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Some Middle School Students Want Behavior Commitment Devices (But Take-up Does Not Affect Their Behavior)

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Abstract and Figures

Commitment devices impose costs on one’s future self for failing to follow through on one’s intentions, offer no additional benefit to one’s future self for following through on the intention, and people voluntarily enroll in them. Enrollment in commitment devices reflects self-awareness that one may lack sufficient self-control to fulfill one’s intentions. There is little experimental research on whether school-age children possess the self-awareness necessary to enroll in a commitment device, despite evidence that children and young adolescents have many positive intentions that they fail to live up to, such as demonstrating improved school conduct or eating healthier. We report the first field experiment examining the demand for, and impact of, commitment devices among middle school students. We offered students a commitment device that imposed future costs for failing to improve in-school conduct. When presented with the opportunity to actively opt-in (default not enrolled), over one-third of students elected to enroll. When presented with the opportunity to actively opt-out (default enrolled), more than half elected to remain enrolled, showing that changing default options can increase commitment device enrollment. Despite demand for the self-control strategy, taking-up the commitment device did not affect student behavior. These findings have implications for youth-based behavioral interventions broadly, as well as those focused on eating behaviors.
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Some Middle School Students Want Behavior Commitment Devices
(But Take-up Does Not Affect Their Behavior)
Carly D. Robinson1*, Gonzalo Pons2*, Angela L. Duckworth2, Todd Rogers4 1
1Harvard University, Graduate School of Education, Cambridge, MA, USA 2
2University of Chicago, Harris School of Public Policy, Chicago, IL, USA 3
3University of Pennsylvania, Department of Psychology, Philadelphia, PA, USA 4
4Harvard University, Kennedy School of Government, Cambridge, MA, USA 5
* Correspondence: 6 Corresponding Author 7 8
Keywords: behavioral interventions, youth, self-control, commitment device, educational 9 intervention, eating behavior 10
Abstract 11
Commitment devices impose costs on one’s future self for failing to follow through on one’s 12 intentions, offer no additional benefit to one’s future self for following through on the intention, and 13 people voluntarily enroll in them. Enrollment in commitment devices reflects self-awareness that one 14 may lack sufficient self-control to fulfill one’s intentions. There is little experimental research on 15 whether school-age children possess the self-awareness necessary to enroll in a commitment device, 16 despite evidence that children and young adolescents have many positive intentions that they fail to 17 live up to, such as demonstrating improved school conduct or eating healthier. We report the first 18 field experiment examining the demand for, and impact of, commitment devices among middle 19 school students. We offered students a commitment device that imposed future costs for failing to 20 improve in-school conduct. When presented with the opportunity to actively opt-in (default not 21 enrolled), over one-third of students elected to enroll. When presented with the opportunity to 22 actively opt-out (default enrolled), more than half elected to remain enrolled, showing that changing 23 default options can increase commitment device enrollment. Despite demand for the self-control 24 strategy, taking-up the commitment device did not affect student behavior. These findings have 25 implications for youth-based behavioral interventions broadly, as well as those focused on eating 26 behaviors. 27
1 Introduction 28
When our best laid plans – like sticking to a healthy diet or completing an assignment on-time go 29 awry, we often must look inward to understand what went wrong. Time and again, despite having the 30 necessary motivation, skills, and knowledge to act in ways that align with their long-term goals (e.g., 31 avoid sugary foods, start the essay early), people succumb to immediate temptations that are at odds 32 with those future aims (e.g., eating a donut, binge watching a TV series) (Duckworth, Gendler, & 33 Gross, 2014; Milkman, Rogers, & Bazerman, 2008). The prevalence of self-control failures has 34 resulted in an array of strategies that help people delay gratification and control their impulses in 35 service of achieving their long-term goals. In the past couple decades, behavioral scientists have 36
Students Want Behavior Commitment Devices
developed and tested strategiesoften referred to as “nudges”that encourage people to engage in 37 desirable behaviors without restricting choice (Benartzi et al., 2017; Thaler, 2008). In this paper, we 38 focus on one behavioral intervention that can help people accomplish their goals by anticipating and 39 planning for future self-control failures: commitment devices (for review see Bryan, Karlan, & 40 Nelson, 2010; Rogers, Milkman, & Volpp, 2014). 41
Commitment devices (CDs) deliberately limit future choices by allowing people to voluntarily 42 impose costly restrictions or penalties on themselves for failing to accomplish their goals (Bryan et 43 al., 2010; Rogers et al., 2014). For example, those who want to eat healthier might agree to deposit 44 money into an account that they can only access again if they improve their diets, or students who 45 want to meet an assignment deadline may ask a friend to change their Netflix password until they 46 turn in the essay. These examples highlight two important features of CDs. First, CDs impose 47 consequences when people fail to achieve their stated goals (e.g., losing money, locked out of Netflix 48 account). Second, people voluntarily elect to use CDs (Rogers et al., 2014). Thus, the take-up of CDs 49 requires individuals to possess the capacities for metacognition (i.e., awareness that their future 50 preferences may not be aligned with their current goals) and prospection (i.e., identifying what 51 consequence would be costly enough to make them forego immediate rewards) (Duckworth et al., 52 2014). 53
Previous experiments have shown that there can be demand among adults for CDs across various 54 domains, including improving healthy food behaviors and academic task performance, although CD 55 take-up rates can be very low (Rogers et al., 2014). No matter the domain, CDs require that 56 individuals be sophisticated enough to recognize their self-control may fail without external 57 consequences. Schwartz and colleagues (2014) conducted an experiment in a large national grocery 58 chain that gave households a 25% discount on healthy foods through a rewards program. Households 59 in the treatment condition had the opportunity to pre-commit to increasing their healthy grocery 60 purchases by 5%, or forfeit the entire 25% discount if they failed to reach their goal. Despite no 61 additional incentive or bonus, the study found that there was significant demand for a CD aiming to 62 improve nutrition habits among adults: over one-third of households voluntarily agreed to increase 63 their healthy grocery purchases or lose their 25% discount. Notably, those households that did enroll 64 in the CD purchased significantly more health food items in the subsequent months. 65
CDs can be utilized to improve eating behaviors in restaurant or cafeteria settings, as well. A study 66 found that up to one-third of customers at a fast food restaurant accepted offers from servers to cut 67 the portions of their high calorie side dishes in half, even if they received no discount (Schwartz, 68 Riis, Elbel, & Ariely, 2012). What’s more, the offer to downsize portions was more effective than the 69 more common practice of calorie labeling (i.e., providing consumers with nutritional information) at 70 reducing the number of calories consumed. 71
