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Vellekoop, Nathanael; Wiederholt, Mirko
Working Paper Inflation expectations and choices of households
SAFE Working Paper, No. 250
Provided in Cooperation with: Leibniz Institute for Financial Research SAFE
Suggested Citation: Vellekoop, Nathanael; Wiederholt, Mirko (2019) : Inflation expectations and choices of households, SAFE Working Paper, No. 250, Goethe University Frankfurt, SAFE - Sustainable Architecture for Finance in Europe, Frankfurt a. M., http://dx.doi.org/10.2139/ssrn.3383452
This Version is available at: http://hdl.handle.net/10419/196146
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Nathanael Vellekoop – Mirko Wiederholt
Inflation Expectations and Choices of Households
SAFE Working Paper No. 250 Inflation Expectations and Choices of Households∗
Nathanael Vellekoop† Mirko Wiederholt‡ Goethe University Frankfurt and SAFE Sciences Po and CEPR
April 2019
Abstract
Do household inflation expectations affect consumption-savings decisions? We link survey data on quantitative inflation expectations to administrative data on income and wealth. We document that households with higher inflation expectations save less. Estimating panel data models with year and household fixed effects, we find that a one percentage point increase in a household’s inflation expectation over time is associated with a 250-400 euro reduction in the household’s change in net worth per year on average. We also document that households with higher inflation expectations are more likely to acquire a car and acquire higher-value cars. In addition, we provide a quantitative model of household-level inflation expectations.
∗We thank our discussants Philippe Andrade, Francesco Bianchi, Jane Ryngaert, and Johannes Wohlfart and seminar and conference participants at AEA 2019, Bundesbank, Cambridge, CEBRA 2018, CESifo, ECB, EEA 2018, Halle, Kiel, NYU, Oxford, SED 2017, Yale, and Z¨urich for helpful comments. We gratefully acknowledge research support from the Research Center SAFE, funded by the State of Hessen initiative for research LOEWE. We thank CentERdata for making the survey data of the Dutch Household Survey available. Satyajit Dutt and Bopjun Gwak provided excellent research assistantship. Results include our own calculations based on microdata made available by Statistics Netherlands. †[email protected] ‡[email protected]
1 1 Introduction
How do individuals form inflation expectations? The answer to this question is of central im- portance for policy-makers. Inflation expectations are viewed as a key determinant of inflation (Bernanke, 2007, Yellen, 2015). In addition, academics and policy-makers have argued that, in the current context of low nominal interest rates, deflationary expectations could cause large falls in output, while an increase in inflation expectations could stimulate spending (Krugman, 1998). Unsurprisingly, research on expectation formation has been an important input into policy-making. The rational expectations revolution has had a large impact on institutional design such as central bank independence. Learning models have affected how central banks think about disinflations. More recent work on limited information-processing capacity of agents is likely to have an effect on the design of policy communication. We contribute to this literature by studying how households update inflation expectations at the micro level. A question that seems equally important is: Do inflation expectations affect choices? The main motivation for studying the formation of inflation expectations is the presumption that they affect choices. There exists a vast literature (encompassing the entire literature on New Keynesian models) that assumes a tight link between inflation expectations and consumption-savings decisions of households, and this tight link is often central for conclusions regarding the propagation of shocks and the effectiveness of policy, but empirically it is still an open question whether such a tight link exists. We therefore view the main contribution of this paper as providing empirical evidence on the relationship between inflation expectations and consumption-savings decisions of households by linking panel survey data on inflation expectations to panel administrative data on income, wealth, and car purchases at the household level. The survey has the unique feature that the inflation expectations of individual households are elicited over several years. We use the Dutch Household Survey. The survey aims to be representative for the Dutch population. Every year households are asked to forecast prices for the next year. Households participate for several years and inflation changes significantly over periods of several years. Since one can track the answers of individual households over time, one can study how individual households update inflation expectations over time. Most papers studying how individuals update inflation expectations over time study data from a survey of professional forecasters or data from the Michigan Survey of Consumers, but professional forecasters
2 do not appear prominently in Macroeconomic models and households are surveyed at most twice in the Michigan Survey of Consumers. We therefore start the paper in Section 2 by studying how households update inflation expectations at the micro level. We find that a noisy information model with two modifications (heterogeneous intercepts, and a small probability that households answer 10% instead of reporting their true inflation expectation) matches well the micro data. We then link the survey data on inflation expectations to administrative data on income and wealth. Dutch households are subject to both a wealth tax and an income tax. Wealth and income data are directly provided to Statistics Netherlands from the tax authorities and banks. Our preferred measure of savings is change in net worth during the year. Net worth at the end of the year is the difference between the value of all assets at the end of the year and the value of all liabilities at the end of the year. The change in net worth during the year is the first difference of net worth at the end of the year. Our main dependent variable