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- v.17; 2020 Mar
Risk and protective factors of identity theft victimization in the United States
a University of Toronto, Factor-Inwentash Faculty of Social Work, 246 Bloor Street West, Toronto, Ontario, M5S1V4, Canada
b University of Minnesota, Twin Cities, School of Social Work, 105 Peters Hall, 1404 Gortner Ave., St. Paul, MN, 55108, USA
c RTI International, Division for Applied Justice Research, 701 13th Street NW, Washington DC, 20005, USA
- • Identity theft is a pervasive problem and a public health issue.
- • Frequent online purchasing behaviors result in greater risk of identity theft.
- • Corporate and government data breaches put consumers at risk for identity theft.
- • Risk factors vary by identity theft subtype.
- • Routine individual preventative behaviors can mitigate identity theft risk.
Identity theft victimization is associated with serious physical and mental health morbidities. The problem is expanding as society becomes increasingly reliant on technology to store and transfer personally identifying information. Guided by lifestyle-routine activity theory, this study sought to identify risk and protective factors associated with identity theft victimization and determine whether individual-level behaviors, including frequency of online purchasing and data protection practices, are determinative of victimization. Data from sequential administrations of the U.S. National Crime Victimization Survey–Identity Theft Supplement (ITS) in 2012 and 2014 were combined (N = 128,419). Using multivariable logistic regression, risk and protective factors were examined for three subtypes: 1) unauthorized use of existing credit card/bank accounts, and unauthorized use of personal information to 2) open new accounts, or 3) engage in instrumental activities (e.g., applying for government benefits, receiving medical care, filing false tax returns). Existing credit card/bank accounts and new accounts identity theft victimization were associated with higher levels of online purchasing activity and prior identity theft victimization. All identity theft subtypes were associated with government/corporate data breaches and other crime victimization experiences. Routine individual-level preventive behaviors such as changing online passwords and shredding/destroying documents were protective. Identity theft subtypes showed divergent socio-demographic risk/protective profiles, with those of higher socioeconomic status more likely to be victims of existing credit card/bank account identity theft. Identity theft is a pervasive, growing problem with serious health and psychosocial consequences, yet individuals can engage in specific protective behaviors to mitigate victimization risk.
Identity theft – defined as the intentional, unauthorized use of a person’s identifying information for unlawful purposes ( Federal Trade Commission, 1998 , Koops and Leenes, 2006 ) – is a growing public health problem. While identity theft is not a new crime, the magnitude of the problem has increased with society’s growing reliance on the electronic transfer and storage of personal information across all forms of commerce and services. Approximately 10% of U.S. adults experienced identity theft in 2016, up from 7% in 2012 ( Harrell, 2019 ), and consumer agencies have seen recorded complaints about identity theft increase almost five-fold since 2001 ( Federal Trade Commission, 2017 ). Even routine, mandatory interactions with government (e.g., filing taxes) and healthcare systems (e.g., health records) involve the online transfer and storage of highly identifiable information, such as social security and medical ID numbers, expanding opportunities for identity thieves to illegally obtain personal information ( Myers et al., 2008 ).
In addition to the rising incidence of identity theft, there is growing recognition of the negative emotional and physical health consequences of financial crimes. One in 10 identity theft victims, roughly 2.6 million people, reported experiencing severe emotional distress following victimization ( Harrell, 2019 ). A quarter of identity theft victims experienced sleep problems, anxiety, and irritation six months after the crime ( Sharp et al., 2004 ), with older adults and minorities experiencing more severe emotional consequences including depression, anger, worry, and sense of vulnerability ( Golladay and Holtfreter, 2017 ). While not specific to identity theft, Ganzini and colleagues ( 1990 ) found significantly higher rates of depression and anxiety among financial crime victims compared to demographically-matched controls. Financial crimes have also been associated with increased rates of hospitalization ( Dong and Simon, 2013 ) and all-cause mortality ( Burnett et al., 2016 ). Identity theft also diminishes public confidence in government and corporate entities, prompting increasingly restrictive access to government databases designed to promote public health research ( Wartenberg and Thompson, 2010 ).
The large number of high-profile data breaches in the 21st century (e.g., Equifax, Yahoo, Anthem, U.S. Office of Personnel Management) introduce the question of whether individual-level characteristics and behaviors affect the risk of identity theft victimization, or whether victimization risk is entirely contingent on corporate and government-level data security practices. Combining 2012 and 2014 data from the Bureau of Justice Statistics’ (BJS) nationally representative National Crime Victimization Survey – Identity Theft Supplement (NCVS-ITS), the current study provides a comprehensive examination of identity theft victimization risk and protective factors across three major identity theft subtypes: 1) Unauthorized use of existing credit card(s) and/or bank account(s) and; Unauthorized use of personal information to 2) open new account(s); or 3) engage in instrumental activities. Although the BJS provides basic descriptive and bivariate statistics from the NCVS-ITS with a focus on socio-demographic variables, a multivariable analysis is necessary to identify whether individual-level online routines and lifestyle behaviors affect the probability of victimization above and beyond risk factors that are largely outside of an individual’s control, such as corporate/government-level data breaches. Only through this more comprehensive analysis that isolates the impact of individual behaviors after controlling for other factors can we begin to understand where to effectively allocate security resources to help reduce the frequency and consequences of identity theft. In contrast to BJS reports that combine both “attempted” and “actual” cases of identity theft in analysis, the current study focuses on identity theft victimization and, therefore, includes only cases of actual identity theft (excluding attempted cases).
2. Theoretical framework
The current paper draws on lifestyle-routine activity theory (L-RAT; Cohen and Felson, 1979 , Hindelang et al., 1978 ) which proposes that individual lifestyles and routine activities influence the risk of crime victimization to the extent that they bring a potential target into contact with offenders or affect the availability of protective measures to prevent the crime ( Cohen et al., 1981 , Miethe and Meier, 1990 , Hindelang et al., 1978 ). L-RAT originally described crimes involving direct victim-perpetrator contact, such as assault and robbery, yet the theory has been modified for application to internet-based crimes in which the victim and perpetrator do not physically or necessarily instantaneously converge, including financial fraud ( Pratt et al., 2010 ) and identity theft ( Reyns, 2013 , Reyns and Henson, 2016 ).
According to L-RAT, individuals with greater visibility to offenders in unguarded/un-protected settings are more likely to be victimized ( Cohen et al., 1981 ). In the context of cyber crimes, online activity could expose a person’s identifying information to offenders if the device is infected with malware, hacked, or personal data is entered into an unsecure website. Identity theft research has generally supported the hypothesis that engagement in routine online commercial activities, such as banking, shopping, emailing/instant messaging, selling goods, downloading media, or higher overall levels of internet usage, is associated with victimization ( Holtfreter et al., 2014 , Reyns, 2013 , Reyns and Henson, 2016 , Williams, 2016 ). Yet beyond individual online activities, data breaches targeting retailers, healthcare insurers/providers, and government entities that store and transfer personal information may also increase risk of identity theft.
