PADM530Wk3

Applying Behavioral Insights in Policy Analysis: Recent

Trends in the United States

Maithreyi Gopalan and Maureen A. Pirog

An understanding of human nature and of the motivations that drive human behavior have always

informed public policies. The use of behavioral research in public policy analysis, which flows largely

from social and cognitive psychology, behavioral economics, and other behavioral sciences, came into

sharp focus in the last decade. Since then, policy initiatives incorporating behavioral insights have

flourished, and thousands of research articles have been published on that topic. A lot of this research

has focused on how behavioral insights used by governments at all levels can improve the delivery of

governmental services and improve compliance and use of government services by the public. We

review recent trends in policy initiatives that specifically incorporate behavioral insights in the

United States and outline a framework for further integrating behavioral insights into the various

stages of policy analysis and policy design.

KEY WORDS: policy analysis, behavioral economics, nudges, policy evaluation

政策分析中行为研究的应用—近期美国趋势

对人类本性和促使人类行为发生的动机的理解一直影响着公共政策。最近十年里, 行为研

究在公共政策分析中的使用十分明显。这些政策分析大致包括社会和认知心理学, 行为经济学,

以及其他行为科学。从那时起, 包含行为研究的政策措施就开始兴起, 成千上万的相关文章也

对其进行了发表。许多这方面的研究聚焦于各级政府使用的行为研究如何能提高政府服务的

提供, 提高公众对该服务的认同和使用。本文检查了近期具体包含美国行为研究的政策措施走

向, 勾勒了一项框架,用于将行为研究进一步整合到政策分析和政策设计的不同阶段。

关键词: 政策分析, 行为经济学, 轻推, 政策评估

Introduction

An e-mail informed by behavioral insights, encouraging the U.S. Department of

Defense (DOD) service members to participate in a thrift savings plan, led to roughly

4,930 new enrollments and $1.3 million in savings in just one month (Social and

Behavioral Sciences Team [SBST], 2015). A series of eight personalized text messages

sent to low-income high school students reminding them to complete required pre-

matriculation tasks, led to a 5.7-percentage-point increase in college enrollment

S82

0190-292X VC 2017 Policy Studies Organization

Published by Wiley Periodicals, Inc., 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington Road, Oxford, OX4 2DQ.

The Policy Studies Journal, Vol. 45, No. S1, 2017

bs_bs_banner

(Castleman & Page, 2015). While the above examples sound like marketing cam-

paigns executed by multinational corporations, they were in fact initiatives pilot-

tested by the U.S. federal government over the last few years ushering in what just

might be a new way by which a government engages with its citizens to improve

social welfare.

Governments have always tried to improve social welfare by introducing poli-

cies that often entail bringing about a change in citizen’s behavior. However, not

until recently has the behavioral paradigm permeated public policy in a more perva-

sive way. We define the behavioral paradigm as the incorporation of findings from

behavioral sciences—such as social and cognitive psychology, and behavioral eco-

nomics—into public policy. We observe such a trend both across countries as well as

in international organizations. For example, in 2015, the World Bank published its

flagship World Development Report titled “Mind, Society, and Behavior,” which

aimed to advance a new framework for development policy based on a “fuller con-

sideration of psychological and social influences.” Similarly, the European Commis-

sion (EC) recently released a report reviewing the use of behavioral insights in

policymaking across several different countries in Europe (Lourenço, Ciriolo,

Almeida, & Xavier, 2016). Simultaneously, several national governments have begun

to integrate the use of evidence-based research from the behavioral sciences in poli-

cymaking by establishing dedicated teams within the bureaucracy. The United King-

dom formed the Behavioral Insights Team (BIT) in 2010, a first-of-its-kind

government entity dedicated to the application of insights from the behavioral scien-

ces to public policy issues. Since then, countries such as Denmark, Sweden, Canada,

Australia, and the United States, have formed dedicated departments or “nudge uni-

ts” to develop and apply such behavioral insights to policymaking.

In September 2015, President Obama issued an executive order titled “Using

Behavioral Science Insights to Better Serve the American People,” and formally

established the SBST. This team, established under the National Science and Technol-

ogy Council in the United States, consists of behavioral scientists tasked with incor-

porating behavioral insights into federal policies and programs. In its first year, SBST

executed several proof-of-concept projects. These projects ranged from text-

messaging campaigns designed to increase college enrollment of low-income stu-

dents to projects intended to increase retirement savings among federal employees.

The growing influence of behavioral insights on public policy is thus undeniable. In

this article, following Chetty (2015), we argue that the incorporation of behavioral

factors should be seen as a “natural progression of (rather than a challenge to) neo-

classical economic tools.”

This article makes three contributions. First, we summarize the recent trends

in the U.S. policy initiatives that have begun to incorporate behavioral insights.

We will primarily focus our review on research of U.S. policy initiatives within

two substantive policy fields—social policy and education policy—defined

broadly, because they are at the forefront of testing and evaluating initiatives

embedded with a behavioral component. We include research published in peer-

reviewed academic journals, working papers, and reports from research think

tanks and government agencies (at the federal, state, and local level) between

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S83

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

2010 and 2015 in our review. Second, we organize the research into a conceptual

framework by adapting the taxonomy used by the EC in its report (Lourenço

et al., 2016). Our article reviews research on policy initiatives embedded with a

behavioral component in the United States and should be viewed as a comple-

ment to the recent EC report. Finally, we identify emerging themes from these

policy initiatives with the specific aim of providing insights for policy design as

well as ex-ante and ex-post policy analyses.1

Our thematic review of research on policy initiatives that have incorporated

insights from behavioral sciences showcase the tremendous promise of this approach

to public policy analysis and policy design. Furthermore, we outline a framework for

incorporating such behavioral insights into all stages of policy analysis and effective

policy design.

Conceptual Framework

We adapt the taxonomy recently used by the EC in this article to classify

research on policy initiatives into three broad categories: behaviorally tested, behavioral-

ly informed, and behaviorally aligned. Figure 1 illustrates our framework for classifying

the research on various behaviorally embedded policy initiatives in this review.

Behaviorally tested policy analysis includes evaluation/analysis of those policy ini-

tiatives that have been rigorously tested in smaller experiments before scale-up or

large-scale implementation. For example, educational interventions aimed at improv-

ing students’ noncognitive outcomes, such as grit and growth mindset (the belief

that intelligence is not innate but can be developed with deliberate practice), were

tested in social psychology labs in universities before being scaled to several schools

(Paunesku et al., 2015). Behaviorally informed policy analysis includes evaluation/anal-

ysis of policy initiatives that have been designed based on previously available

Behaviorally tested policy analysis includes evalua�on/analysis of those ini�a�ves that

have been rigorously tested in smaller lab/field experiments before scale-up.

Behaviorally informed policy analysis includes evalua�on/analysis of those ini�a�ves that

have been designed based on previously available behavioral evidence

Behaviorally aligned policy analysis includes evalua�on/analysis of tradi�onal policy

ini�a�ves; however, the evalua�on/analysis of these policy ini�a�ves is aligned with a

behavioral insight when analyzed post-hoc a�er implementa�on

Behaviorally informed Policy Analysis

Behaviorally aligned Policy Analysis

Behaviorally tested Policy Analysis

Figure 1. Classification of Policy Analysis of Initiatives Embedded with Behavioral Insights.

S84 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

behavioral evidence; however, these initiatives are often not tested as rigorously as

the behaviorally tested initiatives before implementation. For example, based on past

evidence on reminder notices that improved people’s adherence to payment sched-

ules in domains such as savings and child support payments, SBST designed an

e-mail campaign reminding federal student loan borrowers about their repayments.

This policy initiative was not piloted before implementation, given the robust evi-

dence on other similarly tested initiatives. Last, behaviorally aligned policy analysis

includes evaluation/analysis of policy initiatives that are most often traditional poli-

cy tools such as taxes or subsidies that do not explicitly rely on any existing behav-

ioral evidence; however, the evaluation/analysis of these policy initiatives is aligned

with a behavioral insight when analyzed post hoc after implementation. For exam-

ple, Chetty, Friedman, and Saez (2013) observed that people in different states

responded differently to the variation in Earned Income Tax Credit (EITC) policies.

They then used behavioral insights to explore those differences and found that dif-

ferences in people’s knowledge about the EITC’s incentive structure explained such

spatial variation. They showed how the neoclassical model that typically assumes

perfect information or knowledge about tax codes needed to be updated to under-

stand the complexities of human behavioral responses to even traditional policy

interventions, such as tax credits.

The subtle distinction we draw here in our classification of behaviorally embed-

ded policy analysis can help unpack the similarities and differences between how

various behavioral insights are embedded in policy analysis and help identify

emerging themes for more effective policy analysis and design. Finally, behavioral

insights have largely been synonymous with nudges. However, our framework recog-

nizes that policy initiatives that incorporate behavioral insights go well beyond nudg-

ing (Bhargava & Loewenstein, 2015; Lourenço et al., 2016).

For example, Thaler and Sunstein (2003) define a nudge as “any aspect of the

choice architecture that alters people’s behavior in a predictable way without forbid-

ding any options or significantly changing their economic incentives” (Thaler & Sun-

stein, 2008, p. 6). One of the nudge-type initiatives that they highlight relates to the

use of automatic defaults to increase retirement savings. The underlying behavioral

insight was that more people would enroll in a savings plan and likely save more if

the default option in a savings plan was to enroll everybody automatically and let

people “opt out” if they wanted to, rather than enrolling people only when they

“opt in.” Increases of as much as 50 percentage points in savings participation rates

were observed in some studies (Madrian & Shea, 2001). These results have also been

replicated in many subsequent studies on savings plan participation (Beshears, Choi,

Laibson, & Madrian, 2008) as well as in other domains such as organ donations that

have similar opt-in/opt-out structures (Johnson & Goldstein, 2003).

However, nudges are just a subset of the policy initiatives that embed an under-

lying behavioral insight about peoples’ response to a choice architecture. Behavioral

insights often go beyond merely altering choice architecture. For example, monetary

incentives that are tied to specific savings commitments that encourage savings

among low-income individuals have been tested recently and show great promise

(Jones & Mahajan, 2015). These interventions are also designed to encourage savings

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S85

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

just like the automatic savings enrollment default nudges. However, they do so by

providing a new policy tool—commitment devices—that go beyond just changing

the choice architecture for potential savers. Similarly, reminder letters that prompt

action or the reframing of the content of the message in a reminder letter in terms of

gains rather than losses based on insights from loss aversion (Tversky & Kahneman,

1991) to encourage or discourage a behavior are somewhat different from just

nuanced design changes advocated specifically by nudges. We support the perspec-

tive of Lourenço et al. (2016) and review the literature using this broader view of

behavioral insights as applied to policy analyses and research. Thus, we include the

analysis/evaluation of nudge-type policy initiatives within our broader framework

depending on how a nudge-type policy initiative was evaluated. As illustrated in

Figure 1, we observe that most nudge-type policy initiatives were analyzed using

behaviorally tested or behaviorally informed approaches.

A systematic review of research on all policy initiatives that have incorporated a

behavioral insight in the United States is beyond the scope of this article; however,

we employed multiple search strategies to provide a snapshot of such research. First,

we identified appropriate studies in Google Scholar and Thomson Reuters Web of

Science using relevant keyword searches.2 Close to 80,000 published articles, books,

and book chapters emerged in that search within relevant Web of Science categories

between 2010 and 2015. We also conducted manual searches in a variety of relevant

peer-reviewed academic journals, working papers, and reports to sharpen the focus

of our search to include research on initiatives in the United States within the sub-

stantive fields of social and education policy, broadly. From these short-listed studies

(50), we identify key examples in each category (behaviorally tested, informed,

aligned) and use these to illustrate the application of behavioral insights for policy

analysis in the main text of this review. In the Appendix, we include Table A1 sum-

marizing results from the more extensive list of short-listed studies to provide an

easily accessible reference for scholarship in this burgeoning field of study.

