Article

Boon or Bane: 401(k) Loans and Employee Contributions

Research on Aging 2014, Vol. 36(5) 527-556 ª The Author(s) 2013 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0164027513507001 roa.sagepub.com

Jeffrey B. Wenger1,4 and Christian E. Weller2,3

Abstract Economic and behavioral theories arrive at different conclusions about the effect of being allowed to borrow from one’s defined-contribution (DC) retirement plan on people’s contributions to DC plans. Traditional life-cycle models unambiguously suggest that the borrowing option makes people better off than not being able to borrow. Households consequently contribute more to their DC plans than they would absent the borrowing option. Previous research finds that the ability to borrow from a DC plan increases contemporaneous contributions, consistent with traditional models. Behavioral finance, in contrast, suggests that some workers may operate with nonlinear time discounting. They plan on saving more in the future but change their mind and save less than initially planned as time passes. These workers may enjoy higher lifetime utility if they have no loan option because DC plans serve as commitment devices for retirement saving. The money cannot be used prior to retirement. Absent this commitment device, contributions may be lower for some households than would be the case without a DC loan option. We study DC plan contributions for households with 1

Department of Public Administration and Policy, School of Public and International Affairs, The University of Georgia, Athens, GA, USA 2 Department of Public Policy and Public Affairs, McCormack Graduate School, University of Massachusetts, Boston, MA, USA 3 Center for American Progress, Washington, DC, USA 4 RAND Corporation, Santa Monica, CA Corresponding Author: Christian E. Weller, Department of Public Policy and Public Affairs, McCormack Graduate School, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA 02125, USA. Email: [email protected]

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heterogeneous preferences about discounting. We separate households into those that demonstrate inconsistent (or paradoxical) borrowing behavior, which may reflect nonlinear time discounting, and those with more consistent borrowing behavior. We find that a DC loan option raises current savings, but does so more for households with consistent borrowing behavior than for those with inconsistent borrowing behavior. Keywords retirement savings, 401(k), pension loans, defined contribution

Introduction The rise of defined-contribution (DC) retirement savings plans has meant that households have more opportunities to save on their own than in the past. And most households can borrow from their DC plans, an option that typically does not exist in other retirement benefits such as defined-benefit (DB) pensions, which used to be the primary private retirement benefit for U.S. workers but have widely given way to DC plans. The combination of more individual decisions on savings—how much and when to save, for instance—and a growing ability to borrow from DC plans raises the possibility of two conflicting phenomena with respect to households’ retirement savings.1 Americans, on the one hand, need to save more, for instance in DC accounts, to avoid consumption declines in retirement (Center for Retirement Research, 2010). And more control over how much to contribute to a DC plan may make it easier for households to save more than would be the case with DB pensions. Greater credit access through a DC loan option, on the other hand, may make it less likely that people save. Laibson (1997), for example, shows that increased credit card access in the 1970s and 1980s was associated with a decline in the U.S. saving rate. Standard life-cycle models of consumption and saving indicate that the DC loan option will likely increase retirement savings. The assumption in these models is that well-informed workers have stable lifetime preferences, will save in accordance with these preferences, and will save optimally to maintain a preferred level of consumption over their lifetime. With fixed preferences over time, there is no need for added incentives to save and thus also no need for precommitment devices such as limits on DC loans (Bernheim & Rangel, 2005). Individuals and households will save less in their DC retirement accounts if there is no loan option than if they can borrow.

