Malleability of Alcohol Consumption:
Evidence from Migrants∗†
Marit Hinnosaar‡

Elaine M. Liu§

May 2022

Abstract
How malleable is alcohol consumption? Specifically, how much is alcohol consumption driven by the current environment versus individual characteristics? To answer
this question, we analyze changes in alcohol purchases when consumers move from one
state to another in the United States. We find that if a household moves to a state witha higher (lower) average alcohol purchases than the origin state, the household is likely
to increase (decrease) its alcohol purchases right after the move. The current environment explains about two-thirds of the differences in alcohol purchases. The adjustment
takes place both on the extensive and intensive margins.

JEL: I12, L66, D12, I18
Keywords: alcohol, geographic variation, migration, taxes, regulation∗

We thank Philip Cook, Rachel Griffith, Toomas Hinnosaar, Kevin Milligan, Sarah Moshary, Ignacio Monzon, Juan Morales, Mario Pagliero, Joel Waldfogel, Evgeny Yakovlev and seminar participants at the NBERSI Health Economics Workshop, Econometric Society North American Summer Meeting, Baltic Economic
Association, Electronic Health Economics Colloquium, Applied Economics Workshop, National Taiwan University, University of Nottingham, the University of Washington PHEnOM, Vanderbilt University, and the
University of Western Australia for helpful comments and suggestions.
†
Researcher(s) own analyses calculated (or derived) based in part on data from Nielsen Consumer LLC
and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data
Center at The University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ
data are those of the researcher(s) and do not reflect the views of NielsenIQ. NielsenIQ is not responsible
for, had no role in, and was not involved in analyzing and preparing the results reported herein.
‡
University of Nottingham and CEPR, marit.hinnosaar@gmail.com.
§
University of Houston, NBER, IZA, and HCEO, emliu@uh.edu.

11Introduction

Alcohol is one of the leading killers among substances. In 2016, alcohol was responsible for5.3% of all deaths and 7.2% of all premature deaths (among persons 69 years of age andyounger) worldwide (World Health Organization, 2018). Beyond direct health consequences,
excessive alcohol consumption generates a social and economic burden on other people (Cookand Moore, 2000; Cawley and Ruhm, 2011).
Various factors affect alcohol consumption, such as taxes and other regulations, peers,and social norms. To identify each factor’s direct impact, studies have used changes in regulation (e.g. Carpenter, 2004; Marcus and Siedler, 2015) and random assignment of peers(e.g. Eisenberg et al., 2014). These studies give us a well-identified local average treatment
effect in the short term. But over the long term, institutions and cultural norms are known
to interact (Guiso et al., 2016), either magnifying or decreasing each factor’s direct effect.1
For example, peers who do not consume alcohol could vote for stricter alcohol regulation;
strict alcohol regulation could lead to norms of consuming less alcohol and affect how children grow up viewing alcohol, which again changes the norm and leads to a different local
environment. Quantifying the overall impact of the environment is important, as it tells us
how malleable alcohol consumption is and informs us how much room there is for any alcohol
policy. But measuring the combined effect is challenging because it typically requires looking
at a long time horizon. However, over the long term, economic conditions change in otherways that also affect alcohol consumption. Therefore, less is known about the magnitude of
the combined effect.
In this paper, we study how much the current environment drives alcohol purchases.
To answer the question, we analyze changes in alcohol purchases when consumers movefrom one state to another in the United States. The magnitude of the change in movers’
alcohol purchases allows us to measure the relative importance of the current environment.
Understanding how alcohol consumption responds to the current environment is crucial for
designing effective policies.
Our empirical strategy relies on the fact that the environment, including supply conditions, alcohol regulation, taxes, and movers’ peers, changes discretely when consumers move.
If the current environment mainly drives alcohol consumption, we would expect a jump in
the mover’s alcohol purchases in the same direction as the gap between the destination andorigin state. On the other hand, if alcohol consumption is only driven by individual charac1

For an overview of interactions of institutions and cultural norms, see Alesina and Giuliano (2015).

2

teristics, such as personal preferences and past experiences, we would not expect a change
in the mover’s alcohol purchases.
We study the question using a panel of movers in the NielsenIQ scanner data of alcoholpurchases. We observe their alcohol purchases years before and after the move. Our primary
outcome variable is the logarithm of quarterly off-premise alcohol purchases measured in pureethanol. We also measure alcohol purchases separately in beer, wine, and liquor categories
and analyze the extensive margin—whether consumers buy any alcohol at all.
We estimate event study and difference-in-differences regressions with consumer and time
period fixed effects. A possible concern with our identification strategy is that moves occur
due to a shock that changes alcohol purchases. To alleviate the concern, we provide twopieces of evidence. First, we restrict the sample to movers whose observable characteristics,
like household size, employment, and marital status, don’t change, and our results remainsimilar. Second, we compare trends in pre-move purchases of movers to higher versus lower
alcohol-purchasing states. This shows that the movers who chose to go to different states
before the move had similar trends in their purchases.
We find that if a household moves to a state with a higher (lower) average alcoholpurchases than the origin state, the household is likely to increase (decrease) its alcoholpurchases right after the move. About two-thirds of the gap in alcohol purchases between theorigin and destination state closes immediately when a consumer moves. No sizable further
change is seen after the immediate jump. This finding implies that the current environment
explains a large share of the differences in alcohol purchases and that government policies
and regulations targeting the drinking environment (e.g. alcohol availability) could have asignificant impact on the amount of alcohol consumed.
There is some heterogeneity across product types. Consumers adjust their wine purchases
more and their liquor purchases less. The adjustment takes place both on the extensive andintensive margins. On the extensive margin, movers are more (less) likely to buy alcohol
when moving to a state with a larger (smaller) share of consumers buying alcohol. On theintensive margin, movers who bought alcohol before the move adjust the quantity in the
direction of the average purchases in the destination state. There is evidence of asymmetries
in adjustment, but mainly on the extensive margin. Consumers are more likely to adjust
upward (start to purchase alcohol) than downward (stop buying alcohol). Our results arerobust to a number of robustness checks using alternative samples, functional forms, controls,
and geographic aggregation levels.
An important concern about the analysis is underreporting of alcohol purchases. In

3particular, whether the magnitude of underreporting changes at the move. This could happen
if reporting depends on retail conditions, such as alcohol availability in grocery stores, whichvary by state. We provide evidence that our results are not driven by the changes in reporting
related to the retail conditions. When we restrict the sample to moves between states withsimilar retail conditions, movers still adjust their purchases. Another concern is that weonly measure off-premise alcohol purchases, and we provide suggestive evidence that this ispredictive of overall heavy drinking and chronic liver disease and cirrhosis mortality.
Our results based on movers and alcohol purchases might not generalize to the general
population and other products. If movers are more likely to adjust to the new environment,
our results provide an upper bound of how malleable alcohol purchases are in the general
population. However, movers are also interesting in their own right because they are a large
share of the population—more than 30% of the U.S. population has moved across the statelines in their lifetime (Molloy et al., 2011). Furthermore, our findings of the extent of theadjustment are specific to alcohol and don’t necessarily generalize to other products. For
example, we show that movers adjust their cigarette purchases less than alcohol, and they
don’t significantly adjust their food purchases. We also show that our estimation method
replicates the results from the literature of a large adjustment for brands as in Bronnenberg
et al. (2012) and no adjustment for healthy eating choices as in Allcott et al. (2019).
Our work contributes to the ongoing debate about how malleable alcohol consumption
is and how much it is driven by the environment.2 We provide new causal evidence that thecurrent environment explains about two-thirds of the variation in alcohol purchases. The
current environment consists of many factors (including local regulation, norms, and peers),
which in the long-term affect each other, making it difficult to measure the combined effect.
Therefore, the literature mostly estimates the short-term direct effect of either taxes andregulations or peer effects. A notable exception is Yakovlev (2018), who uses a structuralmodel and data on alcohol consumption and peers to estimate the impact of an increase in
the price of vodka in Russia. He finds that peer effects play a large role in magnifying theimpact of the price increase. We contribute to the literature by using an alternative method
based on movers to overcome the difficulties in measuring the combined effect.
Our work also adds a new finding to the literature on how changes in the environment
2

The literature has studied the impact of environmental factors such as alcohol taxes and regulations(Carpenter, 2004; Marcus and Siedler, 2015; Aguirregabiria et al., 2016; Bernheim et al., 2016; Hinnosaar,
2016; Miller and Weinberg, 2017; Illanes and Moshary, 2018; Griffith et al., 2019; Miravete et al., 2019; Seo,
2019; Kueng and Yakovlev, 2021; Gehrsitz et al., 2020), peer effects (Lundborg, 2006; Clark and Lohéac,
2007; Eisenberg et al., 2014), and individual characteristics such as family background, cognitive ability,
discount rate, and self-control (Cutler and Lleras-Muney, 2010; Schilbach, 2019).

4affect consumer behavior. In the case of food and drinks, the question was studied using
movers in the same dataset, the NielsenIQ scanner data, by Bronnenberg et al. (2012); Allcott
et al. (2019); Hut (2020).3 Bronnenberg et al. (2012) study the evolution of brand preferences
and find that 60% of the gap between the destination and the origin average purchases ofgrocery products is bridged immediately after the move. Allcott et al. (2019) and Hut(2020) study how much the healthiness of food purchases changes with a move and findthat the change is very small. Our results align with the evidence of large changes in brandchoices while standing in contrast to little changes in the healthiness of food purchases. We
hypothesize that large regional differences in alcohol regulation (availability and taxes) are
the main reason for the large adjustment in alcohol purchases. So large regional differencesin supply conditions are absent in food healthiness while existing for brands.
More generally, the literature on the convergence of behaviors of migrants has often foundevidence of persistence, for example, for food preferences (Atkin, 2016), living arrangements(Giuliano, 2007), and fertility and female labor force participation (Fernández and Fogli,2009). Our finding of sizable changes in alcohol purchases is not surprising, consideringthat alcohol consumption is a social activity. It is plausible that alcohol consumption ismore influenced by social forces and the environment than other behaviors examined in theliterature.
To quantify the importance of the environment in affecting population health, Finkelstein
et al. (2016, 2018, 2021) develop and employ the same empirical strategy of examining
migrants’ behaviors. Our results on the role of the environment in alcohol purchases add an
important dimension not previously examined in this literature. More generally, our paper
relates to the recent work of Chetty et al. (2016) and Chetty and Hendren (2018b), whichfind that where one grows up is an important factor in affecting long-term outcomes suchas intergenerational mobility and earning. Our paper provides an additional mechanism of
why the environment matters. According to our findings, the current environment largely
determines individuals’ alcohol purchases. Using a simple back-of-envelope calculation would
suggest that if a household of two adults and one child moves from Utah to New Hampshire,
3

Other papers using data of movers have estimated the impact of urban sprawl on obesity (Eid et al., 2008),
the impact of location on healthcare utilization (Finkelstein et al., 2016), food consumption in India (Atkin,2016), intergenerational mobility (Chetty and Hendren, 2018b,a), opioid abuse (Finkelstein et al., 2018),
relative obesity (Liu and Zuppann, 2018), physicians practice styles (Molitor, 2018), mortality (Finkelstein
et al., 2021), and consumer financial distress (Keys et al., 2020). More generally, the same idea of using
movers is used to measure worker and firm effects (Abowd et al., 1999; Card et al., 2013) and teacher
effects (Jackson, 2013; Chetty et al., 2014). The impact of other large changes in the environment on foodconsumption has been studied, for example, by Dragone and Ziebarth (2017) in German reunification.

5the family’s alcohol purchases would increase by $27 per quarter. This shift in alcoholpurchases could affect wealth, earnings, alcohol abuse, and the overall well-being.
Section 2 describes the data. Section 3 describes the empirical strategy. Section 4 presents
our main analysis. Section 5 concludes.

2

Data

NielsenIQ Consumer Panel. We use NielsenIQ Consumer Panel from 2004–2017 tomeasure household-level alcohol purchases (quantity and expenditures). The panel is representative of the U.S. population. The households in the panel are asked to scan all theirgrocery purchases, including alcohol. The reliability of the data has been extensively analyzed (Einav et al., 2010; Zhen et al., 2019).
Each year, the households report demographic characteristics, including income, household composition, marital status, employment status, and geographic location. Withinhousehold variation in demographic characteristics in the NielsenIQ Consumer Panel has
been used widely in the literature, including changes in location (Allcott et al., 2019; Hut,
2020), employment status (Dubé et al., 2018; Hinnosaar, 2018), household composition (Hinnosaar, 2019; Janssen and Parslow, 2021), and income (Dubé et al., 2018; Argente and Lee,2021).
We think of alcohol purchases as a reasonable measure of alcohol consumption. We
acknowledge that some purchases could be used as gifts or consumed by guests. After
presenting the main estimates, we will examine the concern that the household does notnecessarily consume the alcohol it purchased.
All the purchases data is at the household level, which makes the person-level analysis
impossible. Therefore, with a slight abuse of terminology, when talking about individual
characteristics, we mean individual household characteristics. Analyzing household-level
data misses some person-level effects, such as one household member increasing and theother equally decreasing their consumption. To look at some of the person-level effects, inthe robustness analysis, we separately analyze single-adult households.
Sample construction. Our main sample consists of movers. We define a household tobe a mover if its state of residence changes once.4 We exclude from the sample households
4

We analyze robustness to alternative geographic levels, such as county and 3-digit zip code. We focus
on across states moves because a lot of the variation in the alcohol regulation is at the state level, and at
more disaggregate levels, data is noisier.