Behaviorally Tested Policy Analysis

Behaviorally tested policy analysis includes the evaluation/analysis of policy ini-

tiatives that are piloted in labs or smaller field experiments before being scaled up.

These examples showcase how insights from basic research in the behavioral scien-

ces can be harnessed to inform policy.

Behavioral Insight: Provide Timely Information and Increase Saliency of Information

Evidence from behavioral sciences shows that the provision of timely infor-

mation and an increase in the salience of information presented can improve the

take-up of government services. The e-mail campaigns carried out by the SBST,

in collaboration with the DOD (mentioned in the introduction of this article) are

great examples of behaviorally tested policy analyses. While past evidence exists

on how timely, informational messages sent about the benefits of a program to

S86 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

potential beneficiaries result in higher uptake of the savings in other countries (Kar-

lan, McConnell, Mullainathan, & Zinman, 2016), the SBST wanted to ensure that

such informational messages would also work within the context of retirement sav-

ings for military service members in the United States. In one of the most successful

pilot-tests conducted by SBST, the DOD sent approximately 720,000 not-enrolled

service members one of nine e-mails, with messages incorporating various behavior-

al insights—framing the decision to enroll as a “Yes/No” choice, making the bene-

fits of enrollment more salient, clarifying the next steps needed to enroll in the plan,

and/or providing information about the projected financial benefits of retirement

security (SBST, 2015). The positive results ($1.3 million in savings increase in just a

month) from the most effective e-mail message have prompted the DOD to scale up

this intervention. The DOD will be sending periodic e-mails with embedded behav-

ioral framing of messages to service members going forward.

MDRC, a nonprofit education and social policy research organization, has led

several behaviorally tested policy analyses in collaboration with the U.S. Department

of Health and Human Services (U.S. DHHS). They have conducted about 15 random-

ized controlled trials that incorporated behavioral evidence on information provision

as well as information salience. One of MDRC’s most successful policy initiatives, car-

ried out in collaboration with the Texas Office of the Attorney General’s Child Sup-

port Division, provides an example. Several states, including Texas, allow child

support payments to be lowered for an incarcerated parent during his/her prison

term. However, the incarcerated parent has to apply for such an order modification.

Many prisoners fail to apply for a child support modification and accrue very high

child support arrears. To reduce the complexity in the process, MDRC sent a postcard

to a random set of incarcerated parents informing them about the order modification

program and a prefilled (with available personal information) application form. Those

who received the reminders and the simplified application were 11 percent more like-

ly to apply for the child support modification option (Richburg-Hayes et al., 2014).

Interventions that provided timely information have also shown positive effects

on students’ post-secondary outcomes. For example, text messaging campaigns that

reminded students to complete tasks needed for matriculation (Castleman & Page,

2015) improved college enrollment particularly among low-income students by 5.7

percentage points (as compared to the control group of low-income students who

did not receive text messages). Castleman and Page (2015) also evaluated the impact

of a peer mentoring intervention in which college student mentors reached out to a

randomized group of high school students via text messages to help them navigate

their transition to college. Peer mentoring increased college enrollment by 4.5 per-

centage points. Similarly, randomized control trials conducted by researchers in col-

laboration with a nonprofit research think tank, ideas42, included a text messaging

campaign that provided information about student loan borrowing costs to students

in a community college. Those students who received the text messages borrowed

less compared to the students who did not receive text messages, $2,218 compared

to $2,401 (ideas42, 2016). Online campaigns using smartphone apps that reminded

students about the priority deadline for applying for the Free Application for Federal

Student Aid (FAFSA) at a large public university also had a significant impact on

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S87

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

FAFSA application completion rates as well as receipt of financial aid awards

(ideas42, 2016). Lavecchia, Liu, and Oreopoulos, (2014) review several other educa-

tional interventions that have incorporated insights from behavioral sciences, many

of which are behaviorally tested.

Interventions designed to provide effective information to aid decision making

have also been used in other social policy domains. The U.S. Department of Agricul-

ture Food and Nutrition Service (USDA FNS) pilot-tested four initiatives under the

Supplemental Nutrition Assistance Program-Education (SNAP-Ed) project that pro-

vided information to low income children and women about the benefits of healthy

eating (USDA FNS, 2012). These initiatives that included direct and online education

(with substantial variations in program design and levels of exposure) were all rigor-

ously evaluated using randomized control trials and/or quasi-experimental meth-

ods. However, only one of those four educational interventions showed a

statistically significant positive effect on children’s eating behaviors and caregivers’

purchase and offering of healthy food items such as fruits and vegetables.

Interventions that go beyond just providing information or increasing the

salience of information have also been analyzed by several behaviorally tested policy

analyses. A central insight from behavioral sciences is that the framing of a message

and the affective response invoked by the message matters as much as, if not more

than, the specific contents of the message. We review a few behaviorally tested poli-

cy analyses that use such behavioral insights.

Behavioral Insight: Reframe the Information Content to Change the Emotional Affective

Response of Recipient

The DOD collaborated with the SBST to increase re-enrollment of service mem-

bers to the thrift savings plan after pilot-testing the use of another behaviorally

informed e-mail campaign. The e-mail included three behavioral components—a

personalized greeting that included the service member’s name, message emphasiz-

ing the timing (a new year) as an opportunity for service members to make a

renewed commitment with their finances, and clear information about the steps

needed to complete the re-enrollment process. The redesigned e-mail embedded

with behavioral insights led to a 5.2-percentage point increase (from 23.5 to 28.7 per-

cent) in re-enrollments in the first week (SBST, 2015). Based on this result, the DOD

scaled up the effective behavioral messaging for encouraging re-enrollment.

In another similar initiative, MDRC, in collaboration with Franklin County Child

Support Enforcement Agency in Ohio, carried out a randomized control trial to increase

overall collections of child support from noncustodial parents. The team designed

reminder notices that incorporated several behavioral insights that were sent to a ran-

dom group of noncustodial parents. The control group received no reminder notice.

The number of noncustodial parents who made a payment when sent a reminder was

statistically significantly larger (by 3 percentage points) than the number of noncustodi-

al parents in the control group. However, the reminder notice did not result in a statisti-

cally significant increase in total collections per person (Richburg-Hayes et al., 2014).

S88 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Several other interventions implemented by MDRC resulted in treatment effect sizes

that ranged between 2 and 3 percentage points in comparison with the control group.

In another behaviorally tested policy analysis, MDRC evaluated a policy initia-

tive to increase the number of Temporary Assistance for Needy Families (TANF)

recipients in Los Angeles County to sign up for services such as job search assistance,

community service, employment, education, and/or other specialized services as

part of their new welfare-to-work participation rules. MDRC used two different mes-

saging strategies that were embedded with behavioral insights regarding loss aver-

sion (Tversky & Kahneman, 1991). The message that highlighted the losses

participants might experience by not attending a required activity increased the like-

lihood of participation initially by 4 percentage points, although these results were

not sustained over time (Farrell, Smith, Reardon, & Obara, 2016).

Behavioral Insight: Reduce Complexity of Task

Low take-up of government support programs cannot always be improved

by informational messages or reframing of the messages, especially in certain

educational domains for vulnerable populations such as low-income students.

This insight became apparent as a result of another clever behaviorally tested pol-

icy analysis. The FAFSA that students have to fill out to access government aid

and other need-based institutional aid is infamous for its length and complexity.

Research shows that such complexity acts as a significant barrier to many stu-

dents accessing higher education and thereby exacerbates the enrollment gap

between high- and low-income students (Dynarski & Scott-Clayton, 2006).

Researchers, in collaboration with a tax-preparation software company, con-

ducted a randomized field experiment that went beyond informational nudges to

students and parents. Low-income families who were receiving tax preparation

help were offered personal assistance to complete the FAFSA. Due to the large

duplication of information between tax forms and FAFSA, the treated partici-

pants received largely prepopulated FAFSA forms in addition to extra guidance

for completing the rest of the application and automatic online submission.

Treated participants were also provided with personalized aid estimates and

comparisons with tuition costs for nearby colleges. The effects of the personal

assistance were large. High school seniors whose parents received the treatment

were 8 percentage points more likely to have completed two years of college

(going from 28 to 36 percent), during the first three years following the experi-

ment. Families who received aid information but no assistance with the FAFSA

did not experience improved outcomes (Bettinger, Terry Long, Oreopoulos, &

Sanbonmatsu, 2012).

Such heterogeneous, context-dependent effects of several educational interven-

tions highlight the need for designing, implementing, and evaluating several proof-

of-concept projects before scaling up a behaviorally inspired intervention. Social-

psychological interventions that have begun to show tremendous promise in educa-

tion in the United States recently follow such an approach (Yeager & Walton, 2011).

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S89

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Behavioral Insight: Target Students’ Subjective Experiences and Beliefs

These interventions use subtle reading and writing exercises to influence stu-

dents’ subjective experiences and beliefs to promote their educational and psychologi-

cal well-being. Social psychologists have traditionally used lab experiments as a first

step, where the independent variables of interest are manipulated in a controlled

experimental set-up, before implementing tweaked experiments in the field. For

example, Paunesku et al. (2015) show that interventions that target students’ beliefs

about their ability and motivation in school have significant effects on students’ aca-

demic outcomes. Such interventions were implemented to scale (1,500 students in 13

high schools in the United States) after rigorous testing in smaller lab and field

experiments (Aronson, Fried, & Good, 2002; Blackwell, Trzesniewski, & Dweck,

2007). Social-psychological interventions that target students’ feelings of belonging on

campus, especially during their transition to college, are tested in a variety of univer-

sity settings—such as large public universities and smaller selective universities

(Yeager et al., 2016) before being scaled to a variety of colleges. These interventions

were also tested in smaller lab settings (Walton & Cohen, 2011) before being pilot-

tested in the field. The central behavioral insight of such social psychological

interventions is that by precisely targeting students’ subjective experiences in school,

educators can positively impact students’ academic outcomes. Such precise psycho-

logical mechanisms, however, need to be drawn from basic laboratory research on

attitude change and persuasion, and customized to the different contexts in smaller

proof-of-concept studies before large-scale implementation (Yeager & Walton, 2011).

Behavioral Insight: Invoke Social Norms to Promote Desired Behavior

Finally, the above examples seem like most pilot-tested policy initiatives had the

intended effect; however, not all behaviorally tested policy analyses reveal such posi-

tive effects. We argue that reporting and understanding the causes of such null find-

ings are extremely important to move this research forward. For example, behavioral

insights from social psychology have shown that individuals are very sensitive to

social pressure and social norms. Smaller lab and field experiments on charitable giv-

ing have shown that social pressure—an individual’s fundamental dislike to say

“no”—can be used to increase an individual’s charitable giving (Dellavigna, List, &

Malmendier, 2012). Similarly, invoking social norms, that is, description of an indi-

vidual’s peer behavior to encourage/discourage one’s behavior, revealed positive

impacts in some settings; however, such insights might not translate to an alternative

policy domain as the below behaviorally tested policy analysis example reveals.

The SBST, in collaboration with the US DHHS’s Centers for Medicare and Med-

icaid Services (CMS), sent letters informed by behavioral insights to a randomized

group of medical providers with high prescription rates of controlled substances.

