FinTechAdoptionandHouseholdRisk-Takingen.saif.sjtu.edu.cn/junpan/slides_FinTech.pdfLuohan Academy Webinar FinTech Adoption and Household Risk-Taking Jun Pan 5 / 33 AlipayasaOne-StopFinTechApp

FinTech Adoption and Household Risk-TakingLuohan Academy Webinar

Jun Pan

Shanghai Advanced Institute of Finance (SAIF)Shanghai Jiao Tong University

January 12, 2021

Based on joint work with Claire Yurong Hong and Xiaomeng Lu from SAIF

Luohan Academy Webinar FinTech Adoption and Household Risk-Taking Jun Pan 1 / 33

Household Finance in the Age of FinTech

The current wave of “Fin + Tech” development is unique in that▶ FinTech Platforms: created by tech not finance firms.

⋆ Giant user bases, low operational costs, and a culture of “winner-take-all.”▶ Super Apps: financial services delivered directly to households via super apps.

⋆ Free of traditional financial advisors.⋆ All-in-one ecosystems with a wide range of products.

In China, activities central to household finance are taking place on FinTechplatforms via super apps:

▶ Consumption: online consumption accounts for 25% of the total.▶ Investments: 30% of mutual fund purchases takes place on FinTech platforms.▶ Payments: digital payments everywhere.


FinTech in China

Consumption, Investments, and Paymentsaggregated over a random sample of 50,000 individuals

2017 Mar 2017 Sep 2018 Mar 2018 Sep 2019 Mar0

50

100

150

200

250

300

Mill

ion

Yua

n

AlipayTaobaoRisky FundsMoney Market Funds


Super Apps

Taobao from Alibaba Group Alipay from Ant Group


FinTech in China

Consumption, Investments, and Paymentsaggregated over a random sample of 50,000 individuals


50

100

150

200

250

300

Mill

ion

Yua

n

AlipayTaobaoRisky FundsMoney Market Funds


Alipay as a One-Stop FinTech App

Imagine if1 Main-street banks2 Wall Street’s brokers3 Boston’s asset managers4 Connecticut’s insurers

all shrunk to fit into1 a single app designed in

Silicon Valleythat almost everyone used.

– The Economist, Oct 8th 2020

Online Shopping Investment

Offline Consumption

Utility/Phone Bill Payment


Motivations

Campbell (2006), AFA presidential address, on the key challenges of the study ofhousehold finance:

▶ household behavior is difficult to measure.▶ households face constraints not captured by textbook models.

With the arrival of FinTech over the past decade:▶ More data from FinTech platforms have been made available to researchers.▶ Compared with other super-cool data (Swedish, Finnish, Charles Schwab, etc),the FinTech household data are unique – account-level data generated byindividuals on FinTech platforms, most likely via super apps.

▶ Some of the constraints faced by households might be dissipating.


Our Research Questions

Can FinTech lower investment barrier and improve household risk-taking?▶ Under risk-taking: access costs, lack of familiarity, trust, financial education.▶ FinTech platforms: easy access, low costs, brand recognition, repeated usage.▶ FinTech convenience reduces physical costs, increasing participation.▶ Repeated usage of Alipay builds familiarity and trust, increasing risk-taking.

Who benefits the most from FinTech Advancements?▶ The otherwise more constrained investors prior to the arrival of FinTech:

⋆ Individuals who are more risk-tolerant.⋆ Individuals living in areas under-served by financial institutions.

▶ Individual heterogeneity: risk tolerance inferred from consumption volatility.▶ Geographical variation in financial inclusion: areas under-served by financialinstitutions, using the number of local bank branches as a proxy.


Financial Theory on Household Finance

Optimal portfolio choice and consumption:▶ Mean-variance: Markowitz (1952) and Tobin (1958).▶ Dynamic continuous-time models: Merton (1969, 1971).

Insights from Merton’s portfolio problem:▶ The link between the optimal portfolio weight w∗ and risk-aversion γ

w∗ =1

γ

µ− rσ2R

▶ The link between the optimal consumption volatility σc and risk-aversion γ

σc = σw = w∗ σR =

1

γ

µ− rσR

Beyond Merton: alternative specifications of household utility, stochastic interestrates, and time-varying risk premiums.Luohan Academy Webinar FinTech Adoption and Household Risk-Taking Jun Pan 9 / 33

Key Findings: Can FinTech Improve Household Risk-Taking?

