How Do You Identify a Good Manager?

This paper develops a novel experimental method to identify the causal contribution of managers to team performance, and uses it to evaluate which characteristics predict managerial effectiveness and how manager selection mechanisms affect organizational outcomes.

The core identification challenge is that managers are not randomly assigned to teams in the field, and field managers are a highly non-random sample, making it difficult to infer which traits genuinely predict managerial performance. The authors address this by repeatedly randomly assigning managers to multiple teams in a controlled laboratory experiment, then estimating each manager’s average causal contribution to group output after conditioning on group members’ individual productive skills. The intuition is that a good manager is someone who consistently causes their team to produce more than the sum of their parts.

The experiment was conducted at the University of Essex lab with 555 participants (46% female, mean age 25, ethnically diverse) forming 728 groups of three across four rounds. Each group consisted of one manager and two workers who performed a Collaborative Production Task requiring coordination across three problem-solving modules (numerical, spatial, and analytical reasoning). The team score was the minimum module score — a weakest-link structure making coordination essential. Prior to group testing, all participants completed individual assessments of task-specific skill, fluid intelligence (CFIT), emotional perceptiveness (Reading the Mind in the Eyes Test, RMET), economic decision-making skill (the Assignment Game, which measures resource allocation under comparative advantage), Big 5 personality, and demographic characteristics. Manager selection was randomly varied at the session level: in 20 sessions, the participant with the strongest preference for leadership became manager (self-promotion); in 19 sessions, managers were assigned by lottery.

The main quantitative findings are as follows. First, there are large, stable, and statistically significant manager effects: a manager one standard deviation above average improves team performance by approximately 0.23 standard deviations (p = 0.04). This estimate is roughly 90% the size of the combined productive skill coefficient for the two workers (approximately 0.26 sd), indicating that a good manager is roughly twice as valuable as a good individual worker. Manager contributions predict out-of-sample group performance in a leave-one-out procedure (p < 0.01).

Second, among randomly assigned managers, only two predictors significantly explain managerial performance: fluid intelligence (CFIT) and economic decision-making skill (Assignment Game scores), both significant at below the 1% level. Gender, age, and ethnicity do not predict managerial performance.

Third, self-promoted managers perform substantially worse than lottery-assigned managers, by approximately 0.10 standard deviations — roughly equivalent to being assigned a manager with fluid intelligence one full standard deviation below average. The mechanism is overconfidence: people who strongly prefer management roles are significantly more overconfident (d = 0.41 sd, p < 0.01) and exhibit a strong negative correlation between self-reported social skills and actual emotional perceptiveness on the RMET (r = -0.37, p < 0.001). Among self-promoted managers, self-reported extraversion and political skill are negatively correlated with managerial performance (rho = -0.24 and -0.26, p < 0.05); no such negative relationship appears among lottery managers.

Fourth, selecting managers on economic decision-making skill rather than self-promotion improves average manager quality by 0.6 standard deviations — equivalent to replacing an average worker in every group with a worker at the 99th percentile of individual productivity.

The three mechanisms through which good managers improve performance are: (1) monitoring — good managers (1 sd above average) cut monitoring errors from 16% to 8%; (2) optimal task allocation according to comparative advantage — groups with optimally assigned workers score 0.52 sd higher (p < 0.01); (3) worker motivation in late-stage effort — teams led by a 1-sd-above-average manager solve 0.6 more problems in the final two minutes versus only 0.3 more in the first two minutes.

The experiment was conducted in a university lab in the UK, and the sample skews toward graduate students with limited work experience. Generalizability to field settings is supported by prior evidence that peer productivity spillover experiments yield similar magnitudes in lab versus field settings, and that the estimated manager effects are similar to Lazear et al. (2015) estimates from a large employer dataset.

Q: What is the core methodological innovation of this paper? A: The paper requires repeated random assignment of managers to multiple teams, combined with controls for individual productive skill measured prior to group work. This allows identification of each manager’s average causal contribution to group output, rather than confounding management quality with team composition or individual worker ability. The key estimand is the standard deviation of individual manager effects (sigma_alpha), interpreted as the impact of having a manager one standard deviation above average.

Q: How large is the estimated manager effect, and how does it compare to worker effects? A: A manager one standard deviation above average improves team performance by approximately 0.23 standard deviations (p = 0.04 by randomization inference). This is roughly 90% the size of the combined productive skill effect of both workers together (approximately 0.26 sd), implying a good manager is nearly twice as valuable as a good individual worker. Without conditioning on production skills, the manager effect rises to 0.29 sd.

Q: What characteristics predict managerial performance among randomly assigned managers? A: Only two measures predict managerial performance in the lottery arm: fluid intelligence (CFIT) and economic decision-making skill (scores on the Assignment Game), both significant at below the 1% level. These predictors are robust to controls for demographics, education, work experience, emotional perceptiveness, and personality traits. Gender, age, and ethnicity do not predict managerial performance.

Q: What is the “Assignment Game” and why is it a strong predictor? A: The Assignment Game (Caplin et al., 2024) places participants in a simulated managerial role where they must assign fictional workers to tasks. Performing well requires understanding comparative advantage intuitively, managing an attentionally demanding numerical environment, and avoiding biases such as anchoring. The paper argues its strong predictive power reflects that good managers excel at allocating workers according to comparative advantage — which the experiment directly identifies as a key mechanism.

Q: How do self-promoted managers perform relative to lottery-assigned managers? A: Self-promoted managers perform approximately 0.10 standard deviations below lottery managers, and this gap is robust across model specifications. The performance deficit is roughly equivalent to being assigned a manager whose fluid intelligence is one full standard deviation below average. This finding implies that common organizational practice of selecting managers partly via self-nomination actively reduces team productivity.

