
doi: 10.2139/ssrn.2482234
We measure financial literacy in a large sample of LinkedIn members, complementing a standard set of questions with a method that allow us to isolate and distinguish optimism and self-confidence. Like previous work, we find that high literacy respondents are more likely to save for a rainy day, plan for retirement, and are more likely to pay attention to fees when choosing credit cards. However, this is mostly driven by perceived, rather than actual, financial literacy: controlling for self-perceptions, actual literacy has low power to predict financial engagement. Moreover, behavior biases drive participation among low literacy respondents and are associated with mistaken beliefs about financial products and a lower willingness to accept financial advice. This has important implications for policy and for the design of institutions aimed at increasing literacy and protecting consumers from fraud.
Optimism, Economics, G02, Economic statistics, Economic models, Consumer protection, Financial literacy, Studies, Perceptions, Consumers, Decision making, Economic Theory, Consumer fraud
Optimism, Economics, G02, Economic statistics, Economic models, Consumer protection, Financial literacy, Studies, Perceptions, Consumers, Decision making, Economic Theory, Consumer fraud
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 8 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
