Experimental Design
(1) Experimental Design1:
To examine LGBTQ-related discrimination in the hiring process, we employ an audit study, a widely used method in discrimination research:
Step 1: Design a set of fictitious resumes (using accounting positions as an example), consisting of three resumes. The first resume includes an LGBTQ-signal, the second contains an Radical signal in the corresponding section, and the third is a Neutral Resume. Apart from the signal section, all three resumes are identical (e.g., age, educational background).
Step 2: Submit a large number of applications for accounting job postings via recruitment platforms, like Zhilian Zhaopin. However, ensure that no applications are submitted to the same company.
Step 3: Test using the regression model. [Callbacki = α + β1LGBTQi + ϵi]
In this model, Callbacki is a binary variable indicating whether resume i received a callback for an interview, while LGBTQi is a binary variable indicating whether resume i contains LGBTQ-related signals. The coefficient β1 measures the extent to which LGBTQ-related signals influence the probability of receiving a callback. If β1 is significantly negative, it indicates the presence of discrimination against LGBTQ-related signals in the hiring process.
(2) Experimental Design2:
Step1: Recruitment of Evaluators: We plan to recruit 500 professionals from accounting and finance fields with hiring experience. Each evaluator will be paid ¥50 to assess resumes, and they will be informed that their evaluations directly affect candidates’ opportunities.
Step2: Group Division: Evaluators will be divided into three groups: (1)Traditional Recruitment Group (2) AI-Assisted Group: Evaluates resumes with AI-generated compatibility scores as reference. (3) Pure AI Decision Group.
Step 3: Collect Resume: A total of 1,250 resumes will be collected. Among these, 125 resumes (10%) will be modified to include an LGBTQ-signal, resulting in a dataset with 125 LGBTQ-signal resumes and 1,125 regular resumes.
Step 4: Resumes will be randomly distributed to evaluators, with each evaluator reviewing five resumes (one of which contains an LGBTQ-signal).
Step 5: We will collect the scoring data and conduct a post-survey to examine the mechanisms behind the evaluators’ decisions.
Step 6: We will analyze the results using regression analysis, controlling for several factors, including applicant characteristics such as gender, age, GPA, total months of relevant internship experience, computer skills, volunteering experience, marital status, and whether the applicant has children. Additionally, evaluator fixed effects will be included to account for unobserved heterogeneity among evaluators.