Previous studies examining L-RAT and criminal behavior have found that routine activities account for a substantial portion of the association between crime and socio-demographic characteristics ( Osgood et al., 1996 ). It is unknown whether identity theft victimization is correlated with demographic and socioeconomic characteristics—age, income, education, race, residential setting—given that personal information is often obtained through online channels with no direct victim-perpetrator contact. Yet these characteristics influence socio-cultural lifestyles and patterns of consumption that affect how often individuals use their identifying information and for what purposes. Previous researchers have found a positive relationship between income, educational attainment, and identity theft victimization ( Anderson, 2006 , Reyns, 2013 , Reyns and Henson, 2016 , Williams, 2016 ).
Prior studies have inconsistently found that both females ( Anderson, 2006 ) and males ( Holtfreter et al., 2014 , Reyns, 2013 ) are at greater risk of identity theft victimization. Similarly, different studies have shown that younger adults ( Williams, 2016 ), middle-aged adults ( Harrell, 2015 ), and older adults ( Reyns, 2013 ) are at increased risk of victimization. Rather than considering age as a continuous variable or according to arbitrary cut-offs, the current study examined age according to generational cohorts, which may be more indicative of age-cohort-related lifestyles and routine activity trends. The study also examined age and gender risk profiles separately for each identity theft subtype, as differences in how information is obtained and misused could explain previous mixed findings.
According to L-RAT, people with greater measures of protection or security, including social, physical, or safety measures are at lower risk of victimization ( Cohen et al., 1981 , McNeeley, 2015 , Wilcox et al., 2007 ). In the context of identity theft, behaviors such as installing antivirus software, shredding documents, and routinely changing passwords theoretically reduce opportunities for identity thieves to access personal information. This has received mixed results in the identity theft literature. Reyns and Henson (2016) found that protective computer/internet-based behaviors, such as use of antivirus software, deleting emails from unknown senders, and regularly changing passwords, were not related to identity theft victimization. Williams ( 2016 ) found that some security measures (using only one computer, filtering spam email, installing antivirus software and secure browsing) were associated with lower identity theft victimization, while other measures (changing security settings and passwords) were associated with greater victimization. However, existing identity theft research is limited by study designs that have been unable to determine whether reported protective behaviors were enacted as a general precautionary measure (prior to) or in response to (following) identity theft victimization. The current study only considered protective behaviors reported as general preventive measures and excludes protective behaviors enacted in reaction to a victimization experience.
This study combined cross-sectional data (n = 128,419) from a rotating panel design of consecutive, directly comparable 2012 (n = 64,132) and 2014 (n = 64,287) administrations of the NCVS-ITS ( U.S. Department of Justice, 2012 , U.S. Department of Justice, 2014 ). The broader NCVS study used a two-stage, stratified cluster sample design, representing all U.S. residents age 12 years or older living in housing units or group quarters. The ITS surveys were administered to eligible respondents age 16 or older at the end of their NCVS interviews using computer-assisted personal or telephone interviewing. While the ITS survey collected only data about respondent experiences with identity theft, respondents’ demographic data and their experiences with other types of crime victimization were collected through the broader NCVS survey. The overall NCVS-ITS unit response rates for NCVS households, NCVS persons, and ITS persons in 2012 and 2014 were 68.2% and 66.1%, respectively. Selection bias analysis found little or no bias to ITS estimates due to non-response ( Inter-University Consortium for Political and Social Research, 2012 , Inter-University Consortium for Political and Social Research, 2014 ). Data were weighted to be nationally representative but adjusted back to reflect the original sample size and avoid inflated p-values. Further details on NCVS-ITS methods can be found at www.bjs.gov ( Bureau of Justice Statistics, 2014 ).
3.2. Dependent variables
Consistent with empirically derived recommendations to maximize sensitivity and reduce respondent under-reporting in financial exploitation prevalence research ( Burnes et al., 2017 ), the NCVS-ITS measured identity theft victimization using a series of contextually oriented questions describing specific sub-categories, rather than a single, general self-report assessment question. Dependent identity theft variables include the unauthorized use of: 1) existing credit card and/or bank accounts; 2) personal information to open new accounts (e.g., financial, investment, utilities); and 3) personal information for instrumental purposes (e.g. filing false tax returns, obtaining medical services, applying for a job or government benefits). Because the mechanisms of identity exposure and the purposes of identity misuse differ across these three categories, risk and protective factors were assessed separately in the analysis. Victimization status was limited to respondents reporting identity theft within the previous year (1 = yes, 0 = No).
3.3. Independent variables
3.3.1. risk factors.
Potential risk factors for identity theft included: 1) frequency of online purchasing behavior in the past year (none, up to once per month, up to once per week, up to once per day, more than once per day); 2) prior year breach of personal information stored by a company or government (no = 0, yes [but social security number not exposed] = 1, yes [social security number exposed] = 2); 3) number of other forms of victimization experienced in the past year, such as theft and assault (continuous); and 4) whether the respondent experienced prior identity theft victimization during lifetime (yes = 1, no = 0).
3.3.2. Protective factors
Respondents were asked a series of seven questions (no = 0/yes = 1) designed to capture identity theft-related preventive/protective practices within the previous 12 months. The questions asked about the following behaviors: checked credit report; changed passwords on financial accounts; purchased credit monitoring services or identity theft insurance; shredded or destroyed documents containing personally identifying information; checked bank or credit card statements for unfamiliar charges; used computer security software; or purchased identity theft protection services. An affirmative response to each question triggered a follow-up question asking whether the behavior was enacted in response to a misuse of personal information. To address issues of temporal ordering as it relates to routine protective behaviors, respondents who indicated that a behavior was enacted in response to a victimization event in the past 12 months were coded as a “no” for the preventive behavior. To understand whether the seven binary protective practice items loaded onto one or more dimensional factors, a multiple correspondence analysis (MCA) was conducted, which analyzed the underlying structure of the binary/categorical data ( Greenacre & Blasius, 2006 ). As illustrated in the discrimination measures plot ( Appendix A ), two factors emerged based on whether the protective item was purchased or reflected a routine protective behavior. The purchased factor contained two items—credit monitoring services/identity theft insurance and identity theft protection services. The routine protective behavior factor had five items—checked credit report, changed passwords, shredded/destroyed documents, checked bank/credit card statements, used computer security software. These purchase and routine protective behavior variables (continuous) were entered separately into the models.
Age was operationalized according to generational cohorts to reflect age-related lifestyles that could impact exposure to identity theft: millennials (born 1981–1998), Generation X (born 1965–1980), baby boomers (born 1946–1964), and Silent/Greatest (born before 1945) ( Pew Research Center, 2016 ). Additional socio-demographic characteristics included gender (male/female), marital status (married/partnered vs. not married/partnered), education (high school or less, some college, college degree, advanced degree), annual household income ($0–24,999, $25,000–49,999, $50,000–74,999, $75,000 or more), and race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, non-Hispanic Asian American/Pacific Islander/American Indian/Alaska Native [AAPI/AIAN], other). Other control variables included residential setting (urban, rural) and survey administration mode (in-person, telephone).
3.4. Analytic plan
Risk and protective variables and controls were regressed on each subtype of identity theft using multivariable logistic regression. Model fit was tested using the Omnibus Test of Model Coefficients and the Hosmer-Lemeshow Test. Tolerance and variance inflation factor statistics were used to test for multicollinearity in regression models. The existing credit card/ bank account analysis was limited to respondents who reported having a credit card or bank account. Missing data were managed with a fully conditional specification multiple imputation method using five pooled data sets. Analyses were performed using IBM SPSS version 25. Due to the large sample size, a p-value of less than 0.001 was considered statistically significant.
