Reducing student absences at scale by targeting parents’ misbeliefs


Student attendance is critical to educational success, and is increasingly the focus of educators, researchers and policymakers. We report the results of a randomized experiment examining interventions targeting student absenteeism. Parents of 28,080 high-risk students in grades kindergarten to 12th grade received one of three personalized information treatments repeatedly throughout the school year or received no additional information (control). The most effective versions reduced chronic absenteeism by 10% or more, partly by correcting parents’ biased beliefs about their children’s total accumulated absences. The intervention reduced student absences comparably across grade levels, and reduced absences among untreated cohabiting students in treated households. This intervention is easy to scale and is more than one order of magnitude more cost effective than current absence-reduction best practices. Educational interventions that inform and empower parents, such as the one reported here, can complement more intensive student-focused absenteeism interventions.

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Fig. 1: Sample mailings for each treatment condition.
Fig. 2: Absences by treatment condition.
Fig. 3: Treatments corrected parents’ biased beliefs.


  1. 1.

    Balfanz, R. & Byrnes, V. The Importance of Being in School: A Report on Absenteeism in the Nation’s Public Schools (Johns Hopkins Univ., 2012).

  2. 2.

    Gottfried, M. A. Evaluating the relationship between student attendance and achievement in urban elementary and middle schools: an instrumental variables approach. Am. Educ. Res. J. 47, 434–465 (2010).

    Article  Google Scholar 

  3. 3.

    Nauer, K., Mader, N., Robinson, G. & Jacobs, T. A Better Picture of Poverty: What Chronic Absenteeism and Risk Load Reveal About NYC’s Lowest Income Elementary Schools (Center for New York City Affairs, 2014).

  4. 4.

    Gershenson, S., Jacknowitz, A. & Brannegan, A. Are student absences worth the worry in US primary schools? Educ. Finance Policy 12, 137–165 (2017).

    Article  Google Scholar 

  5. 5.

    Allensworth, E. M. & Easton, J. Q. What Matters for Staying On-Track and Graduating in Chicago Public High Schools: a Close Look at Course Grades, Failures, and Attendance in the Freshman Year (Consortium on Chicago School Research, 2007).

  6. 6.

    Goodman, J. Flaking Out: Student Absences and Snow Days as Disruptions of Instructional Time Working Paper 20221 (National Bureau of Economic Research, 2014).

  7. 7.

    Gottfried, M. A. The detrimental effects of missing school: evidence from urban siblings. Am. J. Educ. 117, 147–182 (2011).

    Article  Google Scholar 

  8. 8.

    Byrnes, V. & Reyna, R. Summary of State Level Analysis of Early Warning Indicators (Johns Hopkins Univ., 2012).

  9. 9.

    Schoeneberger, J. Longitudinal attendance patterns: developing high school dropouts. Clearing House 85, 7–14 (2012).

    Article  Google Scholar 

  10. 10.

    Henry, K. L. & Thornberry, T. P. Truancy and escalation of substance use during adolescence. J. Stud. Alcohol Drugs 71, 115–124 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Baker, M. L., Sigmon, J. N. & Nugent, M. E. Truancy Reduction: Keeping Students in School (US Department of Justice, 2001).

  12. 12.

    Jacob, B. A. & Lefgren, L. Are idle hands the devil’s workshop? Incapacitation, concentration, and juvenile crime. Am. Econ. Rev. 93, 1560–1577 (2003).

    Article  Google Scholar 

  13. 13.

    Rohrman, D. Combating truancy in our schools—a community effort. NASSP Bull. 77, 40–45 (1993).

    Article  Google Scholar 

  14. 14.

    Ely, T. L. & Fermanich, M. L. Learning to count: school finance formula count methods and attendance-related student outcomes. J. Educ. Financ. 38, 343–369 (2013).

    Google Scholar 

  15. 15.

    Every Student Succeeds Act (ESSA) (US Department of Education, 2015).

  16. 16.

    Lynch, L., Burwell, S., Castro, J. & Duncan, A. Joint Letter on Chronic Absenteeism (2015);

  17. 17.

    Guryan, J. et al. The Effect of Mentoring on School Attendance and Academic Outcomes: a Randomized Evaluation of the Check and Connect Program Working Paper-16-18 (Northwestern Univ. Institute for Policy Research, 2017).

  18. 18.

    Sutphen, R. D., Ford, J. P. & Flaherty, C. Truancy interventions: a review of the research literature. Res. Social Work Pract. 20, 161–171 (2010).

    Article  Google Scholar 

  19. 19.

    O’Donnell, C. L. Defining, conceptualizing, and measuring fidelity of implementation and its relationship to outcomes in K–12 curriculum intervention research. Rev. Educ. Res. 78, 33–84 (2008).

    Article  Google Scholar 

  20. 20.

