the difference in
Table 3 that does not control for other demographics. The inflation rate also is nearly half a
17

percentage point lower for households in the West than in the East, and almost 0.2 percentage
point lower in the Midwest than in the East. Larger households have higher inflation rates;
the median inflation rate is one-fourth of a percentage point higher for a family of two adults
and two children than for a single adult. In both the OLS and median regressions, inflation
rates with household-level prices are higher for households whose heads are older. Differences
by race are not statistically significant.
The results with barcode-average and CPI prices show similar correlations, except
that the magnitude of the coefficients typically becomes smaller as prices are computed at
higher levels of aggregation. For example, the difference in median inflation rates between the
lowest and highest income groups is 0.1 percentage point with CPI prices and 0.4 percentage
point with barcode-average prices, compared with 0.6 percentage point with household-level
prices. The coefficients on age categories likewise shrink as prices are aggregated, as do the
coefficients on region and household size. Thus, measures of household-level inflation that
use aggregated prices may underestimate the differences in inflation rates across demographic
groups. However, education is an exception to this pattern, with barcode-average prices
producing larger coefficients than household-level prices.
Although Table 4 reveals several statistically and economically significant associations
of household inflation with observables, these effects are small relative to the total amount of
heterogeneity. For example, moving from the bottom to the top of the age distribution raises
median inflation with household-level prices by less than one-tenth as much as moving from
the 25th to the 75th percentile of the overall distribution of inflation. As a result, observables
have little power to predict household inflation rates in the cross section. The R-squared
in the OLS regression is just 1.2 percent, of which three-fourths is explained by the time
dummies; household characteristics explain just 0.3 percent of the cross-sectional variation
in inflation rates.
The remaining columns of Table 4 examine the association between demographics
and the dispersion of inflation rates, as well as one possible explanation for differences in
dispersion: differences in shopping behavior. Column (5) repeats the median regression of
household inflation rates on demographics from column (2) but controls for the number of

18

shopping trips9 that the household made in quarters t and t + 4. All else equal, an increase
in the number of shopping trips in quarter t is associated with a higher inflation rate from
t to t + 4, while an increase in the number of shopping trips in quarter t + 4 is associated
with a lower inflation rate from t to t + 4. This association suggests that households find
lower prices when they make more shopping trips, as many search models would predict.
However, the magnitudes of these effects are relatively small. For example, to reduce the
inflation rate by one-fourth of one percentage point, one would need to double the number
of shopping trips in the final quarter. The combined effect of equal percentage increases in
shopping trips in both the initial and final quarters is close to zero, implying that, all else
equal, a household’s average level of search over time affects the household’s price level but
not its inflation rate. Controlling for the number of shopping trips does not much change the
effects of demographics, compared with the equivalent coefficients in column (2). Thus, the
demographic differences in inflation rates do not appear to result from differences in shopping
frequency.
Columns (6) and (7) of Table 4 show how the dispersion of household inflation rates depends on demographics, without and then with controls for the number of shopping trips. The
interquartile range of inflation rates is lower for higher-income, older and larger households,
and those living outside the Northeast region, especially in the South. The magnitudes are
substantial: The interquartile range is 0.87 percentage point smaller for the highest-income
households relative to the lowest-income households, and at least 1 percentage point smaller
for households with heads at least age 40 relative to households with heads under age 30.
However, dispersion is higher for nonwhite households and has a U-shaped relationship with
education. Controlling for the number of shopping trips does not much change these relationships, but households that make more shopping trips have less-dispersed inflation rates, again
consistent with the predictions of many search models. All else equal, doubling the number of
shopping trips in the initial quarter reduces the interquartile range by 0.13 percentage point,
while doubling the number of trips in the final quarter reduces the interquartile range by 0.35
percentage point.
When consumption shifts toward less-expensive goods as prices change, the Laspeyres
9

The count of shopping trips excludes trips on which the household spent less than $1.

19

index (which weights goods by initial-period consumption) shows higher inflation than the
Paasche index (which weights by final-period consumption). Thus, differences between Laspeyres and Paasche inflation rates show the extent of substitution toward less-expensive
goods. The top panel of figure 5 plots the mean differences between households’ Laspeyres
and Paasche inflation rates and their geometric mean, the Fisher index, for each quarter
in the sample. The Laspeyres inflation rate averages 0.33 percentage point higher than the
Fisher index, which in turn averages 0.30 percentage point higher than the Paasche index.
Hence, on average, the data show that households substitute toward less-expensive goods, as
predicted by standard models of consumer demand. The discrepancy between Laspeyres and
Fisher indexes is known as substitution bias because the Fisher index is an approximately
correct measure of the increase in the cost of living, whereas the Laspeyres index is biased
upward as a measure of the cost of living because it ignores substitution toward lower-priced
goods. The 0.33 percentage point average substitution bias in our household-level inflation
rates is similar to typical estimates of substitution bias in aggregate data, such as the Boskin
Commission’s estimate of a 0.4 percentage point substitution bias in the CPI (Boskin et al.,
1996, Table 3).
The average substitution patterns mask a great deal of heterogeneity. The bottom
panel of figure 5 shows the distribution of the difference between the Laspeyres and Paasche
P
L
, for a typical period, the fourth quarter of 2004 to
− πit,t+4
indexes for each household, πit,t+4

the fourth quarter of 2005. Although the Paasche index measures a lower inflation rate than
the Laspeyres index for the average household, this relationship is far from uniform. Slightly
more than 40 percent of households fail to substitute in the expected direction and have a
higher Paasche index than Laspeyres index.
In standard models, substitution in the unexpected direction — toward goods whose
relative prices have risen — can occur only when households experience preference shocks of
some sort. These shocks could take many forms, ranging from actual shocks to preferences
(such as a strong desire to eat pineapple one year and a strong desire to eat peaches the
next year) to income shocks that influence purchases via non-homothetic preferences. The
large number of households with Paasche inflation rates greater than Laspeyres inflation
rates suggests that the magnitude of these shocks is substantial. Such shocks could also help
20

