Access to Retirement Savings and its Effects on Labor Supply Decisions

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Access to Retirement Savings and its Effects on Labor Supply Decisions Yan Lau Reed College May 2015 IZA / RIETI Workshop

Motivation My Question: How are labor supply decisions affected by access of Retirement Savings Accounts (RSAs)? Highly relevant to policy makers Aging populations mean higher dependency ratios Especially Japan, Germany; most likely US in the future Ameliorate by increasing labor force participation of elderly Delay access to retirement savings and other benefits Consequences for indexing to life expectancy Denmark

Background on Retirement Savings Accounts (RSAs) Focus on RSAs in the US First established in 1970s, growing popularity ever since (Poterba, Venti and Wise, 1994) RSA types: IRAs for everyone Keogh plans for the self-employed 401(k) plans for private firms Thrift Savings Plans for government employees All of these have same basic structure

How do Retirement Savings Accounts (RSAs) Work? Deposit pre-tax dollars into account 1 Invest funds and earn dividends and capital gains over time Withdraw funds when you are older Only then are dollars taxed Tax benefit derived from being in lower bracket Age thresholds define access restrictions: 59.5 = Allowed to start making withdrawals 70.5 = Minimum mandatory withdrawals 1 Slightly different for Roth IRAs, which take post-tax dollars.

How does RSA access affect labor supply? Income effect increases demand for leisure, reduces labor supply For financially constrained participants Desire to maximize tax savings Aging participants enter lower tax brackets as earnings decrease with age Decreasing productivity, worse wage offers Substitute withdrawals for earnings

Literature Review Much of the literature devoted to whether RSAs encourage savings (Sabelhaus, 2000) Extensive literature on retirement decisions Structural models (Gustman and Steinmeier, 1986; Stock and Wise, 1990) Availability of employer pension plans (Stock and Wise, 1990) Social security policy changes (Krueger and Pischke, 1992) In combination with health and other factors (Fields and Mitchell, 1984; French, 2005) Other age thresholds Social security access (Stewart, 1995) Early retirement windows (Brown, 2002; Hogarth, 1988; Lumsdaine et al., 1990; Pencavel, 2001)

Data Survey of Income and Program Participation (SIPP) 2008 Panel, 15 waves Unit of observation is a person-month response to survey Sample Restrictions: Between ages 50 and 80 Household has never owned a business Consumer Price Index (CPI) adjustment to May 2008 dollars Labor supply is measured in usual hours worked per week

Summary Statistics Percent 0.5 1 1.5 2 2.5 50 55 60 65 70 75 80 Age Percent 0 20 40 60 0 10 20 30 40 50 60 70 80 90 100 Usual Hours Worked / Week

(1) (2) (3) Entire Sample RSA Participants Withdrawers % Female 0.550 0.529 0.488 (0.497) (0.499) (0.500) % White 0.811 0.861 0.934 (0.392) (0.346) (0.248) % Black 0.124 0.085 0.043 (0.33) (0.278) (0.202) % Asian 0.034 0.031 0.013 (0.182) (0.173) (0.113) % High School 0.872 0.954 0.959 (0.334) (0.210) (0.197) % College 0.332 0.445 0.434 (0.471) (0.497) (0.496) % Married 0.627 0.695 0.662 (0.484) (0.460) (0.473) Household Size 2.363 2.340 1.904 (1.295) (1.179) (0.779) N 1,325,591 803,864 36,981 % 100 60.64 2.79

(1) (2) (3) Entire Sample RSA Participants Withdrawers Owns RSA 0.606 1 1 (0.489) Withdrew from RSA 0.028 0.046 1 (0.165) (0.209) Withdrawal Amount 53.819 88.749 1929.157 (653.161) (836.903) (3416.832) % Working 0.460 0.591 0.161 (0.498) (0.492) (0.368) Usual Hours / Week 17.600 23.186 4.842 (20.697) (21.153) (12.585) Hours if Working 38.298 39.229 30.033 (11.809) (11.300) (15.027) Earned Income 1680.69 2450.224 395.256 (3263.988) (3867.661) (1838.644) N 1,325,591 803,864 36,981 % 100 60.64 2.79

How do withdrawal behaviors change across the age thresholds? Proportion Withdrawing 0.05.1.15.2 50 55 60 65 70 75 80 Age Proportion of RSA Participants Withdrawing by Age

What are the differences in labor supply decisions of those withdrawing versus those making no withdrawals? Proportion Working 0.2.4.6.8 1 50 55 60 65 70 75 80 Age Non Withdrawing Withdrawing Proportion of RSA Participants Working by Age and Withdrawal

Labor supply decisions Average Usual Hours Worked / Week 0 10 20 30 40 50 55 60 65 70 75 80 Age Non Withdrawing Withdrawing Average Usual Hours Worked per Week of RSA Participants by Age and Withdrawal Status

Labor supply decisions Average Earnings / Month 0 1000 2000 3000 4000 50 55 60 65 70 75 80 Age Non Withdrawing Withdrawing Average Monthly Earnings of RSA Participants by Age and Withdrawal Status

Relationship between RSA Access and Withdrawal Amount withdrawal it = β 0 +β 1 post59.5 it +β 2 post70.5 it +β 3 age it +β 4 age 2 it where: + T t γ 0 + X it γ 1 + µ i + µ FEs + ε it (1) withdrawal it is the dollar amount withdrawn by individual i in period t from his/her RSA(s) post59.5 it and post70.5 it are indicators which take the value of one when individual i is older than 59.5 and 70.5 in period t age it is individual i s age in period t µ FEs are a set of SIPP reference month, SIPP wave, and state fixed effects (FEs) necessary for identification and inference ε it are error terms and where the following variables are only included in certain specifications: T t is a vector of time trends (t, t 2, and month dummies) X it is a vector of controls (sex, marital status, race and ethnicity, and education) µ i are individual i fixed effects.

