Appendix E
Demographic Projections of the Research Workforce in the Biomedical, Clinical, and Behavioral Sciences, 2006-2016 (Using the System Dynamics Simulation Methodology)

OVERVIEW

Appendix D provides demographic projections of the research workforce in the biomedical, clinical, and behavioral sciences for the years 2006-2016 using a traditional statistical (actuarial) approach. This appendix provides additional demographic projections for the same workforces using an alternative approach called system dynamics that is based on the “structure” of the system (i.e., the interconnections among the various entities or parts of the system). In this case, the system under study is the scientific research workforce.

For each of the biomedical, clinical, and behavioral sciences workforces, projections will be shown for the total population along with the populations in the following four (4) demographic categories:

  1. U.S.-trained males

  2. U.S.-trained females

  3. Foreign-trained males

  4. Foreign-trained females

In each projection, the beginning population values are the actual values for 2006, the latest published set of data points. For each of the three major workforces (i.e., biological, clinical, and behavioral sciences), three (3) scenarios will be considered.

  1. Scenario 1 (Moderate Risk): Use 50 percent of the value of the specified annual growth rate for each subgroup of the workforce. This is rated moderate risk because it is the most likely scenario and has the workforce projections that are most expected.

  2. Scenario 2 (High Risk): Use 75 percent of the value of the specified annual growth rate for each subgroup of the workforce. This is rated high risk because it produces very large workforces over the 10-year simulation.

  3. Scenario 3 (Low Risk): Use Ph.D. student growth rates in a “pipeline” model into the workforce. This is rated low risk because it is the most conservative set of projections for the workforces.

Figure E-1 shows the projections for the three major workforces for Scenario 1, the most likely scenario.

SUMMARY PROJECTIONS FOR ALL THREE SCENARIOS

Figures E-2 through E-4 show the projections for each of the three major workforces for each of the three scenarios in line-graph form. Tables E-1 through E-3 then show the projections for each of the three major workforces for each of the three scenarios in table form.

DEMOGRAPHIC DETAILS FOR SCENARIO 1 (MODERATE RISK)

Figure E-5 shows the projections for each of the four demographic groups for the biomedical sciences workforce for Scenario 1 in bar-graph form, and Table E-4 shows the same projections in table form.

Figure E-6 shows the projections for each of the four demographic groups for the behavioral sciences workforce for Scenario 1 in bar-graph form, and Table E-5 shows the same projections in table form.

Figure E-7 shows the projections for each of the four demographic groups for the clinical sciences workforce for Scenario 1 in bar-graph form, and Table E-6 shows the same projections in table form.



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appendix e demographic Projections of the research Workforce in the Biomedical, Clinical, and Behavioral Sciences, 2006-2016 (using the System dynamics Simulation methodology) overvieW workforce. This is rated high risk because it produces very large workforces over the 10-year simulation. Appendix D provides demographic projections of the . Scenario  (Low Risk): Use Ph.D. student growth rates research workforce in the biomedical, clinical, and behav- in a “pipeline” model into the workforce. This is rated low ioral sciences for the years 2006-2016 using a traditional risk because it is the most conservative set of projections for statistical (actuarial) approach. This appendix provides the workforces. additional demographic projections for the same workforces using an alternative approach called system dynamics that Figure E-1 shows the projections for the three major is based on the “structure” of the system (i.e., the intercon- workforces for Scenario 1, the most likely scenario. nections among the various entities or parts of the system). In this case, the system under study is the scientific research Summary ProJeCtioNS for all workforce. three SCeNarioS For each of the biomedical, clinical, and behavioral sci- ences workforces, projections will be shown for the total Figures E-2 through E-4 show the projections for each of population along with the populations in the following the three major workforces for each of the three scenarios four (4) demographic categories: in line-graph form. Tables E-1 through E-3 then show the projections for each of the three major workforces for each 1. U.S.-trained males of the three scenarios in table form. 2. U.S.-trained females 3. Foreign-trained males demograPhiC detailS for SCeNario 1 4. Foreign-trained females (moderate riSk) In each projection, the beginning population values are Figure E-5 shows the projections for each of the four the actual values for 2006, the latest published set of data demographic groups for the biomedical sciences workforce points. For each of the three major workforces (i.e., biologi- for Scenario 1 in bar-graph form, and Table E-4 shows the cal, clinical, and behavioral sciences), three (3) scenarios will same projections in table form. be considered. Figure E-6 shows the projections for each of the four demographic groups for the behavioral sciences workforce . Scenario  (Moderate Risk): Use 50 percent of the for Scenario 1 in bar-graph form, and Table E-5 shows the value of the specified annual growth rate for each subgroup same projections in table form. of the workforce. This is rated moderate risk because it is Figure E-7 shows the projections for each of the four the most likely scenario and has the workforce projections demographic groups for the clinical sciences workforce for that are most expected. Scenario 1 in bar-graph form, and Table E-6 shows the same . Scenario  (High Risk): Use 75 percent of the value projections in table form. of the specified annual growth rate for each subgroup of the 