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One can, however, theorize about instances where imposing borrowing constraints on DC plans may be welfare enhancing by leading to more savings and more retirement income. This may occur if households do not save optimally absent such restrictions, particularly if people have dynamically inconsistent preferences, are myopic, or are unsophisticated, such that their current desire for future savings is undone by their own future decisions to not save more—for example, by borrowing from a DC plan. Restricting access to savings before retirement could raise retirement savings and lifetime consumption and may be welfare enhancing for this subset of households. The contrasting policy prescriptions—no constraints on borrowing from DC plans versus a mixture of constraints and incentives—hinge on beliefs about people’s preferences and how consistent those preferences are over time and how well informed consumers are about satisfying their preferences. The life-cycle model relies on people having stable preferences; the behavioral model allows that some constraints, including limits on loans from DC accounts, may be welfare enhancing. Previous research finds some evidence that loan options increase contribution rates to DC plans (GAO, 1997; VanderHei & Holden, 2001), consistent with the traditional life-cycle model. This research, though, assumes preference stability and standard discounting in models of household retirement savings behavior. Researchers have not explicitly examined the link between heterogeneous time preferences and DC loan options and retirement savings contributions for different types of households. Unobservable preferences may influence households’ savings behavior. We thus develop two household discounting profiles—Type A: standard discounting where people behave in ways that are consistent with the standard model; and Type B: ‘‘inconsistent’’ discounting representing households with nonstandard economic behavior. We use a rich set of variables on attitudes about debt and risks and observations of inconsistent behavior to define these household types. There are many reasons why a household may demonstrate Type B behavior such as hyperbolic discounting, mental accounts, myopia, and lack of financial sophistication. Our data are insufficient to separately test each of these hypotheses. We instead argue that households have heterogeneous preferences and that these lead to different savings behaviors with DC loan options. We study specifically how access to DC loans influences the DC account contributions under heterogeneous preferences. We find evidence that for households with Type B preferences, the effect of having a borrowing option in their DC plans is smaller than for households with Type A (standard discounting) preferences—as theory would predict. Our estimates indicate that the contribution rate for people with Type B preferences is about two

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thirds that of people with standard preferences when the borrowing option is present. This smaller savings effect of DC loan options for households with inconsistent preferences deserves policy makers’ attention since these households may require additional incentives to adequately save for retirement.

Literature Review The economic literature has long recognized that ‘‘observationally equivalent’’ individuals are not the same. This becomes particularly clear when considering people’s preferences for consumption and their resulting choices, including their savings decisions. Some people can plan consistently for the future, as standard economic models predict, while others may lack self-control and thus do not plan consistently for the future as behavioral finance models suggest.

Life-Cycle Model of Consumption The saving model using the intertemporal utility maximizing framework developed by Modigliani and Brumberg (1980) posits that individuals decide early in their life on how much they will consume and save in each period. An individual will try to maximize the utility of lifetime consumption. Economists have typically assumed that it is possible to represent an individual’s preferences with a separable utility function such as: U ðc1 l1 ; . . . cT lT Þ ¼

T X

dt uðct ; lt Þ;

ð1Þ

t¼0

subject to the lifetime budget constraint T X t¼0

pt ðct þ st Þ ¼

T X

pt ðwt lt þ rt kt Þ:

ð2Þ

t¼0

Where c is consumption, l is labor, s is savings, and k is capital; d, w, and r are the discount, wage, and rental rates of capital, respectively. ArrowDebreu prices are represented by p. Consumption choices are limited to the feasible set of consumption bundles. The feasible consumption bundle set is restricted by changes in real income over time, changes in the real return on savings, and liquidity constraints, as Equations 1 and 2 show. The important assumption here is that each individual will maintain the same lifetime preferences at every moment in time (Bernheim & Rangel, 2005). This implies an unambiguous welfare standard. Higher total lifetime consumption is better than lower consumption.

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Individual behavior is also dynamically consistent. An individual would always choose the same consumption bundle at time t þ k, assuming no unexpected changes in circumstances, regardless of whether the decision was made at an earlier or later time. Since the individual always chooses the same bundle at period t þ k, there can be no utility gain from precommitment in savings decisions. Preventing oneself from alternative courses of action in the future, such as saving less and consuming more than the individual had originally planned on, should have no effect on individual consumption choices. On the contrary, such restrictions should reduce utility since they reduce the feasible set of consumption bundles. In the event that individual circumstances change, this could considerably reduce utility. Providing individuals with the option to borrow from their DC accounts would thus improve welfare since it eliminates such restrictions and gives individuals more choices.