Another study found that a majority of college-age adults were willing to self-impose deadlines to 72 overcome procrastination, even when those deadlines were binding and costly (i.e., each day the 73 assignment was turned in after the self-imposed deadline, students received a 1% penalty on their 74 overall grade) (Ariely & Wertenbroch, 2002). These students were willing to risk a lower grade to 75 apply the self-control mechanism of pre-commitment which, in turn, led to improved average grades. 76 These studies suggest that at least a subset of adults possess the requisite skills for metacognition and 77 prospection to recognize that CDs can help activate their self-control. 78
In general, people’s capacities for metacognition and prospection generally improve as they age 79 (Cunningham, Zelazo, Packer, & Van Bavel, 2007; Dimmitt & McCormick, 2012; Duckworth & 80
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Steinberg, 2015; Steinberg et al., 2009), which raises the question of whether there is demand for 81 CDs among school-age children. For instance, in the seminal delay of gratification task where 82 children who wait long enough get to eat two marshmallows rather than just one, preschool children 83 tend to employ ineffective self-control strategies (e.g., imagining eating the marshmallow) (Mischel 84 & Mischel, 1983). By first grade, most children demonstrate awareness of more effective approaches 85 for resisting the temptation to eat the sweet (e.g., covering the marshmallow). But it is not until 86 children reach middle school that children consistently understand how and why to create a more 87 favorable environment for delaying gratification (Mischel & Mischel, 1983). 88
Both in and out of school, children are constantly asked to control their impulses, delay gratification, 89 and regulate their emotional responses (Moffitt et al., 2011). In the past few decades, the youth 90 obesity crisis has given rise to a focus on getting children and adolescents to eat healthier, leading to 91 a greater emphasis on how interventions can be used to beneficially modify youths’ eating behaviors 92 (e.g., Fila & Smith, 2006; Gortmaker et al., 1999). That said, effective strategies for improving eating 93 behaviors and preventing weight gain in school-age children are in short supply (Brownell, Schwartz, 94 Puhl, Henderson, & Harris, 2009). Researchers have found that, while children are informed about 95 healthy eating practices and recommendations, they find it difficult to follow-through on eating 96 healthfully (Croll, Neumark-Sztainer, Story, & Ireland, 2002; Story & Resnick, 1986) even when 97 they intend to so (Fila & Smith, 2006). Whether the desired goal be eating healthier or turning in 98 assignments on time, all self-control strategies depend on higher order mental processes (i.e., 99 metacognition and prospection) to make predictions about and intervene upon lower order processes 100 (Duckworth et al., 2014). Therefore, the extent to which children are sophisticated about their self-101 control problems has implications for the types of interventions that can be used to encourage 102 desirable behaviors in youth across numerous domains (O'Donoghue & Rabin, 2001). 103
Even when demand for a CD exists, people might not opt-in to using it because inaction is an easier 104 alternative (Kahneman, Knetsch, & Thaler, 1991; Samuelson & Zeckhauser, 1988). CDs can only 105 change the behavior of those who agree to use them, so a central challenge is increasing usage. 106 Research shows that take-up rates of CDs are traditionally very low (e.g., Giné, Karlan, & Zinman, 107 2010; Royer, Stehr, & Sydnor, 2015), but also that changing defaults can dramatically change 108 behavior and improve enrollment rates in a range of domains (e.g., Bergman & Rogers, 2017; 109 Carroll, Choi, Laibson, Madrian, & Metrick, 2009; Johnson & Goldstein, 2003). Therefore, requiring 110 people to opt out of a CD, as opposed to opting in, may increase the enrollment rate. Despite the 111 potential for using defaults to influence student behaviors, research suggests most adults fail to 112 understand or use defaults in circumstances where they might be beneficial (Zlatev, Daniels, Kim, & 113 Neale, 2017). Children and young adolescents may stand to benefit most from changes to default 114 decisions because they often have less control over their circumstances relative to adults (Radnitz et 115 al., 2013). 116
If there is demand for CDs among school-age children, the next question is whether they are viable 117 strategies for helping youth follow-through on their intentions. That is, even if young adolescents 118 have the metacognitive skills to recognize they may benefit from CDs, pre-commitment may still not 119 be an effective strategy for impacting their future actions. Given that behaviors established in youth, 120 like healthy eating and positive school conduct, lay the foundations for adult behavior (Kelder, Perry, 121 Klepp, & Lytle, 1994; Lytle, Seifert, Greenstein, & McGovern, 2000; Moffitt et al., 2011), 122 identifying which strategies children can proactively enact to help with self-control failures may have 123 long-term consequences. 124
2 The Present Study 125
Students Want Behavior Commitment Devices
To date, there is little experimental research on whether school-age children would be willing to self-126 impose penalties on themselves for failing to follow-through on their intentions. In this study, we 127 conducted the first field experiment to evaluate the plausibility of offering middle school students a 128 CD, and then assessed whether the opportunity to pre-commit to achieving a goal would improve 129 their behavior relative to students’ simply acknowledging they would like to achieve the goal. First, 130 we explored whether middle school students have metacognitive skills to be aware of their own 131 limited self-control, such that they would voluntarily elect to use a CD to help them accomplish a 132 future behavioral goal. Second, building on prior research on using defaults to increase take-up rates, 133 we tested whether defaulting students who state they want to accomplish a goal into a CD aimed at 134 reaching that goal (with the opportunity to opt out), increased the take-up of the CD. Third, we 135 investigated whether offering students a CD resulted in a greater percentage accomplishing their 136 behavioral goal. Finally, we examined whether teachers can accurately predict which students are 137 self-aware enough of their limited self-control that they would enroll in the CD. 138
To answer these questions, we partnered with five middle schools in three northeastern US states that 139 all operate under a single charter network. This charter network operates free, open-enrollment K-12 140 schools in under-resourced communities. All schools in the charter network utilized a behavior 141 management system modeled after a weekly paycheck. Behavior management systems can take many 142 forms, but tend to focus on promoting positive student behaviors (i.e., following teacher instructions, 143 turning in assignments on-time, prosocial acts, etc.) and reducing negative student behaviors (i.e., 144 tardiness, time-off task, fighting, etc.). Teachers and administrators use the paycheck system to track 145 and incentivize individual students’ school behaviors by awarding symbolic dollars for positive 146 behaviors (i.e., rewards) and subtracting dollars for negative behaviors (i.e., demerits). At the end of 147 each week, students can then spend the symbolic dollars they earn at the school store. The paycheck 148 system provided an opportunity to study whether CDs are a useful strategy for helping children avoid 149 self-control failures for three reasons. First, the paycheck serves as a way to quantify student 150 behavior. Second, students presumably have a great deal of control over their school behaviors, and 151 thus their paychecks. Finally, almost all students want to earn higher paychecks, which facilitates 152 goal-setting. 153