6

whose state of residence changes more than once. Robustness analysis shows that further
restricting the sample to the movers with constant demographic characteristics (employment
status, marital status, household size, the number of members aged 21 and above, income,and occupation) does not substantially change the main estimates.
The dataset has information of the year on the move but not the exact time of moving.
The geographic location of the stores where movers shop confirms that indeed they change
the shopping location during the year of the move (figure A.1 in online appendix). Unsurprisingly, timing is heterogeneous: some movers start shopping in their destination at the
beginning of the move year, while others switch later. We drop the year of the move fromour main sample to avoid mismeasurement associated with not knowing the exact timing of
the move. Dropping the year of the move is not critical for our results. In the robustness
analysis, we assume that households move in the quarter with the first shopping trip in thedestination during the move year. However, this definition of the move’s timing is imprecise
because the store location is known for about half of the shopping trips.
Outcome measures. Our main outcome measures are household-level quarterly alcoholpurchases per adult: quantity of beer, wine, liquor, the total quantity of pure alcohol, andthe total expenditure on alcohol. We calculate the total quantity of pure alcohol from alltypes of alcohol using the following formula: Q(pure alcohol) = 0.4Q(liquor) + 0.12Q(wine)+ 0.045Q(beer). We deflate alcohol expenditures to 2015 dollars using the consumer priceindex for urban consumers.5 We calculate alcohol purchases per adult by dividing household
purchases by the number of persons aged 21 and above.6 To analyze alcohol purchases on
the extensive margin, we calculate a rolling average measure of whether the household hasbought any alcohol in the past year. We construct the extensive margin measure based ona full year instead of a single quarter because consumers might store alcohol and consume
it later. Thus a quarter without any alcohol purchases is not sufficient evidence that they
have stopped consuming alcohol, they might simply consume what they have stored. Whilethe year of the move is otherwise excluded from the main regressions, these time periods
are still used to construct the extensive margin measure. For example, the extensive margin
measure in the first quarter after the move year is based on the first quarter after the moveyear and the last three quarters during the move year.
In our main specification, the outcome variable is the logarithm of quarterly alcohol quantity or expenditures plus one. The results do not depend on the functional form. Robustness
5
6

In the same way, we also deflate all measures of income, prices, and taxes.
We drop households from the sample that do not have any members aged 21 or above.

7

analysis shows that three alternative functional forms (inverse hyperbolic sine transformation, percentile ranks, and actual values) give similar results.
We compute state-level average outcomes using data on non-movers, that is, households
whose state of residence does not change. When calculating state-level averages, we first
average across households in each time period (calendar quarter) and in each state using
sample weights (and information on the number of adults) and then take averages acrosstime periods. To calculate the state-level averages, we use the entire period 2004–2017.
State-level alcohol purchases have remained rather stable over time (figure A.3 in the online
appendix). As a robustness check, we also re-run the analysis using state-level purchases atthe origin state two years before the move year and at the destination state two years after
the move year.
Summary statistics. Online appendix A presents summary statistics. Movers compared
to non-movers are more likely to have higher income, be college-educated, and women areless likely to be employed (table A.1). Movers are also likely to consume more alcohol. Moves
are rather symmetric—moves to states with larger average alcohol purchases are about aslikely as moves to states with smaller average alcohol purchases (figure A.2). Moves takeplace between all regions (table A.2). Movers who move to states with larger average alcoholpurchases have relatively larger pre-move alcohol purchases than those who move to stateswith smaller average purchases (table A.3). In our analysis, these differences among movers
will be absorbed by household fixed effects.

3

Empirical strategy

Our empirical strategy decomposes the variation in alcohol purchases to the current environment versus individual factors. In the main specification, we restrict the sample to movers
and regress mover’s alcohol purchases on the size of the move defined as the difference
between the average outcome variable (measuring alcohol purchases) in the mover’s destination and origin states, and on household and time fixed effects.7 Specifically, we estimate
the following event study regression for mover i in period t:
yit = αi + τt +X

θr(i,t) · ∆i + εit(1)

r(i,t)
7An alternative strategy (in online appendix B) that instead of the size of the move uses household andstate fixed effects and data on both movers and non-movers gives similar estimates.

8where the outcome variable yit is a measure of alcohol purchases. All regressions include
household fixed effects αi and time period fixed effects τt . Index r(i, t) indicates quarters
relative to the move for household i in period t. The first quarter after the year of move in
the new state is indexed by 0. The coefficient θ−1 on the last quarter in the state of origin
is normalized to zero.8
The size of the move ∆i = ȳD,i − ȳO,i measures the difference between the average alcoholpurchases in the mover’s destination and origin state. We calculate the difference ȳD,i − ȳO,ibased on non-movers in the destination and origin as described in section 2.
The coefficients of interest θr(i,t) measure how much individual alcohol purchases change
relative to the gap in average purchases between two areas, ∆i . If, after the move, themover’s alcohol purchases change by the same amount as the gap ∆i , the coefficient θr(i,t)
equals one. However, if there is no change in alcohol purchases after the move, the coefficientθr(i,t) equals zero. The value of the coefficient measures the fraction of the difference between
destination and origin that has been covered. In this way, the size of the jump at thetime of move measures the share of the average difference between areas attributable tothe current environment (as opposed to individual characteristics).9 We calculate standard
errors clustered at the household level.10
In addition to the event-study, we also estimate difference-in-differences regressions. The
specification is the same as equation (1), except that all the coefficients post-move {θr(i,t) :r ≥ 0} are collapsed into one and the coefficients pre-move are normalized to zero.
Identification. The identifying assumption is that the trends in movers’ purchases are notcorrelated with the size of the move. The inclusion of household fixed effects would capture
any time-invariant differences in alcohol purchases at the household level.11 A possible
concern is that movers to higher (lower) alcohol purchasing states would have increased(decreased) their purchases anyway. For example, the moves and changes in alcohol purchases
happen due to getting a new job or unemployment. While in NielsenIQ, we do not know
8

We exclude the calendar year of the move to avoid mismeasurement due to not observing the exact
quarter of the move. By the first quarter in the new state, we mean the first quarter after the year of themove; and by the last quarter in the origin state, we mean the last quarter before the year of the move.
9
Finkelstein et al. (2016) show formally how the coefficient measures the share of variation explained by
the location as opposed to individual characteristics.
10
The estimates retain their level of statistical significance when clustering at the level of origin-destination
states pair.
11
In the robustness check section, we also include non-movers in the event study analysis. The household
fixed effects would also absorb any time-invariant differences between the movers’ and non-movers’ alcoholpurchases.

9why someone moves. We utilize Annual Social and Economic Supplement (ASEC) of the
March Current Population Survey (CPS) from 2013 to 2017. Table 1 summarizes the reasons
why people move across the state lines in CPS data, by the type of states they move to.
One can see that in general the reasons of moving are similar for those who move to ahigher and a lower-consumption state. Therefore, our findings are unlikely to be driven
by differential moving reasons between high and lower-consumption state. In addition, we
provide evidence that we get similar results when we exclude households whose income,marital status, employment, occupation, household size, or the number of members aged 21
and above changes.
Table 1: Reasons of moving across states
Move to a higher-alcohol
consumption state
Panel A: Breakdown of Reasons of Movejob-related reasons.49
family-related reasons.24
housing-related reasons.2other reasons.08
Panel B: Breakdown of Job-Related Reasons
new jobs or transfer.35
lost jobs or to look for job.03
retired.02
easier commute.05
other job related reasons.04
Observations
2596

Move to a lower-alcohol
P-values for
consumption stateCol(1)=Col(2)
.47
.24
.21
.09

0.17
0.87
0.33
0.23

.35
.03
.02
.03
.04
2435

0.91
0.66
0.28
0.00
0.49

Notes: This is imputed using the March Current Population Survey (CPS), 2013-2017. We restrict the sample
to those households who move between states between 2013 and 2017, which is roughly 1.5% of the entire
CPS sample. Family-related reasons include change of marital status, starting new households, starting new
relationship and other family reasons. Job-related reasons include new job or job transfer, looking for a job
or lost a job, easier commute, retired and other job-related reasons. Housing-related reasons include want
to own home, want a new house/apartment, want to move to better neighborhood, etc. We merge MarchCPS with the state-average alcohol consumption level from NielsenIQ to impute whether someone moves toa higher-alcohol consumption or a lower-alcohol consumption state.

To provide additional support for the identifying assumption, we analyze whether the
pre-move trends in alcohol purchases are correlated with the size of the move. Figure 1apresents binned scatter plots of changes in alcohol purchases over three years before themove by the size of the move. While on the figures by eye-balling, one could detect a slight10

Slope = 0.667 (0.117)
.4
.2
0
-.2
-.4

-.4

-.2

0

.2

Pre-move change in quantity: y-1 - y-3.4Pre-post-move change in quantity: y1 - y-1

Slope = 0.163 (0.111)

-.2

-.1

0

.1

.2

-.2

Size of move

-.1

0

.1

.2

Size of move(a) Pre-move change: y−1 − y−3(b) Pre-post-move change: y1 − y−1

Figure 1: Changes in alcohol purchases by the size of move: pre-move (1a) and from pre- topost-move (1b)Notes: Each figure presents a binned scatter plot of the change in the logarithm of alcohol purchases (y-axes)ˆ i (x-axes). For each mover we calculate the size of the move ∆i and group these intoon the size of the move ∆percentiles. The x-axes displays the mean ∆i for movers in each percentile. On figure 1a, the y-axes showsfor movers in each percentile the average log purchases in the last calendar year pre-move minus average log
purchases in the third calendar year pre-move. On figure 1b, the y-axes shows for movers in each percentile
the average log purchases in the first calendar year post-move minus the average log purchases in the lastcalendar year pre-move. The line of best fit is obtained from OLS regression using the 100 data points(percentiles). Its slope coefficient and standard error (in parentheses) are reported on the graph. Figures
1a–1b use the same sample, which includes all movers that move across state lines, limiting the sample tothose that move only once and who are observed continuously from three calendar years before the move to
one calendar year after the move (1339 households).

pre-trend, this is not statistically significant at the 10 percent level. In any case, the small
magnitude of the statistically insignificant pre-trend is in contrast with the large positive
correlation of the size of the move and changes in the three years over the move (figure 1b).

4

Results: Measuring the extent of movers’ adjustment

Event study results. Figure 2 presents event study estimates. It shows a sizable jumpat the time of the move in the quantity of ethanol purchased. This indicates that thecurrent environment plays a sizable role in affecting alcohol purchases. Figure A.4 in online
appendix presents similar results for alcohol expenditures and separately for the quantity ofbeer, liquor, and wine.
It would be interesting to study even longer-term effects than two years after the move
year, but then the sample will get smaller. The event study estimates are based on the

11

1
.5
0
-.5

-8

-6

-4

-2
0
2
Quarters from move4

6

Figure 2: Event study of alcohol purchases (quantity)
Notes: Each figure presents the coefficients θr(i,t) (point estimates and 95% confidence intervals) estimatedfrom equation (1). The coefficient for the last time period before the move is normalized to 0. The dependent
variable is the logarithm of the quantity of total pure alcohol purchased. The regression includes quarteryear dummies and household fixed effects. Sample includes all movers that move across state lines, limiting
the sample to those that move only once and who are observed continuously 2 years before and after themove; the year of the move itself is excluded from the sample; quarters more than 2 years before or after themove are included in the estimation but not shown on the figure (1,379 households and 50,964 observations).
Standard errors are clustered at the household level.

sample of households that are observed continuously for five years: two years before andtwo years after the move year. Few households are in the sample long enough to observe
longer-term effects. Figure A.5 in the online appendix presents results for three and four
years after the move year. It is reassuring that the estimates in the third and fourth year
after the move year also show sizable convergence.
Difference-in-differences results. Panel A of table 2 summarizes the above event-study
estimates, re-estimating regression (1) pooling all time periods before the move and all time
periods after the move.12 The change in alcohol purchases after the move equals about 70%of the destination minus origin difference. There is some heterogeneity across the types ofalcohol—the adjustment (importance of the current environment) is slightly larger for wineand smaller for liquor.
Is the change in alcohol purchases coming from the intensive or extensive margin? According to the 2017 National Survey on Drug Use and Health, 34% of U.S. adults did not
12

When restricting time periods to 2 years before and after the move, results remain similar.