Increased risk-taking with FinTech adoption: moving the FinTech adoptionmeasure from zero to one corresponds to an increase of

▶ 13.6% in risky participation (average=37.5% among 50,000 individuals)▶ 14.0% in risky share (average=45% among 28,393 individuals)▶ 0.52% in portfolio volatility (average=1.77% among 28,393 individuals)

Individual-level increase in risk-taking occurs with individual-level increasein FinTech adoption: tracking the same individual’s FinTech savviness from 2017to 2018, a change from 0 to 1 corresponds to changes of

▶ 1.4% in risky participation▶ 8.7% in risky share

Controlling for risk tolerance: findings remain robust.


Key Findings: Risk-Taking and Consumption Volatility

Higher risk-taking by individuals with higher consumption volatility:▶ Individual consumption volatility as a proxy for risk tolerance.

Components of consumption:▶ Informative: basic consumption and personal development.▶ Non-informative: consumption for enjoyment.

Risk-taking by asset classes:▶ Equity, mixed, index, and QDII funds: positive and significant.▶ Bond fund: positive and insignificant.▶ Gold fund: negative and insignificant.


Key Findings: Who Benefits the Most from FinTech Advancements?

Risk-taking improvement stronger for more risk-tolerant individuals.▶ Individuals with more risk tolerance were otherwise more constrained prior tothe advent of FinTech.

Quantify the FinTech improvement relative the optimal risk-taking.▶ Among investors with high FinTech adoption, the alignment of their σw and σcis substantially closer to the optimal solution of Merton.

Cities under-served by banks benefit more from FinTech penetration.▶ Positive and significant effect of city-level FinTech penetration on risk-taking,controlling for city GDP, population, income, and bank accessibility.

▶ The effect of FinTech penetration on risk-taking is stronger for cities with lowfinancial-service coverage, using the number of local bank branches as a proxy.


Related Literature

Theory: Markowitz (1952), Tobin (1958), and Merton (1969, 1971).Household Finance: Campbell (2006).Risk-Taking and

▶ Consumption Volatility: Mankiw and Zeldes (1991).▶ Familiarity: Hong, Kubic and Stein (2004).▶ Trust: Guiso, Sapienza, and Zingales (2008).

Technology and Investor Behavior:▶ Internet and stock trading: Barber and Odean (2002).▶ FinTech platforms and mutual fund flows: Hong, Lu, and Pan (2020).


Summary Statistics

28,393 Active Users (> 100 RMB Fund Purchases)Consumption FinTech Adoption Risk-Taking

Female

Age

Consum

ption

σC AliFrac

log(A

liCnt)

∆AliFrac

∆log

(AliC

nt)

Particip

ate

RiskySh

are

σW

(%)

Mean 0.61 31.1 2,292 1.21 0.55 3.05 0.08 0.62 0.66 0.45 1.77Median 1.00 30.0 1,396 1.16 0.57 3.12 0.07 0.54 1.00 0.15 0.18Std 0.49 7.8 4,732 0.40 0.22 0.83 0.17 0.76 0.47 0.47 2.97

All 50,000 UsersMean 0.61 30.4 2,155 1.21 0.54 3.01 0.08 0.59 0.38Median 1.00 29.0 1,259 1.16 0.56 3.08 0.07 0.53 0.00Std 0.49 7.8 17,063 0.40 0.22 0.84 0.22 0.67 0.48


Measuring FinTech Adoption

Using individual i’s month-t consumption on Alipay and Taobao:

AliFracit =Alipayit

Alipayit + Taobaoit

2017Q2 2017Q4 2018Q2 2018Q4


Digital Payments via QR-Code Scan


City-Level FinTech Penetration

Average Level of FinTech Penetration 2018 minus 2017


Determinants of FinTech AdoptionAliFrac Log(AliCnt)

All Users Active Users All Users Active UsersσC 0.032*** 0.034*** 0.033*** 0.035*** 0.044*** 0.054*** 0.042*** 0.052***

(11.13) (12.31) (9.47) (10.52) (4.85) (6.31) (3.31) (4.37)Log(C) -0.104*** -0.107*** -0.107*** -0.109*** 0.133*** 0.124*** 0.130*** 0.121***

(-81.59) (-94.95) (-72.19) (-80.70) (19.60) (25.10) (17.15) (20.31)Female -0.054*** -0.050*** -0.055*** -0.051*** -0.170*** -0.160*** -0.190*** -0.180***