explain the wide dispersion of the difference between Laspeyres and Paasche inflation rates.
The online appendix examines the relationship between household demographics and
substitution patterns. As with household-level inflation rates themselves, the difference between Laspeyres and Paasche indexes has an economically and statistically significant correlation with some demographics, but the R-squared is essentially zero: Demographics have
almost no power to explain cross-sectional differences in substitution patterns.
We can also test whether household spending patterns are consistent with simple models of intertemporal substitution. Models in which households can substitute spending over
time generally predict that, all else equal, households will consume more at dates with low
relative prices; the extent of this substitution will depend on the elasticity of intertemporal substitution and on any borrowing or savings constraints the household faces. We test
whether intertemporal substitution goes in the expected direction by decomposing the crosssectional variation in expenditure growth into variation in inflation and variation in quantity
growth. For any household i, the growth rate of expenditure x between quarters t and t + 4
is
ln xi,t+4 − ln xit = ln πit,t+4 + ln qi,t+4 − ln qit

(13)

where πit,t+4 is a household-level inflation rate and qt is an index of the real quantity consumed
at date t. Hence the cross-sectional variation in spending growth can be decomposed as
Var(ln xi,t+4 − ln xit ) = Var(ln πit ) + Var(ln qi,t+4 − ln qit ) + 2Cov(πit , ln qi,t+4 − ln qit ). (14)
Given expenditure growth, which we observe, and household-level inflation, which we calculate, we can recover the growth rate of the quantity index for each household directly from
(13). We can then compute the variances in (14). We use the Laspeyres inflation rate with
household-level prices to measure πit , and expenditure on goods purchased at both t and t + 4
to measure xi,t+4 and xit . (Thus, as throughout the analysis, this decomposition examines
substitution on the intensive but not the extensive margin.) On average across quarters, the
variance of expenditure growth is 0.116, the variance of log inflation is 0.007, the variance
of quantity growth is 0.113, and the covariance between log inflation and quantity growth is
-0.002. Thus, as theory predicts, households spend more when they face low relative prices.
21

Given a specific model of intertemporal choice, one could use the variances and covariances
we observe to recover the average elasticity of intertemporal substitution, though we do not
pursue such an exercise here because the results would depend greatly on details of the model.
The combination of a positive variance of inflation and negative covariance between
inflation and expenditure means that inequality in real consumption growth is almost the
same as inequality in nominal consumption growth. However, while the amount of inequality
is the same in real and nominal terms, households’ positions in the distribution of real expenditure growth are not the same as their positions in the distribution of nominal expenditure
growth.

4. Time-Series Properties of Household-Level Inflation
The long-run impact of heterogeneous inflation rates on households’ welfare depends
on whether the heterogeneity is persistent or whether a household that experiences high
inflation in one year tends to experience an offsetting low inflation rate in the next year.
Figure 6 shows how the persistence of household-level inflation evolves over time.
For each household whose inflation rate is measured in two consecutive one-year periods, we
compute an annualized inflation rate over the two years. The middle panel of the figure shows
the cross-sectional standard deviation of these annualized two-year inflation rates, computed
with Laspeyres indexes. They remain highly dispersed, demonstrating that even over horizons
longer than a year, households experience markedly different inflation rates. (The online
appendix shows that results are similar for Fisher and Paasche indexes and exhibits the
entire distribution of two-year inflation rates for an illustrative time period.) The standard
deviation of two-year inflation rates with barcode-average prices is about halfway between
the results for CPI prices and household-level prices, demonstrating that, as with one-year
rates, variation in prices for a given barcode and variation in the mix of barcodes within
an item stratum are both important sources of variation in household-level inflation rates.
For comparison, the top panel of the figure computes the cross-sectional standard deviation
of one-year inflation rates among the sample of households used to compute two-year rates
(i.e., those with inflation rates observed in consecutive years). The one-year rates are more
dispersed than the two-year rates, showing that heterogeneity in inflation rates does diminish

22

when the inflation rate is computed over a longer time horizon.
The bottom panel of Figure 6 computes the cross-sectional correlation between a
household’s inflation rate in quarter t and its inflation rate in quarter t + 4. The one-year
serial correlation of inflation rates using household-level prices is approximately −0.1, and
precisely estimated, throughout the sample period. This correlation is much less negative
than we would expect if households drew their price levels at random each period from the
cross-sectional distribution of price levels; in that case, the serial correlation would be −0.5.10
Indeed, we can use the serial correlation of inflation rates to characterize a simple
stochastic process for household price levels. Assume that the deviation of a household’s
price level from the aggregate price level consists of a household fixed effect plus an AR(1)
process at an annual frequency. That is,
ln Pit − ln Pt = µi + ρ(ln Pi,t−4 − ln Pt−4 − µi ) + it ,

(15)

where Pit is the price level of household i in quarter t, Pt is the aggregate price level in quarter
t, µi is a household fixed effect, and it is i.i.d. across households and over time with mean zero
and variance σ 2 . Also assume that households’ initial conditions are drawn from the ergodic
distribution, so that the distribution of ln Pit − ln Pt is stationary, which seems reasonable
given the relative stability of the standard deviations and serial correlations shown in Figure
6. Then
Var(πit ) =

2σ 2
1+ρ

(16)

and
Cov(πit , πi,t−1 ) = σ 2
10

ρ−1
,
1+ρ

(17)

The basket of UPCs used to compute a household’s inflation rate from t + 4 to t + 8 includes all UPCs
bought at both t+4 and t+8, and thus may differ from the basket used to compute inflation from t to t+4. In
the web appendix, we test whether the changing basket affects the serial correlation by constructing inflation
rates with a constant basket. These inflation rates are not directly comparable to those in the body of the
paper because the restriction to constant baskets requires us to use a narrower sample of dates, households,
and goods, which covers less than half of the spending in the baseline sample as measured in dollars, number
of purchases, or number of unique UPCs. The serial correlation of inflation rates falls slightly, to −0.23, but
part of the decrease is due to the change in the sample, and we still strongly reject the hypothesis that it is
−0.5.

