Defined Contribution Pension Plans: Determinants of Participation and Contributions Rates Gur Huberman &
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- Com pensation ($1,000) C o ntr ib u ti o
- Compensation ($1,000) Co n tr ib u ti o
- Com pensation ($1,000) C o nt ri b u ti
- Com pensation ($1,000) C o ntr ibuti on
- Compensation ($1,000) Cont ribut ion ( D olla r)
- Assets in Personal Accounts % of Total Households
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18.0% P ar ti cip at io n Co ntributio n--P articipants Co ntributio n--Full Sample P articipatio n Figure 7 Marginal effect of $10,000 increase in compensation on participation and contribution J Finan Serv Res attempt to affect the most people whereas the findings regarding higher income employees should inspire policies that attempt to affect the most savings. Figure 7
and contribution to a $10,000 change in compensation, holding other variables constant. Marginal participation probability peaks at around 15% for those earning $30,000 and declines thereafter. (To appreciate the magnitude, note that the 15% marginal probability is on top of the average participation probability of those earning $30,000 which is 55%.) It is not negative for any compensation level, and for those earning more than $100,000 it is near zero. The marginal effect of compensation on contribution of the eligible employees peaks around a compensation of $60,000 at $1,208. (The $1,208 marginal contribution is on top of the $3,868 average contribution of those earning $60,000.) Even for those earning between $100,000 and $150,000 the marginal contribution ranges between $800 and $500. The -400
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P ar tic ip at io n Contribution--Participants Contribution--Full Sample Participation Figure 9 Excess participation rates and contribution levels of those covered by DB J Finan Serv Res marginal contribution of eligible employees is greater than that of the participants since the marginal participation probability is non-negative. Figure 8
and contribution between women and men, holding other variables constant. The average participation probability of men earning $30,000 is 49% whereas that of women with the same compensation and similar other attributes is 12% higher. The difference declines for higher wages, but even at the highest compensation levels, women ’s participation probabilities are at least 2% higher than men ’s. The contributions of women are higher than men
’s at all compensation levels, and the difference increases (for the whole population) from around $300 (for those earning around $20,000) all the way to $1,500 (for those earning above $100,000). -1000
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21.00% P a rt ici p a ti o n Contribution--Participants Contribution--Full Sample Participation Figure 11 Marginal effect of 100% match on participation and contribution J Finan Serv Res One explanation for the gender difference is that women are residual income earners in their families: many more low-compensation women than men are married to working spouses and among the majority of working couples, women earn less than men. Consistent with this hypothesis, the data show that women tend to live in wealthier neighborhoods than men of comparable compensation, age and tenure. (Not tabulated.) If the participation and contribution decision reflects the family ’s, as opposed to just the employee
’s needs, then low-income women will participate and contribute as if they had higher incomes than their recorded compensation. This observation may explain the gender gap at the low end of the pay scale, although even here the inclusion of the WEALTH variable should control for the “family” effect. Moreover, if this were the only explanation, women at the higher income levels should behave similarly to men of similar income. But they do not: women earning six digit figures have 2% higher participation probabilities and contribute more than a thousand dollars more than their male counterparts. Figure 9
without DB plans. The counterintuitive results on higher participation rates and contributions that transpire from Tables 1 and
2 surface here as well, with the additional insight that they are concentrated among those in the middle of the earnings distribution, peaking at incomes of $40,000 –$45,000. Remarkably, contributions of participants with and without DB plans are statistically indistinguishable at all income levels. The graph suggests that once an employee decides to participate, the contribution level is unaffected by the presence or absence of the DB plan. The reason that contributions of eligible employees with DB plans are higher than of those without DB plans is that participation probabilities are higher. Figure 10
