Managerial Ability and Labor Investment
Bibliographic record
Abstract
The capability of higher-ability managers to acquire and use resources more efficiently than lower-ability managers suggests a positive linear relation between labor investment efficiency (LIE) and managerial ability (MA). However, a puzzle emerges about how the best managers set themselves apart from their peers, calling into question the linearity of the relation between LIE and MA. We explore this puzzle by asking how the highest-ability managers achieve the highest performance levels. We then investigate this puzzle empirically by considering alternatives to a linear relation between LIE and MA. We begin with the distinction that managers achieve the highest performance when they combine efficient exploitation of existing products and services with successful exploration for innovations in products and services. This point is relevant to our puzzle because a firm’s labor needs for exploration are high and unpredictable. Thus, we expect the highest-ability managers to purposefully invest more than predicted by a model of optimal labor investment across firms. In contrast, we expect low-ability managers, who are less able to evaluate, forecast, and make efficient investments, to deviate more from predicted labor investment and vacillate between over- and underinvestment. We present evidence that supports our predictions of nonlinear relations between LIE and MA, with high-ability managers investing more than predicted and low-ability managers over- and underinvesting. We make and test related hypotheses about exploration (investment in research and development), and we probe further by relating future firm performance to over- and underinvestment in labor for different levels of MA. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by the CPA Alberta Education Foundation. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2020.01932 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".