(-16.95) (-16.18) (-16.00) (-15.21) (-14.94) (-13.70) (-16.04) (-14.74)Log(Age) 0.000 -0.002 -0.015 -0.017* -0.816*** -0.818*** -0.861*** -0.863***

(0.03) (-0.27) (-1.61) (-1.86) (-23.04) (-28.26) (-23.49) (-26.97)Log(GDP) 0.023** 0.022** 0.123** 0.129**

(2.50) (2.18) (2.17) (2.07)Log(Income) 0.029*** 0.029*** 0.117*** 0.126***

(4.32) (4.45) (3.28) (3.39)Log(Population) 0.006 0.005 0.0138 0.0233

(0.90) (0.71) (0.35) (0.59)Log(#Branch) -0.003 -0.004 0.0194 -0.0077

(-0.35) (-0.34) (0.40) (-0.13)Citylevel=1 -0.059** -0.059** -0.2612** -0.267**

(-2.50) (-2.65) (-2.20) (-2.22)City FE N Y N Y N Y N YR2 0.210 0.208 0.230 0.230 0.0857 0.086 0.096 0.095N 49,087 50,000 27,886 28,393 49,087 50,000 27,886 28,393


Determinants of Change in FinTech Adoption, from 2017 to 2018∆AliFrac ∆Log(AliCnt)

All Users Active Users All Users Active Usersσc -0.010*** -0.010*** -0.008*** -0.009*** -0.054*** -0.053*** -0.056*** -0.054***

(-3.87) (-3.77) (-2.43) (-2.55) (-6.78) (-6.60) (-5.13) (-4.99)Log(C) -0.026*** -0.026*** -0.023*** -0.022*** -0.098*** -0.093*** -0.094*** -0.091***

(-18.29) (-18.19) (-12.74) (-12.57) (-22.82) (-21.56) (-17.18) (-16.45)Female -0.016*** -0.016*** -0.017*** -0.017*** -0.002 -0.002 -0.017** -0.017**

(-6.79) (-6.86) (-5.41) (-5.61) (-0.27) (-0.35) (-2.02) (-2.01)Log(Age) 0.102*** 0.101*** 0.103*** 0.102*** 0.500*** 0.493*** 0.532*** 0.527***

(16.32) (16.26) (14.90) (14.78) (20.46) (20.16) (17.50) (17.28)Log(GDP) -0.004 -0.009** -0.0302** -0.0485***

(-1.61) (-2.54) (-2.44) (-3.06)Log(Income) -0.005*** -0.005** -0.041*** -0.041***

(-3.24) (-2.55) (-5.37) (-4.86)Log(Population) 0.002 0.001 0.0023 0.009

(1.10) (0.61) (0.33) (1.11)Log(#Branch) -0.006** 0.002 0.0076 0.0221

(-2.10) (0.45) (0.49) (1.15)Citylevel=1 0.005 0.003 0.079*** 0.080***

(1.31) (0.68) (3.01) (2.89)City FE N Y N Y N Y N YR2 0.021 0.021 0.019 0.019 0.0403 0.040 0.046 0.046N 49,087 50,000 27,886 28,393 49,087 50,000 27,886 28,393


Mutual Funds in China

Mutual Funds in China

Yu'ebao 2013/6/13

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

0

2

4

6

8

10

12

14

16

18

Mut

ual F

und

AU

M (

RM

B tr

illio

n)

Equity+Mixed Bond Money Market

08Q1

08Q3

09Q1

09Q3

10Q1

10Q3

11Q1

11Q3

12Q1

12Q3

13Q1

13Q3

14Q1

14Q3

15Q1

15Q3

16Q1

16Q3

17Q1

17Q3

18Q1

18Q3

19Q1

19Q3

0

10

20

30

40

50

60

70

80

90

100

Frac

tion

of F

unds

(%)

←Ant entersPlatforms

Yu'ebao→

Brokers

Banks

Platform Coverage of Actively-Managed Mutual FundsShumi+AntTiantianHowbuyTong Huashun

Source: “FinTech Platforms and Mutual Fund Distribution” by Hong, Lu, and Pan (2020)


Mutual Fund Purchases

Our Sample Market-Wide


Household Consumption

2016 2017 2018 2019 20204

6

8

10

12

14

16

18

20

22

24Monthly Consumption Growth Volatility (%)

Feb

Taobao (our sample)Alipay (our sample)Online (economy wide)Offline (economy wide)

Fraction of Taobao Consumption (%)


