Revealing implicit stereotypes

Alberto Alesina, Michela Carlana, Eliana La Ferrara, Paolo Pinotti 02 February 2019

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Anti-immigrant stereotypes are widespread in most contemporary societies (Alesina et al. 2018). These negative stereotypes are party due to misinformation and partly to rejection of diversity, especially in counties which had been historically homogenous and now receive a large influx of immigrants. Negative stereotyping may lead to discrimination and, possibly, self-fulfilling prophecies by influencing the behaviour of discriminated groups in the direction predicted by stereotypes (Glover et al. 2017, Carlana 2019). In recent years, employees of several corporations and academic institutions have been encouraged to take Implicit Association Tests (IAT) to reveal possible gender-based or racially based stereotypes or to participate in implicit-bias training aimed at increasing awareness of unconscious associations. 

Immigrant children in schools are particularly vulnerable to stereotypes, which may induce them to undertake suboptimal decisions impacting their future careers and well-being. In a recent paper (Alesina et al. 2018), we study the impact of revealing IAT scores to teachers by randomising the timing of disclosure around the date on which they assign term grades. We focus on the Italian context, where mass migration is relatively recent and politically salient, and we collect a unique dataset merging a survey with around 1,400 teachers with administrative data on student outcomes.

Stereotypes: Implicit Association Tests

We try to measure implicit stereotypes using an Implicit Association Test (IAT). This is a computer-based tool, developed by social psychologists, which exploits the reaction time to associations between positive/negative adjectives and native/immigrant names (Greenwald et al. 1995). Recently, IAT scores have also been used by economists when studying race and gender discrimination. The scores predict (not perfectly of course) relevant choices and behaviours in lab experiments and in real-world interactions (Rooth 2010, Glover et al. 2017, Corno et al. 2018).

Over 67 % of the teachers in our sample exhibit moderate to severe degree of associations between immigrant-bad and native-good, i.e. a score greater than 0.35 according to the typical thresholds in the literature (Greenwald et al. 2009), while almost no teachers exhibit the opposite associations.

Teacher-assigned grades and IAT

Immigrant students in Italy receive lower teacher-assigned grades compared to native Italian students, holding constant performance on standardised, blindly graded tests.1  

In principle, lower grades to immigrants may reflect differences in unobservable characteristics compared to natives, which are captured by teacher assigned-grades but not by multiple choice, standardised test scores. The key here is that now we can relate differences between blind and non-blind grades to teachers’ negative biases. In particular, we find that math teachers who are implicitly biased against immigrants give immigrant student lower grades compared to native students, keeping constant their performance in standardised tests. This is not true for literature teachers. 

We suggest two non-mutually exclusive explanations for this difference. First, multiple choice standardised tests may be ill-suited to measure skills evaluated by literature teachers. Second, taking into account the additional difficulties faced by non-native speakers in their subject, literature teachers may impose lower standards on immigrant than on native students. This latter explanation is consistent with additional evidence on differential grading of first- and second-generation immigrants. 

Revealing implicit stereotypes

We designed an experiment aimed at understanding whether increasing awareness of own stereotypes affects teachers’ behaviour. We administer this intervention in 65 schools in Italy, with  6,031 students in grade 8 in the school year 2016-2017 and their 533 teachers (262 in math and 271 in literature).2

We offered the option of receiving feedback on the IAT score by email to all teachers in our sample, and more than 80% of teachers chose to receive it. Teachers in half of the schools (the treated group) received the feedback before the end-of-semester grading, which took place at the end of January 2017. Teachers in the remaining schools (the control group) received the feedback within two weeks after end-of-semester grading. The timing of feedback was randomised across schools to avoid contamination between teachers in the treatment and control groups. 

Receiving the feedback on the IAT before grading shifts the grade in favour of immigrant students. In particular, math teachers eligible for receiving the feedback on the IAT score give on average 0.25 points more to immigrants and 0.15 points less to natives, compared to teachers randomised into the control group. The effect on grading of immigrant students in literature is qualitatively similar, but a bit smaller. 

Furthermore, we elicit teachers’ explicit bias by asking them if immigrant and natives should have the same right to jobs. We find that for both math and literature teachers the effect is driven by individuals that do not report explicit views against immigrants (i.e. who say that immigrants and natives should have the same right to jobs). This suggests that teachers actually react to being revealed as having a bias they were unaware of. 

Our results imply that finding out one’s own IAT score helps to counteract biased behaviour, but it could also induce positive discrimination by teachers whose negative stereotypes do not translate into discriminatory behaviour. Furthermore, the part of the population that responds to the intervention is only that which does not report explicit views against immigrants, suggesting that revealing stereotypes to someone who already explicitly acknowledges it is ineffective. 

References

Alesina, A, M Carlana, E La Ferrara and P Pinotti (2018), “Revealing stereotypes: Evidence from immigrants in schools”, NBER Working Paper 25333. 

Alesina, A, A Miano and S Stantcheva (2018), “Immigration and redistribution”, NBER Working Paper 24733.

Botelho, F, R A Madeira and M A Rangel (2015), “Racial discrimination in grading: Evidence from Brazil”, American Economic Journal: Applied Economics 7(4):37–52.

Burgess, S, and E Greaves (2013), “Test scores, subjective assessment, and stereotyping of ethnic minorities”, Journal of Labor Economics 31(3):535–576.

Corno, L, E La Ferrara and J Burns (2018), “Interaction, stereotypes and performance. Evidence from South Africa”, BREAD Working Paper 549.

Carlana, M (2019), “Implicit stereotypes: Evidence from teachers’ gender bias”, HKS Working Paper.

Glover, D, A Pallais and W Pariente (2017), “Discrimination as a self-fulfilling prophecy: Evidence from French grocery stores”, The Quarterly Journal of Economics 132(3): 1219–1260.

Greenwald, AG, and MR Banaji (1995), “Implicit social cognition attitudes, self- esteem, and stereotypes”, Psychological Review 102: 4–27.

Greenwald, A G, T A Poehlman, E L Uhlmann and M R Banaji (2009), “Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity”, Journal of Personality and Social Psychology 97(1):17.

Hanna, R N, and L L Linden (2012), “Discrimination in grading”, American Economic Journal: Economic Policy 4(4):146–68.

Van Ewijk, R (2011), “Same work, lower grade? Student ethnicity and teachers’ subjective assessments”, Economics of Education Review 30(5):1045–1058.

Endnotes

[1] Previous papers provide show similar results from other countries comparing minority and non-minority students (Botelho et al. 2015, Burgess and Greaves 2013, Hanna and Linden 2012, Van Ewijk 2011). 

[2] We administered the survey with the IAT test in 102 schools but, due to logistical constraints, we only managed to complete the data collection for 65 of them before the end-of-semester grading in January.

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Topics:  Education Migration

Tags:  education, Discrimination, migrants, teachers, Italy, Implicit Association Test

Nathaniel Ropes Professor of Political Economy, Harvard University; and Research Fellow, CEPR

Assistant Professor of Public Policy, Harvard Kennedy School

Professor of Economics, Bocconi University and CEPR Research Fellow

Bocconi University and Fondazione Rodolfo Debenedetti

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