The Efects of Cryptocurrency Wealth on

Household Consumption and Investment∗

Darren Aiello Scott R. Baker Tetyana Balyuk Marco Di Maggio

Mark J. Johnson Jason Kotter†

February 9, 2024

Abstract

This paper uses transaction-level bank and credit card data to identify cryptocurrency investors and assess the impact of crypto wealth fuctuations on household consumption, as

well as on investments in other assets. We fnd an $0.09 MPC from crypto gains, exceeding

most previous estimates from unrealized equity gains. However, crypto investors are mostly

also active equity investors, with many similar consumption patterns across gains from both

asset classes. Households sell cryptocurrencies to increase discretionary and housing expenditures, with the latter causing local house price appreciation.

Keywords: cryptocurrency, Bitcoin, Ethereum, consumption, transaction data, household

balance sheet, real estate

JEL Classification: G51, R21, R31, G23, G11.

∗

We thank Sergey Chernenko and seminar and conference participants at CCAF Alternative Finance Conference,

BYU, Emory, Ohio State (Econ), UBC Finance, UChicago, UCSD Finance, HBS Entrepreneurship, NBER Summer

Institute, NBER TRIO, and Purdue for comments and suggestions. Data provided by Yodlee, Inc. All errors are our

own responsibility.

†Darren Aiello: Brigham Young University; d.a@byu.edu, Scott Baker: Northwestern University Kellogg

School of Management and NBER; s-baker@kellogg.northwestern.edu, Tetyana Balyuk: Emory University Goizueta

Business School; tetyana.balyuk@emory.edu, Marco Di Maggio: Harvard Business School and NBER; mdimaggio@hbs.edu, Mark J. Johnson: Brigham Young University; markjjohnson@byu.edu, and Jason Kotter: Brigham

Young University; jasonkotter@byu.edu
1 Introduction

In the last decade, cryptocurrencies have gone from relative obscurity to a peak global market

capitalization of over $3 trillion. Households in the U.S. have increasingly adopted crypto as part

of their investment portfolio and crypto’s extreme volatility has led to rapid wealth gains for

many investors. While the cryptocurrency market has experienced rapid adoption and growth, it

is unclear whether how households treat this asset class relative to other traditional investments

and whether cryptocurrency drives spillovers to the broader economy. While some attention has

focused on crypto and fnancialstability, lack of data due to the anonymous nature of transactions

on public blockchains has restricted research regarding how the introduction of cryptocurrencies

has afected the investment and consumption behavior of individual households and how it has

spilled over into other asset classes.

In this paper, we use transaction-level data spanning millions of U.S. households’ bank accounts and credit cards to analyze how crypto wealth impacts the real economy. Specifcally, we

are able to trace how household consumption responds to changes in crypto wealth and to assess

the causal efect of this wealth on real asset prices. We identify crypto users based on transfers

into and out of major cryptocurrency exchanges and impute crypto wealth based on the timing

of these transactions. While most crypto users have invested relatively small amounts into this

asset class, many individuals have the equivalent of several months of consumption held in such

accounts (consistent with fndings in Wheat and Eckerd, 2022).

We begin by briefy summarizing the characteristics of crypto users by linking a large, nationally representative set of U.S. households with a complete set of fnancial transactions. This

allows us to compare the income and spending patterns of crypto users to non-crypto users.

We more fully characterize the decision to invest in crypto in Aiello, Baker, Balyuk, Di Maggio,

Johnson, and Kotter (2023). In this paper, we focus primarily on the efect of crypto gains on con1
sumption and investment decisions. We fnd that crypto adopters have higher incomes, which

matches recent survey evidence (Benetton and Compiani, 2024), and are more likely to deposit

money in traditional equity brokerages than non-adopters.

On average, households appear to treat crypto as one asset class within a larger investment

portfolio. Crypto users are more likely to be active traders in equity markets, often simultaneously investing in both crypto assets and traditional equity securities. We also fnd evidence

suggesting that households re-balance their portfolios by selling crypto after large gains and depositing money into traditional brokerages.

We then turn to the key results of the paper, examining how spending patterns change following changes in crypto wealth. We estimate a marginal propensity to consume (MPC) out of

crypto wealth of $0.09. Qualitatively, this mirrors consumption responses to the appreciation of

other asset classes such as housing (e.g., Carroll, Otsuka, and Slacalek, 2011; Aladangady, 2017)

and equities (e.g., Hartzmark and Solomon, 2019; Di Maggio, Kermani, and Majlesi, 2020), but

is about two times larger. At the same time, the MPC is roughly one-third the estimated MPC

from one-time income shocks (e.g., economic stimulus payments in Kaplan and Violante (2014);

Johnson, Parker, and Souleles (2006)) and is much smaller than those found in studies of lottery

winnings, which range from 50% to 100% (e.g., Fagereng, Holm, and Natvik, 2021).

Diferences in consumption out of crypto wealth can be driven both by the characteristics of

the asset class and by the type of investors that participate in crypto markets. To tease this out,

we estimate the MPC out of unrealized equity wealth for both crypto and non-crypto investors.

