I study the effect of the length of the Christmas "shopping season" in the United States (traditionally, beginning the day after US Thanksgiving) on aggregate retail sales. I find a statistically significant increase in per-capita retail sales in November and December (combined) of approximately $6.50 per additional day over the relevant range. The implications of these finding are briefly discussed.Christmas, Retail, Shopping

Emek Basker

English

Research Papers in Economics

’Twas Four Weeks before Christmas:
Retail Sales and the Length of the Christmas Shopping Season
Emek Basker∗
University of Missouri
August 2004
Abstract
I study the eﬀect of the length of the Christmas “shopping season” in the United
States (traditionally, beginning the day after US Thanksgiving) on aggregate retail
sales. I ﬁnd a statistically signiﬁcant increase in per-capita retail sales in Novem-
ber and December (combined) of approximately $6.50 per additional day over the
relevant range. The implications of these ﬁnding are brieﬂy discussed.
JEL Numbers: L81, D12
Keywords: Christmas, Retail, Shopping
1 Introduction
The traditional Christmas “shopping season” in the United States varies from 26-32 days: the
shopping season begins the day after Thanksgiving, which falls on the fourth Thursday in
November.1 The number of days each year for 1967-2000 is plotted in Figure 1. (Leap years
prevent the number of days from following a regular 7-year cycle.)
US media reports speculate each year whether the length of the shopping season will aﬀect
sales. News stories typically argue that shorter shopping seasons reduce consumers’ opportu-
nities to make “impulse” purchases, aﬀecting purchases of both of gifts and items for personal
∗Comments welcome to: emek@missouri.edu. I thank Saku Aura, Eric Gould, Jeﬀ Milyo, Peter Mueser, Pham
Hoang Van and especially Nevet Basker for helpful conversations and comments. All errors are my own.
1Thanksgiving has been a national holiday since 1863, but its date was not set in law. Because Lincoln
celebrated Thanksgiving on the last Thursday of November, it was celebrated on that day until 1939, when
Franklin Roosevelt proclaimed Thanksgiving a week earlier in order to lengthen the Christmas shopping season,
spurring confusion and controversy. In 1941, Congress set Thanksgiving to its current date, the fourth Thursday
in November.
1consumption. In an unscientiﬁc on-line poll by the publication Retail Merchandiser in Decem-
ber 2002, 31% of respondents thought that a shorter shopping season would have a “major
impact” on their sales; only 15% felt that “fewer days have nothing to do with the amount that
consumers need to spend on holiday gifts each year” (Retail Merchandiser 2002).
In this paper, I use data on aggregate retail sales in the US from 1967-2000 to check whether
the number of shopping days really does aﬀect retail sales. I ﬁnd that the number of shop-
ping days matters primarily for the timing of sales. Per capita, US consumers spend approx-
imately $6.50 (in 2000-2002 dollars) more in November and December (combined) for every
additional shopping day between Thanksgiving and Christmas over the relevant range (26-32);
this amounts to an increase of about 3.5% in holiday-related sales per person. The sectors most
strongly aﬀected are electronics, apparel, food and general merchandise.
2 Data
The data come from the US Census Bureau’s Monthly Retail Trade Survey. The survey covers
over 10,000 retail businesses each month, selected from the Business Register, a comprehensive
listing of all Employer Identiﬁcation Numbers (EINs).2 Nominal, seasonally-unadjusted retail
sales data are available for selected two- and three-digit SIC codes such as apparel, drugstores,
general merchandise stores (including department stores and many discount retailers) and hard-
ware stores. Total sales across all retail SIC codes are also given. I use the Consumer Price
Index (all items) to convert nominal sales to real dollars.
For each retail subsector, I calculate the Christmas sales increase – excess sales over other
months – as
HolidayExcessjt ≡ (NovSalesjt + DecSalesjt) − (SeptSalesjt + OctSalesjt) (1)
where j indexes industry and t indexes year, and SeptSales, OctSales, NovSales and Dec-
Sales are, respectively, September-December real (per-capita) retail sales. The variable Hol-
idayExcess is therefore interpretable as excess sales over November and December relative
to a counterfactual in which September- and October-level sales would have been observed in
November and December. The purpose of using October sales to “deﬂate” holiday sales is to
2Details about the survey are available from the Census Bureau’s web site,
http://www.census.gov/mrts/www/noverview.html.
2control for the overall size of the economy. I also calculate
NovExcessjt ≡ NovSalesjt −
1
2
(SeptSalesjt + OctSalesjt) (2)
DecExcessjt ≡ DecSalesjt −
1
2
(SeptSalesjt + OctSalesjt) (3)
JanExcessjt ≡ JanSalesjt −
1
2
(SeptSalesj,t−1 + OctSalesj,t−1). (4)
Summary statistics are shown in Table 1. Industries included in the table (and in the
analysis) are those from which gifts are likely to be selected, and others selling goods associated
