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Or, should racial disparities in the unemployment rate be attributed to other factors such as racial gaps in\neducational attainment? To answer this question, two social scientists conducted the following experiment.1 In response to newspaper ads, the researchers sent out\nr\u00e9sum\u00e9s of fictitious job candidates to potential employers. They varied only the names of job applicants, while leaving the other information in the r\u00e9sum\u00e9s unchanged.\nFor some candidates, stereotypically African-American-sounding names such as Lakisha Washington or Jamal Jones were used, whereas other r\u00e9sum\u00e9s contained\nstereotypically white-sounding names, such as Emily Walsh or Greg Baker. The researchers then compared the callback rates between these two groups and examined\nwhether applicants with stereotypically black names received fewer callbacks than those with stereotypically white names. The positions to which the applications were\nsent were either in sales, administrative support, clerical, or customer services.\n\nData is in the CSV data file resume.csv.\nVariable Description\nfirstname: first name of the fictitious job applicant\nsex: sex of applicant (female or male)\nrace: race of applicant (black or white)\ncall: whether a callback was made (1= yes, 0= no)","file_sha256":"3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"CC BY-NC 4.0","member":"data/resume.csv","object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"agent","sha256":"3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970","transforms":[],"url":"https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/data.zip"},"resume_white":{"columns":null,"description":"resume.csv with white = 1 when race is 'white'","file_sha256":"3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"CC BY-NC 4.0","member":"data/resume.csv","object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"reference","sha256":"3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970","transforms":[{"args":{"args":{"cmp":"eq","column":"race","value":"white"},"name":"white","op":"indicator"},"op":"derive"}],"url":"https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/data.zip"}},"difficulty":"M","id":"qrdata_socsci.377","level":"T2","meta":{"agent_reach":{"blocked_by":["reference_frame:resume_white"],"dtypes":{},"named":{},"reachable":false,"rule":"magi_bench.reach/1"},"answer_format":{"figure_transform":"abs","round_to":3,"unit_scale":1.0},"answer_format_from":{"figure_transform":"racial gap in callback rate","round_to":"nearest thousandth","unit_scale":"1.0: an estimator's figure is on the gold's own scale"},"cluster":"resume.csv","expected_tool":"infer.ols","family":"numerical","headline_keys":{"infer.ols":["coef.white"]},"native":{"answer":"0.030","keywords":["Social science","Statistics","Probability","Conditional probability"],"multiple_choices":null,"question_type":"numerical","reference":"Quantitative Social Science 2.2.3"},"native_id":377,"reference":{"cite":1,"note":"Caminho de refer\u00eancia inferido \u00e0 m\u00e3o.","steps":[{"args":{"dataset":"resume_white","subset":"sex == 'male'","x":["white"],"y":"call"},"tool":"infer.ols","why":"Among male applicants, OLS of callback on the white-name indicator: the difference of the two callback rates."}]},"reference_path":{"calls":[{"args":{"dataset":"resume_white","subset":"sex == 'male'","x":["white"],"y":"call"},"tool":"infer.ols"}],"needs":[],"provenance":"hand_inferred","quotes":{},"reference_datasets":["resume_white"],"source_id":null},"reference_why":"Among male applicants, OLS of callback on the white-name indicator: the difference of the two callback rates."},"question":"What is the racial gap in callback rate among white and black male applicants? 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