We show that much of the diferences in consumption are driven by investor type; the MPC out

of crypto wealth is only modestly higher than the MPC out of equity wealth for crypto investors.

In addition, for both equity and crypto gains, the MPC is signifcantly larger for constrained

households with low savings. On net, households appear to treat cryptocurrencies similarly to

2
more traditional after-tax equity investments.

Overall, our MPC estimates imply that the aggregate efect of increases in retail crypto wealth

for the households in our sample implies an approximate $30 billion increase in consumption.1

If the consumption out of non-retail crypto gains is similar to our estimates, the total U.S. efect

peaked at between $70–$100 billion in early 2021.2

While some of this increased consumption comes in the form of discretionary spending, we

show that following large crypto withdrawals householdstransition from rentersto homeowners

at higher rates. These individual-level changes in housing consumption create an additional path

for crypto returns to spill over into the local real economy—increased demand for homes can create local housing price pressure. Consequently, volatility in crypto markets can infuence not just

crypto investors but also the broader population. However, two challenges make it difcult to

estimate the efect of crypto wealth on house prices. Naïve regression estimates potentially sufer

from reverse causality, as higher prices might cause households to withdraw crypto investments

in order to aford a home. Additionally, counties that become wealthier are likely to simultaneously invest more in all assets. We deal with these concerns by estimating the causal impact of

county-level crypto wealth on local house price growth using two separate natural experiments.

The frst experiment exploits the largest run-up in Bitcoin prices in our sample period (late2017) as a shock to the crypto wealth in a county. Counties that had high per capita crypto

wealth prior to the beginning of the price run-up were highly exposed to a quasi-random 12-

month Bitcoin return of over 1,400%. To alleviate some concerns of reverse causality, we use

a diference-in-diferences methodology, noting an absence of diferential trends in the period

preceding the run-up in prices. To further alleviate concernsthat changesin equity wealth (rather

1The total crypto wealth accumulated in our sample reached a peak of $2,600 per household. Multiplying this

by the number of US households (130 M) and the MPC of $0.088 yields $29.7B. 2Assuming that the U.S. holds between one-third to one-half of the world’s crypto wealth, which is in line with

estimates of the U.S. share of total household wealth.

3
than crypto wealth) drive our results, we show that following the Bitcoin price shock, high crypto

wealth counties experience no change in traditional brokerage withdrawals, but experience a

sharp increase in crypto withdrawals. House prices in high crypto wealth counties grow about

43 basis points faster than house prices in low crypto wealth counties, explaining roughly 11% of

the standard deviation in house price growth.

We extend the concept underlying the diference-in-diferences estimation to the full time

series using a two-stage least squares (2SLS) specifcation based on an approach mirroring that

used by Calvet, Campbell, and Sodini (2009) for studying equity market investors. We use passive

gains in county-level crypto wealth, defned as the value of county crypto wealth 12-months

prior grown by the annual return to Bitcoin and Ethereum, as an instrument for the growth

in the county’s crypto wealth. Because this instrument is based on historical crypto portfolios,

it alleviates concerns about reverse causality stemming from individuals potentially adjusting

their investments in response to near-term spending plans. The quasi-random nature of crypto

returns makes it unlikely that most sources of wealth are simultaneously correlated with both

crypto returns and historical county-level crypto wealth. While crypto returns are positively

correlated with equity market returns in some periods, our 2SLS results are robust to controlling

for county-level changes in equity wealth and using crypto returns in excess of market equity

returns.

Increases in crypto wealth cause signifcant house price growth. The estimates suggest that

an additional dollar of per capita county-level retail crypto wealth increases county house prices

by about $0.15 over the following three months. To interpret this magnitude, consider that crypto

gains lead investors to withdraw large amounts of cash from their crypto brokerages. Extrapolating from our data to the entire U.S. population, about 2.8 million households withdraw at least

$5,000 worth of crypto between 2018 and 2023. While not all of these households will purchase

4
homes, this is a meaningful increase in potential demand—roughly 10.5% relative to the total

number of new home listings over that period. At the county level, a one standard deviation

increase in per capita retail crypto gains leads to a $460 dollar increase in house prices over the

next three months.

Unlike papers which describe characteristics of cryptocurrency investors or crypto trading

behavior (e.g., Benetton and Compiani, 2024; Chava, Hu, and Paradkar, 2022; Divakaruni and

Zimmerman, 2023; Hackethal, Hanspal, Lammer, and Rink, 2022; Makarov and Schoar, 2021), we

examine the interaction of cryptocurrency price fuctuations with household consumption and

investment behavior. Our data allow us to link a broad set of U.S. retail crypto traders to a relatively complete set of other fnancial transactions. One related study is Kogan, Makarov, Niessner,

and Schoar (2023) which uses transaction-level data to characterize the investment decisions of

retail crypto users. Kogan et al. (2023) observe actual crypto and equity trades and document

momentum in crypto investment. Their ability to observe individual transactions and holdings

across both asset classes allows them to shed light on the diferences between equity and crypto

investment decisions, while our ability to observe income, consumption, and real outcomes allows us to speak to the way households treat gains across these two asset classes.

