MARKET INSIGHTS

Fair Weather Trends:
Seasonality and Google Mobility Data
October 2021

Introduction
Google LLC’s Community Mobility Reports offer
data that many have found helpful in tracking economic
developments since the onset of the COVID-19
pandemic. 1 The raw data from these reports can bemisleading, however, as typical seasonal variation
obscures the underlying trend.
According to our analysis, in which we account for thatseasonal variation in the United States, the overall recovery
of U.S. mobility following the massive lockdown-induced
decline in early 2020 has been more muted than the raw
data suggest, and—notably—the more recent perceived
mobility recovery since April 2021 has been essentiallynonexistent.
In this paper, we share a novel methodology for adjusting
Google mobility data (GMD) to account for the effects ofseasonality. This is a particularly difficult problem becauseof the data series’ very short history, which necessitates anadjustment approach that goes beyond traditional
methods often applied to macroeconomic time series.

Background on Google
Mobility Data
Google began sharing its Community Mobility Reports inearly 2020 in response to the onset of the pandemic. GMD
is based on anonymized activity data collected from
Google users who have turned on their “location history.”
Google defines six categories for which mobility data arecompiled: retail & recreation, grocery & pharmacy, parks,transit stations, workplaces, and residential. Google
established “baseline” levels of mobility using median daily
values for the five-week period between January 3, 2020and February 6, 2020. Figure 1 shows changes in mobility
for each category over time relative to that baseline period.
For purposes of this paper, we focus specifically on retail &recreation mobility, as we believe this category maps mostdirectly onto broadly defined economic activity. According
to Google, retail & recreation mobility includes locations
such as restaurants, cafes, shopping centers, theme parks,
museums, libraries, and movie theaters. Other categories,
in our view, offer less by way of potential insights. Timespent in workplaces, for example, is likely a poor proxy for

Figure 1 U.S. Google Mobility Data:
Change in 7-Day Moving Average Relative to January 2020 Baseline(February 15, 2020 to September 30, 2021)
80%
60%
40%
20%
0%
-20%
-40%
-60%
Feb-20

Apr-20Jun-20Retail & Recreation

Aug-20

Oct-20Grocery & Pharmacy

Dec-20Feb-21

Apr-21

Parks

Transit Stations

Jun-21
Workplaces

Aug-21
Residential

Sources: Google LLC; the D. E. Shaw group.

1

Available here.

MARKET INSIGHTS | Fair Weather Trends: Seasonality and Google Mobility Data

1

time spent working given the widespread adoption of
remote work; parks and residential are not closely tied tomarket-based economic activity; and each of grocery &pharmacy and transit stations represents a fairly narrowslice of activity.

The Importance of Adjustingfor Seasonality
Macroeconomic data often show evidence of pronounced
seasonality. For example, Figure 2 shows U.S. retail andfood service sales, as measured by the U.S. Census Bureau,
on an unadjusted basis (orange line) and a seasonallyadjusted basis (blue line). Given the erratic swings in theunadjusted data, it’s much easier to identify important
shifts in the economy—such as the onset of the COVID-19
crisis—from the seasonally-adjusted data series. As aresult, analysts prefer to work with seasonally-adjusted
data when they are available.
One would expect mobility to show a strong seasonalpattern in a manner similar to other economic data;

however, GMD are not seasonally adjusted. Importantly,
GMD’s baseline measurement period falls within atimeframe that is seasonally weak for mobility in the
United States, as people generally spend less time outside
in the winter and are less apt to travel and shop
immediately following the U.S. holiday season. Indeed, formany macroeconomic indicators—such as payroll
employment, retail sales, and housing starts—January is
consistently close to the low point of the year in nonseasonally-adjusted activity. In other words, in the absenceof a pandemic, we would have expected mobility during
the rest of 2020 on average to have been substantiallyhigher than during the GMD baseline period.
With all of this in mind, adjusting GMD to account forseasonality allows for a better read on underlying trends inthe data. However, doing so is not a straightforward
exercise. Traditional methods for seasonal adjustment
identify patterns in the data by looking over a long
historical time series. 2 For GMD, as with many alternative
data sources, its limited history means that use of thosemethods may not be feasible.

