Who is easier to nudge? John Beshears James J. Choi David Laibson Brigitte C. Madrian Sean (Yixiang) Wang

The appeal of defaults Large effect on outcomes If a default isn’t right for somebody, she will (eventually) opt out

The downside of defaults Giving some people the wrong default is inevitable when population is heterogeneous and only one default can be chosen Sometimes it takes people a long time to opt out Open question Who is most vulnerable to getting stuck at a bad default?

Default effects by income in Madrian and Shea (2001)

Why it’s difficult to interpret that graph Low-income workers could persist longer at default because it is closer to their target rate, so less incentive to opt out quickly Relative persistence could change if we chose a different default

What we’d like to know Holding fixed distance between default and target, are low-income workers are more inertial?

Key empirical challenge Distribution of target rates by income group unobserved –Thus, hard to control for default’s distance to target In particular, target rate is unobserved for those who are still at the default. Mixture of –Those for whom default = target –Those for whom default ≠ target, but they haven’t moved there

Objectives Estimate distribution of target rates by income group separately for each company Estimate per-period probability of opting out to each target rate by income group separately for each company Estimate each income group’s probability of remaining stuck at default 2 years after hire when it is not target rate

Our empirical approach Assume target rate doesn’t change over observation period (2 years after hire) Two time intervals after hire –Initial period (usually 2 months): Higher opt-out activity –Later period: Lower opt-out activity Assume monthly probability of opting out to target c i is constant across time during later period –But varies by target rate × company × income group

Intuition for empirical methodology Suppose we observe 20 people opt out to 5% contribution rate in month 3 (start of later period) Consistent with numerous possibilities # people with 5% target who haven’t moved at beginning of month 3 Monthly probability of moving # people with 5% target who haven’t moved at beginning of month % %40 50%20

Intuition for empirical methodology Suppose we also observe 16 people opt out to 5% in month 4 If monthly opt-out probability is constant, then we can infer which possibility is correct Above scenario implies 20 × 0.5 = 10 opt-outs in month 4 → Inconsistent with data # people with 5% target who haven’t moved at beginning of month 3 Monthly probability of moving # people with 5% target who haven’t moved at beginning of month %20

Intuition for empirical methodology Above scenario implies 80 × 0.2 = 16 opt-outs in month 4 → Consistent with data # people with 5% target who haven’t moved at beginning of month 3 Monthly probability of moving # people with 5% target who haven’t moved at beginning of month %80

Intuition for empirical methodology We know from last step how many people have a 5% target but haven’t opted out at beginning of month 3 People with 5% target at beginning of initial period = Opt-outs in initial period + People with 5% target at beginning of later period Probability of opting out to 5% during initial period = Opt-outs in initial period / People with 5% target at beginning of initial period

High vs. low income definition Split employees into those above vs. below sample-wide median income ($61,228)

Sample FirmIndustryHire Dates Covered Sample Size Initial Period Default Rate APharma/HealthJan 2002 – Dec ,96114 months3% BMedical TechJan 2002 – Oct 20035,4523 months3% CManufacturingOct 2008 – Dec 20101,9312 months6% DManufacturingJan 2002 – Dec 20065,1932 months6% EComputer HardwareJan 2002 – Dec 20021,8722 months0% FInsuranceAug 2003 – Dec 20065,8192 months0% GBusiness ServicesJan 2002 – Dec 20033,1652 months0% HIT ServicesMar 2002 – Dec 20048,2892 months0% IPharma/HealthJan 2002 – Dec 20045,45312 months0% JTelecom ServicesJan 2002 – Dec 20032,1692 months0%

Probability of being at default after 2 years when it’s not your target

Adjusting for differences in target distributions Less likely to opt out within 2 years if default is close to target rate Differences between low- and high-income sticking probabilities partially driven by differences in target rates In next graph, set target rate distribution equal to average of low- and high-income for both income groups

Probability of being at default after 2 years when it’s not your target, holding rate preferences fixed

Conclusion Low-income individuals less likely to opt out of default Default choices should place higher weight on low-income individuals’ needs To be explored: Do defaults change target contribution rates?