\nThe data was gathered through an online survey by YouGov. The experiments are incentivized. (2026-01-02). \n
\n\n\nYouGov interviewed 2471 White, Black and Hispanic respondents between the ages of 25 and 54. \n
\n\n\nA sampling frame was constructed by stratified sampling from the full 2016 American Community Survey (ACS) 1-year sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). \n
\n\n\nThe respondents were weighted to the sampling frame using propensity scores. The cases and the frame were combined and a logistic regression was estimated for inclusion in the frame. The propensity score function included age, race/ethnicity, years of education, and region. \n
\n\n\nThe propensity scores were grouped into deciles of the estimated propensity score in the frame and post-stratified according to these deciles. The weights were then post-stratified on 2016 Presidential vote choice, and a four-way stratification of gender, age (4-categories), race (4- categories), and education (4-categories), to produce the final weight. \n
\n\n\nYouGov has provided weights: The respondents were weighted to the sampling frame using propensity scores. The cases and the frame were combined and a logistic regression was estimated for inclusion in the frame. The propensity score function included age, race/ethnicity, years of education, and region. \n
\n\n\nThe propensity scores were grouped into deciles of the estimated propensity score in the frame and post-stratified according to these deciles. The weights were then post-stratified on 2016 Presidential vote choice, and a four-way stratification of gender, age (4-categories), race (4- categories), and education (4-categories), to produce the final weight. \n
"},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2026-01-02"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Social Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"competitiveness"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"risk"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"experiment"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationRelationType":{"typeName":"publicationRelationType","multiple":false,"typeClass":"controlledVocabulary","value":"IsSupplementTo"},"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Dariel et al. (2026) \"The racial and ethnic gap in behavioral measures rivals the gender gap in the United States\", Proceedings of the National Academy of Sciences U.S."}}]},{"typeName":"notesText","multiple":false,"typeClass":"primitive","value":"This entry is a six-files data package totaling 728.10 kB, containing files in .do, .dta, .ods, .pdf and .txt formats.
\n\nIf you use this dataset, please cite: Stoop, J., Dariel, A., Ham, J., & Nikiforakis, N. (2026). The racial and ethnic gap in behavioral measures rivals the gender gap in the United States – Replication Data (Version V1) [Data set]. DataverseNL. \n https://doi.org/10.34894/JQNARD "},{"typeName":"language","multiple":true,"typeClass":"controlledVocabulary","value":["English"]},{"typeName":"grantNumber","multiple":true,"typeClass":"compound","value":[{"grantNumberAgency":{"typeName":"grantNumberAgency","multiple":false,"typeClass":"primitive","value":"NWO Netherlands"},"grantNumberValue":{"typeName":"grantNumberValue","multiple":false,"typeClass":"primitive","value":"VI.Vidi.95.061"}}]},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Stoop, Jan"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2026-01-02"},{"typeName":"dateOfCollection","multiple":true,"typeClass":"compound","value":[{"dateOfCollectionStart":{"typeName":"dateOfCollectionStart","multiple":false,"typeClass":"primitive","value":"2021-01-13"},"dateOfCollectionEnd":{"typeName":"dateOfCollectionEnd","multiple":false,"typeClass":"primitive","value":"2021-02-08"}}]},{"typeName":"kindOfData","multiple":true,"typeClass":"primitive","value":["survey data","experimental data"]},{"typeName":"software","multiple":true,"typeClass":"compound","value":[{"softwareName":{"typeName":"softwareName","multiple":false,"typeClass":"primitive","value":"Stata"},"softwareVersion":{"typeName":"softwareVersion","multiple":false,"typeClass":"primitive","value":"18"}}]},{"typeName":"relatedDatasets","multiple":true,"typeClass":"primitive","value":["The dataset is made available in Stata format, as well as in ODS. It contains information about a subject's gender, race, income and education, as well as the experimental outcomes on competitiveness and risk tolerance."]