10

15

20

25

30

35

40

45

50

BasicEnjoyDevelopment


FinTech Adoption and Risk-Taking

Participate

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tech-Savviness

20

25

30

35

40

45

Ris

ky P

artic

ipat

ion

(%)

Participation i = 29.27 + 15.32 Tech-Savviness i ; R2 = 79%.[13.54]

Tech-Savviness sorted groups

Risky Share

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tech-Savviness

36

38

40

42

44

46

48

50

52

54

Ris

ky S

hare

(%

)

Risky Share i = 38.24 + 12.9 Tech-Savviness i ; R2 = 71%.[11.00]


Portfolio Volatility σW

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tech-Savviness

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

2.2

Por

tfolio

Vol

atili

ty

W (

%)

Wi = 1.53 + 0.43 Tech-Savviness i ; R2 = 38%.

[5.52]




Individual Risk-Taking on Individual FinTech SavvinessParticipate Risky Share Portfolio Volatility (σw)

Tech-Savviness 0.127*** 0.239*** 0.131*** 0.146*** 0.431*** 0.446***(10.47) (17.94) (7.65) (7.80) (4.76) (4.59)

σc 0.037*** 0.019*** 0.052*** 0.018*** 0.345*** 0.163***(7.37) (3.69) (7.87) (2.72) (8.43) (4.07)

Log(C) 0.076*** 0.031*** 0.128***(30.06) (9.03) (5.46)

Female -0.067*** -0.102*** -0.542***(-12.24) (-15.12) (-15.52)

Log(Age) 0.007 -0.171*** -0.861***(0.57) (-11.11) (-10.50)

City FE Y Y Y Y Y YAdjusted R2 0.004 0.024 0.006 0.025 0.004 0.016N 50,000 50,000 28,393 28,393 28,393 28,393



Risk-Taking on FinTech Savviness, Change on Change∆Participate ∆Risky Share ∆Trade Propensity

∆Tech Savviness 0.014** 0.087*** 0.025***(2.08) (5.30) (6.60)

σc 0.009** -0.010 0.000(2.23) (-1.32) (0.09)

Log(C) 0.013*** 0.000 -0.002**(8.25) (0.10) (-2.13)

Female -0.025*** -0.004 0.000(-8.31) (-0.68) (-0.076)

Log(Age) -0.041*** 0.012 -0.005**(-5.98) (0.98) (-2.05)

City FE Y Y YAdjusted R2 0.004 0.154 0.001N 50,000 28,393 50,000


FinTech Adoption and Risk-TakingRisk-Taking on FinTech Savviness, Conditioning on Individual Heterogeneity

Participate Risky Share Portfolio Volatility σwTech-Savviness×σc 0.061** 0.047* 0.029 0.015 0.434** 0.381**

(2.45) (1.86) (1.01) (0.51) (2.32) (2.01)Tech-Savviness×Log(C) 0.037*** 0.004 0.048

(3.40) (0.27) (0.55)Tech-Savviness×Female -0.063*** -0.036 -0.130

(-2.84) (-1.27) (-0.69)Tech-Savviness×Log(Age) -0.113** -0.106* -0.679**

(-2.52) (-1.91) (-2.19)Tech-Savviness 0.165*** 0.341** 0.111*** 0.486** -0.077 2.054

(5.08) (1.96) (2.58) (2.12) (-0.30) (1.55)σc -0.013 -0.005 0.002 0.010 -0.074 -0.043

(-1.04) (-0.41) (0.13) (0.55) (-0.66) (-0.38)Controls & City FE Y Y Y Y Y YAdjusted R2 0.024 0.024 0.025 0.036 0.016 0.016N 50,000 50,000 28,393 28,393 28,393 28,393


Optimal Alignment of Consumption and Investment

All Active Users

1 2 3 4 5 6 7

Normalized Consumption Volatility C

6

7

8

9

10

11

12

13

Nor

mal

ized

Por

tfolio

Vol

atili

ty

W

Wi = 6.54 + 0.79

Ci ; R2 = 62%.

[9.02]

C sorted groups

High and Low FinTech Adoption

1 2 3 4 5 6 7

Normalized Consumption Volatility C

7

8

9

10

11

12

13

Nor

mal

ized

Por

tfolio

Vol

atili

ty

W High FinTech: Wi = 6.23 + 0.91

Ci ; R2 = 77%.

[8.86]

Low FinTech: Wi = 6.65 + 0.58

Ci ; R2 = 45%.

