Gender differences in sabotage: the role of uncertainty and beliefs


We study gender differences in relation to performance and sabotage in competitions. While we find no systematic gender differences in performance in the real effort task, we observe a strong gender gap in sabotage choices in our experiment. This gap is rooted in the uncertainty about the opponent’s sabotage: in the absence of information about the opponent’s sabotage choice, males expect to suffer from sabotage to a higher degree than females and choose higher sabotage levels themselves. If beliefs are exogenously aligned by implementing sabotage via strategy method, the gender gap in sabotage choices disappears. Moreover, providing a noisy signal about the sabotage level from which subjects might suffer leads to an endogenous alignment of beliefs and eliminates the gender gap in sabotage.

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  1. 1.

    Surveyed evidence on gender differences in social and competitive preferences can be found, e.g., in Bertrand (2011) and Croson and Gneezy (2009).

  2. 2.

    Field studies rely on data from sports as reliable company data are not available for research due to the secret nature of sabotage acts (see, e.g., Balafoutas et al. 2012; del Corral et al. 2010; Deutscher et al. 2013; Garicano and Palacios-Huerta 2006; Preston and Szymanski 2003). In sports, information regarding relative performance is usually observable for all contestants.

  3. 3.

    The instructions, translated into English, can be found in "Appendix".

  4. 4.

    Several other contributions have used the same procedure but implement sabotage as a binary choice (Falk et al. 2008; Gürtler et al. 2013; Vandegrift and Yavas 2010). Implementing chosen sabotage has the advantage that sabotage is available to each contestant under the same conditions (e.g., costs, knowledge about the opportunity for sabotage). Keeping the awareness of the sabotage option equal in all treatments is crucial for a between treatment comparison. As our design relies on providing different information about sabotage between treatments, a more subtle form of sabotage, e.g., simply the option to misreport, would not be feasible in our design. If information about sabotage is provided in one treatment, this would also automatically increase the awareness of the option to misreport.

  5. 5.

    We use the sabotage choices from the info treatment as those subjects received the same information about relative performance as the subjects in the learning treatment. In the instructions, subjects were informed that they would receive information about sabotage choices from subjects who had the same information about relative performance.

  6. 6.

    Categories of relative performance and sabotage choices contained an approximately similar number of observations for all combinations of the two dimensions.

  7. 7.

    We elicited the belief about the unconditional choice of the opponent in the strategy treatment before subjects reported their own unconditional choice.

  8. 8.

    Suppose subjects A and B compete against each other and have chosen the unconditional sabotage choices \(s_{u}^{A}=10\) and \(s_{u}^{B}=20\), whereas the relevant conditional choices were \(s_{c}^{A}(20)=0\) and \(s_{c}^{B}(10)=40\). If subject A was randomly drawn, then the pair of sabotage choices \((s_{u}^{A},s_{c}^{B}(s_{u}^{A}))=(10,40)\) was implemented. If subject B was randomly drawn, \((s_{u}^{B},s_{c}^{A}(s_{u}^{B}))=(20,0)\) was implemented.

  9. 9.

    For a more detailed discussion about the benefits of repeated periods and experienced subjects, see, e.g., Harbring and Irlenbusch (2011) or Moffat (2016).

  10. 10.

    As feedback regarding relative performance did not enhance productivity in our sample, our results seem, at first glance, to contrast with previous findings (Azmat and Iriberri 2010; Eriksson et al. 2009; Fu et al. 2015; Kuhnen and Tymula 2012). However, Charness et al. (2013) report that sabotage can offset the positive effect of performance feedback, which might explain our finding.

  11. 11.

    Except for the baseline treatment, subjects were informed about the opponent’s performance before choosing sabotage such that subjects did not have to rely on a belief about the opponent’s performance. To keep the structure and payments constant between treatments, we elicited the belief about performance in all treatments. As displayed in the right panel of Fig. 1, the beliefs about performance are very similar across treatments and gender.

  12. 12.

    This finding indicates that the gender gap in sabotage documented by Dato and Nieken (2014) is not driven by uncertainty about own performance: in a setup where sabotage was selected prior to exerting effort, the authors demonstrated that males chose on average twice the amount of sabotage as females.

