FinTech and Consumer Well-Being in the

Information Age

Bruce Carlin – UCLA

Arna Olafsson – Copenhagen Business School

Michaela Pagel – Columbia University
Motivation

I With the growing use of personal finance technology,

people should be better equipped than ever to make good

choices
Motivation

I With the growing use of personal finance technology,

people should be better equipped than ever to make good

choices

I The empirical challenge is to find data on both access to

information and economic outcomes, and to deal with

endogeneity, reverse causality, and omitted variables
Motivation

I With the growing use of personal finance technology,

people should be better equipped than ever to make good

choices

I The empirical challenge is to find data on both access to

information and economic outcomes, and to deal with

endogeneity, reverse causality, and omitted variables

I Using data from a financial aggregation software, we

show that better technology improves decision-making

I Logging in one more time per month reduces

high-interest unsecured consumer debt by 14 percent

over a two-year period

I Late fees and other financial penalties are reduced too
Data: From a financial aggregation app

I We use a transaction-level panel dataset of spending,

income, balances, limits, and logins by device recorded by

a financial aggregation and service app in Iceland from

2011 to 2016
Data: From a financial aggregation app

I We use a transaction-level panel dataset of spending,

income, balances, limits, and logins by device recorded by

a financial aggregation and service app in Iceland from

2011 to 2016

I The advantages of using Icelandic data include

I Icelanders (almost) never use cash

I App is marketed through banks and we have a fairly

representative sample

I Income and spending are pre-categorized

I App is for information purposes only (no transaction

functionalities)
Data: From a financial aggregation app

I We use a transaction-level panel dataset of spending,

income, balances, limits, and logins by device recorded by

a financial aggregation and service app in Iceland from

2011 to 2016

I The advantages of using Icelandic data include

I Icelanders (almost) never use cash

I App is marketed through banks and we have a fairly

representative sample

I Income and spending are pre-categorized

I App is for information purposes only (no transaction

functionalities)

I Before November 2014, access to this personal

information only occurred via the internet on a desktop or

laptop computer whereas after a smart phone app was

introduced
The financial aggregation app: Screenshots
Raw data: Logins

Based on the raw data, there is an obvious discontinuity in the

propensity of individuals to log into their financial accounts

around the introduction of the mobile app
Raw data: Overdraft interest fees

I Up until the introduction of the app, overdraft interest

increased but there was a trend change at the time of the

app intro
Raw data: Late fees

I Up until the introduction of the app, late fees increased

but there was a trend change at the time of the app intro
Empirical strategy

I We exploit a discontinuity in individual access to financial

accounts arising from the introduction of the app on

November 14, 2014 (RDiT design)
Empirical strategy

I We exploit a discontinuity in individual access to financial

accounts arising from the introduction of the app on

November 14, 2014 (RDiT design)

I The timing of the app introduction is plausibly exogenous

to individual characteristics but sorted individuals into

different frequencies of logins
Empirical strategy

I We exploit a discontinuity in individual access to financial

accounts arising from the introduction of the app on

November 14, 2014 (RDiT design)

I The timing of the app introduction is plausibly exogenous

to individual characteristics but sorted individuals into

different frequencies of logins

I To the best of our knowledge, no other event took place

around the same time
Empirical strategy

I We exploit a discontinuity in individual access to financial

accounts arising from the introduction of the app on

November 14, 2014 (RDiT design)

I The timing of the app introduction is plausibly exogenous

to individual characteristics but sorted individuals into

different frequencies of logins

I To the best of our knowledge, no other event took place

around the same time

I We include individual fixed effects and other

macroeconomic controls
Empirical strategy

I We exploit a discontinuity in individual access to financial

accounts arising from the introduction of the app on

November 14, 2014 (RDiT design)

