ESRI Discussion Paper Series No.353

Inflation Expectations and Consumer Spending: Micro-data Evidence

Junichi Kikuchi
Yoshiyuki Nakazono
July 2020

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “ESRI Discussion Papers” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Inﬂation Expectations and Consumer Spending:
Micro-data Evidence*
Junichi Kikuchi†

Yoshiyuki Nakazono‡

Abstract
This study examines the relationship between inﬂation expectations and consumer
spending. Using a combination of original consumer survey data on inﬂation expectations and scanner data on the actual expenditure, we examine whether higher inﬂation
expectations generate greater current spending. The linked data also allows us to directly estimate the value of the elasticity of intertemporal substitution (EIS) based ona standard macroeconomic model. We ﬁnd that higher inﬂation expectations generate greater current spending compared with one year later and the value of the EIS issigniﬁcantly positive and approximately 0.1. On the other hand, liquidity-constrained
consumers seem to decrease current expenditure in response to higher inﬂation expectations. This evidence implies that the impact of higher inﬂation expectations on consumers’ intertemporal allocation may vary depending on the type of consumer.
JEL Classiﬁcation:

D84; E21; E31; E52

Keywords:

elasticity of intertemporal substitution; Euler equation;
forecast data; inﬂation expectations; survey data

* We thank INTAGE Inc. for the cooperation regarding the online household survey. We also thank thediscussant, Naohito Abe, for invaluable inputs and Natsuki Arai, Kengo Nutahara, Rui Ota, Qing-Yuan Sui,
Naoki Takayama, and participants in the 2019 Japanese Joint Statistical Meeting and the Japanese Economic
Association 2020 Spring Meeting and seminar at the CIGS for their comments and suggestions. Nakazono
acknowledges ﬁnancial support from The Kigawada Foundation (The 21-Seiki Bunka Gakujyutsu Zaidan) and
JSPS KAKENHI Grant Number 19K13649.
†
Yokohama City University‡
Cabinet Ofﬁce, Government of Japan; Yokohama City University

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

1 Introduction
The issue of expectation has been increased interest among policy makers and researchers. While conventional monetary policy cuts nominal interest rates to stimulate the economy, there is no room to reduce short-term nominal interest rates which
are virtually zero under the effective lower bound (ELB). As a result, the major central banks such as the Federal Reserve, European Central Bank, and Bank of Japan,
have implemented unconventional monetary policies designed to engineer higher inﬂation expectations and thereby increase current spending. There is a theoretical basis fora relationship between inﬂation expectations and current spending. Based on the Euler equation, higher inﬂation expectations lower real interest rates via the Fisher equation; this drop in real interest rates discourages consumer saving and stimulates current
spending. Under the ELB, the effect of inﬂation expectation on the real economy has
been the central transmission mechanism for unconventional monetary policies.
However, despite its importance in macroeconomics, the empirical relationship between inﬂation expectations and consumer spending has not yet been fully uncovered.
One of the main reasons is because we do not have concurrent information of consumption growth and inﬂation expectations. Although panel data on consumers’ actual
expenditure are often available, there is, as far as we know, no survey data available forthe same respondents in relation to inﬂation expectations.1 To tackle the issue of thelack of matched data on inﬂation expectations and consumer expenditure, the literatureusually conducts a generalized method of moments to estimate the Euler equation, assuming the choices of instrumental variables (IVs) are valid. However, this approach
is often criticized because the correlation between the independent variable and the IVs
is very weak.2 At any attempt to directly estimate the Euler equation which includes
expected inﬂation rates should use matched data on the actual expenditure and inﬂation
expectations.
By using a survey to ask consumers about their subjective inﬂation expectations
1

For example, the Michigan Surveys of Consumers includes inﬂation expectations and readiness to spend
on durable goods. However, the surveys do not include data on the respondents’ actual spending.
2
Yogo (2004) point outs the weak-instruments problem that can lead to bias in estimators and size distortion
in hypothesis tests and shows that the elasticity of intertemporal substitution (EIS) is less than 1 and not
signiﬁcantly different from 0.

