PERSPECTIVES: Choice and Other Determinants of Employee Contributions to Defined Contribution Plans

by Leslie E. Papke
Social Security Bulletin, Vol. 65 No. 2, 2004

Understanding the role that 401(k) plan characteristics, such as investment choice, play in participation and employee contributions is important as more workers rely on this type of retirement plan and as proposals for Social Security solvency include individual savings plans. Using the 1992 Health and Retirement Study, this article investigates which individual and job characteristics are associated with asset choice in defined contribution plans. Investment choice is found to substantially increase contributions to such plans.


The author is a professor with the Department of Economics, Michigan State University.

The findings and conclusions presented in the Bulletin are those of the authors and do not necessarily represent the views of the Social Security Administration.

Summary

Understanding the role that 401(k) plan characteristics, such as investment choice, play in participation and employee contributions is important as more workers rely on this type of retirement plan. Further, the degree of autonomy in investment decisions is an important issue in the design of private saving plans that are proposed as part of Social Security reform.

In this analysis, I use the 1992 Health and Retirement Study (HRS) to examine the individual and job characteristics that are associated with asset choice in defined contribution plans. The HRS is one of the few surveys that records whether participants have choice over their defined contribution assets. I find that men and women are equally likely to have investment choice in their pension plan and that choice does not vary by marital status. Employees' ability to choose their own investments increases with education and family net worth. Workers in managerial positions are more likely to have choice than are workers in other occupations, and employees in larger establishments and larger firms are also more likely to have investment choice.

I also examine whether having choice over pension investments increases contributions to defined contribution plans. As more workers rely on defined contribution plans, a key issue in retirement income security is to encourage sufficient contributions. (Often employee contributions are required before the employer will make a matching contribution.) A key econometric issue in estimating the effect of choice is the potential endogeneity of choice; that is, do participants with some financial sophistication work at firms that offer plans with investment choice? If so, then it will be difficult to infer the effect of choice on the general population. I address this issue econometrically in two different ways. My preferred estimates indicate that a participant with choice contributes over 8.5 percentage points more annually to their defined contribution plan than a comparable, randomly selected participant without choice. This effect is estimated fairly precisely, and it is the largest effect on contributions. Single and married women are estimated to contribute more (0.83 and 1.03 percentage points, respectively) than married men. Older participants contribute more, but the effect is quite small economically. I also find that the benefits of tax-deferred saving are distributed fairly evenly across income levels. Finally, I submit these results to a series of robustness checks.

From a policy perspective, it is important to understand which plan features encourage employee participation in defined contribution plans. This article adds to a growing literature that suggests that plan attributes other than the employer match rate can play a role in increasing participant contributions. Loan provisions and asset choice may encourage contributions even as employers reduce or eliminate matching provisions in their 401(k) plans.

Introduction

The striking growth of 401(k) plans has vastly expanded the number of individuals who have some discretion regarding the retirement assets in their employer-provided pension plan. In 1998, the most recent year for which the Department of Labor (2003) has released detailed information from Internal Revenue Service (IRS) Form 5500 filings, about 83 percent (30.93 million) of the 37 million participants in 401(k) plans had some control over their investments. These participants owned over $1.25 trillion in assets, accounting for 81 percent of 401(k) assets. Participants usually direct the investment of their own contribution and often that of their employer. The decision to contribute is important for retirement income, because an account is usually not even established for the employee unless a contribution is made. Understanding the role that plan characteristics, such as investment choice, play in participation is critical as more workers rely on this type of retirement plan. This article presents evidence from the 1992 Health and Retirement Study on the prevalence of choice over pension investments and the effect of asset choice on contribution rates.

Recent work points to the importance of plan features in encouraging plan participation and contribution rates. These characteristics include the presence and size of an employer match rate, the fraction of salary matched, participant choice over asset allocation, and loan provisions. Plan features may have unintended consequences as well. For example, the structure of many of the 401(k) plans (often the employer contribution is made in company stock) may encourage employees to hold extremely risky portfolios—that is, holding their pension assets as well as their human capital in one company.