    Chugh, D. & Bazerman, M. H. Bounded awareness: what you fail to see can hurt you. Mind Soc. 6, 1–18 (2007).

    Article  Google Scholar 

  21. 21.

    Simons, D. J. & Chabris, C. F. Gorillas in our midst: sustained inattentional blindness for dynamic events. Perception 28, 1059–1074 (1999).

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Sedikides, C., Gaertner, L. & Toguchi, Y. Pancultural self-enhancement. J. Pers. Soc. Psychol. 84, 60–79 (2003).

    Article  PubMed  Google Scholar 

  23. 23.

    Lee, J. Y. Garrison Keillor: A Voice of America (Univ. Press Mississippi, Jackson, MS, 1991).

  24. 24.

    Kling, J. R., Mullainathan, S., Shafir, E., Vermeulen, L. & Wrobel, M. Comparison Friction: Experimental Evidence from Medicare Drug Plans Working Paper 17410 (National Bureau of Economic Research, 2012).

  25. 25.

    Grubb, M. D. & Osborne, M. Cellular service demand: biased beliefs, learning, and bill shock. Am. Econ. Rev. 105, 234–271 (2015).

    Article  Google Scholar 

  26. 26.

    Becker, G. S. A theory of social interactions. J. Political Econ. 82, 1063–1093 (1974).

    Article  Google Scholar 

  27. 27.

    Heckman, J. J. & Mosso, S. The Economics of Human Development and Social Mobility Working Paper 19925 (National Bureau of Economic Research, 2014).

  28. 28.

    Bursztyn, L. & Coffman, L. C. The school decision: family preferences, intergenerational conflict, and moral hazard in the Brazilian favelas. J. Political Econ. 120, 359–397 (2011).

    Article  Google Scholar 

  29. 29.

    Hastings, J. S. & Weinstein, J. M. Information, School Choice, and Academic Achievement: Evidence from Two Experiments Working Paper 13623 (National Bureau of Economic Research, 2007).

  30. 30.

    Bergman, P. Parent–Child Information Frictions and Human Capital Investment: Evidence from a Field Experiment (Columbia Univ., 2015);

  31. 31.

    Bergman, P. & Rogers, T. The Impact of Defaults on Technology Adoption, and its Underappreciation by Policymakers Faculty Research Working Paper Series RWP17-021 (Harvard Kennedy School, Cambridge, MA, 2017).

  32. 32.

    Kraft, M. A. & Rogers, T. The underutilized potential of teacher-to-parent communication: evidence from a field experiment. Econ. Educ. Rev. 47, 49–63 (2015).

    Article  Google Scholar 

  33. 33.

    Frey, B. S. & Meier, S. Social comparisons and pro-social behavior: testing “conditional cooperation” in a field experiment. Am. Econ. Rev. 94, 1717–1722 (2004).

    Article  Google Scholar 

  34. 34.

    Shang, J. & Croson, R. A field experiment in charitable contribution: the impact of social information on the voluntary provision of public goods. Econ. J. 119, 1422–1439 (2009).

    Article  Google Scholar 

  35. 35.

    Ferraro, P. J., Miranda, J. J. & Price, M. K. The persistence of treatment effects with norm-based policy instruments: evidence from a randomized environmental policy experiment. Am. Econ. Rev. 101, 318–322 (2011).

    Article  Google Scholar 

  36. 36.

    Ferraro, P. J. & Price, M. K. Using nonpecuniary strategies to influence behavior: evidence from a large-scale field experiment. Rev. Econ. Stat. 95, 64–73 (2013).

    Article  Google Scholar 

  37. 37.

    Goldstein, N. J. & Cialdini, R. B. in Social Psychology of Consumer Behavior (ed. Wänke, M.) 273–296 (Psychology Press, New York, NY, 2009).

  38. 38.

    Allcott, H. & Rogers, T. The short-run and long-run effects of behavioral interventions: experimental evidence from energy conservation. Am. Econ. Rev. 104, 3003–3037 (2014).

    Article  Google Scholar 

  39. 39.

    Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J. & Griskevicius, V. Normative social influence is underdetected. Pers. Soc. Psychol. Bull. 34, 913–923 (2008).

    Article  PubMed  Google Scholar 

  40. 40.

    Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J. & Griskevicius, V. The constructive, destructive, and reconstructive power of social norms. Psychol. Sci. 18, 429–434 (2007).

    Article  PubMed  Google Scholar 

  41. 41.

    Coffman, L. C., Featherstone, C. R. & Kessler, J. B. Can social information affect what job you choose and keep? Am. Econ. J. Appl. Econ. 9, 96–117 (2017).

    Article  Google Scholar 

  42. 42.

    Gerber, A. S. & Rogers, T. Descriptive social norms and motivation to vote: everybody’s voting and so should you. J. Polit. 71, 178–191 (2009).

