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Sunk costs in the NBA: the salary cap and free agents

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The previous research on the role sunk costs play in the National Basketball Association (NBA) has come to mixed conclusions. We use a direct measure of sunk costs, player compensation, and a quasi-experimental method that considers the endogenous relationship between compensation and productivity. Using the spike in salary cap for the 2016–2017 season, from new television broadcasting contracts, we find compensation has a significant effect on playing time. Specifically, we use players signing free agent contracts in 2015–2016 or 2016–2017. For both groups, we analyze the change in salary from the pre-contract season to the post-contract season. Using the difference-in-differences, we find the inflated salary cap, from the new television contracts, increased player compensation by 81.7%, on average. Instrumental variables estimations show the increase in compensation significantly affects playing time. The change in salary yields an additional 1.93 min played per game, which is approximately equal to the effect of a one-standard deviation increase in contemporaneous productivity. Furthermore, we find that our conclusions are robust to the use of various advanced analytical measures or traditional box-score statistics.

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  1. In the estimation sample, the correlations are 0.84, 0.73, and 0.71 for w48 and per, ws48 and vorp, and per and vorp respectively. .

  2. Aggregate measures of performance in the NBA have been criticized for their accuracy (see Berri and Schmidt 2010 for example). While these measures have been shown to not be highly correlated with winning, there is evidence that they, especially player efficiency rating, are closely related to people’s beliefs about performance (Berri and Schmidt 2010). For our purposes, we seek to control for expected or perceived productivity; thus, aggregate measures, like player efficiency rating, may be appropriate. Regardless, we present results using aggregate measures and individual box-score measures to analyze robustness. .

  3. Estimations controlling for experience, as a quadratic function, only yield similar results. Experience is identified, since several players appear in the estimations in three periods, they were free agents in both 2015–2016 and 2016–2017. These estimations are available from the author. .

  4. First-difference estimations yield similar results and are available from the author. .

  5. Utilizing clustered bootstrap standard errors, as suggested by Bertrand et al. (2004), the standard error for our difference-in-differences coefficient is 0.193, 0.204, 0.197, and 0.207 for ws48, per, vorp, and box-score measures respectively, all remaining significant at the 1% level. .

  6. Controlling only for a quadratic in experience the inflated cap led to a 60.1% increase in salary. .

  7. Controlling only for a quadratic in experience the increase in playing time for 2016–2017 free agents is 1.67 min per game greater than the increase for 2015–2016 free agents.

  8. Using the first-difference transformation to control for player fixed effects yields estimates of 3.28, 3.46, 3.08, and 1.89 for ws48, per, vorp, and box-score measures, respectively. Full estimation results are available upon request. .


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Correspondence to Quinn Keefer.

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See Tables 10 and 11.

Table 10 Luxury tax.
Table 11 Player comparison

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Keefer, Q. Sunk costs in the NBA: the salary cap and free agents. Empir Econ 61, 3445–3478 (2021).

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