3 Materials and Methods 154
3.1 Participants 155
School enrollment at each of five middle schools ranged from 225 students to 445 students. A total of 156 1,632 fifth through eighth grade students were enrolled in all five schools. All 1,632 students were 157 eligible to participate in the study, except for students excluded based on teachers’ requests (i.e., 158 students with limited English comprehension, cognitive disabilities, or individualized behavior plans 159 that did not involve the paycheck system) (n = 21). Because we randomized students in the week 160 leading up to the intervention we also excluded students after they were randomized into conditions, 161 including students who were opted out of the study by their guardians or did not assent to 162 participating in the study (n = 28), students who did not complete the intervention survey (n = 110), 163 students who were absent on the day of the intervention (n = 143), students who could not earn a 164 higher paycheck goal (because they had already earned the maximum goal) (n = 46), and students for 165 whom the school could not provide reliable paycheck data (n = 79). We did not collect data on 166 student ages, but students typically begin fifth grade at age 10 or 11 and complete eighth grade at age 167 13 or 14. The students in the final sample (N = 1,205) were 52% female, and 22% of students were 168 enrolled in fifth grade, 30% in sixth grade, 26% in seventh grade, and 22% in eighth grade. We 169 received race and free and reduced price lunch data from four of the five schools. In these charter 170
Students Want Behavior Commitment Devices
schools, which serve predominantly under-resourced communities, 74% of students identified as 171 Black, 25% identified as Latino/Hispanic, and 85% received free and reduced priced lunch. 172
3.2 Measures 173
Our main outcome measures were whether students enrolled in the CD and students’ end-of-week 174 paycheck scores. Students earn “dollars” towards their weekly “paycheckfor performing 175 encouraged behaviors (e.g., participating in class activities, demonstrating school values). Students 176 also can have dollars deducted from their weekly paycheck for performing discouraged behaviors 177 (e.g., not turning in homework, not following directions). Any school faculty or staff member can 178 award or deduct dollars from students’ paychecks. At the end of each week, students receive their 179 paycheck along with an itemized list of how and when they earned or lost dollars. Students have the 180 opportunity to purchase items from the school store (e.g., school supplies, toys) or tickets to 181 extracurricular activities (e.g., a pizza party) based on their paycheck balance. In four of the five 182 schools, students start the week with $0 and there are no paycheck caps. In the last school, students 183 start each week with $45 (i.e., a deductive payment model) and paychecks are capped at $50. 184
Prior to the treatment, students responded to questions about their perceptions of the paycheck. These 185 questions were adapted from the Expectancy-Value-Cost (EVC) Scale of student motivation 186 (Kosovich, Hulleman, Barron, & Getty, 2015) to assess students’ expectancy that they can earn a 187 higher paycheck, the value students attribute to the paycheck, and the perceived costs associated with 188 the earning a good paycheck. Based on the EVC theory of motivation, expectancy reflects the extent 189 to which a student thinks he or she can be successful in a task, value reflects the extent to which a 190 student thinks a task is worthwhile, and cost reflects the negative aspects of engaging in a task 191 (Barron & Hulleman, 2015; Wigfield & Cambria, 2010). We use the EVC scale to assess students’ 192 motivation for earning a good paycheck. Each item had four response options: 1 “Strongly Disagree,” 193 2 “Disagree,” 3 “Agree,” and 4 “Strongly Agree.” See Table 1 for the items. 194
***TABLE 1*** 195
3.3 Design and Procedure 196
The five schools sent consent forms home to all student households. Parents and guardians had the 197 opportunity to opt their child out of the study by returning the form to the school, or contacting a 198 member of the research team. 199
In the week leading up to the intervention, the school provided the research team with students’ 200 average paycheck earnings over the past four weeks. A handful of grade leaders computed the 201 paycheck averages based on only the prior three weeks of school (n = 58). The research team used 202 the average paycheck earnings to compute a unique “paycheck goal” for each student that was 10% 203 more than their average paycheck. For example, a student who had an average paycheck of $20 204 would have a paycheck goal of $22. We chose to make the paycheck goal proportional to students’ 205 average earnings as opposed to uniform (i.e., having all students’ paycheck goals be the same 206 amount) because we were concerned that low paycheck earners may become discouraged having to 207 earn a relatively higher percentage of their average paychecks. For instance, setting a goal to earn $2 208 more might seem relatively achievable to students who consistently earn $40 because it is only 5% 209 more than their average paycheck, as compared to students who consistently only earn $10 and 210 would have to earn 20% more than their average paycheck. 211
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We then randomly assigned students to one of three conditions: Opt-in, Opt-out, or Control. In all 212 three conditions, students answered whether they wanted to set a goal to earn a paycheck of 10% 213 over their average paycheck for the upcoming week (yes or no). In the Opt-in condition, if students 214 answered “yes,” they were then offered a CD: They had the opportunity to pre-commit to earning 215 their paycheck goal for that week (e.g., $22), or lose 20% of their average paycheck (e.g., $4) from 216 the next week’s paycheck if they failed to meet their goal. In the Opt-out condition, if students 217 answered “yes” to the initial question, they were defaulted into the aforementioned CD, but could 218 choose to opt-out of the pre-commitment by writing “I would like to drop out” at the bottom of the 219 page. Students in the Control condition only responded to the question whether they wanted to set a 220 goal to earn their paycheck goal, and were not offered the CD. Therefore, any difference between 221 students in the Opt-in/Opt-out conditions and the Control condition can be attributed to the marginal 222 impact of offering a commitment device relative to simply asking students whether they want to 223 achieve a goal or not, which can be perceived as an informational nudge. Prior to the study, the 224 research team conducted numerous pilot tests of the intervention materials with fifth graders from 225 similar backgrounds to ensure that the directions were clear and that students understood what they 226 were being asked to do in each condition. 227
In these schools, students attend homeroom at the beginning of each day, which is the classroom that 228 students assemble in daily with the same teacher before dispersing to other classes. We performed a 229 stratified randomization, using the students’ homeroom as a stratification variable within each school. 230 That is, to ensure that each homeroom had an equal number of students in each condition, we 231 randomly assigned students to one of three conditions within their homeroom. Students were 232 distributed across the three conditions as follows: 391 students in the Opt-in condition (32.45%), 406 233 students in the Opt-out condition (33.69%), and 408 students in the Control condition (33.86%). 234
