1 / 12

Impact of Socioeconomic Standing on Identity Theft in Metropolitan Areas

Impact of Socioeconomic Standing on Identity Theft in Metropolitan Areas. Casey Crowley cc6560a@student.american.edu http://www.eagle1.american.edu/~cc6560a/ American University School of International Service SIS 600.005 – Dr. Assen Assenov. Research Question:

butch
Download Presentation

Impact of Socioeconomic Standing on Identity Theft in Metropolitan Areas

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Impact of Socioeconomic Standing on Identity Theft in Metropolitan Areas Casey Crowley cc6560a@student.american.edu http://www.eagle1.american.edu/~cc6560a/ American University School of International Service SIS 600.005 – Dr. AssenAssenov

  2. Research Question: • What types of people are victims of Identity Theft? Do certain demographics play a role in Identity Theft? Does the type of Industry available in the area play a role in Identity Theft Rates? Research Hypothesis: • The Income, Socioeconomic Standing, Education and Industry of a metropolitan area have a relationship to Identity Theft Rates in that area.

  3. Topics in Identity Theft • Phishing, Pharming and Identity Theft Brody, R.G., Mulig, E., & Kimball, V. (2007) • Phishing: Lure people into giving up their information voluntarily • Pharming: Inserting viruses into links and downloadable material • Trusting the Internet: Cues Affecting Perceived Vulnerability Wogalter, M.S., & Mayhorn, C.B. (2008) • Looks at how trust affects vulnerability • Those who use the Internet more often are more capable of discerning good website from bad websites.

  4. Data Used Unit of Analysis: US Metropolitan Areas Data Sources: • Federal Trade Commission 2009 Identity Theft Report • US Census Bureau 2009 Current Population Survey Reliability: Data available for 340 out of 390 Metropolitan Areas available for analysis Types of Variables: All variables used are Interval-Ratio variables

  5. Variables Dependent Variable: Reported ID Theft Rates (per 100,000) Independent Variables: Age Groups – As a Percentage of the Population Race – As a Percentage of the Population Income Levels – # of Households at a Certain Income Level Industry – Percentage of the Area involved in that Industry Education – Percentage with a Certain Education Level Poverty – Percentage Living in Poverty ForeignBorn – As a Percentage of the Population Speaking Other than English at Home- As a Percentage of the Population Banks – Number Available and Amount (in Millions) Deposited

  6. Scatter Plot Analysis No Correlation Insufficient Data Scatter Plot Analysis allowed variables with no correlation to be dropped. Additionally, some variables had insufficient data for further analysis. Variables dropped included: Industry Variables Bank Variables Income Variables Some Race Variables Correlation!

  7. Descriptive Statistics *Some variables were dropped after Scatter Plot Analysis

  8. Descriptive Statistics (Cont.)

  9. Bivariate Analysis Alpha = .05 Variables that were not statistically significant and were not included in this table include 15-24, 35-44, Asian, Hawaiian/Pacific Islander andAmerican Indian/Alaskan Native.

  10. Bivariate Analysis (cont.) • Alpha=.05 • Areas with higher percentages of poverty have higher rates of ID Theft. • Foreign Born has a strong positive correlation. Speaking other than English at Home has the strongest positive correlation. • High School Diploma has a strong negative correlation. This drops off significantly when looking at the Bachelor’s degree variable. This suggests that some education has a strong correlation on ID theft rates, but that a lot of education does not.

  11. Regression Analysis * Alpha = .05 The Dependent Variable is ID Theft Rate

  12. Findings and Policy Implications • HR: The Income, Socioeconomic Standing, Education and Industry of a metropolitan area have a statistically significant correlation to Identity Theft Rates in that area. • The H0 can be rejected as some variables DO have a statistically significant correlation to Identity Theft Rates. • Income and Industry have no statistically significant correlation to Identity Theft Rates. • Areas with high percentages of minorities and people with foreign connections have higher rates of Identity Theft. • Areas that are largely Caucasian, have slightly older populations, larger percentages of at least a High School Diploma have lower rates of Identity Theft. • Obviously many of these variables cannot be changed. However, these findings suggest that a focus on education, especially at the High School level, can potentially lead to better protection against Identity Theft.

More Related