For those earning less than $42,000 —41% of the sample—participation probability is higher in the presence of company stock; for those earning $30,000 or below —30% of the sample, presence of company stock enhances participation probability by about 7%. (Overall participation probability of employees at this income level is 48%.) For those earning below $35,000 contributions are also higher if company stock is an investable fund, but they are lower for employees earning more than $35,000. Note that conditional on participation, employees contribute less in the presence of company stock. Presumably, employees attracted to the program by the presence of company stock tend to contribute considerably less than those who would participate regardless of the presence of company stock in the investable funds. For those earning above $40,000, the effect of company stock on participation probability is slightly negative (between 0 and −2%), but mostly indistinguishable from zero. More puzzling, and harder to explain is the behavior of contributions: they are lower in the presence of company stock in the investable funds. They can be lower by as much as $400
–750 for those earning between $65,000 and $130,000. Why the presence of company stock should adversely affect contributions is unclear, since its presence in the investable funds can be safely ignored by eligible employees and participants, in which case it would leave participation probabilities and contributions unaffected. But this seems not to be the case. According to Fig. 11 which depicts the relevant sensitivities of participation and contribution to a 100% match, the presence of such a match increases participation at all compensation levels, and such inducement is stronger the lower the compensation. In fact, a 100% match (up to 5% of salary) would lift the average participation rates of those earning $20,000 by 19%. (Recall that the overall participation rate of employees in this income range is 43%.) Contributions of eligible employees at all income levels are higher when a J Finan Serv Res match is available. In fact, for those earning less than $35,000, a 100% match would increase contributions by about $550 –750. 4
12 for average contribution levels at various income levels). The positive impact of a match on all employees ’ contributions is in contrast with its effect on participants only. It seems that only participants earning above $130,000 increase their contributions in response to the match. Participants earning between $35,000 and $120,000 seem to reduce their contributions in response to a match. To understand this counterintuitive finding requires analysis of the relation between the policy variables — existence of the match, the match rate, and the upper limit on the match —and individuals’ choices to participate and, if they participate, how much to contribute. Choi et al. ( 2002 )
2003 ) summarize how a match affects participants ’ choices through income and substitution effects. It is possible that behavioral factors are also necessary to explain the match ’s influence on employees ’ choices. A substantial fraction of the contributions are at or near the point where they exhaust the employer ’s match: the contributions of about 18 (22%) of the participants whose employers offer a match are no more than $100 ($200) away from the upper limit of their employers ’ match. A similar phenomenon is noted by Choi et al. ( 2002 ) on the contribution behavior of participants in one plan that changed the upper limit of match. A substitution effect could lead participants to cluster their contributions at the point where the employer caps the match. But a substitution effect alone does not cause participants to contribute less when the match rate increases. A simple explanation for the propensity to contribute an amount which exhausts the employer ’s match is that the maximal match point is a focal point and interpreted by participants as an implicit suggestion that it is the optimal amount to contribute (Madrian and Shea 2001 ). This explanation is consistent with the behavior gleaned from -2000 -1000
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-8 -6 -4 -2 0 2 4 6 8 Savi n g s R ate (%) Contribution-Male Contribution-Female Savings Rate-Male Savings Rate-Female Figure 12 Contribution levels and savings rates for men and women 4 Using an experiment setting, Duflo et al. ( 2006 ) find that a 50% match increase the take-up rate of low- and middle-income subjects into IRA contribution by 11 percentage points compared to no match; and increase the contribution by $345. These numbers seem to be in the same order of magnitude as our findings. J Finan Serv Res
Fig. 11 , that participants who receive a match contribute less than those who do not receive a match when their incomes are at least $40,000. The following calculations are consistent with this observation. A typical upper limit on the match is 5% of salary. It inspires very different contribution levels for low- and high-income participants. The average (median) contribution of participants earning $40,000 is about $2,740 ($2,315), but 5% of $40,000 is $2,000. Thus, participants earning around than $40,000 who contribute just the matched amount will contribute less than their counter-parts who are offered no match. This effect is strengthened when looking at higher earnings, say $90,000. The average (median) contribution by participants in this income range is $7,121 ($7,239), but 5% of $90,000 is only $4,500. Thus, participants who use the upper limit on the match as their focal point to choose their contribution level will contribute less than those who do not receive a match at