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8 APPENDIX E 250,0 00 225,000 220,642 200,0 00 175,00 0 159,853 168,983 Total Work force 150,0 00 124, 292 125,00 0 Biomedical Sciences 10 0,0 00 Behavioral Sciences Clinical Sciences 75,0 00 52,743 50,0 00 35,320 25,00 0 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-1 Total biomedical, behavioral, and clinical sciences workforces, 2006-2016, scenario 1. E-1.eps SOURCE: NRC analysis. 350,000 317,302 Scenario 1 300,000 Scenario 2 Scenario 3 250,000 220,642 Total Work force 200,000 209,274 159,853 150,000 100,000 50,000 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-2 Total biomedical sciences workforce, 2006-2016. SOURCE: NRC analysis. E-2.eps

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 APPENDIX E 250,000 Scenario 1 225,000 215,115 Scenario 2 Scenario 3 200,000 175,000 168,983 Total Work force 150,000 124, 292 139,198 125,000 10 0,0 00 75,000 50,000 25,00 0 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-3 Total behavioral sciences workforce, 2006-2016. SOURCE: NRC analysis. E-3.eps 80,000 72,957 Scenario 1 70,000 Scenario 2 Scenario 3 60,000 52,743 50,000 Total Work force 48,097 40,000 35,320 30,000 20,000 10,000 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-4 Total clinical sciences workforce, 2006-2016. SOURCE: NRC analysis. E-4.eps

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ssing up to employment in the workforce. In addition, based on detailed data150,599 2013 172,137 134,399 for several pipeline models could be used to show the movement through the 156,100 2014 184,214 135,962 2015 162,203 198,404 137,561 e fields of science, engineering, etc. in addition to the separation of male/female 0 APPENDIX E n. 2016 168,983 215,115 139,198 SOURCE: NRC Analysis TABLE E-1 Biomedical Sciences Workforce Projections TABLE E-3 Clinical Sciences Workforce Projections for for AllWorkforce Projections for AllE - 3 Clinical Science Scenarios Scenarios All Workforce Projections for All Scenarios Table Scenarios edical Science CLINICAL BIOMEDICAL Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 2006 35,320 35,320 35,320 2006 159,853 159,853 159,853 2007 36,327 36,859 36,291 2007 162,950 164,598 162,926 2008 37,441 38,654 37,319 2008 166,423 170,244 166,296 2009 38,680 40,763 38,408 2009 170,339 177,046 169,995 2010 40,061 43,256 39,562 2010 174,782 185,354 174,063 2011 41,605 46,221 40,785 2011 179,854 195,662 178,543 2012 43,335 49,765 42,082 2012 185,684 208,677 183,489 2013 45,279 54,024 43,456 2013 192,437 225,425 188,959 2014 47,470 59,162 44,913 2014 200,321 247,417 195,024 2015 49,943 65,388 46,458 2015 209,607 276,908 201,764 2016 52,743 72,957 48,097 2016 220,642 317,302 209,274 SOURCE: NRC AnalysisSOURCE: NRC analysis. C AnalysisSOURCE: NRC analysis. avioral Science Workforce Projections forable E-4 Breakout of Biomedical Sciences Workforce, 2006-2016, Scenario 1 T All Scenarios TABLE E-2 Behavioral Sciences Workforce Projections for All Scenarios BEHAVIORAL Scenario 1 Scenario 2 Scenario 3 2006 124,292 124,292 124,292 2007 127,049 128,501 125,660 2008 130,079 133,351 127,051 2009 133,414 138,958 128,465 2010 137,091 145,459 129,906 2011 141,149 153,018 131,373 2012 145,634 161,832 132,871 2013 150,599 172,137 134,399 2014 156,100 184,214 135,962 2015 162,203 198,404 137,561 2016 168,983 215,115 139,198 C AnalysisSOURCE: NRC analysis. nical Science Workforce Projections for All Scenarios CLINICAL Scenario 1 Scenario 2 Scenario 3 2006 35,320 35,320 35,320 2007 36,327 36,859 36,291 2008 37,441 38,654 37,319 2009 38,680 40,763 38,408 2010 40,061 43,256 39,562 2011 41,605 46,221 40,785 2012 43,335 49,765 42,082 2013 45,279 54,024 43,456 2014 47,470 59,162 44,913 2015 49,943 65,388 46,458 2016 52,743 72,957 48,097