Participant-friendly features, such as asset choice and loan provisions, may encourage or maintain participation levels during periods in which companies discontinue their contributions to 401(k) plans, for example. Since 2001, a number of large companies have either suspended or discontinued their employer match.1 These companies include Ford Motor Company, DaimlerChrysler, Goodyear, and Charles Schwab & Co. Active control over plan assets may keep participants engaged in the plan—perhaps increasing their own contributions to cover the loss of the match—and allow plans to pass nondiscrimination requirements relatively inexpensively.

The behavior of participants in self-directed individual retirement accounts is also relevant in the discussions of adding personal accounts to Social Security. Further, if future Social Security benefits are reduced, there will be an increased role for personal saving in financing retirement with tax-deferred plans that are not related to employment, such as individual retirement accounts (IRAs).

This article

Asset Choice in Pension Plans

Prior to the availability of 401(k) plans, supplemental defined contribution plans were organized as saving and thrift plans in which the employee made contributions out of after-tax dollars. Employees were generally allowed to direct their contribution, but it was common for the employer's contribution to be constrained, often to company stock. Many of these saving and thrift plans were converted when 401(k) plans became available (generally, 1981) to allow employees to make pretax contributions, but different treatment of the employer contribution continued. The extent of asset choice depends on the organizational form of the plan as well. In some 401(k) forms, even the employee contribution is required to be in company stock. For example, initial investment in company stock is common in profit-sharing plans, and it is required for those organized as employee stock ownership plans.2

Companies are encouraged to provide a diverse offering of assets by section 404(c) of the federal regulations pertaining to fiduciary responsibility. Section 404(c) of the Employee Retirement Income Security Act (ERISA), finalized in 1992, provides conditions under which the sponsor is not liable for loss or breach of fiduciary responsibility that may result from the participant's exercise of control over assets. Broadly, the participant must be able to "exercise independent control" over assets in the account. The sponsor must (1) provide sufficiently varied investment alternatives to allow the participant an opportunity to materially affect the potential returns on assets and account risk; (2) allow the participant to choose from at least three alternative investments, each of which must be diversified and have different risk and return characteristics (employer's securities may not be one of the three); (3) provide sufficient information for the participant to make investment decisions; and (4) allow the participant to change investments with a frequency that is appropriate for the expected market volatility of the investment.3

Perhaps due in part to this regulation, participant direction grew during the 1990s. Wiatrowski (2000) summarizes the trends in investment choice for full-time employees using several years of the Bureau of Labor Statistics' National Compensation Survey. He reports that, in 1985, 90 percent of full-time employees had investment choice over their own contribution, and 48 percent had control over their employers' contribution. By 1997, there was a slight drop in the percentage who could control their own contributions (87 percent), but more than 65 percent had choice over their employers' contribution. Using these data, Wiatrowski also finds that a smaller percentage of participants may choose company stock as an investment option. In 1985, for example, 70 percent of employees could choose employer stock for their contribution, and 61 percent could choose employer stock for their employer's contribution. In 1997, those figures were 42 and 25, respectively.

Recent work relates plan features to participation. Using the 1998 Survey of Consumer Finances, Munnell, Sundén, and Taylor (2001) find that the ability to borrow from the plan increases the 401(k) contribution percentage by about 1 percentage point. Engelhardt and Kumar (2003) estimate an elasticity of 401(k) contributions with respect to the employer match rate of 0.25, suggesting that employee contributions are responsive to matching. In recent work with two data sets—the 1992 National Longitudinal Survey of Mature Women and Wave 1 of the 1992 Health and Retirement Study—Papke (2003) estimates economically large effects of asset choice on the likelihood of participation, contribution rates, and account balances. Her preferred ordinary least squares (OLS) estimates from that paper indicate that participants with choice are about 30 percent more likely to make a contribution to their defined contribution plan. She estimates that participants contribute between 1 and 3 percentage points more of salary annually and have at least $9,000 more in their accounts than comparable participants without investment choice.

Other work indicates that many participants do not change the default choices firms make for them. Choi and others (2002), using administrative data from three companies with automatic enrollment in their 401(k) plans, find that most employees stay with a plan's preselected default contribution rate and the default investment fund chosen by the company. It appears that participant-friendly features such as borrowing and investment choice are associated with a higher probability of participating and higher contribution levels.

Econometric Estimates

This section presents estimates of the determinants of pension plan investment choice and the effect of asset choice on contributions. I use data on pension participants of preretirement age from Wave 1 (1992) of the Health and Retirement Study (HRS). The respondents are aged 51–61 in 1992.