1

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"and matching the results of that survey with the actual expenditure, this study directly
estimates the Euler equation and examines the relationship between inﬂation expectations and consumer spending. We focus on whether consumers change the intertemporal allocation of resources in response to changes in their inﬂation expectations. First,
by combining the original consumer survey on inﬂation expectations and scanner dataon the actual expenditure, we examine whether higher inﬂation expectations generate
greater current spending. Second, we structurally estimate the value of the elasticity
of intertemporal substitution (EIS). The unique data we combine allows us to directly
estimate the deep parameter of the EIS based on a standard model. Third, we identify
those who are facing liquidity constraints and investigate how liquidity-constrained consumers respond to changes in inﬂation expectations. If liquidity-constrained consumers
react to higher inﬂation expectations in a manner that contradicts standard model predictions, the impacts on aggregate spending of unconventional monetary policies aiming
to raise inﬂation expectations may vary among different types of consumer.
This study contributes to the existing literature. First, we ﬁnd that in response tohigher inﬂation expectations, consumers generate greater current spending comparedwith one year later. This suggests that the prediction of higher inﬂation rates inducesa change in the intertemporal allocation for the optimal consumption path. In otherwords, theoretical predictions are consistent with the data. Second, we identify thevalue of the EIS based on a structural model and show that its estimated value is significantly positive and approximately 0.1. Our results suggest that the EIS may be smaller
than those measured in previous studies. Third, we ﬁnd that consumers who are facing
liquidity constraints could not generate greater current spending in response to changes
in their expectations. In fact, our result based on the reduced form estimation suggeststhat liquidity-constrained consumers who live from hand-to-mouth may save more andspend less in response to higher inﬂation expectations. The result indicates that higher
inﬂation expectations may have varying impacts on consumers’ intertemporal allocation. This implies that the macroeconomic effects on current spending of unconventional monetary policies aiming to raise inﬂation expectations might be ambiguous.
Our study is linked to two strands in the literature. First, our study is related to2

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"empirical studies exploring the theoretical relationship between inﬂation expectations
and consumer spending. The literature provides mixed evidence about this relationship. Using survey on the expenditure plan, Ichiue and Nishiguchi (2015) and Ducaet al. (2018) show evidence supporting the predictions of the standard models: consumers expecting higher inﬂation tend to increase current spending. Ehrmann et al.
(2017) and Weber et al. (2015) explore the relationship between inﬂation expectations
and consumer attitudes to spending and show also results in line with the predictionsof macroeconomic theory. On the other hand, Bachmann et al. (2015) provide contradictory evidence. Using U.S. data, they show that consumers expecting higher inﬂation
tend to decrease current spending when nominal interest rates are virtually zero, while
Burke and Ozdagli (2013) report little evidence that consumers respond to changes ininﬂation expectations. While past studies relied on qualitative data based on expenditure plans or consumers’ attitudes to spending, we use the quantitative survey on theactual spending collected by using home scanners and investigate the association between inﬂation expectations and consumer’s actual spending. Furthermore, we identify
consumers with liquidity constraints by exploiting the survey information on the levels of income and ﬁnancial assets and examine how hand-to-mouth consumers react tohigher inﬂation expectations. This study therefore contributes to the existing literatureby exploring the varied impacts of higher inﬂation expectations on consumer spending.
Second, our study is related to the literature on estimating the value of the EIS.
Starting with Hall (1978), Hansen and Singleton (1982), Hansen and Singleton (1983),
and Hall (1988), many papers have attempted to estimate the EIS. Hall (1988) concludes
that the EIS is unlikely to be much above 0.1, and may well be zero, using time-series
data of consumption growth and interest rates. Follow-up papers, however, come up
with mixed results.3 There is therefore no clear consensus on the magnitude of theelasticity of intertemporal substitution; this arises because of the data limitations in

3For example, the EIS Attanasio and Weber (1995) estimated is 0.56, using the Consumer Expenditure
Survey, while Cashin and Unayama (2016) ﬁnd an EIS of 0.21, using data from the Japanese Family Income
and Expenditure Survey. Gourinchas and Parker (2002) estimate the EIS as being between 0.7 to 2.0, using the
U.S. American Consumer Expenditure Survey, while Gary and Kumar (2009) estimate the EIS at 0.74 using
data about 401(k) participation. Among further studies that estimate the EIS are Basu and Kimball (2002),
Barro (2009), and Guvenen (2006), each one of which estimates the EIS differently.

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ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

relation to inﬂation expectations, which are usually unobservable and not available. In
the case of our study, we conducted original survey on consumer inﬂation expectations
and believe that we can provide the more accurate value of the EIS.
The structure of this paper proceeds as follows. Section 2 shows the theoretical prediction of the relationship between inﬂation expectations and consumer spending while
Section 3 describes the survey data we use. Section 4 presents our results and Section
5 discuss the intertemporal allocation of liquidity constrained consumers. Section 6summarizes the ﬁndings and presents the conclusions.

