Cryptocurrency Regulation: Insights from Demographics, Crime Rates, and Traditional Banking Público

Abstract

This paper explores the predictors of cryptocurrency ownership using the U.S. Survey of

Consumer Payment Choice and crime data from the Federal Bureau of Investigation. We

find that adoption of financial technology, being Asian or other race, and moving from high

school to Bachelor’s degree are positive predictors of cryptocurrency ownership. Female,

Hawaiian, and older Americans are less likely to own cryptocurrency. When predicting

ownership of specific cryptocurrencies, we see that these predictors fluctuate, suggesting

that certain groups of investors prefer different coins. The popularly held belief that theft

and cryptocurrencies are positively interlinked is confirmed, but violent crime is negatively

associated. Lastly, we find that paying a credit card or bank account fee in the last year

that indicates financial illiteracy has a positive effect on cryptocurrency ownership.

Table of Contents

1 Introduction 1

1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Economic Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.1 Cryptocurrency Origins . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.2 Current Cryptocurrency Regulation . . . . . . . . . . . . . . . . . . . 4

1.3.3 Cryptocurrency Regulation Prescriptions . . . . . . . . . . . . . . . . 5

2 Data 6

2.1 Survey of Consumer Payment Choice . . . . . . . . . . . . . . . . . . . . . . 6

2.2 National Incident-Based Reporting System . . . . . . . . . . . . . . . . . . . 8

2.3 US Census Bureau Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Descriptive Statistics 8

3.1 Variable Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.2 US State Heat Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4 Methodology 21

4.1 Data Cleaning and Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2 Preliminary Regression Methods . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2.1 Linear Probability Model . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2.2 Logit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.2.3 Probit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.3 ROC Curves and AUC Calculations . . . . . . . . . . . . . . . . . . . . . . . 26

4.4 Control Variable Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.5 Multinomial Logit of Different Cryptocurrency Ownership . . . . . . . . . . 27

4.6 Fee Payments and Financial Riskiness . . . . . . . . . . . . . . . . . . . . . . 27

4.7 Connecting Crime and Survey Data . . . . . . . . . . . . . . . . . . . . . . . 28

4.8 Copula Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.9 Other Instrument Variable Tests . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.9.1 Biprobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.9.2 Two Stage Residual Inclusion . . . . . . . . . . . . . . . . . . . . . . 31

4.10 Impact of Crime Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5 Results 32

5.1 Preliminary Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.1.1 Linear Probability Model . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.1.2 Logit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.1.3 Probit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.2 ROC Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.3 Best Specification for Each Model . . . . . . . . . . . . . . . . . . . . . . . . 34

5.4 Control Variables Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.5 Multinomial Logit By Different Cryptocurrency . . . . . . . . . . . . . . . . 35

5.6 Copula Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.7 Other Instrument Variable Tests . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.8 Crime Level Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6 Conclusion 39Appendix 44

Data Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Preliminary Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

LPM Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Logit Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Probit Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

ROC Curve Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Best Specification Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Control Variable Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Multinomial Logit by Different Cryptocurrency . . . . . . . . . . . . . . . . . . . 56

Crime Rates Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

About this Honors Thesis

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School	Emory College
Department	Economics
Degree	B.A.
Submission	Honors Thesis
Language	English
Research Field	Economics, General Economics, Finance
Palavra-chave	digital assets regulation cryptocurrency
Committee Chair / Thesis Advisor	David Jacho-Chavez, Emory University
Committee Members	Shomu Banerjee, Emory University Jillian Grennan, Emory University

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