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 APPENDIX E 250,00 0 Foreign BIO Female 225,00 0 Foreign BIO Male U.S. BIO Female 200,00 0 U.S. BIO Male 175,000 Breakout of Work force 150,00 0 125,00 0 10 0,00 0 75,000 50,0 00 25,000 0 20 06 20 07 20 08 20 09 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-5 Breakout of biomedical sciences workforce, 2006-2016, scenario 1. SOURCE: NRC analysis. E-5.eps TABLE E-4 Breakout of Biomedical Sciences Workforce, 2006-2016, Scenario 1 BIOMEDICAL - SCENARIO 1 DETAILS US Male US Female Foreign Male Foreign Female 2006 80,268 45,828 23,636 10,121 2007 81,782 46,989 23,943 10,236 2008 83,502 48,218 24,337 10,366 2009 85,455 49,522 24,848 10,515 2010 87,675 50,906 25,517 10,684 2011 90,198 52,378 26,401 10,876 2012 93,066 53,946 27,577 11,095 2013 96,327 55,618 29,147 11,345 2014 100,034 57,403 31,254 11,629 2015 104,250 59,312 34,091 11,953 2016 109,044 61,356 37,919 12,322 SOURCE: NRC NRC analysis. SOURCE: Analysis Table E - 5 Breakout of Behavioral Sciences Workforce, 2006-2016, Scenario 1 BEHAVIORAL - SCENARIO 1 DETAILS US Male US Female Foreign Male Foreign Female

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 APPENDIX E 200,0 00 Foreign BEH Female 180,0 00 Foreign BEH Male U.S. BEH Female 160,0 00 U.S. BEH Male 140,0 00 Breakout of Work force 120,0 00 10 0,0 00 80,0 00 60,000 BIOMEDICAL - SCENARIO 1 DETAILS US Male US Female Foreign Male Foreign Female 40,0 00 2006 80,268 45,828 23,636 10,121 2007 81,782 46,989 23,943 10,236 83,502 00 20,0 2008 48,218 24,337 10,366 2009 85,455 49,522 24,848 10,515 0 2010 87,675 50,906 25,517 10,684 20 06 20 07 20 08 20 09 2010 2011 2012 2013 2014 2015 2016 2011 90,198 52,378 26,401 10,876 Year 2012 93,066 53,946 27,577 11,095 FIGURE E-6 Breakout of behavioral sciences workforce, 2006-2016, scenario 1. 2013 96,327 55,618 29,147 11,345 SOURCE: NRC analysis. 2014 100,034 57,403 31,254 E-6.eps 11,629 2015 104,250 59,312 34,091 11,953 2016 109,044 61,356 37,919 12,322 SOURCE: NRC Analysis Table E - 5 BreakoutBreakout of Behavioral Sciences Workforce, 2006-2016, Scenario 1 1 TABLE E-5 of Behavioral Sciences Workforce, 2006-2016, Scenario BEHAVIORAL - SCENARIO 1 DETAILS US Male US Female Foreign Male Foreign Female 2006 57,593 62,758 1,457 2,484 2007 58,495 64,335 1,464 2,755 2008 59,471 66,066 1,471 3,071 2009 60,525 67,971 1,478 3,440 2010 61,665 70,069 1,485 3,871 2011 62,897 72,384 1,492 4,375 2012 64,230 74,941 1,499 4,964 2013 65,671 77,770 1,507 5,652 2014 67,229 80,901 1,514 6,457 2015 68,914 84,371 1,521 7,398 2016 70,736 88,221 1,529 8,498 SOURCE:SOURCE: Analysis NRC NRC analysis. Table E - 6 Breakout of Clinical Sciences Workforce, 2006-2016, Scenario 1

