Can Experience with the old Rural Residents' Pension Improve the Accuracy of Pension Expectations? Evidence from the China Health and Retirement Longitudinal Study
LV Youji, ZHENG Wei, XIE Zhiwei
School of Finance,Nankai University; School of Economics, Peking University
Summary:
Pension expectations are the critical path connecting the public pension system to individual decision-making, and they directly affect individual welfare through economic variables such as savings and labor supply. Incorrect expectations of future pensions can lead to individual decision-making that deviates from the optimal path, fails to maximize lifetime welfare, and weakens the welfare effects of public pension system reform. Therefore, it is necessary to analyze the main factors affecting the accuracy of pension expectations to provide a decision-making reference for further improving the public pension system. Using data from the China Health and Retirement Longitudinal Study from 2011 to 2018, we examine the impact of experience with the former Rural Residents' Pension (ORBP) on the accuracy of pension expectations among rural residents via the treatment effect model. We find that such experience significantly reduces the degree of pension expectation bias by 0.24, or 34.29% of the sample mean (0.70). This finding holds across several robustness tests, including using the propensity score matching method, restricting the sample to those without migration history, taking into account the sample attrition problem, and shrinking the tails of pension-related variables. We also find the effect to be strengthened among well-educated individuals, those with Internet access, and those without cognitive impairment. In addition, the above effect mainly works by increasing the level of individual caution. The village-level analysis, which uses the cohort difference-in-differences approach, also supports our main findings. Two policy implications can be drawn from the above conclusions. First, we should objectively evaluate the achievements of the historical exploration of the ORBP, and we should not reject it completely because of the exploration's failure. The historical exploration of the ORBP that began in the 1990s regrettably failed, but it was valuable nonetheless. It not only helped guide the subsequent construction of the New Rural Residents' Pension but also significantly improved the accuracy of residents' pension expectations through several channels, such as increasing the degree of prudence and consolidating the cognitive foundation of pensions. From this point of view, the government should actively explore the construction of various types of public systems. After all, practice is the only standard for testing the truth. However, we must also understand that the realization of the educational function of the ORBP will come at a cost, which can be avoided through a more sophisticated system design. Second, the government should focus on groups with lower levels of cognition. We find that lower levels of cognition can significantly hinder the ORBP from exerting the pension expectation accuracy enhancement effect. Thus, differentiated arrangements are needed to provide more accurate institutional education and information disclosure for groups with lower cognition levels, such as those with lower education, a lack of access to information, and cognitive barriers, to help them better understand the public pension system reform and make reasonable pension arrangements accordingly. We make two main contributions. First, in terms of research ideas, we integrate experience with the ORBP into impact factors of pension expectation accuracy. This offers a new perspective from which to interpret pension expectation bias and expands the application scenario of the experience learning effect. Second, we verify that the ORBP significantly improves the pension expectation accuracy of the Residents' Basic Pension participants, demonstrating its positive role in improving the level of residents' pension cognition. This result provides new evidence to support objectively evaluating the impact of the ORBP. In this paper, we explore the impact of experience with the ORBP on the accuracy of pension expectations among rural residents. Limitations pertaining to the calculation method and the impact mechanisms point toward two ways in which future research can be expanded. First, by obtaining longer-term micro-survey data, future research can use residents' expected and real pensions to calculate their pension expectation bias. Second, by obtaining policy trust variables and pension cognition level variables, future research can further characterize how experience with the ORBP affects the accuracy of rural residents' pension expectations.
吕有吉, 郑伟, 谢志伟. 老农保参保经历会提升养老金预期准确性吗?——来自CHARLS的证据[J]. 金融研究, 2023, 522(12): 113-131.
LV Youji, ZHENG Wei, XIE Zhiwei. Can Experience with the old Rural Residents' Pension Improve the Accuracy of Pension Expectations? Evidence from the China Health and Retirement Longitudinal Study. Journal of Financial Research, 2023, 522(12): 113-131.
