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Willingness to share personal information.
We want to estimate what is the effect of the use of PETs on the willingness to share personal information. We construct the latter variable using one specific question from the survey. The survey questions the respondents about their willingness to disclose six types of personal information (name, telephone number, ID number, bank card and account numbers, health information and personal information like tastes and preferences) with seven different types of actors that are: local territories, State services, banks, operators, online retailers, Internet actors like Google or Microsoft and finally social networks. Two dimensions can therefore be analyzed: whether the respondent wants to disclose to a specific actor or not, and whether the respondent is ready to disclose a specific information or not.
With whom are you ready to share these personal information? We consider the willingness to supply personal information by an individual to be the sum of information he or she is willing to share with all the actors. Consequently, the amount of thiswillingness to share ranges from 0 to 49. It is important to notice that this question only relates to the willingness to disclose personal information, and does not allow a measurement of the actual level of disclosure. For example, while it is impossible not to share banking details with the bank, a respondent could still prefer not to share this information with his bank. However, a survey conducted among 2000 representative French Internet users shows that there is little difference between the willingness t disclose and the actual disclosure.8 This table allows us to measure the willingness to disclose of all the respondents, whether this willingness translates into actual disclosure or not. This question has remained the same from 2009 to 2015, we are therefore able to study the evolution of the willingness to disclose over this period with cross-sections.
Survey and data description
We use a survey conducted in May 2015 by ACSEL/Caisse des Dépôts on a sample of 1,000 French Internet users aged 15 years and older. The survey was drawn from access panels (directories of people willing to participate in surveys on a regular basis). The sample is representative of the French Internet population (in terms of age, sex, socioeconomic classification,
urban areas and Internet use). The survey has been conducted using online questionnaires. The main objective of the survey is to measure the level of trust of Internet users in several online services (bank, administration, etc.). The survey is divided into several parts that deal with Internet access and use, e-commerce, payment instruments, online banking, online communication (chats, blogs, etc.), social networks, online administration, cloud services, Internet of things, security and authentication, personal data and privacy. We focus our empirical study on the questions related to e-commerce (frequency of purchase, average monthly spending, trust in online retailers, security and privacy policy, etc.) and payment instruments. We now describe these questions in more detail.
French e-commerce is one of the most developed in Europe. In 2014, 34.7 millions of online consumers (79 percent of French Internet users) spent 57 billion euro with 164,000 online retailers (FEVAD, 2015). In our survey, 81 per cent of the respondents (811 respondents) in 2015 report to have made at least one online purchase during the last 12 months. Figure 3.1 displays the distribution of the frequencies of online purchases. 49 per cent of respondents purchase more than once per month but less than once per week. Overall, 62 per cent of the online consumers claim to make more than one purchase per month.
Econometric model
In this section, we test whether the use of a non-bank payment instrument, when controlling for various other effects, positively influences the frequency of online purchases. The dependent variable, « Frequency of online purchases », is a categorical variable with four categories: « less often than once per month », « more than once per month », « once per week », and « several times per week ». Our main variable of interest w is non-bank, a binary variable indicating whether or not a consumer has used at least one non-bank payment instrument in online purchases (as described in Section 3). There is a potential endogeneity issue if the use of a non bank-payment instrument is correlated with unobservable variables that influence the frequency of online purchases y, such as cash used in online transactions or direct money transfers via online accounts for example. In this case, the coefficients estimated by Ordinary Least Squares (OLS) can be biased. We take into account the potential endogeneity by specifying a system of two equations, that explicitly models the use of a non-bank payment instrument for a consumer i.
Table of contents :
CHAPTER 1: GENERAL INTRODUCTION
CHAPTER 2: ONLINE PRIVACY CONCERNS, PRIVACY-ENHANCING TECHNOLOGIES AND THE WILLINGNESS TO SHARE PERSONAL INFORMATION
2.1 Introduction
2.2 Related literature
2.3 Data description
2.3.1 Source of the data
2.3.2 Willingness to share personal information.
2.4 Model and findings
2.4.1 The model
2.4.2 Findings
2.5 Conclusion
2.6 Appendix 1: OLS estimations
2.7 Appendix 2: Brant test
2.8 Appendix 3: Descriptive statistics
CHAPTER 3: PAYMENT INSTRUMENTS, FINANCIAL PRIVACY AND ONLINE PURCHASES
3.1 Introduction
3.2 Related literature
3.3 Survey and data description
3.4 Econometric model
3.5 Estimation results
3.6 Conclusion
3.7 Appendix: descriptive statistics
CHAPTER 4: FINANCIAL PRIVACY, SCREENING AND PAYMENT INSTRUMENTS
4.1 Introduction
4.1.1 Example 1: American Express
4.1.2 Example 2: OpenBanking
4.1.3 Example 3: FICO Score XD and FinTechs
4.2 Related literature
4.3 Model set-up
4.3.1 Borrowers
4.3.2 The bank
4.3.3 Expected utility functions
4.3.4 Timing
4.4 Case 1: Lending decisions according to a screening technology
4.4.1 Stage 2: lending decisions and payoffs
4.4.2 Stage 1: choice of payment instrument by borrowers
4.5 Case 2: Screening-based lending decisions with financial privacy concerns
4.5.1 Stage 2: lending decisions and payoffs
4.5.2 Stage 1: choice of payment instrument by borrowers
4.6 Case 3: Lending decisions without payment data based screening
4.6.1 Stage 2: lending decisions and payoffs
4.6.2 Stage 1: choice of payment instrument by borrowers
4.7 Conclusion
CHAPTER 5: CONCLUSION
BIBLIOGRAPHY .