The 1992 HRS is a detailed survey that includes pension questions on up to three defined benefit and three defined contribution plans. I restrict the sample to those defined contribution participants who answer the following question about each of their defined contribution plans: Were you able to choose how the money in your account is invested? In the typical regression, there are 1,690 individual defined contribution plan participants and 180 multiple plans, for a total of 1,870 observations.

Note that the HRS question does not explicitly distinguish employer from employee contributions, so it is possible that the employee would report having choice over his or her contributions, even if the employer directs the firm's contributions. Unfortunately, there is no firm-reported pension information in the publicly available HRS data.

Determinants of Choice

In this section, I relate individual characteristics, and the limited characteristics of firms available in the HRS, to the participant-reported ability to choose pension investments.4 Table 1 presents summary statistics for this HRS sample. About 59 percent of the 1,983 respondents report having choice over investments in their pension plan, and they contribute, on average, about 5.05 percent of salary. Single women account for about 13 percent of the sample; single men, about 7 percent; married women, 32 percent; and married men, about 48 percent. The average age in the sample is 54 years. About 58 percent of this sample reports having an individual retirement account, and 42 percent have a defined benefit pension plan in addition to their defined contribution plan—either with the current or a former employer.

Table 1. Summary statistics for respondents answering question about choice in the 1992 Health and Retirement Study
Dependent variable Mean Standard
deviation
Observations
Choice .589 .492 1,983
Contribution percentage 5.050 4.831 1,981
Single female .128 .334 1,981
Single male .071 .257 1,981
Married female .318 .466 1,969
Age 54.456 4.850 1,981
Education 13.488 2.542 1,981
Income (dollars)
25,000–50,000 .302 .459 1,981
50,000–100,000 .441 .497 1,981
More than 100,000 .168 .374 1,981
Net worth (dollars)
50,000–100,000 .189 .391 1,981
100,00–250,000 .351 .478 1,981
250,000–500,000 .182 .386 1,981
More than 500,000 .094 .292 1,981
Employee has—
Defined benefit plan .421 .494 1,981
Individual retirement account .581 .494 1,981
Industry dummies
Agriculture and mining .046 .210 1,908
Manufacturing .275 .446 1,908
Transportation .096 .295 1,908
Wholesale .048 .213 1,908
Retail .074 .262 1,908
Fire and services .403 .491 1,908
Public administration .059 .235 1,908
Occupation dummies
Management .238 .426 1,922
Professional, specialist, and technical .200 .400 1,922
Sales .079 .269 1,922
Clerical .209 .406 1,922
Services .049 .217 1,922
Farming and construction .027 .162 1,922
Machine operators .198 .399 1,922
Number of employees in participant's work location
Less than 100 .450 .498 1,913
100–499 .276 .447 1,913
500 or more .273 .446 1,913
Number of employees in participant's firm a
Less than 100 .166 .372 1,904
100–499 .162 .369 1,904
500 or more .672 .470 1,904
SOURCE: Author's calculations based on data from the 1992 Health and Retirement Study.
a. Participant's estimate of firm's employment in all locations.

What individual and job characteristics are associated with defined contribution plans that offer asset choice? Column 1 of Table 2 presents estimates of a linear probability model of choice as a function of individual and firm characteristics. The ability to choose pension investments does not appear to vary by sex or marital status (married men are the omitted category). Asset choice increases with years of education: a participant with 4 more years of education is 6.4 percentage points more likely to have investment choice than is an otherwise comparable participant. Family net worth between $250,000 and $500,000 is also associated with a higher probability of having choice (9.2 percentage points higher).