Dynamically Inconsistent Preferences There are, however, a number of empirical findings that call into question the life-cycle hypothesis (see, e.g., Bernheim, Garrett, & Maki, 2001; Choi, Laibson, & Madrian, 2004, 2006). These findings, taken together, offer ample evidence that the life-cycle hypothesis cannot adequately predict an important range of individual savings and consumption behaviors. There appears to be a number of retirement savings and consumption behaviors that are influenced by factors that lie outside the purview of the life-cycle hypothesis. We focus only on research that indicates a need to incorporate precommitment mechanisms in retirement savings as a policy tool. One important reason to include precommitment mechanisms, in this view, is that the standard life-cycle model fails to incorporate problems that individuals may have with self-control. Consumption choices for a particular period will differ depending on when people make the choice, now or in the future. Some people may lack self-control and thus prefer ways to precommit to future actions. Gul and Pesendorfer (2001, 2004), Dekel, Lipman, and Rustichini (2009), and Fudenberg and Levine (2006) have developed theories that rely on preferences for commitment or rely on game theoretic solutions to overcome the lack of self-control inherent in standard models.2 Experimental studies find that the temporal proximity of a reward can lead people to seek unusually quick gratification. A person who accepts an immediate small payoff, for instance, rather than waiting a week to receive a larger payoff, may choose the larger, delayed payment if the ‘‘immediate’’ small payoff is delayed by a week and the larger payoff is 2 weeks later.3 Individuals who regularly make these types of choices are thought to have

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dynamically inconsistent preferences (for a review of this literature, see Frederick, Loewenstein, & O’Donoghue, 2002). People with dynamically inconsistent preferences have different sets of preferences, depending on when the choice is made, today or in the future. Some people, who initially indicate that they will save in the future for retirement, instead choose consumption over saving when the future actually arrives. Physical proximity also matters for self-control. Experimenters, who offer small prizes within individuals’ physical control along with larger prizes outside the individual’s physical control, find that individuals are more likely to accept the smaller prizes (Metcalfe & Mischel, 1999). People may choose to forgo saving for retirement, because of physical consumption proximity. They may borrow, for example, from their DC plans because such loans are largely under their control. Cognitive load appears to influence self-control, too. Shiv and Fedorikhan (1999) find that individuals facing a cognitively demanding task (memorizing a seven-digit number) are more likely to choose a reward that is high on the affective scale (chocolate cake) compared to a control group asked to remember a two-digit number. Shiv and Fedorikhan further find that individuals’ private information about their ability to exercise self-control,4 when interacted with the cognitive load, was a significant predictor of choosing the affective reward (cake). Making saving and investment decisions related to DC retirement accounts is likely a cognitively demanding task that could lead to individuals borrowing from their DC plans to finance consumption, especially if individuals already have a hard time exercising self-control. Individuals with dynamically inconsistent preferences will lack selfcontrol particularly when it comes to complex choices, such as saving and investing in a DC plan, and this lack of self-control is correlated with individual characteristics.5 Economists have begun incorporating more psychological insights into their models of saving behavior. The (b, d)-model of separable utility described in Equation 3 is a common approach. This model extends the standard model by including an additional term—b—that adds an extra discount to all decisions made in the future (t þ 1). When b ¼ 1, the model reduces to the usual discounting model. When b < 1, future utility is further discounted, making current consumption more appealing. " # T X kt uðct þ b d uðck Þ : ð3Þ k¼tþ1

Households that have bs that are close to 1 (time-consistent preferences) and households that have dynamically inconsistent preferences (b less than

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1) may rely on precommitment mechanisms differently. We use this split to examine what happens to saving behavior in the presence of a borrowing option for DC plans for these different household types. We denote as Type A households with values of b close to 1. For Type A households, the effect of being able to borrow should raise DC plan contributions. Being able to borrow increases the value of savings if circumstances change and allows for greater consumption smoothing over time. Type B households, however, may save less than those with standard preferences since the borrowing option removes the precommitment device of saving for retirement with a dedicated savings vehicle, such as a DC retirement account. We do not have direct measures of hyperbolic discounting behavior. It may also be the case that households are financially unsophisticated, myopic, or use mental accounts. All of these theoretical reasons can drive unobserved heterogeneity and may lead to different household savings behaviors by household type—given a change in policy such as the ability to borrow from DC accounts.

