|Benefit-Cost Summary Statistics Per Participant|
|Taxpayers||$954||Benefits minus costs||$3,725|
|Participants||$1,975||Benefit to cost ratio||$94.89|
|Others||$831||Chance the program will produce|
|Indirect||$5||benefits greater than the costs||57 %|
|Net program cost||($40)|
|Benefits minus cost||$3,725|
|Detailed Monetary Benefit Estimates Per Participant|
|Benefits from changes to:1||Benefits to:|
|Labor market earnings associated with test scores||$903||$1,989||$886||$0||$3,779|
|Health care associated with educational attainment||$50||($14)||($55)||$25||$6|
|Adjustment for deadweight cost of program||$0||$0||$0||($20)||($20)|
|Detailed Annual Cost Estimates Per Participant|
|Annual cost||Year dollars||Summary|
|Program costs||$39||2016||Present value of net program costs (in 2017 dollars)||($40)|
|Comparison costs||$0||2016||Cost range (+ or -)||60 %|
|Estimated Cumulative Net Benefits Over Time (Non-Discounted Dollars)|
|The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in non-discounted dollars to simplify the “break-even” point from a budgeting perspective. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.|
|Meta-Analysis of Program Effects|
|Outcomes measured||Treatment age||No. of effect sizes||Treatment N||Adjusted effect sizes(ES) and standard errors(SE) used in the benefit - cost analysis||Unadjusted effect size (random effects model)|
|First time ES is estimated||Second time ES is estimated|
|Grade point average^||14||4||2165||0.062||0.085||14||n/a||n/a||n/a||0.145||0.096|
Blackwell, L.S., Trzesniewski, K.H., & Dweck, C.S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78(1), 246-263.
Chao, M.M., Visaria, S., Mukhopadhyay, A., & Dehejia, R. (2017). Do rewards reinforce the growth mindset?: Joint effects of the growth mindset and incentive schemes in a field intervention. Journal of Experimental Psychology: General, 146(10), 1402-1419.
Good, C., Aronson, J., & Inzlicht, M. (2003). Improving adolescents' standardized test performance: An intervention to reduce the effects of stereotype threat. Journal of Applied Developmental Psychology, 24(6), 645-662.
Paunesku, D., Walton, G.M., Romero, C., Smith, E.N., Yeager, D.S., & Dweck, C.S. (2015). Mind-set interventions are a scalable treatment for academic underachievement. Psychological Science, 26(6), 784-793.
Rienzo, C., Rolfe, H., & Wilkinson, D. (2015). Changing mindsets: Evaluation report and executive summary. London, United Kingdom: Education Endowment Foundation.
Yeager, D.S., Romero, C., Paunesku, D., Hulleman, C.S., Schneider, B., Hinojosa, C., . . . Dweck, C. (2016). Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school. Journal of Educational Psychology, 108(3), 374-391.