A. “What Will My Account Really Be Worth? An Experiment on Exponential Growth Bias and Retirement Saving,” by Gopi Shah Goda, Colleen Flaherty Manchester, and Aaron Sojourner (w17927, March 2012, .pdf format, 68p.).
Recent findings on limited financial literacy and exponential growth bias suggest saving decisions may not be optimal because such decisions require an accurate understanding of how current contributions can translate into income in retirement. This study uses a large-scale field experiment to measure how a low-cost, direct-mail intervention designed to inform subjects about this relationship affects their saving behavior. Using administrative data prior to and following the intervention, we measure its effect on participation and the level of contributions in retirement saving accounts. Those sent income projections along with enrollment information were more likely to change contribution levels and increase annual contributions relative to the control group. Among those who made a change in contribution, the increase in annual contributions was approximately $1,150. Results from a follow-up survey corroborate these findings and show heterogeneous effects of the intervention by rational and behavioral factors known to affect saving. Finally, we find evidence of behavioral influences on decision-making in that the assumptions used to generate the projections influence the saving response.
B. “Financial Literacy and the Financial Crisis,” by Leora F. Klapper, Annamaria Lusardi, and Georgios A. Panos (w17930, March 2012, .pdf format, 54p.).
The ability of consumers to make informed financial decisions improves their ability to develop sound personal finance. This paper uses a panel dataset from Russia, an economy in which consumer loans grew at an astounding rate – from about US$10 billion in 2003 to over US$170 billion in 2008 – to examine the importance of financial literacy and its effects on behavior. The survey contains questions on financial literacy, consumer borrowing (formal and informal), saving and spending behavior. The paper studies both the financial consequences and the real consequences of financial illiteracy. Even though consumer borrowing increased very rapidly in Russia, the authors find that only 41% of respondents demonstrate understanding of the workings of interest compounding and only 46% can answer a simple question about inflation. Financial literacy is positively related to participation in financial markets and negatively related to the use of informal sources of borrowing. Moreover, individuals with higher financial literacy are significantly more likely to report having greater availability of unspent income and higher spending capacity. The relationship between financial literacy and availability of unspent income is higher during the financial crisis, suggesting that financial literacy may better equip individuals to deal with macroeconomic shocks.
C. “Limited Life Expectancy, Human Capital and Health Investments: Evidence from Huntington Disease,” by Emily Oster, Ira Shoulson, and E. Ray Dorsey (w17931, March 2012, .pdf format, 40p.).
One of the most basic predictions of human capital theory is that life expectancy should impact human capital investment. Limited exogenous variation in life expectancy makes this difficult to test, especially in the contexts most relevant to the macroeconomic applications. We estimate the relationship between life expectancy and human capital investments using genetic variation in life expectancy driven by Huntington disease (HD), an inherited degenerative neurological disorder with large impacts on mortality. We compare investment levels for individuals who have ex ante identical risks of HD but learn (through early symptom development or genetic testing) that they do or do not carry the genetic mutation which causes the disease. We find strong qualitative support: individuals with more limited life expectancy complete less education and less job training. We estimate the elasticity of demand for college completion with respect to years of life expectancy of 0.40. This figure implies that differences in life expectancy explain about 10% of cross-country differences in college enrollment. Finally, we use smoking and cancer screening data to test the corollary that health capital is responsive to life expectancy.