12

Table 2: Change in alcohol purchases after move. Difference-in-differences estimates.
(1)

(2)
(3)
(4)
(5)
Quantity
Expend.
Total
Beer
LiquorWine
Total
Panel A: Average effects∆· After move
0.708*** 0.689*** 0.608*** 0.821***0.695***(0.082)(0.087)(0.097)(0.093)(0.058)Households
3267
3267
3267
3267
3267
Observations
97860
97860
97860
97860
97860
Intensive margin
Extensive
Quantity conditional on
marginpurchasing alcohol before move
Purchasing
Total
Beer
LiquorWinealcohol
Panel B: Intensive and extensive margins, average effects∆· After move
0.775*** 0.771*** 0.670*** 0.889***0.500***(0.095)(0.101)(0.112)(0.107)(0.055)
Panel C: Intensive and extensive margins, asymmetric effects∆ · 1[∆ > 0]· After move
0.868*** 0.919*** 0.855*** 1.044***0.705***(0.145)(0.146)(0.191)(0.162)(0.087)∆ · 1[∆ < 0]· After move
0.664*** 0.571*** 0.486*** 0.719***
0.291***(0.152)(0.161)(0.157)(0.176)(0.085)Wald test, coef. equality, p-value
0.373
0.133
0.170
0.215
0.002
Households
2722
2722
2722
2722
3267
Observations
83596
83596
83596
83596
86112
Notes: Each column-panel combination presents estimates from a separate regression. Dependent variable islogarithm of alcohol purchases or indicator for purchasing alcohol (column 5 in panels B–C). ∆i = ȳD,i − ȳO,iis the difference in average logarithm of alcohol purchases between the destination and origin state of themover; or in column 5 in panels B–C, the difference in the share of households purchasing any alcohol between
the destination and origin state. 1[∆i > 0] is an indicator for ∆i being strictly positive, that is, a move to astate with larger average alcohol purchases, and 1[∆i < 0] indicates a move to a state with smaller averagealcohol purchases. Each regression includes quarter-year fixed effects and household fixed effects. Sample
includes all movers that move across state lines, limiting the sample to those that move only once, the yearof move is excluded from the sample (3267 households and 97860 observations). In panels B–C columns
1–4, to analyze changes on the intensive margin, the sample is further restricted to households who bought
alcohol before the move (2722 households and 83596 observations). In panels B–C column 5, to analyzechanges on the extensive margin, the outcome variable indicator for purchasing any alcohol is calculated
as a rolling average over four quarters, therefore the number of household-quarters is smaller than in panel
A (3267 households and 86112 observations). In panel C, to test whether the effect is asymmetric, p-valueof the Wald test for the equality of the two coefficients is included. Standard errors (in parentheses) areclustered at the household level. *** Indicates significance at 1 percent level.

drink alcohol in the past year.13 Does the move change whether consumers purchase any
alcohol (extensive margin)? Or is all the adjustment coming from those who purchased alcohol before the move and now change the quantity? To examine the intensive margin (table
13

Source: Substance Abuse and Mental Health Services Administration, National Survey on Drug Use and
Health, 2017, https://datafiles.samhsa.gov/.

13

2, panel B, columns 1–4), we restrict our sample to those who have purchased alcohol before
the move. To analyze the extensive margin (panel B, column 5), the dependent variableindicates whether a household purchased alcohol in the previous 12 months and ∆ the difference between the destination and origin in the share of households purchasing alcohol.14
The results show that movers make large changes in their alcohol purchases both on theintensive and extensive margins.
Is the effect asymmetric? It might be easier to adjust alcohol consumption upwards thancut it down. In the extreme, is all the adjustment only upwards? Panel C of table 2 showsthat adjustment takes place in both directions. On the intensive margin (columns 1-4), themagnitude of adjusting alcohol quantity upwards versus downwards is not different from eachother at 10 percent significance level. On the extensive margin (column 5), the effect is indeed
asymmetric. When moving to a state with a larger share of consumers purchasing alcohol(∆ > 0), movers are more likely to adjust upward (start to purchase alcohol), compared toadjusting downward (stopping purchasing alcohol), when moving to a state with a smaller
share of consumers purchasing alcohol (∆ < 0). The average ∆ in column 5 is about 10%,
implying that on average a non-drinker moving from a low alcohol consumption state to ahigh alcohol consumption state is seven percentage points more likely to purchase alcohol
after the move.
Robustness. Below, we summarize the analysis meant to explore the sensitivity of ourresults to alternative samples, functional forms, controls, clustering, and geographic levels.
Our results are robust to using alternative samples (figure 3a, regressions 1–8). Importantly, the point estimate of the main result remains similar when restricting the sample
to movers whose demographic characteristics (employment status, marital status, household
size, the number of members aged 21 and above, income, and occupation) remain constant.15
This helps to alleviate the concern that a move and a change in alcohol purchases are dueto the same shock in personal circumstances. Results remain the same when aggregate measures are calculated based on only two years before the move year in the state of origin andtwo years after the move year in the state of destination. Results are also robust to whether
the extensive margin measure is calculated based on a year or half a year (table A.12). The
results are not driven by the specific functional form of the outcome variable (figure 3a,14

Nearly one-third of the sample do not purchase any alcohol, which matches the national average fromthe National Survey on Drug Use and Health.
15
As in the dataset, income is reported in 16 intervals, we consider income to be constant if it remains inthe same income interval.

14

(1) Sample: constant demogr. char.
(2) Sample: contin. 2 years before/after(1) Moves between not liquor
control states(3) Sample: balanced panel(4) Sample: incl. move year(5) Sample: incl. non-movers(6) Sample: excl. to/from Utah
(7) Sample: excl. large purchases(2) Moves between states wine
available in grocery stores(8) Sample for aggr. measures: 2 years(9) Actual value of quantity(10) Percentile rank of quantity(11) IHS transformation of quantity(3) Moves between states beer
available in grocery stores(12) Includes demogr. char.
(13) Includes quarter since move FE(14) Clustering on state pairs
0.5
Convergence (coefficient)(a) Changing samples, dependent variables
and regression specifications

1

0

.5
Convergence (coefficient)

1(b) Moves between states with similar alcohol
retail conditions

Figure 3: Robustness checks of difference-in-differences estimations
Notes: Each bar presents a point estimate from a separate regression analogous to that in column 1 panel A in
table 2. The x-axes measures the estimated extent of changes in alcohol purchases after move (∆· After movein table 2). The red vertical line at 0.708 describes the comparison which is the value of the point estimate in
the regression in column 1 in table 2. The dependent variable is the logarithm of the quantity of total ethanolpurchases, except regressions 9-11 on figure 3a where it is either the actual value of the quantity, percentile
rank, or IHS transformation. For percentile ranks, in each time period, all households are ranked by alcoholpurchases; each period, household’s percentile rank is its position in the national distribution; the area-levelchange is measured as a change in the area-level average percentile rank. Each regression includes quarteryear fixed effects and household fixed effects. Demographic characteristics (logarithm of income, household
size, number of adults aged 21+, marital status, employment status, an indicator for children aged 0-5, anddummies for time period relative to move) are included in regression 12 on figure 3a, and quarter since move
fixed effects are also included in 13 on figure 3a. Sample includes all movers that move across the state lines,
limiting the sample to those that move only once. The sample excludes the move year, except in regression 4
on figure 3a. On figure 3a, the sample is further restricted to households whose demographic characteristics(employment status, marital status, household size, the number of members aged 21 and above, income, andoccupation) remain constant (regression 1); or who are observed continuously two years before and after themove (regression 3); or to the balanced panel two years around the move (regression 4). In regressions 5 and13, the sample includes non-movers; in regression 6, it excludes movers to and from Utah. In regression 7,
the sample excludes days when the household made a large alcohol purchase, that is, purchased either morethan a six-pack of beer, more than a typical large bottle of liquor (1.75 liters), or more than four bottles ofwine. The state-level average alcohol purchases are calculated using the entire period of 2004–2017, except inregression 8, where state-level purchases at the origin state are calculated two years prior to the move year,
and at the destination state, two years after the move year. On figure 3b, the sample is further restricted
to moves between states that are not liquor control states (regression 1), moves between states where in
the majority of grocery stores wine (regression 2) or beer (regression 3) is available. Capped spikes present
the 95% confidence intervals with standard errors clustered at the household level, except for regression 12
where the standard errors are clustered on the origin-destination state pair. Details about the estimates arein tables A.4–A.11 and A.16–A.18 in online appendix A.

regressions 9–11). Instead, the results are similar with three alternatives: when we measurealcohol purchases using the actual value of quantity (instead of the logarithm), inverse hy15

perbolic sine transformation, or percentile rank (as used by Chetty and Hendren (2018a)).
Including event time fixed effects or demographic characteristics as controls also does notchange the results (figure 3a, regressions 12–13). When including event time fixed effects we
use the sample of both movers and non-movers, because if we use only movers, time period
fixed effects and event time fixed effects would be perfectly collinear. While in the main
analysis, we cluster standard errors at the household level, the calculated standard errors
remain similar if clustered at the level of origin-destination states pair (figure 3a, regression
14).
We explore robustness to the geographic area, comparing zip codes, counties, states, andcensus regions (tables A.13–A.15 in online appendix). Our results remain similar as long as
movers cross state lines. For moves inside the state, there is much less convergence towards
the destination zip code or county level. Although county- and town-level differences inalcohol regulation exist, we hypothesize that most variation in alcohol purchases are driven
by state-level regulation. For within-state moves, there is little, possibly noisily measured,variation between destination and origin.
Heterogeneity by alcohol retail conditions and initial alcohol purchases. A potential concern is whether our estimates are driven by changes in scanning behavior due tothe move. If households are more likely to report purchases made in grocery stores thanliquor stores, moving to or from a state which sells alcohol in grocery stores could lead to alarge change in reported purchases. To check the validity of this concern, we restrict samples to moves between states with similar retail conditions (figure 3b). The estimates areless precise but not considerably different from those obtained from the full sample (table 2
column 1).
We also explore heterogeneity by before-move alcohol purchases (table A.19 in online
appendix). The point estimates for moderate and heavy drinkers are similar (about 0.7),
but the estimates for heavy drinkers are imprecise (possibly because of the smaller samplesize).
Heterogeneity by demographic characteristics and changes in the characteristics.
Does the effect of the move differ by demographic characteristics and changes in the characteristics? Figure 4 presents the estimates splitting the sample by education, income, age,
the number of adults in the household, and changes in income, job (current occupation andemployment), and the number of adults. It shows the convergence in all the groups. Tables
A.20–A.22 present asymmetric effects, showing that in all groups, alcohol purchases increase16

when moving to states with average higher purchases and decrease when moving to stateswith lower purchases.
(1) College(2) No college(3)Income above median(4) Income below median(5) Age: <45(6) Age: 45-60(7) Age: 61+(8) Income: increase(9) Income: decrease(10) Income: constant(11) Occupation/employment: change(12) Occupation/employment: constant(13) Number of adults = 1(14) Number of adults > 1(15) Number of adults: change
0.51Convergence (coefficient)Figure 4: Heterogeneity by demographic characteristics and changes in the characteristics
Notes: Each bar presents a point estimate from a separate regression analogous to that in column 1 panel
A in table 2. The x-axes measures the estimated extent of changes in alcohol purchases after move (∆·After move in table 2). The red vertical line at 0.708 describes the comparison which is the value of thepoint estimate in the regression in column 1 in table 2. The dependent variable is the logarithm of thequantity of total ethanol purchases. Each regression includes quarter-year fixed effects and household fixed
effects. Sample includes all movers that move across the state lines, limiting the sample to those that move
only once. The sample excludes the move year. In each regression the sample is further restricted by thedescribed characteristic. In regressions 1–7, all characteristics are measured in the last year before themove year. College indicates whether either the male or female household head has college education. Age
indicates the age of the youngest household head. In regressions 8–15, the comparison is between the firstyear after the move year and the latest year before the move year. As in the dataset, income is reported in
16 intervals, in regressions 8–10, we consider income to be constant if it is the same income interval, andincreasing or decreasing correspondingly otherwise. In regressions 11–12, the sample is split by whether at
least one of the household head changed occupation or employment status. In regressions 13–15, the sample
is split by the number of adults, and whether the number of adults changed. Capped spikes present the 95%
confidence intervals with standard errors clustered at the household level. Details about the estimates arein tables A.20–A.22 in online appendix A.

