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Aggregate wages of players and performance in Italian Serie A

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Abstract

This paper investigates the relationship between players’ wages and sport performance in the Italian top professional football league—Serie A. The analysis focuses on 14 seasons from 2001/2002 to 2014/2015. Findings show that aggregate wage expenditure is a robust predictor of success for Italian professional football teams. We first exploited a fixed-effects panel data and eventually we addressed the problem of endogeneity by providing a dynamic IV specification of the model. Based on the System-GMM framework, we employed a model including lagged terms of dependent variables and covariates as instruments to control for endogeneity as well as alternative exogenous instruments to control for geographical/environmental factors and socio-economic factors that could be the actual predictors of performance through an indirect effect on payroll.

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Notes

  1. For details about the organization of Italian Serie A see Baroncelli and Caruso (2011).

  2. Note that Serie A seasons 2001/2002, 2002/2003, and 2003/2004, enrolled 18 teams; since 2004/2005 Serie A has enrolled 20 teams.

  3. The variable points_pct is included in the analysis at the natural log, therefore it is labelled log_points_pct henceforth.

  4. We are aware that players’ salaries are often negotiated over multi-year contracts, as shown by Buraimo et al. (2015). However, since our focus is on total payroll (and not on individual players’ wages) we believe that this feature is not affecting the main idea that higher payrolls reflect a larger availability of talent within the team.

  5. Data for the period 2002–2006 have been provided by G. Rossi. See Bryson et al. (2014) for further details.

  6. The Chi square statistics for the full model specification is 44.99, that allows for rejection of the null hypothesis with p < 0.01.

  7. The F statistic is 0.46, hence the null hypothesis of joint nullity of all the season-specific dummies cannot be rejected at conventional levels of statistical significance.

  8. As a further robustness check, first-differencing is also applied to PCSE estimator (PSAR1-FD) when presenting the main results.

  9. Caruso (2011) analyzes the relationship between crime and sport in Italy. Sironi and Bonazzi (2016) describe the long-lasting impact of crime on victims so preventing also new investments.

  10. The discussion about exogeneity of our instruments is postponed to the results’ section.

  11. First-order autocorrelation cannot be excluded instead, consistently with the dynamic framework of the model: we actually expect that sport success is serially (first-order) correlated. See, among others, Roodman (2009).

  12. The threshold, age*, is simply the maximum of the non-linear function, which is calculated as follows: \(age^{*} = e^{{\left( { - \frac{{\hat{\beta }_{3} }}{{2\hat{\beta }_{4} }}} \right)}}\) where \(\hat{\beta }_{3}\) and \(\hat{\beta }_{4}\) are the estimated coefficients respectively associated to the linear and non-linear terms of age.

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Authors and Affiliations

  1. Institute of Economic Policy and Center for Applied Economics (CSEA), Catholic University of the Sacred Heart, Milan, Italy

    Raul Caruso

  2. Department of Political Science, University of Teramo, Teramo, Italy

    Marco Di Domizio

  3. Department of Economics, Institutions and Development (DISEIS) and Cognitive Science and Communication Research Center (CSCC), Catholic University of the Sacred Heart, Milan, Italy

    Domenico Rossignoli

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  1. Raul Caruso
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  2. Marco Di Domizio
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  3. Domenico Rossignoli
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Correspondence to Domenico Rossignoli.

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Caruso, R., Di Domizio, M. & Rossignoli, D. Aggregate wages of players and performance in Italian Serie A . Econ Polit 34, 515–531 (2017). https://doi.org/10.1007/s40888-017-0062-6

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