Can individual-level research on nudges lead to dramatic system-level change? Yes it can and has, despite what Chater & Loewenstein (C&L) suggest.

Consider the December 2022 regulations passed through the US Congress, which included several mandates: Most US employers will be required to automatically enroll employees into retirement savings programs, auto-escalate employees' contributions unless they opt out, and provide monthly retirement income projections. These “s-frame” mandates were undoubtedly influenced by “i-frame” research on automatic escalation (Thaler & Benartzi, Reference Thaler and Benartzi2004), automatic enrollment (Choi et al., Reference Choi, Laibson, Madrian and Metrick2004), and other behavioral interventions (Goldstein, Hershfield, & Benartzi, Reference Goldstein, Hershfield and Benartzi2016).

And these policies should have massive impact. Data from dozens of firms that have implemented automatic enrollment suggest that it more than triples retirement savings among the poor on average, substantially reduces retirement savings gaps, and increases net wealth at retirement among the poorest income decile by 12 percentage points (according to Choukhmane, Reference Choukhmane2023). This tremendous impact is not limited to retirement savings nudges. For example, interventions that reduce administrative burdens, remind people to enroll in insurance plans, and reduce failures to appear in court also markedly reduce inequities by helping low-socioeconomic status (SES) consumers most (Domurat et al., Reference Domurat, Menashe and Yin2021; Fishbane et al., Reference Fishbane, Ouss and Shah2020; Herd & Moynihan, Reference Herd and Moynihan2019; Johnson, Reference Johnson2022; Mrkva et al., Reference Mrkva, Posner, Reeck and Johnson2021).

Importantly, behavioral insights can also be used to increase profits or harm consumers. Techniques that change the default option or the prominence or presentation of information can sometimes increase company profits by several million dollars (Goldstein et al., Reference Goldstein, Johnson, Herrmann and Heitmann2008; Kohavi & Thomke, Reference Kohavi and Thomke2017; Posner et al., Reference Posner, Simonov, Mrkva and Johnson2023; Reeck et al., Reference Reeck, Posner, Mrkva and Johnson2023), which explains why they are so prevalent among companies like Amazon and AirBnB. A major priority for behavioral policy in the present and future will be preventing these harms and protecting unwitting consumers from companies' tricks (via groups like the United States' FTC and the United Kingdom's CMA). Like helpful nudges, “evil nudges” and dark patterns also have disproportionate impact on the poor and can exacerbate disparities (Luguri & Strahilevitz, Reference Luguri and Strahilevitz2021; Mrkva et al., Reference Mrkva, Posner, Reeck and Johnson2021). Efforts to protect consumers with behavioral insights, regulation, and s-frame mandates have potential to reduce disparities and help consumers. Whether used in situations that help or harm people, or that reduce or exacerbate disparities, what is clear is that effects of behavioral interventions are not always “disappointingly modest” (contrary to what C&L claim).

However, we do agree with the authors that researchers and policymakers should look for s-frame solutions when possible. We sympathize with C&L's desires for policies that have massive positive impact and certainly desire the same thing. Luckily, most behavioral insights teams are already looking at broad solutions that go beyond individual-level behavior (Herd & Moynihan, Reference Herd and Moynihan2019; Mažar & Soman, Reference Mažar and Soman2022). Yet it is important to accelerate plans and efforts to develop and scale these solutions across organizations, better anticipate when these nudges will have positive versus negative distributional effects, and take full advantage of opportunities to turn i-level nudges into changes in systems, policies, and norms.