23

from which it follows that
ρ=1+2

Cov(πit , πi,t−1 )
= 1 + 2Corr(πit , πi,t−1 ),
Var(πit )

(18)

where the second equality uses the stationarity of the distribution. Thus, a serial correlation of
inflation rates of −0.1 implies a serial correlation of price levels of ρ = 0.8. As a result, shocks
to households’ price levels are persistent but not permanent, according to this stochastic
process.
Together, the high cross-sectional variance and low serial correlation of household-level
inflation rates suggest that for individual households, the aggregate inflation rate is almost
irrelevant as a source of variation in the household-level inflation rate. Over the sample period,
our aggregate inflation rate averages 2.7 percent with a standard deviation of 1.9 percentage
points, whereas the cross-sectional standard deviation of household-level one-year inflation
rates averages 6.2 percentage points. If households’ deviations from aggregate inflation are
independent of the aggregate inflation rate, these figures mean that, over time, 91 percent
of the variance of a household’s annual inflation rate comes from heterogeneity — either the
household fixed effect µi or the idiosyncratic shocks it — and only 9 percent from variability
in aggregate inflation.

5. Conclusion
This paper documents massive heterogeneity in inflation rates at the household level,
an order of magnitude larger than that found in previous work, owing to differences across
households in prices paid within the same categories of goods. Such heterogeneity poses a
range of challenges for monetary economics. Optimal policy in most monetary models is
calculated to maximize the welfare of a representative household that faces the aggregate
inflation rate; because extreme inflation rates cause larger welfare losses than small inflation rates, optimal policy could be different if one accounted for heterogeneity in inflation
and for the policy’s effect on heterogeneity. In addition, even in models that relax the
representative-agent assumption by allowing uninsured shocks to generate heterogeneity in
assets and consumption, all households typically face the same inflation rate and hence the
same real interest rate (see, e.g., Kaplan, Moll, and Violante, 2017). Optimal policy might
24

differ if models allowed households to face identical nominal interest rates but, because inflation rates vary, different real rates. Furthermore, the heterogeneity we observe suggests that
movements of the aggregate price level may not be an important determinant of individual
agents’ inflation rates, potentially explaining why households and small firms fail to be well
informed about aggregate inflation and monetary policy (Binder, 2017; Kumar et al., 2015).
Our results also have implications for the measurement of income inequality. Because
inflation is higher on average for lower-income households for the goods in our data, inequality
in real incomes may be rising faster than inequality in nominal incomes. However, to fully
understand the effect of inflation heterogeneity on income inequality, it would be important
to extend the inflation measurements to a more comprehensive set of goods and services than
that measured in the KNCP. Future research could also investigate why inflation is higher
for lower-income households. Jaravel (2016) proposes a theory involving greater rates of
innovation in product categories popular with high-income households, but other explanations
are also possible, such as different shopping behavior at different income levels. For example,
Orhun and Palazzolo (2017) show that liquidity constraints inhibit low-income households
from taking advantage of bulk discounts and temporary sales, while Argente and Lee (2016)
find that high-income households had lower inflation rates during the Great Recession because
they were more able to substitute toward lower-quality goods.
Heterogeneity in realized inflation could also help to explain heterogeneity in inflation
expectations. Allowing heterogeneity only in the allocation of spending across CPI item
strata, Johannsen (2014) shows that demographic groups with greater dispersion in realized
inflation also have greater dispersion in inflation expectations and proposes an imperfect
information model to explain this finding. Our results show that there is substantially more
heterogeneity in realized inflation once we account for differences in the allocation of spending
across goods within item strata and differences in prices paid for identical goods. The KNCP
does not measure inflation expectations, but it would be valuable for future researchers to
collect data on household-level inflation expectations and UPC-level purchasing patterns
within the same dataset so that the relationship between expectations and realized inflation
could be examined while allowing for all sources of heterogeneity.
However, the implications of household-level inflation heterogeneity depend impor25

tantly on whether households can forecast where they will fall in the cross-sectional distribution. If a household has no idea where in the inflation distribution its particular inflation
rate will fall each year, the household’s best way to forecast its own inflation rate is still
to forecast the aggregate inflation rate. In such a case, while heterogeneity in realized inflation rates may have distributional consequences, it should have little impact on inflation
expectations or forward-looking decisions. By contrast, if households can predict whether
their own inflation rates will be above or below average, heterogeneity in inflation rates will
affect expectations and dynamic choices. Our data show that inflation rates at the household
level are only weakly correlated with observables and nearly serially uncorrelated. Thus, we
have little ability to forecast household-level deviations from aggregate inflation, either in the
cross-section or over time, with the limited information available to us as econometricians.
Whether households can use their much larger information sets to make better forecasts of
their idiosyncratic inflation rates is an important question that we leave for further research.