with the holidays (speciﬁcally, food stores and liquor stores).
To gauge the eﬀect of the length of the shopping season on holiday sales, I estimate
ExcessSalesjt = αj + γ · ExcessDayst + β · Xt + εjt (5)
where ExcessSales may be NovExcess, DecExcess or HolidayExcess, and ExcessDays
is the number of days between Thanksgiving and Christmas minus 26 (the minimum number
of shopping days).3 Because of the short time-series dimension (34 years), I add only unem-
ployment rates as control variables.
Since the timing of Christmas is ﬁxed, and only the date of Thanksgiving varies from
one year to the next, a pure substitution eﬀect may occur where more shopping days lead to
relatively more sales in November, but correspondingly fewer sales in December. Alternatively,
November sales may increase while December sales remain unchanged (or not fall by the full
amount).4 Finally, if there is a “habit-formation” element in shopping, the longer consumers
are exposed to advertising and shopping, the more spendthrift they become, in which case a
longer shopping season can translate into more December sales as well as more November sales.
The latter case is consistent with a model such as Laibson’s “cue theory” (Laibson 2001) or
with an intertemporal increasing-returns shopping technology.
3Regressions estimating the eﬀect on January sales use the previous year’s number of days.
4While consumers can still do their Christmas shopping in November, before Thanksgiving, simultaneous
planning for two holidays limits this possibility. This constraint is reﬂected in – and exacerbated by – the fact
that price markdowns begin in earnest only after Thanksgiving (Warner and Barsky 1995).
33 Implementation and Results
Results for retail sales in several categories are shown in Table 2. Column (1) shows the eﬀect
of an added shopping day on November sales, and column (2) shows December sales. Columns
(3) and (4) show total holiday sales. Columns (1)-(3) have no control variables, while the
regressions in column (4) include the US unemployment rates for September-December for each
year (4 controls). Each of these regression is estimated with 34 data points, with the exception
of electronics sales, which are available only for 1992-2000 (9 years). Finally, column (5) shows
the eﬀect on January sales (with no controls); these regressions are estimated with 33 data
points due to the lag structure.
As expected, November sales increase with the number of shopping days in all categories;
for example, adding a shopping day increases excess November sales in drugstores by $0.14
per capita, or 0.35%, and sales in jewelry stores by $0.06, about 1%. With the exception of
food- and liquor-stores’ sales, the eﬀect of the number of shopping days is always positive.
Somewhat surprisingly, sales in December in apparel and food store (and, to a lesser extent,
in general merchandise stores) also increase signiﬁcantly; only sales in furniture stores and
nonstore retailers decrease in December when the number of shopping days increases; these
negative eﬀects are statistically insigniﬁcant. As a result, overall holiday sales increase in many
categories. These results are not sensitive to the inclusion of unemployment rates as control
variables.5
Total per capita retail sales over the 2-month period November-December increase by ap-
proximately $6.50 (in 2000-2002 dollars) with every additional shopping day between Thanks-
giving and Christmas. With average sales in November and December (combined) about $190
higher than the counterfactual of constant sales at the average of September and October over
this period, this amounts to an increase of approximately 3.5% in sales per person. The eﬀect
is therefore both statistically and economically signiﬁcant.
For electronics, the implied increase in sales per capita is approximately $0.70 over the
shopping season, an increase of approximately 5% in “excess” holiday spending. Increased sales
in apparel stores amount to approximately $0.50, and general merchandise sales increase by
5Using only October sales (instead of the average of September and October sales) to normalize holiday sales
increases these point estimates. Using September sales alone decreases them substantially. This is probably
due to a combination of two factors. First, the later is Thanksgiving, the more likely is September to have ﬁve
weekends instead of four, which will mechanically increase September sales and depress October sales. Second,
anticipating a late Thanksgiving, consumers may in fact start their holiday shopping as early as October.
4just over $1.50 for each additional shopping day. Sales at food stores (a category that includes
grocery stores as well as speciality food stores) increase by approximately $0.90, most likely due
to the fact that holiday parties traditionally do not begin until after Thanksgiving. In addition,
some of the increase in food- and liquor-store sales may be gift purchases, and not all of that
amount will be consumed immediately.
Since consumers operate within a budget constraint, increased Christmas sales must come
at the expense of either spending (in other months) or saving. With the notable exception of