2 Theoretical framework and identiﬁcation strategy
2.1 A standard model of consumption in the complete markets
We ﬁrst present the theoretical framework to describe the relationship between inﬂation
expectations and the growth rate of consumption. Suppose that the utility function is
isoelastic. The objective of the consumer is:

max Et

T −t
∑
τ =0

c1−γ − 1(1 + δ)−τ t+τ
,1−γ

subject to the budget constraint:
at+τ +1 = (1 + rt+τ ) at+τ + yt+τ − ct+τ ,and the terminal condition aT = 0. δ is denoted as the rate of time preference and γ −1 isthe elasticity of intertemporal substitution. In this setting, we assume that the consumer
can borrow and save as much as needed. Under the budget constraint, the ﬁrst orderconditions lead to the Euler equation:
[(
Et

ct+1
ct

)−γ

4

1 + rt
1+δ

]
= 1.

(1)

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Rewriting Equation (1) obtains:Et [∆ ln ct+1 ] = γ −1 (rt − δ) .

(2)

Equation (2) shows the optimal consumption path in the complete market.

2.2 Identiﬁcation strategy
Second, we consider how consumption varies over time, especially under the ELB.
Because ∆ ln ct+1 = Et ∆ ln ct+1 + εt+1 and the (linearized) Fisher equation indicatesthat rt = it − Et [πt+1 ], Equation (2) can be written as:∆ ln ct+1 = γ −1 (it − Et [πt+1 ] − δ) + εt+1 ,(3)

where it and Et [πt+1 ] are denoted as the nominal interest rate and inﬂation expectations
at time t. Because it is almost zero under the ELB,4 Equation (3) can be simply writtenas:∆ ln ct+1 = −γ −1 (Et [πt+1 ] + δ) + εt+1 .

(4)Equation (4) suggests that for consumers who are not facing liquidity constraints, thegrowth rate of consumption basically depends only on inﬂation expectations and deepparameters, γ −1 and δ.
Equation (4) simply indicates the theoretical predictions about the relationship between inﬂation expectations and the growth rate of consumption. When consumers
expect a higher inﬂation rate, they change the intertemporal allocation of consumption
and saving; they save less and spend more today. Because higher inﬂation expectations
discourage consumers from saving and encourage them to increase consumption in thecurrent period, the growth rate of consumption ∆ ln ct+1 decreases. Thus, Equation (4)allows us to empirically test the theoretical relationship between inﬂation expectations
and the consumption path.
In order to investigate the theoretical relationship, we use the quarterly survey data
4

Figure 1 shows the development of the short-term nominal interest rates in Japan since the 1980s. It showsthat the short-term nominal interest rates are virtually zero after 2010.

5

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

on inﬂation expectations and the actual expenditure to estimate the following equation:( i )c= −γ −1 × Eti [πt→t+4 ] + Xβ + εit+1 ,∆ ln t+4
cit(5)

where cit , Eti [πt→t+4 ], and X are denoted as individual i’s actual expenditure at time t,
individual i’s inﬂation expectations over the next four quarters (i.e. next year) at time t,
and control variables such as time dummies and the constant term. In addition, Equation (4) allows us to directly estimate the deep parameter, γ −1 . Recall that γ −1 is theEIS. Because the survey linking consumer inﬂation expectations and their actual expenditure is available, we can simply obtain the structural parameter of γ −1 by estimating
Equation (5).

3 Data3.1 Survey of inﬂation expectations
We conduct a quarterly online survey for Japanese households to collect inﬂation expectations from 2015(Q4). Every quarter, approximately 30,000 households answer thequestions regarding their outlook for price changes for the next one, three, and ten years.
Respondents are asked to answer the following questions:“What will the levels of CPI be over the next one-, three-, and ten-year periods
given that the current level of CPI is 10,000? Provide price level ﬁgures over eachperiod, excluding the impact of consumption tax hikes on the price levels.”
The question asks respondents to estimate the CPI levels which they forecast overthe next 1-, 3-, and 10-year periods on average. The questionnaire directly measures
households’ inﬂation expectations in the short, medium, and long term. The questionnaire is unique because the survey allows us to obtain the quantitative answers andalleviate the “round number” problem which Binder (2017) points out. First, we ask
respondents to answer their point estimates by ﬁlling out the ﬁgures which they forecast as the future levels of CPI. While some surveys ask respondents to choose from
options such as “Prices will probably rise” or “Prices will probably fall,” we can obtain

