Macro Roundup

Core of paper, “…Music training has repeatedly been claimed to positively impact children’s cognitive skills and academic achievement (literacy and mathematics). This claim relies on the assumption that engaging in intellectually demanding activities fosters particular domain- general cognitive skills, or even general intelligence. The present meta-analytic review (N = 6,984, k = 254, m = 54) shows that this belief is incorrect. Once the quality of study design is controlled for, the overall effect of music training programs is null (𝑔̅≈ 0) and highly consistent across studies (τ2 ≈ 0). Results of Bayesian analyses employing distributional assumptions (informative priors) derived from previous research in cognitive training corroborate these conclusions. Small statistically significant overall effects are obtained only in those studies implementing no random allocation of participants and employing non-active controls (𝑔̅≈ 0.200, p <.001). Interestingly, music training is ineffective regardless of the type of outcome measure (e.g., verbal, non-verbal, speed-related, etc.), participants’ age, and duration of training. Furthermore, we note that, beyond meta-analysis of experimental studies, a considerable amount of cross-sectional evidence indicates that engagement in music has no impact on people’s non-music cognitive skills or academic achievement. We conclude that researchers’ optimism about the benefits of music training is empirically unjustified and stems from misinterpretation of the empirical data and, possibly, confirmation bias…”

Supporting Evidence

“…This meta-analysis - which includes 38 studies, 118 effect sizes, and 3,085 participants - found an overall effect of𝑑̅= 0.16. It also highlighted that the impact of music training on cognitive skills and academic performance was a function of the quality of the study’s experimental design. Specifically, the magnitude of the music-induced effects was significantly smaller (around zero) in those studies implementing active controls and random allocation of the participants to groups….”

“…The overall effect size of the RVE intercept model was𝑔̅= 0.184, SE = 0.041, 95% CI, m = 54, k = 254, df = 38.36, p <.001, τ2 = 0.041, I2 = 43.16%. Different values of within-study effect-size correlation (ρ) did not significantly affect the results (𝑔̅range 0.184- 0.185, τ2 = 0.041). The random-effect (RE) model (with Cheung & Chan’s correction) yielded very similar estimates:𝑔̅= 0.176, SE = 0.037, p <.001, τ2 = 0.033. Overall, the results showed a small and moderately heterogeneous overall effect of music training on cognitive and academic outcomes. The results were not affected by modeling choices (i.e., ρ values and procedure for modeling nested data)…. Baseline difference and Type of controls were the only two statistically significant moderators (p =.031 and p =.035, respectively) and accounted for part of the true heterogeneity (τ2 = 0.038, I2 = 34.87%). Age was not significant (p =.403), neither was Allocation (p =.518). No significant differences were found across the four broad groups of outcome measures (all ps ≥.624; Holm’s correction for multiple comparisons), nor across the more fine-grained categorization (eight levels, all ps ≥.362), and there was no difference between cognitive skills and measures of academic achievement (p =.981). Duration of training was not significant either (p =.266, p =.952, and p =.662 for hours, weeks, and sessions, respectively).

Giovanni Sala and Fernand Gobet, "Cognitive and academic benefits of music training with children: A multilevel meta-analysis,"Memory & Cognition, July 29, 2020, https://link.springer.com/article/10.3758/s13421-020-01060-2

Paper finds no evidence that participation in high school sports has positive spillovers on wages, LFP or college attendance, highly correlated but its all self-selection. Core of paper, "...We examine the extent to which participation in high school athletics has beneficial effects on future education, labor market, and health outcomes. Due to the absence of plausible instruments in observational data, we use recently developed methods that relate selection on observables with selection on unobservables to estimate bounds on the causal effect of athletics participation. We analyze these effects in the US separately for men and women using three different nationally representative longitudinal data sets that each link high school athletics participation with later-life outcomes. We do not find consistent evidence of individual benefits reported in many previous studies - once we have accounted for selection, high school athletes are no more likely to attend college, earn higher wages, or participate in the labor force. However, we do find that men (but not women) who participated in high school athletics are more likely to exercise regularly as adults. Nevertheless, athletes are no less likely to be obese...."
Their evidence

Positive outcomes driven by selection,"...Athletic participation is strongly positively correlated with a number of outcomes—including high school graduation, college attendance, college graduation, wages, exercise habits, and absence of obesity—but we find that this correlation is almost completely due to selection. For most of the outcomes that we consider, we find that even if the correlation between athletic participation and unobservable characteristics is a small fraction of the correlation between athletic participation and observable characteristics, then there is no effect of sports.Across several different outcomes and different samples, we find no consistent benefit from high school sports. However, in a few cases that we discuss below, we do find statistically significant effects from sports participation that are arguably causal...."