Students completed the intervention in their homerooms via a paper-based survey. On the day of the 235 intervention, teachers told students they had the opportunity to participate in a research study about 236 their school experiences and paychecks. Teachers passed out pre-labeled individual envelopes to 237 students with the help of the research team. On-site research assistants were available to answer any 238 teacher or student questions and, when necessary, administer the survey. The envelopes ensured that 239 student answers would not be seen by teachers and so teachers remained blind to condition 240 assignment. The teachers read aloud the implementation script to all students, and then instructed 241 students to open their envelopes and silently read the assent form on the first page of the survey 242 packet. 243
After assenting to participating in the study, students completed the remainder of the survey. The 244 final page of the packet varied randomly across conditions (see Supplementary Materials for sample 245 survey). Once students completed the survey, students placed their survey packets back into the 246 envelopes and passed the envelopes to the teacher. 247
The day after the survey, all students received a note from the research team in their homeroom. 248 Students in the Control condition or who did not take-up the CD received a generic note thanking 249 them for participating. Students in either the Opt-in or Opt-out condition who enrolled in the CD 250 received a note reminding them of their paycheck goal for the week, and that they would lose dollars 251 off their next week’s paycheck if they failed to meet their goal. 252
At the end of the week, students’ paychecks also included an attached note. Students in the Control 253 condition or who did not take-up the CD again received a generic note thanking them for 254 participating. Students who enrolled in the CD and earned their paycheck goal received a 255
Students Want Behavior Commitment Devices
congratulatory note. Students who enrolled in the CD and failed to earn their paycheck goal were 256 notified that they did not earn their goal, and that the deduction would be reflected in their next 257 week’s paycheck. Within 10 days of the receiving their paycheck, students completed a short follow-258 up survey that asked questions about their experience in the study. 259
In the months before the study was administered to students, the research team introduced the study 260 to teachers in staff meetings. After receiving an overview of the study, each teacher was asked to 261 predict whether each student in their homeroom class would take-up the CD or not. 262
In accordance with human subject protection, this procedure and experiment were approved and 263 overseen by the Harvard University Institutional Review Board (Protocol #13-2091). Before 264 analyzing the data, we registered our study design, hypotheses, and analysis plan on AEA RCT 265 Registry. 266
3.4 Analytic Details 267
We conducted regression analyses with paycheck earnings as the dependent variable, controlling for 268 students’ homeroom, average pre-treatment paycheck earnings, and pre-treatment math grades. For 269 regression analyses with CD take-up as the dependent variable, we controlled for students’ 270 homeroom and pre-treatment math grades. All results are robust to the exclusion of these covariates. 271 We do not include covariates in any other analyses. We evaluated our hypotheses using 95% 272 confidence intervals to emphasize the range of plausible values for the treatment effect, in addition to 273 p-values (Cumming, 2014). 274
4 Results 275
4.1 Balance Equivalence and Descriptive Statistics 276
We checked to ensure the three conditions were balanced across covariates. For a breakdown of 277 participating students’ demographics by condition (see Table 2). A multinomial logistic regression 278 predicting condition assignment with available pre-treatment variables for all students, such as math 279 grade, proportion of female students, proportion of students in each school, proportion of students in 280 each grade, and average pre-treatment paycheck earnings, was not statistically significant (LR χ2 (20) 281 = 12.29, p = 0.906). For the four schools that provided student race information, we checked for 282 whether students’ race was balanced across conditions. The distribution of race in the Control, Opt-in 283 and Opt-out conditions, respectively, were 72.87%, 75.43%, 73.33% for Black, χ2 (2) = 0.58, p = 284 0.749, and 24.61%, 26.62%, 23.81% for Hispanic/Latino, χ2 (2) = 0.68, p = .713. 285
***TABLE 2*** 286
Students’ earned an average pre-treatment paycheck of $41.34 (SD = 17.91) and an average post-287 treatment paycheck of $39.98 (SD = 21.77). In the weeks leading up to the intervention, students in 288 higher grades tend to earn higher paychecks than students in lower grades, B = 1.41, SE = 0.48, t = 289 2.93, p = .003, CI [0.47, 2.36]. On average, eighth graders earned $44.17, seventh graders earned 290 $41.62 sixth graders earned $39.99, and fifth graders earned $40.03. 291
4.2 Perceptions about the Paycheck 292
Students’ responses to the adapted EVC Scale of student motivation demonstrated how they 293 perceived their weekly paychecks. Specifically, we were interested in how motivated students were 294
Students Want Behavior Commitment Devices
to earn good paychecks as a check to determine if improving their paycheck was a reasonable goal 295 for students to set. A principal component analysis with varimax rotation confirmed the latent 3-296 factor structure as suggested by Kosovich and colleagues (2015), KMO = .82; 2 (45, 1,103) = 297 4123.83, p < .001; see Supplementary Materials). In favor of a more parsimonious analysis, we 298 computed the simple average score of the items for each factor. The mean score for students’ 299 perceived expectancy of the paycheck was 3.35, SD = 0.51, SE = 0.015, CI [3.33, 3.38]. The mean 300 score for students’ perceived value of the paycheck was 2.82, SD = 0.75, SE = 0.022, CI [2.78, 2.87]. 301 The mean score for students’ perceived costs associated with earning a good paycheck was 2.06, SD 302 = 0.62, SE = 0.018, CI [2.03, 2.10]. These results suggest that, on average, students agreed that they 303 could earn a better paycheck and that the paycheck was valuable, but disagreed that the costs 304 associated with earning a good paycheck were too high. Overall, students appear to be motivated to 305 earn good paychecks. 306
4.3 Desire to Set a Goal to Earn a Higher Paycheck 307
Across the three conditions, 75.6% of students said that they wanted to set a goal to increase their 308 paycheck by 10%. In the Control condition, which was not influenced by the CD narrative in the 309 survey, 79.9% of students indicated they wanted to set the goal. The percentage of students that 310 wanted to set the goal was significantly different across conditions with 80.1% of students in the Opt-311 in condition indicating they wanted to set the paycheck goal, but only 67% of students in the Opt-out 312
condition expressing interest in setting the paycheck goal, χ2 (2) = 24.59, p < .001. This difference 313 may be due to the fact that if students in the Opt-out condition read to the bottom of the page before 314 making their decision, they would have realized they would have to take an extra step to write, “I 315 would like to drop out” if they did not want to take-up the CD. Students in the Opt-in condition only 316 had to respond yes or no. We find no other evidence that students assigned to the Opt-out condition 317 differ from students assigned to the Opt-in and Control conditions on any other dimensions. 318
4.4 Take-up of Commitment Device 319