Our paper also contributes to a large literature that assesses the impact of changes in income

or asset prices on consumption behavior. Baker, Nagel, Wurgler, et al. (2007), Hartzmark and

Solomon (2019), and Di Maggio, Kermani, and Majlesi (2020) look at equity markets and fnd that

the MPC out of capital gains is on the order of 0.04. Case, Quigley, and Shiller (2005), Aladangady

(2017), Berger, Guerrieri, Lorenzoni, and Vavra (2018), and Chen, Michaux, and Roussanov (2020)

examine consumption responses to changes in home values and broadly fnd the MPC out of

housing wealth to be roughly in line with capital gains. Beyond asset price fuctuations, there is

also a large body of work that assesses the MPC out of shocks to either persistent or transitory

5
income (e.g., Jappelli and Pistaferri, 2014; Agarwal and Qian, 2014; Baker, 2018; Baker, Farrokhnia,

Meyer, Pagel, and Yannelis, 2020).

Comparing these consumption decisions to the those following equity or housing gains sheds

light on how households treat crypto relative to other asset classes. Moreover, we leverage regional wealth shocksto testspatial variation in economic impacts, akin to work such as ChodorowReich, Nenov, and Simsek (2021), Hartman-Glaser, Thibodeau, and Yoshida (2023), and Grifn,

Kruger, and Mahajan (2023) who exploit such regional variation in wealth changes to study consumption and house prices.

The rest of the paper proceeds as follows. Section 2 describes our transaction-level data set.

Section 3 explores the role crypto plays in household investment decisions. Section 4 examines

consumption responses to crypto wealth at the household level. Section 5 presents estimates of

the causal efect of county-level crypto wealth on local house prices. Section 6 concludes.

2 Data

2.1 Transaction Data

Our data provider is a large fnancial aggregation and analytics frm that specializes in utilizing anonymized bank, credit, and debit card transaction data across millions of American households. This provider contracts primarily with fnancial institutions and FinTech frms to provide

data and personal fnancial management services to their customers and an ability to aggregate

fnancial information across a user’s fnancial accounts. As a consequence, conditional on banking with a given fnancial institution, there is no additional selection of users into the database

and attrition is minimal.

Our data are limited to bank, credit card, and debit card transactions, excluding transactions

6
made within other types of accounts (e.g., brokerage accounts), though we can generally observe

deposits to and withdrawals from those accounts. Each individual transaction contains a number

of pieces of information. For instance, we are able to observe the precise date and amount of

a transaction and whether the transaction was made in person or remotely. Using information

from the textual description accompanying the transaction, transactions are categorized into one

of 43 diferent categories (e.g. salary, ATM withdrawal, groceries, mortgage payments, medical

spending). Merchant names and physical locations at a city or zip code level are also observable

for the majority of transactions.

The full database spans over 60 million American users and billions of transactions from

June 2010 until September 2023. The database experiences a substantial expansion of users in

the early years, so we focus on data from 2014 onward to mitigate concerns about changes in

the population. While these data allow us to see substantial detail surrounding users’ fnancial

transactions, we do not observe demographic information such as age, gender, or race. However,

for a large fraction of users, we are able to impute the zip code of their residence based on the

physical location of merchants that frequently appear in transactions.3

2.1.1 Validation of Consumer Transaction Data

Due to its size and granularity, transaction data from a variety of diferent providers has been

increasingly utilized in research to answer questions about the behavior of individuals and the

broader economy. Baker and Kueng (2022) provide a review of literature involving transaction

data and some of the advantages and disadvantages inherent in its use. Balyuk and Williams

(2023) utilize the same data provider as in this paper to study the rollout of peer-to-peer fnancial

3Thisimputed zip code representsthe zip code in which they most frequently are seen making physicalspending

transactions in a given year. We limit these transactions to Grocery, Restaurant, Gasoline, General Merchandise,

Home Improvement, and Pharmacy transactions.

7
transfer technology and Di Maggio, Williams, and Katz (2022) use these data to study buy now,

pay later fnancing.

While our data are not drawn randomly from the population, in general it appearsto be highly

representative of the broader economy. Many other transaction databases have samples derived

from a highly selected sample of the general population (e.g., those interested in using a FinTech

app to borrow or to help pay down debt). In contrast, our data provider works with large fnancial

institutions that cover a sizable fraction of the U.S. population, limiting worries about a highly

selected sample.

To validate that the data are broadly representative, we compare our observed spending data

to data obtained from merchants in the Census Retail Sales Surveys. These surveys are used by

the Census Bureau to estimate monthly retail sales in the U.S. by merchant category. In Figure 1,

we aggregate observable transactions from our data to a monthly level for a range of categories

(Auto and Gas, General Merchandise, Groceries, Personal/Family, Medical, and Restaurants). The

fgure shows that trends in spending from 2015 to 2023 are very similar across our data and the

Census Retail Sales survey. On average, the correlation in monthly spending from these two

sources is over 0.90. The series with the lowest correlation, Healthcare and Medical, is also the

category in which we would expect the largest share of pre-tax or third-party spending, driving

a wedge between observable spending among households and revenue reported by retailers. The

data also appears to be broadly representative across counties. In Appendix Figure A.1, we plot a

binscatter of county weights by population vs. county weights by users in our transaction data.