Table 1 provides a description of the weighted sample of victims across identity theft subtypes. Across identity theft subtypes, victims were proportionally more female, Caucasian, belonged to the Baby Boomer generation, and lived in urban settings. Whereas victims of existing credit card/bank account identity theft tended to belong to higher income households, victims of new accounts and instrumental purposes identity theft tended to belong to lower-income households.
Descriptive characteristics of weighted (sample-size-adjusted) victim samples across identity theft victimization subtypes.
Table 2 presents the prevalence of identity theft victimization overall and by subtype. The prevalence of overall identity theft victimization (any type) was 6.2% in the combined 2012/2014 sample (95%CI = 6.0%–6.3%). The most common form of victimization was existing credit card or bank account identity theft, with a prevalence of 5.6% (95%CI = 5.5%–5.8%).
Identity theft victimization frequencies.
4.1. Risk factors
Table 3 presents results from the multivariable analysis of risk and protective factors of identity theft victimization for each subtype. Higher levels of online purchasing behavior were significantly associated with increasing odds of existing credit card/bank account and new accounts identity theft victimization; those engaging in daily online shopping were more than five times as likely to be victims of existing credit card/bank account identity theft as those not engaging in online purchasing (OR = 5.74, 95%CI = 4.31–7.64). Persons reporting breached personal information from a company or government were significantly more likely to experience identity theft, particularly if social security information was exposed (instrumental purposes: OR = 8.05, 95%CI = 5.66–11.46; new accounts: OR = 3.83, 95%CI = 2.67–5.51; existing credit/bank account: OR = 1.46, 95%CI = 1.26–1.68). Those reporting other NCVS victimizations were between 29% (existing credit/bank account: OR = 1.29, 95%CI = 1.23–1.35) and 46% (new accounts: OR = 1.46, 95%CI = 1.32–1.62) more likely to be victims of identity theft with each successive crime. Individuals with a history of identity theft victimization were 28% more likely to be victimized by existing credit/bank account identity theft in the past year than those with no prior history (OR = 1.28, 95%CI = 1.19–1.37).
Multivariable logistic regression models predicting identity theft victimization.
Note: All multivariable logistic regression models, except the New Accounts model, satisfied the Omnibus Test of Model Coefficients (p < 0.01). All multivariable logistic regression models satisfied the Hosmer-Lemeshow Test (p > 0.05). Across models, independent variables had tolerance of 0.70 or above and variance inflation factor of 1.43 or below, indicating no concern of multicollinearity.
CI = Confidence interval; OR: Odds ratio; SSN: Social Security Number; AAPI/AIAN = Asian American/Pacific Islander/American Indian/Alaskan Native. ***p < 0.001, (two-tailed tests).
4.2. Protective factors
Individuals engaging in a higher number of proactive, routine protective behaviors, such as shredding documents and updating passwords, were between 25% (existing credit/bank account: OR = 0.76, 95%CI = 0.75–0.78) and 35% (new accounts: OR = 0.66, 95%CI = 0.61–0.71) less likely to experience identity theft victimization with each additional protective behavior. Purchasing credit monitoring services and identity theft insurance, however, was associated with significantly higher odds of new accounts (OR = 1.62, 95%CI = 1.28–2.06) identity theft.
4.3. Socio-Demographic controls
Across all identity theft subtypes, baby boomers were most likely to be victims (existing credit/bank account: OR = 1.38, 95%CI = 1.29–1.48; new accounts: OR = 1.70, 95%CI = 1.32–2.20; instrumental: OR = 1.79, 95%CI = 1.32–2.42). Unmarried/un-partnered persons were 63% (OR = 1.63, 95%CI = 1.28–2.09) more likely to experience instrumental forms of identity theft. Higher levels of education were associated with increasingly higher odds of both existing credit card/bank account and new accounts forms of identity theft. Compared to non-Hispanic whites, existing credit/bank account victimization was less likely among Hispanic (OR = 0.85, 95%CI = 0.78–0.93), Black (OR = 0.78, 95%CI = 0.71–0.86), and AAPI/AIAN (OR = 0.78, 95%CI = 0.70–0.87) persons. Persons living in households in the highest income bracket were most likely to experience existing credit/bank account identity theft (OR = 1.38, 95%CI = 1.25–1.52) compared to those in the lowest income households. As a methodological finding, respondents who participated in a telephone rather than in-person interview were significantly less likely to report identity theft victimization.
Approximately 1 out of every 15 adults aged sixteen years or older in the U.S. – over 16 million people – experience some form of identity theft each year. In addition to direct losses, consequences may include damaged credit, legal fees, loss of trust, and health outcomes such as stress, anxiety, and depression ( Harrell, 2015 , Golladay and Holtfreter, 2017 ). Among victims who experienced the misuse of personal information for instrumental purposes, approximately 56% suffered moderate to severe distress, a similar percentage as seen among victims of violence ( Harrell, 2015 ).
As large-scale data breaches have become an unfortunate part of our growing tech-based marketplace, this analysis examined whether online purchasing behavior and personal data security practices affect the risk of identity theft victimization, or whether becoming a victim is largely contingent on corporate and government-level data breaches. Findings provide support for the L-RAT model of victimization which suggests that individual lifestyle routines and degree of protective measures/guardianship influence the likelihood of victimization.
Respondents who stated that their information was part of a large data breach were significantly more likely to report all forms of identity theft, particularly when their social security numbers were exposed. Victims of identity theft for instrumental purposes were eight times as likely to say their social security numbers were exposed in a data breach compared to non-victims, likely because that form of identity theft requires social security numbers to access government benefits and other services. Although it is not possible to assess whether data breaches directly caused identity theft incidents, data breaches were significantly correlated with the misuse of identity information.
L-RAT proposes that routine lifestyle behaviors contribute to crime victimization risk. In the present study, individual risk and protective behaviors were consistent and strong (magnitude) predictors. Similar to findings using a Canadian sample ( Reyns & Henson, 2016 ), increasing levels of online purchasing activity were associated with incrementally higher odds of financial account and new account identity theft. Participating in commercial activities online reflects a major societal innovation and lifestyle shift that has allowed consumers to purchase products conveniently and globally, but entering personal data online entrusts vendors to safely store and manage this data. For example, Holtfreter et al. (2015) found that individuals who placed an order with a company they had never done business with before were significantly more likely to be victims of identity theft. While the NCVS ITS does not ask respondents what online retailers they have made purchases from, it is likely that as the frequency of online shopping increases, the odds of using an unsecured payment portal or having information exposed in a retail data breach increases. Further innovations in online security and payment systems are required to protect users’ information, and future research should explore precisely how online purchasing activities expose personal information.