  13. 13.

    Unless stated otherwise, we estimated panel Tobit models with bootstrapped standard errors to account for the censoring of our data. To check the robustness of our results, we estimated double hurdle regressions to take the possible two-step structure of our data (first, the decision to sabotage or not and, second, the decision about the amount of sabotage) into account. The results are reported in Tables 10, 11, 12, 13 and 14 in Appendix.

  14. 14.

    In the info treatment, the sabotage choices might critically depend on the relative performance difference. Importantly, however, the gender gap in sabotage choices does not depend on the relative performance: using the same relative performance categories as in the learning treatment, the gap turns out to be highly significant in every performance category (\(p=0.004\) in trailing, \(p=0.007\) in close, and \(p=0.002\) in leading).

  15. 15.

    The information was displayed graphically to subjects (see Fig. 5 in Appendix for the original graphs). When reporting results from the category trailing (leading), we refer to the beliefs of trailing (leading) subjects in the learning treatment.

  16. 16.

    This result is also true if we split the dataset into relative performance categories as in the learning treatment. There is no significant gender gap in all relative performance categories (\(p=0.785\) for trailing, \(p=0.611\) for close, and \(p=0.212\) for leading).

  17. 17.

    To keep procedures and payoff as identical as possible between the treatments, we have elicited the belief about the opponent’s unconditional sabotage choice. The beliefs do not differ significantly between females and males (males 18.04 points vs. females 23.18 points; \(p=0.214\)). Although the belief is not relevant for any of the sabotage choices in the strategy treatment, this nicely complements the result of aligned sabotage choices between females and males.

  18. 18.

    Subjects are categorized as trailing, being in a close competition, or leading by implementing the same categories as in the learning treatment.

  19. 19.

    Again, the information was displayed graphically (see Figs. 6 and 7 in Appendix for the original graphs).

  20. 20.

    For the corresponding graphs depicting the empirical response functions, see Fig. 10 in Appendix for males and Fig. 11 in Appendix for females.


  1. Amegashie, J. A. (2015). Sabotage in contests. In R. D. Congleton & A. L. Hillman (Eds.), Companion to the political economy of rent seeking, 9 (pp. 138–149). Cheltenham: Edward Elgar.

    Google Scholar 

  2. Andreoni, J., & Miller, J. (2002). Giving according to garp: An experimental test of the consistency of preferences for altruism. Econometrica, 70(2), 737–753.

    Google Scholar 

  3. Azmat, G., & Iriberri, N. (2010). The importance of relative performance feedback information: Evidence from a natural experiment using high school students. Journal of Public Economics, 94(7), 435–452.

    Google Scholar 

  4. Azmat, G., & Petrongolo, B. (2014). Gender and the labor market: What have we learned from field and lab experiments? Labour Economics, 30, 32–40.

    Google Scholar 

  5. Balafoutas, L., Lindner, F., & Sutter, M. (2012). Sabotage in tournaments: Evidence from a natural experiment. Kyklos, 65(4), 425–441.

    Google Scholar 

  6. Bénabou, R., & Tirole, J. (2016). Mindful economics: The production, consumption, and value of beliefs. The Journal of Economic Perspectives, 30(3), 141–164.

    Google Scholar 

  7. Benistant, J., & Villeval, M. C. (2019). Unethical behavior and group identity in contests. Journal of Economic Psychology, 72, 128–155.

    Google Scholar 

  8. Bertrand, M. (2011). Chapter 17—New perspectives on gender. In D. Card & O. Ashenfelter (Eds.), Handbook of Labor Economics (Vol. 4b, pp. 1545–1592). Amsterdam: Elsevier.

    Google Scholar 

  9. Betz, M., O’Connell, L., & Shepard, J. M. (1989). Gender differences in proclivity for unethical behavior. Journal of Business Ethics, 8(5), 321–324.

    Google Scholar 

  10. Blau, F. D., & Kahn, L. M. (2017). The gender wage gap: Extent, trends, and explanations. Journal of Economic Literature, 55(3), 789–865.