Another common concern when using transaction data is whether we are able to observe

the totality of income and consumption transactions associated with a given user. For this data

source, we observe a complete picture of a household’s transactions if the household only banks

8
with and uses credit cards from fnancial institutions that contract with this aggregating service.4

While this is unlikely to be true for all users, we focus our household-level analysis on a subset

of high-quality users where this is more likely to be the case. The data provider ranks the quality

of the transaction data based on completeness and account tenure. We focus on a sub-sample of

165,609 users drawn randomly from the top 10% of the sample based on this measure.

2.2 Identifying Cryptocurrency Exchange Transactions

Leveraging the textual descriptions and merchant information that accompany each transaction in our database, we are able to identify transactionsthat represent depositsto or withdrawals

from popular cryptocurrency exchanges. We assemble a list of major crypto exchanges and do

substantial manual inspection to identify all variants of textstringsthat denote a transaction with

major exchanges (e.g., ‘Coinbase.com debit card purchase’ or ‘Gemini Trust Co Txfer’). These

exchanges include Coinbase, Binance, Gemini, Crypto.com, Kucoin, Cryptohub, Blocket, CEX.io,

and Bitstamp. Our focus on crypto exchanges meansthat we will necessarily underestimate retail

crypto wealth, because some investors hold cryptocurrency in private wallets obtained through

direct purchases or mining. It is difcult to determine how much retail cryptocurrency is held

of-exchange, but Makarov and Schoar (2021) estimate that since 2015, approximately 75% of total

Bitcoin transactions have occurred through exchanges. This fnding suggests that transactions

with retail crypto exchanges are likely to capture the majority of crypto deposits and withdrawals.

While users interact with exchanges using bank transfers, debit cards, and credit cards, the

vast majority of transactions are through a checking account, with credit cards making up less

than 2% of cryptocurrency exchange transactions. In addition, while we observe both deposits

and withdrawals, nearly 90% of transactions with one of these exchanges are deposits, refecting

4We referto a userin our data as a household, which is accurate if the household has combined fnancial accounts.

However, it is possible that some individuals in our data live in the same household but maintain separate accounts.

9



2.4 Makeup of Cryptocurrency Investors

Cryptocurrency is a rapidly growing asset class, with a global market value of about $1.6 trillion as of early 2024. Despite itsrapid growth, the decentralized, anonymous nature of blockchain

transactions has made it difcult to understand who invests in crypto and what drives this investment decision. In contemporaneous work, Kogan et al. (2023), Chava, Hu, and Paradkar

(2022), Divakaruni and Zimmerman (2023), and Hackethal et al. (2022) begin to shed light on

these questions. We expand on this work by providing evidence based on actual cryptocurrency

transactions for a large, nationally representative set of U.S. households. Because we observe not

only crypto transactions, but a complete set of payment transactions, we are the frst to be able

to characterize investment, income, and consumption patterns of crypto investors.

We more fully describe the characteristics of crypto users in Aiello et al. (2023). Here, we

focus on a few key features of the development of retail crypto markets relevant for our later

analysis. Figure 3 plots the evolution of deposits to and withdrawals from crypto exchanges. We

examine how aggregate crypto deposits and withdrawals, summed across a 10% sample of the

60 million households in our transaction data, correlate with crypto returns (defned based on

a market value weighted index of Bitcoin and Ethereum). The four panels of the fgure show

crypto deposits, withdrawals, new users, and net deposits. The salience of large crypto returns

is evident: Both the number of new users and total crypto deposits spike following large runups in crypto prices. In fact, the single largest jump in new users occurs in late 2017, following

the largest 12-month crypto return in our sample. Interestingly, though, withdrawals also spike

around this time, suggesting that at least some households cash out their crypto gains.

An advantage of ourtransaction data isthat we can observe spending patternsfor both households that invest in cryptocurrencies and those that do not. In Table 2, we show the average

amount of monthly income, spending, and the fraction of spending made up of various categories

13
for crypto investors vs. non-crypto investors. A few key patterns emerge from the data. Crypto

adopters have higher incomes than non-adopters: Average monthly income is $7,467 for crypto

investors relative to $6,648 for non-investors. Crypto adopters also actively invest substantially

more in traditional brokerage accounts.9

Despite income diferences, overallspending patterns are relatively similarfor crypto adopters

and non-adopters. The largest diferences are in discretionary spending. Consistent with having

higher disposable income, crypto investors spend about 1.1 percentage points more of their budgets on entertainment/travel and restaurants than non-crypto investors. Crypto investors also

spend substantially less using cash or checks.