Background on DC loans A DC loan enables the borrower to act like a bank to herself, albeit within some limits (GAO, 1997). Households that have the option to borrow from their DC plan can borrow up to $50,000, or one half the vested balance from the account, whichever is less. Loans must be repaid within 5 years, except for loans that have been taken out for the first-time purchase of a home. Home loans can be repaid over a period of up to 15 years. Loan repayment is not tax deductible and neither are interest payments unless the primary residence secures the loan. The interest rates on these loans are generally favorable. For instance, GAO (1997) finds that of those 401(k) plans that allowed borrowing in 1996, approximately 70% charged an interest rate equal or less than the prime rate plus one percentage point in 1996. Borrowers can incur penalties if they fail to repay their pension loan. The outstanding loan amount is then considered a taxable distribution from the DC plan and subject to income tax on the outstanding loan amount plus an additional 10% as excise tax. The excise tax disappears for borrowers over the age of 59½ years of age. Pension loans have risen over time (Weller & Wenger, 2008). More people have DC plans, their balances have grown, and DC loans have become more prevalent. Sunde´n and Surette (2000), for instance, find that the share of families with a DC loan outstanding rose to 5.3% in 1998 from 2.1% in 1992. VanDerhei, Holden, and Alonso (2010) report that an average of 21% of those permitted to have loans, had a loan outstanding in 2009, compared to 18%

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in each year from 2006 to 2008, and 19% in 2004 and 2005. Based on the data reported in VanDerhei et al., this is the highest percentage of households with loans outstanding since data started being collected in 1996. Loan reasons and borrower characteristics may raise the likelihood of repayment, although research investigating this has been lacking. However, even when loans are repaid, retirement savings may be reduced. The exact impact on total retirement savings will depend on the interest rate charged for the loan, the interest rate earned on savings, whether the borrower keeps up with contributions to the retirement savings plan in addition to repaying the loan, and when the loan is taken out. A loan taken out early in a worker’s career can reduce retirement savings by more than 20% (GAO, 1997; Munnell & Sunde´n, 2004; Weller &Wenger, 2008). At the same time, though, research finds that the borrowing increases the contribution amount, consistent with the predictions of standard discounting in a life-cycle model.6 GAO (1997), for instance, finds, based on the 1992 Survey of Consumer Finances (SCF), that when plans offer a loan option, workers significantly increased the contribution rate. Similarly, VanDerhei and Holden (2001) find that a loan option increased contribution rates by 0.6 percentage points compared to participants who did not have such a loan option. These analyses, though, ignore the heterogeneity of discounting types and thus ignore the possibility of welfare losses from the elimination of a precommitment device. An additional and related problem arises because the data on DC plan characteristics, such as borrowing options, are self-reported in many surveys. The majority of empirical analyses on pension loans rely on household survey data, such as the SCF, which we also use in our study. Some survey respondents originally answer that they don’t know if their employer allows for pension loans in their DC plan, and many workers have limited information about the details of their retirement plan (Mitchell, 1988; Starr, & Sunde´n, 1999). The ‘‘don’t know’’ answer in the SCF is then imputed as having a DC borrowing option or not. This imputation assumes that the original responses are unbiased—that is, knowledge about the loan option is not endogenously determined. This is unlikely to be the case with heterogeneous savers. Those savers who are most likely to borrow in the future may be more likely to be Type B households and will be more likely than standard Type A households to inquire about borrowing options from their DC plans from their employers. We believe that selection on knowing whether a household has a DC borrowing option is likely to impact the estimates of the link between the borrowing option and DC plan contributions. We will address both the imputing and endogeneity of having a DC borrowing option in our empirical analysis. We will use household

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characteristics to separate Type A (standard discounters) from Type B households and we investigate the possibility of endogeneity in households’ responses related to borrowing options.