Abadie, A., D. Drukker, J.L. Herr, and G.W. Imbens. 2004. “Implementing Matching Estimators for Average Treatment Effects in Stata”, Stata Journal, 4(3), pp.290~311.
[18]
Alessie, R., M. Van Rooij, and A. Lusardi. 2011. “Financial Literacy and Retirement Preparation in the Netherlands”, Journal of Pension Economics & Finance, 10(4), pp.527~545.
[19]
Bai, C.-E., W. Chi, T.X. Liu, C. Tang, and J. Xu. 2021. “Boosting Pension Enrollment and Household Consumption by Example: A Field Experiment on Information Provision”, Journal of Development Economics, 150, pp.102622.
[20]
Baldini, M., C. Mazzaferro, and P. Onofri. 2019. “Pension Expectations, Reforms and Macroeconomic Downturn in Italy. What Can Microdata Tell Us?”, Applied Economics, 51(13), pp.1396~1410.
[21]
Barrett, A., I. Mosca, and B. Whelan. 2015. “How Well-Informed Are Pension Scheme Members on Their Future Pension Benefits? Evidence from Ireland”, Journal of Aging & Social Policy, 27(4), pp.295~313.
[22]
Bissonnette, L. and A. Van Soest. 2015. “Heterogeneity in Consumers' Income and Pension Expectations”, Journal of Pension Economics & Finance, 14(4), pp.439~465.
[23]
Bottazzi, R., T. Jappelli, and M. Padula. 2006. “Retirement Expectations, Pension Reforms, and Their Impact on Private Wealth Accumulation”, Journal of Public Economics, 90(12), pp.2187~2212.
[24]
Cai, F., J. Giles, P. O’Keefe, and D. Wang. 2012. “The Elderly and Old Age Support in Rural China”. The World Bank.
[25]
Chan, S. and A.H. Stevens. 2004. “Do Changes in Pension Incentives Affect Retirement? A Longitudinal Study of Subjective Retirement Expectations”, Journal of Public Economics, 88(7~8), pp.1307~1333.
[26]
Chen, Y., Z. Fan, X. Gu, and L.-A. Zhou. 2020. “Arrival of Young Talent: The Send-Down Movement and Rural Education in China”, American Economic Review, 110(11), pp.3393~3430.
[27]
Chen, Y., Q. Fan, X. Yang, and L. Zolotoy. 2021. “CEO Early-Life Disaster Experience and Stock Price Crash Risk”, Journal of Corporate Finance, 68,p.101928.
[28]
Chen, Y. and L.-A. Zhou. 2007. “The Long-Term Health and Economic Consequences of the 1959-1961 Famine in China”, Journal of Health Economics, 26(4), pp.659~681.
[29]
Cheng, L., H. Liu, Y. Zhang, and Z. Zhao. 2018. “The Health Implications of Social Pensions: Evidence from China's New Rural Pension Scheme”, Journal of Comparative Economics, 46(1), pp.53~77.
[30]
Chetty, R., J.N. Friedman, S. Leth-Petersen, T.H. Nielsen, and T. Olsen. 2014. “Active vs. Passive Decisions and Crowd-Out in Retirement Savings Accounts: Evidence from Denmark”, Quarterly Journal of Economics, 129(3), pp.1141~1219.
[31]
Chiang, Y.-M., D. Hirshleifer, Y. Qian, and A.E. Sherman. 2011. “Do Investors Learn from Experience? Evidence from Frequent IPO Investors”, Review of Financial Studies, 24(5), pp.1560~1589.
[32]
Choi, J.J., D. Laibson, B.C. Madrian, and A. Metrick. 2009. “Reinforcement Learning and Savings Behavior”, Journal of Finance, 64(6), pp.2515~2534.
[33]
Delavande, A. and S. Rohwedder. 2011. “Individuals' Uncertainty About Future Social Security Benefits and Portfolio Choice”, Journal of Applied Econometrics, 26(3), pp.498~519.
