Project Title:
Discrimination Against Immigrants of the Same Ethnic Group in Europe
Role: Data Analyst and Statistician
Project Description: This project investigated which social groups are most likely to discriminate against immigrants of the same race or ethnic group as the majority in the host country. The study utilised data from the European Social Survey (ESS) Wave 7 to examine social-demographic factors, social satisfaction, and attitudes towards immigrants in the UK, Germany, and France.
Background: Racial discrimination, defined as differential treatment based on race, is a global issue closely linked to immigration. While many studies have extensively analysed discrimination against immigrants, few have focused on immigrants who share the same race or ethnic group as the majority in the host country. This study aims to fill that gap by investigating which social groups are most likely to discriminate against same-ethnicity immigrants and exploring the potential reasons behind this phenomenon. As an Asian female studying abroad, I often encountered members of my own ethnicity in group discussions. I noticed that when I communicated in my native language versus English, I sometimes received different attitudes. This observation sparked my curiosity to explore intra-ethnic discrimination. After reviewing the literature, I discovered the Black Sheep Effect within immigrant communities, motivating my research on this topic.
Key Responsibilities:
Data Collection and Preparation:
Utilised the ESS Wave 7 dataset, focusing on variables related to demographic information, social satisfaction, and attitudes towards immigrants.
Filtered and cleaned the dataset to ensure accuracy and relevance, excluding invalid responses and handling missing data.
Coding and Statistical Analysis:
Utilised R for data manipulation, coding, and analysis. Key tasks included recoding categorical variables into binary formats and creating new composite variables to address multicollinearity.
Conducted descriptive statistics to summarise the dataset and provide an overview of key variables such as educational level, gender, age, income, life satisfaction, economic satisfaction, and attitudes towards immigrants.
Performed correlation analysis to investigate the relationships between independent variables and the dependent variable (discrimination against same-ethnic immigrants).
Developed logistic regression models to test the hypotheses, adjusting for multicollinearity by combining highly correlated variables into new composite variables.
Hypotheses Testing:
Hypothesis 1 (H1): Explored the impact of socio-demographic factors (age, gender, education) on discriminatory attitudes. Found significant correlations between educational level and discrimination in all three countries, with higher education associated with lower discrimination.
Hypothesis 2 (H2): Examined the relationship between social satisfaction (income, life satisfaction, economic satisfaction, government satisfaction) and discriminatory attitudes. Identified a significant negative correlation, indicating that lower social satisfaction is associated with higher discrimination.
Hypothesis 3 (H3): Analysed attitudes towards immigrants' impact on various societal aspects (economy, crime, job market, taxes and services) and their influence on discrimination. Found strong support for the black sheep effect, where negative attitudes towards immigrants correlate with higher discriminatory behaviour.
Results Interpretation:
The logistic regression models indicated that education level is the most significant predictor of discrimination, with higher education reducing the likelihood of discriminatory attitudes.
Social satisfaction factors showed a negative correlation with discrimination, supporting the hypothesis that dissatisfaction with society leads to higher discrimination against same-ethnic immigrants.
Attitudes towards immigrants' societal impact also significantly influenced discriminatory behaviour, with concerns about economy and living conditions being key factors.
Project Outcomes:
Identified education as a crucial factor in reducing discrimination, suggesting that policies aimed at improving educational access and quality could help mitigate discriminatory attitudes.
Demonstrated the importance of social satisfaction in shaping attitudes towards immigrants, highlighting the need for social policies that address economic and governance satisfaction to reduce discrimination.
Provided empirical evidence supporting the black sheep effect, contributing to the understanding of intra-group discrimination dynamics and informing interventions to foster more inclusive societies.
Skills and Tools:
Data Collection and Analysis: R
Statistical Analysis: Descriptive Statistics, Correlation Analysis, Logistic Regression
Data Preparation: Data Cleaning, Recoding Variables