Figure 4 shows how the geography of cryptocurrency wealth evolves over time. We aggregate

total crypto wealth value to the county level and divide it by the number of households in the

county. We then show the county maps at year-end 2015, 2017, 2019, and 2021. In 2015, most

coastal counties had wealth of less than $100 per household, while much of the interior of the U.S.

had no crypto participation. During the initial run-up in crypto prices in 2017, dozens of counties

throughout the U.S. began to accumulate crypto wealth of $1,000 per household or more. By the

end of 2021, most populated U.S. counties had crypto wealth of at least $1,000 per household, and

some counties had crypto wealth of tens of thousands of dollars per household. The largest per

capita crypto values are concentrated in counties located in California, Nevada, and Utah. The

geographic variation suggests the possibility that crypto wealth might have diferential efects

on the local economy across counties, which we investigate in Section 5.

9Note that we do not observe pre-tax 401K contributions orsimilarinvestmentsthat are withheld from paychecks

and thus underestimate the dollar amount of traditional investments made by households.

14
3 Investment after Growth in Crypto Wealth

The summary stats in Table 2 suggest that crypto users are more likely than non-crypto users

to have traditional brokerage investments. Aiello et al. (2023) provide additional evidence that

crypto investors are more likely to be sophisticated investors. To the extent that crypto investors

are fnancially sophisticated, we would expect them to rebalance large crypto gains into traditional investments. However, polling data suggests that household crypto investors might view

crypto as a substitute for traditional investing. For example, a Pew Research Center Poll in 2022

found that among those respondents who say they have invested in cryptocurrency, 78% say one

of their motivations was to have a diferent way to invest, 54% claim that they think it is easier to invest in crypto than in traditional investments, and 39% say they are more confdent in

cryptocurrencies than in other investments.10

We evaluate the relation between crypto gains and future investment at the household level

to shed light on the extent to which crypto users rebalance crypto portfolio gains. In Figure 5, we

plot a cross-sectional bin scatter of total brokerage deposits against total cryptocurrency deposits.

There is a strong, positive correlation between the two types of deposits. However, for most

households the total amount of brokerage deposits is substantially larger than the total amount

of crypto deposits. This relation fattens at the high end of crypto deposits. For instance, the

average user who deposits $20,000 into cryptocurrency exchanges is observed to invest two to

three times as much in traditional brokerage accounts while the average user who invests $50,000

into cryptocurrency exchanges deposits about the same amount in brokerages. This evidence

suggests that there are two types of retail crypto investors. For most investors, crypto makes

up a small portion of an investment portfolio dominated by traditional brokerage deposits. In

10https://www.pewresearch.org/fact-tank/2022/08/23/46-of-americans-who-have-invested-in-cryptocurrencysay-its-done-worse-than-expected/.

15




ever, crypto investors have an MPC out of equity gains of $0.07 (Table 3 column (4)), nearly 40%

higher. These results suggest that part of the higher MPCs out crypto wealth is due to diferences

in investor type.

We next investigate whether households treat crypto losses diferently from gains. We split

the sample into household-quarters with an average crypto gain over the past year versus an

average crypto loss. We include non-crypto investors in both samples. The results are reported

in Table 4. Column (1) shows that the MPC out of crypto gains is about $0.08. The MPC out of

crypto losses, reported in column (2), is similar ($0.11). We test if the diference in these MPCs

is statistically signifcant by estimating the MPC in the full sample and including an interaction

between an indicator for average crypto losses with the level of average quarterly crypto gains.

The coefcient on this interaction is positive, but insignifcant. Given this fnding, we would

anticipate meaningful reductions in spending following crypto crashes.

Another dimension in which we might expect heterogeneity in consumption responses is

liquidity constraints. In most models of consumption, liquidity or credit constrained households

consume more out of an exogenous wealth shock. We split our sample of households into low

and high savings subsamples based on their average annual net savings (total income less total

spending). We then re-estimate our 2SLS specifcation separately for each sub-sample in Table 5.

Households that on average save relatively little of their income respond much more strongly to

crypto gains. These relatively constrained householdsspend nearly $0.14 of every dollar of crypto

gains. In contrast, the less constrained high savings households only spend about $0.05 per dollar

of crypto gains. These diferences are statistically signifcant at the 10% level (see column (3)).

This variation in the consumption response to crypto gains is very similar to the variation in

the consumption response to traditional investment gains. In columns (4)–(6) of of Table 5 we

show that low savings households consume much more of their equity gains—roughly $0.10 per

20
dollar—while high savings households have a much smaller MPC of about $0.03.

One diference in consumption responses across crypto and traditional investments is with

respect to income. We split households into high and low income based on the sample median

in the frst quarter that the household enters the data. In columns (1) through (3) of Internet

Appendix Table A.4, we fnd that the MPC out of crypto gains increases in income, although the

diferences are not statistically signifcant. In contrast, columns (4) through (6) show that the

MPC out of equity gains decreases in income.

The transaction data that we use allows us to categorize consumption. In Internet Appendix

Table A.5, we explore how diferent categories of consumption respond to changes in crypto

wealth. The largest efect is in spending by check; this spending represents nearly 50% of the

overall MPC.15 There is also a large increase in spending on general merchandise. Most of the

remaining consumption is split evenly entertainment/travel, groceries, and restaurants. Consequently, it looks like households use crypto gains to increase durable spending (most check

spending is likely to be on durable goods), as well as to increase discretionary spending.