Figure 2 U.S. Retail and Food Services Sales(January 2010 to July 2021)
650
600
550

$bn

500
450
400
350
300
250
2010

2011

2012

2013

2014

2015

2016

Not Seasonally Adjusted

2017

2018

2019

2020

2021

Seasonally Adjusted

Sources: U.S. Census Bureau; the D. E. Shaw group.

2

For example, X-13ARIMA-SEATS (available here) is the seasonal adjustment technique used for a variety of the U.S. government’s
macroeconomic indicators, such as the monthly payroll employment estimate from the U.S. Bureau of Labor Statistics. This method requires
several years of data in order to uncover seasonal patterns.

MARKET INSIGHTS | Fair Weather Trends: Seasonality and Google Mobility Data

2Our Alternative Approach

Validating Our Approach

Our new approach to seasonally adjusting GMD addresses
this limited history by combining a prior about the underlying
drivers of seasonal variation with a richer cross-sectional
technique that makes use of U.S.-state-specific data. By
contrast, traditional methods of seasonal adjustment are
mostly agnostic about why seasonal patterns exist.

Before applying our seasonal adjustment approach to
GMD, we need to make an initial assessment of its
reliability. To do so, we apply the methodology describedin the previous section to a data series for which wealready have known seasonal factors estimated in the
traditional manner. Our aim is to show that we canroughly reproduce these seasonality estimates, by firstusing a long history of data and then restricting the data
history to more closely resemble what is available for GMD.

We believe that seasonality in economic data is driven
mostly by weather (i.e., certain activities are more feasible
or more popular depending on the season) and by societal
or cultural features of the calendar (e.g., the start of theschool year or timing of the U.S. Thanksgiving holiday).
We focus specifically on weather, which we think is ofprimary importance when examining mobility. We use
temperature as a proxy for weather because of its regularseasonality and ready availability at the state level. 3
Our approach to estimating a temperature-based
seasonality adjustment then consists of three steps. First, weconstruct a panel dataset of our quantity of interest (i.e.,
state-level mobility) that is not seasonally adjusted, as wellas corresponding data for average state-level temperatures
for each observation period. 4 Next, we compute a simple
regression of changes in mobility on changes in temperature
to estimate typical sensitivity of mobility to temperature.
Finally, we construct an expected average path fortemperature over the course of the year at the national
level, and we use our sensitivity estimates to calculate
expected seasonal change based on that temperature path.
Key to our approach is the availability of state-level datafor both independent (temperature) and dependent(mobility) variables, which provides a broad cross-sectionof data to compensate for a time series history that is too
brief to estimate their relationship reliably. Our approach
benefits from the fact that temperature often has
significant independent variation in the cross-section ofstates, such as the extreme cold experienced by Texas inFebruary 2021 or the heatwave that descended upon anumber of western states in summer 2021 (while weather
outcomes in the rest of the country were more typicalduring these periods).

Because our approach focuses specifically on
weather—as compared to holidays, for example—it ismost appropriately applied to series for which we believethat seasonal variation is primarily due to weather
patterns. Employment in the leisure and hospitality
industry is an ideal test case, as it is significantly influenced
by weather, is measured and readily available at the statelevel, and is intuitively quite closely linked to retail &recreation mobility.
Starting first with a panel of ten years of monthly datafrom the contiguous 48 U.S. states, we run a pooled
regression and find with statistical significance that anincrease in temperature of 10°F yields, on average, a 2.0%
rise in the level of non-seasonally adjusted employment in
the leisure and hospitality industry. (Complete results forthis regression and the GMD regression described below
are available upon request.) Given that the national
average temperature usually varies by about 40°F during
the year, this implies seasonal swings in leisure andhospitality employment of about 8%, as shown by theorange line in Figure 3. Notably, this is almost exactly the
amplitude of seasonality reflected in the official seasonal
factors from the U.S. Bureau of Labor Statistics (BLS),
shown by the blue line in Figure 3. As the graph indicates,
the pattern of seasonality estimated by BLS is somewhat
more nuanced than our technique affords, with its
seasonality estimate deviating slightly from our moreclassic “sine wave” pattern at the start of the school year inAugust–September and at the peak of the U.S. holiday
season in December.

