Personal Experiences and Expectations about Aggregate Outcomes

• DOI: http://doi.org/10.1111/jofi.12819
• Author: THERESA KUCHLER,BASIT ZAFAR
• Date: 01 October 2019
• Published date: 01 October 2019

THE JOURNAL OF FINANCE •VOL. LXXIV, NO. 5 •OCTOBER 2019

Personal Experiences and Expectations about

Aggregate Outcomes

THERESA KUCHLER and BASIT ZAFAR∗

ABSTRACT

Using novel survey data, we document that individuals extrapolate from recent per-

sonal experiences when forming expectations about aggregate economic outcomes.

Recent locally experienced house price movements affect expectations about future

U.S. house price changes and higher experienced house price volatility causes re-

spondents to report a wider distribution over expected U.S. house price movements.

When we exploit within-individual variation in employment status, we find that in-

dividuals who personally experience unemployment become more pessimistic about

future nationwide unemployment. The extent of extrapolation is unrelated to how

informative personal experiences are, is inconsistent with risk adjustment, and is

more pronounced for less sophisticated individuals.

EXPECTATIONS PLAY A KEY ROLE IN ECONOMIC models of decision-making under

uncertainty. Recent work explores empirical measures of expectations to in-

form the modeling of the expectation formation process (see Barberis et al.,

2015; Fuster, Laibson, and Mendel, 2010). This work shows that personal ex-

periences have a substantial effect on expectations of aggregate economic out-

comes (see, e.g., Malmendier and Nagel, 2011,2016; Malmendier, Nagel, and

Yan , 2017). Little is known, however, about what exactly constitutes the rele-

vant set of “personal experiences.” For instance, local house price movements

can differ substantially across the United States.1But do differences in these

∗Theresa Kuchler is with Stern School of Business, New York University. Basit Zafar is with

Arizona State University. Theresa Kuchler and Basit Zafar have read the Journal of Finance’s

disclosure policy and have no conflicts of interest to disclose. We are grateful to Markus Brun-

nermeier, Suzanne Chang, Eduardo Davila, Xavier Gabaix, Ed Glaeser, Camelia Kuhnen, Ulrike

Malmendier, Stefan Nagel, Alexi Savov, Johannes Stroebel, Michael Weber, an anonymous ref-

eree; and seminar participants at the AEA 2016 Annual Meetings, New York University, National

University of Singapore, London School of Economics, Baruch, Christmas Meeting of German

Economists Abroad, Reserve Bank of India, Bank of Spain, Society of Economic Dynamics, and the

LMU Munich Workshop on Natural Experiments and Controlled Field Studies for helpful sugges-

tions. We thank Luis Armona, John Conlon, Michael Kubiske, and Max Livingston for excellent

research assistance. Any errors that remain are ours. The views expressed in this paper do not

necessarily reflect those of the Federal Reserve Bank of New York or the Federal Reserve System

as a whole.

1For instance, in Arizona, prices increased dramatically during the boom, with annual increases

of up to 30% in 2005, followed by deep drops in the subsequent bust of over 25% in 2008. During

the same time, house prices in Indiana were quite stable, with average changes of less than 1%

per year.

DOI: 10.1111/jofi.12819

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2492 The Journal of Finance R

locally experienced house prices lead individuals to have different expec-

tations about aggregate price changes despite witnessing the same aggre-

gate price movements? Similarly, unemployment rates rose during the finan-

cial crisis throughout the United States, but does personally experiencing

unemployment—rather than simply witnessing times of high unemployment—

affect individuals’ expectations about the aggregate unemployment rate? Fur-

ther, do the answers to these questions depend on individual characteristics?

And what do the answers to these questions have to say about the expectation

formation process?

In this paper, we address the above questions in order to better understand

how individuals form expectations. We focus on expectations about house price

changes and unemployment, since there tend to be substantial differences be-

tween local or personal experiences and aggregate measures in both domains.

Housing and labor markets therefore offer a rich empirical setting to ana-

lyze which types of personal experiences affect expectations and whether their

effects vary with individual characteristics. In addition, both markets are of

interest in and of themselves. House price expectations play an important

role in understanding housing booms and busts (e.g., Piazzesi and Schneider,

2009; Goetzmann, Peng, and Yen, 2012; Glaeser, Gottlieb, and Gyourko, 2012;

Burnside, Eichenbaum, and Rebelo, 2016; Glaeser and Nathanson, 2017; Case,

Shiller, and Thompson, 2012; Bailey et al., 2018), while employment expecta-

tions affect the speed of economic recovery after recessions, and can influence

households’ job search behavior (see Carroll and Dunn, 1997; Tortorice, 2011;

Hendren, 2017). Our results therefore shed light on how expectations about

these two key aggregate outcomes are formed while also providing insights

into the expectation formation process more generally.