Off-premise versus on-premise alcohol purchases. We might be worried that wemeasure only changes in off-premise purchases, and households are substituting to on-premise(bar) purchases. First, the adjustment in off-premise alcohol purchases is interesting in its

17

own right. Alcohol consumption in bars is a social activity that is likely to adjust to thelevel of new peers when moving. An adjustment in off-premise purchases is more surprising.
Second, we explore changes in at-home food purchases. Assuming that on-premise alcoholpurchases are related to food consumption outside the home, we expect changes in on-premise
alcohol consumption to be reflected in grocery purchases. We estimate the same regression
as for alcohol also for grocery purchases (figure 5a). We exclude from the food purchases
typical breakfast food products because alcohol is mostly consumed at lunch or dinner times.
The adjustment in food purchases is considerably lower than in alcohol. While the estimates
are imprecise, they rule out adjustment about half the size of that for alcohol.
Lastly, we compare alcohol purchases from the NielsenIQ dataset to heavy drinking measures from the Behavioral Risk Factor Surveillance System (BRFSS) and chronic liver diseaseand cirrhosis mortality from the Centers for Disease Control and Prevention (CDC). We findthat localities with higher off-premise alcohol purchase in NielsenIQ dataset have higher
shares of heavy drinkers in BRFSS (figure A.6) and higher mortality rates related to chronic
liver disease (figure A.7).
Cigarette purchases. For robustness, we also analyze convergence in cigarette purchases.
Some characteristics tobacco shares with alcohol, while others are different. As in the caseof alcohol, tobacco regulation also considerably differs by state. This might lead to large
adjustments after the move, but tobacco consumption is likely to be somewhat less socialand more addictive than alcohol. That is, if we exclude alcohol abuse cases, then for atypical consumer, addiction is not the main reason for consuming alcohol. Because tobacco
consumption is less social and more addictive, the adjustments after the move might besmaller than in the case of alcohol. We estimate the same regression for cigarettes as foralcohol (figure 5a). The estimated convergence is considerably lower than for alcohol (it issignificant at the one percent level): 0.29 for cigarettes versus 0.71 for alcohol. It is possiblethat the convergence is lower because cigarette consumption is more addictive and less social.
Comparison with previous literature. To compare with the literature, we use ourestimation method to estimate the importance of the current environment in brand choice
as in Bronnenberg et al. (2012) and healthy eating choices as in Allcott et al. (2019). Figure
5b presents the estimates. We focus on the most popular beer brands to compare the resultswith Bronnenberg et al. (2012). We estimate the average change in alcohol purchases to equal
0.54 for the most popular beer brands, similar to 0.6 across all grocery brands (not only beer)in Bronnenberg et al. (2012). To compare with Allcott et al. (2019), we focus on soda and18

Bud Light
Coors Light
Miller Light(1) Food calories
Budweiser
Natural Light(2) Food expenditures

Busch Light
Regular soda

(3) Cigarettes quantity
-.2
0
.2
.4
.6
Convergence (coefficient)

Low-calorie soda
Fruits & vegetables-.5(a) Food and cigarettes

0.5Convergence (coefficient)

1(b) Beer brands, soda, fruits and vegetables

Figure 5: Change in purchases after the move. Difference-in-differences estimates
Notes: Each bar presents a point estimate from a separate regression, where dependent variable is thelogarithm of quantity of purchases. The x-axes measures the estimated extent of changes in alcohol purchases
after move (∆· After move in table 2). Each regression includes quarter-year dummies and household fixed
effects. The sample of 3267 households, includes all movers that move across state lines, limiting the sample
to those that move only once, the year of the move is excluded from the sample. In figure 5a, the dependent
variable is the logarithm of either calories or expenditures of food, excluding typical breakfast foods, orthe logarithm of the quantity of cigarettes. In figure 5b, the dependent variable is either the logarithm ofquantity of one of the six most sold brands of beer; or the logarithm of quantity of regular soda, low-calorie
soda, or fruits and vegetables. Capped spikes present the 95% confidence intervals with standard errors
clustered at the household level. Details about the estimates are in tables A.23–A.25 in online appendix A.

fruits and vegetables, which over- or under-consumption plays a role in the healthiness of diet.
Similar to the findings by Allcott et al. (2019), for none of these categories is the estimatedconvergence statistically significantly different from zero, the average point estimate is 0.03.
Which characteristics are common to high-alcohol-consumption environments?
We explore the question in online appendix C. First, using data on both movers and nonmovers, we regress alcohol purchases on household, state, and time fixed effects in orderto quantify the role of environment in explaining the variation in alcohol purchases. Then,
we measure which state-level characteristics are correlated with the estimated state-level
location effects. We find that high-alcohol-consumption environments are more likely tohave alcohol available in grocery stores and have lower alcohol prices. The correlation does
not necessarily describe causal effects and instead could capture endogeneous responses to19voters’ and consumers’ preferences. This could happen both across states and time. For
example, during economic downturns, governments tend to increase alcohol taxes (because
other revenue sources have decreased), and alcohol consumption also tends to increase during
economic downturns. This positive correlation between taxes and consumption does notmean that higher taxes lead to more drinking. In cross-section, in states with high demand,
voters may support lower taxes and fewer restrictions on alcohol availability.
Limitations. While the length of our panel and the identification strategy provide advantages in answering the question in the paper, the analysis also has limitations. Below
we address the concerns about underreporting, on-premise purchases, purchases versus consumption, and external validity.
As with any consumption survey data, underreporting is a concern with our dataset.
Cook (2007) describes that other survey-based measures of alcohol consumption capture
about half the consumption in the alcohol tax data. In our analysis, underreporting is notnecessarily a problem, as long as the magnitude of underreporting is not correlated with thedirection of the move. We might be worried that underreporting depends on retail conditions,which vary by state. But when we restrict the sample to moves between states with similar
retail conditions, movers still adjust their purchases. Furthermore, in the main analysis, wedrop the year of the move, which should alleviate the concern that consumers are too busy
to report purchases during the move.
Our dataset includes only off-premise alcohol purchases, excluding alcohol consumed inbars and restaurants. First, we argue that any adjustment in off-premise alcohol purchases
is surprising and interesting in its own right because compared to on-premise purchases, it
is less social. Nevertheless, one might worry that we capture only substitution between offand on-premise purchases. To alleviate the concern, we provide three pieces of evidence.
First, off-premise alcohol purchases are likely to change when there are other changes inpersonal circumstances. But our main results remain similar when we limit the sample tomovers whose demographic characteristics (employment status, marital status, household
size, the number of members aged 21 and above, income, and occupation) stay constant.
Second, suppose alcohol purchases in bars tends to increase after moving because movers
want to get to know new colleagues, or decrease because they don’t yet know anyone. In
both cases, if that is reflected in off-premise purchases, we would observe a temporary shortterm increase/decrease immediately after the move. Instead, we observe a lasting impact
over two years (in the event study framework), suggesting these temporary effects are not20

driving our results. Third, changes in off-premise versus on-premise alcohol consumption arelikely reflected in food consumption at home. But we find no evidence of large adjustmentsof food purchases after the move.
Could the alcohol purchases be for someone else (as gifts or for a party) instead of own
consumption? Since we only observe purchases instead of consumption, we cannot know who
consumed it. Suppose households moving to regions where alcohol consumption is high after
the move more often bring alcohol as a gift to a dinner party. While those moving to regions
where alcohol consumption is low less often choose alcohol as a gift to bring to a party. If
alcohol is brought to a party, most likely, the household itself will consume alcohol at the
party. In this way, gifts may reflect the household’s own consumption. One might also beconcerned that households host more parties in regions where average alcohol consumption
is high and fewer parties in regions with low average alcohol consumption. Since parties
tend to involve some food, we would expect corresponding changes in food consumption,
but we show that food purchases do not change in this way (table A.26). One might still beconcerned that households moving to regions where alcohol consumption is high after themove could start to host more parties with alcohol, while those moving to regions wherealcohol consumption is low could start to host fewer parties with alcohol. To alleviate theconcern, we re-estimated the regressions while excluding from the analysis days when thehousehold made a large alcohol purchase. The estimated convergence (figure 3a and tableA.7) is only slightly higher compared to our main estimates (0.79 versus 0.71).
Finally, do our findings based on movers extend to the general population? First, we
argue that movers are interesting in their own right because their share in the population islarge. Each year about 2% of the U.S. population moves across the state lines,16 and morethan 30% of the U.S. population has moved across the state lines in their lifetime (Molloy
et al., 2011). Second, if movers are more likely to adjust to a new environment, then ourestimates give an upper bound of how malleable alcohol consumption is. Furthermore, ourresults show that movers are not particularly open-minded regarding all products. Using thesame identification strategy, we find no evidence of large adjustment in purchases of fruits
and vegetables, regular soda and diet soda, and total food purchases. Similarly, Allcott
et al. (2019) and Hut (2020) find little change in movers’ healthiness of food purchases. Thissuggests that alcohol is a particular type of product (like brands studied by Bronnenberg
et al. (2012)) for which the current environment makes a large difference.
16

In 2010-2019, the average annual interstate migration rate equals 2.3 according to the American Community Survey 1-year estimates: https://www.census.gov/data/tables/time-series/demo/
geographic-mobility/state-to-state-migration.html.

21

5Conclusion

Analyzing the purchases of households that move across states, we find robust evidence
that alcohol purchases are strongly affected by the current environment. About two-thirdsof geographic variation in alcohol purchases is due to the current environment instead ofindividual characteristics. This finding suggests that government policies and regulations
that target the drinking environment (e.g. alcohol availability) could considerably impact
the amount of alcohol consumed.
While we quantify the overall importance of the environment, our analysis does not determine the main environmental factors that increase alcohol consumption. We provide reduced
form suggestive evidence that high-alcohol-consumption environments have higher alcohol
availability in grocery stores and lower prices. To identify the separate impact of various
factors, a more fruitful way is to examine the effect of exogeneous changes in regulations andtaxes, as is done in recent research (Marcus and Siedler, 2015; Bernheim et al., 2016; Illanes
and Moshary, 2018; Miravete et al., 2019; Seo, 2019; Kueng and Yakovlev, 2021).

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Yakovlev, E. (2018): “Demand for Alcohol Consumption in Russia and Its Implication for
Mortality,” American Economic Journal: Applied Economics, 10, 106–149.
Zhen, C., M. Muth, A. Okrent, S. Karns, D. Brown, and P. Siegel (2019): “Dodifferences in reported expenditures between household scanner data and expenditure
surveys matter in health policy research?” Health Economics, 28, 782–800.
26

Share of trips
.4
.6
.8
.2
0

0

.2

Share of trips
.4
.6
.8

1

Online appendix: Additional figures and tables
1

A

-8

-4

-1Move year

1

5

8

-8

-4

-1Move year

1

Quarters after move

Quarters after move

OriginOriginDestination(a) Balanced panel

58

Destination(b) Unbalanced panel

Figure A.1: Share of shopping trips to the destination versus origin state of residence

.03
.01

.02

Share of movers

.03
.02

0

0

.01

Share of movers

.04

.04

.05

.05

Notes: The shares are calculated using all trips to stores in NielsenIQ Retail Scanner Data. The shares
do not necessarily sum to one because there is a very small share of shopping trips in states other thanthe origin or destination. The sample includes all movers that move across state lines, limiting the sample
to those that move only once. On figure A.1a, the sample is further restricted to those who are observed
continuously 2 years before and after the move year, while on figure A.1b, the sample includes the movers
who are not observed continuously around the move.

-.2

-.1

0

.1

.2

-1

Destination-origin difference in average log. quantity

-.5

0

.5

1

Destination-origin difference in average log. expenditures(a) Quantity(b) Expenditures

Figure A.2: Distribution of destination-origin difference in the logarithm of alcohol purchases

A1

50
40
30
20
0

10

State rank by expenditures in 2011-2017

50
40
30
20
10

State rank by quantity in 2011-2017

0

0

10

20

30

40

50

0

State rank by quantity in 2004-2010

10

20

30

40

50

State rank by expenditures in 2004-2010(a) Quantity(b) Expenditures

Figure A.3: State ranked by average alcohol purchases before and after 2010
Notes: States ranked by average alcohol purchases of nonmovers in 2004-2010 vs 2011-2017. The dashed line
is a 45-degree line.

A2

1.5
1

1

.5

.5

-.5

0

0
-.5

-8

-6

-4

-2
0
2
Quarters from move

4

6

-8

-6

-2
0
2
Quarters from move

4

6

4

6

(b) Quantity of liquor

-.5

-.5

0

0

.5

.5

1

1

1.5

(a) Quantity of beer

-4

-8

-6

-4

-2
0
2
Quarters from move

4

6

-8

(c) Quantity of wine

-6

-4

-2
0
2
Quarters from move(d) Alcohol expenditures

Figure A.4: Event study of alcohol purchases
Notes: Each figure presents the coefficients θr(i,t) (point estimates and 95% confidence intervals) estimatedfrom equation (1). The coefficient for the last time period before the move is normalized to 0. The dependent
variable is the logarithm of quantity of beer, liquor, or wine purchased (figures A.4a–A.4c), or alcohol
expenditures (figure A.4d). Each regression includes quarter-year dummies and household fixed effects.
Sample includes all movers that move across state lines, limiting the sample to those that move only once
and who are observed continuously 2 years before and after the move; the year of the move itself is excluded.
Standard errors are clustered at the household level.

