Motivated statistical discrimination

While any kind of discrimination can have fatal consequences for the discriminated, a precise identification of the source of discrimination has important implications for effective policy interventions. Researchers typically categorize discrimination as either taste-based or statistical discrimination based on either accurate or inaccurate beliefs. Bohren et al. (2019) argue, that if discrimination stems from inaccurate beliefs instead of animus towards a particular group, a simple information treatment can mitigate discrimination and increase welfare. I suggest a more careful analysis of the formation of inaccurate beliefs. When an agent does not hold randomly inaccurate beliefs, but instead holds inaccurate beliefs due to motivated reasoning, resulting discrimination looks like inaccurate statistical discrimination when it really is a form of motivated discrimination. I show why it is important to separate motivated discrimination from taste-based or inaccurate statistical discrimination. In particular, I analyze the effect of biased information processing on discriminatory behavior and show that people selectively attend to and interpret information in line with their motives - if they have the necessary 'wiggle room' to do so. In that case, they update their beliefs in direction of their motives and ultimately discriminate based on these beliefs. I also show that limiting this 'wiggle room' can be an effective measure to fight motivated statistical discrimination.