all. 5 Discussion and Concluding Remarks This is the first study on 401(k) participation and contribution to use non-survey individual- level data that covers a large number of plans (companies) and includes information about non-participants. It offers a few novel and counterintuitive observations on participation in and contributions to 401(k) plans, and provides sharp estimates of sensitivities of these choices to various individual and plan-level variables. The surprising findings are: women are more aggressive users of 401(k) plans; coverage by a DB plan does not adversely affect usage of a 401(k) plan; matching programs positively affect participation rates and contribution of all employees, especially low-income ones, but negatively affect contributions of mid-to-higher income participants. Other studies have considered some of the issues covered here. However, the results reported here are particularly compelling because of the size and nature of the data used — actual employee records, including non-participants ’ records, from hundreds of plans. Individual-level data are important because in general, it is inappropriate to estimate a relation on an aggregate level and then infer that an analogous relation holds at the individual level —a problem known as the “aggregation bias” (see, e.g., Freedman 2001
; Garrett
2003 ). For example, at plan level, a $10,000 increase in average compensation would increase average contribution by $480, while at individual level the same coefficient is $907. Further, since individuals choose whether to participate in 401(k) plans and how much to contribute to them, records of non-participants are essential to analyze the participation and contribution decision. Figure 12
levels for each gender using predicted contribution imputed from two-sided Tobit coefficients. By the nature of corner solution at zero, the predicted unconditional contribution amount (i.e., accounting for non-participation) could go negative in which case the predicted observed contribution would be zero. The Figure shows that contributions rise with compensation, which is not surprising. It also shows that savings rates rise with compensation, a more remarkable finding. Whether those who earn more also save a larger fraction of their incomes has been a well known question, going back decades prior to Friedman ’s ( 1957
) classic work on the consumption function. Recently, Dynan et al. ( 2004 ) re-visit the issue and conclude that those with higher expected lifetime earnings also have higher savings rates. J Finan Serv Res In general, the estimation of expected lifetime earnings and of savings is thorny, perhaps intractable. This study uses 2001 compensation records of wage earners, whose incomes fluctuate less than those of the self-employed. The unit of observation is the wage earner, not the household. And savings are narrowly defined as contribution to a 401(k) plan. Keeping these simplifications in mind, Fig. 12 shows quite clearly that savings rate in DC accounts increase with compensation. The gender difference also transpires from Fig. 12 : Relatively more women save, and they save more than men, a result consistent with the findings of Warner and Pleeter ( 2001
) who study US military personnel offered a choice between a lump sum and an annuity upon release from the US armed forces. At the contemporary US government borrowing rate of 7% the present values of the annuities were double those of the corresponding lump sums. Nonetheless, most people took the lump sums. Warner and Pleeter estimate that women ’s probability of choosing the annuity was 2% higher than men ’s. Generalizing this finding, one would expect that women are more likely to participate in and contribute to DC plans that defer current consumption into future on favorable terms. One explanation for the gender difference is that women have a stronger taste for saving, perhaps because they live longer on average. A second explanation starts with the assumption that participation and contribution decisions are made at the household, not the employee level. Women are more likely than men to have working spouses, and women ’s working spouses earn more than men ’s working spouses. Thus, comparing a man and a woman with the same income, the woman is likely to live in a household with a higher income, and therefore more likely to participate in a DC plan, and contribute more to it. Without information about marital status, it is difficult to identify such effects. However, the analysis control for the wealth level of the neighborhood in which the household lives (and women do overall live in wealthier neighborhood compared to men of equal earning power), which should to some extent offset the first effect. The second explanation suggests that the gender difference should be strongest for low-income employees and disappear for high-income employees. The differences between women ’s and men’s participation probabilities are indeed highest for those earning less than $40,000, where they exceed 10%. But these differences are still around 2 –3% for those earning over $100,000. Also, the gender gap in contribution rates increases rather than decreases with compensation. Thus, the data are consistent with one prediction of the second explanation, but suggest that it is not the only valid explanation. This paper ’s findings on DB plans and matching programs are also relevant to those interested in promoting savings. New tax-preferred savings programs can attract new savings (i.e., money that would have been used for consumption) or money from competing savings channels. In the latter case, there would be no increase in aggregate savings. This study shows that surprisingly, other things equal, employees covered by DB plans tend to participate more in, and contribute about the same amount to DC plans once they participate. It is possible that employees have separate mental accounts (Shefrin and Thaler 1992 ; Thaler