A3

1
.5

1

0

.5

-.5

0
-.5

-8

-6

-4

-2
0
2
4
6
Quarters from move

8

10

-4

(a) 2 years before, 3 years after

-2

0

2
4
6
8
10
Quarters from move1214(b) 1 year before, 4 years after

Figure A.5: Event study of alcohol purchases
Notes: Each figure presents the coefficients θr(i,t) (point estimates and 95% confidence intervals) estimatedfrom equation (1). The coefficient for the last time period before the move is normalized to 0. The dependent
variable is the logarithm of quantity of total ethanol purchased. Each regression includes quarter-year
dummies and household fixed effects. Sample includes all movers that move across state lines, limiting thesample to those that move only once and who are observed continuously: on figure A.5a, 2 years before and3 years after the move year and on figure A.5b, 1 year before and 4 years after the move year. The year of
the move itself is excluded. Quarters more than 2 years before or 3 years after the move year are included in
the estimation but not shown on the figure A.5a. Similarly, quarters more than 1 year before or 4 years after
the move year are included in the estimation but not shown on the figure A.5b. On figure A.5a, the sample
includes 1,045 households and 40,896 observations. On figure A.5b, the sample includes 1,089 households
and 42,420 observations. Standard errors are clustered at the household level.

A4

1

1.5

2

2.5

3

12
10
8
6
4
2

4

6

8

% heavy drinkers in the county (BRFSS)

Slope = 0.528 (0.140)***2% heavy drinkers in the state (BRFSS)

Slope = 1.110 (0.234)***0Off-premise ethanol purchases per adult (Nielsen)

1

2

3

4

5

Off-premise ethanol purchases per adult (Nielsen)(a) State level(b) County level

Figure A.6: Off-premise alcohol purchases (Nielsen) vs the share of heavy drinkers (BRFSS)
at state and county level
Notes: Each dot represents either a state (figure A.6a) or a county (figure A.6b). Figure A.6b includes only
counties with at least 100 respondents in both BRFSS and Nielsen’s dataset. Y-axis reflects the share ofheavy drinkers (defined by BRFSS as “adult men having more than two drinks per day and adult women
having more than one drink per day”) in a given locality in 2007 BRFSS dataset. X-axis displays the yearly
off-premise alcohol purchases per adult in a given county or state calculated using Nielsen sample weights(and information on the number of adults) from the NielsenIQ Consumer Panel of 2007. We choose 2007
since that was the first year with a larger number of households (approximately 60,000) in the NielsenIQ
Consumer Panel. The line of the best fit is obtained from OLS regressions. Its slope coefficient and standard
error (in parentheses) are reported on the graph. The coefficients are significant at 1% level on both figures.

A5

5

10

15

20

25

Chronic liver disease and cirrhosis mortality rate (CDC), 2014-2020

Slope = 1.697 (0.749)**

.5

1

1.5

2

2.5

3

off-premise ethanol purchases per person(Nielsen), 2007-2013

Figure A.7: Off-premise alcohol purchases (Nielsen) vs cirrhosis and chronic liver diseasemortality rate at state level (CDC)
Notes: Each dot represents a state. X-axis reflects the average annual off-premise alcohol purchases per adultin a given state calculated using Nielsen sample weights (and information on the number of adults) from theNielsenIQ Consumer Panel of 2007-2014. Y-axis displays the average annual age-adjusted mortality rate per
100,000 population for cirrhosis and chronic liver disease at state level between 2014 and 2020 from the CDC
website. We choose 2007 since that was the first year with a larger number of households (approximately
60,000) in the NielsenIQ Consumer Panel. The mortality rate data is only available for 2005, 2014-2020
from the CDC. We lag the outcome by many years since chronic liver disease and cirrhosis are caused by
years of excessive drinking. The line of the best fit is obtained from OLS regressions. Its slope coefficientand standard error (in parentheses) are reported on the graph. The coefficients are significant at 5% level.

A6

Table A.1: Summary statistics
Ever-movers Non-movers(1)(2)
Demographic characteristics
Household income
69414.963820.3
Household size
2.4
2.7
Average age of household heads
50.8
49.9
College
0.597
0.522
Male household head employed
0.749
0.765
Female household head employed
0.588
0.614
Race: white non-Hispanic
0.810
0.785
Married
0.642
0.646First observed residence
Northeast
18.2
16.9
Midwest
22.8
25.5
South
36.5
38.0
West
22.5
19.6
Alcohol purchases
Quantity of total pure alcohol
0.64
0.49
Quantity of beer
3.69
3.37
Quantity of liquor
0.73
0.53
Quantity of wine
1.54
1.05
Alcohol expenditures
27.50
20.45
Purchasing alcohol
0.74
0.66
Number of households
3267
172827
Notes: All characteristics are measured during the first year in the sample. In the survey, income is reported
in intervals; to calculate Household income in dollars, midpoints of the intervals are used. Income andexpenditures are deflated to 2015 dollars using the consumer price index for urban consumers. For householdswith two heads, College indicates whether at least one of them has attended college. Alcohol purchases aremeasured per adult per quarter. Quantity of alcohol is measured in liters and expenditures in dollars.
Purchasing alcohol is an indicator variable for whether the household has purchased any alcohol during thecurrent and past 3 quarters. Ever-movers include all movers that move across state lines, limiting the sample
to those that move only once. Non-movers includes all households that don’t ever move across state lines. For
all demographic characteristics (except marital status) and alcohol purchases, the differences between movers
and non-movers are statistically significantly different from zero at 1 percent significance level according to
the Wilcoxon rank-sum test.

A7

Table A.2: Migration patterns (percentages)
Origin region
Northeast
MidwestSouth
West

Destination region
Northeast Midwest South
4.4
1.3
10.9
1.0
6.9
10.9
3.1
6.3
22.0
1.3
3.0
7.1

Notes: The sample of movers.

A8

West
1.7
4.0
5.0
11.1

Table A.3: Comparison of movers to states where alcohol consumption is lower versus higher
than in the state of origin
Movers to states
t-test Wilcoxonwith alcohol purchases
test
lower
higherp-value p-value
(1)
(2)
(3)
(4)
Demographic characteristics
Household income
70316.968398.3
0.003
0.005
Household size
2.4
2.5
0.007
0.000
Average age of household heads
51.0
50.5
0.053
0.006
College
0.610
0.582
0.001
0.001
Male household head employed
0.740
0.760
0.016
0.016
Female household head employed 0.588
0.589
0.943
0.943
Race: white non-Hispanic
0.815
0.805
0.180
0.180
Married
0.639
0.646
0.412
0.412
First observed residence
Northeast
25.2
10.4
Midwest
21.8
23.8
South
36.8
36.1
West
16.1
29.8
Alcohol purchases
Quantity of total pure alcohol
0.58
0.71
0.000
0.000
Quantity of beer
3.24
4.20
0.000
0.000
Quantity of liquor
0.69
0.77
0.106
0.000
Quantity of wine
1.37
1.74
0.001
0.004
Alcohol expenditures
26.18
28.99
0.026
0.000
Purchasing alcohol
0.72
0.75
0.092
0.092
Number of households
1731
1536
Notes: All demographic characteristics and alcohol purchases are measured during the first year in thesample. In the survey, income is reported in intervals; to calculate Household income in dollars, midpoints of
the intervals are used. Income and expenditures are deflated to 2015 dollars using the consumer price index
for urban consumers. For households with two heads, College indicates whether at least one of them has
attended college. Alcohol purchases are measured per adult per quarter. Quantity of alcohol is measured inliters and expenditures in dollars. Column 3 presents the p-value of the t-test and column 4 of the Wilcoxon
rank-sum test for whether the difference between the movers to states with higher versus lower alcohol
consumption is significantly different from zero. The sample includes all movers that move across state lines,
limiting the sample to those that move only once.

A9

Table A.4: Robustness: Change in alcohol purchases after move, alternative samples
Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: Sample: continuously 2 years before and after∆· After move 0.669*** 0.616*** 0.650*** 0.886*** 0.687***(0.106)(0.114)(0.129)(0.130)(0.077)
Households
1379
1379
1379
1379
1379
Observations
50964
50964
50964
50964
50964
Panel B: Sample: balanced panel 2 years around the move∆· After move 0.651*** 0.553*** 0.708*** 0.811*** 0.632***(0.101)(0.104)(0.116)(0.134)(0.075)
Households
1379
1379
1379
1379
1379
Observations
22064
22064
22064
22064
22064
Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. The sample includes
all movers that move across state lines, limiting the sample to those that move only once, the year of themove is excluded from the sample. In panel A, the sample is further restricted to those who are observed
continuously from 2 years before the move to 2 years after the move; quarters more than 2 years before orafter the move are also included in the estimation. In panel B, the sample is restricted to the balanced panelof households observed 2 years before and after the move. Each regression includes quarter-year dummies
and household fixed effects. Standard errors are clustered at the household level. *** Indicates significanceat the 1 percent level, ** at a 5 percent level, * at a 10 percent level.

A10

Table A.5: Robustness: Change in alcohol purchases after move, households with constantdemographic characteristics
Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: Sample: constant demographics 3 years around the move∆· After move 0.655*** 0.572*** 0.430** 1.016*** 0.663***(0.156)(0.148) (0.200) (0.209)(0.109)Households
656
656
656
656
656
Observations
8392
8392
8392
8392
8392
Panel B: Sample: constant demographics 5 years around the move∆· After move 0.723*** 0.474**
0.223 1.329*** 0.823***(0.221)(0.239) (0.249) (0.331)(0.163)
Households
271
271
271
271
271
Observations
3424
3424
3424
3424
3424
Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. The sample includes all
movers that move across state lines, limiting the sample to those that move only once. The sample includes
time periods within two years before and after the year of the move. The year of the move is excluded fromthe sample. The sample is restricted to those whose employment status, marital status, household size, thenumber of members aged 21 and above, income, and occupation stay constant three years around the move(from the year before to the year after the move year) on panel A; or five years around the move (fromtwo years before to two years after the move year) on panel B. As in the dataset, income is reported in
16 intervals, we consider income to be constant if it remains in the same income interval. Each regression
includes quarter-year dummies and household fixed effects. Standard errors are clustered at the household
level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10 percent level.

A11

Table A.6: Robustness: Change in alcohol purchases after move, alternative samples
Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: Sample: includes the year of move∆· After move 0.698*** 0.678*** 0.608*** 0.798*** 0.671***(0.077)(0.081)(0.088)(0.085)(0.053)
Households
3267
3267
3267
3267
3267
Observations
110928110928110928110928110928Panel B: Sample: includes non-movers∆· After move 0.719*** 0.707*** 0.610*** 0.830*** 0.704***(0.083)(0.087)(0.097)(0.094)(0.058)Households
176094176094176094176094176094
Observations
3110488 3110488 3110488 3110488 3110488
Panel C: Sample: Excludes Utah∆· After move 0.723*** 0.703*** 0.621*** 0.844*** 0.716***(0.085)(0.089)(0.099)(0.097)(0.060)Households
3206
3206
3206
3206
3206
Observations
96348
96348
96348
96348
96348
Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. In panel A, the sample
includes all movers that move across state lines, limiting the sample to those that move only once. In panel
B, the sample adds non-movers. In panel C, the sample excludes movers to and from Utah. Each regression
includes quarter-year dummies and household fixed effects. Standard errors are clustered at the household
level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10 percent level.

A12

Table A.7: Robustness: Change in alcohol purchases after move, sample excludes large
purchases∆· After move
Households
Observations

Total
(1)
0.785***(0.083)3267
97860

Quantity
Beer
Liquor(2)(3)
0.719*** 0.749***(0.086)(0.094)
3267
3267
97860
97860

Wine
(4)
0.914***(0.088)3267
97860

Expend.
Total(5)0.763***(0.058)3267
97860

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. The sample includes
all movers that move across state lines, limiting the sample to those that move only once, the year of themove is excluded from the sample. The sample excludes days when the household made a large alcoholpurchase: purchasing either more than a six-pack of beer, more than a typical large bottle of liquor (1.75
liters), or more than four bottles of wine. These thresholds don’t imply that smaller quantities are consumed
by a single household in one sitting. Instead, the thresholds reflect alcohol purchasing and storage behavior,
and allow price sensitive households to obtain quantity discounts. Each regression includes quarter-year
dummies and household fixed effects. Standard errors are clustered at the household level. *** Indicatessignificance at the 1 percent level, ** at a 5 percent level, * at a 10 percent level.