Empirical Analysis The SCF data allow us to compare trends over time and also offer comprehensive details on borrowers. The Federal Reserve’s triennial SCF provides detailed information on households’ assets and debt, including loans from DC plans. Consistent data for our descriptive statistics are available from 1989 to 2007. Unless otherwise noted, our analysis focuses on families between the ages of 25 and 64 who have a DC plan.7 Unlike most data that include imputed values, the SCF contains five imputed values for each missing variable. Because of this the SCF contains five ‘‘replicates’’ for each unique observation. In order to use the data correctly, estimates must be made for each replicate individually and then combined (often by the arithmetic or geometric average) using rules discussed in detail in Rubin (1987). All of our estimates use this method.

Defining Preference Heterogeneity The previous discussion of retirement savings policy argues that we should account for the systematic heterogeneity that characterizes savers. We need to operationalize this potential heterogeneity to correctly estimate the models. We use two approaches to ensure the robustness of our conclusion. We first separate our sample by choices related to debt and second by planning horizons. We identify households in our sample that engage in financial decisions that do not easily fit into an optimizing framework,8 and thus their lifetime consumption. Specifically, if the household has an outstanding credit card balance beyond the grace period, we compare the credit card interest rate for the card with the largest balance to the interest rate on their home equity line of credit (HELOC). We denote households with credit card interest rates larger than HELOC interest rates as Type B households. All other households are Type A households.9 We measure preference heterogeneity as any household that carries a credit card balance but also has untapped home equity at a lower interest rate. Our assumption is that these households are not optimizing in the standard way if they choose a higher-cost form of credit when a lower-cost one is available to them. We expect that these households’ DC plan contributions may be differentially influenced by the option to borrow—relative to those households we denote as Type A. Approximately 68% of households in our sample are Type A, a percentage that has varied from 59% in 1989 to 73% in 2001.

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Economic theory provides a foundation but no unique operationalization for saver heterogeneity. We, like all researchers, need to select a reasonable approach to operationalize heterogeneity, but the resulting operationalization is only one possible way to capture saver heterogeneity. We use an alternative operationalization that does not capture savers’ behavior but only their attitudes as described below to test the robustness of our conclusion that saver heterogeneity matters for differences in saving behavior. We classify households as Type B if they have an outstanding credit card balance and could borrow the same amount against their home equity at a lower rate. This captures household behavior that does not fit into an optimizing framework, while holding their liquidity preferences relatively constant. An alternative approach of classifying households as Type B if they have an outstanding credit card balance that they could pay off with their liquid assets ignores households’ liquidity preferences. Such an approach could thus erroneously classify these households as exhibiting systematic biases, when they are acting rational. Our approach of matching two easily accessible forms of credit hence allows for an apples-to-apples comparison in household behavior to uncover systematic biases. Our preferred definition of Type A and Type B households has some limitations. First, it may be possible that HELOCs are not as easily accessible as credit cards due to application fees for HELOCs. This should pose a minor issue since the higher costs are also offset by the tax advantages for HELOCs. Second, households need to have outstanding credit card balances and home equity for us to define them as Type B. It is thus likely that some Type A households would be classified as Type B if they owned a home. Consequently, it is likely that estimates for the savings effect of a DC loan option for Type A households underestimates the real savings effect. As a check on robustness for our definition of preference heterogeneity, we also divide households by their self-described planning horizon. Short-term planners are those with planning horizons of fewer than 5 years, medium term indicates a 5- to 10-year planning horizon, and long-term planners have horizons set at more than 10 years. Short-term planners should be more likely to have dynamically inconsistent preferences after controlling for observable characteristics such as age, while long-term planners should be more likely to be standard discounters. Short-term planners make up 43%, medium-term planners comprise 32%, and long-term planners comprise the remaining 25% of the sample. The share of short-term planners is thus close to the share of households defined as Type B households by our earlier definition. Importantly for our work, it appears that planning horizon and our division into Type A and Type B households are only somewhat correlated

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(rho ¼ –.05). In our sample, for instance, two thirds of those with mediumterm planning horizons were Type A households. We prefer the division by objective decision criteria over the division based on self-reported planning horizons. Differences in planning horizons may not necessarily indicate differences in preferences, but rather differences in circumstances. Additionally, households may not act according to their own stated planning horizons (Laibson, 1997).