Based on past evidence that medical providers respond to normative messages that

provided feedback about the providers’ own vaccination rates compared to those of

their peers (Kiefe et al., 2001), the SBST letter included details about the medical

S90 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

providers’ prescription rates compared to that of their peers’ rates of controlled-

substance prescription. The control group did not receive any letter. In this case, no

significant impact was seen in the subsequent year (Sacarny, Yokum, Finkelstein, &

Agrawal, 2016). The null results spurred more analysis into the mechanisms of

behavior change. Subsequent randomized control trials using letters with revised

language based on more recent psychological evidence are currently under way.

Behaviorally Informed Policy Analysis

In contrast to behaviorally tested policy analyses, behaviorally informed policy

analyses include the evaluation/analysis of initiatives that are not explicitly tested

either as lab or field experiments before being implemented to scale. These initia-

tives, in most cases, are based on past behavioral evidence that has been rigorously

tested in another (often related) policy domain. For example, information provision

strategies that have been proven to work based on several other pilot tests in related

policy domains, have often been implemented to scale without additional tests. We

review a few behaviorally informed analyses of initiatives that were adapted to new

policy issues based on robust past evidence.

Behavioral Insight: Provide Timely Information and Increase Saliency of Information

Providing reminders to encourage people to follow through on a desired course

of action has shown huge promise in many domains such as personal savings (Kar-

lan et al., 2016) and child support payments (Richburg-Hayes et al., 2014). Based on

such evidence, the SBST designed an e-mail campaign reminding federal student

loan borrowers about their repayments. The SBST and the Department of Educa-

tion’s Office of Federal Student Aid (FSA) sent a reminder e-mail to over 100,000 bor-

rowers who had missed their first payment. The e-mail specified that the borrower

had missed a payment, and included additional salient information about the steps

needed for the borrower to complete payment including an easily accessible link to

the service provider’s payment system. The above policy initiative was evaluated

using a quasi-experimental pre-post design (i.e., the overall payment rates were com-

pared before and after the e-mail campaign). Although the SBST team did not use

the more rigorous experimental approach to this evaluation, their pre-post compari-

son suggests that the reminder e-mail led to a 29.6 percent increase in the fraction of

borrowers making a payment by the end of the first week after delivery of e-mail.

Overall, by the end of the first week after the e-mail reminder, student payment

amounts went up by 0.8 percentage points (SBST, 2015).

Similarly, based on past evidence on how timely notices increased the use of tax

credits, the SBST, in collaboration with the FSA, sent informational e-mails about

income-driven repayment (IDR) plans to approximately three million student bor-

rowers. The e-mail included information about the eligibility criteria for IDR plans,

the benefits of IDR, costs of not enrolling in IDR, and easily accessible online links to

reach the service provider. To evaluate the impact of this initiative, SBST varied the

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S91

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

timing of sending these e-mails—e-mails were sent in two waves three weeks apart

from each other. The informational e-mail led to a substantial increase in applications

for IDR plans within 20 days of the e-mail being sent. Among the group that

received the e-mail, 4,327 applied for IDR as opposed to the 982 IDR applications

received from the comparison group who had not yet received the informational

e-mail. The SBST and the FSA were most concerned about the impact of the e-

mail campaign on the seriously delinquent (90–180 days), approximately 800,000

student loan borrowers. Based on the positive results of these initiatives, the FSA

has continued to collaborate with the SBST on initiatives designed to simplify

the use of IDR. The ongoing efforts range from revising the IDR application form

to innovative communication campaigns targeting struggling student borrowers

based on scientific evidence from other domains such as take-up of tax credits

postinformational notices (SBST, 2015). However, it is important to note that rig-

orous behaviorally informed policy evaluations of such initiatives described

above are exceptions rather than the rule.

Behavioral Insight: Increase Salience of Information to Mitigate Effects of Limited

Attention

The U.S. federal government has also implemented certain regulations in the

financial sector that have been evaluated in behaviorally informed policy analyses.

For example, the Credit Card Accountability Responsibility and Disclosure (CARD)

Act of 2009 mandated changes to credit card statements to protect consumers from

financial institutions that previously had taken advantage of consumers’ limited

attention by obfuscating the true costs of certain financial instruments. These legislat-

ed changes were based on past research on people’s cognitive biases such as limited

attention. The new law required financial institutions to disclose the length of time it

would take to pay off a credit card balance in full if borrowers only pay the mini-

mum monthly amount. This new law increased the salience of fees, and other costs

to consumers to mitigate the effects of limited attention. Additionally, credit card

companies had to disclose the minimummonthly payment needed to pay off the bal-

ance in three years. Agarwal, Chomsisengphet, Mahoney, and Stroebel (2015) evalu-

ate the effectiveness of the CARD act using a quasi-experimental research design

and find that the information disclosure requirements only had a negligible (but sta-

tistically significant) effect on borrowers’ repayment behavior. Account holders who

paid at a rate that would repay the balance within three years increased by less than

a percentage point (0.4 percentage points on a base of 5.3 percent). However, other

evaluations that tested the mechanism of information disclosure reducing consumer

indebtedness related to payday loans lends some support to such regulations. A

Jamal Poverty Action Lab led-study by Bertrand and Morse (2011) showed that inter-

ventions that provided in-depth information regarding the cost of payday loans to a

randomized group of low-income consumers significantly reduced their borrowing

frequency, and overall borrowing amounts.

S92 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Behavioral Insight: Reduce Choice Overload

Similarly, psychological research has shown that many individuals suffer from

choice overload—the inability to meaningfully compare choices when too many

choices are provided at once. For example, in a series of classic experiments, Iyengar

and Lepper (2000) showed that an extensive-choice context not only increased the

burden on mental resources and the time and energy required to make a choice, but

also reduced their overall satisfaction. In some cases, the overwhelming number of

choices even paralyzed some individuals, preventing them from being able to make

any decision at all. A regulatory approach undertaken by the U.S. government to

facilitate better decision making that has been informed by research on choice over-

load is a mandate to standardize product attributes. For example, the federal govern-

ment mandated that the Medicare supplemental insurance plans (Medigap) for

senior citizens must conform to one of 10 standardized plan options (Medicare

Improvements for Patients and Providers Act 2008). These plans, denoted with let-

ters of the alphabet (as delineated by the CMS), are standardized across 47 states. For

example, the level of coverage (or benefits) under Plan A in Florida is the same as

that of Plan A in Indiana.

The number of product choices, or the outcomes of senior citizens who chose

these designated products were not explicitly tested using lab or field experiments;

however, such behaviorally informed regulatory approaches seem to provide a

promising avenue for incorporating insights based on evidence from basic psycho-

logical (and other behavioral sciences) research. However, we highlight the need to

evaluate such initiatives using behaviorally informed empirical analysis to under-

stand both the impact of the initiative on the outcome of interest and the mediating

mechanisms that the underlying behavioral insights presume. Such an evaluation is

particularly pertinent in light of a recent review of the literature on the impacts of

laws and regulations that require public information disclosure (Loewenstein, Sun-

stein, & Golman, 2014). The above review finds that while information disclosure, in

many cases, does not affect the behavior of the recipients of the information, it seems

to significantly affect the behavior of the providers of information.

Behaviorally Aligned Policy Analysis

Last, behaviorally aligned policy analysis includes evaluation/analysis of policy

initiatives that are most often traditional policy tools such as taxes or subsidies that

do not explicitly rely on any existing behavioral evidence; however, the evaluation/

analysis of these policy initiatives is aligned with a behavioral insight when analyzed

post hoc after implementation.

Behavioral Insight: Differential Awareness of Programmatic Components Affect Take-Up

For example, Chetty et al. (2013) show that the EITC, the largest anti-poverty

program in the United States, affects labor supply decisions of people differentially

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S93

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

based on their knowledge of the tax code. They demonstrate how the neoclassical

model of labor supply that typically assumes perfect information about tax codes

needs to be updated to incorporate the effects of imperfect information on traditional

policy instruments such as tax credits.

Past research on the EITC had demonstrated the effect of the tax credit in

increasing the labor force participation of low-income workers. However, the evi-

dence on the intensive margin, that is, hours of work and earnings (conditional on

increased labor force participation) was mixed (Eissa & Hoynes, 2006). Chetty et al.

(2013) exploited the variation in the tax-credit top-up levels across states to identify

the effects of EITC on wage earning using detailed tax-return data, not previously

available. One of the researchers’ primary insights was that the claimants differed in

their responses to EITC (measured using the distribution of EITC claimants’ levels of

reported incomes right around the EITC refund-maximizing amounts) both within

and across the states. The researchers hypothesized that the differential response

might be driven by differences in peoples’ knowledge about the EITC’s incentive

structure and used empirical techniques to unpack those differences. This insight

helped explain the spatial variation in responses to EITC. The researchers also help

explain how information diffusion might drive such differential response across the

intensive and extensive margins uncovered by earlier research.

Behavioral Insight: Reframing the Timing and Mode of Delivery of Programmatic

Components Affect Take-Up

Another example of a behaviorally aligned policy analysis is conducted by

Richards and Sindelar (2013). They evaluate existing proposals to encourage healthy

food choices in the Supplemental Nutrition Assistance Program (SNAP), one of the

largest food assistance programs in the United States, using behavioral principles.

For example, they evaluate the proposal of subsidizing purchases of healthy foods in

the SNAP using a behavioral lens. They recommend changes to the timing and

mode of delivery of subsidy that would increase the salience of the subsidy thereby

promoting healthy eating behavior. They also make innovative recommendations to

changes in the SNAP to promote healthy eating that includes the use of default

options to encourage healthy food choices and commitment devices that can be har-

nessed in addition to traditional price subsidies. We classify the above analysis as

behaviorally aligned policy analysis because the proposals evaluated by the authors

have not been implemented to date. However, the ex-ante evaluation of the proposed

reforms to SNAP using behavioral insights is an excellent example of how behavioral

insights can drive policy reforms. We also hope that such reforms are pilot tested

and evaluated using behaviorally tested/informed policy analyses in the future.

Behavioral Insight: Can Default Options Result in Crowd-Out Effects?

Another excellent example of a behaviorally aligned policy analysis is the use of

innovative empirical strategies and the use of “big data” to explicate the behavioral

S94 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

lever underlying a policy initiative, and/or the mechanisms of behavioral change.

For example, a primary concern regarding initiatives that boost savings using auto-

matic enrollment options (described earlier) has been that increases one observes in

savings produced by automatic enrollment in savings plans might in fact be offset

by reductions in savings (or increases in borrowing) in other savings accounts.

Chetty, Friedman, Leth-Petersen, Nielsen, and Olsen, (2014) explore such a hypothe-

sis empirically. They study the impacts of defaults on total savings of individuals by

exploiting variation in employers’ contributions to retirement savings accounts (i.e.,

for all practical purposes similar to an automatic enrollment default option) using a

rich panel data from Denmark. They analyze the savings behavior of employees who

switch jobs and experience variations in employer contributions to their retirement

savings account and find limited evidence for crowd-out effects. Specifically, they

find that employees who move to a firm with a more generous pension contribution

(at least 3 percentage points higher than the prior employer) on average reduce their

own savings contribution by just 0.56 percentage points with no change in their sav-

ings in any other taxable account.

In the same study, Chetty et al. (2014) also compare the effectiveness of tax subsi-

dies for pension contributions with the effects of automatic enrollment defaults into

employer pension program. The automated enrollment into pension savings has

huge impacts relative to tax subsidies. Essentially, a dollar of government expendi-

ture on tax subsidies for pensions increases total savings by only 1 cent whereas the

effect of an automatic enrollment default into pension savings is approximately 80

cents. The authors estimate that approximately 85 percent of individuals are “passive

savers” who are unresponsive to subsidies (and also unresponsive to changes in any

employer contribution amounts); 15 percent of individuals are “active savers” who

respond to tax subsidies and reallocate their savings to other tax-saving instruments.