Table A.8: Robustness: Change in alcohol purchases after move. Aggregate purchases calculated based on two years prior the move year in the state of origin and two years after themove year in the state of destination∆· After move
Households
Observations

Total
(1)
0.706***(0.080)3267
97860

Quantity
Beer
Liquor(2)(3)
0.661*** 0.618***(0.083)(0.091)
3267
3267
97860
97860

Wine
(4)
0.815***(0.092)3267
97860

Expend.
Total(5)0.693***(0.057)3267
97860

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. The average purchases
are calculated based on two years prior the move year in the state of origin and two years after the moveyear in the state of destination. The sample includes all movers that move across state lines, limiting thesample to those that move only once, the year of the move is excluded from the sample. Each regression
includes quarter-year dummies and household fixed effects. Standard errors are clustered at the household
level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10 percent level.

A13

Table A.9: Robustness: Change in alcohol purchases after move, functional form
Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: Inverse Hyperbolic Sine transformation of purchases∆· After move 0.710*** 0.695*** 0.612*** 0.819*** 0.697***(0.084)(0.085)(0.097)(0.091)(0.056)
Households
3267
3267
3267
3267
3267
Observations
97860
97860
97860
97860
97860
Panel B: Percentile rank of purchases∆· After move 0.710*** 0.699*** 0.627*** 0.775*** 0.704***(0.051)(0.070)(0.078)(0.059)(0.051)
Households
3267
3267
3267
3267
3267
Observations
97860
97860
97860
97860
97860
Panel C: Actual value of purchases∆· After move 0.641*** 0.527*** 0.519*** 0.743*** 0.540***(0.122)(0.154)(0.157)(0.197)(0.127)
Households
3267
3267
3267
3267
3267
Observations
97860
97860
97860
97860
97860
Notes: In panel
the outcome
variable is the Inverse Hyperbolic Sine transformation of alcohol purchases A, √
that equals ln x + x2 + 1 , where x is the quantity or expenditures of alcohol purchases. In panel B, theoutcome variable is the percentile rank of alcohol purchases that equals (Rankit − 1)/(Nt − 1), where Rankit
is household i’s rank in the national distribution of alcohol purchases calculated for a given time period t,
and Nt is the number of households in that time period. In panel C, the outcome variable is the quantity
or expenditure of alcohol purchases. In each panel, ∆i is the difference in average outcome variable between
the destination and origin state of the mover. Each regression includes quarter-year dummies and household
fixed effects. The sample includes all movers that move across state lines, limiting the sample to those thatmove only once; the year of the move is excluded from the sample. Standard errors are clustered at thehousehold level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10 percent
level.

A14

Table A.10: Robustness: Change in alcohol purchases after move, including additional variables
Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: Sample of movers, includes demographic char.
∆· After move
0.689*** 0.679*** 0.595*** 0.806*** 0.686***(0.082)(0.087)(0.097)(0.093)(0.058)Demogr. Char.
Yes
Yes
Yes
Yes
Yes
Households
3267
3267
3267
3267
3267
Observations
97860
97860
97860
97860
97860
Panel B: Sample of movers and nonmovers, includes quarter since move FE∆· After move
0.698*** 0.673*** 0.610*** 0.805*** 0.687***(0.082)(0.087)(0.096)(0.093)(0.058)
Quarter since move FE
Yes
Yes
Yes
Yes
Yes
Households
176094176094176094176094176094
Observations
3110488 3110488 3110488 3110488 3110488
Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. The sample includes all
movers that move across state lines, limiting the sample to those that move only once; the year of the moveis excluded from the sample. In panel B, the sample also includes non-movers. Each regression includes
quarter-year dummies and household fixed effects. In panel A, regressions also include the logarithm ofincome, household size, number of adults aged 21+, marital status, employment status, and an indicator
for children aged 0-5. In panel B, regressions include event time fixed effects. In the panel B, we include
non-movers, because if we only use movers, event time fixed effects and time period fixed effects would be
perfectly collinear. Standard errors are clustered at the household level. *** Indicates significance at the 1
percent level, ** at a 5 percent level, * at a 10 percent level.

A15

Table A.11: Robustness: Change in alcohol purchases after move; clustering at origindestination states pair
Quantity
Total
Beer
Liquor
Wine
(1)
(2)
(3)
(4)
Clustering at origin-destination states pair∆· After move 0.708*** 0.689*** 0.608*** 0.821***(0.083)(0.091)(0.101)(0.105)
Households
3267
3267
3267
3267
Observations
97860
97860
97860
97860

Expend.
Total(5)0.695***(0.064)3267
97860

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression
includes quarter-year dummies and household fixed effects. The sample includes all movers that move acrossstate lines, limiting the sample to those that move only once; the year of the move is excluded from thesample. Standard errors are clustered at the origin-destination states pair (1,012 clusters). *** Indicatessignificance at the 1 percent level, ** at a 5 percent level, * at a 10 percent level.

A16

Table A.12: Robustness: Change in alcohol purchases after move, extensive margin, aggregate and asymmetric effects. Difference-in-differences estimates. Extensive margin calculated
based on half a year.
(1)
(2)
(3)
(4)
Extensive margin: whether bought alcohol in half-a-year
Purchasing
Alcohol
Beer
LiquorWine
Panel A: Average effects∆· After move
0.619*** 0.706*** 0.592*** 0.664***(0.050)(0.071)(0.078)(0.055)
Households
3267
3267
3267
3267
Observations
93944
93944
93944
93944
Panel B: Asymmetric effects∆ · 1[∆ > 0]· After move
0.841*** 0.945*** 0.902*** 0.938***(0.078)(0.108)(0.122)(0.090)∆ · 1[∆ < 0]· After move
0.386*** 0.421*** 0.317*** 0.389***(0.075)(0.110)(0.120)(0.080)Wald test, coef. equality, p-value
0.000
0.002
0.002
0.000
Households
3267
3267
3267
3267
Observations
93944
93944
93944
93944
Notes: Each column-panel combination presents estimates from a separate regression. Dependent variable
is an indicator for purchasing beer (column 1), liquor (column 2) or wine (column 3); it is calculated as arolling average over two quarters. ∆i = ȳD,i − ȳO,i is the difference in the share of households purchasing any
beer (column 1), liquor (column 2) or wine (column 3) between the destination and origin state. 1[∆i > 0]
is an indicator for ∆i being strictly positive, that is, a move to a state with a larger share of households
purchasing that category of alcohol products, and 1[∆i < 0] indicates a move to a state with smaller share ofhouseholds purchasing that category of alcohol products. Each regression includes quarter-year fixed effects
and household fixed effects. Sample includes all movers that move across state lines, limiting the sample tothose that move only once; the year of the move is excluded from the sample. In panel B, to test whether theeffect is asymmetric, p-value of the Wald test for the equality of the two coefficients is included. Standard
errors (in parentheses) are clustered at the household level. *** Indicates significance at 1 percent level.

A17

Table A.13: Robustness: Change in alcohol purchases after move; geographic area is definedas state; move inside versus across census regions
Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: move across census regions∆· After move 0.766*** 0.806*** 0.613*** 0.900*** 0.754***(0.105)(0.107)(0.121)(0.127)(0.076)
Households
1815
1815
1815
1815
1815
Observations
54292
54292
54292
54292
54292
Panel B: move inside census region∆· After move 0.622*** 0.500*** 0.598*** 0.714*** 0.616***(0.134)(0.148)(0.159)(0.138)(0.089)Households
1452
1452
1452
1452
1452
Observations
43568
43568
43568
43568
43568
Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression
includes quarter-year dummies and household fixed effects. The sample includes all movers that move acrossstate lines, limiting the sample to those that move only once; the year of the move is excluded from thesample. Standard errors are clustered at the household level. *** Indicates significance at the 1 percent
level, ** at a 5 percent level, * at a 10 percent level.

A18

Table A.14: Robustness: Change in alcohol purchases after move; geographic area is definedas county; move inside versus across states
Total
(1)
∆· After move
Households
Observations∆· After move
Households
Observations

0.646***(0.177)
543
16336
0.275
(0.168)947
29900

Quantity
Beer
Liquor
Wine
(2)
(3)
(4)
Panel A: move across states
0.461*** 0.625*** 0.995***(0.173)(0.183)(0.187)
543
543
543
16336
16336
16336
Panel B: move inside state
0.055
0.308
0.103
(0.167)(0.208)(0.220)
947
947
947
29900
29900
29900

Expend.
Total(5)0.746***(0.131)543
16336
0.298*(0.159)947
29900

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin county of the mover. Each regression
includes quarter-year dummies and household fixed effects. The sample includes movers that move acrosscounty borders, limiting the sample to those that move only once, and move between counties where at any
time period there are at least 50 households in the dataset; the year of the move is excluded from the sample.
Standard errors are clustered at the household level. *** Indicates significance at the 1 percent level, ** at
a 5 percent level, * at a 10 percent level.

A19

Table A.15: Robustness: Change in alcohol purchases after move; geographic area is definedas 3-digit zip code; move inside versus across states
Total
(1)
∆· After move
Households
Observations∆· After move
Households
Observations

0.422***(0.152)
701
21032
0.183
(0.125)1262
39568

Quantity
Beer
Liquor
Wine
(2)
(3)
(4)
Panel A: move across states
0.649*** 0.362* 0.655***(0.158) (0.188) (0.149)
701
701
701
21032
21032
21032
Panel B: move inside state-0.0620.337
0.142(0.138) (0.213) (0.135)
1262
1262
1262
39568
39568
39568

Expend.
Total(5)0.596***(0.106)70121032
0.251**(0.109)1262
39568

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference inaverage logarithm of alcohol purchases between the destination and origin 3-digit zip code of the mover.
Each regression includes quarter-year dummies and household fixed effects. The sample includes movers
that move across 3-digit zip codes, limiting the sample to those that move only once, and move between 3
digit zip codes where at any time period there are at least 50 households in the dataset; the year of the moveis excluded from the sample. Standard errors are clustered at the household level. *** Indicates significanceat the 1 percent level, ** at a 5 percent level, * at a 10 percent level.

A20

Table A.16: Heterogeneity: Change in alcohol purchases after move; separating by retailconditions: liquor control states
Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: Moves between non-liquor-control states∆· After move 0.665*** 0.624*** 0.676*** 0.907*** 0.717***(0.107)(0.117)(0.122)(0.127)(0.079)Households
1738
1738
1738
1738
1738
Observations
52060
52060
52060
52060
52060
Panel B: Moves between liquor control states∆· After move 0.899** 1.017***0.535
0.584** 0.829***(0.423)(0.352)(0.411)(0.292)(0.244)
Households
198
198
198
198
198
Observations
5496
5496
5496
5496
5496
Panel C: Moves: from CS to not CS; or from not CS to CS∆· After move 0.770*** 0.765*** 0.503*** 0.750*** 0.648***(0.136)(0.138)(0.168)(0.151)(0.090)Households
1331
1331
1331
1331
1331
Observations
40304
40304
40304
40304
40304
Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression
includes quarter-year dummies and household fixed effects. The sample includes movers that move acrossstate lines, limiting the sample to those that move only once, the year of the move is excluded from thesample. In panel A, the sample is further restricted to moves between states that are not liquor control
states; in panel B, to moves between states that are liquor control states; in panel C, to the remaining moves(either from liquor control state to non-control state or from non-control state to control state). Standard
errors are clustered at the household level. *** Indicates significance at the 1 percent level, ** at a 5 percent
level, * at a 10 percent level.

A21

Table A.17: Heterogeneity: Change in alcohol purchases after move; by retail conditions:
whether wine available in the majority of grocery stores∆· After move
Households
Observations∆· After move
Households
Observations

Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: Moves between states wine available
0.504*** 0.386** 0.732*** 0.511*** 0.401***(0.147)(0.183)(0.152)(0.162)(0.109)
1795
1795
1795
1795
1795
53660
53660
53660
53660
53660
Panel B: Moves to/from wine not available
0.807*** 0.772*** 0.490*** 0.993*** 0.799***(0.100)(0.099)(0.120)(0.115)(0.068)
1472
1472
1472
1472
1472
44200
44200
44200
44200
44200

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression includes
quarter-year dummies and household fixed effects. The sample includes movers that move across state lines,
limiting the sample to those that move only once, the year of the move is excluded from the sample. In panel
A, the sample is further restricted to moves between states where wine is available in more than 80% grocery
stores (calculated from the NielsenIQ Retail Scanner Data as described in Online Appendix D); in panel B,
to moves to/from states where wine is not available in more than 80% of the grocery stores. There is notenough moves between the states where wine is not available in the grocery stores to estimate analogous
regressions for those. Standard errors are clustered at the household level. *** Indicates significance at the
1 percent level, ** at a 5 percent level, * at a 10 percent level.

