Source: ChatGPT 4
Source: Gallup
Source: ChatGPT 4
Source: TV2
Source: ChatGPT 4
Social Mechanism | Minority | Majority |
---|---|---|
Divergent Awareness | Integration → awareness of group's enduring marginalized status. | Minority integration → impression of successful diversification of society. |
Divergent Definition | Claim to equality → increased sensitivity & confidence to interpret subtle events as discrimination. | Maintain way of life and privileges → set high standards for what constitutes discrimination and be accused of it. |
Divergent Opportunity Structure | ... there are 937=13.3 in which a minority experiences it. | For every interaction in which a majority member engages in or witnesses discrimination, ... ← |
Three social mechanisms could explain a trend of divergence.
Do the 3 mechanisms drive perceptions away from "reality"?
The field lacks a methodology to measure misperceptions of discrimination – a crucial barrier.
How accurate do majority and minority citizens
perceive ethno-racial discrimination?
We cannot observe
hidden intentions.
→ Price: Fictitious cases identify discrimination in the aggregate!
Claim 1: Majority citizens tend to overperceive prevalence of minority discrimination.
Claim 2: Lack of awareness does not explain low support for anti-discrimination legislation.
Source: Schaeffer, Krakowski, and Olsen (2023)
Source: Schaeffer, Krakowski, and Olsen (2023). Post-stratification weighted results. nHiring=1,045, nHousing=1,041, nPolitics=1,043, and nSchools=1,041 mainstream-majority Danes.
Source: Schaeffer, Krakowski, and Olsen (2023). Post-stratification weighted results. nHiring=1,045, nHousing=1,041, nPolitics=1,043, and nSchools=1,041 mainstream-majority Danes.
Source: Haaland and Roth (2023)
Source: Haaland and Roth (2023)
Source: Schaerer, du Plessis, Nguyen, van Aert, Tiokhin, Lakens, Giulia Clemente, Pfeiffer, Dreber, Johannesson, Clark, and Luis Uhlmann (2023)
Source: Schaeffer, Krakowski, and Olsen (2023); Post-stratification weighted results. n = 769.
Source: Schaeffer, Krakowski, and Olsen (2023); Post-stratification weighted results. n = 769.
Source: Østergaard (2020)
Claim 3: Typically, people do not expect ethno-racial discrimination,
but when they do, their expectations tend to be wrong.
Claim 4: Perceived and expected discrimination are costly.
→ Observe actual € sent.
→ Survey expected € received.
True first name & city of residence from registers
Alisina, Ahmad, Anne, Binyamin, Joyce, Somaia, Sarah, Hayriye, Saibe, Björn, Salem, Fabienne, Sadet, Linda, Margarita, Ali, Joseph, Mhd Kheir, Baran, Bahaa, Jebran, Reno, Seiji, Irina, Ajsel, Christine, Rahim, Yaw Abrefa, Mark, Anjali Dev, Elmar, Anke, Laura, Heiko, ...
library(text)# Predict ethnicity of first names.zeroshot_ethnic <- text::textZeroShot( # The list of first names to be predicted. sequences = vornamen, # sequences = vornamen[1:20], # The possible origins for the first names. candidate_labels = c( "deutscher", "muslimischer", "türkischer", "ostasiatischer", "osteuropäischer", "christlicher"), # Indicates whether multiple genders should be predicted for each first name. multi_label = TRUE, # The template for the hypothesis that is generated for each first name. hypothesis_template = "Das ist ein {} Vorname.", # The model that is used to predict the gender. # model = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli") %>% model = "michaelp11/zeroshot-classification-de") %>% # Renames the column `sequences` to `vorname`. rename(vorname = sequence)
library(text)# Predict ethnicity of first names.zeroshot_ethnic <- text::textZeroShot( # The list of first names to be predicted. sequences = vornamen, # sequences = vornamen[1:20], # The possible origins for the first names. candidate_labels = c( "deutscher", "muslimischer", "türkischer", "ostasiatischer", "osteuropäischer", "christlicher"), # Indicates whether multiple genders should be predicted for each first name. multi_label = TRUE, # The template for the hypothesis that is generated for each first name. hypothesis_template = "Das ist ein {} Vorname.", # The model that is used to predict the gender. # model = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli") %>% model = "michaelp11/zeroshot-classification-de") %>% # Renames the column `sequences` to `vorname`. rename(vorname = sequence)
→ Observe actual € sent to others.