is this measure of savings during the year. Our explanatory variable of interest is the inflation expectation reported during the year. We include year and household fixed effects and control for income, wealth, and a large number of observable household characteristics taken mainly from the administrative data. We find that the coefficient on inflation expectations is negative and statistically significant at the 1% level. A one percentage point increase in a household’s inflation expectation over time is associated with a 250 euro reduction in the household’s change in net worth per year on average. We also include other household expectations and obtain similar results. We also link the survey data on inflation expectations to administrative data on car purchases. Statistic Netherlands provides registry data containing ownership of cars in each month for each per- son. The source of the data is the government agency that registers car ownership. Car registration is mandatory for purposes of liability insurance. For all households in the matched admin-survey data we construct a variable whether the household acquires a car in the 12 months after inflation expectations are elicited. We impute the value of the purchased car from the import value of the car and the depreciation schedule that the tax authorities use. We find that households with higher inflation expectations are more likely to acquire a car and acquire higher-value cars. Krugman (1998) argued that in a liquidity trap, where nominal interest rates are at zero, the central bank can still stimulate consumer spending by raising inflation expectations. This argument
3 has had a large impact on the literature on forward guidance (e.g., Eggertsson and Woodford, 2004). This argument has also had a large impact on the literature on government spending multipliers, since government spending multipliers can be large when government spending increases inflation, inflation expectations, and thereby consumer spending (Christiano, Eichenbaum and Rebelo, 2011). To the best of our knowledge, this paper is the first paper that provides a direct estimate of the partial equilibrium effect of an increase in inflation expectations on the level of consumer spending. We find that the effect has the predicted sign–an increase in expected inflation stimulates consumer spending–and is large–a one percentage point increase in a household’s inflation expectation over time is associated with a 250 euro reduction in annual savings on average. It is important to emphasize that we provide an estimate of the partial equilibrium effect of an increase in inflation expectations on consumer spending, since we focus on idiosyncratic variation in inflation expectations, while aggregate variation in inflation expectations is captured through time fixed effects. General equilibrium effects can be computed with a general equilibrium model and will depend on the specificities of the country, for example, through the degree of price stickiness and the conduct of monetary policy. The vast literature on New Keynesian models has identified key determinants of sign and size of these general equilibrium effects, taking as given that there exists a link between inflation expectations and consumption-savings decisions at the household level. We provide evidence in favor of such a link. Given our results in Section 2 of this paper, it also seems important that a general equilibrium model features realistic assumptions about the expectation formation of households. There exist only a few papers that study the relationship between inflation expectations and choices of households using microdata. Most papers in this literature examine the relationship between quantitative inflation expectations and answers to qualitative questions on “readiness to spend” (Bachmann et al., 2015, D’Acunto et al., 2018, Andrade et al., 2017, Arioli et al., 2017). These papers use the Michigan Survey of Consumers (MSC) or similar surveys for other countries. Another group of papers exploit recent innovations in the Federal Reserve Bank of New York’s Survey of Consumer Expectations (SCE): Armantier et al. (2015) find that inflation expectations co-move in a meaningful way with investment choices in a financially incentivized field experiment, while Crump et al. (2018) estimate the elasticity of intertemporal substitution, exploiting the fact that the SCE elicits quantitative measures of both inflation and spending growth expectations.
4 Malmendier and Nagel (2016) investigate the relationship between their model-implied inflation expectations and financial decisions reported in the Survey of Consumer Finances (SCF) at the cohort level. We take a different approach to the existing papers in this literature by linking panel survey data on inflation expectations to panel administrative data on income, wealth, and car purchases at the household level. To the best of our knowledge, our paper is the first paper that links survey data on expectations to administrative data on income and wealth.1’2 The rest of the paper is organized as follows. Section 2 presents the survey data on inflation expectations and our results on how individual households update inflation expectations over time. Section 3 introduces the administrative data on income, wealth, and car purchases and studies the empirical relationship between inflation expectations and choices. Section 4 concludes.
2 Inflation Expectations of Households
In this section, we investigate how households update inflation expectations over time. The main goal of this section is to present the survey data on inflation expectations and to make the point that a standard model of the dynamics of the average inflation expectation also matches fairly well the dynamics of individual-level inflation expectations.
2.1 Data
The inflation expectations microdata is from the DNB Household Survey, conducted annually since 1993 and administered by CentERdata at Tilburg University. The survey aims to be representative for the Dutch population. Households participate for several years.3 Since one can track individual households over time, one can study how individual agents update inflation expectations over time.