The resultsin thissection show that households change their consumption behaviorfollowing

increases in crypto wealth. While the MPC out of crypto wealth is larger than the MPC out of

equity wealth, much of this is driven by the fact that crypto investors consume more out of all

types of investment gains. Overall, consumption behavior out of crypto wealth looks remarkably

similar to consumption behavior out of equity wealth, suggesting that investors treat these assets

in broadly similar ways.

15The data do not allow us to determine what cash/check expenses are for. Note that cash/check purchases make

up about 17–19% of overall household spending, on average (see Table 2).

21


the size of the withdrawal. Table 7 reports results for mortgage expenses following withdrawals

larger than $5,000 and for those larger than $10,000. Columns (1) and (2) show that larger crypto

withdrawals are followed by even larger increases in mortgage spending. For example, users who

withdraw at least $10,000 from crypto exchanges increase their mortgage spending by $597 over

the next year, about 20% more than the efect from withdrawing at least $5,000.

This increase in mortgage spending could be driven by new house purchases, but also could

represent households prepaying their existing mortgage. In columns (3) and (4) of Table 7, we

re-estimate the event study using an indicator for a new homeowner as the outcome variable. We

defne a monthly indicator equal to one if a household spends more than $2,500 total on mortgage

payments in the next six months after spending less than $100 total in the 6 months before the

crypto withdrawal. Using this indicator as a proxy for new homeownership, we fnd that a crypto

withdrawal of at least $10,000 increases the probability of transitioning into homeownership by

about 8.2 percentage points, or about a 90% increase relative to the sample mean.

In Internet Appendix Table A.6, we fnd that households also spend more on their monthly

mortgage following a withdrawal from a brokerage account, but the efects do not seem to depend on the size of the withdrawal. Households also transition into homeownership after large

withdrawals from traditional brokerages, but the rates are roughly half those following crypto

withdrawals.

5 Aggregate Impact of CryptoWealth on Local House Prices

In Section 4.1, we show that households spend more on housing following large withdrawals

from crypto exchanges. These individual-level house purchase decisions might put price pressure

on local housing markets, particularly since Figure 4 shows that household crypto wealth is ge24




diferences estimates may refect the efect of equity wealth rather than crypto wealth.

We take three steps to alleviate concerns that our diference-in-diferences experiment might

be contaminated by equity returns. First, we compare the pattern of Bitcoin returns with S&P

500 returns in the months surrounding the crypto wealth shock (see Appendix Figure A.5). S&P

500 returns are relatively fat or even falling during the crypto wealth shock. Second, while high

crypto wealth counties have a large spike in crypto withdrawals following the Bitcoin run-up,

Appendix Figure A.6 shows no discontinuous change in withdrawals from brokerage accounts

around this event, suggesting that high crypto wealth counties are not realizing especially large

equity gains during the post-period. Finally, we control for county level per capita equity gains

over the last year.18

We estimate the diference-in-diferencesspecifcation in Equation 11 and report the resultsin

Table 8. We estimate both the traditional diference-in-diferences coefcient using an indicator

for high crypto wealth counties (columns (1) and (3)), as well as a continuous version where we

interact the post indicator with the log county crypto wealth per capita as of 2016 (columns (2)

and (4)).19 Across both specifcations, high crypto wealth counties experience higher house prices

in the months after the Bitcoin price run-up relative to low crypto wealth counties.

The estimated efect of crypto wealth on county house prices in column (1) indicates that

house prices grow about 43 basis points faster in the post-period in high crypto wealth counties

relative to low crypto wealth counties, or roughly 11% of the standard deviation in house price

growth over 2018. In dollar terms, the estimate in column (3) indicates that house prices are

about $2,005 higher in high crypto wealth counties in the nine months following the Bitcoin price

shock. This is about a one percent increase in prices relative to the median county house price.

18Results are robust to omitting this control. See Section 5.2 for details on the calculation of county equity gains. 19The sample sizes difer across these specifcations because we omit the middle tercile of county crypto wealth

from the sample when using the high crypto wealth indicator.

29



6 Conclusion

Households in the U.S. have increasingly adopted cryptocurrency as a component of their

investment strategy, in part due to the extreme volatility that has led to rapid wealth gains for

some investors. This paper is the frst to document consumption responses to this newfound

crypto wealth and identify spillover efects from this wealth on local house prices. Using fnancial transaction-level data for millions of U.S. households, we show that household crypto

investors appear to treat crypto as one piece of an investment portfolio and use crypto wealth to

increase their consumption. The MPC out of crypto wealth is somewhat higher than the MPC out

of equity wealth, but much lowerthan the MPC out of lottery winnings. Overall, consumption behavior following crypto gains is broadly similar to consumption behavior following equity gains.

Together, this evidence suggests that for the average household, investing in cryptocurrencies is

treated much the same as investing in after-tax brokerage accounts.

Households also withdraw crypto gainsto purchase housing—both to enterthe market as new

buyers and to upgrade their existing housing. This increased spending on housing puts upward

pressure on local house prices, particularly in areas that are heavily exposed to crypto assets. In

the aggregate, growth in county-level crypto wealth causes county house prices to increase.