Thus, automated defaults appear to work better than tax subsidies that require

actions on the part of savers. This study is consistent with others that lends increas-

ing confidence to the notion that automated defaults embedded in policy instru-

ments seem to work well. While the above studies are carried out using data from

Denmark, we include this example in our review to showcase the importance of

such behaviorally aligned policy analyses.

Behavioral Insight: Reduce Complexity of Task

Finally, behavioral insights that reveal that individuals suffer from choice over-

load can be used to inform behaviorally aligned policy analyses as well. Specifically,

the impacts of poverty on cognitive capacity (Mani, Mullainathan, Shafir, & Zhao,

2013), raises several concerns about the design and impact of several policy initiatives.

For example, Bhargava, Loewenstein, and Sydnor, (2015) find that, while everyone

struggles when choosing from a complex choice set, low-income households particu-

larly struggle more when making complex choices. They analyze the health plan

choices of employees at a large U.S. firm to examine the effects of choosing from a

complex choice set. As a consequence, when the government offers numerous and

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S95

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

complex options to citizens, it can result in lower average welfare and also have

adverse distributional consequences. From a policy perspective, this is salient when

offering health insurance plans under health care exchanges as these plans vary

across parameters such as deductibles, copay rates, and out-of-pocket maximums.

Lessons for Policy Analysis and Design

As the above review demonstrates, behavioral insights have informed public

policy and continue to do so increasingly across several policy domains. While there

has been an overwhelming agreement about the usefulness of incorporating behav-

ioral insights into policy analysis, the exact approach for how to do so remains

unclear (Congdon, 2013). In this section, we provide some thoughts on how to apply

behavioral insights into the various stages of policy analysis—ex-ante policy analysis,

ex-post policy analysis, and future policy design. We distinguish between ex-post poli-

cy analysis—that occurs upon or after the policy has been implemented—and ex-ante

policy analysis—that occurs before the policy is implemented—to better delineate

the specific insights that can be gained from the incorporation of the behavioral per-

spective in each of those domains.

As Figure 2 illustrates, the thematic organization of the analyses of policy ini-

tiatives embedded with a behavioral element (i.e., behaviorally tested, behavioral-

ly informed, and behaviorally aligned initiatives) can be used to inform the

various stages of policy analysis and policy design. First, we illustrate how les-

sons from behaviorally informed and behaviorally tested policy analysis can inform

ex-ante policy analysis. For example, evidence from behavioral sciences can be

used to enhance our understanding of the underlying policy problem that a poli-

cy initiative is trying to solve. Specifically, we describe how a diagnosis of the

policy problem can reveal how peoples’ psychological impediments may interact

with traditionally defined policy problems such as market failures and govern-

ment delivery of services.

Figure 2. Framework for Applying Behavioral Insights in Policy Analysis and Policy Design.

S96 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Second, we describe how lessons from the behaviorally tested policy analysis can

be effectively applied to future policy design. We show how behavioral insights can

provide a wider repertoire of policy tools at a government’s disposal to intervene

and influence behavior change. Furthermore, behavioral testing of policy initiatives

can also be used to study and understand the mechanisms of behavior change that

can act as building blocks for designing more effective future policies. We also high-

light how the government can combine traditional policy tools with newer behavior-

ally enhanced policy tools to intervene cost-effectively.

Third, we discuss how lessons from behaviorally aligned policy analysis can

inform ex-post policy analysis. We show how one can better evaluate the impact of

existing policies (that may use traditional policy tools such as taxes/subsidies) if we

incorporate the rich evidence available from behavioral sciences.

Finally, we show how a fuller incorporation of behavioral insights into the vari-

ous stages of policy analysis entails the need to view the policy process itself as cycli-

cal. Lessons from ex-post policy analysis should indeed inform ex-ante policy analysis

and future policy design. We describe such examples in the last section where past

behaviorally aligned ex-post policy analysis have provided new insights that have

driven the implementation and evaluation of new policy initiatives.

Lessons for Ex-Ante Policy Analysis

One of the primary steps in any ex-ante policy analysis is to understand the

underlying policy problem that a policy initiative is intending to solve, that is, a

rationale for government intervention in the first place. Traditional ex-ante policy

analysis, based primarily within the neoclassical welfare economics framework, has

focused broadly on two categories of situations that demand government interven-

tion—efficiency and equity. Inefficiency within the neoclassical framework has been

explored primarily in the form of various market failures and equity demands that

the government intervene to alleviate poverty and/or redistribute resources within a

society even in situations that do not necessarily promote efficiency.

The incorporation of behavioral insights into ex-ante policy analysis demands

that we reframe how we think about the underlying policy problem that the policy

initiative is trying to solve (Congdon, 2013). The primary taxonomy of market fail-

ures—public goods, externalities, natural monopolies (or other inefficient market

structures), and information asymmetry—can be enhanced with another category

that includes peoples’ psychological impediments such as imperfect optimization,

bounded self-control, and nonstandard preferences3 (Congdon, Kling, & Mullainathan,

2011, p. 20; Madrian, 2014). Psychological impediments can also be explored as an

underlying factor that may exacerbate or attenuate any of the existing categories of

market failure or effective delivery of government programs (Congdon et al., 2011).

We believe that a diagnosis of the policy problem that analyzes the interaction

between psychological impediments and the existing sources of market failure, and

the delivery of other governmental programs can be used to integrate behavioral

insights more comprehensively into policy analysis. Using a case study of a policy

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S97

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

initiative that we reviewed earlier, we illustrate how a behavioral diagnosis of the

underlying policy problem can be incorporated into an ex-ante policy analysis.

Nonprofit research organizations such as ideas42 and MDRC have developed

systematic approaches to diagnose a policy problem in the delivery of government

programs by applying behavioral principles. This approach, referred to as

“behavioral diagnosis and design” (Richburg-Hayes et al., 2014), or “behavioral

mapping” (Hall, Galvez, & Sederbaum, 2014), includes a series of steps that aims to

methodically diagnose the psychological impediments that result in the deviation of

programmatic outcomes from a policy’s intended effects. Before MDRC sent out

reminder postcards to incarcerated parents in Texas, they carried out a diagnosis of

the decision-making environment of an incarcerated parent in the child support

order modification policy context in Texas. Researchers identified several psychologi-

cal impediments in the existing decision-making environment that an incarcerated

parent faced. For example, many parents avoided even opening the letter notifying

their eligibility for order modification due to a negative emotional response they had to

any communication from the child support office. Neither the content of the letter

nor the process of application for child support order modification was simple, add-

ing to the cognitive load that the parents already faced. The interventions that MDRC

designed included reminder postcards and effective reframing of the order modifica-

tion message to address each of these psychological impediments.

Such a diagnosis should also be carried out to understand how psychological

impediments may interact with market failures. For example, education provision in

the United States is a classic example of a public good (with some features of a posi-

tive externality). Traditional ex-ante policy analysis advocates government interven-

tion in education provision to mitigate the potential market failure that might result

in underconsumption of education. College education in the United States is thus

heavily subsidized by the government. However, college enrollment and completion

rates, especially for racial minority and first-generation students, are lower than that

of white and continuing-generation students (Ifill, Radford, Cataldi, Wilson, & Hill,

2016). Evidence from social psychology has shown how students’ sense of belonging,

particularly for racial minority students and first-generation students on campus, can

affect their engagement and performance in college (Walton & Cohen, 2011). Such

impediments to the psychological processes that affect students’ persistence and per-

formance in college further exacerbates the market failure of underconsumption of

education. Social psychological interventions that target and promote students’ sense

of belonging are being behaviorally tested across college campuses in the United

States (collegetransitioncollaborative.org) to mitigate such externalities. Such policy

initiatives can often complement the traditional policy tools that address the public

good/positive externality nature of education provision using subsidized loans.

Lessons for Policy Design

Having diagnosed the policy problem, behavioral insights can (and should) be

applied to effective policy design. First, behavioral insights can be used to enhance the

S98 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

policy toolkit—default options, e-mail reminders, and text campaigns are examples

of new policy tools that have been ushered in by evidence from behavioral sciences

research. Traditional policy design has predominantly focused on the use of price

incentives and regulations to change behavior—provide subsidies or tax credits to

encourage a particular behavior, tax those behaviors that need to be curtailed, or reg-

ulate markets to encourage/discourage behaviors. While price incentives are incredi-

bly powerful in many instances in changing behavior, there are limits to the impact

price incentives alone can have. The size of incentives, the structure of incentives, the

framing of the incentive message, and the salience of the message strongly influence

behavior change (Kamenica, 2012). Fryer, Levitt, List, and Sadoff (2012) analyze the

impact of reframing a teacher incentive program using principles of loss aversion in

nine schools in Chicago. They find that reframing the incentive structure using loss

aversion (i.e., teachers are paid in advance and asked to give back the money if their

students do not improve) had significant effects on students’ math test scores—stu-

dents whose teachers received the reframed incentive structure showed between 0.2

and 0.4 standard deviation gains in math test scores.

Second, behavioral insights can (and should) be applied to tweak the traditional

policy tools such as taxes and subsidies. For example, text/e-mail reminders can

enhance the effectiveness of existing policy tools such as the subsidized FAFSA student

loans.

Third, well-designed pilot studies can be used as a first stage before rolling out

policy initiatives across the state/country. The UK BIT team uses a “test, learn, adapt”

approach for policy design (Lourenço et al., 2016). The approach is based on three key

principles: “Test,” that is, the identification of various policy interventions that can be

evaluated and analyzed for their effectiveness; “learn” by measuring the results and

identifying “what works”; and “adapt” using findings from the above initiatives and

their effectiveness to adjust and design future policy intervention accordingly. We pro-

pose that an additional consideration about “how” it works is also crucial in the design

of new policies. The mediating mechanisms through which behavior change can be

influenced as well as the contexts with which these mediating mechanisms interact

and covary demands as much attention as “what works” to move this research

forward.

The main advantage of testing an intervention in a controlled experiment (in a

lab or in smaller field experiments) is that the underlying theory and mechanism of

change can be better understood (Mortensen & Cialdini, 2010). Recently, economists

have also argued for the use of such mechanism experiments as a precursor to larger

policy evaluations using randomized control trials (Ludwig, Kling, & Mullainathan,

2011). They argue that if a hypothesized causal mechanism is not effective in a con-

trolled experimental set-up, a policy evaluation using a larger randomized control

trial might be wasteful. Conversely, if the causal mechanism proves effective, espe-

cially under multiple contexts, a policy evaluation using a well-designed randomized

control trial targeting the causal mechanism of change is not only more cost-effective

but will also provide more insights into the efficacy of the intervention. Meta-

analysis of experimental studies and policy evaluations must also strive to under-

stand and review the candidate mediating mechanisms along with providing

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S99

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

estimates of effect sizes across multiple trials and contexts as was the case with the

growth mindset studies (Burnette, Boyle, Vanepps, Pollack, & Finkel, 2013).

Lessons for Ex-Post Policy Analysis

Behavioral insights often enhance the evaluation of existing policy impacts when

incorporated meaningfully in an ex-post policy analysis. As discussed earlier, Chetty

et al. (2013) incorporate several behavioral insights to estimate the precise impacts of

the EITC on both the number of hours worked and wage earnings of individuals

across states.