References
Angeletos, G. and Jennifer La’O, 2009, “Incomplete Information, Higher-Order Beliefs and
Price Inertia,” Journal of Monetary Economics 56, S19–S37.
Argente, David, and Munseob Lee, 2016, “Cost of Living Inequality During the Great Recession,” manuscript, University of Chicago.
Binder, Carola, 2017, “Fed speak on main street: Central bank communication and household
expectations,”Journal of Macroeconomics 52, 238–251.
Boskin, Michael J., Ellen R. Dulberger, Robert J. Gordon, Zvi Griliches, and Dale Jorgenson,
1996, “Toward a More Accurate Measure of the Cost of Living,” Final Report to the Senate
Finance Committee from the Advisory Commission to Study the Consumer Price Index,
accessed March 17, 2016, at https://www.ssa.gov/history/reports/boskinrpt.html.
Bronnenberg, Bart J., Jean-Pierre Dubé, Matthew Gentzkow, and Jesse M. Shapiro, 2015,
“Do Pharmacists Buy Bayer? Informed Shoppers and the Brand Premium,” Quarterly
Journal of Economics 130(4), 1669–1726.

26

Bureau of Labor Statistics, 2015, “The Consumer Price Index,” in Handbook of Methods,
Washington: Bureau of Labor Statistics.
Einav, Liran, Ephraim Leibtag, and Aviv Nevo, 2010, “Recording Discrepancies in Nielsen
Homescan Data: Are They Present and Do They Matter?” Quantitative Marketing and
Economics 8(2), 207–239.
Federal Open Market Committee, 2016, “Statement on Longer-Run Goals and Monetary
Policy Strategy,” adopted effective Jan. 24, 2012, amended effective Jan. 26, 2016. Accessed March 22, 2016, at http://www.federalreserve.gov/monetarypolicy/files/
FOMC_LongerRunGoals_20160126.pdf.
Handbury, Jessie, 2013, “Are Poor Cities Cheap for Everyone? Non-homotheticity and the
Cost of Living Across U.S. Cities,” manuscript, University of Pennsylvania.
Hobijn, Bart, and David Lagakos, 2005, “Inflation Inequality in the United States,” Review
of Income and Wealth 51(4), 581–606.
Hobijn, Bart, Kristin Mayer, Carter Stennis, and Giorgio Topa, 2009, “Household Inflation
Experiences in the U.S.: A Comprehensive Approach,” Federal Reserve Bank of San Francisco Working Paper 2009-19.
Jaravel, Xavier, 2016, “The Unequal Gains from Product Innovations,” manuscript, Stanford
University.
Johannsen, Benjamin K., 2014, “Inflation Experience and Inflation Expectations: Dispersion
and Disagreement Within Demographic Groups,” Finance and Economics Discussion Series
2014-89, Board of Governors of the Federal Reserve System.
Kaplan, Greg, and Guido Menzio, 2015, “The Morphology of Price Dispersion,” International
Economic Review 56(4), 1165–1206.
Kaplan, Greg, Benjamin Moll, and Giovanni L. Violante, 2017, “Monetary Policy According
to HANK,” manuscript, Princeton University.

27

King, Robert G., 1982, “Monetary Policy and the Information Content of Prices,” Journal
of Political Economy 90(2), 247–279.
Kilts Center Archive of the Nielsen Company, 2013a, “Consumer Panel Dataset FAQ’s.”
Kilts Center Archive of the Nielsen Company, 2013b, “Consumer Panel Dataset Manual.”
Kumar, Saten, Hassan Afrouzi, Olivier Coibion, and Yuriy Gorodnichenko, 2015, “Inflation
Targeting Does Not Anchor Inflation Expectations: Evidence from Firms in New Zealand,”
Brookings Papers on Economic Activity. Fall 2015, 151–208.
Lucas, Robert E., Jr., 1972, “Expectations and the Neutrality of Money,” Journal of Economic Theory 4(2), 103–124.
Lucas, Robert E., Jr., 1975, “An Equilibrium Model of the Business Cycle,” Journal of
Political Economy 83(6), 1113–1144.
Michael, Robert T., 1979, “Variation Across Households in the Rate of Inflation,” Journal of
Money, Credit and Banking 11(1), 32–46.
Nimark, Kristoffer, 2008, “Dynamic Pricing and Imperfect Common Knowledge,” Journal of
Monetary Economics 55(2), 365–382.
Orhun, A. Yeşim, and Mike Palazzolo, 2017, “Frugality is Hard to Afford,” manuscript,
University of Michigan.
Prais, S.J., 1959, “Whose Cost of Living?” Review of Economic Studies 26(2), 126–134.
Rao, J.N.K., C.F.J. Wu, and K. Yue, 1992, “Some Recent Work on Resampling Methods for
Complex Surveys,” Survey Methodology 18, 209–217.
Reis, Ricardo, 2006, “Inattentive Consumers,” Journal of Monetary Economics 53(8), 1761–
1800.
Sims, Christopher A., 2003, “Implications of Rational Inattention,” Journal of Monetary
Economics 50(3), 665–690.

28

9
−3

percentage
points
0
3
6
2004

2005

2006

2007

CPI
KNCP aggregate

2008

2009

CPI food at home
+/− 2 s.e.

2010

2011

2012

CPI ex energy

Figure 1: Aggregate inflation rates.
CPI inflation rates computed as annual percentage change in quarterly average of monthly index values.
Inflation rate plotted for each quarter is the change in price index from that quarter to the quarter one year
later. Vertical bars show an interval of ± 2 bootstrap standard errors around each point estimate. Source for
CPI data: Bureau of Labor Statistics.

29

−6

−4

percentage points
−2
0
2
4

6

(a) distribution of deviations between KNCP and CPI stratum inflation rates

2004

2005

2006

2007

2008

2009

2010

2011

10th, 90th percentiles

median

25th, 75th percentiles

mean

2012

−2

0

percentage points
2
4

6

8

(b) aggregate inflation rates computed with KNCP and CPI prices

2004

2005

2006

2007

2008

KNCP prices

2009

2010

2011

2012

CPI prices

Figure 2: Comparison of KNCP and CPI inflation rates at the item stratum level.
Panel (a) shows distribution across item strata of difference between inflation rate in KNCP data and that
published for CPI. Panel (b) shows average inflation rates across all item strata using KNCP data and using
published CPI indexes, weighted by distribution of spending in KNCP. CPI inflation rates are annual percent
change in quarterly average of monthly indexes. Inflation rate plotted for each quarter is change in price
index from that quarter to the quarter one year later.