Table 2. Linear probability model of choice and linear models of the percentage of salary participants contribute to a defined contribution plan
Dependent variable Choice
(1)
Percentage of
salary contributed
Ordinary
least squares
(2)
Two-stage
ordinary
least squares
(3)
Choice   2.936***
(.214)
8.551***
(2.027)
Single female -.050
(.041)
.579*
(.355)
.825*
(.429)
Single male .020
(.049)
.547***
(.395)
.441
(.450)
Married female -.032
(.030)
.832***
(.284)
1.032***
(.339)
Age -.00011
(.002)
.076***
(.021)
.080***
(.026)
Education .016***
(.006)
-.036
(.053)
-.132*
(.072)
Income (dollars)
25,000–50,000 -.016
(.045)
.932***
(.350)
.995**
(.440)
50,000–100,000 .011
(.048)
1.175***
(.392)
1.059**
(.479)
More than 100,000 .015
(.056)
1.005**
(.497)
.845**
(.598)
Net worth (dollars)
50,000–100,000 .045
(.038)
.657**
(.302)
.352
(.402)
100,00–250,000 .043
(.036)
1.045***
(.302)
.732**
(.399)
250,000–500,000 .092**
(.042)
1.806***
(.387)
1.234**
(.504)
More than 500,000 .057
(.053)
.900*
(.488)
.589
(.566)
Employee has—
Defined benefit plan .134***
(.025)
1.451***
(.228)
.540
(.423)
Individual retirement account .025
(.026)
.141
(.227)
.049
(.273)
Number of employees in participant's work location
100–499 .072**
-.030
   
500 or more .071**
-.031
   
Number of employees in participant's firm a
100–499 .074*
-.043
   
500 or more .107***
-.038
   
Industry and occupation dummies Yes Yes Yes
Constant .151
(.176)
-4.320
(1.503)
-5.513
(1.886)
Observations 1,865 1,888 1,865
R2 .1084 .1980  
SOURCE: Author's calculations based on data from the 1992 Health and Retirement Study.
NOTE: Standard errors that are robust to heteroskedasticity and to correlation across mulitple plans for an individual are in parentheses.
* = statistically significant at the 10 percent level.
** = statistically significant at the 5 percent level.
*** = statistically significant at the 1 percent level.
a. Participant's estimate of firm's employment in all locations.

The individual characteristic with the largest economic effect on the choice probability is having a defined benefit plan, either with the current employer or with a previous job. Having a defined benefit plan raises the probability of having asset choice in one's defined contribution plan by 13.4 percentage points. This suggests that individuals with a taste for saving, as evidenced by their pension participation, may prefer defined contribution plans with self-direction features. Such features allow them to choose pension assets to achieve their preferred asset allocation across tax-deferred and non-tax-deferred accounts.

The probability of having choice also varies for a few industry and occupation categories (these coefficients are not reported in Table 2). Workers in public administration are estimated to be 17 percentage points more likely than workers in agriculture and mining to have choice in their pension plan (none of the other industry coefficients differed from the omitted category). Workers in service, farming and construction, and machine operator occupations are estimated to be significantly less likely to have choice than workers in managerial positions (from 10 to 24 percentage points).

I also include two sets of firm-size dummies in the linear probability model of choice. The first set is based on the number of employees in the participant's work location, and the second set is the participant's estimate of the firm's employment in all locations. The four dummy variables that are included are statistically significant in the choice equation, suggesting that participants in larger firms (more than 99 employees) are between 7 and 10 percentage points more likely to have investment choice in their pension plan.

Since choice is a binary variable, I also estimate a probit model containing the same explanatory variables as in Table 2. The estimates, reported in column 1 of Table 3, are qualitatively similar to those from the linear probability model. In particular, the direction and statistical significance of the coefficients are the same. As in the linear probability model, firm size has an important effect on choice.

Table 3. Probit model of choice and a linear model of the percentage of salary participants contribute to a defined contribution plan
Dependent variable Choice
(1)
Percentage
of salary
contributed
(instrumental
variables)
(2)
Choice   9.096***
(2.001)
Single female -.137
(.110)
.847*
(.441)
Single male .067
(.132)
.427
(.463)
Married female -.085
(.083)
1.052***
(.347)
Age -0.00062
(.0068)
.081***
(.027)
Education .045***
(.016)
-.141*
(.073)
Income (dollars)
25,000–50,000 -.047
(.120)
1.001**
(.455)
50,000–100,000 .019
(.129)
1.046**
(.494)
More than 100,000 .037
(.156)
.827
(.615)
Net worth (dollars)
50,000–100,000 .123
(.101)
.325
(.415)
100,00–250,000 .117
(.099)
.703*
(.411)
250,000–500,000 .263**
(.120)
1.182**
(.517)
More than 500,000 .155
(.150)
.560
(.581)
Employee has—
Defined benefit plan .371***
(.068)
.452
(.422)
Individual retirement account .068 .040
Number of employees in participant's work location
100–499 .202**
(.082)
 