We expected that defaulting students into enrolling in the CD would increase take-up rates. A logistic 320 regression confirmed this hypothesis, and Figure 1 shows that a significantly higher percentage of 321 students enrolled in the CD in the Opt-out condition (52.9%) than in the Opt-in condition (36.2%), 322 = 0.76, SE = 0.16, z = 4.79, p < 0.001, CI [0.45, 1.08]. Student age did not affect demand, as CD 323 take-up did not differ by grade. 324
***FIGURE 1*** 325
4.5 Teachers’ Prediction of Students’ Commitment Device Take-up 326
We expected that teachers’ predictions of students’ take-up of the CDs would be uncorrelated with 327 actual take-up of the CD. For this analysis we used a logistic regression, clustering the standard 328 errors by teacher and homeroom. When we pooled the two treatment conditions together, we found 329 no association between teacher predictions and actual enrollment, B = 0.257, SE = 0.196, z = 1.31, p 330 = .190, CI [-0.127, 0.641]. However, when we distinguished between treatment conditions, teachers 331 were somewhat more accurate in their predictions when the student was assigned to the Opt-in 332 condition. That is, a teacher’s prediction that a student in the Opt-in condition would take-up the CD 333 was associated with a 12-percentage point increase in the probability of that student enrolling in the 334 CD, B = 0.534, SE = 0.239, z = 2.23, p = .026, CI [0.065, 1.004]. 335
4.6 Commitment Device and Student Paychecks 336
Students Want Behavior Commitment Devices
We hypothesized that students in the treatment conditions who had the opportunity to enroll in the 337 CD (i.e., the Opt-in and Opt-out conditions) would earn higher paychecks as compared to students in 338 the Control condition. However, we found no evidence to support the hypothesis that the CD offer 339 impacted students’ subsequent behavior across multiple model specifications. 340
An intent-to-treat (ITT) analysis using OLS regression showed that there was no significant 341 difference on end-of-week paycheck earnings between the Control condition and either of the CD 342 conditions. Model 1 in Table 3 confirms that, compared to the Control condition, both confidence 343 intervals for the Opt-in condition estimate and Opt-out condition estimate include zero. In both the 344 Opt-in and Opt-out conditions the estimated coefficients are very small (0.393 and -0.096 dollars, 345 respectively). These coefficients translate to effect sizes of 0.018 and -0.004 standard deviations, 346 respectively, further signifying that we have no evidence that the treatment groups differed from the 347 control group in the population. The results remain unchanged when limiting the analysis to students 348 who initially indicated that they wanted to set the paycheck goal (see Model 2 in Table 3). 349
Additionally, having the opportunity to enroll in the CD did not affect the probability that students 350 met their paycheck goal (see Model 3 in Table 3). The difference in take-up between conditions is 351 not statistically significant: 37% of students in the Control condition met their paycheck goal, while 352 39% of students in the Opt-in condition and 37% of students in the Opt-out condition met their 353 paycheck goal. 354
Finally, we performed a treatment-on-the-treated (TOT) analysis, using condition assignment as an 355 instrument for CD take-up (a two stage least squares [2SLS] regression). We found no significant 356 differences between the Control condition and either treatment condition (see Models 4 and 5 in 357 Table 3). The coefficients in Model 4 represent the difference in paycheck scores between each 358 treatment condition and the Control condition, while the coefficient in Model 5 shows the difference 359 in paycheck scores between the Opt-in and Opt-out conditions. 360
Standardizing students’ paychecks within each school and grade did not meaningfully change the 361 results nor did conducting a difference-in-difference regression on student paycheck scores (see 362 Supplementary Materials). 363
***TABLE 3*** 364
4.7 Pre-Treatment Paycheck Performance 365
As an exploratory analysis, we investigated whether pre-treatment paycheck average was correlated 366 with enrolling in the CD and meeting the paycheck goal. In both treatment conditions, students’ pre-367 treatment paycheck average was negatively associated with taking up the CD, B = -0.021, SE = 368 0.004, z = -4.81, p < .001, CI [-0.029, -0.012]. In the Control condition, students’ pre-treatment 369 paycheck average was negatively associated with meeting their paycheck goal, B = -0.021, SE = 370 0.006, z = -3.28, p = .001, CI [-0.033, -0.008]. 371
5 Discussion 372
As researchers, practitioners, and policymakers increasingly turn to behavioral science, those 373 working with school-age children must consider which nudges are developmentally appropriate for 374 youth. Our study starts to shed light on whether CDs could be an effective strategy for helping 375 school-age children follow-through on their intentions. While we advance knowledge about school-376
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age children’s metacognition and how defaults may impact the take-up of CDs, we do not find 377 evidence that this CD impacts middle school students’ school behavior. 378
5.1 Students’ Metacognition 379
This study reveals that middle school students are more self-aware of their limited self-control than 380 we might realize. When given the opportunity, over one-third of students in the Opt-in condition 381 voluntarily elected to enroll in a CD to help them follow-through on their goal to improve their 382 school conduct. While this study focuses on a more general in-school behavior, there is little reason 383 to believe this capacity for metacognition would not transfer to other domains (e.g., nutrition and 384 exercise). Therefore, a sizable fraction of students appear to possess the metacognitive skills to 385 understand that there may be a gap between their current goals (e.g., to earn a higher paycheck, to eat 386 more healthfully) and their future preferences (e.g., talking to their friend during class, selecting 387 pizza over salad in the cafeteria). 388
The demand among middle school students to employ a tool that will help them follow-through on an 389 intended behavior, even when failure to follow-through is associated with negative consequences, 390 could inform future strategies that attempt to improve young adolescents’ self-control. Instead of 391 hoping school-age children simply develop more “willpower” when facing temptations that conflict 392 with their long-term goals, future interventions can leverage young adolescents’ willingness to 393 proactively modify their situation in ways that reduce the desirability of succumbing to anticipated 394 in-the-moment impulses (Duckworth et al., 2014; Duckworth, Gendler, & Gross, 2016). 395
5.2 Defaults Increase Commitment Device Take-up 396
Despite the potential of using pre-commitment as a self-control strategy, enrollment rates of CDs 397 tend to be low (Rogers et al., 2014). The desire to increase the percentage of people who take-up a 398 CD is often at odds with the voluntary aspect of the approach. We found that requiring students to 399 opt-out of the CD, as opposed to convincing them to proactively opt-in, maintained personal agency 400 and increased the percentage of students who enrolled in the CD by 46% (or 17 percentage points). 401 This finding builds on past research that opt-out or default framing increases take-up among adults 402 across many domains (e.g., taxes and organ donation). We contribute to this body of evidence by 403 demonstrating that defaults are also effective in the educational domain and with middle school-age 404 children. 405
As hypothesized, teachers were not very accurate when predicting which of their students would 406 take-up the CD. However, teachers were more accurate in predicting whether a student would take-407 up the CD if they had to proactively opt-in. This suggests that, like adults in other fields (Zlatev et 408 al., 2017), teachers and other adults may not be aware of the impact that defaults can have on youth 409 participation, and that default choice architecture holds potential for increasing youth-focused 410 intervention enrollment rates across many domains. 411
