Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-23T23:25:36.108Z Has data issue: false hasContentIssue false
  • English
  • Français

Alternative measures of non-cognitive skills and their effect on retirement preparation and financial capability

Published online by Cambridge University Press:  28 February 2019

Gema Zamarro
Gema Zamarro*
Affiliation:
University of Arkansas & University of Southern California, Fayetteville, USA
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Individuals are increasingly asked to take responsibility for preparing for retirement and available financial products to do so are growing in sophistication. A better understanding of how non-cognitive skills influence financial capability and retirement preparation could help effective policy design. This area of research has been hampered by the struggle to find reliable measures of these skills. I argue that questionnaires themselves can be seen as performance tasks, such that measures of survey effort could lead to meaningful measures of non-cognitive skills. I exploit the fact that I observe respondents taking multiple survey modules covering different topics in different moments of time to build survey effort measures in a nationally representative internet panel. I use survey effort measures along with self-reports to study the role of non-cognitive skills on retirement preparation and financial capability. My results show that non-cognitive skills can have a significant role, beyond the role of cognitive ability.

Keywords

Financial capabilitynon-cognitive skillsretirement preparationC83C91D14
Type
Article
Information
Journal of Pension Economics & Finance , Volume 20 , Special Issue 4: New Longitudinal Data for Retirement Analysis and Policy , October 2021 , pp. 532 - 546
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Almlund, M, Duckworth, A, Heckman, J and Kautz, T (2011) Personality psychology and economics. In Hanushek, EA, Machin, S and Wossmann, L (eds), Handbook of the Economics of Education. Amsterdam: Elsevier, 1–181.Google Scholar
Cheng, A, Zamarro, G and Orriens, B (2018) Personality as a predictor of unit nonresponse in an internet panel. Sociological Methods and Research 1–27. https://journals.sagepub.com/doi/abs/10.1177/0049124117747305.Google Scholar
Costa, PT Jr and McCrae, RR (1992) Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) Professional Manual. Odessa, FL: Psychological Assessment Resources, Inc.Google Scholar
Dobbie, W and Fryer, RG (2015) The medium-term impacts of high-achieving charter schools. Journal of Political Economy 123(5), 985–1037.CrossRefGoogle Scholar
Duckworth, AL and Quinn, PD (2009) Development and validation of the short grit scale (GRIT–S). Journal of Personality Assessment 91(2), 166–174.CrossRefGoogle Scholar
Duckworth, AL and Yeager, DS (2015) Measurement matters assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher 44(4), 237–251.CrossRefGoogle ScholarPubMed
Duckworth, AL, Peterson, C, Matthews, MD and Kelly, DR (2007) Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology 92(6), 1087–1101.CrossRefGoogle ScholarPubMed
Frederick, S (2005) Cognitive reflection and decision making. The Journal of Economic Perspectives 19(4), 25–42.CrossRefGoogle Scholar
Galla, BM, Plummer, BD, White, RE, Meketon, D, D'Mello, SK and Duckworth, AL (2014) The Academic Diligence Task (ADT): assessing individual differences in effort on tedious but important schoolwork. Contemporary Educational Psychology 39(4), 314–325.CrossRefGoogle ScholarPubMed
Hershey, DA and Mowen, JC (2000) Psychological determinants of financial preparedness for retirement. The Gerontologist 40(6), 687–697.CrossRefGoogle ScholarPubMed
Hitt, CE (2015) Just Filling In the Bubbles: Using Careless Answer Patterns on Surveys as a Proxy Measure of Noncognitive Skills. UARK-EDRE WP 2015-06. http://www.uaedreform.org/downloads/2015/07/edre-working-paper-2015-06.pdf.Google Scholar
Hitt, CE, Trivitt, JR and Cheng, A (2016) When you say nothing at all: the predictive power of student effort on surveys. Economics of Education Review 52, 105–119.CrossRefGoogle Scholar
Huang, JL, Curran, PG, Keeney, J, Poposki, EM and DeShon, RP (2012) Detecting and deterring insufficient effort responding to surveys. Journal of Business and Psychology 27(1), 99–114.CrossRefGoogle Scholar
Hurd, MD, Duckworth, A, Rohwedder, S and Weir, DR (2012) Personality Traits and Economic Preparation for Retirement. MRRC WP 2012-09. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.391.2886&rep=rep1&type=pdf.Google Scholar
John, OP, Donahue, EM and Kentle, RL (1991) The Big Five Inventory—Versions 4a and 54. Berkeley, CA: Institute of Personality and Social Research, University of California.Google Scholar
Johnson, JA (2005) Ascertaining the validity of individual protocols from web-based personality inventories. Journal of Research in Personality 39(1), 103–129.CrossRefGoogle Scholar
Knoll, MAZ and Houts, CR (2012) The financial knowledge scale: an application of item response theory to the assessment of financial literacy. The Journal of Consumer Affairs 3, 381–410.CrossRefGoogle Scholar
Krosnick, JA, Narayan, S and Smith, WR (1996) Satisficing in surveys: initial evidence. New Directions for Evaluation, Special Issue: Advances in Survey Research, 70, 29–44.CrossRefGoogle Scholar
Lipkus, IM, Samsa, G and Rimer, BK (2001) General performance on a numeracy scale among highly educated samples. Medical Decision Making 21(1), 37–44.CrossRefGoogle ScholarPubMed
Lusardi, A, Michaud, P and Mitchell, O (2017) Optimal financial knowledge and wealth inequality. Journal of Political Economy 125(2), 431–477.CrossRefGoogle ScholarPubMed
Mather, N and Jaffe, LE (2016) Woodcock-Johnson IV: Reports, Recommendations, and Strategies. Hoboken, NJ: John Wiley & Sons.Google Scholar
Meade, AW and Craig, SB (2012) Identifying careless responses in survey data. Psychological Methods 17(3), 437–455.CrossRefGoogle ScholarPubMed
O'Conner, PJ, Sullivan, GL and Jones, WH (1982) An evaluation of the characteristics of response quality induced by follow-up survey methods. Advances in Consumer Research 9, 257–259.Google Scholar
Parise, G and Peijnenburg, K (forthcoming) Noncognitive abilities and financial distress: evidence from a representative household panel. The Review of Financial Studies.Google Scholar
Soland, J (2018) Can the amount of time students spend on test items help illustrate social-emotional learning needs? Initial evidence from an international achievement test. Paper presented at the American Education Finance and Policy 43rd Annual Conference, Portland, Oregon.Google Scholar
Toplak, ME, West, RF and Stanovich, KE (2014) Assessing miserly information processing: an expansion of the cognitive reflection test. Thinking & Reasoning 20, 147–168.CrossRefGoogle Scholar
Tourangeau, R and Yan, T (2007) Sensitive questions in surveys. Psychological Bulletin 133(5), 859–883.CrossRefGoogle ScholarPubMed
West, MR, Kraft, MA, Finn, AS, Martin, R, Duckworth, AL, Gabrieli, CFO and Gabrieli, JDE (2016) Promise and paradox: measuring students’ Non-cognitive skills and the impact of schooling. Educational Evaluation and Policy Analysis 38(1), 148–170.CrossRefGoogle Scholar
Zamarro, G, Cheng, A, Shakeel, M and Hitt, C (2018) Comparing and validating measures of non-cognitive traits: performance task measures and self-reports from a nationally representative internet panel. Journal of Behavioral and Experimental Economics 72, 51–60.CrossRefGoogle Scholar