30

0

.2

density
.4 .6

.8

(a) Laspeyres indexes, 5+ barcodes

−5

0

5

10

household inflation rate (%)

0

.2

density
.4 .6

.8

(b) Laspeyres indexes, 25+ barcodes

−5

0

5

10

household inflation rate (%)

0

density
.2 .4 .6 .8

(c) Laspeyres indexes, 30%+ matched spending

−5

0

5

10

household inflation rate (%)

household−level prices

barcode−average prices

stratum−average prices

CPI prices

Figure 3: Distributions of household-level inflation rates, fourth quarter of 2004 to fourth
quarter of 2005.
Kernel density estimates using Epanechnikov kernel. Bandwidth is 0.05 percentage point for inflation rates
with household-level and barcode-average prices and 0.005 percentage point for inflation rates with CPI prices.
Data on 23,635 households with matched consumption in 2004q4 and 2005q4. Plots truncated at -5 percent
and 10 percent.

31

0

2

4

6

8

10

(a) Interquartile range

2004

2005

2006

2007

2008

2009

2010

2011

2012

household−level prices

barcode−average prices

stratum−average prices

CPI prices

−10

−5

0

5

10

15

(b) Evolution of the distribution of household inflation rates with household−level prices

2004

2005

2006

2007

2008

2009

2010

10th, 90th percentiles

25th, 75th percentiles

mean

aggregate index

2011

2012
median

Figure 4: Measures of the dispersion of household-level inflation rates.
Calculated with Laspeyres indexes. Vertical bars in panel (a) show an interval of ± 2 bootstrap standard
errors around each point estimate. Mean in panel (b) is calculated on data from 1st to 99th percentiles of
distribution of inflation rates with household-level prices at each date.

32

−.5

percentage points
−.25
0
.25

.5

(a) Mean differences of Laspeyres and Paasche indexes from Fisher index

2004

2005

2006

2007

2008

Laspeyres

2009

2010

2011

2012

Paasche

0

.05

density
.1

.15

.2

(b) Distribution of household−level difference between Laspeyres and Paasche indexes,
fourth quarter of 2004 to fourth quarter of 2005

−10

−5
0
5
10
difference between household Laspeyres and Paasche inflation rates (%)

15

Figure 5: Difference between Laspeyres, Paasche, and Fisher indexes.
Vertical bars in panel (a) show an interval of ± 2 bootstrap standard errors around each point estimate. Panel
(b) shows a kernel density estimate using Epanechnikov kernel and bandwidth of 0.05 percentage point; data
are on 23,635 households with matched consumption in 2004q4 and 2005q4, and plot is truncated at 1st and
99th percentiles of distribution of inflation rates with household-level prices.

33

0

2

4

6

8

(a) Standard deviation of one−year inflation rates

2004

2005

2006

2007

2008

2009

2010

2011

2012

2011

2012

2011

2012

0

2

4

6

8

(b) Standard deviation of two−year inflation rates

2004

2005

2006

2007

2008

2009

2010

−.4 −.2

0

.2

.4

(c) Within−household serial correlation of annual inflation rates

2004

2005

2006

2007

2008

2009

2010

household−level prices

barcode−average prices

stratum−average prices

CPI prices

Figure 6: Evolution of the persistence of household-level inflation rates.
Calculated with Laspeyres indexes. Calculations for each quarter use the subset of households for which
inflation with household-level prices is observed and falls between the 1st and 99th percentiles of the distribution in both that quarter and the quarter one year ahead. Vertical bars show an interval of ± 2 bootstrap
standard errors around each point estimate.

34

Table 1: The dispersion of household-level inflation rates.
A. Interquartile range
Household-level prices:
Laspeyres
Fisher
Paasche
Barcode-average prices:
Laspeyres
Fisher
Paasche
Stratum-average prices:
Laspeyres
Fisher
Paasche
CPI prices:
Laspeyres
Fisher
Paasche

mean

s.d.

min

max

7.33
7.13
7.37

0.74
0.72
0.76

6.23
6.12
6.34

8.99
8.92
9.18

3.99
3.87
3.98

0.77
0.75
0.76

3.06
2.95
3.03

5.73
5.68
5.81

1.96
1.83
1.95

0.95
0.88
0.92

0.89
0.91
0.92

3.96
3.84
3.92

1.61
1.57
1.62

0.80
0.77
0.78

0.71
0.70
0.71

3.77
3.53
3.42

B. Difference between 90th and 10th percentiles
Household-level prices:
Laspeyres
15.87 1.44 13.67 19.74
Fisher
15.32 1.36 13.27 18.84
Paasche
15.83 1.38 13.76 19.48
Barcode-average prices:
Laspeyres
6.39 1.18
4.85
8.94
Fisher
6.15 1.14
4.70
8.69
Paasche
6.31 1.14
4.84
8.82
Stratum-average prices:
Laspeyres
4.12 1.93
1.93
7.92
Fisher
3.78 1.75
1.92
7.99
Paasche
4.07 1.89
1.97
8.41
CPI prices:
Laspeyres
3.30 1.66
1.41
7.69
Fisher
3.21 1.58
1.38
7.17
Paasche
3.33 1.61
1.44
7.07
C. Ratio of variance with common prices
with household prices
Barcode-average prices:
Laspeyres
0.38 0.07
Fisher
0.37 0.07
Paasche
0.37 0.07
Stratum-average prices:
Laspeyres
0.14 0.15
Fisher
0.10 0.09
Paasche
0.12 0.11
CPI prices:
Laspeyres
0.07 0.07
Fisher
0.07 0.08
Paasche
0.07 0.08

to variance

0.29
0.29
0.30

0.58
0.58
0.59

0.03
0.02
0.03

0.71
0.41
0.44

0.01
0.01
0.01

0.30
0.30
0.30

Averages from 2004q1 through 2012q3 of dispersion measures for each date.
35

Table 2: Quantile regressions of household-level inflation rates
on aggregate inflation.
(1) Aggregate index
Decile
1
2
3
4
5
6
7
8
9