500 or more .203**
(.090)
Number of employees in participant's firm a
100–499 .191*
(.113)
 
500 or more .283***
(.101)
 
Industry and occupation dummies Yes Yes
Constant -.952
(.484)
-5.636
(1.940)
Observations 1,865 1,865
SOURCE: Author's calculations based on data from the 1992 Health and Retirement Study.
NOTE: Standard errors that are robust to heteroskedasticity and to correlation across mulitple plans for an individual are in parentheses.
* = statistically significant at the 10 percent level.
** = statistically significant at the 5 percent level.
*** = statistically significant at the 1 percent level.
a. Participant's estimate of firm's employment in all locations.

Econometric Results for Employee's Contribution Percentage

Pension investment choice may affect the level of participation in a pension plan. Some behavioral theories of saving suggest that a change in the economic environment, such as requiring participants to choose assets in a pension plan, may stimulate saving.5 With employer-provided pensions in place, individuals are more likely to learn that others think saving is important. This section presents models explaining the employee's contribution percentage to the defined contribution plan as a function of choice and of individual and employment characteristics. In this HRS sample, the average contribution percentage is 5.05 percent of salary, with a standard deviation of 4.81. The median contribution is 5.00 percent, and the mean of those who contribute is 7.10 percent, with a standard deviation of 4.27. About 29 percent (573) report a zero contribution.

Simple tabulations indicate that employees are more likely to participate when investment choice is present. While 50.37 percent of those without choice report a zero contribution, only 13.76 percent of those with choice report a zero contribution.

Table 2 also presents estimates of linear models of the percentage of salary the participant contributes to his or her defined contribution plan. The ordinary least square estimates are in column 2. Standard errors are corrected for heteroskedasticity. Controlling for individual characteristics, financial characteristics, and industry and occupation, choice over pension assets is estimated to increase the annual contribution by 2.9 percentage points. This increase, a 43 percent increase relative to the unconditional mean contribution of 5.05 percent, is an economically large effect and is precisely measured.

Having a defined benefit plan or an individual retirement account also increases the percentage contributed. These dummy variable coefficients indicate a taste for saving: a participant with a defined benefit plan is predicted to contribute about 1.5 percentage points more to his or her defined contribution plan. The sign of the coefficient on the IRA dummy is also positive but is imprecisely measured. The percentage of salary contributed is greater for each included category of income and net worth relative to the omitted categories (income less than $25,000 and net worth less than $50,000), but the difference is generally close to 1 percentage point. (The largest effect is 1.8 percentage points for the highest net worth category—$250,000 to $500,000.) This suggests that the benefits of tax-deferred saving, in percentage terms at least, are fairly evenly spread among the medium- to high-income participants.

These OLS estimates indicate that participant-direction of pension assets has a statistically significant and economically large effect on pension contributions. However, one might argue that choice is an endogenous variable in these regressions. That is, participants with some financial sophistication and taste for saving join firms that offer plans with investment choice. Unobserved saving heterogeneity may remain in the error term despite my attempt to control for saving propensity by including the ownership of an individual retirement account and participation in a defined benefit plan. Pension plans with participant-direction features may be more common in certain industries and occupations. I include those dummy variables as well to allow individuals to sort on that basis. Unfortunately, there are a limited number of pension plan features in the publicly available HRS data.

Ideally, we could find one or more instrumental variables (IV) for the choice variable in the contribution equation. Such a variable must be exogenous in the contribution equation, that is, it must be properly omitted from the equation and uncorrelated with unobservables, such as taste variables, in that equation. In addition, the IV candidates must be partially correlated with choice. Instrumental variables are difficult to come by without some kind of natural experiment that would exogenously cause some firms to offer choice when they might not have otherwise. In the publicly available HRS, the possibilities are rather limited. Nevertheless, the results for the linear probability choice models are suggestive. In particular, firm size has a significant effect on choice. It may also be reasonable to assume that although individuals with a taste for saving may select different industries and occupations, there is no systematic sorting of those with a taste for saving into certain firm sizes, either by their work location or number of employees in the entire firm. As with most applications of instrumental variables, this assumption can be questioned. However, I can partly test exogeneity of the firm-size variables using a test of the overidentifying restrictions; the results are reported below.