A22

Table A.18: Heterogeneity: Change in alcohol purchases after move; by retail conditions:
whether beer available in the majority of grocery stores∆· After move
Households
Observations∆· After move
Households
Observations

Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A: Moves between states beer available
0.660*** 0.582*** 0.730*** 0.696*** 0.597***(0.105)(0.135)(0.113)(0.112)(0.075)
2392
2392
2392
2392
2392
71324
71324
71324
71324
71324
Panel B: Moves to/from beer not available
0.766*** 0.765***
0.222
1.092*** 0.831***(0.132)(0.111)(0.180)(0.170)(0.092)
875
875
875
875
875
26536
26536
26536
26536
26536

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression
includes quarter-year dummies and household fixed effects. The sample includes movers that move acrossstate lines, limiting the sample to those that move only once, the year of the move is excluded from thesample. In panel A, the sample is further restricted to moves between states where beer is available in morethan 80% of the grocery stores (calculated from the NielsenIQ Retail Scanner Data as described in Online
Appendix D); in panel B, to moves to/from states where beer is not available in more than 80% of thegrocery stores. There is not enough moves between the states where beer is not available in the grocery
stores to estimate analogous regressions for those. Standard errors are clustered at the household level. ***Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10 percent level.

A23

Table A.19: Heterogeneity: Change in alcohol purchases after move; heavy drinkers andmoderate drinkers∆· After move
Households
Observations∆· After move
Households
Observations

Quantity
Expend.
Total
Beer
Liquor
Wine
Total
(1)
(2)
(3)
(4)
(5)
Panel A. Sample: Heavy drinkers
0.740** 0.797**
0.522
1.236*** 0.539***(0.354)(0.346)(0.455)(0.395)(0.171)
521
521
521
521
521
15880
15880
15880
15880
15880
Panel B. Sample: Moderate drinkers
0.714*** 0.702*** 0.656*** 0.779*** 0.750***(0.079)(0.093)(0.091)(0.094)(0.067)
2201
2201
2201
2201
2201
67716
67716
67716
67716
67716

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression
includes quarter-year dummies and household fixed effects. The sample includes movers that move acrossstate lines, limiting the sample to those that move only once, the year of the move is excluded from thesample. In panel A, the sample is further restricted to movers whose before move alcohol purchases where
above the 80th percentile (heavy drinkers); in panel B, to movers who purchased alcohol before the move
but who purchases were below the 80th percentile (moderate drinkers). Standard errors are clustered at thehousehold level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10 percent
level.

A24

Table A.20: Heterogeneity: Change in alcohol purchases after move; by education, income,and age
Income > median
Age
Yes
No
<45
45-60
61+
(3)
(4)
(5)
(6)
(7)
Panel A. Average effects∆· After move
0.746*** 0.657*** 0.736*** 0.679*** 0.553*** 0.726*** 0.770***(0.103) (0.135) (0.121) (0.112) (0.140) (0.126) (0.158)
Households
1935
1332
1615
1652
1010
1329
928
Observations
57808 40052 49508 48352 24492 42536 30832
Panel B. Asymmetric effects∆ · 1[∆ > 0]· After move
0.846*** 0.870*** 1.054*** 0.659*** 0.473** 0.974*** 0.949***(0.149) (0.218) (0.193) (0.159) (0.224) (0.198) (0.218)∆ · 1[∆ < 0]· After move
0.624*** 0.417** 0.341* 0.701*** 0.635*** 0.440** 0.532*(0.170) (0.204) (0.178) (0.186) (0.228) (0.187) (0.272)
Wald test, coef. equality, p-value 0.358
0.165
0.013
0.872
0.649
0.070
0.263
Households
1935
1332
1615
1652
1010
1329
928
Observations
57808 40052 49508 48352 24492 42536 30832
College
Yes
No
(1)
(2)

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression includes
quarter-year dummies and household fixed effects. The sample includes movers that move across state lines,
limiting the sample to those that move only once, the year of the move is excluded from the sample. In each
regression the sample is further restricted by the described characteristic. All characteristics are measured
in the last year before the move year. College indicates whether either the male or female household headhas college education. Age indicates the age of the youngest household head. Standard errors are clusteredat the household level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10
percent level.

A25

Table A.21: Heterogeneity: Change in alcohol purchases after move; by changes in income
and occupation and employment
Increase(1)∆· After move
Households
Observations∆ · 1[∆ > 0]· After move∆ · 1[∆ < 0]· After move
Wald test, coef. equality, p-value
Households
Observations

0.714***(0.166)939
26832
0.598**(0.236)0.837***(0.300)
0.572
939
26832

IncomeOccupation/employment
Decrease Constant Change
Constant
(2)
(3)
(4)
(5)
Panel A. Average effects
0.689*** 0.715*** 0.504***
0.917***(0.147)(0.123)(0.109)(0.123)
957
1371
1624
1643
29500
41528
48360
49500
Panel B. Asymmetric effects
0.932*** 0.933*** 0.531***1.185***(0.245)(0.181)(0.171)(0.183)0.415*
0.434** 0.472***
0.601***(0.212)(0.183)(0.173)(0.194)
0.145
0.066
0.824
0.040
957
1371
1624
1643
29500
41528
48360
49500

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression
includes quarter-year dummies and household fixed effects. The sample includes movers that move acrossstate lines, limiting the sample to those that move only once, the year of the move is excluded from thesample. In each regression the sample is further restricted by the described characteristic. The comparison
is between the first year after the move year and the latest year before the move year. As in the dataset,income is reported in 16 intervals, in columns 1–3, we consider income to be constant if it remains in thesame income interval, and increasing or decreasing otherwise. In columns 4–5, the sample is split by whether
at least one of the household head changed occupation or employment status. Standard errors are clusteredat the household level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10
percent level.

A26

Table A.22: Heterogeneity: Change in alcohol purchases after move; by the number of adults
in the household, and changes in the number of adults∆· After move
Households
Observations∆ · 1[∆ > 0]· After move∆ · 1[∆ < 0]· After move
Wald test, coef. equality, p-value
Households
Observations

Number of adults
1>1Change(1)(2)(3)
Panel A. Average effects
0.666*** 0.768*** 0.590***(0.193)(0.105)(0.171)
690
1896
681
22324
55076
20460
Panel B. Asymmetric effects
0.679*** 0.982*** 0.734***(0.253)(0.174)(0.261)0.650** 0.512*** 0.436*(0.325)(0.160)(0.256)
0.946
0.074
0.446
690
1896
681
22324
55076
20460

Notes: Dependent variable is the logarithm of alcohol purchases. ∆i = ȳD,i − ȳO,i is the difference in averagelogarithm of alcohol purchases between the destination and origin state of the mover. Each regression
includes quarter-year dummies and household fixed effects. The sample includes movers that move acrossstate lines, limiting the sample to those that move only once, the year of the move is excluded from thesample. In each regression the sample is further restricted by the number of adults aged 21 or above, or thechange in the number of adults. In columns 1–2, the number of adults remains constant, and in column 3, itchanges. The comparison is between the first year after the move year and the latest year before the moveyear. Standard errors are clustered at the household level. *** Indicates significance at the 1 percent level,** at a 5 percent level, * at a 10 percent level.

A27

Table A.23: Change in food purchases after move. Dependent variable: the logarithm offood purchases∆· After move
Households
Observations

Calories(1)
0.138(0.098)3267
97860

Expenditures
(2)
0.136
(0.093)3267
97860

Notes: Dependent variable is the logarithm of food of purchases, either calories (column 1) or expenditures(column 2). Food purchases include all grocery purchases, except breakfast foods and random weight purchases. ∆i = ȳD,i − ȳO,i is the difference in average logarithm of the calories or expenditures of purchases
between the destination and origin state of the mover. The sample includes all movers that move across statelines, limiting the sample to those that move only once, the year of the move is excluded from the sample.
Each regression includes quarter-year dummies and household fixed effects. Standard errors are clusteredat the household level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, * at a 10
percent level.

Table A.24: Change in cigarette purchases after move. Dependent variable: the logarithmof cigarettes quantity∆· After move
Households
Observations

Cigarettes quantity(1)
0.293***(0.109)3267
97860

Notes: Dependent variable is the logarithm of cigarettes quantity. ∆i = ȳD,i − ȳO,i is the difference inaverage logarithm of cigarettes quantity between the destination and origin state of the mover. The sample
includes all movers that move across state lines, limiting the sample to those that move only once, the yearof the move is excluded from the sample. Each regression includes quarter-year dummies and household
fixed effects. Standard errors are clustered at the household level. *** Indicates significance at the 1 percent
level, ** at a 5 percent level, * at a 10 percent level.

A28

Table A.25: Change in purchases after move: beer brands, soda, and fruits and vegetables(1)∆· After move
Households
Observations∆· After move
Households
Observations∆· After move
Households
Observations

Bud Light
0.377***(0.113)3267
97860
Budweiser
0.531**(0.207)3267
97860
Regular soda
0.088
(0.098)3267
97860

Quantity(2)(3)
Panel A: Beer
Miller Light
Coors Light
0.625***
0.667***(0.129)(0.202)
3267
3267
97860
97860
Panel B: Beer
Natural LightBusch Light
0.484**
0.569**(0.210)(0.284)
3267
3267
97860
97860
Panel C: Other products
Low-calorie soda
Fruits & vegetables-0.1290.136(0.114)(0.105)
3267
3267
97860
97860

Notes: Dependent variable is the logarithm of quantity of purchases. ∆i = ȳD,i − ȳO,i is the difference inaverage logarithm of the quantity of purchases between the destination and origin state of the mover. The
sample includes all movers that move across state lines, limiting the sample to those that move only once,
the year of the move is excluded from the sample. In panel A and B, the dependent variable is one of the six
most sold brands of beer. In panel C, dependent variable is either regular soda, low-calorie soda, fruits andvegetables. Each regression includes quarter-year dummies and household fixed effects. Standard errors are
clustered at the household level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, *at a 10 percent level.

A29

Table A.26: Change in food purchases after move as a response to move to high or low
alcohol consumption region. Dependent variable: the logarithm of food purchases∆ log. alcohol quantity · After moveCalories(1)-0.045(0.154)∆ log. alcohol expend. · After move
Households
Observations

3267
97860

Expenditures
(2)

0.019
(0.027)3267
97860

Notes: Dependent variable is the logarithm of food of purchases, either calories (column 1) or expenditures(column 2). Food purchases include all grocery purchases, except breakfast foods and random weight purchases. ∆i = ȳD,i − ȳO,i is the difference in average logarithm of total alcohol quantity or expenditures
between the destination and origin state of the mover. The sample includes all movers that move acrossstate lines, limiting the sample to those that move only once, the year of the move is excluded from thesample. Each regression includes quarter-year dummies and household fixed effects. Standard errors are
clustered at the household level. *** Indicates significance at the 1 percent level, ** at a 5 percent level, *at a 10 percent level.

A30

B

Online appendix: Alternative decomposition of alcohol purchases to individual characteristics and location
effects

Using data on both movers and non-movers, we estimate the following equation for household
i in state j in period t:
yijt = αi + γj + τt + ρit + εijt
(2)where households are indexed by i, states by j, and periods by t, and where α-s are household
fixed effects, γ-s are state fixed effects, τ -s are time period fixed effects, and for movers ρitare relative period effects since the move.
This approach provides an alternative way of measuring the importance of the environment and, as such, it works as a robustness check for the analysis described in the main
text.
Table B.1 presents the additive decomposition of the difference between the high and low
alcohol consumption areas. It reports the overall difference between the two areas R and R0
in average log. consumption ŷR − ŷR0 , the difference in estimated state-level location effectsγ̂ −γ̂
γ̂R − γ̂R0 , and the share of the difference due to the location effects Slocat (R, R0 ) = ŷRR −ŷR00 .
R
While to identify the location effects equation (2) uses the variation from movers, theresults of the decomposition need not to be the same as the difference-in-differences estimatesin table 2. There are two main differences. First, here, the model is estimated using data also
from non-movers, which helps to measure time varying effects. Second, here, the comparision
is between two groups instead of all households.
The estimates (table B.1) show that about 60% of the difference between the above- andbelow- median alcohol consumption states and 70% of the difference between the states inthe top and bottom alcohol-consumption deciles is due to the current environment. Overall,
the results are similar to those from the event study and difference-in-differences analysis in
the main text.