Slope
1.011
1.013
1.026
1.052
1.093
1.137
1.198
1.243
1.305

(0.015)
(0.009)
(0.008)
(0.008)
(0.007)
(0.009)
(0.010)
(0.012)
(0.019)

(2) Median inflation

Intercept
−7.602
−4.609
−2.810
−1.448
−0.264
0.944
2.286
4.189
7.491

(0.058)
(0.039)
(0.031)
(0.027)
(0.026)
(0.030)
(0.034)
(0.046)
(0.066)

Slope
0.997
0.973
0.966
0.978
1.000
1.030
1.073
1.100
1.112

(0.011)
(0.006)
(0.005)
(0.003)
(0.000)
(0.004)
(0.006)
(0.009)
(0.014)

Intercept
−7.445
−4.421
−2.606
−1.224
0.000
1.242
2.641
4.595
8.036

(0.044)
(0.022)
(0.019)
(0.013)
(0.000)
(0.014)
(0.022)
(0.035)
(0.053)

The table shows the slope and intercept from quantile regressions of
household-level inflation rates, computed with Laspeyres indexes and
household-level prices, on measures of the overall inflation rate. In panel
(1), the overall inflation rate is the aggregate CPI for the KNCP universe
of goods. In panel (2), the overall inflation rate is the median of household
inflation rates. Sample contains 835,386 household-quarter observations.
Bootstrap standard errors are in parentheses.

36

Table 3: Cumulative inflation rates at different levels of household income.
Household income

difference

<$20,000

$20,000–
$39,999

$40,000–
$59,999

$60,000–
$99,999

≥$100,000

(<$20,000 vs.
≥$100,000)

34.35
(0.90)
33.25
(0.66)
32.96
(0.86)

32.37
(0.58)
31.11
(0.57)
30.61
(0.64)

29.90
(0.60)
28.26
(0.63)
27.64
(0.60)

27.84
(0.55)
25.86
(0.56)
25.72
(0.60)

25.74
(0.65)
24.23
(0.71)
24.98
(0.63)

8.61
(1.10)
9.02
(0.99)
7.98
(1.08)

fraction of population
0.17
average age of household head(s)
<30
0.01
30–39
0.08
40–49
0.13
50–59
0.22
60–69
0.22
≥70
0.34
highest education of household head(s)
less than high school
0.1
high school diploma
0.47
some college
0.31
bachelor’s degree
0.1
graduate degree
0.02
Census region
Northeast
0.19
Midwest
0.22
South
0.42
West
0.18
mean # household members
1.74
has children
0.16
white
0.81
black
0.13
Asian
0.01
other nonwhite
0.05
Hispanic
0.07

0.25

0.19

0.22

0.16

0.02
0.1
0.16
0.21
0.22
0.29

0.02
0.14
0.22
0.26
0.2
0.16

0.01
0.16
0.27
0.29
0.17
0.09

0.01
0.13
0.31
0.34
0.15
0.07

0.05
0.44
0.33
0.14
0.04

0.02
0.33
0.35
0.22
0.08

0.01
0.2
0.32
0.31
0.15

0
0.09
0.23
0.37
0.31

0.17
0.23
0.39
0.2
2.15
0.21
0.8
0.12
0.01
0.07
0.1

0.17
0.24
0.39
0.21
2.51
0.29
0.79
0.11
0.02
0.08
0.11

0.19
0.23
0.35
0.23
2.72
0.33
0.78
0.1
0.03
0.08
0.12

0.21
0.21
0.32
0.26
2.79
0.31
0.79
0.08
0.06
0.07
0.12

cumulative inflation (%)
over 9 years ending in:
2013q1
2013q2
2013q3

Calculated with Laspeyres indexes and household-level prices. Bootstrap standard errors are in parentheses.
Demographics are averages over the sample period.

37

38
0.012
0.009

(0.145)
(0.137)
(0.137)
(0.139)
(0.140)
(0.108)
(0.107)
(0.110)
(0.118)
(0.040)
(0.033)
(0.046)
(0.018)
(0.094)
(0.029)
(0.049)
(0.125)
(0.067)
(0.056)

0.244
0.297
0.362
0.430
0.453
-0.029
-0.102
-0.163
-0.137
-0.175
-0.063
-0.429
0.110
0.074
-0.033
0.063
-0.089
-0.025
-0.085

-

(0.039)
(0.041)
(0.045)
(0.050)

-0.126
-0.258
-0.468
-0.597

-

-0.019
-0.013
0.053
-0.092

-0.029
0.041
-0.254
0.058
-0.153
0.012

-0.120
-0.162
-0.238
-0.267

0.160
0.218
0.262
0.287
0.276

-0.093
-0.185
-0.267
-0.404

coeff.

(0.027)
(0.070)
(0.045)
(0.036)

(0.025)
(0.023)
(0.027)
(0.012)
(0.066)
(0.020)

(0.054)
(0.054)
(0.058)
(0.063)

(0.089)
(0.090)
(0.090)
(0.088)
(0.090)

(0.025)
(0.027)
(0.028)
(0.035)

std. err.

(3) Median,
barcode prices

-

-0.061
-0.010
0.007
-0.010

-0.025
0.006
-0.039
0.026
0.048
-0.029

-0.062
-0.092
-0.125
-0.141

0.066
0.085
0.111
0.104
0.065

-0.061
-0.077
-0.099
-0.124

coeff.