5.3 When Commitment Devices Fail 412
Despite finding that there was demand for the self-control strategy, we found no evidence that 413 enrollment in the CD impacted student behavior as measured by their paychecks. Compared to 414 students in the control condition, students in the treatment conditions did not earn higher paychecks, 415 nor were they more likely to meet their paycheck goals. We do not interpret our results as evidence 416 that CDs cannot impact student behavior in any domain. Rather, we interpret them as suggesting that 417 the present CD intervention was ineffective (i.e., the small effect sizes and that the confidence 418
Students Want Behavior Commitment Devices
intervals for the treatment effect included zero). Absence of evidence, after all, is not the same as 419 evidence of absence. 420
Additionally, our experiment cannot speak to why the CD did not significantly improve student 421 behavior, but there are a few potential explanations that we hope can be addressed in future research. 422
First, the standard goal of increasing their average paycheck by 10% for all students meant that 423 students with higher paycheck averages had larger and more difficult goals to achieve. For example, 424 a student with an average paycheck of $50 needed to earn $5 more than usual, or risk losing $10. But, 425 a student with an average paycheck of $10 only needed to earn $1 more dollar than usual, and only 426 risked losing $2. The relatively larger goal could have decreased the motivation of students with 427 higher average paychecks before the intervention to take-up the CD. In support of this explanation, 428 we found that having a higher pre-treatment average paycheck was associated with lower likelihood 429 of enrolling in the CD. Thus, it is possible that at least a subset of these students with high initial 430 paychecks acted rationally, recognizing that the paycheck goal was unrealistic and weighing that 431 against the relatively higher stakes. Future studies might explore how the target goal impacts the 432 enrollment and effectiveness of CDs. 433
Second, while young adolescents may possess the metacognitive awareness that they have limited 434 self-control, they may nevertheless struggle to make use of self-control strategies. Self-control tends 435 to improve as children age (Duckworth & Steinberg, 2015; Eisenberg, Duckworth, Spinrad, & 436 Valiente, 2014), and middle school students may be too young to effectively employ CDs. This study 437 suggests that more research is needed to determine at what age CDs become a viable strategy for 438 discouraging undesirable behavior. 439
Finally, middle school students may lack the requisite capacity for prospection which is needed to 440 identify what consequences, specifically, will be costly enough to motivate their future selves to 441 forego immediate temptation (Duckworth et al., 2014). That is, students may believe that the threat of 442 losing 20% of their average paycheck would incite them to avoid temptation, but they incorrectly 443 predicted the extent to which they valued their paycheck relative to their short-run impulses. Mochon 444 and colleagues (2016) found that shoppers for whom eating behavior CDs were effective (i.e., they 445 met their goal of purchasing more nutritional foods) were those that had the most to lose by failing to 446 meet their pre-commitment, suggesting the consequence must be costly enough to evoke behavior 447 change. Therefore, understanding the extent to which school-age children can predict what costs are 448 associated with controlling their impulses will be an important next step. This will be especially 449 important if children need to forecast what will motivate them, for example, to follow-through on 450 their goal of eating healthy in the face of the daily onslaught of unhealthy foods they likely encounter 451 (Harris, Bargh, & Brownell, 2009; Lee, 2012). 452
Our findings suggest that CDs intended to help middle school students follow-through on their 453 intentions may be more effective if they target specific, momentary behaviors, rather than ambiguous 454 behaviors over long periods of time. For instance, youth may be better served by an intervention that 455 emulates Schwartz and colleagues (2012) portion downsizing intervention where the target behavior 456 is restricted at the moment students express demand for the CD, as opposed to offering a CD that 457 requires students to modify their own future behaviors without additional self-control reinforcements. 458
5.4 Limitations 459
There a few limitations to consider when interpreting the results of this study. First, because we 460 focused on a school network for its paycheck system, our sample is necessarily limited to the student 461
Students Want Behavior Commitment Devices
populations these schools serve. In this case, these schools serve students from underserved 462 neighborhoods which may limit the generalizability of these results. Future research might explore 463 how middle school students from other backgrounds react to the opportunity to take-up a 464 commitment device. Second, our study required students to be in attendance on a single school day 465 and provide their assent to participate. While there is no reason to think that our study would 466 influence students’ attendance, it is possible that students who were absent or did not assent to 467 participate would have lower baseline levels of self-control, leaving us with a sample of students who 468 have higher self-awareness of their self-control limitations. Finally, because we had a limited 469 window for conducting the study (one week) we were not able to test the impact of the intervention 470 on students’ behavior in subsequent weeks. Thus, we do not know if students who took-up the CD 471 and lost 20% of their earnings because they failed to meet their paycheck goal would have learned 472 from their mistakes. Future studies on CDs might explore how people learn and adapt their behaviors 473 after self-control failures. 474
5.5 Conclusion 475
While the importance of self-control to healthy development is well-established, there are few 476 examples of translational research on self-control interventions targeting youth in real-world settings. 477 The present study suggests that some adolescents are willing to impose penalties on themselves for 478 failing to reach their goals, and that a default framing can increase the take-up of CDs. That said, 479 although CDs have been shown to help adults exercise self-control in the short-term in service of 480 achieving their long-term goals, we did not find evidence that CDs are effective at encouraging 481 middle school students to improve their school behaviors. Future research should explore at what 482 age, in what domains, and in what form nudge interventions, such as CDs, are developmentally 483 appropriate for effectively improving self-control. 484
6 References 485
Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by 486 precommitment. Psychological Science, 13(3), 219-224. 487 Barron, K. E., & Hulleman, C. S. (2015). Expectancy-value-cost model of motivation. International 488 encyclopedia of social and behavioral sciences, 261-271. 489 Benartzi, S., Beshears, J., Milkman, K. L., Sunstein, C. R., Thaler, R. H., Shankar, M., . . . Galing, S. 490 (2017). Should Governments Invest More in Nudging? Psychological Science, 0(0), 491 0956797617702501. doi:doi:10.1177/0956797617702501 492 Bergman, P., & Rogers, T. (2017). Is this technology useless? How seemingly irrelevant factors 493 affect adoption and efficacy. HKS Faculty Research Working Paper Series RWP17-021. 494 Brownell, K. D., Schwartz, M. B., Puhl, R. M., Henderson, K. E., & Harris, J. L. (2009). The need 495 for bold action to prevent adolescent obesity. Journal of Adolescent Health, 45(3), S8-S17. 496 Bryan, G., Karlan, D., & Nelson, S. (2010). Commitment Devices. Annual Review of Economics, 497 2(1), 671-698. doi:10.1146/annurev.economics.102308.124324 498 Carroll, G. D., Choi, J. J., Laibson, D., Madrian, B. C., & Metrick, A. (2009). Optimal defaults and 499 active decisions. The Quarterly Journal of Economics, 124(4), 1639-1674. 500 Croll, J., Neumark-Sztainer, D., Story, M., & Ireland, M. (2002). Prevalence and risk and protective 501 factors related to disordered eating behaviors among adolescents: relationship to gender and 502 ethnicity. Journal of Adolescent Health, 31(2), 166-175. 503 Cumming, G. (2014). The new statistics: why and how. Psychol Sci, 25(1), 7-29. 504 doi:10.1177/0956797613504966 505
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Cunningham, W. A., Zelazo, P. D., Packer, D. J., & Van Bavel, J. J. (2007). The iterative 506 reprocessing model: A multilevel framework for attitudes and evaluation. Social Cognition, 507 25(5), 736-760. 508 Dimmitt, C., & McCormick, C. B. (2012). Metacognition in education. In K. R. Harris, S. Graham, 509 T. C. Urdan, C. B. McCormick, G. M. Sinatra, & J. Sweller (Eds.), APA educational 510 psychology handbook, Vol 1: Theories, constructs, and critical issues (Vol. 1, pp. 157-187). 511 Washington, DC: American Psychological Association. 512 Duckworth, A. L., Gendler, T. S., & Gross, J. J. (2014). Self-control in school-age children. 513 Educational Psychologist, 49(3), 199-217. 514 Duckworth, A. L., Gendler, T. S., & Gross, J. J. (2016). Situational strategies for self-control. 515 Perspectives on Psychological Science, 11(1), 35-55. 516
Duckworth, A. L., & Steinberg, L. (2015). Unpacking self‐control. Child development perspectives, 517 9(1), 32-37. 518 Eisenberg, N., Duckworth, A. L., Spinrad, T. L., & Valiente, C. (2014). Conscientiousness: origins in 519 childhood? Dev Psychol, 50(5), 1331-1349. doi:10.1037/a0030977 520 Fila, S. A., & Smith, C. (2006). Applying the theory of planned behavior to healthy eating behaviors 521 in urban Native American youth. International Journal of Behavioral Nutrition and Physical 522 Activity, 3(1), 11. 523 Giné, X., Karlan, D., & Zinman, J. (2010). Put your money where your butt is: a commitment 524 contract for smoking cessation. American Economic Journal: Applied Economics, 2(4), 213-525 235. 526 Gortmaker, S. L., Peterson, K., Wiecha, J., Sobol, A. M., Dixit, S., Fox, M. K., & Laird, N. (1999). 527 Reducing obesity via a school-based interdisciplinary intervention among youth: Planet 528 Health. Archives of pediatrics & adolescent medicine, 153(4), 409-418. 529 Harris, J. L., Bargh, J. A., & Brownell, K. D. (2009). Priming effects of television food advertising 530 on eating behavior. Health Psychol, 28(4), 404-413. doi:10.1037/a0014399 531 Johnson, E. J., & Goldstein, D. G. (2003). Do defaults save lives? Science, 302, 1338-1339. 532 Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss 533 aversion, and status quo bias. The Journal of Economic Perspectives, 5(1), 193-206. 534 Kelder, S. H., Perry, C. L., Klepp, K.-I., & Lytle, L. L. (1994). Longitudinal tracking of adolescent 535 smoking, physical activity, and food choice behaviors. American journal of public health, 536 84(7), 1121-1126. 537 Kosovich, J., Hulleman, C., Barron, K., & Getty, S. (2015). Developing a practical measure of 538 motivation: Expectancy-Value-Cost in middle school science and mathematics. Journal of 539 Early Adolescence, 35(5-6), 790-816. 540 Lee, H. (2012). The role of local food availability in explaining obesity risk among young school-541 aged children. Social science & medicine, 74(8), 1193-1203. 542 Lytle, L. A., Seifert, S., Greenstein, J., & McGovern, P. (2000). How do children's eating patterns 543 and food choices change over time? Results from a cohort study. American Journal of Health 544 Promotion, 14(4), 222-228. 545 Milkman, K. L., Rogers, T., & Bazerman, M. H. (2008). Harnessing Our Inner Angels and Demons: 546 What We Have Learned About WantShould Conflicts and How That Knowledge Can Help 547 Us Reduce Short-Sighted Decision Making. Perspectives on Psychological Science, 3(4), 548 324-338. doi:10.1111/j.1745-6924.2008.00083.x 549 Mischel, H. N., & Mischel, W. (1987). The development of children’s knowledge of self-control 550 strategies. In Motivation, intention, and volition (pp. 321-336). Springer, Berlin, Heidelberg. 551 Mochon, D., Schwartz, J., Maroba, J., Patel, D., & Ariely, D. (2016). Gain without pain: The 552 extended effects of a behavioral health intervention. Management Science, 63(1), 58-72. 553
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Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., . . . Ross, S. 554 (2011). A gradient of childhood self-control predicts health, wealth, and public safety. 555 Proceedings of the National Academy of Sciences, 108(7), 2693-2698. 556 O'Donoghue, T., & Rabin, M. (2001). Choice and procrastination. The Quarterly Journal of 557 Economics, 116(1), 121-160. 558 Radnitz, C., Loeb, K. L., DiMatteo, J., Keller, K. L., Zucker, N., & Schwartz, M. B. (2013). Optimal 559 defaults in the prevention of pediatric obesity: from platform to practice. Journal of food & 560 nutritional disorders, 2(5), 1. 561 Rogers, T., Milkman, K. L., & Volpp, K. G. (2014). Commitment devices: using initiatives to change 562 behavior. Jama, 311(20), 2065-2066. 563 Royer, H., Stehr, M., & Sydnor, J. (2015). Incentives, commitments, and habit formation in exercise: 564 evidence from a field experiment with workers at a fortune-500 company. American 565 Economic Journal: Applied Economics, 7(3), 51-84. 566 Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of risk and 567 uncertainty, 1(1), 7-59. 568 Schwartz, J., Riis, J., Elbel, B., & Ariely, D. (2012). Inviting consumers to downsize fast-food 569 portions significantly reduces calorie consumption. Health Affairs, 31(2), 399-407. 570 Schwartz, J., Mochon, D., Wyper, L., Maroba, J., Patel, D., & Ariely, D. (2014). Healthier by 571 precommitment. Psychological Science, 25(2), 538-546. 572 Steinberg, L., Graham, S., O’Brien, L., Woolard, J., Cauffman, E., & Banich, M. (2009). Age 573 differences in future orientation and delay discounting. Child Development, 80(1), 28-44. 574 Story, M., & Resnick, M. D. (1986). Adolescents' views on food and nutrition. Journal of Nutrition 575 Education, 18(4), 188-192. 576 Thaler, R. H. (2008). Nudge : improving decisions about health, wealth, and happiness. New Haven. 577 Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: 578 Definitions, development, and relations to achievement outcomes. Developmental 579 Review, 30(1), 1-35. 580 Zlatev, J. J., Daniels, D. P., Kim, H., & Neale, M. A. (2017). Default neglect in attempts at social 581 influence. Proceedings of the National Academy of Sciences, 114(52), 13643-13648. 582
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7 Tables 584
7.1 Table 1. Items and factors of paycheck perceptions. 585
I know I can earn a better paycheck
I believe that I can be successful in earning a better paycheck
I am confident that I can earn a better paycheck
I think my paycheck is important
I value my paycheck
I think my paycheck is useful
Earning a good paycheck requires too much time
Because of other things that I do, I don't have time to earn a good paycheck
I'm unable to put in the time needed to earn a good paycheck
I have to give up too much to earn a good paycheck
7.2 Table 2. Balance table and descriptive statistics. 588
0.953 b
0.995 b
a p-value from a F-statistic. b p-value from a χ2 statistic. Note: A multinomial logistic
regression model predicting condition assignment with these variables was not jointly
statistically significant (LR χ2 (20) = 12.29, p = 0.906).
