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Dsicrimination. Overview. Discrimination is the situation where people get treated differently by the market because of the colour of their skin, their gender, their religion, sexual orientation or whatever even though these characteristics are irrelevant for the purpose being considered.
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Overview • Discrimination is the situation where people get treated differently by the market because of the colour of their skin, their gender, their religion, sexual orientation or whatever even though these characteristics are irrelevant for the purpose being considered. • In labour market context normally interested in discrimination in wages and employment. • a large number of theories of discrimination but I will only describe 2 of the most prominent • Becker’s theory of prejudice • Statistical discrimination
Becker’s Theory of Prejudice • based on the prejudice of agents in the labour market, employers, workers and customers. • sometimes called a ‘tastes’ theory. • idea is that some people are prejudiced so will be prepared to pay a price to avoid certain groups • this then affects the demand for labour of those groups.
A simple exampleEmployer Discrimination • Suppose some employers are prejudiced against black people so that, if black and white wages were the same, they would always prefer to employ white workers. • But they do care about profits so that, if the black wage is sufficiently below the white, the extra money they can save by employing black workers is sufficient to overcome their prejudice.
More formally • wage for white workers is Ww • wage for black workers is Wb • Firm f will choose to employ black workers if: • where df is the firm’s discrimination coefficient.
The discrimination coefficient • If df=0 the employer is not prejudiced and employs whichever group of workers is cheapest • a larger value of df implies the employer is more prejudiced.
The Market as a Whole • The market will have a white wage and a black wage • For given wages all firms with df below df* will employ only black workers, those with df above df* will employ only white workers • Where:
Overall demand for black workers is: • Overall demand for white workers is: • Market wages will equate supply to demand • What will equilibrium look like?
White wage /Black wage Relative Supply Relative Demand 1 White employment /Black employment Equilibrium with No Prejudice
Equilibrium with prejudice White wage /Black wage Relative Supply Relative Demand 1 White employment /Black employment
Consequences • Wages and employment lower for black workers • But also profits are lower for prejudiced employers as they only hire expensive white workers • Becker argued that competitive markets would drive out employer discrimination • Though not customer discrimination • Is this true?
Statistical Discrimination • in a world of imperfect information, employers will base decisions on observable characteristics (like race and gender) that do not directly affect productivity because they are correlated with unobserved characteristics that do affect productivity • Many variants of this theory – I will give one
A Simple Model • Suppose true productivity of a worker is p – assume this is not observed by employers • Employers do observe an imperfect signal of productivity (e.g. education), s, • they are assumed to know the distribution of p in the population as a whole.
Expected productivity of someone with signal s can be shown to be (don’t worry about the details): • μ is mean productivity in population as a whole • Θ is a measure of how informative the signal is • In a competitive labour market workers will receive a wage equal to their expected productivity
theory starts by assuming that θ is lower for whites than blacks. • The reasons why this should be the case are not entirely clear but lets not get into that. • Wages of whites and blacks will now be:
black and white workers with the same level of education will now get different wages so there is some discrimination • But average level of wages across will simply reflect the average level of productivity so there can only be discrimination overall if • If not true then educated black workers will get less than their white counterparts but uneducated black workers will get more with the average across all groups being zero.
This may not seem very interesting but there are reasons why average productivity may end up different • Consider choice of education • Suppose s is chosen to maximise: • This leads to first-order condition:
Implication • Education increasing in θ • If θ is lower for blacks then they will acquire less education and average productivity will be lower • In equilibrium blacks get paid less than whites because they have lower productivity but this is the outcome of a rational decision-making process in which they have lower returns to skills because employers find it harder to evaluate their skills.
key point in models of statistical discrimination • your fate is determined not just by what you do but by what people like you do. • This can lead to self-fulfilling expectations that are discriminatory in nature.
Empirical Evidence on Discrimination • Much empirical evidence on discrimination puts a dummy variable for gender or race in an earnings function, controls for lots of other things and pronounces that the coefficient on the race variable is a measure of discrimination. • this is not very credible evidence – one can always argue that important variables have been omitted or that some variables that are included should themselves be part of measured discrimination (e.g. occupation).
Audit Studies • These are experimental studies • These have a long history in academic research, even TV documentaries. • basic idea is to get two people to apply for jobs with identical (fake) CVs but who differ in race/gender. • One then observes what happens to them.
Problems with audit studies • Most studies of this type have very small samples because it is expensive, • there are concerns that the participants may be knowingly participating in the experiments producing the desired results. • Bertrand-Mullainathan found a clever way around this
Bertrand-Mullanaithan • Instead of sending out people they sent out cv’s. • this enabled them to: • more tightly control the information available to employers • to dramatically increase the sample size – they have about 5000 job applications. • Use black-sounding names to pick up discrimination • Treatment is correlated with being black but not perfectly so
Conclusions • But they also measure outcomes conditional on various other covariates • location, Boston, Chicago • Gender • occupation and industry • quality of cv • address of applicant • Their main conclusions are that: • those with ‘black’ names have lower call-back rates than those with ‘white’ names • this disadvantage is larger for those with better observable skills • there is little other systematic variation across other characteristics of the worker or job • it is hard for the theories described above to explain all of these findings.
Interpretation of the Estimates • two issues I want to single out for discussion • can one equate the effect of having a ‘black’ name to the effect of being black? • Is this really a randomised experiment?
What do we measure • denote by X the binary variable of having a ‘black’ name and B that of being ‘black’. • Suppose we can write the outcome equation as: • Here β1 is effect of being black, β2the additional effect of being black and having a black name and β3 the effect of having a black name if white (the omitted category is white with a white name).
Bertrand-Mullanaithan estimate the difference in outcomes for those with a black name and those without i.e. they estimate:
This is not directly interpretable • If it is only race that matters and name matters only because it is an indicator of race then we would have the restrictions β2=β3=0 • Then we have: • i.e. an under-estimate of the true effect of being black unless being black and having a black name are perfectly correlated.
Is it a true random experiment? • the assignment to the cv’s of the names is random so random from the perspective of the researchers. • But this does not mean the name will necessarily be regarded as random by the employers. • In fact the researchers don’t want it to be regarded as randomly assigned as they want the employers to infer something about the race of the applicant. • But perhaps the employer infers all sorts of other things from the name.
Fryer and Levitt • show that blacks with distinctively ‘black’ names come from lower socio-economic backgrounds, poorer neighbourhoods etc. • So, employers might be inferring a lot more than just race from these names. • If this is right then there will be an omitted variables bias.
Omitted Variables Bias • suppose that the outcome variable is related to two factors, W1 that is observable and W2 that is not according to: • One can assume that the name is independent of the observable characteristics but not necessarily the unobservable. • In this case Bertrand-Mullanaithan will estimate:
Implication • Only if the only omitted variable believed to be correlated with having a black name is being black will this be what we want to estimate. • Bertrand-Mullanaithan are aware of this problem and investigate the correlation between callback rates and the average mother’s education for the particular name not finding a significant effect. • But Fryer and Levitt, which is a non-experimental study find that controlling for factors observable at birth, having a ‘black’ name has little impact on a range of outcomes (albeit not all those one would like) in later life.
Other studies • Bertrand-Mullainathan study now been repeated in many countries • Always seem to find the name matters • Perhaps suggests discrimination in general not inferring economic disadvantage as that varies country to coountry
Conclusion • Bertrand and Mullainathan results provocative • However we interpret the results, we would hope that name would be irrelevant • They show it is not