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 APPENDIX E 60,000 Fore ign CLIN Fe male Fore ign CLIN M ale 50,000 U.S. CLIN Fe male U.S. CLIN M ale 40,000 Breakout of Work force 30,000 20,000 10,000 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-7 Breakout of clinical sciences workforce, 2006-2016, scenario 1. SOURCE: NRC analysis. E-7.eps TABLE E-6 Breakout of Clinical Sciences Workforce, 2006-2016, Scenario 1 CLINICAL - SCENARIO 1 DETAILS US Male US Female Foreign Male Foreign Female 2006 9,457 14,706 6,359 4,798 2007 9,737 15,368 6,378 4,844 2008 10,055 16,096 6,398 4,893 2009 10,417 16,902 6,417 4,944 2010 10,829 17,797 6,436 4,998 2011 11,299 18,794 6,456 5,056 2012 11,835 19,909 6,475 5,116 2013 12,446 21,159 6,495 5,179 2014 13,143 22,566 6,515 5,246 2015 13,938 24,154 6,534 5,317 2016 14,846 25,952 6,554 5,391 RCE: NRC Analysis analysis. SOURCE: NRC le E-7: Breakout of Biomedical Sciences Workforce, 2006-2016, Scenario 2 BIOMEDICAL - SCENARIO 2 DETAILS US Male US Female Foreign Male Foreign Female

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 APPENDIX E demograPhiC detailS for SCeNario 2 (high riSk) Figure E-8 shows the projections for each of the four demographic groups for the biomedical sciences workforce for Scenario 2 in bar-graph form, and Table E-7 shows the same projections in table form. 350,0 00 325,00 0 Foreign BIO Female Foreign BIO Male 30 0,0 00 U.S. BIO Female 275,00 0 U.S. BIO Male 250,00 0 225,000 Number in Work force 200,0 00 CLINICAL - SCENARIO 1 DETAILS 175,00 0 US Male US Female Foreign Male Foreign Female 150,00 0 2006 9,457 14,706 6,359 4,798 125,00 0 2007 9,737 15,368 6,378 4,844 2008 10,055 16,096 6,398 4,893 10 0,0 00 2009 10,417 16,902 6,417 4,944 75,0 00 2010 10,829 17,797 6,436 4,998 50,0 00 2011 11,299 18,794 6,456 5,056 25,00 0 2012 11,835 19,909 6,475 5,116 2013 12,446 21,159 6,495 5,179 0 20 06 20 07 20 08 20 09 2010 2011 2012 2013 2014 2015 2016 2014 13,143 22,566 6,515 5,246 Year 2015 13,938 24,154 6,534 5,317 FIGURE E-8 2016 14,846 25,952 6,554 8.eps 5,391 Breakout of biomedical sciences workforce, 2006-2016, scenario 2. E- SOURCE: NRC analysis. SOURCE: NRC Analysis Table E-7: BreakoutBreakout of Biomedical Sciences Workforce, 2006-2016, Scenario 2 2 TABLE E-7 of Biomedical Sciences Workforce, 2006-2016, Scenario BIOMEDICAL - SCENARIO 2 DETAILS US Male US Female Foreign Male Foreign Female 2006 80,268 45,828 23,636 10,121 2007 82,594 47,588 24,119 10,297 2008 85,406 49,507 24,820 10,511 2009 88,808 51,605 25,863 10,770 2010 92,923 53,908 27,439 11,084 2011 97,903 56,441 29,852 11,466 2012 103,933 59,238 33,578 11,929 2013 111,235 62,333 39,367 12,490 2014 120,078 65,769 48,398 13,171 2015 130,791 69,591 62,528 13,998 2016 143,771 73,855 84,676 15,000 SOURCE: NRC NRC analysis. SOURCE: Analysis Table E-8 Breakout of Behavioral Sciences Workforce, 2006-2016, Scenario 2