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ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

the numeric measures to capture consumers’ inﬂation forecasts. For example, when a
respondent answers 10,080, 10,600, and 11,000 as her forecasts for the price levels overthe following 1-, 3-, and 10-year periods respectively, her forecasts for annualized inﬂation rates over the next 1-, 3-, and 10-year periods (or the next 4, 12, and 40 quarters)
are calculated as 0.80%, 1.96%, and 0.96%, respectively. We call them “spot” rates anddenote Eti [πt,t+q ] as household i’s inﬂation forecasts over the next q-quarter. We canalso compute “forward” rates: an annualized forward rate for years n through n + k
is calculated from the forecasts of price levels over the next n- and n + k-year. When
she answers 10,080, 10,600, and 11,000 as the price level forecasts over the next 1-, 3-,
and 10-year periods (or the next 4, 12, and 40 quarters), the forward rates Eti [πt+4,t+12 ]and Eti [πt+12,t+40 ] are 2.55% and 0.53%, respectively. The quantitative measures tocapture inﬂation forecasts make our survey unique.

Years later
Forecast on price levels

1-year 3-year 10-year
10,080 10,600 11,000↓

Annualized inﬂation rates
Years later
Inﬂation expectations: π e“Spot” inﬂation rates
1-year 3-year 10-year
0.80% 1.96% 0.96%“Forward” inﬂation rates
1- to 3-year 3- to 10-year
2.55%
0.53%

Second, asking respondents to provide ﬁgures for the aggregate price levels can mitigate the “round number” problem which Binder (2017) points out. As Binder (2017)
argues, the literature on cognition and communication documents that when a survey
asks respondents to answer point predictors, people use round numbers to convey uncertainty. She shows that a large proportion of the respondents to the Michigan Surveys
of Consumers and the Federal Reserve Bank of New York Survey of Consumer Expectations report inﬂation forecasts that are a multiple of ﬁve. Our survey could also
include a-multiple-of-ﬁve forecasts. However, because our measures to capture inﬂation
expectations are calculated by point predictors of particular price levels, the measures
we compute are not always a multiple of ﬁve. In fact, Binder (2017) reports that approximate half of the forecasts were a multiple of ﬁve in the case of the University of7

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Michigan Surveys of Consumers, while our survey shows that only approximately oneﬁfth of forecasts are a multiple of ﬁve. Thus, our survey can alleviate the problem thatarises as a result of rounding behavior.
Tables 1 and 2 show “spot” and “forward” inﬂation forecasts of households, respectively.5 Based on the simple average, Table 1 shows that inﬂation expectations for the
1-year and 3-year horizons are above 2.0%, while 10-year-ahead forecasts are at 1.5%.
Table 2 shows that the average of inﬂation forecasts for the 1- to 3-year horizons isalmost 2.0%, and those for the 3- to 10-year horizons are almost 1.0%. Our measure
to capture households’ inﬂation expectations is reasonable in sense that respondents’covariates explain the level of forecasts. The average forecasts of female, lowly qualiﬁed, and lower-income respondents are higher than those of male, highly qualiﬁed, andhigher-income earners. These evidences are similar in both Tables 1 and 2.

3.2 Data about the consumption expenditure
The data we use is the panel data (SCI-personal) on the consumption expenditure, collected by a marketing company, Intage. We use the data that records day-to-day shopping information collected on an ongoing basis from 50,000 consumers aged 15–79 allover Japan. The data captures the proﬁle of these consumers in detail, including aspects such as income, education, and ﬁnancial assets. We can see who bought what,
when, where, how many, and at what price. This data covers items which that households purchase frequently, such as food (except for fresh food, prepared food, and lunchboxes), beverages, daily miscellaneous goods, cosmetics, pharmaceutical products, andcigarettes.6 We combine the inﬂation survey with the consumption expenditure fromthe same respondents and empirically test the theoretical relationship with inﬂation expectations and consumer spending.

56

The (annualized) inﬂation forecasts exclude all forecasts of inﬂation above 25 and below −2 percent.
Table 3 shows the basic statistics of the data.

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ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

4 Estimation results
4.1 Testing the theoretical prediction and estimating the structural parameter of the EIS
As presented in Section 2, the data we use allows us to directly estimate the value of theEIS. We regress the growth rate of consumption on inﬂation expectations for not only
shorter-term but also longer-term horizons. Our empirical framework is based on thefollowing equation:(ln(where ln

cit+q
cit

cit+q
cit

)
i
= −γ −1 × Eti [πt→t+k ] + Xβ + εt+q
,

(6)