In support of the guardianship principle of L-RAT, proactive individual behaviors, like shredding personal documents and routinely changing account passwords, significantly reduced the likelihood of identity theft. Unfortunately, the Pew Research Center ( Olmstead & Smith, 2017 ) found that half of U.S. respondents were not educated about everyday security practices. Given that routine safety behaviors reduce risk of identity theft, consumer protection efforts need to focus on educating consumers on the basics of online security. Purchasing external credit monitoring and identity theft protection services did not reduce risk and was related to greater likelihood of new accounts identity theft victimization. Perhaps respondents who purchased these services had some knowledge that their identity may be misused. Another explanation is that some criminal entities have reached a level of sophistication to evolve techniques ahead of current industry protection standards ( Moore et al., 2009 ).
This study found that exposure to other types of crime, as well as prior experiences with identity theft, were associated with a greater risk of identity theft victimization. Personal information may be stolen during the course of other crimes directly (e.g., theft of wallets, bank statements) or indirectly through theft of devices that contain personal information. This result is consistent with financial fraud research—prior fraud victimization increases the odds of re-victimization ( Titus et al., 1995 ). An underground system exists for identity theft where specified pieces of stolen identifying information are bundled and sold to other criminals, thereby increasing the odds that it is used for various identity crimes over time ( Moore et al., 2009 ). Services for identity theft victims should include help contacting the major credit bureaus to place a temporary freeze or fraud alert on credit reports to prevent criminals from opening new accounts with victims’ stolen credentials.
The socioeconomic and demographic risk patterns found in this study were roughly consistent with the predictions of L-RAT. In general, members of Generation X and the baby boomers, now between the ages of 39 and 73, were at the highest risk of most types of identity theft. This likely reflects the socioeconomic capacity and consumption patterns among Generation X and baby boomers relative to millennials. Together, these older generations constitute the bulk of the U.S. workforce and, therefore, have the economic means to engage in consumer activities where identities may be exposed. Longitudinal data is needed to determine whether the association between middle to late adulthood and increased risk of identity theft is indeed due to lifestyles or whether age has an independent effect.
Compared to Hispanic, Black, and Asian respondents, White respondents and those with higher educational attainment experienced significantly higher risk of existing credit card/bank account identity theft. Individuals with higher socioeconomic status have more purchasing power ( Charron-Chénier et al., 2017 ), have more access to credit ( Haushofer & Fehr, 2014 ), own more internet-enabled devices that store and transfer personal information, and are more likely to use credit cards ( Greene & Stavins, 2016 ). In support of L-RAT, this suggests that the association between existing credit card/bank account identity theft and demographic/socioeconomic profiles is related to lifestyle factors where there is greater reliance on these financial instruments, and thus more opportunities for criminals to intercept account information.
While the NCVS Identity Theft Supplement is one of the most comprehensive sources of data on identity theft, the survey likely underestimates the true extent of the problem. First, the NCVS excluded adult sub-populations who may be particularly vulnerable, such as those living with cognitive impairment and/or in institutional settings. Second, the literature on financial fraud victimization finds that people tend to under-report victimization in survey research ( Beals et al., 2015 ), and this self-report error likely extends to the issue of identity theft. Finally, the nonresponse group is likely disproportionately represented by victims who are reluctant to provide personal information in response to a survey. Another limitation of the study was that data on other potentially important behavioral variables, such as the extent of online downloading, online financial account management, types of websites visited, and presence of malware, hacking or phishing events, were unavailable. To better understand risk of identity theft victimization within the L-RAT paradigm, measures are needed to account for system-level security practices among corporate and government entities, but this is beyond the scope of the NCVS.
5.2. Health implications
Identity theft victimization affects tens of millions of Americans each year. Financial exploitation, in general, is associated with major health-related consequences such as increased rates of hospitalization and all-cause mortality. Victims of identity theft experience severe mental/emotional distress, particularly among minority and older adult populations ( Harrell, 2019 , Golladay and Holtfreter, 2017 ). Given the increasing scope of this problem, the development of effective primary prevention strategies is critically needed and should focus on promoting relatively unintrusive and feasible everyday practices such as routinely changing financial account passwords, shredding documents, and checking credit reports and financial statements. The prevalence of this problem indicates that healthcare professionals will encounter patients who are victimized by identity theft on a regular basis. Healthcare settings represent an important place to both recognize vulnerable adults and provide victims with preventive education to mitigate the risk of identity exposure.
This study comprehensively examined the risk of different forms of identity theft victimization in the U.S. Although other research indicates that Americans have inadequate knowledge of cybersecurity practices ( Olmstead & Smith, 2017 ), findings from the current study demonstrated the importance of this knowledge in keeping personal information safe. Yet individual actions alone are not enough. As investment in cybersecurity grows, criminals respond with increasingly sophisticated and evolving techniques such as hacking, malware, and skimming to overcome these controls ( Pontell, 2009 ). Reducing the incidence of identity theft requires greater public/private investment in robust, dynamic data security systems and encryption tools, and more collaboration between criminal justice and law enforcement agencies to investigate and prosecute identity theft crimes.
CRediT authorship contribution statement
David Burnes: Conceptualization, Formal analysis, Data curation, Writing - original draft, Writing - review & editing. Marguerite DeLiema: Conceptualization, Writing - original draft, Writing - review & editing. Lynn Langton: Conceptualization, Methodology, Writing - original draft, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix B Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2020.101058 .
Multiple Correspondence Analysis Discrimination Measures Plot.
Appendix B. Supplementary data
The following are the Supplementary data to this article:
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The Financial and Psychological Impact of Identity Theft Among Older Adults
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Marguerite DeLiema, David Burnes, Lynn Langton, The Financial and Psychological Impact of Identity Theft Among Older Adults, Innovation in Aging , Volume 5, Issue 4, 2021, igab043, https://doi.org/10.1093/geroni/igab043
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Society’s growing reliance on technology to transfer private information has created more opportunities for identity thieves to access and misuse personal data. Research on identity theft specifically among adults aged 65 and older is virtually nonexistent, yet research focusing on victims of all ages indicates a positive association between age, minority status, and more severe economic and psychological consequences.
Identity theft measures come from a sample of more than 2,000 self-reported victims aged 65 and older from the nationally representative National Crime Victimization Survey Identity Theft Supplements administered in 2014 and 2016. Regression was used to examine how socioeconomic status, demographic characteristics, and incident-specific factors relate to how much money is stolen, the likelihood of experiencing out-of-pocket costs, and emotional distress among older identity theft victims.
Older Black identity theft victims were more likely to have greater amounts of money stolen and were more likely to feel distressed by the incident than older White victims. The most disadvantaged older adults living at or below the federal poverty level were significantly more likely to suffer out-of-pocket costs. The length of time information was misused, experiencing subsequent financial problems and problems with friends/family, and the hours spent resolving identity theft were positively associated with emotional distress. Among those aged 65 and older, age was not significantly associated with losses or emotional distress.
Older adults living in poverty need more resources to assist with recovery and reporting identity theft to law enforcement. Limiting the extent of losses from identity theft and reducing the length of time information is misused may reduce the emotional toll of identity theft on older victims.
Translational Significance: More than 7% of older adults are victims of identity theft each year, and a third experience moderate to severe emotional distress following the incident. We find that victims who can least afford it suffer out-of-pocket costs, and that Black and female victims are more likely to report distress. Victim service organizations should pay special attention to these groups and individuals who lack the social capital to advocate for their financial recovery. Greater psychological support is needed to help older adults recover, in addition to training on how to protect their information from future misuse.
