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Home / Publications / Research / Economic Brief / 2021

Economic Brief
August 2021, No. 21-25

How Much Does Household Consumption Impact
Business Cycles?
Article by: Christian Matthes and Felipe F. Schwartzman

We identify shocks to household consumption using cross-sectoral information.
We find that those shocks have accounted for close to 40 percent of prepandemic business cycle fluctuations in the U.S. Such shocks have the
characteristics of demand shocks: They increase (or decrease) output, inflation
and interest rates. The results imply that one might be able to significantly
stabilize business cycles by stabilizing consumption fluctuations.
Household consumption is the largest expenditure component of GDP. Accordingly,
policymakers and business economists pay a great deal of attention to its ebbs and flows.
So when recessions hit, it isn't surprising that such attention translates into policy in the
form of tax rebates or other transfers to stimulate consumption, as was the case in 2001
and 2008/2009.
Most recently, the large cash transfers to families mandated by the American Rescue Plan
Act have generated debate on whether they would be too stimulative and "overheat" the
economy. Despite the attention of policymakers, until recently, macroeconomic theory has
mostly not considered household consumption as an independent driver of economic

Drivers of Business Cycles
Fluctuations in household demand as an important source of business cycles gained
prominence after the 2007-09 recession.2 Newly available evidence suggested that steeply
declining housing prices led to destruction of household wealth and reduced consumption.3
Even then, debate remained over the extent to which the household demand channel was
the most relevant one, as compared to losses of collateral for entrepreneurs and general
curtailment of credit by the banking system.4

In our recently updated paper, "The Consumption Origins of Business Cycles: Lessons from
Sectoral Dynamics," we use information available in the cross-section of industries to
provide evidence that macroeconomic disturbances (or "shocks") that initially impacted
household consumption were a key driver of GDP contractions and expansions prior to the
pandemic. Apart from a loss of housing wealth that mainly affects consumption, such
shocks might include fluctuations in consumer sentiments, consumer credit access or
employment uncertainty. In our study, we find that such shocks combine to account for as
much as 40 percent of output fluctuations since the mid-1970s.
We also find that those shocks behave like prototypical "demand" shocks, impacting not
only aggregate consumption and output, but also inflation and interest rates. At the same
time, consumption shocks had little impact on corporate credit spreads and measured total
factor productivity, implying that they are distinct from shocks to corporate credit or

How We Examined Effects of Household Consumption
To identify how shocks to consumption impact business cycles, we use information
available in the cross-section of industries. Intuitively, a negative shock to household
consumption should have most of its initial impact on sectors heavily oriented toward
consumer goods production (such as the apparel sector), rather than on sectors also
geared toward businesses (such as the software sector). Also, being demand shocks, they
should lead to greater price changes in those consumption-oriented sectors.5
The strategy we use is designed to avoid a few potential pitfalls.
Sector Sensitivity
The sensitivity of different sectoral prices and quantities to shocks is measured relative to
their sensitivity to all shocks. This ensures that our methodology does not capture just the
greater cyclical sensitivity of durable or luxury goods but also the increased sensitivity of
particular sectors to particular business cycle shocks.
Categorizing Shocks
Our procedure identifies shocks correctly even if there are other shocks that may have
similar sectoral impact. For example, a generalized shock to the financial sector would
affect household consumption as well as financing to firms. Our methodology allows us to
exclude such possibilities through the common assumption that, being exogenous, the
time-series behavior of different shocks is uncorrelated.
We further sharpen our results by explicitly identifying other candidate drivers of economic
fluctuations using analogous schemes. Thus, for example, shocks to technological progress
affects sectors that are more intensive in research and development, shocks to government

expenditures affects mostly those that sell most of their output to the government, and so
Using Multiple Assumptions and Averaging the Results
Our methodology explicitly considers that our identification assumption is imprecise and
incorporates the resulting uncertainty in our estimation procedure. For example, a
consumption shock may affect sectors differentially depending on their precise position on
production networks.
We accommodate the possibility of model misspecification by identifying the consumption
shock several times. In each case, we impose an identification assumption that is a little bit
different from our preferred one. Our results are then (weighted) averages of those
possibilities, and we describe the uncertainty surrounding those results incorporating those
variants. (Said another way, we imposed our identification assumptions through Bayesian
priors.) Because we use extensive cross-sectoral data, we can obtain fairly precise

Correlations with GDP
Figure 1 below validates our identification assumptions. It shows the correlation between
various time-series and leads and lags of GDP:
The gray line shows the autocorrelations for GDP. Its value is 1 at 0 lags and declines
symmetrically around it.
C shows consumption as measured by BEA aggregate consumption.
HML IP shows the difference between high and low consumption share sectors in the
FRB Industrial Production Index. HML π and HML C show the same difference for
inflation and consumption growth among Bureau of Economic Analysis personal
consumption expenditure categories, respectively.

Figure 1: Correlation with Lags of GDP











Source: Christian Matthes and Felipe Schwartzman, "The Consumption Origins
of Business Cycles: Lessons from Sectoral Dynamics," Federal Reserve Bank of
Richmond Working Paper No. 19-09, June 24, 2021.
Notes: The horizontal axis refers to the quarterly lag of GDP with negative
numbers corresponding to leads. HML IP is the difference between high and
low consumption share sectors in the Federal Reserve Board's Industrial
Production Index. HML π and HML C refer to the same difference for inflation
and consumption growth among BEA personal consumption expenditure

The correlation of consumption with GDP is larger for negative lags, indicating that
consumption precedes GDP fluctuations. The relationship is even more pronounced if we
focus instead on the difference between high and low consumption-share sectors. Relative
increases in the production or prices of sectors with high consumption shares tend to be
most correlated with output one year afterwards.