)
and Eti [πt→t+k ] are denoted as the consumption expenditure growth

rates by individual i from t to t + q and inﬂation forecasts by individual i over the
next k quarters at time t, respectively. A vector X includes the control variables suchas the ﬁxed effects, time dummies, and aggregate inﬂation rates.7 Because we use the( ci )
quarterly data, when q = 4 and k = 12, ln t+4
is the annual growth rate of individualcit

i’s expenditure and Eti [πt→t+12 ] is the inﬂation forecast for the next 12 quarters (i.e.
the next 3 years) at time t.8 We focus on the parameter γ −1 in Equation (6).
Table 4 summarizes the estimation results using the entire sample. Panels (A) and(B) use the annual and the semiannual growth rates of consumption as the dependent
variable, respectively. First of all, we conﬁrm that higher inﬂation expectations arenegatively associated with the growth rate of consumption, that is, higher inﬂation expectations generate greater current spending compared to future spending. In fact, thesign of the relationship between the growth rate of consumption and inﬂation expectations are negative in all cases.9 While the model we present in Section 2 predicts thenexus only between the annual growth rate of consumption and inﬂation expectations
7

The growth rate of consumption on the left-hand side is deﬂated by the inﬂation rate which is computedby the change in the CPI (excluding perishables).
8
The combination of (q, k) = (4, 4) exactly matches the model in Section 2; the annual growth rate of
consumption expenditure from t to t + 4 is inﬂuenced by inﬂation expectations for the next one year from t to
t + 4.
9Note that the estimated coefﬁcient is −γ −1 , but γ −1 (that is, EIS) is reported in Tables 4 to 8.

9

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"for the next one year, the table suggests that higher inﬂation expectations for the next
three year signiﬁcantly generate greater current expenditure compared to future expenditure. The evidence shows that our benchmark results are robust and are consistent
with the theoretical prediction of the standard macroeconomic model, that higher inﬂation expectations induces consumers to change their intertemporal allocation and tosave less and spend more.
Second, Table 4 also shows the value of the EIS (γ −1 ). The value of the coefﬁcientis our primary focus. The estimate in the ﬁrst column in Panel (A) is approximately
0.1. The results are similar to those in Panel (B), using the semiannual growth rate of
consumption as the dependent variable. The value of the coefﬁcient is approximately
0.1. The values in Panel (A) and (B) are much smaller to those suggested by previous
studies.10

4.2 Robustness check
We check the robustness of our benchmark results using the subsamples. While weshow the estimate results of the EIS in Table 4, the sample covers all the respondents.
We reestimate the values of the EIS using the subsamples from consumers who seem not
to face liquidity constraints, speciﬁcally, the subsample of wealthy or highly educatedconsumers. Table 5 shows the results. Wealthy and highly educated consumers are deﬁned as those whose household income is 9 million yen a year or more or have ﬁnancial
assets over 10 million yen, and have a bachelor’s degree or higher, respectively. Panels (A) and (B) use the annual and the semiannual growth rates of consumption as thedependent variable, respectively. The results in both panels show that higher inﬂation
expectations are negatively associated with current expenditure, that is, higher inﬂation
expectations increase the growth rate of consumption. The relationship is signiﬁcant:the growth rate of consumption is related to inﬂation expectations for the next one andthree years. The evidence supports our benchmark results. Furthermore, the value ofγ −1 is approximately 0.2, which is slightly higher than those which our estimation results suggest in the previous section. The results imply that the EIS for consumers who
10

Jappelli and Pistaferri (2017) summarize that the estimated EIS in the literature ranges from 0.2 to 2.0.

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ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

can access credit more easily becomes slightly higher than that of those who cannot.
We also check the robustness of our benchmark results by taking the ﬁrst difference of Equation (6) and using the lag of the inﬂation expectations as the instrumental
i [πvariable (IV: Et−1
t−1→t+k−1 ]). Taking the ﬁrst difference of Equation (6) can omit

the consumer i’s speciﬁc effects. Using the ﬁrst lag of the independent variable as the
IV can alleviate the endogenous problem in the estimating equation when inﬂation expectations in the right-hand side might be correlated with the error term (ε) due to theomitted variable(s). Table 6 shows the robustness of our benchmark results. Panels (A)and (B) use the ﬁrst difference and the IV as the dependent variable, respectively. The
estimated EIS is below 0.1, but signiﬁcantly positive, when we use inﬂation expectations for the next one year. The results in both panels support the above results: higher
inﬂation expectations generate greater current consumption and the EIS is much lower
than those in the previous studies.11