Dep. Var.: (1) (2) (3) (4) (5) Withdrawal Amt. OLS OLS OLS FEs Weighted Post 59.5 62.661*** 62.659*** 61.298*** 51.519*** 61.621*** (5.815) (5.795) (5.915) (8.575) (7.306) Post 70.5 116.186*** 116.181*** 116.573*** 111.311*** 133.869*** (15.638) (15.642) (15.161) (20.409) (19.822) Age -1.937-1.95-3.547-40.034*** -1.972 (6.962) (6.985) (6.662) (13.688) (8.407) Age Squared 0.053 0.053 0.066 0.345*** 0.053 (0.06) (0.06) (0.058) (0.119) (0.073) Female -50.654*** -48.473*** (5.221) (4.534) Married -8.663* -9.724* (4.827) (5.582) Constant -113.32-78.53-105.64 1220.36*** -191.61 (200.28) (198.77) (196.12) (404.12) (246.33) SIPP & State FEs Yes Yes Yes Yes Yes Time Trends - Yes Yes Yes Yes Controls - - Yes - Yes Individual FEs - - - Yes - N 803,864 803,864 803,864 803,864 803,864 R-Square 0.015 0.016 0.018 0.146 0.019

Relationship between Withdrawal Amount and Labor Supply hours it = β 0 +β 1 withdrawal it +β 2 age it +β 3 age 2 it+t t γ 0 +X it γ 1 +µ FEs +ε it (2) where hours it is the usual hours worked per week for individual i in period t (which in SIPP is a month) and similar notation is defined as before. Tobit regression because hours it has corner solutions at zero

Dep. Var.: (1) (2) (3) (4) (5) Hours Worked Tobit Tobit Tobit Weighted OLS Withdrawal Amt. -1.394*** -1.406*** -1.580*** -1.543*** -0.642*** (Thousands) (0.151) (0.154) (0.162) (0.181) (0.044) Age 5.628*** 5.543*** 5.462*** 5.710*** -1.729*** (0.433) (0.444) (0.458) (0.470) (0.251) Age Squared -0.067*** -0.066*** -0.065*** -0.067*** 0.002 (0.004) (0.004) (0.004) (0.004) (0.002) Female -5.455*** -5.322*** -3.795*** (0.454) (0.454) (0.277) Married -3.601*** -3.491*** -2.11*** (0.462) (0.507) (0.289) Constant -74.97*** -72.61*** -68.29*** -78.57*** 126.29*** (13.20) (13.53) (16.02) (16.81) (8.55) Marginal Effects (Evaluated at withdrawal amount of $2000) E[hours] withdrawal -0.896*** -0.903*** -1.011*** -1.002*** (0.092) (0.092) (0.1096) (0.111) E[hours hours>0] withdrawal -0.693*** -0.697*** -0.783*** -0.777*** (0.070) (0.070) (0.073) (0.085) SIPP & State FEs Yes Yes Yes Yes Yes Time Trends - Yes Yes Yes Yes Controls - - Yes Yes Yes Individual FEs - - - - -

Summary Delaying access to RSA funds or changing the timing of mandatory minimum withdrawals can have appreciable effects On RSA withdrawal patterns On labor supply decisions Any policy decision to shift RSA age thresholds should be approached and considered in a thoughtful manner Results apply to shifts in age thresholds of other policies such as social security and pension access

Moving Forward Joint household labor supply decisions Structural modeling Include more SIPP years

Bibliography Brown, Charles (2002) Early Retirement Windows, Working Paper 2002-028, University of Michigan, Michigan Retirement Research Center. Fields, Gary S. and Olivia S. Mitchell (1984) Retirement, Pensions, and Social Security, Vol. 1 of MIT Press Books: The MIT Press. French, Eric (2005) The Effects of Health, Wealth, and Wages on Labour Supply and Retirement Behaviour, The Review of Economic Studies, Vol. 72, No. 2, pp. 395 427. Gustman, Alan L and Thomas L Steinmeier (1986) A Structural Retirement Model, Econometrica, Vol. 54, No. 3, pp. 555 584, May. Hogarth, Jeanne M. (1988) Accepting an Early Retirement Bonus an Empirical Study, Journal of Human Resources, Vol. 23, No. 1, pp. 21 33. Krueger, Alan and Jorn-Steffen Pischke (1992) The Effect of Social Security on Labor Supply: A Cohort Analysis of the Notch Generation, Journal of Labor Economics, Vol. 10, No. 4, pp. 412 437. Lumsdaine, Robin L., James Stock, and David Wise (1990) Efficient Windows and Labor Force Reduction, Journal of Public Economics, Vol. 43, No. 2, pp. 131 159. Pencavel, John (2001) The Response of Employees to Severance Incentives: The University of California s Faculty, 1991-94, Journal of Human Resources, Vol. 36, No. 1, pp. 58 84. Poterba, James M., Steven F. Venti, and David A. Wise (1994) Targeted Retirement Saving and the Net Worth of Elderly Americans, The American Economic Review, Vol. 84, No. 2, pp. 180 185, May. Sabelhaus, John (2000) Modeling IRA Accumulation and Withdrawals, National Tax Journal, Vol. 53, No. 4, pp. 865 876, December. Stewart, Jay (1995) Do Older Workers Respond to Changes in Social Security Benefits? A Look at the Time Series Evidence, Working Paper 271, US Department of Labor, Bureau of Labor Statistics. Stock, James and David Wise (1990) Pensions, the Option Value of Work, and Retirement, Econometrica, Vol. 58, No. 5, pp. 1151 1180.