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 APPENDIX E Figure E-9 shows the projections for each of the four demographic groups for the behavioral sciences workforce for Scenario 2 in bar-graph form, and Table E-8 shows the same projections in table form. 250,000 Foreign BEH Female 225,000 Foreign BEH Male U.S. BEH Female 200,000 U.S. BEH Male 175,000 Number in Work force 150,000 125,000 100,000 75,000 50,000 25,000 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-9 Breakout of behavioral sciences workforce, 2006-2016, scenario 2. SOURCE: NRC analysis. E-9.eps TABLE E-8 Breakout of Behavioral Sciences Workforce, 2006-2016, Scenario 2 BEHAVIORAL - SCENARIO 2 DETAILS US Male US Female Foreign Male Foreign Female 2006 57,593 62,758 1,457 2,484 2007 58,966 65,165 1,467 2,902 2008 60,509 67,936 1,478 3,429 2009 62,242 71,135 1,489 4,092 2010 64,190 74,842 1,499 4,928 2011 66,378 79,148 1,510 5,982 2012 68,838 84,162 1,521 7,311 2013 71,602 90,014 1,532 8,988 2014 74,710 96,856 1,544 11,105 2015 78,204 104,868 1,555 13,778 2016 82,132 114,264 1,567 17,154 SOURCE: NRC NRC analysis. SOURCE: Analysis Table E-9 Breakout of Clinical Sciences Workforce, 2006-2016, Scenario 2 CLINICAL - SCENARIO 2 DETAILS US Male US Female Foreign Male Foreign Female

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 APPENDIX E Figure E-10 shows the projections for each of the four demographic groups for the clinical sciences workforce for Scenario 2 in bar-graph form, and Table E-9 shows the same projections in table form. 80,000 Foreign CLIN Female 70,000 Foreign CLIN Male U.S. CLIN Female 60,000 U.S. CLIN Male 50,000 Number in Work force 40,000 BEHAVIORAL - SCENARIO 2 DETAILS US Male US Female Foreign Male Foreign Female 30,000 2006 57,593 62,758 1,457 2,484 2007 58,966 65,165 1,467 2,902 20,000 2008 60,509 67,936 1,478 3,429 2009 62,242 71,135 1,489 4,092 2010 64,190 74,842 1,499 4,928 10,000 2011 66,378 79,148 1,510 5,982 2012 68,838 84,162 1,521 7,311 0 2013 71,602 90,0142006 2007 2008 2009 2010 2011 8,988 2013 1,532 2012 2014 2015 2016 2014 74,710 96,856 1,544 Year 11,105 2015 FIGURE78,204 E-10 Breakout of 104,868 1,555 13,778 clinical sciences workforce, 2006-2016, scenario 2. 2016 SOURCE: NRC analysis. 114,264 82,132 1,567 17,154 RCE: NRC Analysis E-10.eps le E-9 Breakout of Clinical Sciences Workforce, 2006-2016, Scenario 2 2. TABLE E-9 Breakout of Clinical Sciences Workforce, 2006-2016, Scenario CLINICAL - SCENARIO 2 DETAILS US Male US Female Foreign Male Foreign Female 2006 9,457 14,706 6,359 4,798 2007 9,887 15,716 6,388 4,868 2008 10,408 16,886 6,417 4,944 2009 11,040 18,252 6,446 5,026 2010 11,808 19,859 6,475 5,115 2011 12,741 21,765 6,505 5,211 2012 13,877 24,040 6,534 5,315 2013 15,259 26,773 6,564 5,427 2014 16,943 30,076 6,594 5,549 2015 18,995 34,088 6,624 5,682 2016 21,496 38,982 6,654 5,825 RCE: NRC Analysisanalysis. SOURCE: NRC le E-10 Breakout of Biomedical Sciences Workforce, 2006-2016, Scenario 3