5 How do consumers with liquidity constraints respond to a change in their inﬂation expectations?
Consumption theories do not predict that liquidity-constrained consumers can always
smooth their consumption path. Zeldes (1989) shows that consumers who are not constrained can more easily choose the timing of their spending, while those who are constrained may not be able to increase current spending in response to a higher inﬂation
expectation. In fact, Bachmann et al. (2015) ﬁnd that higher inﬂation expectations induce consumers with liquidity constraints reduce current expenditure compared to future expenditure.
Before moving on to estimation, how should we identify consumers facing liquidity
constraints? While there are many studies that suggest ways to carry out such an identiﬁcation, including Jappelli et al. (1998), Carroll (2001), Kohara and Horioka (2006),
and Ludvigson (1999), we follow Zeldes (1989) and Jappelli (1990) and use the data on

11

We further check the robustness of our benchmark results by excluding liquidity-constrained consumers
from the sample as reported in Table 7. We deﬁne liquidity-constrained consumers in Section 5.

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ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

income and ﬁnancial assets to identify consumers with liquidity constraints. We assume
that consumers with a household income below 4 million yen and ﬁnancial assets worth
3 million yen or less are facing liquidity constraints.
Table 8 shows the results of the reduced form estimation using the sample fromliquidity-constrained consumers. Panels (A) and (B) in the table use the annual and thesemiannual growth rates of consumption as the dependent variable, respectively. Panel(A) shows the signiﬁcantly positive relation between the growth rate of consumption
and inﬂation expectations; higher inﬂation expectations seem to generate lower current expenditure compared to one year later. Panel (B) in the table also shows thatthe relationship between inﬂation expectations and the growth rate of consumption ispositive. These results suggest that in contrast to the predictions of standard macroeconomic theory, higher inﬂation expectations may induce liquidity-constrained consumers
to increase current saving and decrease current spending. The evidence that consumers
with liquidity constraints may fail to smooth their consumption in response to higher
inﬂation expectations under the ELB is consistent with Bachmann et al. (2015), whichdocuments the negative correlation between inﬂation expectations and the readiness tospend on durables inside the ELB using the Michigan Survey of Consumers.

6 Conclusion
This study examines the relationship between inﬂation expectations and consumer spending, focusing on whether consumers change the intertemporal allocation of resources in
response to changes in their inﬂation expectations. First, using a combination of original
consumer survey on inﬂation expectations and scanner data on the actual expenditure,
we examine whether higher inﬂation expectations generate greater current spending.
Second, we structurally estimate the value of the EIS. It is hard for researchers to identify the value of the EIS via the Euler equation mainly due to the lack of the linked
data on consumer inﬂation expectations and the consumption expenditure. The uniquedata we combine allows us to directly estimate the deep parameter of the EIS basedon a standard model. Third, we identify those who are facing liquidity constraints and

12

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

investigate how liquidity-constrained consumers respond to changes in inﬂation expectations. If liquidity-constrained consumers react to higher inﬂation expectations in amanner that contradicts standard model predictions, the impacts on aggregate spending of unconventional monetary policy aiming to raise inﬂation expectations may vary
among different types of consumer.
There are three contribution of our study. First, we ﬁnd that in response to higher
inﬂation expectations, consumers generate greater current spending compared with one
year later. This suggests that the prediction of higher inﬂation expectations induces achange in the intertemporal allocation for the optimal consumption path. In other words,
theoretical predictions are consistent with the data. Second, we identify the value of the
EIS based on a structural model and show that its estimated value is signiﬁcantly positive and approximately 0.1. Our results suggest that the EIS may be smaller than thosemeasured in previous studies. Third, we ﬁnd that consumers who are facing liquidity
constraints could not generate greater current spending in response to higher inﬂation
expectations. The evidence indicates that higher inﬂation expectations may have varying impacts on consumers’ intertemporal allocation.

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ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

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ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

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ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

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10%
8%
1-year government bond yield
Overnight call rate

6%
4%
2%
0%
-2%

1985

1990

1995

2000

2005

2010

2015

2020

Figure 1: Development of the short-term nominal interest rates (Source: Ministry of Finance,
Japan and Bank of Japan)

17

Table 1: Basic statistics of households’ inﬂation forecasts: “Spot” forecasts
1-year average
3-year average
10-year average
Obs.
Mean Median
Obs.
Mean Median
Obs.
Mean Median
0.5% 143,612
2.1%
0.9% 144,806
1.5%
1.0% 144,835
All
2.5%
0.8%
69,4742.4%
1.0%
69,8431.6%
1.0%
69,802
Female
2.8%
Male
2.2%
0.5%
73,6941.8%
0.6%
74,5171.3%
1.0%
74,5892.8%
1.0%64,2122.3%
1.0%64,6501.6%
1.0%64,671High school graduate or below
Four-year college graduate or above
2.2%
0.5%
73,7061.8%
0.6%
74,3401.3%
1.0%74,1592.7%
0.9%
63,0772.3%
1.0%
63,5701.6%
1.0%63,625
Annual income below 4 million yen
Annual income 9 million yen and above 2.1%
0.5%
32,4291.7%
0.7%
32,6651.2%
1.0%
32,638
Note: The forecasts of inﬂation above 25 and below −2 percent are trimmed. The data cover from 2015Q4.