Q: Why do self-promoted managers underperform? A: The paper attributes underperformance primarily to overconfidence. People strongly preferring management roles are significantly more overconfident than those without strong preferences (d = 0.41 sd, p < 0.01). Self-promoted managers specifically overestimate their social skills: among them, self-reported people skills are strongly negatively correlated with actual emotional perceptiveness on the RMET (r = -0.37, p < 0.001), and self-reported extraversion and political skill are negatively correlated with managerial performance (rho = -0.24 and -0.26, p < 0.05). None of these negative relationships appear among lottery managers.

Q: Who wants to be a manager, and does it differ by gender? A: The three variables most strongly correlated with wanting to be in charge are extraversion, risk appetite, and being male. The relationship between high extraversion and preference for management is driven largely by men. Women are much less likely to nominate themselves for leadership roles despite being equally or more effective on average — a finding consistent with broader experimental evidence on gender and leadership self-selection.

Q: How large are the potential gains from skill-based manager selection? A: Compared to self-promotion, selecting managers based on economic decision-making skill yields managers who are 0.6 standard deviations better in terms of estimated manager effects. In terms of group performance, this is equivalent to replacing an average worker in every group with a worker at the 99th percentile of individual productivity. Selecting on both economic decision-making and fluid intelligence outperforms random assignment, selection on social skills, or selection on worker task performance (the Peter Principle).

Q: What are the three mechanisms through which good managers improve team performance? A: First, monitoring: good managers (1 sd above average) reduce monitoring errors — defined as having a worker on a module substantially above the minimum score at task end — from 16% to 8% (bivariate correlation with manager performance = -0.40, p < 0.001). Second, optimal task allocation: the probability of finding the optimal comparative-advantage-based assignment is positively associated with manager performance (rho = 0.19, p < 0.01), and groups with always-optimal starting assignments score 0.52 sd higher than those with never-optimal assignments (p < 0.01). Third, worker motivation: team performance in the final two-minute period is about 50% more influential for overall outcomes than the first two minutes (p = 0.038), and 1-sd-above-average managers generate 0.6 more problems solved in the final period versus 0.3 in the first, consistent with differential motivational effects emerging over time.

Q: What is the Peter Principle, and how does this paper relate to it? A: The Peter Principle refers to the practice of promoting employees based on their performance as line workers rather than their suitability for management — promoting individuals to their level of incompetence. Benson et al. (2019) document this selection pattern empirically. This paper shows that selecting managers on worker task skill is inferior to selecting on economic decision-making skill or fluid intelligence, confirming that task skill is not the right criterion for manager selection even if it predicts individual worker output.

Q: How does the paper validate that manager effects are real and not noise? A: The paper uses randomization inference with 5,000 simulated allocations to compute p-values, obtaining p = 0.04 for the main manager effect. Robustness checks include controlling for pre-existing social relationships, manager risk appetite, variance of individual scores, and granular skill measures — all yielding estimates near 0.22 sd. A leave-one-out out-of-sample prediction test confirms manager contributions significantly predict held-out group performance (p < 0.01), while the analogous worker out-of-sample estimate is less than half the magnitude and not statistically significant.

Q: What are the scope conditions on the experimental results? A: The experiment is conducted in a university lab in the UK with graduate students averaging 25 years of age and two years of work experience, limiting direct generalizability to experienced workers or senior management. The task lasts approximately 15 minutes, which may not capture longer-run managerial dynamics. Compensation equalized average earnings between managers and workers, which differs from most real-world settings. The authors note their effect-size estimates closely match Lazear et al. (2015) from a large employer, and that Herbst and Mas (2015) find lab peer-productivity experiments generalize to the field.

Manager Effect (sigma_alpha): The standard deviation of individual managers’ average causal contributions to group performance, estimated via repeated random assignment and conditioning on individual productive skill. Represents the impact of having a manager one standard deviation above average, estimated at approximately 0.23 standard deviations of group output.

Collaborative Production Task: A novel lab group task in which a manager and two workers solve problems across three modules (numerical, spatial, analytical reasoning), with team score defined as the minimum module score (weakest-link structure). Managers are responsible for worker assignment, monitoring, and motivation; workers face no financial performance incentives.

Economic Decision-Making Skill: Defined by Caplin et al. (2024) as the ability to make good resource allocation decisions, assessed via the Assignment Game in which participants must optimally assign workers to tasks under comparative advantage. The single strongest predictor of managerial performance in the lottery arm.

Monitoring Failure: Defined in the paper as having any group member working on a module at task end whose score is substantially greater (e.g., 10 points higher) than the minimum module score — meaning the worker’s effort is not contributing to the group score. Occurs in 16% of groups overall; managers one sd above average reduce this to 8%.

Self-Promotion (as selection mechanism): A treatment condition in which the participant with the strongest stated preference for being manager (on a 1-10 scale) is assigned the managerial role. Contrasted with lottery assignment; self-promoted managers perform approximately 0.10 sd worse than lottery managers.

Overconfidence (in managerial context): The gap between self-assessed skill (particularly social/interpersonal skill) and objectively measured skill (e.g., RMET score). Self-promoters are significantly more overconfident (d = 0.41 sd), and overconfidence is strongly negatively correlated with actual emotional perceptiveness (r = -0.33, p < 0.001).

Comparative Advantage Allocation: The practice of assigning each worker to the module in which they have the highest relative (not absolute) performance advantage. Captured via whether a manager selects the optimal one-to-one assignment given pre-measured individual module scores; groups with always-optimal allocation score 0.52 sd higher.