According to cryptocurrency advocates, crypto returns have been mostly uncorrelated with

other asset classes. Furthermore, recent crashesin cryptocurrency markets have appeared to have

limited contagion efects on broader fnancial markets. While crypto may have limited spillover

efects onto other fnancial assets, our results show that crypto investment does afect real assets.

As a result, the distribution of crypto wealth has meaningful implications for the real economy.

33
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Figure 1. Spending in Data vs. Census Retail Sales. Each panel displays two monthly series from January 2015–June 2023. The

solid line displays total sales in the specifed category from the Census Retail Sales. The dotted line displays spending per user in the specifed

category as observed in the data from the large transaction aggregator.

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Figure 2. Cryptocurrency Assets Held through Coinbase. This fgure shows the percentage of various cryptocurrencies held

on Coinbase in 2019 and 2020. Source: Coinbase S-1 fled on March 23, 2021.

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(a) Crypto Deposits (b) Crypto Withdrawals

(c) New Crypto Users (d) Crypto Net Deposits

Figure 3. Crypto Adoption and Crypto Portfolio Activity. This fgure shows the relation between retail crypto activity

(solid line) and a value-weighted Bitcoin-Ethereum index (dashed-line). Figure (a) depicts fows of deposits into cryptocurrencies. Figure (b)

shows withdrawals or redemption of crypto. Figure (c) shows the number of new crypto users in the month, where a new user is defned by the

frst deposit into crypto greater than $5. Finally, Figure (d) shows the net deposits into crypto which is the total deposits minus withdrawals.

39
40

Figure 4. Crypto User Geography over Time. This fgure shows the geographic evolution of crypto activity over time. We identify transacti

exchanges and assume that deposits and withdrawals represent either buying or selling into a value-weighted Bitcoin-Ethereum index at that day’s price. We 

transactions to calculate the total crypto wealth at the county level. The four panels show snapshots of county-level crypto wealth per capita in December 

2021.
Figure 5. Cryptocurrency Deposits and Equity Investments. This fgure depicts a cross-sectional bin-scatter plot with a

quadratic ftted line of total deposits to brokerages against total cryptocurrency exchange deposits. Underlying data are at a user level. We limit

the plot to users who have cumulatively deposited less than $100,000 to crypto exchanges for ease of exposition due to outliers.

41
Figure 6. Monthly Mortgage and Rent Spending around First Large Bitcoin Withdrawal. Each panel plots the

coefcients on an event-study regression for the months before and after a user frst withdraws at least $5,000 from a cryptocurrency exchange.

The top panel shows monthly mortgage spending around this event, while the bottom panel shows spending on monthly rent.

42
(a) Crypto Wealth by County

(b) Bitcoin Returns (c) Crypto Withdrawals

Figure 7. Crypto County Wealth and Withdrawals during the Bitcoin Run-up. The map in Panel (a) highlights

counties that have per capita crypto wealth in the top tercile (dark red) and bottom tercile (light pink) as of December 2016; these are the treated

and control counties in our diference-in-diference analysis. Panel (b) shows Bitcoin’s year over year return in the months surrounding the price

run-up. The timing of our treatment is determined by the trend break in Bitcoin returns; the vertical line separates the sample into pre- and

post-treatment periods. Panel (c) shows average per capita crypto withdrawals separately for counties with high (top tercile) and low (bottom

tercile) crypto wealth as of December 2016.

43

Table 1

Summary Statistics

This table reports summary statistics for the main variables used in the paper. Panel A reports descriptive statistics for the sample of households

used in our MPC analysis in Tables 3–5. This panel is made up of quarterly data from 2015 to 2023 and includes both crypto adopters and

non-crypto adopters. Panel B summarizes the sample used in the crypto withdrawal event study in Tables 6 and 7. This sample is limited to

crypto adopters who withdraw more than $5,000 of crypto in a single month. We limit the sample to the 24-months surrounding the frst such

withdrawal. These withdrawal eventsspan the entire sample from 2014–2023. Panel C summarizesthe county-levelsample used in our diferencein-diferences analysis in Table 8, which includes the 18 months surrounding the Bitcoin run-up in 2017. Panel D shows summary statistics for

the county-level sample used in our 2SLS analysis in Table 9. This sample is estimated over the full sample from 2015–2023.

Variable Obs. Mean Std. Dev. Q5 Q25 Q50 Q75 Q95

Panel A: MPC Household-level Sample

Total Quarterly Spending 165,609 14,391 11,887 3,689 6,909 11,209 17,969 35,314

Total Quarterly Income 165,609 19,699 14,181 4,492 9,219 15,832 26,113 48,615

Conditional on Crypto User

Avg. Quarterly Crypto Gains 26,622 146 762 -122 -9 -0 18 795

Crypto Exit 26,622 0.098 0.298 0 0 0 0 1

In Final User Period

Crypto Wealth 26,622 11,010 88,110 0 74 464 2,840 34,191

Cumulative Crypto Deposits 26,622 6,212 14,831 25 200 922 4,221 34,074

Cumulative Crypto Withdrawals 26,622 3,614 32,396 0 0 0 600 12,725

Panel B: Household-level Withdrawal Event Sample

Total Monthly Spending, Annualized 38,471 79,664 82,505 -36,253 29,529 57,294 102,126 879,676