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 APPENDIX E demograPhiC detailS for SCeNario 3 (loW riSk) Figure E-11 shows the projections for each of the four demographic groups for the biomedical sciences workforce for Scenario 3 in bar-graph form, and Table E-10 shows the same projections in table form. 225,000 Fore ign BIO Fe male Foreign BIO M ale 200,000 U.S. BIO Fe male U.S. BIO M ale 175,000 150,000 Number in Work force 125,000 100,000 75,000 50,000 25,000 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-11 Breakout of biomedical sciences workforce, 2006-2016, scenario 3. SOURCE: NRC analysis. E-11.eps TABLE E-10 Breakout of Biomedical Sciences Workforce, 2006-2016, Scenario 3 BIOMEDICAL - SCENARIO 3 DETAILS US Male US Female Foreign Male Foreign Female 2006 80,268 45,828 23,636 10,121 2007 80,747 46,823 24,295 11,060 2008 81,255 47,858 25,008 12,175 2009 81,792 48,934 25,776 13,494 2010 82,358 50,051 26,602 15,052 2011 82,953 51,211 27,490 16,889 2012 83,577 52,416 28,444 19,052 2013 84,230 53,666 29,465 21,597 2014 84,913 54,963 30,559 24,588 2015 85,626 56,308 31,730 28,101 2016 86,369 57,702 32,981 32,223 SOURCE:SOURCE: Analysis NRC NRC analysis. Table E-11 Breakout of Behavioral Sciences Workforce, 2006-2016, Scenario 3 BEHAVIORAL - SCENARIO 3 DETAILS US Male US Female Foreign Male Foreign Female

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8 APPENDIX E Figure E-12 shows the projections for each of the four demographic groups for the behavioral sciences workforce for Scenario 3 in bar-graph form, and Table E-11 shows the same projections in table form. Fore ign BEH Fe male Fore ign BEH M ale 150,000 U.S. B EH Fe male U.S. B EH M ale 125,000 100,000 Number in Work force 75,000 BIOMEDICAL - SCENARIO 3 DETAILS US Male US Female Foreign Male Foreign Female 2006 80,268 45,828 23,636 10,121 2007 80,747 50,000 46,823 24,295 11,060 2008 81,255 47,858 25,008 12,175 2009 81,792 48,934 25,776 13,494 2010 82,358 25,000 50,051 26,602 15,052 2011 82,953 51,211 27,490 16,889 2012 83,577 52,416 28,444 19,052 2013 84,230 0 53,666 29,465 21,597 2014 84,913 54,963 2007 2008 30,559 2010 2011 2012 2013 201424,588 2006 2009 2015 2016 2015 85,626 56,308 31,730 28,101 Year 2016E-12 86,369 of behavioral sciences workforce, 2006-2016, scenario 3. 32,223 57,702 32,981 FIGURE Breakout SOURCE: NRC Analysis SOURCE: NRC analysis. E-12.eps Table E-11 Breakout Breakout of Behavioral Sciences Workforce, 2006-2016, Scenario 3 TABLE E-11 of Behavioral Sciences Workforce, 2006-2016, Scenario 3 BEHAVIORAL - SCENARIO 3 DETAILS US Male US Female Foreign Male Foreign Female 2006 57,593 62,758 1,457 2,484 2007 57,491 63,830 1,605 2,735 2008 57,391 64,907 1,750 3,003 2009 57,293 65,990 1,892 3,291 2010 57,197 67,078 2,031 3,600 2011 57,102 68,172 2,167 3,932 2012 57,010 69,273 2,301 4,287 2013 56,920 70,379 2,432 4,669 2014 56,831 71,493 2,560 5,078 2015 56,744 72,613 2,686 5,518 2016 56,659 73,739 2,809 5,991 SOURCE: OURCE: NRC analysis. S NRC Analysis Table E - 12: Breakout of Clinical Sciences Workforce, 2006-2016, Scenario 3