1By contrast, D’Acunto et al. (2019) link administrative data on cognitive-ability (IQ) tests with survey data on inflation expectations, planned spending, planned saving, and planned borrowing. In particular, respondents are asked about their inflation expectations, whether now is the right moment to spend on durables, and whether they plan to save or borrow. 2Another distinguishing feature of our work is that we include household fixed effects, which is rare in the literature on inflation expectations and choices. A notable exception is Burke and Ozdagli (2013) who study data on expectations and choices from a series of survey modules appended to the RAND’s American Life Panel over a four-year period. 3For comparison, households are surveyed at most twice in the Michigan Survey of Consumers. Households participate in the Federal Reserve Bank of New York’s Survey of Consumer Expectations for at most 12 months.
5 The data are collected through the Internetpanel of CentERdata. Households without a computer and/or access to the Internet are provided a basic computer and an Internet connection. The DNB Household Survey consists of six questionnaires. The questionnaire “Health and Income” includes several questions about inflation expectations. Beginning with the 2008 wave, the main quantitative question on inflation expectations is:
“What is the most likely (consumer) prices increase over the next twelve months, do you think?”
Possible answers are:
1%, 2%, 3%, . . . , 10%
Respondents are then asked four questions regarding their subjective CDF. In the years 1993-2002, households were only asked for a point prediction. The questions read:
“Do you expect prices in general to rise, to remain the same, or to go down, in the next 12 months?”
“If the answer is rise: By what percentage do you expect prices in general to rise in the next 12 months?”
In the years 2003-2007, households were only asked for their subjective CDF:
“We now would like to learn what you expect will happen to the prices in the next twelve months. What will be the minimum percentage prices could increase over the next twelve months, do you think? If you think prices will decrease, you can fill in a negative percentage by using a minus in front of the number.”
“What is the maximum percentage prices will increase over the next twelve months, do you think?”
Calling the answers MIN and MAX, the respondents were asked 4 questions, with i ∈ {2, 4, 6, 8}:
“How likely do you think that it is that the increase in prices in the next twelve months i(MAX−MIN) will be less than 10 + MIN?”
6 For the years 2003-2007, where households were only asked for their subjective CDF, we estimate the mean of the subjective CDF using a piece-wise linear interpolation over the probability density function. Figure 1 shows the cross-sectional distribution of point predictions made in the year 2012. Recall that households were asked: “What is the most likely (consumer) prices increase over the next twelve months, do you think?” Possible answers were: 1%, 2%,..., 10%. As one can see, there is large cross-sectional heterogeneity in the answers. Some households answered 1%, while other households picked 10%. The large majority of respondents made a very good forecast that year: two thirds of households answered 2% or 3% in 2012, and CPI inflation turned out to be 2.5% in 2013. Figure 2 illustrates the dynamics of the cross-sectional distribution of inflation expectations. The cross-sectional distribution of inflation expectations for year t is described by the 10th per- centile (dots), the 90th percentile (small dashes), and the mean (large dashes). To allow comparison between expectations and realizations, we also plot the time series for realized CPI inflation (solid line). The four numbers reported for year t refer to the distribution of forecasts made in year t-1 for year t and the realization in year t. The vertical lines mark changes in the survey questions. Cross-sectional heterogeneity in inflation expectations is large in all years and the cross-sectional mean of inflation expectations has properties which are familiar from the literature on predictability of average forecast errors (Coibion and Gorodnichenko, 2012 and 2015). In the case of a difference between realized inflation in year t and the average forecast made in year t-1 for year t, the average inflation expectation moves in the direction of realized inflation but the average forecast error is highly persistent. A persistent average forecast error can arise because: (i) inflation expectations and realized inflation move in opposite directions (see the average inflation expectation for year 2002 and realized inflation in year 2002, where euro coins and banknotes were introduced), and (ii) inflation expectations adjust sluggishly (see the period since November 2013, where the main policy rate of the European Central Bank has been 25 basis points or less). Finally, the changes in the survey questions did not coincide with unusual changes in the cross-sectional mean of re- ported inflation expectations, but they may have coincided with small changes in the cross-sectional heterogeneity in reported inflation expectations. The panel component of the survey data allows us to track the answers of individual households