3

We are aware of past work finding a relationship between mobility and weather, for example Wu, Mooring, and Linz (2021). Although theseauthors focused on the relationship between temperature and park visitations, their study is primarily focused on public health and so is distinct
from our proposed application.
4

Temperature data are sourced from the National Centers for Environmental Information at the U.S. Department of Commerce’s National
Oceanographic and Atmospheric Administration.

MARKET INSIGHTS | Fair Weather Trends: Seasonality and Google Mobility Data

3

Next, we confirm whether a similar result can be obtainedby using a more limited time series history, akin to what isavailable for GMD. Using state-level data only since thebeginning of 2020, 5 we find that we can replicate almost
exactly the same estimated sensitivity of employment totemperature that was established using the ten-year history,
as shown by the red line in Figure 3. In other words, astate-level approach allowed us to meaningfully reduce thelength of the required time series without significantly
degrading the fidelity of our estimates in this case. 6

Because our estimated sensitivities of employment totemperature, derived from both a longer and a shortertime series, line up closely with the official seasonallyadjusted data, we are more confident that we can apply
the same methodology to mobility, for which seasonality
adjustments don’t readily exist.

Figure 3 Estimated Seasonal Factors for Leisure and Hospitality Employment and BLS Seasonal Factor(May 2020 to May 2021)
1.08

1.04

1.00

0.96

0.92
May-20Jul-20

Sep-20Nov-20Jan-21

Mar-21

May-21

Estimated Seasonal Factor for Leisure and Hospitality (1 Year of Data)
Estimated Seasonal Factor for Leisure and Hospitality (10 Years of Data)
BLS Seasonal Factor
Sources: U.S. Bureau of Labor Statistics; the D. E. Shaw group.

5

Recognizing that the months of April, May, and June 2020 experienced unusually large employment changes associated with the imposition andsubsequent relaxation of COVID-19-related lockdowns, we include binary control variables for those three months to avoid affording that periodof time an outsized distorting influence on the estimated temperature sensitivity given the smaller sample.

6

Some might contend that the causal importance of weather in driving mobility is overstated, reasoning that it is the school vacation schedule in
the United States, not temperature, that primarily drives the seasonal pattern. This concern may have some validity, although if the school
vacation schedule were the dominant factor, we would not expect our technique using only one year of data to so closely replicate estimatedseasonality in leisure and hospitality employment.

MARKET INSIGHTS | Fair Weather Trends: Seasonality and Google Mobility Data

4

Adjusting and Reinterpreting
Mobility Data
We now apply our methodology to GMD. To do so, we
run a simple regression of changes in retail & recreation
mobility on changes in temperature using U.S.-state-level
data in order to determine the typical sensitivity of mobility
to temperature. 7 We find with statistical significance that a
10°F increase in temperature translates into a roughly

2.9% rise in the level of retail & recreation mobility. As
one might expect, Figure 4 shows that our estimatedseasonal variation for mobility (blue line) exhibits a similar
pattern to that of leisure and hospitality employment
referenced in the previous section (orange line), although
it does so with somewhat greater amplitude.

Figure 4 Estimated Seasonal Factors: U.S. Google Mobility Data (Retail & Recreation)and Leisure and Hospitality Employment(May 2020 to May 2021)
1.08

1.04

1.00

0.96

0.92
May-20Jul-20

Sep-20Nov-20U.S. Google Mobility Data (Retail & Recreation)

Jan-21

Mar-21

May-21

Leisure and Hospitality Employment

Source: the D. E. Shaw group.

7

In this case, we use a fixed effects panel regression using both state- and month-fixed effects, although the results are qualitatively similar using
either a simple pooled regression or a regression using binary control variables for the COVID-19 shock months.