A final lesson from behavioral sciences-inspired approach to public policy is an

appreciation for the cyclical nature of the policy process. For example, lessons from

behaviorally tested policy analyses—both positive and null findings—have been sub-

sequently used to design other policy initiatives. Chetty et al. (2013) found that peo-

ples’ differential knowledge about EITC across the different states resulted in

differential EITC take-up rates. That insight provided the input for other pilot-tests

that directly evaluated the mediating mechanism—knowledge about tax codes

(Bhargava & Manoli, 2015; Chetty & Saez, 2013). Chetty and Saez (2013) conducted

an experiment with 43,000 EITC clients of tax-preparation software company H&R

Block. Half of the tax filers were randomly selected to receive information from their

tax preparer about the marginal incentive structure of the EITC, while the other half

did not. They found that this informational intervention had no effect on individuals’

earnings in the subsequent year on average. However, in another experiment,

Figure 3. Stylistic Example of a Behaviorally Enhanced Policy Analysis. Note: The above figure is adapted from the figure 9.1 included in Weimer and Vining (2015, p. 205).

S100 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Bhargava and Manoli (2015) mailed eligible individuals simplified information about

the EITC to 35,000 individuals who were eligible for the EITC but did not file the tax

forms needed to claim it. They found that such an informational mailing intervention

raised EITC filing rates significantly.

It is interesting to note that a potential reason why providing information about

EITC eligibility might have increased EITC take-up rates, but information about the

EITC tax code structure did not appear to havemuch impact on earnings, could indeed

be another behavioral lever—cognitive overload (Chetty, 2015). For example, individu-

als might have paid more attention to information that they have unclaimed benefits,

that is, money on the table, as compared to information that their marginal wage is dif-

ferent from what they mistakenly calculate it to be in the absence of EITC. The second

information needs additional mental processing and steps to realize immediate gains.

Such behavioral insights that can uncover heterogeneous treatment effects of the EITC

deserve muchmore attention and are fruitful lines of future research.

In all, we summarize the steps involved in a behaviorally enhanced policy analy-

sis using a stylistic example. Figure 3, an example adapted from Weimer and Vining

(2015),4 illustrates how a diagnosis of the policy problem that identifies psychological

impediments can enrich the standard neoclassical framework of linking the policy

problem (i.e., the rationale of government intervention) to various policy alternatives.

As Figure 3 illustrates, the first step in any policy analysis within the neoclassical

framework is the examination of the presence or potential for market failure that pro-

vides a rationale for government intervention. Our example starts with a similar pre-

mise. However, if there is evidence, or if theory suggests that there is a potential for

market failure, we further recommend that the policy analyst carry out a diagnosis to

identify the psychological impediments that might exacerbate or attenuate the market

failure. Such a diagnosis can result in the design and implementation of initiatives

that can be evaluated using behaviorally informed and tested policy analyses. In the

absence of any source of psychological impediment, we recommend the use of tradi-

tional policy tools such as taxes, subsidies, and/or regulations. However, we suggest

that the policy analyst explore an ex-post policy analysis of these traditional policy

tools using relevant behavioral levers, as evidenced from behaviorally aligned policy

analyses we reviewed earlier.

Similarly, even in the absence of a market failure, government intervention can

be justified on the grounds of equity, especially in the context of antipoverty pro-

grams. A diagnosis of the decision-making environment faced by the targets of such

policies will help shed light onto the policy alternatives that might be effective. The

take-up rates of such policy interventions and the effect on the outcome of interest

can (and should) be evaluated using the behavioral paradigm as shown in the stylis-

tic example of a behaviorally enhanced policy analysis.

Criticisms

The proliferation of behavioral sciences-inspired research for designing public

policies is not without its share of critics. Some critics contend that such an over-

reliance on low-cost interventions has distracted governments from implementing

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S101

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

more ambitious policies that rely on traditional policy instruments. They argue that

while traditional policy instruments such as taxes and/or subsidies might be costlier

to implement and harder to receive bipartisan support, taxes and subsidies may

have a much larger potential to change behavior. For example, in a provocative edi-

torial article titled “Economics Behaving Badly,” George Loewenstein and Peter Ubel

(2010) argued that informational e-mails encouraging lower energy use show just a

modest impact on driving/household energy consuming behavior. However, a tradi-

tional price-based solution such as a well-calibrated carbon tax policy could unleash

a much larger impact by aligning energy prices that internalize the externality; how-

ever, such an effort lacks political will. Similarly, studies shows that information pro-

vision interventions such as laws mandating calorie labeling in restaurant menus

have shown just a modest impact on the provision and selection of healthy food

options (Elbel, Kersh, Brescoll, & Beth Dixon, 2009; Namba, Auchincloss, Leonberg,

& Wootan, 2013), thereby doing very little to combat a public health issue such as

obesity. Instead, Loewenstein and Ubel (2010) argue that the lack of political will to

end the corn subsidies that result in lower costs of high-fructose corn syrup and low-

priced unhealthy processed foods continue to exacerbate the obesity epidemic.

The small effect sizes of many behaviorally enhanced policy interventions have

been endlessly debated by both vehement critics and ardent defenders. Critics argue

that some of the problems that behaviorally embedded policy initiatives are trying to

solve are systemic, and can thus not be solved through just small and simplistic tweaks

to policy design features (Bhargava & Loewenstein, 2015). However, defenders main-

tain that such small interventions resulting in marginal changes have the potential to

add up to more than the sum of its parts. Indeed, the famous social psychologist that

many consider to be the pioneer of this line of applied behavioral science, Professor

Daniel Kahneman, has repeatedly communicated his optimism for incorporating

behavioral insights into the public policy process. He emphasizes that these initiatives

have the potential to achieve “medium-sized gains by nanosized investments” (Kahne-

man, 2013). By analyzing and explicating the relative costs and benefits of behaviorally

embedded policy initiatives, we hope that public policy researchers can move past

such polarized reactions. We believe that behavioral insights complement—and do not

replace—the need for traditional policy tools such as taxes and subsidies.

Finally, some critics have questioned the ethics behind nudge-type policy initia-

tives. The criticism rests on the claim that these policy initiatives might result in

policymakers manipulating citizens’ choices by relying on certain automatic psycho-

logical processes of citizens (Bovens, 2009). While Hansen and Jespersen (2013) con-

tend that not all nudges rely on automatic psychological processes, Thaler and

Sunstein (2008) have argued that most nudges are liberty preserving because they do

not alter the overall availability of choices to an individual. Hansen and Jespersen

(2013) also emphasize that it is important to distinguish between transparent and

nontransparent nudges when evaluating the ethicality of using such behaviorally

enhanced policy initiatives. Most recently, Steffel, Williams, and Pogacar (2016) show

how most nudges can be made completely transparent without reducing their bene-

fits. We encourage more research to explore the ethics and the ultimate welfare

implications of the use of this behavioral paradigm to policymaking. However, we

S102 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

argue that such research should be conducted within the larger context of evaluating

relative trade-offs and benefits to cost comparisons of alternative policy initiatives.

Conclusion

Insights from behavioral sciences hold tremendous promise for applied policy

analytic work. In less than a decade since the publication of Nudge (Thaler & Sun-

stein, 2008)—viewed by many as the beginning of behavioral insights permeating

the policy process—several key advances have been made to fully incorporate such

insights into policy. However, there is not much consensus about how these insights

can be fully incorporated into policy analysis. Our review aims to contribute toward

building such a coherent approach for applying behavioral insights into the various

stages of policy analysis and policy design.

First, behavioral insights should be integrated into ex-ante policy analysis more

thoroughly by incorporating a diagnosis of the underlying policy problem—be it an

analysis of a market failure or an antipoverty/equity concern that demands govern-

ment intervention. Specifically, the interaction of peoples’ psychological impedi-

ments that might exacerbate or attenuate the policy problem(s) needs to be explored

before policy tools are designed and implemented to resolve the policy problem.

Second, behavioral insights should be incorporated into policy design and

implementation. Automatic defaults, reminder e-mails, and text campaigns that

enhance take up of governmental programs and social psychological interventions

that help smooth psychological frictions for vulnerable populations such as students

and low-income households, enhance the policy toolkit at the disposal of the govern-

ment. Such behaviorally informed policy tools can also be used in combination with

existing tools such as taxes and subsidies to enhance the intended consequences of

government intervention. The behavioral revolution in public policy not only encour-

ages the use of rigorous pilot-testing of policy initiatives using randomized control

trials to increase the internal validity of causal impacts, but also to understand the

mechanism of change underlying the policy initiative.

Last, behavioral insights enhance ex-post policy analysis by providing better mod-

els for peoples’ behavioral responses to policy changes. By incorporating various

behavioral levers in empirical models that evaluate policy impacts, we gain a better

understanding of the expected and unexpected consequences of policies. Furthermore,

such an integration of behavioral insights highlights the need to view the various stages

of the policy process as a cyclical and reinforcing process. The lessons from one stage of

the policy process such as ex-post policy analysis should inform the design of the new

policy tools and the evaluation of policy alternatives in an ex-ante policy analysis.

As the field matures, and our understanding of human behavior continues to

improve, we see that some problems can be improved through minor tweaks to poli-

cy design features. However, some other policy problems need a fundamental

rethinking of the underlying assumptions of human nature. Classic social psycholo-

gy research of behavior change carried out by Kurt Lewin entailed the study of a ten-

sion system consisting of conflicting forces in the environment that simultaneously

push and pull an individual’s behavior. Lewin distinguished between two kinds of

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S103

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

conflicting forces—restraining forces and driving forces—that form the basis for

behavior change. “Driving forces” are those that help promote behavior change. In

contrast, “restraining forces” are those that preclude behavior change. Kahneman

(2013) explains how the Lewinian approach for identifying the restraining forces

entails answering the question: “Why don’t people already do what I wish they do?”

Just as Lewin favored reducing the “restraining forces” over increasing the

“driving forces” for behavior change, the first decade of the nudges approach to poli-

cy making has followed a similar trend of targeting the “restraining forces.” Behav-

iorally informed and tested policy analyses of initiatives that involve changes in

framing, or tweaks to choice architecture can essentially be classified as the govern-

ment/change agent reducing the “restraining forces” that precludes human beings

from making choices that are in their best interest. However, we recommend that an

over-reliance on eliminating the “restraining forces” should not preclude a deeper

exploration of other “driving forces” that can be harnessed for behavior change. For

example, in a recent Huffington Post article, Lamberton and Castleman (2016) call

for Nudge 2.0—an expanded nudge toolkit especially in education that can go

beyond simplifying information or providing reminders that enable students to fol-

low through on their commitments. They call for additional interventions that pro-

vide professional assistance that can aid students’ decision making by specifically

incorporating their identity, beliefs, psychological biases, and emotions. We call for

further research to explore the impact of educational interventions based on basic

social psychological theory that can support rather than merely nudge students into

making decisions that enable them to achieve a high-quality educational experience.

Finally, we recommend the “pragmatic approach” advocated by economist Raj

Chetty (2015) for the incorporation of behavioral elements and factors in the policy

process. Rather than debating the validity of a behavioral approach as being in con-

trast to the neoclassical framework/assumptions, behavioral insights should be

judged by the usefulness of its predictions and empirical validity. Scholars and prac-

titioners carrying out policy analysis work must strive to incorporate behavioral

insights into policy design and all the stages of policy analysis.

Maithreyi Gopalan is a Ph.D. candidate at the School of Public and Environmental

Affairs at Indiana University, Bloomington. She has completed a doctoral minor in

Psychology and holds a Master’s in Economics. Her research interests lie in bring-

ing psychological insights to bear on social and education policy.

Maureen A. Pirog is Rudy Professor of Policy Analysis at the School of Public and

Environmental Affairs at Indiana University, Bloomington. She is also a distin-

guished visiting professor at the University of Johannesburg.

Notes

The authors thank two anonymous reviewers, Dave Warren, Shannon Lea Watkins, and Michael D. Per-

kins for their thoughtful and encouraging comments on earlier drafts of this manuscript. Any remaining

errors are our own.