Summary Statistics on Pension Loans and Contributions We summarize DC loans for Type A and Type B households in Table 1. Type B households have been consistently more likely to have a pension loan than Type A households (for every year except 1992). While the likelihood of having a DC loan is higher for Type B households, the change over the business cycle has been much larger. Type B households were nearly twice as likely as Type A households to have a DC loan at the end of the 1990s. Type B households tend to have higher loan amounts both on average and at the median, conditional on having a DC loan. Table 1 also shows that there was a general trend toward larger loan amounts for both groups of households (all figures are in 2010 dollars). Loan amounts initially declined from 1989 to 1992 but then rose throughout the late 1990s and early 2000s for both household types. The growth of loan amounts started in 1992 for Type A households and in 1995 for Type B households. The data on DC loan changes show both a higher probability and a larger loan amount for Type B households relative to Type A, reflecting possibly less self-restraint.10 These data may also shed light on whether households know about their DC plans’ borrowing options. The data in Table 1 show that Type B households have a higher propensity of DC loans than Type A households do. It may be the case that they are more knowledgeable about DC loan options from their employers or have sought out this information due to the fact that they have revolving credit card debt. A summary of the unimputed data allows us to investigate the extent of a respondent’s knowledge about their DC plans’ features.11 Type B households are indeed more likely, albeit only slightly, to know about their DC plans’ features. In our sample, a nearly identical percentage of Type A and Type B households reported that they didn’t have the option to borrow (22.5% and 22.4%, respectively), 51.6% of Type A households indicated that they could borrow from their DC plans compared to 53.0% of Type B households, and 7.2% of Type A households said they didn’t know compared to only 5.8% of Type B households. There are an additional 18.7% of households who either refused

538 8.5% 7.1% $2,026 $2,894 $4,346 $6,968

1992 9.2% 13.6% $2,704 $2,704 $5,458 $5,273

1995 10.2% 19.3% $3,178 $4,830 $6,323 $6,886

1998 8.6% 15.3% $3,041 $4,678 $7,296 $8,187

2001

9.5% 14.8% $4,064 $4,942 $9,149 $8,377

2004

10.7% 11.6% $4,000 $5,000 $6,238 $7,105

2007

Note. In all instances, the demographic characteristics refer to the head of household. Type A households do not have credit card debt with interest rates above their home equity line of credit rate; Type B households do hold such debt. Inflation adjustments are done using the Bureau of Labor Statistics’ CPI-U-RS. All dollar amounts are in 2007 dollars. Shares are in percentage. Changes in shares are in percentage points. Changes in dollar amounts are in percentage. Only observations for households with a DC plan are included when we calculate the share of households with a DC loan. Only data for households with DC loans are considered for the median and mean loan amounts. Only households between the ages of 25 and 64 are included. Authors’ calculations based on Board of Governors (2009).

Average loan amount

2.5% 6.0% $6,494 $4,871 $8,548 $9,628

A B A B A B

Share of people with a DC plan and a loan Median loan amount

Type Type Type Type Type Type

1989

Year

Table 1. Share of Households With a DC Loan and Loan Amounts (1989–2007).

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to answer the question or gave conflicting answers and had to have their values imputed. It seems possible that our sample of Type B households are more knowledgeable about their DC plans’ loan provision than is the Type A sample. Our primary research question is whether the existence of a borrowing option in a DC plan leads to increased DC contribution rates. Second, we are interested in whether there are meaningful differences in contribution rates for our subsamples of Type A and Type B households. Our summary data in Table 2 suggest that having a loan option in a DC plan is associated with higher contributions for both Type A and Type B households. The data also suggest that there is no difference overall between groups. However, Type A households have slightly higher employee contribution rates when the option to borrow exists relative to Type B households. Employee contributions are slightly higher when there is an option to borrow, although the effect is slightly larger for Type B households. The median total contribution rate from employers and employees amounted to 8.3% for Type A households that had a borrowing option compared to 7.4% for Type B households with the option to borrow. These contribution rates are approximately 1% higher than for the respective households, who did not have a borrowing option in their DC plans.