MARKET INSIGHTS | Fair Weather Trends: Seasonality and Google Mobility Data

5

With these results in hand, we can now adjust the raw
GMD series for expected seasonal variation. Figure 5 shows
the original, non-seasonally-adjusted U.S. retail & recreation
mobility data (orange line) and our seasonally-adjusted
series (blue line).
Five takeaways stand out from our adjusted results whencompared to the unadjusted data in Figure 5:
1. The sharp fall in activity evident in GMD after the
COVID-19 crisis hit in early 2020 was in fact more
dramatic than the raw data suggested because, absent
the crisis, mobility would have been seasonally
increasing in springtime.
2. Our seasonally-adjusted data indicate an increase ineconomic activity in late summer and fall 2020, in
contrast to the decline in raw mobility that seemedinconsistent with the signal from official economic dataat the time, such as nonfarm payroll employment.
3. We find that a modest portion of the upturn in mobility
after vaccines became widely available in spring 2021

was seasonally expected, but a robust pace ofimprovement remained even after seasonal adjustment.
4. Mobility has actually edged down a bit since April 2021
after seasonal adjustment, suggesting that normalizationin mobility since the spring has not provided as muchcontinued support to economic recovery as manymight believe.
5. Finally, our seasonally-adjusted measure suggests thatmobility is still down substantially (about 15%) relative
to the pre-COVID baseline, presenting a more
pessimistic picture of the overall normalization that has
taken place as compared to the unadjusted decrease of
only 6.4%. Although not necessarily directlycomparable, this seasonally-adjusted number aligns
more closely with the level gaps apparent in other key
indicators such as leisure and hospitality employment,
reported by the BLS, which was down roughly 14%
relative to the pre-COVID trend.

Figure 5 U.S. Google Mobility Data Retail & Recreation:
Change in 7-Day Moving Average Relative to January 2020 Baseline(February 15, 2020 to September 30, 2021)
20%

10%Baseline

0%

-10%

5

-20%
4
-30%
3

2
-40%

-50%
1
-60%
Feb-20

Apr-20Jun-20Aug-20

Oct-20Dec-20Not Seasonally Adjusted

Feb-21

Apr-21

Jun-21

Aug-21

Seasonally Adjusted

Sources: Google LLC; the D. E. Shaw group.

MARKET INSIGHTS | Fair Weather Trends: Seasonality and Google Mobility Data

6

Conclusion and Directions
for Future Work
Google’s Community Mobility Reports have been of greatinterest and utility to many since the early days of thepandemic. As we have seen, the insights derived from
GMD are crucial to understanding the economic effects ofthe pandemic; in turn, we believe it’s important to refine
those data by adjusting for the effects of seasonality. Weview this as just one example of how macroeconomic
analysis can be improved by a thoughtful approach tointerpreting alternative data sources.
With respect to GMD, we believe our approach is a helpful
step, although we have identified a number of promisingdimensions along which our work could be extended:• Improve model precision: We have assumed a simple,• Expand scope: In principle, this seasonal adjustment ofmobility data could be applied to any country or region
that is sufficiently large as to have substantial
temperature variation in the geographic cross-section(e.g., Canada or the euro area). The technique could
also be applied more broadly than the specific case of
GMD, to include other alternative data measures withsignificant seasonal variation.
Data do not speak for themselves. To make any
compilation of raw data more analytically useful,researchers must apply techniques that render that data
clean, consistent, and interpretable. Google’s Community
Mobility Reports typify a range of new alternative data
sources that can help inform macroeconomic analysis. We
hope this paper will contribute to making mobility dataand other innovative data sources increasingly valuable for
economic research.

linear relationship between temperature and mobility.
Additional work might relax that assumption and allow
for a non-linear relationship between the two quantities,
potentially revealing more nuanced patterns of
seasonality. Further, we could envision the introductionof two factors to represent climatic variation, such astemperature and precipitation (the typical seasonality forwhich is slightly out of phase with temperature). Finally,a mobility measure could be constructed to adjust for
actual (as opposed to expected) variation intemperature, which might be of interest if national
average temperatures are unusual in a given period.
• Capture additional drivers of variation: Our approach

could be extended to incorporate holiday effects,
perhaps by introducing plausible estimates of themagnitude of such effects from an external source andoverlaying these estimates on the seasonal patternderived using our technique. Alternatively, it might bepossible to scale off-the-shelf seasonality estimatesavailable for macroeconomic data series such as
employment, inclusive of holiday effects, by the peakto-trough amplitude of seasonality that we estimate formobility.

