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3. .

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1. (2007) for details. The Project STAR public use file, which is the basis for the empirical analysis in this paper, combines these data such that students can be followed throughout their scholastic careers until the end of high school. In what follows, I present the main independent and dependent variables that I draw from this dataset.
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2. (2007). Due to the selective nature of this subsample, I chose not to analyze fourth-grade test scores. ponents in reading and math. The second- and third-grade versions of the Stanford Achievement Test further include tests of word study skills and listening skills.
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3. – ONLINE APPENDIX – A Data appendix In this appendix, I provide additional details about the Project STAR data. The appendix is very similar, and in parts identical, to the data appendix prepared for a previous paper, in which I use data from the same experiment (Bietenbeck, 2020). Project STAR was planned and implemented by a consortium of researchers from four universities and various state institutions in Tennessee. The experiment ran from the beginning of kindergarten until the end of third grade, but some researchers continued to collect data on participating students in the years afterwards, see Finn et al.
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4. —. 2005b. “Stepwise Multiple Testing as Formalized Data Snooping.” Econometrica 73:1237– 1282.
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5. Academic motivation. As described in the main text, students participating in Project STAR were assessed on their academic motivation and self-concept using the Self-Concept and Motivation Inventory (SCAMIN; Milchus, Farrah, and Reitz, 1968) in the spring of each year from kindergarten through third grade. The group-administered, standardized psychological test asks students to indicate pictorially their response to different situations. Based on the answers, a motivation score and a self-concept score are calculated for each student. These scores are included in the public use file.
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10. B Results from additional analyses Online Appendix Figure B.1: Randomization check like in Feld and Zoelitz (2017), distribution of p-values 0 .05 .1 fraction of tests 0 .2 .4 .6 .8 1 p Male 0 .05 .1 fraction of tests 0 .2 .4 .6 .8 1 p Black 0 .05 .1 fraction of tests 0 .2 .4 .6 .8 1 p Free lunch 0 .05 .1 fraction of tests 0 .2 .4 .6 .8 1 p Age 0 .05 .1 fraction of tests 0 .2 .4 .6 .8 1 p
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44. Online Appendix Table B.4: Randomization check like in Chetty et al. (2011) Male Black Free lunch Age Pred. achievement (1) (2) (3) (4) (5) p-value .14 .99 .26 .30 .69 Observations 2,861 2,766 2,730 2,845 2,868 Notes: The table reports results from a test for random assignment of students to classes similar to the one conducted in Chetty et al. (2011). The intuition of this test is that if students were indeed randomly assigned to classes, then class dummies should not predict their predetermined characteristics. For this table, I regressed each of the variables indicated in the column headers on school-by-entry-grade fixed effects and class dummies (leaving out one dummy per school-by-entry-grade cell to avoid collinearity). I then conducted an F test for the joint significance of all class dummies. The table reports the corresponding p-values.
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48. Regarding the reliability of the early elementary form, Drummond and McIntire (1975) calculate five-months test-retest correlations of motivation scores of 0.37 and 0.51 in samples of first and second grade students, respectively. Using data from Project STAR, I find a one-year test-retest correlation of 0.31 for both first-grade and secondgrade motivation scores. These values are broadly similar to test-retest correlations found for personality traits in children: for example, Measelle et al. (2005) document one-year test-retest correlations for Big Five traits ranging from 0.33 to 0.59 in children aged six to seven, and a meta study by Roberts and DelVecchio (2000) finds an average test-retest correlation of 0.43 for Big Five Traits in children aged six to eleven. The available evidence paints a different picture of the quality of the SCAMIN preschool/kindergarten form, which was administered in the spring of kindergarten.
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56. Several previous studies and my own analysis of data from Project STAR indicate a high quality of the SCAMIN early elementary form, which was administered in grades 1-3. Thus, Finn and Cox (1992) point out its strong content validity due to the careful and structured approach taken when creating questions. McIntire and Drummond (1976) show that the motivation score based on the early elementary form correlates with a conceptually related score from the more widely used Coopersmith Self-Esteem Inventory Scales, providing some evidence of construct validity. My results in Section 4 establish criterion validity, as they show that motivation scores predict a wide range of contemporaneous and future outcomes.
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57. Shure, N. 2017. “Non-Cognitive Peer Effects in Secondary Education.” Unpublished.
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60. Steinmayr, R., and B. Spinath. 2009. “The Importance of Motivation as a Predictor of School Achievement.” Learning and Individual Differences 19:80–90.
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61. Tests in personality psychology are often judged by their levels of content-related, construct-related, and criterion validity (Borghans et al., 2008). Content-related validity concerns qualitative judgments by experts about whether a test adequately represents the psychological construct of interest. Construct-related validity refers to the degree to which a test actually measures what it claims to measure and is often assessed using factor analysis. Criterion validity concerns the ability of a test to predict contemporaneous and future outcomes. Finally, another important measure of test quality is reliability, as captured for example by test-retest correlations.
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62. The analysis is based on the method by Oster (2019) and compares the coefficient estimates and R 2 values from baseline regressions (Panel A) with those from regressions which additionally control for achievement in kindergarten (Panel B). Specifications in both panels are identical to those in Table 2. The lower rows in Panel B show estimates of δ, which is the ratio of the impact of unobservables to the impact of the controls for achievement in kindergarten that would drive the coefficient on motivation to zero. To compute δ, one needs to make an assumption about the hypothetical maximum R 2 achievable if all relevant controls were observed, the Rmax.