590 591
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7.3 Table 3. Commitment device and student paycheck results. 592
Met Goal
(-1.648, 2.434)
(-1.909, 2.701)
(-0.044, 0.080)
(-2.109, 1.916)
(-1.772, 2.988)
(-0.059, 0.062)
Take-up CD x
(-4.495, 6.754)
Take-up CD x
(-3.908, 3.531)
(-4.8, 3.135)
Did not want
to set
paycheck goal
Control group
* p<0.1; ** p<0.05; *** p<0.01
Notes: 95% Confidence Intervals are given in parenthesis. All models control for homeroom, average
pre-treatment paycheck, and pre-treatment math grade. “Take-up CD” variables in model 4 and 5 are
instrumented using condition assignment (CD = commitment device). The sample size reduction in
model 3 is due to strata that were excluded due to perfect prediction.
Students Want Behavior Commitment Devices
8 Figures 596
Figure 1. Percentage of students who took-up commitment device and achieved their goal by 597 treatment condition. 598
Error bars represent 95% CI. Students in the Control condition did not have the opportunity to take-600 up the commitment device. 601
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Current theories suggest that people understand how to exploit common biases to influence others. However, these predictions have received little empirical attention. We consider a widely studied bias with special policy relevance: the default effect, which is the tendency to choose whichever option is the status quo. We asked participants (including managers, law/business/medical students, and US adults) to nudge others toward selecting a target option by choosing whether to present that target option as the default. In contrast to theoretical predictions, we find that people often fail to understand and/or use defaults to influence others, i.e., they show "default neglect." First, in one-shot default-setting games, we find that only 50.8% of participants set the target option as the default across 11 samples (n = 2,844), consistent with people not systematically using defaults at all. Second, when participants have multiple opportunities for experience and feedback, they still do not systematically use defaults. Third, we investigate beliefs related to the default effect. People seem to anticipate some mechanisms that drive default effects, yet most people do not believe in the default effect on average, even in cases where they do use defaults. We discuss implications of default neglect for decision making, social influence, and evidence-based policy.
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We examine the extended effects of an incentive-based behavioral health intervention designed to improve nutrition behavior. Although the intervention successfully improved the target behavior, less is known about any spillovers, positive or negative, that impacted the program's net benefit. This novel examination presents an opportunity to advance our knowledge of this important question, particularly because many theories predict that balancing behaviors in other domains (e.g., reduced exercise) can occur. Our results show a positive and long-lasting persistence effect for the treatment group, even after the incentive was removed. Moreover, we observe no negative spillover effects into related domains such as exercise, and no negative impact on customer loyalty. These results support the use of incentive-based interventions and highlight the importance, for both theory and practice, of examining their extended effects.
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The term "optimal defaults" refers to imparting pre-selected choices which are designed to produce a desired behavior change. The concept is attractive to policymakers because it steers people toward desirable behaviors while preserving free choice through the ability to opt out. It has been found to be a powerful behavioral determinant in areas such as pension plan enrollment, organ donation, and green energy utilization. We discuss how optimal defaults can be applied to pediatric obesity prevention in several domains including public policy, institutional, private sector, and home environment. Although there are obstacles to overcome in implementing optimal defaults, it is a promising component to incorporate in a multi-level strategy for preventing pediatric obesity.
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Conflicts between immediately rewarding activities and more enduringly valued goals abound in the lives of school-age children. Such conflicts call upon children to exercise self-control, a competence that depends in part on the mastery of metacognitive, prospective strategies. The process model of self-control organizes these strategies into five families corresponding to sequential phases in the process by which undesired and desired impulses lose or gather force over time. Situation selection and situation modification strategies involve choosing or changing physical or social circumstances. Attentional deployment and cognitive change strategies involve altering whether and how objective features of the situation are mentally represented. Finally, response modulation strategies involve the direct suppression or enhancement of impulses. The process model of self-control predicts that strategies deployed earlier in the process of impulse generation and regulation generally will be more effective than those deployed later. Implications of this self-control perspective for school-age children are considered.
Although observers of human behavior have long been aware that people regularly struggle with internal conflict when deciding whether to behave responsibly or indulge in impulsivity, psychologists and economists did not begin to empirically investigate this type of want/should conflict until recently. In this article, we review and synthesize the latest research on want/should conflict, focusing our attention on the findings from an empirical literature on the topic that has blossomed over the last 15 years. We then turn to a discussion of how individuals and policy makers can use what has been learned about want/should conflict to help decision makers select far-sighted options. © 2008, Association for Psychological Science. All rights reserved.
Exercising self-control is often difficult, whether declining a drink in order to drive home safely, passing on the chocolate cake to stay on a diet, or ignoring text messages to finish reading an important paper. But enacting self-control is not always difficult, particularly when it takes the form of proactively choosing or changing situations in ways that weaken undesirable impulses or potentiate desirable ones. Examples of situational self-control include the partygoer who chooses a seat far from where drinks are being poured, the dieter who asks the waiter not to bring around the dessert cart, and the student who goes to the library without a cell phone. Using the process model of self-control, we argue that the full range of self-control strategies can be organized by considering the timeline of the developing tempting impulse. Because impulses tend to grow stronger over time, situational self-control strategies—which can nip a tempting impulse in the bud—may be especially effective in preventing undesirable action. Ironically, we may underappreciate situational self-control for the same reason it is so effective—namely, that by manipulating our circumstances to advantage, we are often able to minimize the in-the-moment experience of intrapsychic struggle typically associated with exercising self-control.
Unhealthy behaviors are responsible for a large proportion of health care costs and poor health outcomes.1 Surveys of large employers regularly identify unhealthy behaviors as the most important challenge to affordable benefits coverage. For this reason, employers increasingly leverage incentives to encourage changes in employees’ health-related behaviors. According to one survey, 81% of large employers provide incentives for healthy behavior change.2 In this Viewpoint, we discuss the potential and limitations of an approach that behavioral science research has shown can be used to influence health behaviors but that is distinct from incentives: the use of commitment devices (Table).3
We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.