MARKET INSIGHTS | Fair Weather Trends: Seasonality and Google Mobility Data

7

THIS DOCUMENT IS PROVIDED TO YOU FOR INFORMATIONAL PURPOSES ONLY AND DOES NOT CONSTITUTEINVESTMENT ADVICE OR AN OFFER TO SELL (OR THE SOLICITATION OF AN OFFER TO BUY) ANY SECURITY, INVESTMENTPRODUCT, OR SERVICE.
THE VIEWS EXPRESSED IN THIS DOCUMENT ARE SOLELY THOSE OF THE D. E. SHAW GROUP AS OF THE DATE OF THISDOCUMENT, ARE SUBJECT TO CHANGE WITHOUT NOTICE, AND MAY NOT REFLECT THE CRITERIA EMPLOYED BY ANYPERSON OR ENTITY IN THE D. E. SHAW GROUP TO EVALUATE INVESTMENTS OR INVESTMENT STRATEGIES. SIMILARLY,
THE INFORMATION CONTAINED IN THIS DOCUMENT IS PRESENTED SOLELY WITH RESPECT TO THE DATE OF THISDOCUMENT (UNLESS OTHERWISE INDICATED) AND MAY BE CHANGED OR UPDATED AT ANY TIME WITHOUT NOTICE TOANY OF THE RECIPIENTS OF THIS DOCUMENT. THE INFORMATION CONTAINED IN THIS DOCUMENT HAS BEEN
DEVELOPED BY THE D. E. SHAW GROUP AND/OR OBTAINED FROM SOURCES BELIEVED TO BE RELIABLE; HOWEVER, THE
D. E. SHAW GROUP DOES NOT GUARANTEE THE ACCURACY, ADEQUACY, OR COMPLETENESS OF SUCH INFORMATION.
FURTHER, THIS DOCUMENT CONTAINS PROJECTIONS AND OTHER FORWARD-LOOKING STATEMENTS REGARDING
FUTURE EVENTS, TARGETS, OR EXPECTATIONS. SUCH STATEMENTS ARE BASED IN PART ON CURRENT MARKETCONDITIONS, WHICH WILL FLUCTUATE AND MAY BE SUPERSEDED BY SUBSEQUENT MARKET EVENTS OR OTHER
FACTORS. HISTORICAL MARKET TRENDS ARE NOT RELIABLE INDICATORS OF FUTURE MARKET BEHAVIOR OR THE FUTUREPERFORMANCE OF ANY PARTICULAR INVESTMENT AND SHOULD NOT BE RELIED UPON AS SUCH.
MORE GENERALLY, NO ASSURANCES CAN BE GIVEN THAT ANY AIMS, ASSUMPTIONS, EXPECTATIONS, AND/OROBJECTIVES DESCRIBED IN THIS DOCUMENT WILL BE REALIZED. NONE OF THE ENTITIES IN THE D. E. SHAW GROUP; NORANY OF THEIR RESPECTIVE AFFILIATES; NOR ANY SHAREHOLDERS, PARTNERS, MEMBERS, MANAGERS, DIRECTORS,PRINCIPALS, PERSONNEL, TRUSTEES, OR AGENTS OF ANY OF THE FOREGOING SHALL BE LIABLE FOR ANY ERRORS (AS A
RESULT OF NEGLIGENCE OR OTHERWISE, TO THE FULLEST EXTENT PERMITTED BY LAW IN THE ABSENCE OF FRAUD) IN
THE PRODUCTION OR CONTENTS OF THIS DOCUMENT, OR FOR THE CONSEQUENCES OF RELYING ON SUCH CONTENTS.
NEITHER THIS DOCUMENT NOR ANY PART OF THIS DOCUMENT MAY BE REPRODUCED OR DISTRIBUTED WITHOUT THE
PRIOR WRITTEN AUTHORIZATION OF THE D. E. SHAW GROUP.
COPYRIGHT © 2021 D. E. SHAW & CO., L.P. ALL RIGHTS RESERVED.

MARKET INSIGHTS | Fair Weather Trends: Seasonality and Google Mobility Data

8