There is a growing body of research on the predictors and consequences of financial victimization of older adults. Existing research focuses primarily on two types of victimization—financial abuse/exploitation (a form of elder abuse), in which the perpetrator occupies a position of expected trust like a friend, family member, or caregiver ( Hall et al., 2016 ); and financial fraud and scams, where a stranger uses a false promise or fabricated threat to deceive the victim into paying money ( DeLiema, 2018 ). Limited research to date has examined the impact of the third form of financial victimization—identity theft—on older adults, despite the increasing prevalence of this serious crime ( Harrell, 2019 ).
Identity theft is the intentional, unauthorized use of a person’s identifying information for unlawful purposes ( Federal Trade Commission [FTC], 1998 ). It includes infiltration into a person’s existing accounts, using a person’s identity to open new accounts, or using personal information to obtain instrumental goods and services such as health care and public benefits ( Harrell, 2019 ). Similar to financial fraud, the vast majority of identity theft victims do not have a preexisting personal relationship with the perpetrator. Yet unlike fraud, most incidents do not involve a direct exchange of information or payment. Rather, identifying information is taken and used without the victim’s knowledge or consent, such as through a data breach or malware attack.
Prior research demonstrates that victims experience severe monetary and nonmonetary consequences following financial victimization. Fraud victims report feeling embarrassed and ashamed, angry, stressed, and anxious, with some reporting depression and strained relationships with family and friends ( Button et al., 2014 ; Financial Institution Regulatory Authority 2015 ). Sharp et al. (2003) found that maladaptive psychological and somatic symptoms increased post identity theft victimization. Longitudinal research has demonstrated that elder mistreatment, including financial exploitation, is associated with increased risks of poor mental and physical health outcomes ( Acierno et al., 2017 ), hospitalization ( Dong & Simon, 2013 ), and mortality ( Lachs et al., 1998 ).
Negative financial, social, and emotional outcomes may be more prevalent and severe among older retired victims who lack employment opportunities to make up their losses or who are unable to navigate the process of resolving the incident with financial institutions and credit bureaus. Additionally, because older generations have relatively greater wealth than younger generations ( Gale et al., 2020 ), they may experience higher levels of theft. Indeed, consumer fraud reports indicate that adults in their 80s experience 3–4 times higher median losses per scam ($1,600) than adults aged 20–49 ( FTC, 2020 ).
Using data from 2012 and 2014 National Crime Victimization Survey (NCVS) Identity Theft Supplements (ITS), Burnes et al. (2020) showed that baby boomers were significantly more likely than millennials to be victims of identity theft. Results from the 2016 ITS show that older adults suffered an estimated $2.5 billion in financial losses ( Harrell, 2019 ). In addition to direct losses, other costs include financial and legal troubles and ruined credit. These consequences may be more severe for older adults with physical or cognitive impairments that make it difficult to contact multiple credit bureaus and financial institutions to report identity misuse. Older adults also have lower knowledge of cybersecurity practices to safeguard their identities from continued misuse ( Nicholson et al., 2019 ).
Several recent studies have examined the financial, psychological, and health consequences of identity theft among U.S. adults of all ages. The 2016 NCVS–ITS shows that 12% of victims experienced out-of-pocket costs, with average losses of $690 ( Harrell, 2019 ). Reynolds (2020) found that unmarried victims and those with lower incomes and educational attainment were significantly more likely to experience out-of-pocket costs following identity theft, as were Hispanic/Latino respondents. Age was positively associated with out-of-pocket costs for incidents that involved misuse of bank account information. Reynolds (2020) also found that the risk of out-of-pocket costs differed by the type of identity theft, such that those who experienced misuse of credit card information were significantly more likely to be reimbursed than victims of bank account identity theft.
Using data from the 2012 NCVS–ITS, Randa and Reynes (2020) examined the predictors of emotional distress among all adults. Thirty-two percent of victims reported that the identity theft incident caused them moderate to severe distress. Older adults were significantly more likely to report distress, as were women and those with lower household incomes. The time spent resolving the incident with credit bureaus and financial institutions was also positively related to distress. Using the same data, Golladay and Holtfreter (2017) examined the emotional and physical consequences following identity theft victimization. Similarly, they found that older adults, minorities, and those who suffered higher losses reported an increasing number of emotional consequences—worry/anxiety, anger, depression, vulnerability, feeling unsafe, confused, violated, etc. There was also a negative association between emotional consequences and socioeconomic status, suggesting that those who are better off financially suffer less in the aftermath of victimization.
The current body of research suggests that identity theft victimization has a disproportionate negative impact on older adults and low-income people, but no studies have specifically examined the correlates of financial and psychological consequences among older victims. Using combined data on victims from the 2014 and 2016 NCVS–ITS, we examine how socioeconomic status, demographic characteristics, and other incident-related factors relate to the total amount stolen, out-of-pocket costs, and emotional distress among victims aged 65 and older, controlling for the type of identity theft experienced. Results offer insight into what groups are in greatest need of resources for emotional support and financial recovery, as well as greater identity protection.
This study is restricted to respondents aged 65 and older who reported identity theft victimization occurring in the past 12 months in the 2014 and/or 2016 NCVS–ITS survey ( N = 2,513). These cross-sectional ITS surveys were administered during 6-month periods in each of the years and are consistent in survey content and methodology. They were combined for additional statistical power and more robust estimates. The ITS is administered to respondents aged 16 and older at the end of their NCVS interview using computer-assisted personal interviewing or computer-assisted telephone interviewing. Respondents are asked whether they have experienced different types of misuse of identifying information during the prior year. Those who answer affirmatively are asked to think about the most recent incident and answer more detailed, incident-specific questions about the nature and consequences of the experience.
The current study focuses specifically on the aftermath of identity theft victimization (not attempted identity theft) where the older victim was not in a trust relationship with the perpetrator (financial abuse) and did not willingly provide their personal information to the perpetrator in response to a scam solicitation (fraud). Respondents are asked how long their information was misused before they discovered the identity theft. Those who selected “Not applicable, not actually misused” (i.e., attempted identity theft) were removed from the sample ( n = 50, 1.9%). Victims who experienced identity theft resulting from a scam (i.e., stated that the incident occurred after they responded to a scam email/phone call [ n = 45, 1.7%]) were also excluded. Although the vast majority of respondents did not know the identity of the perpetrator (93%), those who did and reported that it was a relative, caregiver, or someone working in the home, housemate, friend, or neighbor ( n = 68, 2.5%) were excluded to avoid overlap with the definition of financial exploitation/abuse by a trusted individual.
The broader NCVS study uses a two-stage, stratified cluster sample design representing U.S. residents living in housing units or group quarters. The overall NCVS–ITS unit response rate was 66% in 2014 and 61% in 2016. Selection bias analysis found little or no bias to ITS estimates due to nonresponse ( US Department of Justice, 2014 , 2016 ). Data were weighted to reflect a nationally representative sample in regard to age, gender, and race/ethnicity and to compensate for survey nonresponse and aspects of the staged sampling design. Further details on NCVS–ITS methods and the survey instruments can be found at https://bjs.ojp.gov/ .
Total amount stolen.