S104 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

1. We adopt a broad definition of policy analysis in this article that includes Weimer and Vining’s (2015)

description of “policy analysis,” “policy research,” and “academic research.” Weimer and Vining

define “policy analysis” as a systematic assessment of alternative policy choices for policy problems

that is largely carried out by analysts in a variety of public organizational settings such as federal, state,

and local agencies. In contrast, they define “policy research” as pertaining to policy evaluations that

aim at predicting the impact of changes driven by policies or the impact of changes in outcomes that

can be “altered by public policy” (p. 26). Finally, they define “academic research” as empirical and the-

oretical analysis of public policy issues that aims to “contribute to a better understanding of society”

(p. 25) that are not always relevant to specific public policies. This includes analyses published more

traditionally in peer-reviewed academic journals. Given the blurring distinction between these catego-

ries, and how the empirical orientation and practice of policy analysts in governmental agencies have

also begun to resemble academic and policy research, we adopt a broad definition of policy analysis in

this review.

2. Key words used: nudges, behavioral economics, behavioral science, behavioral insights, interventions,

behavioral insights for social policy, behavioral insights for education policy, behavioral foundation of

public policy.

3. Imperfect optimization: category of psychological impediments that refers to errors people make when

choosing among alternatives; bounded self-control: category of psychological impediments that reflects

peoples’ general tendency to not take action that has future benefits even when they recognize such

benefits and would like to take action; nonstandard preferences: category of psychological impediments

that pertains to people having preferences that are different from standard model. In this case, people

are not making errors in choosing, or are not struck with the inability to take action even when they

intend to. Their preferences (accurately identified and executed) are usually assumed away in the stan-

dard economicmodels.

4. Weimer and Vining (2015) include an additional category—government failure—that explores situa-

tions in which government intervention might fail. In those cases, they advocate policy solutions such

as deregulation, legalization, and privatization. A burgeoning literature in political science and public

administration is incorporating behavioral insights to understand and solve some sources of govern-

ment failure. These problems most often pertain to problems of direct democracy and representative

government that essentially explore the cognitive biases in electoral processes as well as in bureaucra-

cy; however, a review and analysis of the insights from those studies to the policy process is beyond

the scope of this review.We thus focus just on how behavioral insights can enhance our understanding

of the sources of market failure and promote government delivery of antipoverty programs in the con-

text of policy analysis and evaluation.

References

Agarwal, Sumit, Souphala Chomsisengphet, Neale Mahoney, and Johannes Stroebel. 2015. “Regulating

Consumer Financial Products: Evidence from Credit Cards.” The Quarterly Journal of Economics 130

(1): 111–64.

Aronson, Joshua, Carrie B. Fried, and Catherine Good. 2002. “Reducing the Effects of Stereotype Threat

on African American College Students by Shaping Theories of Intelligence.” Journal of Experimental

Social Psychology 38: 113–25.

Avery, Christopher. 2013. Evaluation of the College Possible Program. National Bureau of Economic Research

Working Paper 19562. http://www.nber.org/papers/w19562. Accessed December 31, 2016.

Bergman, Peter. n.d. Parent-Child Information Frictions and Human Capital Investment: Evidence from a Field

Experiment. Working Paper. http://www.columbia.edu/~psb2101/BergmanSubmission.pdf.

Accessed December 31, 2016.

Bertrand, Marianne, and Adair Morse. 2011. “Information Disclosure, Cognitive Biases, and Payday

Borrowing.” The Journal of Finance 66 (6): 1865–93.

Beshears, John, James J. Choi, David Laibson, and Brigitte C. Madrian. 2008. “The Importance of Default

Options for Retirement Savings Outcomes: Evidence from the United States.” In Lessons from Pension

Reform in the Americas, ed. Stephen J. Kay, and Tapen Sinha. New York: Oxford University Press, 59–87.

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S105

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Bettinger, Eric, Bridget Terry Long, Philip Oreopoulos, and Lisa Sanbonmatsu. 2012. “The Role of Appli-

cation Assistance and Information in College Decisions: Results from the H&R Block FAFSA

Experiment.” The Quarterly Journal of Economics 127 (3): 1205–42.

Bhargava, Saurabh, and George Loewenstein. 2015. “Behavioral Economics and Public Policy 102:

Beyond Nudging.” American Economic Review: Papers & Proceedings 105 (5): 396–401.

Bhargava, Saurabh, George Loewenstein, and Justin Sydnor. 2015. Do Individuals Make Sensible Health

Insurance Decisions? National Bureau of Economic Research Working Paper No. 21160. http://www.

nber.org/papers/w21160. Accessed December 31, 2016.

Bhargava, Saurabh, and Dayanand Manoli. 2015. “Psychological Frictions and the Incomplete Take-Up of

Social Benefits: Evidence from an IRS Field Experiment.” American Economic Review 105 (11): 3489–529.

Blackwell, Lisa S., Kali H. Trzesniewski, and Carol S. Dweck. 2007. “Implicit Theories of Intelligence Pre-

dict Achievement Across an Adolescent Transition: A Longitudinal Study and an Intervention.”

Child Development 78 (1): 246–63.

Bovens, Luc. 2009. “The Ethics of Nudge.” In Preference Change: Approaches from Philosophy, Economics and

Psychology, ed. Till Gr€une-Yanoff, and Sven Ove Hansson. Amsterdam: Springer, 207–19.

Burnette, Jeni L., Ernest H. O. Boyle, Eric M. Vanepps, Jeffrey M. Pollack, and Eli J. Finkel. 2013. “Mind-

Sets Matter: A Meta-Analytic Review of Implicit Theories and Self-Regulation.” Psychological Bulletin

139 (3): 655–701.

Castleman, Benjamin L., and Lindsay C. Page. 2015. “Can Personalized Text Messages and Peer Mentor

Outreach Increase College Going Among Low-Income High School Graduates?” Journal of Economic

Behavior & Organization 115: 144–60.

Chetty, Raj. 2015. “Behavioral Economics and Public Policy: A Pragmatic Perspective.” American Economic

Review: Papers & Proceedings 105 (5): 1–33.

Chetty, Raj, John N. Friedman, Soren Leth-Petersen, Torben Nielsen, and Tore Olsen. 2014. “Active vs.

Passive Decisions and Crowd-Out in Retirement Savings Accounts: Evidence from Denmark.” The

Quarterly Journal of Economics 129 (3): 1141–219.

Chetty, Raj, John N. Friedman, and Emmanuel Saez. 2013. “Using Differences in Knowledge across Neighbor-

hoods to Uncover the Impacts of the EITC on Earnings.” American Economic Review 103 (7): 2683–721.

Chetty, Raj, and Emmanuel Saez. 2013. “Teaching the Tax Code: Earnings Responses to an Experiment

with EITC Recipients.” American Economic Journal: Applied Economics 5 (1): 1–31.

Congdon, William J. 2013. “Psychology and Economic Policy.” In Behavioral Foundations of Public Policy,

ed. Eldar Shafir. Princeton, NJ: Princeton University Press, 465.

Congdon, William J., Jeffrey R. Kling, and Sendhil Mullainathan. 2011. Policy and Choice: Public Finance

through the Lens of Behavioral Economics. Washington, DC: Brookings Institution Press.

Cook, Philip J., Kenneth Dodge, Roland G. Fryer, Jonathan Guryan, Jens Ludwig, Susan Mayer, Harold

Pollack, and Steinberg Laurence. 2014. The (Surprising) Efficacy of Academic and Behavioral Intervention

with Disadvantaged Youth: Results from a Randomized Experiment in Chicago. National Bureau of Eco-

nomic Research Working Paper No. 19862. http://www.nber.org/papers/w19862. Accessed

December 31, 2016.

Dechausay, Nadine, and Caitlin Anzelone. 2016. Cutting Through Complexity: Using Behavioral Science to

Improve Indiana’s Child Care Subsidy Program. OPRE Report No. 2016-03. Washington, DC: Office of

Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of

Health and Human Services.

Dechausay, Nadine, Caitlin Anzelone, and Leigh Reardon. 2015. The Power of Prompts Using Behavioral

Insights to Encourage People to Participate. OPRE Report No. 2015-75. Washington, DC: Office of Plan-

ning, Research and Evaluation, Administration for Children and Families, U.S. Department of

Health and Human Services.

Dellavigna, Stefano, John A. List, and Ulrike Malmendier. 2012. “Testing for Altruism and Social Pressure

in Charitable Giving.” The Quarterly Journal of Economics 127 (1): 1–56.

Dynarski, Susan M., and Judith E. Scott-Clayton. 2006. The Cost of Complexity in Federal Student Aid: Les-

sons from Optimal Tax Theory and Behavioral Economics. National Bureau of Economic Research Work-

ing Paper No. 12227. http://www.nber.org/papers/w12227.pdf. Accessed December 31, 2016.

S106 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Eissa, Nada, and Hilary W. Hoynes. 2006. “Behavioral Responses to Taxes: Lessons from the EITC and

Labor Supply.” Tax Policy and the Economy 20: 73–110.

Elbel, Brian, Rogan Kersh, Victoria L. Brescoll, and L. Beth Dixon. 2009. “Calorie Labeling and Food

Choices: A First Look at the Effects on Low-Income People in New York City.” Health Affairs 28 (6):

w1110–21.

Farrell, Mary, Jared Smith, Leigh Reardon, and Emmi Obara. 2016. Framing the Message: Using Behavioral

Economics to Engage TANF Recipients. OPRE Report No 2015-02. Washington, DC: Office of Planning,

Research and Evaluation, Administration for Children and Families, U.S. Department of Health and

Human Services.

Farrell, Mary, Peter Baird, Bret Barden, Mike Fishman, and Rachel Pardoe. 2013. "The TANF/SSI Disabili-

ty Transition Project: Innovative Strategies for Serving TANF Recipients with Disabilities." OPRE

Report 2013-51. Washington, DC: Office of Planning, Research and Evaluation, Administration for

Children and Families, U.S. Department of Health and Human Services. http://www.mdrc.org/

sites/default/files/tanf_ssi_disability_transition_project_fr.pdf. Accessed December 31, 2016.

Fryer, Roland G. 2013. Information and Student Achievement: Evidence from a Cellular Phone Experiment. http://

scholar.harvard.edu/files/fryer/files/million_manuscriptjune2013_0.pdf. Accessed December 31, 2016.

Fryer, Roland G., Steven D. Levitt, John List, and Sally Sadoff. 2012. Enhancing the Efficacy of Teacher Incen-

tives through Loss Aversion: A Field Experiment. National Bureau of Economic Research Working

Paper No. 18237. http://www.nber.org/papers/w18237. Accessed December 31, 2016.

Hall, Crystal C., Martha M. Galvez, and Isaac M. Sederbaum. 2014. “Assumptions About Behavior and

Choice in Response to Public Assistance: A Behavioral Decision Analysis.” Policy Insights from the

Behavioral and Brain Sciences 1 (1): 137–43.

Hansen, Pelle Guldborg, and Andreas Maaløe Jespersen. 2013. “Nudge and the Manipulation of Choice:

A Framework for the Responsible Use of the Nudge Approach to Behaviour Change in Public Poli-

cy.” European Journal of Risk and Regulation 1: 3–28.

Harackiewicz, Judith M., Christopher S. Rozek, Chris S. Hulleman, and Janet S. Hyde. 2012. “Helping

Parents to Motivate Adolescents in Mathematics and Science: An Experimental Test of a Utility-

Value Intervention.” Psychological Science 23 (8): 899–906.

ideas42. 2016. Using Behavioral Economics for Postsecondary Success. http://www.ideas42.org/wp-content/

uploads/2016/09/Nudging-For-Success-FINAL.pdf. Accessed December 31, 2016.

