Abstract
Knowledge is an important resource for citizens in a democracy, but it is often in short supply when it comes to aspects of public policy. This chapter explores what Americans know about Social Security, a large federal social insurance program in the United States. Statistical analyses reveal that individual characteristics are strongly related to knowledge about Social Security and knowing program facts predicts preferences toward reform options.
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Notes
- 1.
The question and answers are interpreted as the legal incidence of the Social Security tax (i.e., it is literally paid by workers and employers), not the economic incidence (i.e., the employee pays it all, directly, and indirectly through lower wages).
- 2.
Economists might argue that while this is true in nominal or real (i.e., inflation adjusted) terms, it might not be true according to present discounted value (i.e., the benefit of a stream of future payments discounted to reflect the time value of money and investment risk). Thus, this fact is contestable, but the mass public is more likely to encounter the perspective that appears in the mass media (e.g., Morin & Russakoff (2005) state “…most [people] incorrectly believe that retirees, on average, retirees receive less in benefits than they contribute to the system” p. A01).
- 3.
The statistical simulations are for a respondent who takes on the modal or mean values of each independent variable (i.e., a white, female, Independent, moderate, employed, non-southerner, registered voter, rural, non-AARP member who is not on Social Security but who has the average values of income, education, and age).
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Appendix
Appendix
Tables 14.1 and 14.2 show which factors are statistically significant (in bold type) at or near conventional levels ( p < 0.10, two-tailed) as well as those that are not. Positive entries indicate that the factor increases the probability of knowing a fact while negative coefficients signal the opposite. However, the direction of the coefficient is most important for variables that are statistically significant; in the other instances, it is not possible to be sure that the relationships are any different from zero.
With all of the statistical analyses presented in the chapter, the claims are probably better cast as associational rather than causal because the data are cross-sectional. In other words, surveys represent a diverse population, but with cross-sectional data analyses it is not always possible to be certain that one variable causes another. A strong case for causality is especially hard to sustain when it comes to the effects of knowledge on policy attitudes. However, there are some linkages, such as the relationship between static demographic factors like race or gender in which we can be fairly safe in predicting that the relationship goes from these factors to knowledge and not the other way around. Nevertheless, even in this situation it is still possible that an omitted relevant factor exists.
Due to space limitations, the output of the statistical models predicting Social Security reform preferences is not shown. The policy preference models opinions control for whether people knew the program was not going bankrupt in addition to the socio-demographic influences in Tables 14.1 and 14.2. The ideology and partisanship are not highly correlated (Pearson’s r < 0.49), but the substantive conclusions hold if the ideology terms are removed.
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Barabas, J. (2011). Social Security Knowledge. In: Lamdin, D. (eds) Consumer Knowledge and Financial Decisions. International Series on Consumer Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0475-0_14
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