Respondents reported how much money (in dollars) identity thieves initially obtained in the incident, regardless of whether these losses were ultimately recovered or reimbursed. In nearly a third of the incidents, identity thieves did not obtain any money, but among those who had money stolen, median losses were $200.00 and mean losses were $1,111.04 (standard deviation [ SD ] = 4,877.70). Based on the distribution, values were recoded into four categories: $0 (reference category; 30% of total), $1–100 (25%), $101–500 (21%), and $501 and greater (17%). Approximately 7% ( n = 201) of victims did not know how much money was stolen and were excluded from this analysis.
Out-of-pocket costs are monetary losses that are not reimbursed or recovered following victimization. Because only 7% of older victims experienced out-of-pocket costs, this variable is treated as dichotomous where 0 = no loss and 1 = any loss. Of those who experienced an out-of-pocket cost ( n = 161), the median loss amount is $200.00 with a mean of $1,453.47 ( SD = 9,854.13). Those who did not know whether they suffered out-of-pocket costs (8%) were excluded from this analysis ( n = 209).
On a 4-point Likert scale, respondents were asked to rate how distressing the misuse of their personal information was to them. Responses included “not at all distressing,” “mildly distressing,” “moderately distressing,” and “severely distressing.” Following the convention used in prior studies ( Golladay & Holtfreter, 2017 ; Randa & Reynes, 2020 ), the item was dichotomized such that those who rated their distress as moderate or severe were coded as “1” (34%).
Types of identity misuse.
Because the likelihood of being reimbursed or having funds recovered varies based on the nature of identity theft, types of identity misuse were divided into five categories based on how the respondent answered the ITS victimization screening questions. The reference category is existing credit card account : “During the past 12 months, has someone used or attempted to use one or more of your existing credit cards without your permission?” (yes = 1). Other existing accounts include respondents who said yes to one or both of the following questions: “Has someone, without your permission, used or attempted to use your existing checking or savings account, including any debit or ATM cards?” (yes = 1), and/or “Has someone misused or attempted to misuse another type of existing account such as your telephone, cable, gas or electric accounts, online payment accounts like Paypal, insurance policies, entertainment account like iTunes, or something else?” Before answering the items on existing bank account and credit card identity theft, respondents were first asked if they owned either of these accounts. If not, that particular item was skipped. New accounts identity theft was measured using the question: “Has someone, without your permission, used or attempted to use your personal information to open any NEW accounts such as wireless telephone accounts, credit card accounts, loans, bank accounts, online payment accounts, or something else?” (yes = 1). The fourth category is instrumental identity theft that was measured using the following item: “Has someone used or attempted to use your personal information for some other fraudulent purpose, such as filing a fraudulent tax return, getting medical care, applying for a job or government benefits; giving your information to the police when they were charged with a crime or traffic violation, or something else?” (yes = 1). Multiple types of identity theft were defined as a single incident of information exposure (e.g., a stolen wallet) that results in the multiple types of identity theft as described in the categories above.
Educational attainment was coded as 0 = less than high school, 1 = high school or GED equivalent, 2 = some college/Associate degree, and 3 = Bachelor’s degree or higher. Percent of federal poverty level (FPL) was an ordinal variable that measured a respondent’s household income as a percentage above, at, or below the FPL as determined by the U.S. Department of Health and Human Services. It is a more robust measure than simply using household income because it takes into account the household size. Harrell et al. (2014) provide additional information on how this measure was calculated.
Age was coded continuously. Race was coded as 0 = White, non-Latino; 1 = Black/African American, non-Latino; 2 = Latino; 3 = other race/ethnicity, non-Latino. Sex was 1 = female. Marital status was 1 = married.
Respondents were asked whether they experienced banking and/or credit problems following identity theft and if they were successful in clearing up the financial and credit issues associated with the misuse of their information. Those who said “yes” were coded as 1 = incident resolved. Time to discovery measured how much time passed between when the victim’s information was misused and when they discovered the misuse, where 0 = one day or less, 1 = more than a day but less than a week, 2 = at least a week, but less than 1 month, 3 = 1 month to less than 6 months, 4 = 6 months or more, and 5 = unknown. Time to resolve was measured continuously as the number of hours it took the victim to clear up any financial and/or credit problems associated with identity theft. Respondents were asked if the incident caused them to have significant problems with family members or friends, including getting into more arguments or fights, not feeling they could trust them as much, or not feeling as close to them as before ( Subsequent problems with family/friends ; 1 = yes). They were also asked if they experienced any credit-or banking-related problems as a result of identity theft, such as being turned down for a line of credit, a loan, or a checking account; having to pay a higher interest rate; or having checks bounce ( Subsequent financial and/or credit problems ; 1 = yes). They were also asked if they contacted a bank, credit card company, or other financial institution following the incident ( Contacted financial institution ; 1 = yes). This behavior may also affect whether the victim was able to recover all or a portion of their stolen funds or reverse unapproved charges. Multiple ID theft incidents measured whether the victim experienced other separate incidents of identity theft within the past 12 months (1 = yes), and prior victimization measured whether the respondent experienced identity theft victimization occurring prior to the past 12 months (1 = yes).
Population weights were applied in all analyses. Models were analyzed in SPSS 25 using complex samples procedures to account for the address-based sampling design of the NCVS. Using ordinal regression, the total amount stolen was regressed on demographic and socioeconomic victim characteristics ( N = 2,307). The four levels of the dependent variable were $0 stolen (reference), $1–100, $101–500, and $501 or more. Additional independent variables included the type of identity theft (existing credit card = reference) and whether the victim contacted their financial institution to report the incident. Using logistic regression, out-of-pocket costs were regressed on the same demographic and socioeconomic characteristics, as well as the type of identity theft and whether the victim contacted their financial institution following the incident ( N = 2,302).
In a final logistic regression, emotional distress was regressed on demographic and socioeconomic characteristics, type of identity theft, and other incident-specific factors ( N = 2,160). These additional factors included banking and/or credit problems = 1, incident resolved = 1, time to discovery (ordinal), hours spent resolving incident (continuous), subsequent problems with friends/family = 1, subsequent financial and/or credit problems = 1, multiple identity theft incidents = 1, and prior identity theft victimization = 1. The sample size dropped due to missing responses on the additional independent variables.
Table 1 presents sample characteristics. Approximately half of the identity theft victims surveyed were female (51%) and 65% were married. The mean age was 72 years. Forty-four percent of victims had a bachelor’s degree or higher. The majority lived in a suburban environment (56%), followed by urban (28%) and rural (16%). Eighty-eight percent were White (non-Latino), 5% were Black, and 4% were Latino. In 2016, the federal poverty threshold for a two-person household was an annual household income of less than $16,000. Five percent of older identity theft victims in this sample were at or below 100% FPL (adjusted for their household size), whereas 36% were at or above 501% FPL.
Weighted Sample Characteristics
Total Amount Stolen
Few victim characteristics were associated with the total amount of money stolen (Model 1, Table 2 ). However, older Black victims of identity theft were more likely to have higher dollar amounts stolen than older White victims (odds ratio [OR] = 1.50, 95% confidence interval [CI] = 1.08–2.07, p = .016). Victim age, sex, educational attainment, marital status, area of residence, and poverty level were not associated with the amount stolen. Type of identity theft reported was significant such that experiencing multiple types of identity theft was related to increasing amounts of money stolen relative to existing credit card identity theft (OR = 1.59, 95% CI = 1.11–2.27, p = .012). Amount stolen was negatively associated with contacting a financial institution (OR = 0.41, 95% CI = 0.28–0.61, p < .001), meaning that having less money stolen decreases the odds of contacting a financial institution by nearly 60%.