    Google Scholar 

  11. Bock, O., Baetge, I., & Nicklisch, A. (2014). hroot: Hamburg registration and organization online tool. European Economic Review, 71, 117–120.

    Google Scholar 

  12. Bolton, G. E., & Ockenfels, A. (2000). ERC: A theory of equity, reciprocity, and competition. American Economic Review, 90(1), 166–193.

    Google Scholar 

  13. Bordalo, P., Coffman, K., Gennaioli, N., & Shleifer, A. (2019). Beliefs about gender. American Economic Review, 109(3), 739–773.

    Google Scholar 

  14. Borghans, L., Heckman, J. J., Golsteyn, B. H. H., & Meijers, H. (2009). Gender differences in risk aversion and ambiguity aversion. Journal of the European Economic Association, 7(2–3), 649–658.

    Google Scholar 

  15. Brandts, J., & Charness, G. (2011). The strategy versus the direct-response method: A first survey of experimental comparisons. Experimental Economics, 14(3), 375–398.

    Google Scholar 

  16. Brink, W. D., Eaton, T. V., Grenier, J. H., & Reffett, A. (2019). Deterring unethical behavior in online labor markets. Journal of Business Ethics, 156(1), 71–88.

    Google Scholar 

  17. Carpenter, J., Matthews, P. H., & Schirm, J. (2010). Tournaments and office politics: Evidence from a real effort experiment. American Economic Review, 100(1), 504–17.

    Google Scholar 

  18. Charness, G., & Levine, D. I. (2010). When is employee retaliation acceptable at work? Evidence from quasi-experiments. Industrial Relations, 49(4), 499–523.

    Google Scholar 

  19. Charness, G., Masclet, D., & Villeval, M. C. (2013). The dark side of competition for status. Management Science, 60(1), 38–55.

    Google Scholar 

  20. Charness, G., & Rabin, M. (2002). Understanding social preferences with simple tests. The Quarterly Journal of Economics, 117(3), 817–869.

    Google Scholar 

  21. Chowdhury, S. M., & Gürtler, O. (2015). Sabotage in contests: A survey. Public Choice, 164, 135–155.

    Google Scholar 

  22. Conrads, J., Irlenbusch, B., Rilke, R. M., Schielke, C., & Walkowitz, G. (2014). Honesty in tournaments. Economics Letters, 123(1), 90–93.

    Google Scholar 

  23. Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of Economic Literature, 47(2), 448–474.

    Google Scholar 

  24. Dato, S., & Nieken, P. (2014). Gender differences in competition and sabotage. Journal of Economic Behavior & Organization, 100, 64–80.

    Google Scholar 

  25. Davis, S. F., Grover, C. A., Becker, A. H., & McGregor, L. N. (1992). Academic dishonesty: Prevalence, determinants, techniques, and punishments. Teaching of Psychology, 19, 16–20.

    Google Scholar 

  26. del Corral, J., Prieto-Rodriguez, J., & Simmons, R. (2010). The effect of incentives on sabotage: The case of spanish football. Journal of Sports Economics, 11(3), 243–260.

    Google Scholar 

  27. Deutscher, C., Frick, B., Gürtler, O., & Prinz, J. (2013). Sabotage in tournaments with heterogeneous contestants: Empirical evidence from the soccer pitch. The Scandinavian Journal of Economics, 115(4), 1138–1157.

    Google Scholar 

  28. Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., & Wagner, G. G. (2011). Individual risk attitudes: Measurement, determinants, and behavioral consequences. Journal of the European Economic Association, 9(3), 522–550.

    Google Scholar 

  29. Dreber, A., & Johannesson, M. (2008). Gender differences in deception. Economics Letters, 99, 197–199.

    Google Scholar 

  30. Eriksson, T., Teyssier, S., & Villeval, M.-C. (2009). Self-selection and the efficiency of tournaments. Economic Inquiry, 47(3), 530–548.

    Google Scholar 

  31. Erkal, N., Gangadharan, L., & Nikiforakis, N. (2011). Relative earnings and giving in a real-effort experiment. American Economic Review, 101(7), 3330–3348.