Name | N | % |
---|---|---|
Majority with German name | 10650 | 53.0 |
Minority with Christian name | 330 | 1.6 |
Minority with East-Asian name | 1310 | 6.5 |
Minority with East-European name | 1640 | 8.2 |
Minority with German name | 3430 | 17.1 |
Minority with Muslim name | 2110 | 10.5 |
Minority with Turkish name | 630 | 3.1 |
→ Observe actual € sent to others.
Name | N | % |
---|---|---|
Majority with German name | 10650 | 53.0 |
Minority with Christian name | 330 | 1.6 |
Minority with East-Asian name | 1310 | 6.5 |
Minority with East-European name | 1640 | 8.2 |
Minority with German name | 3430 | 17.1 |
Minority with Muslim name | 2110 | 10.5 |
Minority with Turkish name | 630 | 3.1 |
→ Observe actual € sent to others.
Name | N | % |
---|---|---|
Majority with German name | 3137 | 37.3 |
Minority with East-Asian name | 922 | 11.0 |
Minority with German name | 2414 | 28.7 |
Minority with Muslim name | 1484 | 17.7 |
Minority with Turkish name | 445 | 5.3 |
What did their game partner's entrust them?
Act Discrij=€ij−¯€j(Germ. name)
(−0.488€+−0.113€)×3×23×2=−2.4€
−2.4€20€=−12%
Claim 6: People tend to overinterpret ambiguous signals as evidence of discrimination.
Claim 7: Majority members overinterpret signals of advantage more than minorities overinterpret signals of disadvantage.
On average, your ten game partners sent and thereby entrusted 6 € to you.
In addition, your ten game partners played trust games with other 68 more participants. 25 of these other participants had names that sound typically German.
Below you see a selection of three of these participants with names that sound typically German.
On average, your game partners sent and thereby entrusted 8 € to these three persons:
Annegret from Hamburg
Klaus from Berlin
Hartmut from München
The three have thus received 2 € more than you. How do you rate this result?
On average, your ten game partners sent and thereby entrusted 8 € to you.
In addition, your ten game partners played trust games with other 68 more participants. 25 of these other participants had names that sound typically German.
Below you see a selection of three of these participants with names that sound typically German.
On average, your game partners sent and thereby entrusted 6 € to these three persons:
Annegret from Hamburg
Klaus from Berlin
Hartmut from München
The three have thus received 2 € less than you. How do you rate this result?
On average, your ten game partners sent and thereby entrusted 7 € to you.
In addition, your ten game partners played trust games with other 68 more participants. 25 of these other participants had names that sound typically German.
Below you see a selection of three of these participants with names that sound typically German.
On average, your game partners sent and thereby entrusted 7 € to these three persons:
Annegret from Hamburg
Klaus from Berlin
Hartmut from München
The three have thus received the same amount as. How do you rate this result?
Thank you for your attention!
Claim 1: Majority citizens tend to overperceive prevalence of minority discrimination.
Claim 2: Lack of awareness does not explain low support for anti-discrimination legislation.
Claim 3: Typically, people do not expect ethno-racial discrimination, but when they do, their expectations tend to be wrong.
Claim 4: Perceived and expected discrimination are costly.
Claim 5: People tend to overinterpret ambiguous signals as evidence of discrimination.
Claim 6: Majority members overinterpret signals of advantage more than minorities overinterpret signals of disadvantage.
Haaland, I. and C. Roth (2023). "Beliefs About Racial Discrimination and Support for Pro-Black Policies". In: Review of Economics and Statistics, pp. 40-53.
Østergaard, A. (2020). Detektor: Efter racismedebat – Dansk Folkeparti anerkender forskelsbehandling.
Schaeffer, M., K. Krakowski, and A. L. Olsen (2023). "Correcting Misperceptions about Ethno-Racial Discrimination: The Limits of Evidence-Based Awareness Raising to Promote Support for Equal-Treatment Policies". In: SocArXiv.
Schaeffer, M., K. Krakowski, A. Romarri, et al. (2024). "Far-right Electoral Success Exacerbates Administrative Discrimination Against Minorities: Evidence from a Field Experiment in Italy". In: Unpublished manuscript.
Schaerer, M., C. du Plessis, M. H. B. Nguyen, et al. (2023). "On the trajectory of discrimination: A meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions". In: Organizational Behavior and Human Decision Processes, p. 104280.
Source: ChatGPT 4
Source: Gallup
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