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"18

Table 2: Basic statistics of households’ inﬂation forecasts: “Forward” forecasts
3 to 10-year average
1 to 3-year average
Mean Median Observation
Mean Median Observation
0.4%
141,6861.1%
0.6%
141,667
All
1.8%
0.7%
68,192
1.1%
0.6%
68,131
Female
2.0%
Male
1.7%
0.4%
73,0581.0%
0.6%
73,107
High school graduate or below
2.0%
0.9%
63,1391.1%
0.6%
63,061
Four-year college graduate or above
1.6%
0.4%
73,0621.0%
0.5%
72,975
1.6%
0.4%
53,7820.9%
0.6%
53,822
Annual income below 4 million yen
Annual income 9 million yen and above 1.6%
0.5%
32,1351.0%
0.6%
32,157
Note: The forecasts of inﬂation above 25 and below −2 percent are trimmed. The data covers the range from
2015Q4.

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

19

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Table 3: Basic statistics of household expenditure
Purchase amount (yen)Mean Median
Obs.
All

20,49917,153

1,242,452

FemaleMale

24,182
14,822

21,537
11,593754,335
486,236High school graduate or below
Four-year college graduate or above

22,987
18,518

20,161
14,930684,242503,353

18,915
Households’ annual income below 4 million yen
Households’ annual income 9 million yen and above 22,357

15,85018,502621,072
226,564

Households with liquidity constraints
Households without liquidity constraints

15,77918,85736,766
270,681

18,64922,48520

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Table 4: Do consumers increase current spending in response to changes in their expectations?
ln(cit+4 /cit ) = −γ1−1 × Eti [πt→t+k ] + Xβ + εit+4
Panel (A)

1 year

3 year10 yearEti [πt→t+4 ]Eti [πt→t+12 ]Eti [πt→t+40 ]γ1−10.102***(0.021)0.111***(0.009)0.053(0.072)

YES
YES
84,625YES
YES
85,422YES
YES
85,191

Random effectTime ﬁxed effectObservationsln(cit+2 /cit ) = −γ2−1 × Eti [πt→t+k ] + Xβ + εit+2
Panel (B)

1 year

3 year10 yearEti [πt→t+4 ]Eti [πt→t+12 ]Eti [πt→t+40 ]γ2−1

0.064***(0.021)0.134***(0.024)0.087(0.063)

Random effect
YES
YES
YES
Time ﬁxed effect
YES
YES
YES
Observations
110,792
111,696
111,480
Note: Standard errors in parentheses are clustered at individual levels, and ***, **, and * indicate 1%, 5%, and
10% signiﬁcance, respectively. Time dummy and constantterm are included as the control variables.

21

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Table 5: Do the wealthy or the highly educated consumers increase current spending in
response to changes in their expectations?
ln(cit+4 /cit ) = −γ1−1 × Eti [πt→t+k ] + Xβ + εit+4
Panel (A)
Wealthy consumers
γ1−1

1 year(k = 4)

3 year(k = 12)

10 year(k = 40)Eti [πt→t+4 ]Eti [πt→t+12 ]Eti [πt→t+40 ]0.196***(0.042)0.277***(0.032)0.280(0.170)YES
YES
29,676YES
YES
29,872

YES
YES
29,661

Random effectTime ﬁxed effectObservationsln(cit+4 /cit ) = −γ2−1 × Eti [πt→t+k ] + Xβ + εit+4
Panel (B)Consumers with a higher degree
γ2−1

1 year(k = 4)

3 year(k = 12)

10 year(k = 40)Eti [πt→t+4 ]Eti [πt→t+12 ]Eti [πt→t+40 ]0.152***(0.045)0.199**(0.082)0.045(0.135)YES
YES
43,800YES
YES
44,265YES
YES
44,130Random effectTime ﬁxed effectObservations

Note: Panel (A) uses the subsample from consumers with more than 9 million yen
a year as household’s income or ﬁnancial assets over 10 million yen. Panel (B) uses
the subsample from consumers with a bachelor’s degree or higher. Standard errors inparentheses are clustered at individual levels, and ***, **, and * indicate 1%, 5%, and
10% signiﬁcance, respectively.