[4.42]

High FinTech AdoptionLow FinTech Adoption


Which Component of Consumption is More Informative?

Dependent Variable: Portfolio Volatility σwBasic Development Enjoy

Tech-Savviness 0.438*** 0.143 0.467*** -0.103 0.464*** 0.151(4.43) (0.78) (4.80) (-0.31) (4.76) (0.45)

σc 0.049** -0.032 0.052** -0.053 0.025 -0.034(2.50) (-0.60) (2.37) (-0.87) (1.25) (-0.56)

Tech-Savviness×σc 0.147* 0.193* 0.112(1.72) (1.85) (1.00)

Log(C) -0.555*** -0.554*** -0.569*** -0.568*** -0.578*** -0.578***(-15.23) (-15.24) (-16.53) (-16.54) (-16.61) (-16.62)

Female -0.855*** -0.856*** -0.860*** -0.861*** -0.882*** -0.881***(-10.51) (-10.54) (-10.59) (-10.60) (-10.61) (-10.59)

Log(Age) 0.127*** 0.128*** 0.110*** 0.108*** 0.125*** 0.123***(5.46) (5.46) (4.63) (4.52) (5.32) (5.30)

City FE Y Y Y Y Y YAdjusted R2 0.0156 0.0157 0.0155 0.0157 0.0154 0.0155N 28,393 28,393 28,393 28,393 28,393 28,393


Improved Risk-Taking in Which Asset Classes?

Dependent Variable: Participation in Asset ClassBond Mixed Equity Index QDII Gold

Tech-Savviness 0.040*** 0.015 0.149*** 0.115*** 0.045*** 0.021 0.072*** 0.046*** 0.024*** 0.002 0.140*** 0.115***(7.37) (0.96) (14.03) (4.67) (7.96) (1.56) (9.85) (2.75) (6.93) (0.17) (18.11) (6.23)

Tech-Savviness×σc 0.021* 0.028 0.019** 0.021 0.019** 0.021(1.79) (1.38) (1.89) (1.57) (2.21) (1.43)

σc 0.003 -0.008 0.020*** 0.006 0.011*** 0.001 0.016*** 0.004 0.004** -0.006 -0.004 -0.016*(0.95) (-1.24) (4.60) (0.53) (3.68) (0.11) (4.75) (0.57) (2.15) (-1.34) (-1.09) (-1.75)

Log(C) 0.016*** 0.016*** 0.054*** 0.054*** 0.017*** 0.017*** 0.030*** 0.030*** 0.010*** 0.010*** 0.030*** 0.030***(12.66) (12.65) (25.71) (25.72) (13.18) (13.21) (13.66) (13.64) (11.16) (11.16) (15.32) (15.33)

Female -0.003 -0.003 -0.048*** -0.048*** -0.020*** -0.020*** -0.039*** -0.039*** -0.012*** -0.012*** -0.045*** -0.045***(-1.12) (-1.08) (-10.68) (-10.58) (-8.14) (-8.11) (-12.77) (-12.76) (-6.81) (-6.74) (-14.77) (-14.76)

Log(Age) 0.022*** 0.022*** 0.039*** 0.039*** 0.001 0.001 -0.013 -0.013 0.001 0.001 -0.034*** -0.034***(4.98) (4.97) (3.65) (3.65) (0.22) (0.21) (-1.55) (-1.56) (0.39) (0.37) (-5.08) (-5.10)

City FE Y Y Y Y Y Y Y Y Y Y Y YR2 0.0036 0.0037 0.0186 0.0187 0.0058 0.0059 0.0107 0.0107 0.0048 0.0049 0.0153 0.0154N 50,000 50,000 50,000 50,000 50,000 50,000 50,000 50,000 50,000 50,000 50,000 50,000


Which Cities Benefit More from FinTech Advancements?

Level

0.3 0.4 0.5 0.6 0.7City Tech-Penetration

0

10

20

30

40

50

60

70

80

90

100

Risk

y Sh

are

(%)

Low Coverage: Risky Share=0.28+0.57 Tech-Penetration [2.62]; R2 =5%

High Coverage: Risky Share=0.55+0.01 Tech-Penetration [0.07]; R2 =0%

Low Bank CoverageHigh Bank Coverage

Change

0.02 0.00 0.02 0.04 0.06 0.08 0.10Change in City Tech Penetration

-50

-35

-20

-5

10

25

40

55

70

Chan

ge in

Risk

y Sh

are

(%)