(0.011)
(0.019)
(0.017)
(0.013)

(0.009)
(0.008)
(0.011)
(0.005)
(0.024)
(0.007)

(0.023)
(0.024)
(0.025)
(0.025)

(0.028)
(0.025)
(0.027)
(0.027)
(0.027)

(0.010)
(0.011)
(0.013)
(0.013)

std. err.

(4) Median,
CPI prices

(0.042)
(0.038)

0.352
-0.409
-

(0.048)
(0.136)
(0.070)
(0.060)

(0.041)
(0.034)
(0.045)
(0.018)
(0.100)
(0.030)

(0.103)
(0.102)
(0.104)
(0.114)

(0.162)
(0.152)
(0.156)
(0.156)
(0.158)

(0.039)
(0.039)
(0.043)
(0.047)

std. err.

0.074
-0.072
-0.035
-0.065

-0.169
-0.061
-0.430
0.109
0.066
-0.032

-0.040
-0.115
-0.169
-0.146

0.288
0.360
0.437
0.515
0.527

-0.136
-0.271
-0.479
-0.599

coeff.

(5) Median
household prices

-

0.825
1.442
0.317
-0.110

-0.350
-1.018
-0.285
-0.363
-0.932
0.281

-0.280
-0.118
-0.099
0.024

-0.775
-1.031
-1.187
-1.293
-1.214

-0.399
-0.597
-0.706
-0.873

coeff.

(0.099)
(0.213)
(0.141)
(0.094)

(0.084)
(0.078)
(0.091)
(0.030)
(0.199)
(0.054)

(0.167)
(0.165)
(0.180)
(0.185)

(0.270)
(0.257)
(0.251)
(0.257)
(0.256)

(0.079)
(0.085)
(0.086)
(0.096)

std. err.

(6) Interquartile
range
household prices

-

-0.194
-0.506

0.907
1.535
0.337
-0.114

-0.380
-1.064
-0.310
-0.313
-0.838
0.234

-0.222
-0.072
-0.028
0.054

-0.621
-0.774
-0.879
-0.892
-0.872

-0.338
-0.553
-0.655
-0.830

coeff.

(0.047)
(0.049)

(0.095)
(0.222)
(0.137)
(0.097)

(0.081)
(0.078)
(0.093)
(0.032)
(0.190)
(0.053)

(0.172)
(0.169)
(0.184)
(0.188)

(0.268)
(0.260)
(0.255)
(0.263)
(0.258)

(0.076)
(0.085)
(0.084)
(0.097)

std. err.

(7) Interquartile
range
household prices

Dependent variable is difference between household inflation rate and aggregate inflation rate for the equivalent universe of goods. Sample contains 835,386 householdquarter observations. Bootstrap standard errors in parentheses. In columns (1), (2), (5), (6), and (7), household inflation rate is computed with household-level prices;
column (3) uses barcode-average prices, and column (4) uses CPI prices. All columns use Laspeyres indexes. Column (1) shows results from ordinary least squares
regression; columns (2), (3), (4), and (5) from median regression; and columns (6) and (7) from interquartile range regression. Regressions include time dummy variables.
Omitted categories are: income less than $20,000; white; non-Hispanic; heads’ average age less than 30; heads’ highest education less than high school diploma; Northeast
region. Count of shopping trips excludes trips on which the household spent less than $1.

R2
R2 (time dummies only)

household income
$20,000–$39,999
-0.206
(0.055)
$40,000–$59,999
-0.420
(0.052)
$60,000–$99,999
-0.587
(0.059)
≥$100,000
-0.731
(0.065)
average age of household head(s)
30–39
0.384
(0.205)
40–49
0.451
(0.203)
50–59
0.511
(0.206)
60–69
0.552
(0.199)
≥70
0.552
(0.203)
highest education of household head(s)
high school diploma
-0.064
(0.127)
some college
-0.138
(0.127)
bachelor’s degree
-0.251
(0.128)
graduate degree
-0.285
(0.139)
Census region
Midwest
-0.179
(0.049)
South
-0.140
(0.046)
West
-0.517
(0.054)
# household members
0.089
(0.022)
has children
-0.115
(0.118)
has children ×
0.002
(0.034)
# household members
black
0.058
(0.064)
Asian
-0.074
(0.156)
other nonwhite
-0.033
(0.087)
Hispanic
-0.068
(0.068)
log(# of shopping trips)
initial quarter
final quarter

std. err.

coeff.

coeff.

std. err.

(2) Median,
household prices

(1) OLS,
household prices

Deviation of household inflation from aggregate inflation

Table 4: Regressions of household-level inflation rates on household demographics and shopping behavior.

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Benjamin Chabot, Eric Ghysels, and Ravi Jagannathan

WP-14-27

Early Life Environment and Racial Inequality in Education and Earnings
in the United States
Kenneth Y. Chay, Jonathan Guryan, and Bhashkar Mazumder

WP-14-28

Poor (Wo)man’s Bootstrap
Bo E. Honoré and Luojia Hu

WP-15-01

Revisiting the Role of Home Production in Life-Cycle Labor Supply
R. Jason Faberman

WP-15-02

2

Working Paper Series (continued)
Risk Management for Monetary Policy Near the Zero Lower Bound
Charles Evans, Jonas Fisher, François Gourio, and Spencer Krane
Estimating the Intergenerational Elasticity and Rank Association in the US:
Overcoming the Current Limitations of Tax Data
Bhashkar Mazumder

WP-15-03

WP-15-04

External and Public Debt Crises
Cristina Arellano, Andrew Atkeson, and Mark Wright

WP-15-05

The Value and Risk of Human Capital
Luca Benzoni and Olena Chyruk

WP-15-06

Simpler Bootstrap Estimation of the Asymptotic Variance of U-statistic Based Estimators
Bo E. Honoré and Luojia Hu