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 APPENDIX E Figure E-13 shows the projections for each of the four demographic groups for the clinical sciences workforce for Scenario 3 in bar-graph form, and Table E-12 shows the same projections in table form. 50,000 Foreign CLIN Female Foreign CLIN Male U.S. CLIN Female U.S. CLIN Male 40,000 Number in Work force 30,000 20,000 10,000 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year FIGURE E-13 Breakout of clinical sciences workforce, 2006-2016, scenario 3. SOURCE: NRC analysis. E-13.eps TABLE E-12 Breakout of Clinical Sciences Workforce, 2006-2016, Scenario 3 CLINICAL - SCENARIO 3 DETAILS US Male US Female Foreign Male Foreign Female 2006 9,457 14,706 6,359 4,798 2007 9,591 15,283 6,439 4,978 2008 9,728 15,890 6,520 5,181 2009 9,869 16,528 6,604 5,408 2010 10,013 17,199 6,689 5,661 2011 10,160 17,904 6,777 5,944 2012 10,311 18,645 6,866 6,259 2013 10,466 19,424 6,958 6,608 2014 10,624 20,242 7,052 6,995 2015 10,785 21,101 7,148 7,424 2016 10,950 22,004 7,246 7,897 RCE: NRC Analysisanalysis. SOURCE: NRC le E - 13 Data for US-Trained Ph.D.s

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0 APPENDIX E deSCriPtioN of data uSed for 2001 to 2006) and the past 7 years of data (i.e., 1999 to 2006). WorkforCe ProJeCtioNS The numbers in these columns that are shaded gray are the annual growth rates used for those demographic groups in the Table E-13 shows the data for U.S.-trained Ph.D.s. In workforce projections. To mitigate large changes, the smaller Table E-13, the values in the rightmost columns are the aver- of the two annual growth rates is typically used, or the most age annual growth rates using the past 5 years of data (i.e., reasonable value is used based on inspection. TABLE E-13 Data for U.S.-Trained Ph.D.s Clinical PhD's Annual Avg Growth 1995 1997 1999 2001 2003 2006 Last 5 yrs Last 7 yrs Males Emp, in S&E 5464 6629 6782 7406 6595 7477 0.2% 1.5% Males Emp, out of S&E 740 478 541 546 1760 1600 38.6% 27.9% Males Unemp, Seeking Work 103 71 101 3 88 6 18.8% -13.4% Males Unemp, Not Seeking, Not Retired 49 99 39 74 29 34 -10.8% -1.7% Males Retired 520 550 880 858 907 862 0.1% -0.3% Males Postdoc 212 204 139 136 206 340 30.0% 20.6% Females Emp, in S&E 6051 7087 7997 9358 9505 11375 4.3% 6.0% Females Emp, out of S&E 575 307 685 846 2084 2172 31.3% 31.0% Females Unemp, Seeking Work 68 99 102 124 168 124 0.1% 3.1% Females Unemp, Not Seeking, Not Retired 217 289 294 332 349 486 9.2% 9.4% Females Retired 299 407 428 503 765 868 14.5% 14.7% Females Postdoc 273 310 292 280 254 549 19.2% 12.6% Biomedical PhD's Annual Avg Growth 1995 1997 1999 2001 2003 2006 Last 5 yrs Last 7 yrs Males Emp, in S&E 52075 56819 60727 62814 59582 60794 -0.6% 0.0% Males Emp, out of S&E 5052 4370 3818 4657 10032 10772 26.3% 26.0% Males Unemp, Seeking Work 824 538 561 713 1240 464 -7.0% -2.5% Males Unemp, Not Seeking, Not Retired 1089 1159 1205 1337 1385 796 -8.1% -4.8% Males Retired 5533 5252 6939 7617 8010 8312 1.8% 2.8% Males Postdoc 5973 7355 7080 6342 5706 7442 3.5% 0.7% Females Emp, in S&E 16928 24119 22257 25768 28068 29814 3.1% 4.9% Females Emp, out of S&E 2687 2289 2500 3434 4967 6604 18.5% 23.5% Females Unemp, Seeking Work 408 487 576 305 792 582 18.2% 0.1% Females Unemp, Not Seeking, Not Retired 1670 2290 2280 2576 3116 2302 -2.1% 0.1% Females Retired 1082 1667 1533 1831 1924 3033 13.1% 14.0% Females Postdoc 4218 5169 5745 5332 4547 6526 4.5% 1.9% Behavioral PhD's Annual Avg Growth 1995 1997 1999 2001 2003 2006 Last 5 yrs Last 7 yrs Males Emp, in S&E 48571 50030 51792 51820 25702 45454 -2.5% -1.7% Males Emp, out of S&E 6242 4881 5025 5634 25609 10668 17.9% 16.0% Males Unemp, Seeking Work 284 395 418 281 5888 224 -4.0% -6.6% Males Unemp, Not Seeking, Not Retired 579 723 583 649 524 302 -10.7% -6.9% Males Retired 4630 5214 5638 5982 509 6512 1.8% 2.2% Males Postdoc 714 1171 640 763 6325 945 4.8% 6.8% Females Emp, in S&E 34103 39240 42004 45131 31908 47806 1.2% 2.0% Females Emp, out of S&E 4271 2926 3598 4996 18568 10715 22.9% 28.3% Females Unemp, Seeking Work 289 277 554 509 4171 449 -2.4% -2.7% Females Unemp, Not Seeking, Not Retired 1763 2061 2621 2769 2486 2333 -3.2% -1.6% Females Retired 1329 1637 2328 2992 708 4775 11.9% 15.0% Females Postdoc 1154 1460 1524 1374 4386 1455 1.2% -0.6% SOURCE: National Science Foundation Survey of Doctoral Recipients, 1995 - 2006 SOURCE: Data adapted from National Science Foundation Survey of Doctoral Recipients, 1995-2006. Table E - 14 Data for Foreign-Trained Ph.D.s