    Google Scholar 

  32. Falk, A., Fehr, E., & Huffman, D. B. (2008). The power and limits of tournament incentives. Working paper.

  33. Fang, F., Bennett, J. W., & Casadevall, A. (2013). Males are overrepresented among life sicence researchers committing scientific misconduct. mBio, 4, 1–3.

    Google Scholar 

  34. Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3), 817–868.

    Google Scholar 

  35. Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10, 171–178.

    Google Scholar 

  36. Fischbacher, U., & Föllmi-Heusi, F. (2013). Lies in disguise—An experimental study on cheating. Journal of the European Economic Association, 11(3), 525–547.

    Google Scholar 

  37. Fischbacher, U., Gächter, S., & Fehr, E. (2001). Are people conditionally cooperative? Evidence from a public goods experiment. Economics Letters, 71(3), 397–404.

    Google Scholar 

  38. Flory, J. A., Leibbrandt, A., & List, J. A. (2015). Do competitive workplaces deter female workers? A large-scale natural field experiment on job entry decisions. The Review of Economic Studies, 82(1), 122–155.

    Google Scholar 

  39. Flory, J. A., Leibbrandt, A., & List, J. A. (2016). The effects of wage contracts on workplace misbehaviors: Evidence from a call center natural field experiment. Working paper.

  40. Fu, Q., Ke, C., & Tan, F. (2015). Success breeds succes or pride goes before a fall? Teams and individuals in multi-contest tournaments. Games and Economic Behavior, 94, 57–79.

    Google Scholar 

  41. Garicano, L., & Palacios-Huerta, I. (2006). Sabotage in tournaments: Making the beautiful game a bit less beautiful. CEPR discussion paper no. 5231.

  42. Gneezy, U., Niederle, M., & Rustichini, A. (2003). Performance in competitive environments: Gender differences. The Quarterly Journal of Economics, 118(3), 1049–1074.

    Google Scholar 

  43. Gürtler, O., Münster, J., & Nieken, P. (2013). Information policy in tournaments with sabotage. The Scandinavian Journal of Economics, 115(3), 932–966.

    Google Scholar 

  44. Harbring, C., & Irlenbusch, B. (2005). Incentives in tournaments with endogenous prize selection. Journal of Institutional and Theoretical Economics JITE, 161(4), 636–663.

    Google Scholar 

  45. Harbring, C., & Irlenbusch, B. (2008). How many winners are good to have? On tournaments with sabotage. Journal of Economic Behavior & Organization, 65(3), 682–702.

    Google Scholar 

  46. Harbring, C., & Irlenbusch, B. (2011). Sabotage in tournaments: Evidence from a laboratory experiment. Management Science, 57(4), 611–627.

    Google Scholar 

  47. Hermann, B., & Orzen, H. (2008). The appearance of homo rivalis: Social preferences and the nature of rent seeking. CeDEx discussion paper no. 2008–10.

  48. Kuhnen, C. M., & Tymula, A. (2012). Feedback, self-esteem and performance in organizations. Management Science, 58(1), 94–113.

    Google Scholar 

  49. Leibbrandt, A., Wang, L. C., & Foo, C. (2017). Gender quotas, competition, and peer review: Experimental evidence on the backlash against women. Management Science, 64, 3501–3516.

    Google Scholar 

  50. Lundeberg, M. A., Fox, P. W., & Punćcohaŕ, J. (1994). Highly confident but wrong: Gender differences and similarities in confidence judgments. Journal of Educational Psychology, 86(1), 114.

    Google Scholar 

  51. Moffat, P. G. (2016). Experimetrics. Basingstoke: Palgrave.

    Google Scholar 

  52. Muehlheusser, G., Roider, A., & Wallmeier, N. (2015). Gender differences in honesty: Groups versus individuals. Economics Letters, 128, 25–29.

    Google Scholar 

  53. Niederle, M., & Vesterlund, L. (2007). Do women shy away from competition? Do men compete too much? The Quarterly Journal of Economics, 122(3), 1067–1101.

    Google Scholar 

  54. Papadopoulos, F. C., Skalkidis, I., Parkkari, J., & Petridou, E. (2006). Doping use among tertiary education students in six developed countries. European Journal of Epidemiology, 21, 307–313.