Impacts of Shocks on Output Fluctuations
We find that shocks originating in household consumption demand account for close to 40
percent of output fluctuations at business cycle frequencies. In comparison, we find that
shocks to corporate credit account for 18 percent of output fluctuations, shocks to
government consumption account for 14 percent and shocks to energy account for 11
percent. Monetary and technology shocks have the smallest impacts at 5.6 percent and 7.5
percent, respectively.
As mentioned above, household consumption shocks affect the economy in the way that
one might expect aggregate demand shocks to do. We measure the responses to a onestandard deviation shock in several areas. We find that output, inflation and interest rates
increase on impact, while total factor productivity, government spending and credit spreads
do not. The impact on output, inflation and interest rates is persistent, lasting for more than
two years and beyond.

How should we interpret the estimated household consumption shocks? We compare the
time-path of the consumption shock inferred through our method with other time series
that were not used in estimation.
Figure 2 shows how the consumption shock correlates with the household wealth of the
bottom 90 percent of the wealth distribution.6 The two series correlate well, especially after
the late 1990s and very strongly around the 2007-09 recession.

Figure 2: Comparing Household Wealth
Fluctuations and Consumption Shocks









Household Consumption Shock
Growth Rate of the Average Wealth of a U.S. Family (Excluding Top 10%)

Sources: Christian Matthes and Felipe Schwartzman, "The Consumption Origins
of Business Cycles: Lessons from Sectoral Dynamics," Federal Reserve Bank of
Richmond Working Paper No. 19-09, June 24, 2021.
Notes: The correlation for these two datasets is 0.55.

Figure 3 shows the correlation with consumer sentiment, and the correlation is even
stronger than with housing wealth. Together, those exercises suggest a role for shocks to
both household wealth and consumer sentiment as central driving forces in business

Figure 3: Comparing Consumer Sentiment and
Household Consumption Shocks








Household Consumption Shock



Consumer Sentiment

Sources: Christian Matthes and Felipe Schwartzman, "The Consumption Origins
of Business Cycles: Lessons from Sectoral Dynamics," Federal Reserve Bank of
Richmond Working Paper No. 19-09, June 24, 2021.
Notes: The correlation for these two datasets is 0.68.

As the U.S. economy recovers from the COVID-19 pandemic, debate has emerged around
the extent to which unprecedented government assistance might lead to a stronger
recovery and inflationary pressures. Our results suggest that shocks that led to increased
consumption in the last decades have had such effects and, in fact, have explained a large
fraction of business cycle fluctuations in the U.S. More generally, they validate consumption
stabilization policies as a useful lever for broader business cycle stabilization.
Christian Matthes is an associate professor at Indiana University, and Felipe Schwartzman is
a senior economist at the Federal Reserve Bank of Richmond.


At the same time, it has long been recognized that survey measures of consumer "sentiments"
— which ask consumers about their perception of current and future economic conditions — are
useful metrics for the state of the economy, as explored in the 1995 paper "Consumer Confidence
and Economic Fluctuations" by John G. Matsusaka and Argia M. Sbordone. This connection has
also been explored in structural work by the 2019 working paper "Survey Data and Subjective
Beliefs in Business Cycle Models" by Anmol Bhandari, Jaroslav Borovicka and Paul Ho.
Accordingly, in some theories, shocks affecting those sentiments have been elevated to major
sources of economic fluctuations, as seen in the 2013 papers "Animal Spirits, Financial Crises and

Persistent Unemployment" by Roger E.A. Farmer and "News or Noise? The Missing Link" by Ryan
Chahrour and Kyle Jurado.

Previously, early theories of business cycles tended to focus on the ebbs and flows of

inventories, fixed investment and housing. John Maynard Keynes, for example, famously
connected fluctuations to the "animal spirits" of capital investors. Regarding other theories,
monetarists focused on the unsteady hand of policymakers, and real business cycle theorists
focused on the acceleration or slowing of technological progress.

For example, see my (Felipe's) working paper "Local Scars of the U.S. Housing Crisis," coauthored with Saroj Bhattarai and Choongryul Yang.

A 2015 book by Atif Mian and Amir Sufi, "House of Debt," argues for the consumption demand
channel, whereas a 2015 paper by Manuel Adelino, Antoinette Schoar and Felipe Severino,
"House Prices, Collateral and Self-Employment," emphasizes the collateral channel as being more
important. A 2018 paper by Mark Gertler and Simon Gilchrist, "What Happened: Financial
Factors in the Great Recession," put greater emphasis on the shortfall of banking credit as
captured by higher bond spreads.

The strategy of using information about the different sensitivity of cross-sectional units to a

shock to identify it in the time-series follows my (Felipe's) own prior work using a structural model
(as seen in my 2014 paper "Time to Produce and Emerging Market Crises") and historical
narrative (as seen in my 2015 paper "The Benefits of Commitment to a Currency Peg" with Scott
Fulford). The strategy that we adopt here provides an alternative to those approaches.

The bottom 90 percent of the wealth distribution is as measured by Emmanuel Saez and
Gabriel Zucman in their 2016 paper "Wealth Inequality in the United States Since 1913."

This article may be photocopied or reprinted in its entirety. Please credit the authors,
source, and the Federal Reserve Bank of Richmond and include the italicized statement
Views expressed in this article are those of the authors and not necessarily those of the Federal
Reserve Bank of Richmond or the Federal Reserve System.

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