Empirical Strategy Households with standard discounting (Type A) should demonstrate a stronger response to having a DC loan option than hyperbolic discounters/mental accounts households (Type B). Consequently, we should see that contribution rates are systematically higher for Type A households than for Type B households, when a borrowing option is available in their DC plans. We estimate models of the determinants of DC plan contributions to examine if this is the case. The dependent variable is the employee’s share of earnings contributed to DC plans. The primary explanatory variable of interest to us is whether a plan allows for pension loans. Additional explanatory variables include the employer’s contribution rate, wealth, credit card debt to income, non-pension debt to income, age, education, race, ethnicity, marital status, union membership, and year controls. Our empirical strategy proceeds as follows. We first present a baseline model that estimates the raw effect of having a borrowing option in a DC plan on contributions. This model does not control for preference heterogeneity. We then estimate separate models for Type A and Type B households to gain a sense of the link between preference heterogeneity and contribution decisions. This approach of estimating separate models for both subsamples allows the parameters on all other explanatory variables to vary, essentially

540 7.0% 4.1% 2.9% 15.1% 5.3% 9.8%

14.4% 6.2% 8.2%

–0.7 0.9 –1.6

1.3 1.1 0.3

Does not have option to borrow Difference

8.3% 5.2% 3.1%

Has option to borrow

15.3% 5.4% 10.0%

7.4% 4.5% 2.9%

Has option to borrow

12.0% 4.8% 7.2%

6.7% 4.0% 2.8%

3.3 0.5 2.8

0.7 0.6 0.1

Does not have option to borrow Difference

Type B

Note. In all instances, the demographic characteristics refer to the head of household. Type A households do not have credit card debt with interest rates above their home equity line of credit rate; Type B households do hold such debt. Inflation adjustments are done using the Bureau of Labor Statistics’ CPI-U-RS. All dollar amounts are in 2007 dollars. Shares are in percentage. Changes in shares are in percentage points. Changes in dollar amounts are in percentage. Only observations for households with a DC plan are included when we calculate the share of households with a DC loan. Only data for households with DC loans are considered for the median and mean loan amounts. Only households between the ages of 25 and 64 are included. Authors’ calculations based on Board of Governors (2009).

Median contribution rates Median total contribution Median employee contribution Median employer contribution Mean contribution rates Average total contribution Average employee contribution Average’ employer contribution

Contributions relative to earnings

Type A

Table 2. Median and Mean Contribution Rates, by Household Type, and Borrowing Option.

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controlling for the possibility that there are systematic demographic differences across the two samples. Further, we reestimate the two separate models for Type A and Type B households using an instrumental variable (IV) approach to account for the possibility that a household’s indication of having a borrowing option in their DC plan is endogenous. We use households’ unimputed answers as well as a set of industry and occupation dummies as instruments in our IV regression to provide an unbiased estimate of behavioral effects of the ability to borrow. Our motivation here is simple. Households that are unaware of the option to borrow from their DC plans should not be influenced by the option in their DC plan contribution decisions. Moreover, we estimate separate models for samples of households divided by planning horizon. This allows us to control for the robustness of our results. We again estimate separate IV regressions, using the same instruments as indicated above.

Effect of Borrowing Option on Contribution Rates Table 3 presents our OLS estimates of the effect of having a borrowing option in a DC plan on the percentage of earnings a household contributes to their DC plan.12 The first column presents our baseline model for all households with a DC plan. It indicates that having a borrowing option raises the household’s contribution rate by approximately 1.4 percentage points. We also find that Type B households allocate approximately 0.4 percentage points less income to their DC plan than Type A households do. Female-headed households contribute approximately 0.1 percentage point less to their DC plans compared to maleheaded households, while households with less education have lower contributions—even controlling for other debts relative to income and the household’s net worth. Being married raises DC contribution rates, although at a miniscule level. Finally, having less than a college education monotonically reduces contribution rates, with the smallest reduction being households with some college and the largest being those without a high school diploma (Table 3). We find that the borrowing option has a slightly smaller effect on Type B households than for Type A households, when we estimate the model separately for the two subsamples.13 This difference, however, is not statistically significant, although we reject the null hypothesis for pooling Type A and Type B households into one regression. The small difference is suggestive, but based on this set of naı¨ve estimations, it appears that both Type A and Type B households respond in substantively similar ways to the loan incentives of the DC plans.

