PURPOSE: To analyze the relationship among National Institutes of Health (NIH) R01 Type 1 applicant degree, institution type, and race/ethnicity, and application award probability. METHOD: The authors used 2000-2006 data from the NIH IMPAC II grants database and other sources to determine which individual and institutional characteristics of applicants may affect the probability of applications being awarded funding. They used descriptive statistics and probit models to estimate correlations between race/ethnicity, degree (MD or PhD), and institution type (medical school or other institution), and application award probability, controlling for a large set of observable characteristics. RESULTS: Applications from medical schools were significantly more likely than those from other institutions to receive funding, as were applications from MDs versus PhDs. Overall, applications from blacks and Asians were less likely than those from whites to be awarded funding; however, among applications from MDs at medical schools, there was no difference in funding probability between whites and Asians, and the difference between blacks and whites decreased to 7.8%. The inclusion of human subjects significantly decreased the likelihood of receiving funding. CONCLUSIONS: Compared with applications from whites, applications from blacks have a lower probability of being awarded R01 Type 1 funding, regardless of the investigator's degree. However, funding probability is increased for applications with MD investigators and for those from medical schools. To some degree, these advantages combine so that applications from black MDs at medical schools have the smallest difference in funding probability compared with those from whites.
PURPOSE: To analyze the relationship among National Institutes of Health (NIH) R01 Type 1 applicant degree, institution type, and race/ethnicity, and application award probability. METHOD: The authors used 2000-2006 data from the NIH IMPAC II grants database and other sources to determine which individual and institutional characteristics of applicants may affect the probability of applications being awarded funding. They used descriptive statistics and probit models to estimate correlations between race/ethnicity, degree (MD or PhD), and institution type (medical school or other institution), and application award probability, controlling for a large set of observable characteristics. RESULTS: Applications from medical schools were significantly more likely than those from other institutions to receive funding, as were applications from MDs versus PhDs. Overall, applications from blacks and Asians were less likely than those from whites to be awarded funding; however, among applications from MDs at medical schools, there was no difference in funding probability between whites and Asians, and the difference between blacks and whites decreased to 7.8%. The inclusion of human subjects significantly decreased the likelihood of receiving funding. CONCLUSIONS: Compared with applications from whites, applications from blacks have a lower probability of being awarded R01 Type 1 funding, regardless of the investigator's degree. However, funding probability is increased for applications with MD investigators and for those from medical schools. To some degree, these advantages combine so that applications from black MDs at medical schools have the smallest difference in funding probability compared with those from whites.
Authors: Donna K Ginther; Walter T Schaffer; Joshua Schnell; Beth Masimore; Faye Liu; Laurel L Haak; Raynard Kington Journal: Science Date: 2011-08-19 Impact factor: 47.728
Authors: Dowin Boatright; David Ross; Patrick O'Connor; Edward Moore; Marcella Nunez-Smith Journal: JAMA Intern Med Date: 2017-05-01 Impact factor: 21.873
Authors: Jillian H Hurst; Katherine J Barrett; Matthew S Kelly; Betty B Staples; Kathleen A McGann; Coleen K Cunningham; Ann M Reed; Rasheed A Gbadegesin; Sallie R Permar Journal: Pediatrics Date: 2019-08 Impact factor: 7.124
Authors: Angela R Bazzi; Cristina Mogro-Wilson; Nalini Junko Negi; Jennifer M Reingle Gonzalez; Miguel Ángel Cano; Yessenia Castro; Alice Cepeda Journal: Mentor Tutoring Date: 2017-05-31
Authors: Tisha M Felder; Kathryn L Braun; Heather M Brandt; Samira Khan; Sora Tanjasiri; Daniela B Friedman; Cheryl A Armstead; Kolawole S Okuyemi; James R Hébert Journal: Prog Community Health Partnersh Date: 2015