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Pay inequity effects on back-office employees’ job performances: the case of a large insurance firm

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Abstract

Using personnel data on back-office employees of an insurance company, we investigate the effect of pay inequity—as measurable and, hence, perceivable by an individual—on the particular individual’s job performance. We calculate three measures of pay inequity, “inequality,” “envy,” and “altruism,” for total pay as well as, separately, for three pay components, salary, commissions, and year-end bonuses. Job performance is measured by the value of commissions for new contracts that accrue to the sales agents who are serviced by the back-office employee. Quantile panel regressions show that inequity effects on performance distinctly differ over the performance distribution and across the different pay components. Further, results depend on the time structure of pay and, possibly also, on pay secrecy rules.

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Notes

  1. Cohn et al. (2011), Gächter and Thöni (2010), and Stark and Hyll (2011) provide recent and more detailed discussions.

  2. Artz (2008) shows that the effect also varies with firm size. Using the same estimation technique as applied in Green and Heywood (2011), Pouliakas and Theodossiou (2009) reports no significant effect in their data.

  3. Compare the discussions in Gächter and Thöni (2010) and Grund and Westergaard-Nielsen (2008), for instance.

  4. Akerlof (1982) constitutes the seminal contribution.

  5. This terminology is well introduced in the business literature; compare e.g. Core (1997) and Wade et al. (2006) and the respective procedures to calculate “excess” pay.

  6. Nevertheless, for ease of discussion, we often use the term “effect” to denote such correlations.

  7. We have checked and found that using the more detailed educational information does not affect the significance levels of other variables or the model fit. To our minds, this feature confirms that the job does not require skills that are dependent on higher education.

  8. Choosing other deciles does not change our results qualitatively.

  9. Thus, our two-step procedure serves two goals: first, it provides an intuitive interpretation of the first-step random effects estimate residuals as “excess performances.” Second, these residuals are random variables themselves which can be subjected to the estimation technique proposed by Arulampalam et al. (2007). This technique involves a fixed-effect regression in its first step. Yet, recall that Hausman tests suggest the random effects model to estimate the productivity model.

  10. Chandra and Kaiser (2014) uses this approach to study the differential effects of reader homogeneity on magazines across various percentiles of the advertisement rate distribution. We wish to thank Ulrich Kaiser, University of Zurich, for letting us use his Stata routine for panel quantile regressions.

  11. Chandra and Kaiser (2014) discuss this argument.

  12. Inclusion of the inequity measures in the random effects regressions reported in Table 2 does not yield significant coefficients either.

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Authors and Affiliations

  1. Organization, Personnel and International Management (OPIM) Group, Faculty of Business, Economics and Statistics, Department of Business Administration, University of Vienna, Oskar-Morgenstern-Platz 1, 1090, Vienna, Austria

    Yingchao Zhang & Oliver Fabel

  2. Division of Entrepreneurship and Innovation, The Royal Institute of Technology, Lindstedtsvägen 30, 6 th floor, 10044, Stockholm, Sweden

    Christian Thomann

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  1. Yingchao Zhang
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  2. Oliver Fabel
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Correspondence to Yingchao Zhang.

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Zhang acknowledges that significant parts of her work were done while she was affiliated with the Institute of Statistics and Operations Research, Faculty of Business, Social and Economic Studies, University of Graz, Austria.

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Zhang, Y., Fabel, O. & Thomann, C. Pay inequity effects on back-office employees’ job performances: the case of a large insurance firm. Cent Eur J Oper Res 23, 421–439 (2015). https://doi.org/10.1007/s10100-015-0381-z

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