“…In Table 1 and Table 2 we present basic descriptive statistics of athletes and non-athletes for men and women, respectively. Each table contains a list of outcomes and control variables from each survey, along with the respective sample means for non-athletes, athletes, and the full sample. We denote with an asterisk sample means of athletes that are statistically different from non-athletes at the 5% level. The descriptive statistics provide a high-level understanding of how selection on observables and unobservables might mitigate the effects of sports. We divide the variables into three categories: background characteristics, school characteristics, and outcomes…Athletes tend to have higher cognitive test scores, be disproportionately white, have parents with higher levels of education, be more likely to co-reside with parents, and come from homes with higher incomes. In short, our basic summary statistics reveal that athletes are strongly positively selected on personal and family background traits….On the school side, athletes are less likely to be absent from school, more likely to be found in private schools and schools with smaller student bodies, more likely to be found in rural schools, and more likely to attend schools that are more racially segregated. These results hold for both men and women, and are in line with existing literature and theory. Namely, overwhelmingly white, private, and rural schools provide more opportunities for student athletes, for whatever reason. Possible explanations include differences in school funding, or that it is statistically easier to make the team at a school with a smaller student body….In addition to observing that athletes have different background and school contexts, we also observe that athletes have very different adult outcomes. They attain higher levels of education, measured either by grades completed or degrees attained. Athletes also earn more as adults: about 15 percent higher wages for men, and about 12 percent higher wages for women. Athletes are much more likely to report exercising regularly. Male athletes are neither more nor less likely to be obese as adults, while female athletes are much less likely to be obese. Athletes of both genders report a higher frequency of alcohol abuse as adults….The results in Tables 1 and 2 are striking in that the different surveys exhibit not only the same sign of sports effects, but also many of the same magnitudes, in spite of the fact that athletic participation is measured quite differently across the three surveys….”
Educational outcomes

“….Tables 4 and 5 contain estimates of α, the effect of sports participation on educational outcomes, from all three surveys, respectively for men and women. The first two rows of the table report OLS estimates, while the remaining rows report RCR estimates. Consistent with the large body of prior research on the topic, as well as the evidence reported in Tables 1 and 2, when we assume that students are randomly assigned to be athletes, we find economically large and statistically significant effects of sports participation on high school graduation, college attendance, and college graduation. Furthermore, the inclusion of explanatory variables dramatically reduces the size of the estimated effect, by more than half in some cases. The estimated effects are slightly smaller for women, but the general pattern is very similar.....thus, the correlation between sports participation and college graduation propensity for men in the NELS:88 is robust to a large amount of selection on unobservables, although we would not reject a null hypothesis of a null effect if λ = 1. On the other hand, for women we find little evidence to support a causal effect of sports participation on the educational outcomes that we examine. For all three of these samples, even if the correlation with the unobservables is only half as much as the correlation with the observables, there is no beneficial effect of sports participation….”

“…Tables 8 and 9 contain estimates for our measures of health and risky behaviors: regular exercise, obesity, and alcohol abuse. In the case of regular exercise, both surveys indicate that male athletes are more likely to participate in regular adult exercise. Interestingly, after controlling for observables, the athlete/non-athlete difference does not change much (and actually increases in the Add Health sample). This result is graphically depicted in Figure 1 (b) for the NELS:88….”

Their takeaway, "...Our largely null results inform the policy debate on high school sports by providing evidence against claims that sports foster skills that improve educational or labor market outcomes. However, despite having very little human capital value, sports may still have a place in high school as a social or cultural activity. We generally confirm the assertion of sports commentator Heywood Hale Broun, who said, “Sports reveals character, it doesn’t build it”

Michael Ransom and Tyler Ransom, "Do High School Sports Build or Reveal Character?Institute of Labor Economics, October 2017, https://docs.iza.org/dp11110.pdf