    Article  Google Scholar 

  43. 43.

    Keane, L. D. & Nickerson, D. W. When reports depress rather than inspire: a field experiment using age cohorts as reference groups. J. Political Mark. 14, 381–390 (2015).

    Article  Google Scholar 

  44. 44.

    Rogers, T., Green, D. P., Ternovski, J. & Young, C. F. Social pressure and voting: a field experiment conducted in a high-salience election. Elect. Stud. 46, 87–100 (2017).

    Article  Google Scholar 

  45. 45.

    Karlan, D., McConnell, M., Mullainathan, S. & Zinman, J. Getting to the top of mind: how reminders increase saving. Manag. Sci. 62, 3393–3411 (2016).

    Article  Google Scholar 

  46. 46.

    Balfanz, R. & Byrnes, V. Meeting the Challenge of Combating Chronic Absenteeism (Johns Hopkins Univ., 2013).

  47. 47.

    Balu, R., Porter, K. & Gunton, B. Can Informing Parents Help High School Students Show Up for School? (MDRC, 2016).

  48. 48.

    Allcott, H. Social norms and energy conservation. J. Public Econ. 95, 1082–1095 (2011).

    Article  Google Scholar 

  49. 49.

    Rogers, T. & Feller, A. Discouraged by peer excellence: exposure to exemplary peer performance causes quitting. Psychol. Sci. 27, 365–374 (2016).

    Article  PubMed  Google Scholar 

  50. 50.

    Beshears, J., Choi, J. J., Laibson, D., Madrian, B. C. & Milkman, K. L. The effect of providing peer information on retirement savings decisions. J. Finance 70, 1161–1201 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Nickerson, D. W. Is voting contagious? Evidence from two field experiments. Am. Political Sci. Rev. 102, 49–57 (2008).

    Article  Google Scholar 

  52. 52.

    Goetzel, R. Z., Hawkins, K., Ozminkowski, R. J. & Wang, S. The health and productivity cost burden of the “top 10” physical and mental health conditions affecting six large US employers in 1999. J. Occup. Environ. Med. 45, 5–14 (2003).

    Article  PubMed  Google Scholar 

  53. 53.

    Ten Brummelhuis, L. L., Johns, G., Lyons, B. J. & ter Hoeven, C. Why and when do employees imitate the absenteeism of co-workers? Organ. Behav. Hum. Decis. Process. 134, 16–30 (2016).

    Article  Google Scholar 

  54. 54.

    Robinson, K. & Harris, A. L. The broken compass: parental involvement with children’s education. J. Educ. Res. 108, 345–346 (2014).

    Google Scholar 

  55. 55.

    Valencia, R. R. The Evolution of Deficit Thinking: Educational Thought and Practice (Falmer Press, Bristol, PA, 1997).

  56. 56.

    Henderson, A. T. & Mapp, K. L. A New Wave of Evidence: The Impact of School, Family, and Community Connections on Student Achievement (National Center for Family and Community Connections with Schools, 2002).

  57. 57.

    Rosenbaum, P. R. Observational Studies 1–17 (Springer, New York, NY, 2002).

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We thank the Laura and John Arnold Foundation, Pershing Square Venture Fund for Research on the Foundations of Human Behavior and IES/ICF/REL MidAtlantic number 14JTSK0003 for funding support. We thank J. Lasky-Fink, J. Ternovski and S. Subramanyam for research support. We thank T. Wolford, A. Reitano and W. Hite for district partnership and collaboration. We thank B. Balfanz, G. Basse, M. Bazerman, P. Bergman, H. Chang, L. Coffman, D. Deming, C. Fox, H. Gehlbach, A. Gelber, F. Gino, E. Glaeser, M. Gottfried, D. Green, H. Hoynes, L. John, G. King, D. Laibson, M. Laitin, S. Mullainathan, M. Norton, L. Page, L. Pierce, S. Reardon and J. Schwartzstein for feedback on earlier drafts. No funders had any role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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T.R. and A.F. designed the experiment, oversaw data analysis and wrote the manuscript.

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Correspondence to Todd Rogers.

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Competing interests

The authors declare that they had no competing financial interests while this project was being conducted. In the light of the results of this and other projects, T.R. and A.F. started an organization to help US schools implement this intervention to reduce student absenteeism. It is called In Class Today and worked with two school districts at the time of initial submission, including the school district in which the experiment reported in this manuscript was conducted—SDP.

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Supplementary Notes, Supplementary Methods, Supplementary Tables 1–28, Supplementary Discussion, Supplementary Figures A–D

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Rogers, T., Feller, A. Reducing student absences at scale by targeting parents’ misbeliefs. Nat Hum Behav 2, 335–342 (2018).

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