Fraud victimization has profound economic and social implications. With the widespread leakage of private information and ever-evolving fraud schemes, people frequently encounter fraud schemes. Millions of people suffer from fraud victimization with enormous economic losses every year. A Federal Trade Commission survey reveals that 15.9 percent of the respondents were victims of fraud in 2017, which represents approximately 40 million U.S. adults. The direct monetary costs incurred by victimization could reach $50 billion (Brenner et al. 2020).
The issue is of greater importance in developing countries. In China, for instance, fraud incidents have grown at an annual rate of 20 to 30 percent in the past decade. According to a report by the Government of China, nearly half of internet users encountered fraud schemes in 2018, and 28 percent of them suffered economic losses. Based on judicial data, fraud is one of the most frequent crimes, accounting for 32 percent of cybercrime, involved 46,000 fraudsters during 2016–18.
The previous literature, mostly in criminology, has examined the behavioral patterns of fraudsters (Levi 2008). Behind the commonly encountered fraud phone calls and emails are well-trained fraudsters in criminal organizations with sophisticated technologies. Those organizations are armed with details about potential victims and “scripts” instructing fraudsters on how to persuade potential victims to buy their stories and make money transfers. However, it remains unclear why fraud victims are victimized. In a recent working paper, we investigate fraud victimization from the perspective of household financial conditions. Specifically, how and why do household credit constraints affect fraud victimization when facing fraud schemes?
Using the urban sample of a novel, nationally representative data set on fraud victimization and household finance in China, we find that households facing credit constraints is a key determinant of fraud victimization. Our estimation results show that being credit constrained is associated with a 2.3 percentage points increase in the probability of becoming a victim and a 20.4 percent increase in the total amount of subsequent economic losses for those being approached.
To deal with potential omitted variable bias and reverse causality issues, we employ the instrumental variables (IV) approach. Exposure to a nationwide property privatization reform (PPR) and accessibility to local formal finance (bank density) are used as exogenous shifters of credit constraints. The two IVs can simultaneously capture the demand- and supply-side effects on households’ credit constraints. The key identifying assumptions are that the timing and implementation of the nationwide PPR are likely to be unrelated to unobserved determinants of fraud victimization, and the distribution of bank branches is exogenous to households and individuals. The IV estimation results confirm that credit constraints lead to higher probability of fraud victimization and higher subsequent economic losses.
Fraud victimization involves two steps: being approached and subsequently being victimized. If being approached is not random, and the probability of being approached and of being victimized are jointly determined by unobservables, selection bias is a concern. To address this issue, we implement the Heckman selection model, where we first model the probability of being approached and then, conditional on being approached, we estimate the models of the main outcomes. Since individuals who engage more frequently in online shopping face a higher risk of exposure to fraud schemes (Holtfreter et al. 2008; Reisig and Holtfreter 2013), we exploit e-commerce coverage at the community level as an exogenous source of variation to determine the probability of being approached by fraudsters. Our main findings remain robust.
We further rule out the confounding effect of information acquisition and financial literacy on fraud outcomes. Since credit-constrained households may fail to acquire antifraud information released by banks or other credit institutions, a natural concern is that it may be the lack of acquisition of antifraud information, rather than credit constraints, that causes fraud victimization. Furthermore, a higher level of financial literacy helps people better understand financial products, make better financial decisions, and distinguish legitimate investment projects from fraud schemes. The estimated impact of credit constraints on fraud victimization may thus be confounded by financial literacy. To address these concerns, we add indicators of information acquisition and financial literacy in the main model. Our results indicate that neither information acquisition nor financial literacy drives the link between credit constraints and fraud victimization.
Analyses on potential mechanisms suggest that the personal discount rate (impatience) and the need to expand the social network are important pathways through which credit constraints affect fraud victimization. Borrowing constraints can shape people’s preferences on current versus future consumption (Harrison et al. 2002). Credit constraints may lead to a higher discount rate over the future and thus make people more prone to believe well-disguised fraud schemes that promise an egregious return within a short period. In addition, to obtain social collateral, households with severe credit constraints would engage in activities to expand their social networks (Karlan et al. 2009); more social interaction itself, along with the associated behavior changes such as emphasis on cooperative behavior, could make them more susceptible to becoming victims.
We believe our study has implications for antifraud policy. Current policies on combating fraud victimization emphasize antifraud campaigns and increasing financial literacy or, more generally in the case of containing crime, strengthening law enforcement and improving the legal environment have been thought to be effective in defeating crime (Ehrlich 1996; Di Tella and Schargrodsky 2004). We provide a complementary perspective and evidence that policies focusing on the provision of financial services and credit to households may be as important. When encountering credit-related fraud schemes, sufficient access to credit provided to households would greatly reduce the risk of exposure to fraud schemes and allow these households to exhibit more patience—or be less subject to the temptation of a quick payoff.
Brenner, L., Meyll, T., Stolper, O., & Walter, A. (2020). “Consumer fraud victimization and financial well-being.” Journal of Economic Psychology, 76, 102243.
Levi, M. (2008). “Organized fraud and organizing frauds: Unpacking research on networks and organization.” Criminology & Criminal Justice, 8(4), 389–419.
Holtfreter, K., Reisig, M. D., & Pratt, T. C. (2008). “Low self-control, routine activities, and fraud victimization.” Criminology, 46(1), 189–220.
Reisig, M. D., & Holtfreter, K. (2013). “Shopping fraud victimization among the elderly.” Journal of Financial Crime, 20(3), 324–337.
Harrison, G. W., Lau, M. I., & Williams, M. B. (2002). “Estimating individual discount rates in Denmark: A field experiment.” American Economic Review, 92(5), 1606–1617.
Karlan, D., Mobius, M., Rosenblat, T., & Szeidl, A. (2009). “Trust and social collateral.” Quarterly Journal of Economics, 124(3), 1307–1361.
Ehrlich, I. (1996). “Crime, punishment, and the market for offenses.” Journal of Economic Perspectives, 10(1), 43–67.
Di Tella, R., & Schargrodsky, E. (2004). “Do police reduce crime? Estimates using the allocation of police forces after a terrorist attack.” American Economic Review, 94(1), 115–133.
Source : blogs.worldbank.org