}]},"geospatial":{"displayName":"Geospatial Metadata","name":"geospatial","fields":[{"typeName":"geographicCoverage","multiple":true,"typeClass":"compound","value":[{"country":{"typeName":"country","multiple":false,"typeClass":"controlledVocabulary","value":"United States"}}]}]},"socialscience":{"displayName":"Social Science and Humanities Metadata","name":"socialscience","fields":[{"typeName":"unitOfAnalysis","multiple":true,"typeClass":"primitive","value":["The basic unit of analysis is at the subject level."]},{"typeName":"universe","multiple":true,"typeClass":"primitive","value":["YouGov interviewed 2471 White, Black and Hispanic respondents between the ages\nof 25 and 54. A sampling frame was constructed by stratified sampling from the\nfull 2016 American Community Survey (ACS) 1-year sample with selection within\nstrata by weighted sampling with replacements (using the person weights on the\npublic use file).\n\nThe respondents were weighted to the sampling frame using propensity\nscores. The cases and the frame were combined and a logistic\nregression was estimated for inclusion in the frame. The propensity\nscore function included age, race/ethnicity, years of education, and region.\nThe propensity scores were grouped into deciles of the estimated propensity\nscore in the frame and post-stratified according to these deciles.\n\nThe weights were then post-stratified on 2016 Presidential vote choice, and\na four-way stratification of gender, age (4-categories), race (4-\ncategories), and education (4-categories), to produce the final weight."]},{"typeName":"timeMethod","multiple":false,"typeClass":"primitive","value":"Cross-sectional"},{"typeName":"dataCollector","multiple":false,"typeClass":"primitive","value":"YouGov"},{"typeName":"targetSampleSize","multiple":false,"typeClass":"compound","value":{"targetSampleActualSize":{"typeName":"targetSampleActualSize","multiple":false,"typeClass":"primitive","value":"2468"}}},{"typeName":"collectionMode","multiple":true,"typeClass":"primitive","value":["See YouGov's website for more information about sampling procedures."]},{"typeName":"researchInstrument","multiple":false,"typeClass":"primitive","value":"Structured"},{"typeName":"weighting","multiple":false,"typeClass":"primitive","value":"YouGov has provided weights:\n\nThe respondents were weighted to the sampling frame using propensity\nscores. The cases and the frame were combined and a logistic\nregression was estimated for inclusion in the frame. The propensity\nscore function included age, race/ethnicity, years of education, and region.\nThe propensity scores were grouped into deciles of the estimated propensity\nscore in the frame and post-stratified according to these deciles.\n\nThe weights were then post-stratified on 2016 Presidential vote choice, and\na four-way stratification of gender, age (4-categories), race (4-\ncategories), and education (4-categories), to produce the final weight."}]},"journal":{"displayName":"Journal Metadata","name":"journal","fields":[{"typeName":"journalVolumeIssue","multiple":true,"typeClass":"compound","value":[{"journalVolume":{"typeName":"journalVolume","multiple":false,"typeClass":"primitive","value":"Proceedings of the National Academy of Sciences"}}]},{"typeName":"journalArticleType","multiple":false,"typeClass":"controlledVocabulary","value":"brief report"}]},"dansDataVaultMetadata":{"displayName":"Data Vault Metadata","name":"dansDataVaultMetadata","fields":[{"typeName":"dansDataversePid","multiple":false,"typeClass":"primitive","value":"doi:10.34894/JQNARD"},{"typeName":"dansDataversePidVersion","multiple":false,"typeClass":"primitive","value":"1.0"},{"typeName":"dansBagId","multiple":false,"typeClass":"primitive","value":"urn:uuid:ba109018-8ab5-4214-8ddf-3e1381470fe4"},{"typeName":"dansNbn","multiple":false,"typeClass":"primitive","value":"urn:nbn:nl:ui:13-7a570025-4095-4640-a124-fccafd161247"}]}},"files":[{"description":"","label":"doi-10.34894-jqnard_data_package_2026.zip","restricted":false,"version":1,"datasetVersionId":32444,"dataFile":{"id":574628,"persistentId":"","filename":"doi-10.34894-jqnard_data_package_2026.zip","contentType":"application/zip","friendlyType":"ZIP Archive","filesize":392145,"description":"","storageIdentifier":"surf://store:19b7eefc95a-dc52ecfe8f30","rootDataFileId":-1,"checksum":{"type":"SHA-1","value":"e764475c0c19c9638026c0f425702365e219edc4"},"tabularData":false,"creationDate":"2026-01-02","publicationDate":"2026-01-02","lastUpdateTime":"2026-01-02T16:40:48Z","fileAccessRequest":true}}]}}}