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63. The p-values in italics and brackets in the next row are corrected for multiple hypothesis testing using the procedure by Romano and Wolf (2005a,b).
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64. The public use file contains Stanford Achievement Test scores for all students who took these tests. However, it contains CTBS scores only for students who were on grade level, i.e. students who attended grade 5/6/7/8 in 1991/1992/1993/1994, respectively.
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65. This implies that test scores are not observed for a number of students who had been retained in grade by those years.16 Diane Schanzenbach generously provided me with a different version of the Project STAR data, which contains CTBS scores for students who attended grades 5-8 in Tennessee in any year between 1990 and 1997. Test scores are provided as scale scores, which are comparable across grade levels (Finn et al., 2007). In order to increase sample size, I define test scores for a given grade level as scores obtained in the school year in which participating students were supposed to be in that grade (e.g., eighth-grade scores are defined as scores obtained in 1994, even though some students were attending seventh grade in that year).
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66. Thus, Davis, Sellers, and Johnston (1988) analyzed the form’s questions using factor analysis and found that they could recover the motivation and self-concept subscales only after disregarding some of the questions, which casts doubt on its construct validity. Moreover, Online Appendix Table A.1 shows that kindergarten motivation scores do not predict any of the measures of educational success studied in the paper, indicating that it has very low (or indeed no) criterion validity.
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67. Whitmore, D. 2005. “Resource and Peer Impacts on Girls’ Academic Achievement: Evidence from a Randomized Experiment.” American Economic Review 95:199–204.
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68. Winter, D.G., O.P. John, A.J. Stewart, E.C. Klohnen, and L.E. Duncan. 1998. “Traits and motives: Toward an integration of two traditions in personality research.” Psychological Review 105:230–250.
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69. Wong, M.M., and M. Csikszentmihalyi. 1991. “Motivation and Academic Achievement: The Effects of Personality Traits and the Duality of Experience.”Journal of Personality 59:539– 574.
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35. Can Simple Psychological Interventions Increase Preventive Health Investment?. (2019). Orkin, Kate ; John, Anett ; Haushofer, Johannes.
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36. Some Contributions of Economics to the Study of Personality. (2019). Heckman, James ; Kautz, Tim ; Jagelka, Tomas.
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37. Parenting Values Moderate the Intergenerational Transmission of Time Preferences. (2019). Brenøe, Anne ; Epper, Thomas ; Brenoe, Anne Ardila.
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38. The Effects of Chess Instruction on Academic and Non-Cognitive Outcomes: Field Experimental Evidence from a Developing Country. (2019). Nicholas, Aaron ; Lee, Wang-Sheng ; Islam, Asad.
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39. Self-Control: Determinants, Life Outcomes and Intergenerational Implications. (2019). Schildberg-Hoerisch, Hannah ; Dahmann, Sarah C. ; Cobb-Clark, Deborah ; Schildberg-Horisch, Hannah ; Kamhofer, Daniel A.
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40. Collective intertemporal decisions and heterogeneity in groups. (2019). Glätzle-Rützler, Daniela ; Sutter, Matthias ; Lergetporer, Philipp ; Glaetzle-Ruetzler, Daniela.
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41. Some Contributions of Economics to the Study of Personality. (2019). Heckman, James ; Kautz, Tim ; Jagelka, Tomas.
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42. Economic behavior of children and adolescents – A first survey of experimental economics results. (2019). Sutter, Matthias ; Glatzle-Rutzler, Daniela ; Zoller, Claudia.
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43. Self-Control: Determinants, Life Outcomes and Intergenerational Implications. (2019). Schildberg-Hoerisch, Hannah ; Dahmann, Sarah C. ; Schildberg-Horisch, Hannah ; Kamhofer, Daniel A ; Cobb-Clark, Deborah A.
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44. Collective Intertemporal Decisions and Heterogeneity in Groups. (2019). Glätzle-Rützler, Daniela ; Sutter, Matthias ; Lergetporer, Philipp ; Glatzle-Rutzler, Daniela.
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45. Are economic preferences shaped by the family context? The impact of birth order and siblings’ sex composition on economic preferences. (2018). Sutter, Matthias ; Schmidt, Ulrich ; de Miranda, Katharina Lima ; Friedl, Andreas ; Detlefsen, Lena.
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46. Evaluating intergenerational persistence of economic preferences: A large scale experiment with families in Bangladesh. (2018). Zimmermann, Klaus ; Sutter, Matthias ; Chowdhury, Shyamal.
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47. Are Economic Preferences Shaped by the Family Context? The Impact of Birth Order and Siblings Sex Composition on Economic Preferences. (2018). Sutter, Matthias ; Schmidt, Ulrich ; de Miranda, Katharina Lima ; Friedl, Andreas ; Detlefsen, Lena.
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48. Goals and Gaps: Educational Careers of Immigrant Children. (2018). Carlana, Michela ; Pinotti, Paolo ; la Ferrara, Eliana ; Laferrara, Eliana .
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49. Financial Education in Schools: A Meta-Analysis of Experimental Studies. (2018). Menkhoff, Lukas ; Kaiser, Tim.
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50. Are Economic Preferences Shaped by the Family Context? The Impact of Birth Order and Siblings Sex Composition on Economic Preferences. (2018). Sutter, Matthias ; Schmidt, Ulrich ; de Miranda, Katharina Lima ; Friedl, Andreas ; Detlefsen, Lena.
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