7 over time. Several thousand households participated in the survey. It is difficult to visualize several thousand paths for the reported inflation expectation. We therefore present transition matrices. In Table 1, we study the answers of all households with an observation in the year after the first observation. The entries are conditional probabilities. The first row contains the relative frequency of answers in year two given that the answer in year one was 1%, the second row contains the relative frequency of answers in year two given that the answer in year one was 2%, and so on. The diagonal entries are 0.47, 0.42, 0.33, 0.36, and 0.30. On average more than one third of households gave the same answer in year two as in year one. The fraction is higher for initially low answers and lower for initially high answers. Table 2 repeats the exercise for all households who provided an answer in the two years after providing the first answer. The first panel reports transition probabilities comparing answers in years one and two. The second panel shows transition probabilities comparing answers in years two and three. The third panel reports transition probabilities comparing directly answers in year one and year three. The two one-year transition matrices in Table 2 are similar to the one-year transition matrix in Table 1, suggesting that there is nothing special about the first year of being in the survey. The last transition matrix comparing directly answers in years one and three reveals a striking feature of the expectations data. The (1,1) entry of panel three (“1 to 3”) is much larger than the product of row one of panel one (“1 to 2”) and column one of panel two (“2 to 3”). This means that answers do not follow a Markov process with common transition probabilities. A household has a higher probability of going from an answer of 2% in year two to an answer of 1% in year three if the household already said 1% in year one. The same observation applies to the other diagonal entries of the last panel. Households tend to return to answers they have given in the past. Table 3 confirms this finding. In Table 3, we repeat the exercise for all households with three observations in the three years after the first observation. The different panels in the table are the three one-year transition matrices and the transition matrix comparing directly the answers in years one and four. The diagonal entries of the last transition matrix (“1 to 4”) are again much larger than the probabilities implied by a Markov process with common transition probabilities and the one-year transition matrices reported in the first three panels. Households tend to return to answers they have given in the past. To identify this feature of the data, one needs at least three
8 observations per household. The panel data also reveal that extreme answers come to a large extent from households who report a fairly normal inflation expectation before or after. The first row of any one-year transition matrix shows that households who say 1% in a given year have a 3-4 percent probability of saying 6% or more in the next year. Moreover, the last row of any one-year transition matrix shows that households who say 6% or more in a given year have a high probability of providing a non-extreme answer in the next year. In sum, there is large cross-sectional heterogeneity in reported inflation expectations, the average forecast error is highly persistent, households tend to return to answers they have given in the past, and extreme answers come to a large extent from households who provide fairly normal answers before and after. This completes the description of the survey data on inflation expectations. In the following two subsections, we ask whether a noisy information model of expectation formation can simulta- neously match the average inflation expectation and the transition matrices for individual inflation expectations.
2.2 Model
In this subsection, we propose a model of household-level inflation expectations.4 In the model, households receive noisy signals on inflation. The noise has an aggregate component and an id- iosyncratic component. The aggregate component of noise is interpreted as noise in official inflation statistics or noise in media reports on inflation. The idiosyncratic component of noise is interpreted as coming from randomness in perception due to limited attention. Noisy signal models of belief formation, where the noise is interpreted as coming from limited attention (Sims, 2003, Woodford, 2003, Ma´ckowiak and Wiederholt, 2009), have gained popularity in the empirical literature on expectation formation (Patton and Timmermann, 2010, Coibion and Gorodnichenko, 2012 and 2015), since the noise alone generates many features of survey data on inflation expectations: (i) sluggish adjustment of the average expectation due to Bayesian updat- ing in the presence of noisy signals, (ii) cross-sectional dispersion and idiosyncratic transitions in
4Malmendier and Nagel (2016) propose a model of cohort-level inflation expectations, but across cohort variation in inflation expectations is small in our sample.
9 expectations due to the idiosyncratic component of noise, and (iii) unwarranted movements in the average expectation due to the aggregate component of noise (e.g., the sharpest increase in the average inflation expectation in our sample took place in forecasts in 2001 for 2002 at a time where the news media warned about the inflationary effects of the introduction of euro coins and notes). We modify such a standard noisy signal model of belief formation by introducing an assumption that generates individual-specific intercepts in the equation for household-level inflation expecta- tions, because we documented that households tend to return to answers given in the past (see Section 2.1). While the presence of those intercepts will improve the model’s ability to match household-level expectations data, the precise origin of those intercepts will not be important and we will discuss two alternative ways of generating these intercepts at the end of this subsection. Households’ perceived law of motion for inflation is
πt = (1 − ρ) c + ρπt−1 + ut, (1) where πt is the inflation rate in year t, ρ ∈ (−1, 1] is the autocorrelation coefficient, c ∈ R is 2 a constant, and ut ∼ i.i.d.N 0, σu is the inflation innovation in year t. In every period, each household i receives a noisy signal on inflation. According to the household, the signal is generated as follows
sit = πt + εit, (2)