Ifill, Nicole, Alexandria Walton Radford, Forrest Emily Cataldi, David Wilson, and Jason Hill. 2016.

“Persistence and Attainment of 2011–12 First-Time Postsecondary Students After 3 Years (BPS:12/

14).” NCES 2016-401. U.S. Department of Education. Washington, DC: National Center for Educa-

tion Statistics. https://nces.ed.gov/pubs2016/2016401.pdf. Accessed February 8, 2017.

Iyengar, Sheena S., and Mark R. Lepper. 2000. “When Choice Is Demotivating: Can One Desire Too

Much of a Good Thing?” Journal of Personality and Social Psychology 79 (6): 995–1006.

Johnson, Eric J., and Daniel Goldstein. 2003. “Do Defaults Save Lives?” Science 302 (5649): 1338–39.

Jones, Damon, and Aparajit Mahajan. 2015. Time-Inconsistency and Saving: Experimental Evidence from Low-

Income Tax Filers. National Bureau of Economic Research Working Paper No. 21272. http://www.

nber.org/papers/w21272. Accessed December 31, 2016.

Kahneman, Daniel. 2013. “Foreword.” In Behavioral Foundations of Public Policy, ed. Eldar Shafir. Prince-

ton, NJ: Princeton University Press.

Kamenica, Emir. 2012. “Behavioral Economics and Psychology of Incentives.” Annual Review of Economics

4: 13.1–13.26.

Karlan, Dean, Margaret McConnell, Sendhil Mullainathan, and Jonathan Zinman. 2016. “Getting to the

Top of Mind: How Reminders Increase Saving Getting to the Top of Mind: How Reminders Increase

Saving.” Management Science 62 (12): 3393–411.

Karlan, Dean, and Jonathan Zinman. 2012. Borrow Less Tomorrow: Behavioral Approaches to Debt Reduction.

http://crr.bc.edu/wp-content/uploads/2012/05/FSP-WP-2012-1.pdf. Accessed February 7, 2017.

Kiefe, Catarina, Jeroan Allison, Dale Williams, Sharina Person, Michael Weaver, and Norman Weissman.

2001. “Improving Quality Improvement Using Achievable Benchmarks for Physician Feedback: A

Randomized Control Trial.” Journal of American Medical Association 285 (22): 2871–79.

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S107

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Kling, Jeffrey R., Sendhil Mullainathan, Eldar Shafir, Lee Vermeulen, and Marian Wrobel. 2012.

“Comparison Friction: Experimental Evidence from Medicare Drug Plans.” The Quarterly Journal of

Economics 127: 199–235.

Lamberton, Cait, and Benjamin Castleman. 2016. “Nudge 2.0: A Broader Toolkit for Lasting Behavior

Change.” The Huffington Post. http://www.huffingtonpost.com/cait-lamberton/nudge-20-a-

broader-toolki_b_10108728.html. Accessed January 17, 2017.

Lavecchia, Adam M., Heidi Liu, and Philip Oreopoulos. 2014. Behavioral Economics of Education: Progress

and Possibilities. National Bureau of Economic Research Working Paper No. 20609. http://www.

nber.org/papers/w20609. Accessed December 31, 2016.

Liebman, Jeffrey B., and Erzo F. P. Luttmer. 2011. Would People Behave Differently If They Better Understood

Social Security? Evidence from a Field Experiment. National Bureau of Economic Research Working

Paper No. 17287. http://www.nber.org/papers/w17287. Accessed December 31, 2016.

Loewenstein, George, Cass R. Sunstein, and Russell Golman. 2014. “Disclosure: Psychology Changes

Everything.” Annual Review of Economics 6: 391–419.

Loewenstein, George, and Peter Ubel. 2010. “Economics Behaving Badly.” The New York Times (July 14).

Lourenço, Joana Sousa, Emanuele Ciriolo, Sara Rafael Almeida, and Troussard Xavier. 2016. Behavioural Insights

Applied to Policy European Report 2016. EUR 27726 EN. European Commission. doi:10.2760/903938.

Luca, Michael, and Jonathan Smith. 2013. “Salience in Quality Disclosure: Evidence from the U.S. News

College Rankings.” Journal of Economics & Management Strategy 22 (1): 58–77.

Ludwig, Jens, Jeffrey R. Kling, and Sendhil Mullainathan. 2011. “Mechanism Experiments and Policy

Evaluation.” Journal of Economic Perspectives 25 (3): 17–38.

Madrian, Brigitte C. 2014. “Applying Insights from Behavioral Economics to Policy Design.” Annual

Review of Economics 6: 663–88.

Madrian, Brigitte C., and Dennis F. Shea. 2001. “The Power of Suggestion: Inertia in 401(k) Participation

and Savings Behavior.” The Quarterly Journal of Economics 116 (4): 1149–87.

Mani, Anandi, Sendhil Mullainathan, Eldar Shafir, and Jiaying Zhao. 2013. “Poverty Impedes Cognitive

Function.” Science 341 (6149): 976–80.

Manoli, Day, and Nick Turner. 2016. Nudges and Learning: Evidence from Informational Interventions for Low-

Income Taxpayers. National Bureau of Economic Research Working Paper No. 20718. http://www.

nber.org/papers/w20718.pdf. Accessed December 31, 2016.

Mayer, Susan E., Ariel Kalil, Philip Oreopoulos, and Sebastian Gallegos. 2015. Using Behavioral

Insights to Increase Parental Engagement: The Parents and Children Together (PACT) Intervention.

National Bureau of Economic Research Working Paper No. 21602. http://www.nber.org/

papers/w21602.

Mortensen, Chad R., and Robert B. Cialdini. 2010. “Full-Cycle Social Psychology for Theory and

Application.” Social and Personality Psychology Compass 4 (1): 53–63.

Namba, Alexa, Amy Auchincloss, Beth L. Leonberg, and Margo G. Wootan. 2013. “Exploratory Analysis

of Fast-Food Chain Restaurant Menus Before and After Implementation of Local Calorie-Labeling

Policies.” Prevention of Chronic Disorders 10: 120224.

Oreopoulos, Philip, Robert S. Brown, and Adam M. Lavecchia. 2014. Pathways to Education: An Integrated

Approach to Helping At-Risk High School Students. National Bureau of Economic Research Working

Paper No. 20430. http://www.nber.org/papers/w20430. Accessed December 31, 2016.

Patterson, Mark, Saurabh Bhargava, and George Loewenstein. 2017. “An Unhealthy Attitude? New

Insight into the Modest Effects of the NLEA.” Working Paper. http://dd42c9be-a-62cb3a1a-s-sites.

googlegroups.com/site/sbhargav/NLEA_Patterson_Bhargava_Loewenstein.pdf?attachauth=ANoY7

cruyLyrfHnbRQjFridCWG-KAzqohiCpxrNWePQBeTUjZKoTNRPLowi1I0NHEbZmSQQSBeFwnMa

I7cmOVXquxTkEWqJ6nHynL8wtS9SyP2OaNA1wbXV2N_IgRGd-C8lM7kpMMq_j4t3FBaGh94LTodi

hb9EK-c6o1Fw06WuZTusNHUv1hrnUiVqy8eZb9dCQ1VY47Sq1REyhRgUMXpK62wwfNKrn1JuCjt

rvEODyWnEOjMD8rX0%3D&attredirects=0. Accessed February 7, 2017.

Paunesku, David, Gregory M. Walton, Carissa Romero, Eric N. Smith, David S. Yeager, and Carol S.

Dweck. 2015. “Mind-Set Interventions Are a Scalable Treatment for Academic Underachievement.”

Psychological Science 26 (6): 784–93.

S108 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Richards, Michael R., and Jody Sindelar. 2013. “Rewarding Healthy Food Choices in SNAP: Behavioral Economic Applications.” The Milbank Quarterly 91 (2): 395–412.

Richburg-Hayes, Lashawn, Caitlin Anzelone, Nadine Dechausay, Saugato Datta, Alexandra Fiorillo, Louis Potok, Matthew Darling, and John Balz. 2014. Behavioral Economics and Social Policy: Designing Innovative Solutions for Programs Supported by the Administration for Children and Families. OPRE Report No. 2014-16a. Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.

Rosinger, Kelly. 2016. Can Simplifying Financial Aid Information Impact College Enrollment and Borrowing?

Experimental and Quasi-Experimental Evidence. EdPolicyWorks Working Paper Series No. 49. http:// sbst.gov/download/2015%20SBST%20Annual%20Report.pdf. Accessed February 7, 2017.

Sacarny, Adam, David Yokum, Amy Finkelstein, and Shantanu Agrawal. 2016. “Medicare Letters to Curb Overprescribing of Controlled Substances Had No Detectable Effect on Providers.” Health Affairs 35 (3): 471–79.

Social and Behavioral Sciences Team (SBST). 2015. Annual Report. https://sbst.gov/assets/files/2015- annual-report.pdf. Accessed December 31, 2016.

Starc Amanda. 2014. “Insurer Pricing and Consumer Welfare: Evidence from Medigap.” The RAND Jour- nal of Economics 45 (1): 198–220.

Steffel, Mary, Elanor Williams, and Ruth Pogacar. 2016. “Ethically Deployed Defaults: Transparency and Con- sumer Protection Via Disclosure and Preference Articulation.” Journal of Marketing Research 53 (5): 865–80.

Thaler, Richard, and Cass Sunstein. 2003. “Libertarian Paternalism.” American Economic Review 93 (2): 175–79.

———. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven, CT: Yale Uni- versity Press.

Tversky, Amos, and Daniel Kahneman. 1991. “Loss Aversion in Riskless Choice: A Reference-Dependent Model.” The Quarterly Journal of Economics 106: 1039–61.

U.S. Department of Agriculture Food and Nutrition Service (USDA FNS). 2012. Snap Education and Evalu- ation Study (Wave I): Final Report. http://www.fns.usda.gov/sites/default/files/SNAPEdWaveI_0. pdf. Accessed February 7, 2017.

———. 2014. Evaluation of the Healthy Incentives Pilot (HIP) Final Report. http://www.fns.usda.gov/snap/ healthy-incentives-pilot-final-evaluation-report. Prepared by Abt Associates for the U.S. Department of Agriculture, Food and Nutrition Service. http://www.fns.usda.gov/sites/default/files/HIP- Final.pdf. Accessed February7, 2017.

U.S. Department of Education. 2013. Education Department Releases College Scorecard to Help Students Choose Best College for Them. Washington, DC: U.S. Government Printing Office.

Walton, Gregory M., and Geoffrey Cohen. 2011. “A Brief Social-Belonging Intervention Improves Aca- demic and Health Outcomes of Minority Students.” Science 331: 1447–51.

Weimer, David L., and Aidan R. Vining. 2015. Policy Analysis: Concepts and Practice New York: Routledge.

Yeager, David S., and Gregory M. Walton. 2011. “Social-Psychological Interventions in Education: They’re Not Magic.” Review of Educational Research 81 (2): 267–301.

Yeager, David S., Gregory M. Walton, Shannon T. Brady, Ezgi N. Akcinar, David Paunesku, Laura Keane, Donald Kamentz et al. 2016. “Teaching a Lay Theory Before College Narrows Achievement Gaps at Scale.” Proceedings of National Academy of Sciences 113 (24): E3341–48.

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S109

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Appendix

Table A1. Overview of Short-Listed Policy Initiatives with Behavioral Insights

Study Results Citation

Behaviorally Tested Policy Analysis Examples

Increasing number of incarcerated noncustodial parents (NCP) who complete an application for modification of their child support order

Compared to control group, treated NCPs were 11 percentage points more likely to complete the order modification.