542

Age squared/100

Age

Other race

Hispanic

Black

Adjusted net worth ($100 K)

Other debt to income

Mortgage debt to income

Employer contribution Percentage Credit card debt to Income

DC loan allowed

Type B household dummy

–0.0042** (0.0014) 0.0144** (0.0015) 0.0127** (0.0039) –0.0041 (0.0084) 0.0001 (0.0003) –0.0013 (0.0015) –0.0001** (0.0000) –0.0039 (0.0024) –0.0029 (0.0031) 0.0034 (0.0040) –0.0011þ (0.0006) 0.0000* (0.0000)

Base model (1)

0.0149** (0.0020) 0.0097* (0.0048) –0.0080 (0.0132) –0.0001 (0.0008) –0.0017 (0.0020) –0.0001** (0.0000) –0.0053 (0.0035) –0.0075 (0.0048) –0.0003 (0.0052) –0.0016* (0.0008) 0.0000* (0.0000)

Type A household (2)

0.0135** (0.0020) 0.0197** (0.0056) 0.0015 (0.0091) 0.0001 (0.0003) –0.0009 (0.0021) –0.0000 (0.0001) –0.0016 (0.0036) 0.0026 (0.0039) 0.0103þ (0.0059) –0.0000 (0.0009) 0.0000 (0.0000)

Type B household (3)

0.0549** (0.0157) 0.0135* (0.0058) –0.0058 (0.0135) –0.0002 (0.0008) –0.0017 (0.0022) –0.0001** (0.0000) –0.0071þ (0.0037) –0.0081 (0.0056) 0.0017 (0.0055) –0.0023** (0.0009) 0.0000** (0.0000)

IV Type A household (4)

(continued)

0.0227 (0.0206) 0.0197** (0.0056) 0.0024 (0.0093) 0.0001 (0.0004) –0.0009 (0.0021) –0.0000 (0.0001) –0.0020 (0.0038) 0.0028 (0.0040) 0.0106þ (0.0060) –0.0001 (0.0010) 0.0000 (0.0000)

IV Type B household (5)

Table 3. OLS and IV Regressions of Percentage of Income Allocated by Employee to Defined Contribution Account, by Household Type.

543

–0.0081** (0.0029) –0.0125** (0.0025) –0.0178** (0.0033) –0.0103** (0.0016) –0.0076** (0.0018) –0.0004 (0.0016) 0.0727** (0.0124) Yes 7,407 .064

Base model (1) –0.0047 (0.0035) –0.0109** (0.0030) –0.0180** (0.0044) –0.0099** (0.0023) –0.0075** (0.0026) –0.0006 (0.0023) 0.0838** (0.0161) Yes 5,034 .059

Type A household (2) –0.0162** (0.0052) –0.0172** (0.0046) –0.0167** (0.0046) –0.0109** (0.0024) –0.0077** (0.0026) –0.0002 (0.0023) 0.0507* (0.0207) Yes 2,365 .075

Type B household (3) –0.0032 (0.0037) –0.0117** (0.0032) –0.0138** (0.0054) –0.0081** (0.0027) –0.0063* (0.0029) 0.0019 (0.0027) 0.0665** (0.0189) Yes 5,034 –

IV Type A household (4)

–0.0158** (0.0053) –0.0171** (0.0046) –0.0157** (0.0055) –0.0109** (0.0024) –0.0078** (0.0026) –0.0001 (0.0023) 0.0454 þ (0.0233) Yes 2,365 –

IV Type B household (5)

Source. Type A households do not have credit card debt with interest rates above their home equity line of credit rate; Type B households do hold such debt. Contribution amounts are conditional on having either an employer or employee contribution. Authors’ calculations based on Board of Governors (2008). All figures are in percentage. Differences are in percentage points. Estimates correct for multiple imputation of missing data by estimating each replicate separately and using the combining rules set out by Rubin (1987). Note that 8 observations had imputed values that changed within an observation and were later dropped. Note. þ p