WP-15-07

Bad Investments and Missed Opportunities?
Postwar Capital Flows to Asia and Latin America
Lee E. Ohanian, Paulina Restrepo-Echavarria, and Mark L. J. Wright

WP-15-08

Backtesting Systemic Risk Measures During Historical Bank Runs
Christian Brownlees, Ben Chabot, Eric Ghysels, and Christopher Kurz

WP-15-09

What Does Anticipated Monetary Policy Do?
Stefania D’Amico and Thomas B. King

WP-15-10

Firm Entry and Macroeconomic Dynamics: A State-level Analysis
François Gourio, Todd Messer, and Michael Siemer

WP-16-01

Measuring Interest Rate Risk in the Life Insurance Sector: the U.S. and the U.K.
Daniel Hartley, Anna Paulson, and Richard J. Rosen

WP-16-02

Allocating Effort and Talent in Professional Labor Markets
Gadi Barlevy and Derek Neal

WP-16-03

The Life Insurance Industry and Systemic Risk: A Bond Market Perspective
Anna Paulson and Richard Rosen

WP-16-04

Forecasting Economic Activity with Mixed Frequency Bayesian VARs
Scott A. Brave, R. Andrew Butters, and Alejandro Justiniano

WP-16-05

Optimal Monetary Policy in an Open Emerging Market Economy
Tara Iyer

WP-16-06

Forward Guidance and Macroeconomic Outcomes Since the Financial Crisis
Jeffrey R. Campbell, Jonas D. M. Fisher, Alejandro Justiniano, and Leonardo Melosi

WP-16-07

3

Working Paper Series (continued)
Insurance in Human Capital Models with Limited Enforcement
Tom Krebs, Moritz Kuhn, and Mark Wright

WP-16-08

Accounting for Central Neighborhood Change, 1980-2010
Nathaniel Baum-Snow and Daniel Hartley

WP-16-09

The Effect of the Patient Protection and Affordable Care Act Medicaid Expansions
on Financial Wellbeing
Luojia Hu, Robert Kaestner, Bhashkar Mazumder, Sarah Miller, and Ashley Wong

WP-16-10

The Interplay Between Financial Conditions and Monetary Policy Shock
Marco Bassetto, Luca Benzoni, and Trevor Serrao

WP-16-11

Tax Credits and the Debt Position of US Households
Leslie McGranahan

WP-16-12

The Global Diffusion of Ideas
Francisco J. Buera and Ezra Oberfield

WP-16-13

Signaling Effects of Monetary Policy
Leonardo Melosi

WP-16-14

Constrained Discretion and Central Bank Transparency
Francesco Bianchi and Leonardo Melosi

WP-16-15

Escaping the Great Recession
Francesco Bianchi and Leonardo Melosi

WP-16-16

The Role of Selective High Schools in Equalizing Educational Outcomes:
Heterogeneous Effects by Neighborhood Socioeconomic Status
Lisa Barrow, Lauren Sartain, and Marisa de la Torre
Monetary Policy and Durable Goods
Robert B. Barsky, Christoph E. Boehm, Christopher L. House, and Miles S. Kimball

WP-16-17

WP-16-18

Interest Rates or Haircuts?
Prices Versus Quantities in the Market for Collateralized Risky Loans
Robert Barsky, Theodore Bogusz, and Matthew Easton

WP-16-19

Evidence on the within-industry agglomeration of R&D,
production, and administrative occupations
Benjamin Goldman, Thomas Klier, and Thomas Walstrum

WP-16-20

Expectation and Duration at the Effective Lower Bound
Thomas B. King

WP-16-21

4

Working Paper Series (continued)
The Term Structure and Inflation Uncertainty
Tomas Breach, Stefania D’Amico, and Athanasios Orphanides

WP-16-22

The Federal Reserve’s Evolving Monetary Policy Implementation Framework: 1914-1923
Benjamin Chabot

WP-17-01

Neighborhood Choices, Neighborhood Effects and Housing Vouchers
Morris A. Davis, Jesse Gregory, Daniel A. Hartley, and Kegon T. K. Tan

WP-17-02

Wage Shocks and the Technological Substitution of Low-Wage Jobs
Daniel Aaronson and Brian J. Phelan

WP-17-03

Worker Betas: Five Facts about Systematic Earnings Risk
Fatih Guvenen, Sam Schulhofer-Wohl, Jae Song, and Motohiro Yogo

WP-17-04

The Decline in Intergenerational Mobility After 1980
Jonathan Davis and Bhashkar Mazumder

WP-17-05

Is Inflation Default? The Role of Information in Debt Crises
Marco Bassetto and Carlo Galli

WP-17-06

Does Physician Pay Affect Procedure Choice and Patient Health?
Evidence from Medicaid C-section Use
Diane Alexander

WP-17-07

Just What the Nurse Practitioner Ordered:
Independent Prescriptive Authority and Population Mental Health
Diane Alexander and Molly Schnell

WP-17-08

How do Doctors Respond to Incentives?
Unintended Consequences of Paying Doctors to Reduce Costs
Diane Alexander

WP-17-09

Closing the Gap: The Impact of the Medicaid Primary Care Rate Increase on Access
and Health Unintended Consequences of Paying Doctors to Reduce Costs
Diane Alexander and Molly Schnell

WP-17-10

Check Up Before You Check Out: Retail Clinics and Emergency Room Use
Diane Alexander, Janet Currie, and Molly Schnell

WP-17-11

The Effects of the 1930s HOLC “Redlining” Maps
Daniel Aaronson, Daniel Hartley, and Bhashkar Mazumder

WP-17-12

Inflation at the Household Level
Greg Kaplan and Sam Schulhofer-Wohl

WP-17-13

5