Richburg-Hayes et al. (2014)

Increasing enrollment of service members into thrift savings plan

Compared with no message, e-mails informed by behavioral insights led to roughly 4,930 new enrollments and $1.3 million in savings in a month.

SBST (2015)

Increasing collections of child support arrears from noncustodial parents through behaviorally informed reminders

Compared to no message, reminders increased number of NCPs making a payment by 2.9 percentage points.

Richburg-Hayes et al. (2014)

Increasing re-engagement of TANF in welfare-to-work initiatives

Compared to control group, members who took action and became positively engaged 30 days after their scheduled appointment increased by 3.6 percentage points, a 14 percent increase. However, after 60 days no sustained impact was found.

Farrell et al. (2016)

Educating SNAP recipients on benefits of healthy eating

In one out of four programs statistically significant effects found. Compared to control group, treated children increased consumption of fruits, vegetables, and low-fat milk.

USDA FNS (2012)

Reducing prescription rates of controlled-substances by invoking social comparisons

Intervention did not show any statistically significant effects on prescription rates of controlled substances.

Sacarny et al. (2016)

Increasing information disclosure about cost of payday loans

Modest, but statistically significant impacts of providing information about cost of payday loans (effects vary across treatment arms).

Bertrand and Morse (2011)

Increasing take up of EITCs through information provision

Compared to control group, decreasing complexity increased take up of EITC by 6 percentage points.

Bhargava and Manoli (2015)

Increasing earnings responses to EITC through intervention teaching the structure of tax code

Intervention did not show any statistically significant effects on wage earnings of treated individuals.

Chetty et al. (2013)

Aiding Federal Health Insurance marketplace enrollment through reminders

Compared to no message, those who were sent the most effective reminder were 0.53 percentage points more likely to enroll in health insurance plan.

SBST (2015)

Increase renewal of low-income households’ child care subsidies

Parents in treated child care centers that reminded the parents to renew their child care subsidy, increased on-time renewal rate by 2.4 percentage points.

Dechausay, Anzelone, and Reardon (2015)

Increasing engagement of low-income single workers receiving EITC supplements

Compared to control group, treated EITC supplement recipients were 7.1 percentage points more likely to attend meetings.

Dechausay et al. (2015)

Compared to control group, social pressure of turning down door-to-door

Dellavigna et al. (2012)

S110 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Table A1. cont.

Study Results Citation

Increased charitable giving by invoking social norms (and social pressure)

charity raising campaign increases charitable giving among treated participants.

Increasing Healthy Food Choices in the Supplemental Nutritional Assistance Program (SNAP)

Compared to control group, treated participants who received 30 cents for every SNAP dollar spent on targeted fruits and vegetables, increased their consumption of fruits and vegetables by 26 percent.

USDA FNS (2014)

Encouraging optimal choice in Medicare drug plan selection through information provision

Compared to no information provision, intervention caused an average decline in predicted consumer cost of about $100 a year among letter recipients.

Kling, Mullainathan, Shafir, Vermeulen, and Wrobel (2012)

Increasing collections from individuals with outstanding nontax debt appealing to social norms

Compared to the standard collection letter, those who received showed no difference in payment rates.

SBST (2015)

Increasing labor force participation among older workers through provision of information about Social Security provisions

Compared to no information, intervention increased labor force participation one year later by 4 percentage points.

Liebman and Luttmer (2011)

Reducing debt among low-income consumers using reminder notices, peer support, and goal setting interventions

Intervention did not show any statistically significant effects on debt reduction.

Karlan and Zinman (2012)

Commitment devices and monetary incentives to reduce time-inconsistent preferences in savings among low-income tax filers

Effects vary across treatment arms. Compared to control group, treated low-income tax filers increase soft-commitment to save by 30–35 percentage points. Immediate incentive effect on savings is nearly twice as large as the delayed incentive effect.

Jones and Mahajan (2015)

Increasing college enrollment of low-income students using prefilled FAFSA forms

Compared to control group, high school seniors whose parents received the treatment were 8 percentage points more likely to have completed two years of college (going up from 28 to 36 percent).

Bettinger et al. (2012)

Increasing students’ growth mindset (ability to view intelligence as malleable) and a sense of purpose in schools

Compared to control group, at-risk students who received the growth mindset intervention received higher GPA and were 6.4 percentage points more likely to perform satisfactorily in core courses.

Paunesku et al. (2015)

Increasing students’ sense of belonging in college

Compared to control group, treated ethnic minority and first generation students received higher GPA and were less likely to drop out from college in the sophomore year (effects vary across three studies reported in the paper).

Yeager et al. (2016)

Text messaging campaign reminding students to complete pre-matriculation tasks

Compared to control group, students who received the text messages were 5.7 percentage points more likely to enroll in college.

Castleman and Page (2015)

Peer mentoring campaign to help high school seniors navigate transition to college

Effects vary across sites. In some sites, compared to control group, treated students were 4 percentage points more likely to enroll in colleges.

Castleman and Page (2015)

ideas42 (2016)

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S111

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Table A1. cont.

Study Results Citation

Text messaging campaign to reduce student loan borrowing costs in a community college

Compared to control group, those students who received the text messages borrowed less ($2,218 compared to $2,401).

Online intervention using smartphone app to increase FAFSA application completion rates

Compared to control group, incoming freshmen who received the reminders were 22 percent more likely to complete FAFSA applications.

ideas42 (2016)

Increasing students’ academic outcomes through parental engagement via calls and text

Compared to control group, students whose parents were eligible for treatment experienced a 0.23 standard deviation increase in GPA and improvement in classroom behavior by 6 percentage points. Treated parents were 7.9 percentage points more likely to attend parent-teacher conferences.

Bergman (n.d.)

Increasing students’ enrollment in STEM courses through parental engagement

Compared to control group parents who received no brochures about value of STEM courses, students of treated parents enrolled in nearly one more semester of STEM courses.

Harackiewicz, Rozek, Hulleman, and Hyde (2012)

Increasing college enrollment of disadvantaged youth using counseling and tutoring services

Compared to control group students, treated students were 30 percentage points more likely to apply to four-year colleges, submitted more college applications to selective colleges, and were 15 percentage points more likely to enroll in college.

Avery (2013)

Increasing parental engagement in subsidized preschool programs through behavioral tools

Compared to the control group parents, treated parents who received text reminders, goal-setting, and social rewards increased usage of reading application by one standard deviation.

Mayer, Kalil, Oreopoulos, and Gallegos (2015)

Increasing efficacy of teacher incentives framing using loss aversion

Students whose teachers received the reframed incentive structure using loss aversion (teachers are paid in advance and asked to give back the money if their students do not improve) sufficiently showed between 0.201 and 0.398 standard deviation gains in math test scores

Fryer et al. (2012)

Providing cognitive behavioral therapy to disadvantaged youth in Chicago

Compared to control group, participation in treatment increased math test scores by 0.65 standard deviation and expected graduation rates by 14 percentage points.

Cook et al. (2014)

Increasing student performance using cell phone, texts, and other phone-based reading activities

Students that received cellular phones prompting reading activities/ informational texts were 15 percentage points more likely to report feeling more focused or excited about doing well in school, but no detectable effect on attendance, suspensions, or test scores.

Fryer (2013)

Behaviorally Informed Policy Analysis Examples

Impact of regulations mandating information disclosure—CARD Act of 2009

Information disclosure requirements only had a negligible (but statistically significant) effect on borrowers’ repayment behavior. Account holders

Agarwal et al. (2015)

S112 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Table A1. cont.

Study Results Citation

responding to information disclosed increased by less than a percentage point.

Regulations mandating standardization of Medigap insurance plans across the United States

To the best of our knowledge, we are not aware of any rigorous evaluation of the specific standardization policy. However, there is some evidence that a restriction on insurance plan differentiations might result in suboptimal consumer welfare.

Starc (2014)

Exploring informational interventions to help TANF recipients with disabilities who were also eligible for Social Security Disability Insurance (SSDI) recipients

Results vary across sites. Integrated Placement and Support model used by SSDI program showed promise for use by TANF recipients with disabilities.

Farrell, Baird, Barden, Fishman, and Pardoe (2013)

Increasing student loan payments using reminders

Reminder e-mail led to a 29.6 percent increase in the fraction of borrowers making a payment by the end of the first week after delivery of e-mail.

SBST (2015)

Informing student borrowers about IDR plans

The informational e-mail led to a substantial increase in applications for IDR plans within 20 days of the e-mail being sent. Among the group that received the e-mail, 4,327 applied for IDR as opposed to the 982 IDR applications received from the comparison group who had not yet received the informational e-mail.

SBST (2015)

Simplifying information about IDR plans

Ongoing evaluation SBST (2015)

College Scorecard: Provision of standardized information about colleges to improve postsecondary college choices

To the best of our knowledge, we are not aware of any rigorous evaluation of this initiative.

U.S. Department of Education (2013), hereafter USDOE

Financial Aid Shopping Sheet: Provision of standardized information about graduation rate, loan default rate across various colleges

Ongoing evaluation. Preliminary quasi-experimental results show a 2.6 percentage point decrease in the share of students borrowing federal loans in colleges that adopted the “shopping sheet”

USDOE (2013); Rosinger (2016)

Behaviorally Aligned Policy Analysis Examples

Identifying spatial variation in EITCs take-up rates across states in the United States

Spatial variation in take-up rates of EITC across states identified using behavioral insights about knowledge diffusion.

Chetty et al. (2013)

Identifying behaviorally informed proposals to encourage healthy food choices in the SNAP

Identify behaviorally aligned proposal reforms in the SNAP to encourage healthy eating.

Richards and Sindelar (2013)

Identifying crowd-out effects of automatic enrollment default options on total savings

Identity negligible crowd-out effects on (total savings) of automatic enrollment default options.

Chetty et al. (2014)

Understanding individuals’ choices of health insurance plans

Identify how low-income households make suboptimal health insurance plan choices when provided a complex choice set.

Bhargava et al. (2015)

Understanding the impact of Nutrition Labeling and Education

Limited impact of NLEA on attitudes and behavior of individuals, consistent with previous literature. Using

Patterson, Bhargava, and Loewenstein (2017)

Gopalan/Pirog: Applying Behavioral Insights in Policy Analysis S113

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense

Table A1. cont.

Study Results Citation

Act (NLEA)1990 on consumer attitudes and behaviors

behavioral models in their analysis, authors illuminate the mechanisms resulting in limited impact of NLEA.

Identifying longer term effects of an EITC eligibility information provision intervention

Effects of information provision about EITC eligibility attenuates EITC take-up rates from 80 to 22 percent within a year.

Manoli and Turner (2016)

Understanding the impact of menu-labeling laws on availability of healthy food choices in low-income neighborhoods

Overall availability of healthy food choices remained low over the period. However, restaurants located in areas that implemented calorie labeling increased their healthier entr�ee options.

Namba et al. (2013)

Understanding the impact of increasing salience of university ranking system

Using a natural experiment, authors find that a one-rank improvement leads to a 1-percentage-point increase in the number of applications to that college.

Luca and Smith (2013)

Understanding the impact of free tutoring, group mentoring, and cash incentive to improve college choices

Eligibility to intervention components increases graduation rates from high school by 15 percentage points, college enrollments by 19 percentage points and test scores by 0.15 standard deviations.

Oreopoulos, Brown, and Lavecchia (2014)

S114 Policy Studies Journal, 45:S1

15410072, 2017, S1, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1111/psj.12202 by A

m erican Public U

niversity System , W

iley O nline L

ibrary on [16/12/2023]. See the T erm

s and C onditions (https://onlinelibrary.w

iley.com /term

s-and-conditions) on W iley O

nline L ibrary for rules of use; O

A articles are governed by the applicable C

reative C om

m ons L

icense