We analyze data from the Survey of Consumer Expectations (SCE), a rela-

tively new monthly online survey of approximately 1,200 U.S. household heads,

fielded by the Federal Reserve Bank of New York since 2012. The survey elic-

its consumer expectations about various economic outcomes, including house

price and labor market changes, and collects rich data on respondents’ personal

backgrounds and economic situations. Two features of the survey are important

for our purposes. First, the survey is a panel that tracks the same individuals

monthly for up to 12 months. Second, the data contain respondents’ ZIP code

information, which allows us to exploit variation in locally experienced house

prices to estimate the effect of past experience on expectations. We use the

entire history of locally experienced house price changes to measure each indi-

vidual’s personal experience, and find that past locally experienced house prices

significantly affect expectations about future changes in U.S. house prices.2For

instance, respondents in ZIP codes with a 1-percentage-point higher change in

house prices in the previous year expect the one-year-ahead increase in U.S.

house prices to be 0.1 percentage points higher. We find that this reliance on

2Our ability to exploit within-cohort variation in experiences allows us to conduct additional

analysis, for instance, estimating the horizon over which individuals’ experiences matter, which

prior literature has been unable to do due to data limitations.

Personal Experiences and Expectations about Aggregate Outcomes 2493

local experiences increases the cross-sectional dispersion in expectations by

nearly 9%. Consistent with Malmendier and Nagel (2016) in the case of in-

flation expectations, we also find that more recently experienced house price

changes have a substantially stronger effect than earlier ones. The SCE also

elicits respondents’ subjective distribution of future house price changes. We

can therefore also investigate the impact of experiences on the second moment

of house price expectations. We find that respondents who experience more

volatile house prices locally report a wider distribution over expected future

national house price movements: respondents who experienced a 1-percentage-

point higher standard deviation in ZIP code- or metro-level house price changes

in the past five years expect the standard deviation of one-year-ahead expected

house price changes to be 0.045 and 0.27 percentage points higher, respectively.

Turning to the effect of personal unemployment experiences on U.S. unem-

ployment expectations, we leverage the rich panel component of the survey

to focus on individuals who experience job transitions (individuals who were

previously employed and lose their jobs, or who were unemployed and find

a new job) and exploit this within-individual variation in personal experi-

ences to estimate their effect on expectations about aggregate unemployment.3

We find that experiencing unemployment leads respondents to become sig-

nificantly more pessimistic about future U.S. unemployment: they expect the

likelihood of U.S. unemployment increasing in the next year to be 1.44 percent-

age points higher than when employed (relative to the average stated likelihood

of 37%).4

We next explore potential mechanisms consistent with the observed extrap-

olation from personal experiences to aggregate outcomes, and the resulting

implications for understanding how individuals form expectations.5First,

the effect of personal experiences on expectations about aggregate outcomes

suggests that respondents either do not know or do not optimally use all

relevant publicly available information. All respondents in our sample are

forming expectations about the same aggregate outcome—in our case, the

change in U.S. house prices or nationwide unemployment. Therefore, the op-

timal weighting of any piece of public information should be the same for each

respondent, irrespective of whether this information happens to be local or not.

This is not what we find. Second, we analyze whether respondents optimally

rely on personal experiences because of otherwise limited information. In this

case, respondents should rely more heavily on their personal information when

it is more informative about the aggregate outcome. We find, however, that

3Few previous studies (Keane and Runkle, 1990; Madeira and Zafar, 2015) have used the panel

dimension of survey expectations, largely as a result of data limitations.

4The stated expectations in our survey data are predictive of actual outcomes: Respondents

who believe that they are more likely to lose their job are indeed more likely to subsequently do

so. Expectations about future house price changes are related to whether respondents consider

housing a good investment.

5We follow the literature that takes extrapolation to be “the formation of expected returns

. .. based on past returns” (Barberis et al., 2018). The psychology literature suggests several

underlying biases that can contribute to such extrapolation.