1999 ) for different accounts of retirement money, and when choosing whether to participate in, and how much to contribute to DC plans, they do not take into account whatever rights they have in their employers ’ DB plans. Of course, there could be a selection effect at work here: retirement savings-conscientious employees are more likely attracted to firms that provide both DB and DC plans. Moreover, the stronger usage of DC plans by those already covered by DB plans suggests that the presence of a DB plan increases awareness of the need to save for retirement. The overall impression is that employees save as much in 401(k) plans with or without a DB plan. Thus, the evidence presented here is consistent with the view of Poterba et al. J Finan Serv Res ( 1996
) ( “believing that IRA and 401(k) contributions represent new saving”) and not that of Engen et al. ( 1996
) ( “we conclude that little, if any, of the overall contributions to existing saving incentives have raised saving. ”) The implication, then, is that the introduction of new tax-preferred savings programs will likely increase overall savings. This conclusion, however, is not airtight. It is possible that those eligible for DB benefits who are aggressive contributors to 401(k) plans make their contributions with money that would be saved through other channels not covered in the records used in this study. Match programs increase participation rates, and contributions, primarily of low-income employees. This finding clearly suggests that voluntary participation and contributions in individual retirement accounts are likely to increase if the government were to match the contributions. Moreover, the match will have the strongest impact on the low-income members of society. And, if policy makers find it desirable to limit the subsidy to high- income people, match rates could be set to decline with income. It does not seem surprising that an employer ’s match program should increase overall contributions since it increases the compensation, albeit in a deferred form. However, individuals in the medium income range, conditional on participation, seem to contribute less when the match is generous. Two explanations come to mind. First, if plan participants have desired levels of total savings, they will contribute less in the presence of employer match than what they would in the absence of such matching. Another influence comes from upper limits on the contribution that is matched, which all plans have. (Usually it is 5 –6% of compensation.) The upper limit can serve as a focal point suggesting a desired contribution. Choi et al. ( 2002
) indeed report that many participants contribute exactly to the point where matching ends. To the extent that retirement savings rate increases with compensation, such a focal point will increase contributions of low-income participants and reduce that of mid-to-high-income participants, relative to their counterparts who receive no or little match. This is a study of choices of employees eligible to participate in 401(k) plans in 2001. Most of the employees ’ choices were probably made prior to 2001, and a shortcoming of the study is the records ’ silence on the timing of the employees’ choices. Some employee attributes changed over time —certainly age and tenure, and most likely compensation. Moreover, some plan characteristics may have changed between the time an employee made his most recent choice and 2001. A long panel of records can potentially fix some of these issues, but not all. Ameriks and Zeldes ( 2004
) point out that the separation of age, cohort and time effects requires assumptions outside the panel records. The exploratory data analysis summarized here speaks to a variety of subfields. First, to the growing community of students of retirement plans in general and defined contribution plans in particular. Second, to those interested in savings behavior and especially how it varies across income groups. And third, to those interested in gender differences in decision making. Additionally, this descriptive paper is likely to inform discussions on designs of retirement and savings plans. Acknowledgement The authors are grateful to Steve Utkus and Gary Mottola from Vanguard® Center for Retirement Research. They made the data available and provided us with constructive comments throughout the process. We also thank Pierre Azoulay, Geert Bakeart, Charlie Calomiris, Sarah Holden, Brigitte Madrian, Jim Powell, Thomas Steinberger, Ed Vytlacil, Elke