food and alcohol, whose January sales declines exactly oﬀset holiday increases, there does not
appear to be a “hangover eﬀect” on January sales. By process of elimination, savings over this
period must be reduced, although I do not have direct evidence of such an eﬀect.
4 Conclusion
For most items, I ﬁnd that increasing the number of shopping days increases spending in
November while leaving December sales unaﬀected, suggesting that consumers may be more
constrained by the time available for Christmas shopping. For several retailer categories, in-
cluding apparel, electronics and general merchandise, I ﬁnd that December sales increase when
the number of shopping days (in November) increases, suggesting either high-frequency “habit-
formation” or intertemporal increasing returns to shopping. This could be due to ﬁxed costs
of learning the layout of the stores and the identity of the stores with the best deals, or to
increasing returns to internalizing the “Christmas spirit”.
If Waldfogel (1993 and 2002) is right, these ﬁndings imply that a longer shopping seasons
is associated with a larger “deadweight loss” of Christmas. These issues should be studied
further, preferably using micro-level data.
This ﬁnding has potential macroeconomic implications. Wen (2002) ﬁnds a large role for
seasonal shocks in explaining aggregate business cycles, and argues that large synchronized
shocks such as those induced by Christmas almost inevitably have eﬀects on economic output.
If longer shopping seasons have larger eﬀects, as this paper suggests, this could provide a
powerful exogenous instrument for studying the eﬀect of demand shocks on business cycles.
5References
[1] Laibson, David (2001). “A Cue-Theory of Consumption.” Quarterly Journal of Economics,
116:1, 81-119.
[2] Retail Merchandiser (2002). “A shortened holiday” (Retail Merchandiser Online Poll, De-
cember 1, 2002).
[3] Waldfogel, Joel (1993). “The Deadweight Loss of Christmas.” American Economic Review,
83:5, 1328-1336.
[4] Waldfogel, Joel (2002). “Does Consumer Irrationality Trump Consumer Sovereignty? Evi-
dence from Gifts and Own Purchases.” University of Pennsylvania mimeo.
[5] . Warner, Elizabeth J. and Robert B. Barsky (1995). “The Timing and Magnitude of Re-
tail Store Markdowns: Evidence from Weekends and Holidays.” Quarterly Journal of Eco-
nomics, 110:2, 321-352.
[6] Wen, Yi (2002). “The Business Cycle Eﬀects of Christmas.” Journal of Monetary Economics
49:6, 1289-1314.
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Figure 1: Length of Shopping Season, 1967-2000
Table 1: Retail Sales per Capita, 1967-2000
Type of Holiday
Business September October November December January Excess ($)
Total Sales 754.59 779.10 787.40 936.13 677.28 189.85
Apparel 38.93 40.45 44.22 66.57 31.28 31.41
Drugstores 26.42 27.41 27.30 36.62 26.53 10.09
Electronicsa 14.83 14.86 17.37 26.16 14.69 13.84
Sporting
Goods 5.17 4.88 5.42 8.54 4.28 3.90
Furniture 25.25 26.23 27.70 30.81 23.05 7.03
General
Merchandise 87.86 94.48 112.52 169.56 69.02 99.74
Hardware 5.56 5.79 5.64 6.59 4.46 0.87
Jewelry 5.02 5.33 6.78 16.98 4.25 13.41
Food Stores 154.78 157.20 155.63 170.69 152.59 14.33
Liquor Stores 11.09 11.34 11.73 16.20 10.44 5.50
Nonstore
Retailers 20.18 23.10 25.29 25.87 17.52 7.87
Average sales per capita, 2000-2002 dollars. Holiday Excess is the diﬀerence
between November + December sales and September+October sales.
a1992-2000 only
7Table 2: Eﬀect of Shopping Days on Retail Sales
November December Holiday Holidaya January
Total 3.9022*** 2.8993 6.8014** 6.4988** -0.6774
(1.2319) (2.1681) (2.6742) (2.4915) (1.5324)
Apparel 0.1468 0.4290** 0.5758*** 0.5615*** -0.1530
(0.1351) (0.1581) (0.2008) (0.1681) (0.1629)
Drugstores 0.1444*** 0.0404 0.1848** 0.2080** -0.0246
(0.0390) (0.0676) (0.0841) (0.0856) (0.0543)
Electronicsb 0.1559 0.5086 0.6645 0.7157** 0.2382
(0.0935) (0.3662) (0.4465) (0.1888) (0.1462)
Sporting 0.0280 0.0591 0.0871 0.1011 0.0145
Goods (0.0242) (0.1013) (0.1083) (0.1100) (0.0302)
Furniture 0.1770** -0.0114 0.1657 0.1430 0.1213
(0.0683) (0.1036) (0.1500) (0.1402) (0.0844)
General 0.5246 0.9910* 1.5155* 1.5597* -0.3477
Merchandise (0.3751) (0.5639) (0.8611) (0.8148) (0.2567)
Hardware 0.0120 0.0016 0.0136 0.0037 -0.0063
(0.0154) (0.0527) (0.0598) (0.0630) (0.0365)
Jewelry 0.0588** 0.1154 0.1742 0.1724 -0.0402
(0.0245) (0.1409) (0.1606) (0.1619) (0.0376)
Food Stores -0.2213 1.1983*** 0.9770** 0.8685** -0.9898***
(0.3713) (0.2279) (0.3941) (0.4025) (0.3381)
Liquor Stores -0.0332 0.1708 0.1377 0.1149 -0.1355***
(0.0322) (0.1228) (0.1436) (0.1400) (0.0303)
Nonstore 0.4555*** -0.0365 0.4190 0.2813 -0.0133
Retailers (0.1170) (0.3301) (0.4198) (0.4287) (0.1382)
* signiﬁcant at 10%; ** signiﬁcant at 5%; *** signiﬁcant at 1%
Coeﬃcients represent the marginal eﬀect of an added shopping day on sales.
per capita. Each cell is a separate regression. Standard errors in parentheses.
aIncludes covariates: October-December unemployment rates
b1992-2000 only
8

'Twas Four Weeks before Christmas: Retail Sales and the Length of the Christmas Shopping Season

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