Factors Associated With Increasing Amounts of Money Stolen and Out-of-Pocket Costs Following Identity Theft
a Incorporates household size.
As given in Model 2, Table 2 , more socioeconomic and demographic characteristics were associated with out-of-pocket costs, which are financial losses that are not reimbursed by financial institutions or recovered by victims. Older victims who identified as single (divorced, widowed, separated, never married) were significantly less likely than older married victims to have out-of-pocket costs (OR = 0.54, 95% CI = 0.34–0.84, p = .007). In addition to experiencing significantly higher amounts stolen, older Black victims showed a trend ( p < .1) toward being more likely to experience out-of-pocket costs (OR = 1.86, 95% CI = 0.90–2.84, p = .09). Victims who identified their race as “other,” which includes the categories of Asian, Pacific Islander, and Indigenous, were significantly more likely to suffer out-of-pocket costs than older White victims (OR = 3.60, 95% CI = 1.69–7.67, p = .001). Those living at or below the FPL (0–100% FPL) were significantly more likely to experience out-of-pocket costs relative to those living at 501% or more FPL (OR = 4.93, 95% CI = 2.50–9.73, p < .001). Relative to those who reported existing credit card identity theft, those who reported other existing account identity theft were significantly less likely to suffer out-of-pocket costs (OR = 0.54, 95% CI = 0.35–0.83, p = .005), suggesting that this type of identity theft is less likely to involve financial losses. Victim age, sex, educational attainment, and urbanicity were not significantly associated with out-of-pocket costs. Unlike the total amount stolen, whether the victim contacted their financial institution was not significant for out-of-pocket costs.
Table 3 presents the results of emotional distress regressed on victim demographic and socioeconomic characteristics, along with incident-related factors that may affect psychological outcomes following victimization. Female victims were 40% more likely to report distress than male victims (OR = 1.40, 95% CI = 1.13–1.74, p = .002). Relative to older White victims, older Black victims were 76% more likely to experience emotional distress (OR = 1.76, 95% CI = 1.14–2.70, p = .010), and those who identified their race/ethnicity as “other” were 46% less likely to report distress (OR = 0.54, 95% CI = 0.30–0.96, p = . 034).
Factors Associated With Emotional Distress Following Identity Theft ( N = 2,160)
Victims who suffered out-of-pocket costs were 87% more likely to report emotional distress relative to those with no out-of-pocket costs (OR = 1.87, 95% CI = 1.11–3.14, p = .018). Even after controlling for out-of-pocket costs, those who had between $101 and $500 stolen were 38% more likely to feel distressed (OR = 1.38, 95% CI = 1.02–1.87, p = .038), and those who had $501 or more stolen were two and a half times as likely to feel distressed (OR = 2.46, 95% CI = 1.76–3.44, p < .001) compared to those who had no money taken. Other existing account identity theft was negatively associated with experiencing distress (OR = 0.69, 95% CI = 0.53–0.89, p = .004). Relative to those who discovered their identity had been misused within the same day of the theft, those who discovered it a week to 1 month later were 58% more likely to feel distressed (OR = 1.58, 95% CI = 1.18–2.14, p = .003), and those who did not discover the incident until 1 month to 6 months later were more than twice as likely to feel distressed (OR = 2.21, 95% CI = 1.53–3.18, p < .001), although discovering the incident more than 6 months later had no effect. Experiencing multiple incidents of identity theft within the same year was also significantly associated with distress (OR = 1.41, 95% CI = 1.10–1.81, p = .007). Older victims who reported that identity theft led to subsequent financial and credit problems were 94% more likely to experience emotional distress (OR = 1.94, 95% CI = 1.02–3.71, p = .045), and those who stated that the incident negatively affected their relationships with friends and/or family members were 11 times more likely to report moderate to severe emotional distress (OR = 11.59, 95% CI = 2.31–58.16, p = .003).
This is the first study to examine the financial and psychological outcomes of identity theft among older adult victims. Although only 7% of older victims experience out-of-pocket costs associated with identity theft, 34% describe the experience as moderately to severely distressing, indicating that the harm resulting from personal information misuse extends beyond direct financial losses.
Incident-specific factors are important contributors to distress. The more money that is stolen from the victim during the incident, the greater the odds of emotional distress, regardless of whether losses are recovered or reimbursed. Also, the longer information is misused before the crime is discovered, the more subsequent financial and credit problems, and the more hours spent resolving the incident, the greater the likelihood of distress. Our findings reflect results from a smaller survey of a few hundred adult victims that found that the magnitude of financial loss, the duration of misuse of personal information, and the amount of time spent resolving the effects of the crime are all factors that increase perceived distress ( Li et al., 2019 ).
Beyond incident-specific characteristics, we find that older Black victims and older female victims are significantly more likely to report emotional distress, controlling for other demographic and socioeconomic characteristics. Prior work using the ITS shows that minorities experience higher levels of distress than Caucasian individuals ( Golladay & Holtfreter, 2017 ), and Burnes et al. (2020) found that Black respondents were 58% more likely to report instrumental identity theft relative to other race and ethnic groups. This subtype of identity theft may be particularly stressful for victims because it involves using the victim’s personal identity to obtain benefits and services that the victim is entitled to, such as health care, tax refunds, and enrollment in government programs. The higher prevalence of instrumental identity theft in Black communities may help account for their higher levels of distress, although this type of identity theft was not significantly associated with distress in the current models.
Older Black victims were also more likely to have increasing amounts of money stolen relative to older White victims, although there were no statistically significant differences in out-of-pocket costs. Rather, those who are Asian, Pacific Islander, Indigenous, mixed race, or other race/ethnicity were more likely to suffer out-of-pocket costs. Controlling for out-of-pocket costs, this group was counterintuitively less likely to report emotional distress following the incident. More research is needed to better understand the relationship between identity theft and distress among older adults who belong to these minority groups.
We find that the poorest older Americans are more likely to suffer out-of-pocket costs. Specifically, relative to the wealthiest victims aged 65 and older, those who live at or below the FPL are nearly 5 times as likely to bear a financial burden following the incident, even after accounting for the type of identity theft and whether the victim contacted their financial institution. Consistent with findings from the general U.S. adult population ( Copes et al., 2010 ; Reynolds, 2020 ), our results illustrate the importance of social and economic capital in addressing identity theft incidents. To resolve identity theft, the FTC recommends that victims contact their financial institutions or the company involved in the incident, change their passwords, request that money be reimbursed or charges reversed, contact all three credit bureaus to place fraud alerts, and report the incident to authorities. Depending on the severity of the incident, victims may also need to place a freeze on their credit, write to credit bureaus to request corrections to their credit reports, close unauthorized new accounts, write to debt collectors explaining the situation, report to the Social Security Administration, and replace government-issued IDs. These tasks can place a tremendous burden on low-income older adults, many of whom lack access to broadband internet, supportive ties who can advocate on their behalf, or the knowledge and wherewithal to negotiate with powerful financial institutions. Research is needed to determine whether wealthy and/or White older adults are treated differently by their financial institutions when they report identity theft, and whether they are more likely to have account safeguards in place or a client/customer status that helps keep their information safe.