I The timing of the app introduction is plausibly exogenous

to individual characteristics but sorted individuals into

different frequencies of logins

I To the best of our knowledge, no other event took place

around the same time

I We include individual fixed effects and other

macroeconomic controls

I No effects found for a placebo mobile app introduction

taking place two years earlier
Empirical strategy

I We exploit a discontinuity in individual access to financial

accounts arising from the introduction of the app on

November 14, 2014 (RDiT design)

I The timing of the app introduction is plausibly exogenous

to individual characteristics but sorted individuals into

different frequencies of logins

I To the best of our knowledge, no other event took place

around the same time

I We include individual fixed effects and other

macroeconomic controls

I No effects found for a placebo mobile app introduction

taking place two years earlier

I Complement within-individual identification with a

cross-sectional diff-in-diff (DiD) approach
Regression Results

I One extra log in was associated with 224.1 Icelandic

Krona ($1.86 USD) fewer penalties incurred per month

over a two-year period
Regression Results

I One extra log in was associated with 224.1 Icelandic

Krona ($1.86 USD) fewer penalties incurred per month

over a two-year period

I Effects are mainly driven by the reduction in overdraft

interest

I 176.8 Krona per month for overdraft interest

I Based on average $1,356 overdraft debt and $13 interest

expense per month, this is a reduction of roughly 14%.

I 53.2 Krona per month for late fees
Regression Results

I One extra log in was associated with 224.1 Icelandic

Krona ($1.86 USD) fewer penalties incurred per month

over a two-year period

I Effects are mainly driven by the reduction in overdraft

interest

I 176.8 Krona per month for overdraft interest

I Based on average $1,356 overdraft debt and $13 interest

expense per month, this is a reduction of roughly 14%.

I 53.2 Krona per month for late fees

I Increase in credit card use versus overdrafts same time

I Each additional login associated with a 2.36% increase in

fraction of expenditures purchased with credit card

I Credit card offers 30-50 day float.
Generational Differences: App Logins

Millennials > Gen X’ers > Baby Boomers

By 2016, 52% of Millenials versus 27% of Baby Boomers
Generational Differences: Coholding Puzzle

I Baby Boomers and Gen Xers incur higher financial

penalties than Millennials

I Higher overdraft interest

I More likely to incure late fees (28.6% and 33.1% versus

18.4%)

I More likely to incur NSF fees (31.4% and 39.8% versus

27.4%)
Generational Differences: Coholding Puzzle

I Baby Boomers and Gen Xers incur higher financial

penalties than Millennials

I Higher overdraft interest

I More likely to incure late fees (28.6% and 33.1% versus

18.4%)

I More likely to incur NSF fees (31.4% and 39.8% versus

27.4%)

I Older generations are more liquid

I Higher cash reserves

I 1.52 months of discretionary expenditures for Baby

Boomers versus 1.09 for Millennials.
Generational Differences: Coholding Puzzle

I Baby Boomers and Gen Xers incur higher financial

penalties than Millennials

I Higher overdraft interest

I More likely to incure late fees (28.6% and 33.1% versus

18.4%)

I More likely to incur NSF fees (31.4% and 39.8% versus

27.4%)

I Older generations are more liquid

I Higher cash reserves

I 1.52 months of discretionary expenditures for Baby

Boomers versus 1.09 for Millennials.

I Baby Boomers and Gen Xers have more severe coholding

behavior

I Higher unnecessary overdrafts ($150.9 and $124.5 versus

$81.01)

I Higher annual coholding costs ($19.98 and $16.49 versus

$10.73)
Conclusion

* We document and quantify the welfare effects of better

access to information using a unique panel of account

logins and economic outcomes
Conclusion

* We document and quantify the welfare effects of better

access to information using a unique panel of account

logins and economic outcomes

I Empirically important given the growth of on-line

education, social learning, and electronic access to

information
Conclusion

* We document and quantify the welfare effects of better

access to information using a unique panel of account

logins and economic outcomes

I Empirically important given the growth of on-line

education, social learning, and electronic access to

information

* Implications for people of different generations

I Different adoption rates and patterns of use of financial

instruments

I May have policy implications that vary across the

population