Column 3 of Table 2 contains two-stage least squares (2SLS) estimates of the contribution equation, where the four firm-size dummies included in column 1 are used as instrumental variables for choice. The 2SLS estimate of the effect of choice is substantially larger than the OLS estimate, suggesting that a participant with choice contributes over 8.5 percentage points more annually to his or her defined contribution plan than a comparable participant without choice. This effect is estimated fairly precisely, and it is the largest effect. Single and married women are estimated to contribute more (0.83 and 1.03 percentage points, respectively) than married men. Older participants contribute more, but the effect is quite small economically. Participants with higher income and higher net worth are estimated to contribute about 1 percentage point more than those in the lowest income and net worth categories, as in the OLS estimates. The 2SLS coefficient on the defined benefit indicator is positive but not statistically significant. Apparently, the real causal effect of having a defined benefit plan is zero.

The difference between the OLS and 2SLS estimates of the choice coefficient are practically large. Nevertheless, because the 2SLS standard error is about 10 times larger than the OLS standard error, the difference between OLS and 2SLS could be due to sampling error. That is not the case here. I use a regression-based Hausman test, made robust to heteroskedasticity, to determine whether the difference between OLS and 2SLS is statistically significant. The statistic is computed by obtaining the reduced form residuals from the linear probability model for choice and then including them as a regressor in the contribution equation. The expanded equation is estimated by OLS, and the heteroskedasticity-robust t statistic on the reduced form residuals is a valid test statistic. Assuming that the firm-size dummies are exogenous, the null hypothesis is that choice is exogenous. A significant t statistic on the reduced form residuals rejects exogeneity of choice (Wooldridge 2000). When I carry out this test, the coefficient on the reduced form residual is −5.513 and its p-value is .001, suggesting that the unobservables affecting choice are actually negatively correlated with unobservables in the error in the structural model.

The story about unobserved taste for saving being positively correlated with choice means we would expect OLS to have an upward bias. On average, then, we would expect the 2SLS estimate to be smaller than the OLS estimate. There are several reasons the opposite might occur. First, of course, firm size might not be exogenous, in which case the 2SLS estimates could have an upward bias. (I offer a test of this below.) If firm size is positively correlated with unobserved taste for saving, we expect an upward bias. Because the correlation between choice and firm size is not perfect, a modest amount of correlation between firm size and taste for saving can lead to a large asymptotic bias in 2SLS.6 A second possibility is that choice is measured with error, in which case OLS could have a downward bias. Unfortunately, the direction of bias for OLS is unclear in this application, as choice is a binary variable that cannot satisfy the classical errors-in-variables model. Still, when IV estimates are unexpectedly higher than OLS estimates, measurement error is often cited as a possibility.

A third, more subtle possibility comes from the literature on treatment effect. In a simple bivariate setting, Imbens and Angrist (1994) characterize the probability limit of the IV estimator of the treatment effect of a binary endogenous explanatory variable. In my application, the Imbens and Angrist results imply that the IV estimator consistently estimates the average effect for participants whose choice status is induced by a change in firm size. It could be that this effect is larger than the effect for the population as a whole.7

Since I have one endogenous variable—choice—and four instruments (the firm-size dummies in column 1 of Table 2), I am able to test the three overidentifying restrictions. The heteroskedasticity-robust regression-based statistic is 5.036. Under the null hypothesis that all instrumental variables are uncorrelated with the structural error, this is the outcome of a chi-square random variable with three degrees of freedom; the p-value is about .167, so the firm-size dummies pass the overidentification test if we use standard significance levels.8

Finally, rather than using a standard 2SLS procedure, where the reduced form of choice is linear, I use the fitted choice probabilities from the probit model in Table 3 as a single instrumental variable for choice in the contribution equation. This is the most efficient instrumental variable, since the fitted probabilities are the best predictor of choice. These instrumental variable estimates are reported in column 2 of Table 3 (they are generally similar to the 2SLS estimates in Table 2).

