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Who Has the Time? Community College Students’ Time-Use Response to Financial Incentives


We evaluate the effect of performance-based scholarship programs for post-secondary students on student time use and effort and whether these effects are different for students we hypothesize may be more or less responsive to incentives. To do so, we administered a time-use survey as part of a randomized experiment in which community college students in New York City were randomly assigned to be eligible for a performance-based scholarship or to a control group that was only eligible for the standard financial aid. This paper contributes to the literature by attempting to get inside the “black box” of how students respond to a monetary incentive to improve their educational attainment. We find that students eligible for a scholarship devoted more time to educational activities, increased the quality of effort toward and engagement with their studies, and allocated less time to leisure. Additional analyses suggest that students who were plausibly more myopic (place less weight on future benefits) were more responsive to the incentives, but we find no evidence that students who are arguably more time constrained were less responsive to the incentives.

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  1. Mayer et al. (2015) found that program group students were awarded $2209 more in total financial aid (including $2002 of PBS money) than the control group students. There was no difference between the program and control group students in terms of total loans received. This evidence suggests that the only financial difference between groups is the PBS.

  2. Some students were randomly assigned to a second treatment group that was eligible to receive the PBS during the regular semesters plus a PBS for one consecutive summer term worth up to $1300 (for a total of $3900). Because we focus on regular semester outcomes during which the incentive structures for the two treatment groups are identical, results are not presented separately for the two treatment groups.

  3. “Continued enrollment at mid-semester” was determined by whether the student attended class at least once in the first three weeks of the semester and at least once during the fourth or fifth weeks of the semester.

  4. Equated credits are given in developmental education classes and do not count towards a degree or certificate.

  5. See Richburg-Hayes et al. (2011) for more background on the New York demonstration.

  6. The MDRC PBS study includes one additional participant for whom we did not receive contact information and thus were unable to survey.

  7. Instead of or in addition to increasing time devoted to education and learning, students may improve the quality of their effort by adopting more effective learning strategies. Researchers have also documented a relationship between perceived self-efficacy and academic performance. Finally, cognitive psychologists distinguish between internal motivation and external motivation (e.g., Deci 1975; Deci and Ryan 1985) and document that more positive educational outcomes are associated with greater levels of internal motivation (e.g., Pintrich and De Groot 1990). Therefore, one concern is that providing external motivation through incentives may reduce a student’s internal motivation (e.g., Deci et al. 1999).

  8. Because we focus on the first semester after random assignment, data from the first cohort were not included as we were only able to first survey them in the second semester after random assignment.

  9. The baseline data were collected by MDRC at the time participants were enrolled in the study and before they were randomly assigned to a program or control group.

  10. Randomization-pool fixed effects reflect the community college and cohort in which the participant was recruited. See the table notes for the full list of baseline control variables.

  11. As an alternative, we used factor analysis to identify empirically-determined principal components. The results roughly suggested that variables reflecting academic effort should be grouped together and those reflecting time spent on non-academic time should be grouped together. We prefer our approach because it is more intuitive and it is possible to identify exactly which outcomes contribute to each domain.

  12. The analytic sample only includes study participants from cohorts 2 (Spring 2009) and 3 (Fall 2010) because we were unable to survey the first cohort in the first semester after random assignment.

  13. P value = 0.066 for estimate including controls.

  14. Home production includes time spent on personal care, sleeping, eating and drinking, performing household tasks, and caring for others. Leisure activities include participating in a cultural activity, watching TV/movies/listening to music, using the computer, spending time with friends, sports, talking on the phone, volunteering, religious activities, and other leisure activities.

  15. Educational activities includes: “Hours spent on all academics in the last 24 hours,” “Hours studied in past 7 days,” “Prepared for last class in last 7 days,” and “Attended most/all classes in last 7 days.” Quality of educational input includes academic self-efficacy and the MSLQ index. Non-academic activities includes “Hours on household production,” “Hours on leisure,” “Nights out for fun in the past 7 days,” “Hours worked in last 24 hours,” and “Hours worked in the past 7 days.” Unintended consequences includes “Strongly agree/agree have taken challenging classes,” “Ever felt had to cheat,” “indices of external motivation and internal motivation,” “Ever asked for a re-grade,” and “Very satisfied/satisfied with life.” We do not include whether an individual had “ever enrolled” in a postsecondary institution in the “all academic activities” index as it represents an academic decision on the extensive margin rather than the intensive margin, and participants were recruited on campus after they had made the decision to enroll.

  16. In fact, in Table 5 the estimate of the impact on unintended consequences is negative and statistically significant. The literature is largely silent on why the incentives would have decreased unintended consequences, and we hesitate to speculate.

  17. The baseline characteristics between the treatment and control groups for these subgroups (e.g. age, race, primary language) were also statistically balanced. The p-values for the omnibus F-tests for treatment and control balance are: 0.756 for those with more than 11 years of schooling, 0.162 for those with less than 11 years of schooling, 0.973 for those without young children, and 0.98 for those with young children. Results are economically similar but statistically noisy if we control for baseline characteristics.


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We thank Eric Auerbach, Elijah de la Campa, Ross Cole, Laurien Gilbert, Ming Gu, Steve Mello, Lauren Sartain, and Ini-Abasi Umosen for expert research assistance; Leslyn Hall and Lisa Markman Pithers with help developing the survey; Reshma Patel for extensive help in understanding the MDRC data. Orley Ashenfelter, Alan Krueger, Jonas Fisher, Derek Neal, Reshma Patel, Lashawn Richburg-Hayes, and Shyam Gouri Suresh as well as seminar participants at Cornell University, the Federal Reserve Bank of Chicago, Federal Reserve Bank of New York, Harvard University, Michigan State, Princeton University, University of Chicago, University of Pennsylvania, and University of Virginia provided helpful conversations and comments. This paper was also presented by Cecilia Rouse as the Presidential Address at the 88th International Atlantic Economic Conference in Miami, 17-20 October 2019. Some of the data used in this paper are derived from data files made available by MDRC. We thank the Bill & Melinda Gates Foundation and the Princeton University Industrial Relation Section for generous funding. The authors remain solely responsible for how the data have been used or interpreted. Any views expressed in this paper do not necessarily reflect those of the Federal Reserve Bank of Chicago or the Federal Reserve System. Any errors are ours.

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Correspondence to Cecilia Elena Rouse.

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Barrow, L., Rouse, C.E. & McFarland, A. Who Has the Time? Community College Students’ Time-Use Response to Financial Incentives. Atl Econ J 48, 35–52 (2020).

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  • Higher education
  • Educational investment
  • Time use
  • Incentives
  • Financial aid


  • J24
  • D03
  • I20