Weber, Steve Zeldes, an anonymous referee, and seminar participants at Columbia, Rice, Wharton Pension Research Council, New York Federal Reserve Bank, CEPR workshop on Financing Retirement in Europe, and Wharton Workshop on Household Portfolio Choice and Financial Decision Making for their helpful comments. J Finan Serv Res Appendix Data Construction The original data set provided by the Vanguard consists of 926,104 records of 401(k) eligible employees. The following criteria cause elimination of observations: (1) The employee is hired after January 1, 2001. (His recorded annual contribution might be inaccurate.) (2) The employee is less than 18 years old (He might not be the decision maker.) (3) The employee ’s annual compensation is less than $10,000 or greater than $1 million. 793,794 records survive. The key variables deferral rate, contribution, and compensation appear in all the records. All other individual variables have missing values that are more concentrated in the non-participant sub-sample. 12.8% of the observations do not report gender, among which 62.5% are non-participants; the same percentages for age, tenure and wealth are 12.3% (62.2), 12.2 (62.1%) and 25.6% (76.4). Elimination of all the observations with missing values would cause the study to be based on a partially truncated sample, which is likely to bias the results due to the influence of the selection. Hence the choice to replace them with imputed values. The imputed values are calculated as follows: (1) unidentified gender variables are recorded as the percentage of females in the plan (a record identified as a female being 1 and as a male being 0); (2) missing age and tenure values are replaced with their respective plan mean age or tenure. To fill in the missing values of wealth, the following regression is estimated on non-missing values: Log WEALTH ð Þ ¼ b 0 þ b
1 Log COMP
ð Þ þ b
2 FEMALE
þ b 3 AGE þb 4 AGE 2 þ b
5 Log DCASSETS ð Þ þ e;
where DCASSETS is the total assets in the defined-contribution accounts. The specification above was chosen among various models for both within-sample goodness-of-fit and out- of-sample robustness. The next step is to predict out-of-sample values and map the predicted values to the closest IXI brackets. With the exception of the wealth level, IXI, missing values do not account for a significant proportion (about 10%) of observations, and the symmetric trimming method (Honore et al. 1997
) is applied to conduct sensitivity check. That is, for each variable create an artificial sample by first taking only records that report that variable (but may miss other variables). The second step eliminates a given number of participants ’ records at random, so that the participation rate in the subsample matches that of the original sample. (This second step eliminates records of participants, since systematically it is the non- participants whose records are likely to miss values.) Then estimate coefficients for the resulting sub-sample. Repeating this process many times (e.g., 30 or 50), the coefficients estimated on the full sample (with missing values imputed) are close to the average of coefficients estimated on those symmetrically trimmed sub-samples. For the wealth variable the same sensitivity check cannot be used because only a low proportion of non-participant records have this information. Instead, the comparison is between the inputted wealth variables and the general distribution of wealth in the population. The following figure plots the histogram of wealth distribution for the population (IXI), of the non-missing Vanguard data, and the amended Vanguard data. After J Finan Serv Res filling in the missing values, the sample studied here no long over-represents the wealthy households. The sample still under-represents the very poor households (those with negative or less than $1,000 in balance), and over-represent the lower-middle to middle households (with balances ranging from $5,000 to $100,000), which is consistent with evidences from the Survey of Consumer Finance and the Current Population Survey that 401(k) eligible employees are overall financially better-off than the general population in the lower end. Further, estimating the main regressions excluding the wealth variable, the coefficients on all variables except compensation show little variation. (The loading on compensation is increased, which is expected.) References Agnew J, Balduzzi P, Sunden A (2003) Portfolio choice and trading in a large 401(k) plan. Am Econ Rev 93 (1):193 –215 (March) Ameriks J, Zeldes SP (2004) How do household portfolio shares vary with age? Working Paper, Columbia University Bajtelsmit, VL, Bernasek, A (1999) Why do women invest differently than men? Financ Couns Plan 7:294 –305
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e un de r 1K 1. 0- 2. 5K 2. 5- 5. 0K 5. 0- 10 K 10 -2 5K 25 -5 0K 50 -7 5K 75 -1 00 K 10 0- 12 5K 12 5- 15 0K 15 0- 17 5K 17 5- 20 0K 20 0- 22 5K 22 5- 25 0K 25 0- 30 0K 30 0- 35 0K 35 0- 40 0K 40 0- 45 0K 45 0- 50 0K 50 0- 75 0K 75 0K -1 M ab ov e 1 M Assets in Personal Accounts % of Total Households IXI (population) Vanguard--non-missing Vanguard--amended J Finan Serv Res
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