Low Coverage: ∆ Risky Share=-0.11+2.33∆ Tech Penetration[2.82]; R2 =8%

High Coverage: ∆ Risky Share=-0.01-0.74∆ Tech Penetration [1.90]; R2 =3%

Low Bank CoverageHigh Bank Coverage



Individual Risk-Taking on City-Level Tech-PenetrationParticipate Risky Share Portfolio Volatility σw

Tech-Penetration 0.272*** 0.212*** 0.207*** 0.304*** 0.310** 0.254* 0.759** 0.676 0.594(3.51) (2.92) (2.84) (2.64) (2.25) (1.89) (2.17) (1.60) (1.41)

Tech-Penetration*Log(#Branch) -0.048 -0.546*** -0.803**(-0.69) (-4.26) (-2.00)

Log(#Branch) -0.002 0.02 -0.018 0.229*** -0.033 0.331*(-0.26) (0.62) (-1.52) (3.88) (-0.89) (1.78)

Log(GDP) 0.01 0.011 -0.018 -0.006 0.007 0.024(1.04) (1.13) (-0.99) (-0.33) (0.12) (0.44)

Log(Population) -0.009 -0.009 0.033*** 0.034*** 0.01 0.01(-1.36) (-1.35) (2.73) (2.84) (0.26) (0.28)

Log(Income) 0.010* 0.011** 0.013 0.024** 0.047 0.063*(1.87) (1.98) (1.30) (2.36) (1.48) (1.94)

CityLevel=1 0.037 0.042 -0.011 0.042 0.009 0.088(0.95) (1.05) (-0.15) (0.58) (0.04) (0.38)

Constant 0.206*** 0.234*** 0.238*** 0.398*** 0.395*** 0.437*** 0.847*** 0.887*** 0.948***(5.21) (6.66) (6.69) (7.10) (5.91) (6.65) (4.98) (4.32) (4.59)

Observations 287 287 287 287 287 287 287 287 287R-squared 0.065 0.113 0.115 0.024 0.058 0.116 0.016 0.03 0.044



Individual Risk-Taking on City-Level Tech-Penetration, Change on Change∆Participate ∆Risky Share ∆Trade Propensity

∆Tech-Penetration 0.149* 0.090 1.469** 0.400 0.084** 0.056(1.76) (1.07) (2.44) (0.99) (2.03) (1.40)

Log(#Branch) -0.002 0.005 -0.019 0.103*** 0.000 0.003(-1.53) (1.25) (-1.19) (3.63) (-0.40) (1.27)

∆Tech-Penetration*Log(#Branch) -0.150* -2.714*** -0.071*(-1.81) (-4.79) (-1.68)

Log(GDP) 0.004* 0.003 -0.018 -0.034* -0.001 -0.001(1.66) (1.39) (-0.92) (-1.78) (-0.69) (-1.02)

Log(Population) -0.002 -0.002 0.019* 0.017* 0.000 0.000(-1.10) (-1.13) (1.77) (1.67) (0.14) (0.08)

Log(Income) 0.001 0.001 0.022* 0.015 0.000 0.000(0.91) (0.60) (1.84) (1.53) (0.15) (-0.06)

CityLevel=1 0.004 0.001 -0.001 -0.053** 0.004** 0.002(1.08) (0.30) (-0.04) (-2.25) (1.97) (1.32)

Constant 0.052*** 0.054*** -0.079*** -0.046*** -0.002 -0.001(15.04) (16.00) (-3.86) (-3.16) (-0.94) (-0.42)

Observations 287 287 287 287 287 287R-squared 0.034 0.042 0.105 0.207 0.036 0.045


Conclusions

We study how FinTech can help households move toward optimal risk-taking:▶ FinTech adoption improves risk-taking, more for risk-tolerant individuals.▶ FinTech can help improve the alignment of risk-taking and consumption.▶ Cities with low banking coverage benefit more from FinTech penetration.

Interpretations of our findings:▶ FinTech convenience reduces physical costs, increasing participation.▶ Repeated usage of Alipay builds familiarity and trust, increasing risk-taking.

Future of FinTech:▶ Brighter for emerging economies lacking financial infrastructures.▶ From Tech to Fin, more content building.


Documents

FinTechAdoptionandHouseholdRisk-Takingen.saif.sjtu.edu.cn/junpan/slides_FinTech.pdfLuohan Academy Webinar FinTech Adoption and Household Risk-Taking Jun Pan 5 / 33 AlipayasaOne-StopFinTechApp