Tackling Statistical Discrimination


Yes — there is such a thing as statistical discrimination (though it is technically an economic theory)! It is defined as ‘a theory of inequality between demographic groups based on stereotypes not arising from prejudice.’ It happens when rational, information-seeking decision markers, just like you and I, use observable characteristics of individuals, such as the physical traits that are used to categorise gender or race, as a proxy for otherwise unobservable characteristics. So in the absence of direct information about an individual’s productivity, qualifications, or even criminal background, a decision-maker may substitute group averages (either real or imagined) or stereotypes to fill the information void. This is labelled ‘statistical’ because stereotypes may be based on the discriminated group’s average behaviour.

Unlike other theories of discrimination, statistical discrimination does not assume any sort of animosity or even preference bias toward a particular race or gender on the part of the decision-maker. In fact, the decision-maker in statistical discrimination theory is considered to be a rational, information-seeking profit maximiser.

Statistical discrimination can happen when a woman is offered lower wages than a male counterpart because women are perceived to be less productive on average. The inequality can also occur as a result of the self-enforcing cycle of discrimination. The theory is that the individuals from the discriminated group are ultimately discouraged from higher performance on those outcome-relevant characteristics because of the existence of such ‘first moment’ statistical discrimination. For example, individuals from the discriminated group may be less likely to obtain the skills and education to equally compete with other candidates because their average or assumed return on investment from those activities is less than non-discriminated groups.

So, what are we doing about this?

The first step is to try to get as much information as possible! So, in January, at the first ever United Nations World Data Forum in Cape Town we published the DFID Data Disaggregation Action Plan. It sets out the practical steps we will take to ensure that everyone is counted in global development statistics. It will not be possible to realise delivery of the Global Goals and overcome statistical discrimination for everyone without considerable improvements to disaggregation. Our action plan sets the foundation for immediate short term change. It also sets out the actions we need to take as we work across the global development system to get tools, methods and standards in place to support a longer term change.

You can find out about our work on data by downloading the DFID Data Disaggregation Action Plan or by watching the short video above!

Kim Bradford Smith