A31

Table B.1: Additive decomposition of log. alcohol purchases to location and household effects
Quantity
Expenditures
Above & Top & Top & Above & Top & Top &below bottom bottom below bottom bottom
median25%
10%
median
25%
10%
(1)
(2)
(3)
(4)
(5)
(6)
Difference in average log. purchases:
Overall
0.109
0.169 0.219
0.522
0.800 0.995
Due to location 0.066
0.102 0.154
0.361
0.438 0.576
Share of difference due to:
Location
0.607
0.601 0.705
0.692
0.548 0.579(0.123) (0.107) (0.141) (0.089) (0.081) (0.109)
Notes: Table is based on the estimates from equation (2). Dependent variable is log. quantity (columns
1–3) or log. expenditures (columns 4–6). Each regression includes quarter-year dummies, indicators forquarters since move, state fixed effects, and household fixed effects. Each column defines areas R and R0
based on percentiles of average log. consumption. The first row measures the overall difference in averagelog. consumption ŷR − ŷR0 , where ŷR is calculated in 3 steps: first, taking averages across households in agiven state in a given quarter to calculate the state-quarter average; second, calculating the average acrosstime to obtain the state average; third, calculate average across the states in region R. The second row
measures the difference due to location effect γ̂R − γ̂R0 , and the third row reports the share of the difference
due to location effects. Sample includes movers and non-movers (3,110,488 consumer-quarters). Standard
errors (in parentheses) are calculated using a bootstrap with 50 repetitions at the household level.

C

Online appendix: Characteristics of high-alcoholconsumption environments

Which characteristics are common to environments with high alcohol consumption? To
answer the question, first, using data on both movers and non-movers, we regress alcoholpurchases on household, state, and time fixed effects in order to quantify the role of locations
in explaining the variation in alcohol purchases. Second, we measure which state-level characteristics are correlated with the estimated state-level location effects. For the first step,
we use the estimates of equation (2) from online appendix B. This gives us the estimates ofthe state-level location effects. Using the estimates we proceed to step two.
Correlates of state-level location effects. We study which state-level characteristics
are correlated with the estimated state-level location effects (γ̂). The state-level characteristics describe alcohol availability, taxes, and prices. Two types of measures are used to capture
alcohol availability. First, whether the state is a control state (i.e., has a state monopoly
over selling wine or liquor). Even in states where alcohol is not restricted to be sold only in
A32

state-owned liquor stores, there could still be dry counties and restrictions on the number ofalcohol licenses available for grocery stores. Therefore, we also measure availability as thepercentage of grocery stores selling alcohol. Alcohol price includes excise taxes and, as such,
captures both taxes and market conditions. Details of the construction of the characteristics
are in the Online Appendix D.
Figure C.1 presents the correlates of estimated location effects: bivariate OLS estimates
are presented on the left, and post-Lasso multivariate regression estimates are on the right.
The bivariate OLS estimates show that states, where more grocery stores are selling stronger
alcohol and where alcohol prices are lower, have statistically significantly higher location
effects. In the post-Lasso multivariate regression, alcohol price and the share of grocery stores
selling wine remain significant at the 90% level.17 Similar estimates for alcohol expendituresand for beer, wine, and liquor quantities, are presented on figures C.2–C.5.
We note that figures C.1–C.5 do not necessarily describe causal effects and instead could
capture endogeneous responses to voters’ and consumers’ preferences. This could happen
both across states and time. For example, during economic downturns, governments tend toincrease alcohol taxes (because other revenue sources have decreased), and alcohol consumption also tends to increase during economic downturns. This positive correlation between
taxes and consumption does not mean that higher taxes lead to more drinking. In crosssection, in states with high demand, voters may support lower taxes and fewer restrictions
on alcohol availability.

17

For the share of grocery stores selling wine, the p-value equals 0.085, and for alcohol price, p < 0.001.

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Bivariate OLS

Post-Lasso% grocery stores selling beer
% grocery stores selling wine
% grocery stores selling liquor
Control state: wine
Control state: liquor
Log. beer tax
Log. wine tax
Log. liquor tax
Log. alcohol price
-.2

-.1

0

.1

-.2

-.1

0

.1

Figure C.1: Correlates of average place effects
Notes: Figure presents point estimates and 95% confidence intervals from bivariate OLS regressions (left)
and a post-Lasso multivariate regression (right) of average state level location effects on state-level characteristics. Average state-level location effects (γ̂) are estimated from equation (2) where dependent variable
is the logarithm of alcohol quantity, and the sample includes movers and non-movers (3,110,488 householdquarters). In the OLS, Lasso, and Post-Lasso regressions the unit of observation is a state and the sample
includes 49 observations (48 mainland states and Washington, D.C.), except for the excise taxes, where thesample includes only non-control states. All covariates are standardized to mean zero and standard deviation
one. Post-Lasso estimates are obtained by first running a Lasso regression on the full set of covariates andthen an OLS regression using the set of covariates chosen by Lasso. In the Lasso regression, the penalty levelis chosen by the theory-based rule by Belloni et al. (2012), which is also applicable with heteroscedasticity.

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Bivariate OLS

Post-Lasso% grocery stores selling beer
% grocery stores selling wine
% grocery stores selling liquor
Control state: wine
Control state: liquor
Log. beer tax
Log. wine tax
Log. tax liquor
Log. alcohol price
-1

-.5

0

.5

-1

-.5

0

.5

Figure C.2: Correlates of average place effects, log. expenditure of alcohol
Notes: Figure presents point estimates and 95% confidence intervals from bivariate OLS regressions (left) anda post-Lasso multivariate regression (right) of average state-level location effects on state-level characteristics.
Average state-level location effects (γ̂) are estimated from equation (2) where dependent variable is thelogarithm of alcohol expenditures, and the sample includes movers and non-movers (3,110,488 householdquarters). In the OLS, Lasso, and Post-Lasso regressions the unit of observation is a state and the sample
includes 49 observations (48 mainland states and Washington, D.C.), except for the excise taxes, where thesample includes only non-control states. All covariates are standardized to mean zero and standard deviation
one. Post-Lasso estimates are obtained by first running a Lasso regression on the full set of covariates andthen an OLS regression using the set of covariates chosen by Lasso. In the Lasso regression, the penalty levelis chosen by the theory-based rule by Belloni et al. (2012), which is also applicable with heteroscedacity.

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Bivariate OLS

Post-Lasso% grocery stores selling beer
% grocery stores selling wine
% grocery stores selling liquor
Control state: wine
Control state: liquor
Log. beer tax
Log. wine tax
Log. tax liquor
Log. alcohol price
-.4

-.2

0

.2

-.4

-.2

0

.2

Figure C.3: Correlates of average place effects, log. quantity of beer
Notes: Figure presents point estimates and 95% confidence intervals from bivariate OLS regressions (left) anda post-Lasso multivariate regression (right) of average state-level location effects on state level characteristics.
Average state-level location effects (γ̂) are estimated from equation (2) where dependent variable is thelogarithm of beer quantity, and the sample includes movers and non-movers (3,110,488 household-quarters).
In the OLS, Lasso, and Post-Lasso regressions the unit of observation is a state and the sample includes
49 observations (48 mainland states and Washington, D.C.), except for the excise taxes, where the sample
includes only non-control states. All covariates are standardized to mean zero and standard deviation one.
Post-Lasso estimates are obtained by first running a Lasso regression on the full set of covariates and thenan OLS regression using the set of covariates chosen by Lasso. In the Lasso regression, the penalty level ischosen by the theory-based rule by Belloni et al. (2012), which is also applicable with heteroscedacity.

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Bivariate OLS

Post-Lasso% grocery stores selling beer
% grocery stores selling wine
% grocery stores selling liquor
Control state: wine
Control state: liquor
Log. beer tax
Log. wine tax
Log. tax liquor
Log. alcohol price
-.1

-.05

0

.05

.1

-.1

-.05

0

.05

.1

Figure C.4: Correlates of average place effects, log. quantity of liquor
Notes: Figure presents point estimates and 95% confidence intervals from bivariate OLS regressions (left) anda post-Lasso multivariate regression (right) of average state-level location effects on state level characteristics.
Average state-level location effects (γ̂) are estimated from equation (2) where dependent variable is thelogarithm of liquor quantity, and the sample includes movers and non-movers (3,110,488 household-quarters).
In the OLS, Lasso, and Post-Lasso regressions the unit of observation is a state and the sample includes
49 observations (48 mainland states and Washington, D.C.), except for the excise taxes, where the sample
includes only non-control states. All covariates are standardized to mean zero and standard deviation one.
Post-Lasso estimates are obtained by first running a Lasso regression on the full set of covariates and thenan OLS regression using the set of covariates chosen by Lasso. In the Lasso regression, the penalty level ischosen by the theory-based rule by Belloni et al. (2012), which is also applicable with heteroscedacity.

A37

Bivariate OLS

Post-Lasso% grocery stores selling beer
% grocery stores selling wine
% grocery stores selling liquor
Control state: wine
Control state: liquor
Log. beer tax
Log. wine tax
Log. tax liquor
Log. alcohol price
-.6

-.4

-.2

0

-.6

-.4

-.2

0

Figure C.5: Correlates of average place effects, log. quantity of wine
Notes: Figure presents point estimates and 95% confidence intervals from bivariate OLS regressions (left) anda post-Lasso multivariate regression (right) of average state-level location effects on state level characteristics.
Average state-level location effects (γ̂) are estimated from equation (2) where dependent variable is thelogarithm of wine quantity, and the sample includes movers and non-movers (3,110,488 household-quarters).
In the OLS, Lasso, and Post-Lasso regressions the unit of observation is a state and the sample includes
49 observations (48 mainland states and Washington D.C.), except for the excise taxes, where the sample
includes only non-control states. All covariates are standardized to mean zero and standard deviation one.
Post-Lasso estimates are obtained by first running a Lasso regression on the full set of covariates and thenan OLS regression using the set of covariates chosen by Lasso. In the Lasso regression, the penalty level ischosen by the theory-based rule by Belloni et al. (2012), which is also applicable with heteroscedacity.

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D

Online appendix: Variable definitions

Control state (for wine or liquor) and beer, wine, and liquor state-level excise
taxes. Data about control states and state-level alcohol excise tax rates is obtained fromThe Urban-Brookings Tax Policy Center.18 The dataset includes yearly data from 2004–2017.
It includes indicators of whether the state is a wine control state and liquor control state.
It also includes excise tax rates per gallon of beer, wine, and liquor. It does not include tax
rates on wine and beer for control states (because the control states set the alcohol prices).
We deflate tax rates to 2015 dollars using the consumer price index for urban consumers. For
all the variables, we calculate averages across all years in 2004-2017. In the OLS regressions
on figure C.1, dependent variables are logarithms of the average (across years) alcohol taxes
plus one cent.
Percent of grocery stores selling beer, wine, or liquor. To calculate these measures,
we use data from NielsenIQ Retail Scanner Data from 2006-2017.19 We start with a sample
of all food (grocery) stores that in a given calendar year sold at least one unit of commonlybought grocery products: milk (dairy refrigerated milk) or bread. For each store and year,
we measure whether the store sold at least a unit of a given alcohol product (beer, wine, or
liquor). Then for each year and alcohol product, we calculate the share of stores selling it.
Finally, for each product, we take the simple average of the shares across years. We define
beer products as all beer products excluding near beer. We exclude from wine products
non-alcoholic wine and wine-flavored refreshments. Liquor products consist of brandy, gin,rum, tequila, vodka, and whiskey, excluding all products that can have lower alcohol content
than regular liquor (for example, ready-made cocktails).
Alcohol price. To generate the state-level aggregate alcohol prices, we use NielsenIQ
Consumer Panel from 2004-2017. We calculate prices in the following steps. First, we convert
all the prices into comparable units (price per unit of ethanol). Second, we calculate theweighted average price of ethanol separately for each product group (beer, liquor, and wine)for each year and state. It is calculated as the weighted average of brand and product size pairprices in a given product group, year, and state. The weight of a brand and product size pair
18

The Urban-Brookings Tax Policy Center. 2020. "State Alcohol Excise Taxes". (https://www.
taxpolicycenter.org/statistics/state-alcohol-excise-taxes, accessed July 8, 2020.)
19
While we would like to use the same time period as in our main dataset, years 2004–2005 are not available
in NielsenIQ Retail Scanner Data. This is unlikely to be a problem because over time there are not manychanges in alcohol regulation that could affect alcohol availability.

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is equal to its share in volume in that product group in a given year. To allow market sharesto evolve, the weights vary across years. Each year weights are the same across states, toavoid that aggregate price is artificially cheaper in states where consumers purchase cheaper
brands. We don’t use price data on local brands, to be able to compare prices of the same
products across states. Third, we calculate weighted average price of ethanol for each stateand year, averaging across the product groups (beer, liquor, and wine). The weight of a
product group is equal to its share in volume (including all brands, local and non-local) in agiven year. Again, weights are constant across states and vary over years. Fourth, we deflate
all the prices to 2015 dollars using the consumer price index for urban consumers and take
simple average over years. We have also calculated aggregate prices in alternative ways, andit has led to similar estimates.

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