    Google Scholar 

  55. Preston, I., & Szymanski, S. (2003). Cheating in contests. Oxford Review of Economic Policy, 19(4), 612–624.

    Google Scholar 

  56. Price, C. R. (2012). Gender, competition, and managerial decisions. Management Science, 58(1), 114–122.

    Google Scholar 

  57. Rigdon, M. L., & D’Esterre, A. P. (2015). The effects of competition on the nature of cheating behavior. Southern Economic Journal, 81(4), 1012–1024.

    Google Scholar 

  58. Selten, R. (1967). Die strategiemethode zur erforschung des eingeschränkt rationalen verhaltens im rahmen eines oligopolexperiments. In H. Sauermann (Ed.), Beiträge zur experimentellen Wirtschaftsforschung (pp. 136–168). Tübingen: Mohr.

    Google Scholar 

  59. Sheremeta, R. M. (2010). Experimental comparison of multi-stage and one-stage contests. Games and Economic Behavior, 68(2), 731–747.

    Google Scholar 

  60. Vandegrift, D., & Yavas, A. (2010). An experimental test of sabotage in tournaments. Journal of Institutional and Theoretical Economics JITE, 166(2), 259–285.

    Google Scholar 

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We would like to thank the participants of the Economic Science Association Meeting, Heidelberg, SFB-TR 15 Young Researcher Workshop, Bonn, Stavanger Workshop on Incentives and Motivation, Stavanger, Economics Seminar Series University of East Anglia, Norwich, ULME Economics Seminar, University Ulm, 20th Colloquium on Personnel Economics, Zurich, EEA-ESEM 2018, Cologne, and in particular Subhasish Chowdhury, Matthias Kräkel, Anders Poulsen, Ed Lazear, Bettina Rockenbach, Dirk Sliwka, and Robert Sudgen for their helpful comments as well as Niklas Wagner for programming the experimental software. Financial support by the Deutsche Forschungsgemeinschaft (DFG), Grant SFB/TR 15, is gratefully acknowledged

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See Tables 7, 8, 9, 10, 11, 12, 13, 14 and Figs. 5, 6, 7, 8, 9, 10, 11.

Table 7 Panel Tobit regressions with pooled data of mixed gender sessions of the baseline and info or the info and learning treatments, respectively
Fig. 5

Information shown to subjects in the learning treatment (mixed gender sessions)

Fig. 6

Information shown to male subjects in single genders sessions of the learning treatment

Fig. 7

Information shown to female subjects in single genders sessions of the learning treatment

Fig. 8

Average performance over periods (mixed gender sessions)

Fig. 9

Average sabotage over periods (mixed gender sessions)

Fig. 10

Empirical Response Function of males in the strategy treatment (single gender sessions)

Fig. 11

Empirical Response Function of females in the strategy treatment (single gender sessions)

Table 8 Panel Tobit regressions regressions for the info and learning treatments (mixed gender sessions) with beliefs about sabotage as the dependent variable
Table 9 Tobit regressions with subject averages of sabotage (unconditional choice) as the dependent variable and controls for risk attitude, social preferences, lying, and status seeking
Table 10 Double hurdle regressions for the baseline, info treatment, and learning treatments (mixed gender sessions) with belief about sabotage as the dependent variable
Table 11 Double hurdle regressions for the baseline and info treatments (mixed gender sessions) with sabotage as the dependent variable
Table 12 Double hurdle regressions for the learning and strategy treatments (mixed gender sessions) with sabotage as the dependent variable
Table 13 Double hurdle regressions for the baseline and info treatments (mixed gender sessions) with sabotage as the dependent variable
Table 14 Double hurdle regressions for the info and learning treatments (mixed gender sessions) with beliefs about sabotage as the dependent variable

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Dato, S., Nieken, P. Gender differences in sabotage: the role of uncertainty and beliefs. Exp Econ 23, 353–391 (2020).

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  • Gender
  • Sabotage
  • Tournament
  • Belief formation

JEL Classification

  • J16
  • M12
  • C91