22

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Table 6: Do consumers increase current spending in response to changes in their expectations? Robustness check by taking the ﬁrst differences (Panel (A)) and using the IV (Panel(B)).
∆ ln(cit+4 /cit ) = −γ1−1 × ∆Eti [πt→t+k ] + Xβ + εit+4
1 year(k = 4)

3 year(k = 12)

10 year(k = 40)∆Eti [πt→t+4 ]∆Eti [πt→t+12 ]∆Eti [πt→t+40 ]

γ1−1

0.086**(0.031)0.170***(0.021)0.028(0.028)Time ﬁxed effectObservations

YES
37,880YES
38,546

YES
38,199Panel (A)First differenceiln(cit+4 /cit ) = −γ1−1 × Et−1
[πt−1→t+k−1 ] + Xβ + εit+4

Panel (B)

1 year(k = 4)

3 year(k = 12)

10 year(k = 40)

IV

i
Et−1
[πt−1→t+3 ]iEt−1[πt−1→t+11 ]i
Et−1[πt−1→t+39 ]γ1−1

0.036**(0.012)0.103***(0.029)0.061*(0.029)Random effectTime ﬁxed effectObservations

YES
YES
71,050YES
YES
25,286YES
YES
36,958

Note: Standard errors in parentheses are clustered at individual levels, and ***,**, and * indicate 1%, 5%, and 10% signiﬁcance, respectively.

23

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Table 7: Do consumers increase current spending in response to changes in their expectations? Robustness check without liquidity constrained consumers.
ln(cit+4 /cit ) = −γ1−1 × Eti [πt→t+k ] + Xβ + εit+4
Panel (A)γ1−1

Random effectTime ﬁxed effectObservations

1 year(k = 4)

3 year(k = 12)

10 year(k = 40)Eti [πt→t+4 ]Eti [πt→t+12 ]Eti [πt→t+40 ]0.158***(0.017)0.298***(0.033)0.295***(0.060)YES
YES
52,157

YES
YES
51,538YES
YES
50,723ln(cit+2 /cit ) = −γ2−1 × Eti [πt→t+k ] + Xβ + εit+2
Panel (B)

γ2−1
Random effectTime ﬁxed effectObservations

1 year(k = 4)

3 year(k = 12)

10 year(k = 40)Eti [πt→t+4 ]Eti [πt→t+12 ]Eti [πt→t+40 ]0.102***(0.019)0.187***(0.034)0.196**(0.078)YES
YES
69,844

YES
YES
68,993

YES
YES
67,992∆ ln(cit+4 /cit ) = −γ3−1 × ∆Eti [πt→t+k ] + Xβ + εit+4
Panel (C)

1 year(k = 4)

3 year(k = 12)

10 year(k = 40)∆Eti [πt→t+4 ] ∆Eti [πt→t+12 ] ∆Eti [πt→t+40 ]γ3−1Time ﬁxed effectObservations

0.181***(0.036)0.315***(0.064)0.003(0.065)

YES
23,616

YES
23,616

YES
23,192

Note: Standard errors in parentheses are clustered at individual levels,and ***, **, and * indicate 1%, 5%, and 10% signiﬁcance, respectively.

24

ESRI Discussion Paper Series No.353"Inflation Expectations and Consumer Spending: Micro-data Evidence"

Table 8: Do consumers with liquidity constraints increase current spending in response tochanges in their expectations?
ln(cit+4 /cit ) = α1 × Eti [πt→t+k ] + Xβ + εit+4
Panel (A)α1Fixed effectTime ﬁxed effectObservations

1 year(k = 4)

3 year(k = 12)

10 year(k = 40)Eti [πt→t+4 ]Eti [πt→t+12 ]Eti [πt→t+40 ]0.155**(0.064)0.415***(0.098)0.507*(0.229)

YES
YES
5,487

YES
YES
5,577

YES
YES
5,605

ln(cit+2 /cit ) = α2 × Eti [πt→t+k ] + Xβ + εit+2
Panel (B)α2Fixed effectTime ﬁxed effectObservations

1 year(k = 4)

3 year(k = 12)

10 year(k = 40)Eti [πt→t+4 ]Eti [πt→t+12 ]Eti [πt→t+40 ]0.116**(0.050)0.0801(0.080)0.491*(0.231)

YES
YES
6,303

YES
YES
6,397

YES
YES
6,427

Note: Standard errors in parentheses are clustered at individuallevels, and ***, **, and * indicate 1%, 5%, and 10% signiﬁcance, respectively.

25