[34]
Dolls, M., P. Doerrenberg, A. Peichl, and H. Stichnoth. 2018. “Do Retirement Savings Increase in Response to Information About Retirement and Expected Pensions?”, Journal of Public Economics, 158, pp.168~179.
[35]
Ehling, P., A. Graniero, and C. Heyerdahl-Larsen. 2018. “Asset Prices and Portfolio Choice with Learning from Experience”, Review of Economic Studies, 85(3), pp.1752~1780.
[36]
Gao, M., Y.-J. Liu, and Y. Shi. 2020. “Do People Feel Less at Risk? Evidence from Disaster Experience”, Journal of Financial Economics, 138(3), pp.866~888.
[37]
Giné, X. and D. Yang. 2009. “Insurance, Credit, and Technology Adoption: Field Experimental Evidencefrom Malawi”, Journal of Development Economics, 89(1), pp.1~11.
[38]
Guiso, L., T. Jappelli, and M. Padula. 2013. “Pension Wealth Uncertainty”, Journal of Risk and Insurance, 80(4), pp.1057~1085.
[39]
Gustman, A.L. and T.L. Steinmeier. 2005. “Imperfect Knowledge of Social Security and Pensions”, Industrial Relations: A Journal of Economy and Society, 44(2), pp.373~397.
[40]
Knüpfer, S., E. Rantapuska, and M. Sarvimäki. 2017. “Formative Experiences and Portfolio Choice: Evidence from the Finnish Great Depression”, Journal of Finance, 72(1), pp.133~166.
[41]
Kuchler, T. and B. Zafar. 2019. “Personal Experiences and Expectations about Aggregate Outcomes”, Journal of Finance, 74(5), pp.2491~2542.
[42]
Landerretche, O.M. and C. Martínez. 2013. “Voluntary Savings, Financial Behavior, and Pension Finance Literacy: Evidence from Chile”, Journal of Pension Economics & Finance, 12(3), pp.251~297.
[43]
Lei, X., C. Zhang, and Y. Zhao. 2013. “Incentive Problems in China's New Rural Pension Program”//, Research in Labor EconomicsEmerald Publishing Ltd: 181~201.
[44]
Liu, T. and L. Sun. 2016. “Pension Reform in China”, Journal of Aging & Social Policy, 28(1), pp.15~28.
[45]
Liu, H., Q. Sun, and Z. Zhao. 2014. “Social Learning and Health Insurance Enrollment: Evidence from China's New Cooperative Medical Scheme”, Journal of Economic Behavior & Organization, 97, pp.84~102.
[46]
Maddala, G.S. 1983. “Limited-Dependent and Qualitative Variables in Econometrics”. Cambridge University Press.
[47]
Malmendier, U. and S. Nagel. 2016. “Learning from Inflation Experiences”, Quarterly Journal of Economics, 131(1), pp.53~87.
[48]
Malmendier, U., D. Pouzo, and V. Vanasco. 2020. “Investor Experiences and Financial Market Dynamics”, Journal of Financial Economics, 136(3), pp.597~622.
[49]
Mastrobuoni, G. 2011. “The Role of Information for Retirement Behavior: Evidence Based on the Stepwise Introduction of the Social Security Statement”, Journal of Public Economics, 95(7), pp.913~925.
[50]
Okumura, T. and E. Usui. 2014. “The Effect of Pension Reform on Pension-Benefit Expectations and Savings Decisions in Japan”, Applied Economics, 46(14), pp.1677~1691.
[51]
Seru, A., T. Shumway, and N. Stoffman. 2010. “Learning by Trading”, Review of Financial Studies, 23(2), pp.705~739.
[52]
Van Duijn, M., M. Mastrogiacomo, M. Lindeboom, and P. Lundborg. 2013. “Expected and Actual Replacement Rates in the Pension System of the Netherlands: How and Why Do They Differ?”, Journal of Pension Economics & Finance, 12(2), pp.168~189.