Conclusion

In this paper, I use an instrumental variable approach to the problem of potential endogeneity of investment choice in a contribution equation. My preferred estimates indicate that a participant with choice contributes over 8.5 percentage points more annually to his or her defined contribution plan than does a comparable participant without choice. This is an economically large effect: the unconditional mean of contributions is about 5 percentage points of salary. I also find that the benefits of tax-deferred saving are distributed fairly evenly across income levels.

From a policy perspective, it is important to understand which plan features encourage employee participation in defined contribution plans. This article adds to a growing literature that suggests that plan attributes other than the employer match rate can play a role in increasing participant contributions. Loan provisions and asset choice may encourage contributions even as employers reduce or eliminate matching provisions in their 401(k) plans.

Notes

1. See Munnell and Sundén (2003) for a detailed discussion.

2. Employee stock ownership plans must invest primarily in qualified securities of the employer (debt instruments are not included). Plan administrators must allow participants nearing retirement to diversify at least 25 percent of the account (see Employee Benefit Research Institute 1997). Stock-option plans and stock purchase plans (the latter allows employees to purchase company stock at a discount) are not tax-qualified defined contribution plans.

3. See section number 2550.404c-1, Rules and Regulations for Fiduciary Responsibility, ERISA section 404(c) plans of the Code of Federal Regulations Pertaining to PWBA at http://www.dol.gov/dol/allcfr/pwba.

4. Prior research finds that many participants may not understand their pension arrangements (see, for example, Gustman and Steinmeier 2004), which is an important caveat about HRS self-reported pension data.

5. See Thaler and Shifrin (1981). Bernheim (1997) compares behavioral theories of saving with those that come from the traditional life-cycle hypothesis.

6. See, for example, Wooldridge (2000), Section 15.1 for a discussion.

7. Wooldridge (2000), Section 18.4.2 discusses the Imbens and Angrist (1994) characterization in this setting.

8. Unfortunately, this test tends to have low power because it is effectively based on comparing two different IV estimates. See Wooldridge (2002), Section 6.2.2.

References

Bernheim, B.D. 1997. "Rethinking Saving Incentives." In Fiscal Policy: Lessons from Economic Research, edited by A.J. Auerbach, 259–311. Cambridge, MA, and London: MIT Press.

Choi, J., D. Laibson, B. Madrian, and A. Metrick. 2002. For Better or for Worse: Default Effects and 401(k) Savings Behavior. NBER Working Paper No. 8651. Cambridge, MA: National Bureau of Economic Research.

Department of Labor, Pension and Welfare Benefits Administration. 2003. Abstract of 1998 Form 5500 Annual Reports. Private Pension Plan Bulletin No. 11 (Winter). Washington, DC: DOL.

Employee Benefit Research Institute. 1997. Fundamentals of Employee Benefit Programs. 5th ed. Washington, DC: EBRI.

Engelhardt, G.V., and A. Kumar. 2003. Understanding the Impact of Employer Matching on 401(k) Saving. Research Dialog 76 (June). New York: TIAA-CREF Institute.

Gustman, A.L., and T. Steinmeier. 2004. "What People Don't Know About Their Pensions and Social Security." In Private Pensions and Public Policies, edited by W.G. Gale, J.B. Shoven, and M.J. Warshawsky, 57–125. Washington, DC: Brookings Institution.

Imbens, G.W., and J.D. Angrist. 1994. "Identification and Estimation of Local Average Treatment Effects." Econometrica 62:467–476.

Munnell, A.H., A. Sundén, and C. Taylor. 2001. "What Determines 401(k) Participation and Contributions?" Unpublished paper. Boston College, Chestnut Hill, MA.

Munnell, A.H., and A. Sundén. 2003. Suspending the Employer 401(k) Match. Issue Brief No. 12 (June). Chestnut Hill, MA: Center for Retirement Research at Boston College.

Papke, L.E. 2003. "Individual Financial Decisions in Retirement Plans: The Role of Participant-Direction." Journal of Public Economics 88:39–61.

Thaler, R.H., and H. Shefrin. 1981. "An Economic Theory of Self-Control." Journal of Political Economy 89:392–406.

Wiatrowski, W.J. 2000. "Putting Stock in Benefits: How Prevalent Is It?" Compensation and Working Conditions. Fall:2–7.

Wooldridge, J.M. 2000. Introductory Econometrics: A Modern Approach. Cincinnati, OH: South-Western.

_______. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.