Lagged Monthly Income 38,471 10,617 8,383 0 4,667 8,393 14,466 40,751

New Homeowner Indicator 38,471 0.092 0.290 0 0 0 0 1

Crypto Withdrawal >$5,000 2,577 16,507 23,119 5,000 6,782 9,851 16,464 340,277

Panel C: County-level Dif-in-Dif Sample

Median County House Price 27,747 188,325 122,492 80,988 117,536 157,217 218,831 393,308

Log(Median County House Price) 27,747 12.0 0.5 11.3 11.7 12.0 12.3 12.9

Annual House Price Growth 27,747 4.4 3.9 -1.8 2.1 4.3 6.8 10.7

Log(County Crypto Wealth per capita, Dec. 2016) 27,747 2.2 1.4 0.0 1.2 2.2 3.1 4.7

Panel D: County-level 2SLS Sample

Median County House Price 179,681 205,782 141,456 82,237 123,790 168,928 241,487 445,545

3-month Change in Median County House Prices 179,681 4,058 6,958 -1,810 1,072 2,777 5,499 14,402

Annual per capita County Crypto Gains 179,681 199 3,144 -922 -17 20 164 1,520

Annual per capita County Equity Gains 169,499 2,246 2,300 -37 1,096 1,871 3,206 5,391

45
Table 2

Summary Statistics of Sample and Crypto Users

This table shows average monthly income and spending for cryptocurrency users and non-cryptocurrency users and the diference between the

two. Data are based on a user-level panel of monthly transaction data from 2014 to 2023. ***, **, and * indicate statistical signifcance in the

diference in means at the 1%, 5%, and 10% levels, respectively.

Variable Crypto Users Non-Crypto Users Diference

Total Income 7,467 6,648 819∗∗∗

Total Spending 4,979 4,738 241∗∗∗

Traditional Investment 260 152 107∗∗∗

74∗∗∗ Crypto Investment 74 0

61∗∗∗ Crypto Gains 61 0

Percent of Spending:

AutoFuel 5.2 4.8 0.4∗∗∗

Cable/Telecom -10.2 -10.3 0.1

Cash/Check 16.8 19.4 -2.7∗∗∗

Charity 0.4 0.5 -0.2

Education 0.2 0.6 -0.4

Entertainment/Travel 7.6 6.5 1.1∗∗∗

Gen. Merch. 21.1 21.1 -0.1

Groceries 9.2 9.4 -0.2∗∗∗

Insurance 4.9 5.5 -0.6∗∗∗

Medical 1.9 2.1 -0.2∗∗∗

Mortgage 8.8 8.2 0.7∗∗∗

Rent 2.3 1.8 0.5∗∗∗

Restaurants 10.2 9.1 1.0∗∗∗

Utilities 3.7 3.9 -0.2∗∗∗

46







Internet Appendix

Figure A.1. County Weights by Population vs. Transaction Users. This fgure shows a binscatter of county weights based

on county population vs county weights based on the number of households in our transaction database.

54
Figure A.2. Distribution of Investment Wealth. These fgures show the distribution of investment wealth. The top fgure

presents the distribution of total crypto portfolio values as of December 2021 for our sample of crypto users. The bottom fgure shows the

distribution of equity portfolio values for U.S. households based on the 2016 Survey of Consumer Finances (SCF).

55
Figure A.3. Distribution of Daily Returns. These fgures show the distribution of Bitcoin and equity daily returns between 2014

and 2022. The top fgure presents the distribution of daily Bitcoin returns and the S&P 500. The second one shows Bitcoin returns and Tesla

returns.

56
Figure A.4. Large Crypto Withdrawals. This fgure shows the number of frst-time large crypto withdrawals (greater than $5,000)

each month for our sample of crypto users. We use this sample in our withdrawal event study reported in Tables 6 and 7.

57
Figure A.5. Bitcoin and S&P 500 Rolling 12-month Returns. This fgure shows the 12-month holding period returns each

month for holding Bitcoin and the S&P 500. The fgure plots the returns on separate axes, with Bitcoin returns on the left axis. The red line in

the fgures indicates the pre- and post-periods used in our diference-in-diferences analysis reported in Table 8, which we defne based on the

beginning of the Bitcoin price run-up.

58
Figure A.6. Equity Investment Withdrawals around Bitcoin Run-up by Crypto Wealth. This fgure shows

county-level per capita withdrawals from traditional brokerages each month separately for high and low crypto wealth counties. High (low)

crypto wealth counties are defned based on the top (bottom) tercile of per capita crypto wealth as of December 2016. Investment withdrawals

are identifed as credits to the user’s account from retail trading platforms such as Fidelity, Charles Schwabb, Robinhood, Acorns, etc. The red

line in the fgure indicates the pre- and post-periods used in our diference-in-diferences analysis reported in Table 8, which we defne based on

the beginning of the Bitcoin price run-up.

59