This is the first study to show the negative impact of identity theft on social relationships after controlling for other victim and incident-level characteristics. Maintaining strong positive social and emotional relationships is critical for health and well-being in later life ( Cho et al., 2015 ; Litwin & Shiovitz-Ezra, 2011 ). Findings here illustrate that victims who reported that identity theft caused significant problems with family members or friends were 12 times as likely to experience emotional distress, suggesting that identity theft can have severe ramifications for older adults’ well-being. Qualitative research is needed to understand how identity theft victimization leads to relationship discord. One possibility is that family members blame the older victim for the incident, assuming that they did not keep their personal information secure or that they waited too long to take action. Victim blaming is common in fraud and is likely a driver of low rates of reporting ( Cross, 2015 ; Cross et al., 2016 ). Future studies are needed to understand the role that family and friends play in helping victims recover from identity theft. Family participation in working to protect an older adult from identity crimes, such as providing account oversight and coaching on cybersecurity practices, may be a critical factor in keeping them safe against future victimization.
Implications and Future Research
Findings suggest that limiting the extent of losses and reducing the length of time information is misused prior to detection may reduce the emotional toll of identity theft. Older adults in particular should increase surveillance of their identifying information by using identity protection software, two-step authentication features, signing up for credit alerts, and applying low spending limits on credit cards. Other personal protection behaviors, such as routinely changing passwords, making passwords complicated and varying them for each account, monitoring financial transactions, and locking up or shredding documents, are also important for preventing identity theft. Future research should examine the impact of these identity protection behaviors specifically among older adults. Moreover, this study excluded many older adults who experienced attempted identity theft. Using NCVS–ITS data, additional research may explore how these individuals differ from victims, particularly in regards to their identity protection behaviors.
Given that the length of identity misuse is strongly related to emotional distress, financial institutions should act swiftly to stop suspicious transactions before charges can escalate, and organizations should not delay in informing their customers, employees, and law enforcement of data breaches that involve personal or payment information. Unfortunately, Lacey and Cuganesan (2004) report that a minority of organizations report possible data breaches to law enforcement agencies, indicating that consumers also fail to learn about potential information exposure.
Like identity theft, very little research has been done to examine the outcomes of fraud victimization on older adults. To that end, the Bureau of Justice Statistics recently released a new fraud supplement that assesses the prevalence of different types of fraud. The questionnaire includes information on the amount lost and the emotional impact on victims. Although the amount varied by scam type, victims lost an average of $700 per incident and 53% reported socioemotional problems as a consequence of victimization ( Morgan, 2021 ). Future research should compare how the outcomes of fraud victimization compare to the outcomes of identity theft, and whether Black and female victims also experience higher levels of emotional distress.
Some research has explored how identity theft might affect consumers’ trust in the marketplace, particularly their confidence and willingness to engage in online transactions ( Chakraborty et al., 2016 ; Roberts et al., 2013 ). Avoiding the transfer of personal information online is near impossible in today’s society, as most companies and government agencies rely on the internet to do business with consumers. Future research should examine how identity theft victimization affects older consumers’ trust in government agencies and other institutions, and whether it affects online shopping and sharing of personal information in online environments.
The coronavirus pandemic has created new risks of identity theft as many older adults have turned to the internet to meet their shopping, banking, and even health care consultation needs. While the NCVS–ITS data used in this study were collected prior to the pandemic, it should be noted that identity theft was prevalent following the steep rise in joblessness that disproportionately affected low-income and minority workers. International criminals filed for U.S. unemployment benefits using the stolen identities of American citizens, siphoning off approximately $36 billion from the program, or 10% of all funds expended for unemployment benefits under the CARES Act ( Office of the Inspector General, 2020 ). It is unknown how these crimes affected older adults in particular, and whether they have influenced older adults’ confidence in exchanging personal information with the government.
Although the ITS is one of the most comprehensive sources of data on identity theft, the survey excludes individuals with severe cognitive impairment and those who live in institutional settings (e.g., psychiatric care, long-term care, nursing homes). The impacts of identity theft on these vulnerable older adults are not known, although victim research on fraud indicates that cognitive decline and dementia are correlates of increased risk ( Boyle et al., 2019 ).
Unfortunately, the ITS does not include measures of whether older adults may be experiencing cognitive decline or other mental or physical health conditions that could affect distress and the ability to recover losses. Moreover, the ITS uses a 1-year reference period and it may be difficult for older victims with cognitive impairment to accurately remember details of the incident and how they felt about it. Identity theft is an unusual crime in that the consequences, such as diminished credit scores or unexplained credit card charges, may be overlooked by some victims and therefore underreported.
Although the survey has relatively high response rates and no strong evidence of bias, it is possible that older adults who refuse to participate in the NCVS or the ITS may be more reluctant to provide personal information in a survey because they have experienced identity theft previously. This would mean that more victims in the nonresponse group are not represented in the data.
Emotional distress was measured as a single item and was recoded from four levels into a dichotomous variable. Although this has been the convention used in prior studies ( Golladay & Holtfreter, 2017 ; Randa & Reynes, 2020 ), a binary treatment reduces information and may conceal nonlinear relationships between distress and other variables. To test for differences in effects, we performed a post hoc ordinal regression. Emotional distress (four levels) was regressed on the same independent variables. Only two substantive differences emerged: Victims of other race/ethnic backgrounds were no longer significantly less likely to experience distress relative to non-Hispanic White victims ( p = .318) and experiencing more than one separate incident of identity theft within the past year was only marginally associated with distress ( p = .066). Future research should consider using a more comprehensive, multi item measure of distress.
Findings from this study largely align with studies that examine the impact of identity theft victimization on adults of all ages, although older adults may present additional vulnerabilities, such as cognitive decline and isolation, which could increase their risk of serious outcomes. New programs and services are needed to help older victims recover, with a particular focus on low-income people and those who lack the ability to advocate for themselves. Advocates may assist older victims with contacting multiple financial institutions and credit bureaus, filing complaints, and freezing their credit. Additional services might include victim support groups and other psychological resources, as well as information for family caregivers on how to support older victims. Future research should assess whether cybersecurity training can help older adults secure their identity information and reduce their risk of future identity crimes.
The research reported herein was performed pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Retirement and Disability Consortium through the University of Wisconsin Center for Retirement Security, project number WI21-11. The opinions and conclusions expressed are solely those of the author(s) and do not represent the opinions or policy of SSA or any agency of the Federal Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the contents of this report.
The authors have no conflicts of interest to report.
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Study on online identity theft and identity-related crime
Final report, publication metadata, available languages and formats, english (en).
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- Published: 2022
- Corporate author(s): Directorate-General for Migration and Home Affairs ( European Commission ) , ICF
- Themes: Law and justice
- Subject: computer crime , EU action , EU Member State , fight against crime , identity theft , recommendation (EU) , regulatory policy , report
- Released on EU Publications: 2022-03-18
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