In a variety of social contexts, measuring merit or performance is a crucial step toward enforcing meritocratic ideals. At the same time, workable measures are bound to obfuscate the fuzziness and ambiguity of merit, i.e. to reify performance into an artificially crisp and clear-cut thing – such as a rating for example. This talk explores how the reification of employee performance in organizations breeds inequality in employee compensation. It reports the findings of a large-scale experiment asking participants to divide a year-end bonus between a set of employees based on the reading of their annual performance reviews. In the experiment’s non-reified condition, reviews are narrative evaluations. In the reified condition, the same narrative evaluations are accompanied by a crisp rating of the employees’ performance. I show that participants reward employees more unequally when performance is reified, even though employees’ levels of performance do not vary across conditions: the bonus gap between top- and bottom-performing employees increases by 20% between the non-reified and reified conditions; and it rises by another 10% when performance is presented as a quantified score. Further analyses suggest that reification acts by making participants more accepting of the idea that individuals are indeed more or less talented and valuable, thereby increasing their willingness to reward them unequally. This has direct implications for understanding the legitimacy of inequality in contemporary societies – and ultimately for working toward curbing this inequality.
Listen to podcast bellow:
How the Reification of Merit Breeds Inequality: theory and experimental evidence