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 APPENDIX E Table E-14 shows the data for foreign-trained Ph.D.s. It annual growth rates used for the various foreign-trained should be noted that information regarding foreign-trained Ph.D. groups in the workforce projections. Where there are Ph.D. students is not as well documented as the information “blanks” in the 2003 or 2006 data, values have been assumed for U.S.-trained Ph.D. students. In Table E-14, the values to be the same as either the preceding data or the succeeding in the rightmost column are the average annual growth data. These cells are shaded gray and will show no growth rates using the past 3 years of data (e.g., 2003 to 2006) between 2003 and 2006 because the same numbers are used because there are no data available for 2001. These are the for both years. TABLE E-14 Data for Foreign-Trained Ph.D.s Clinical PhD's Annual Avg Growth 1995 1997 1999 2001 2003 2006 Last 3 yrs Males Emp, in S&E 6073 5629 4982 4621 4716 0.7% Males Emp, out of S&E 956 465 680 1328 1344 0.4% Males Unemp, Seeking Work 81 205 123 123 0.0% Males Unemp, Not Seeking, Not Retired 74 177 389 176 176 0.0% Males Retired 1223 1137 821 1672 213 -29.1% Males Postdoc Females Emp, in S&E 1007 1051 1689 5494 3841 -10.0% Females Emp, out of S&E 172 185 204 641 846 10.7% Females Unemp, Seeking Work 163 89 52 52 0.0% Females Unemp, Not Seeking, Not Retired 232 142 229 59 -24.7% Females Retired 596 824 621 367 442 6.8% Females Postdoc Biomedical PhD's Annual Avg Growth 1995 1997 1999 2001 2003 2006 Last 3 yrs Males Emp, in S&E 6760 6246 6864 21386 20381 -1.6% Males Emp, out of S&E 68 85 1908 2275 6.4% Males Unemp, Seeking Work 135 175 737 332 -18.3% Males Unemp, Not Seeking, Not Retired 121 73 189 222 648 64.0% Males Retired 471 708 873 1508 2207 15.5% Males Postdoc Females Emp, in S&E 2575 2781 2622 8859 8857 0.0% Females Emp, out of S&E 178 96 244 461 826 26.4% Females Unemp, Seeking Work 176 128 647 269 -19.5% Females Unemp, Not Seeking, Not Retired 406 236 331 704 169 -25.3% Females Retired 71 318 298 1584 744 -17.7% Females Postdoc Behavioral PhD's Annual Avg Growth 1995 1997 1999 2001 2003 2006 Last 3 yrs Males Emp, in S&E 987 667 827 690 1044 17.1% Males Emp, out of S&E 776 573 672 397 259 -11.6% Males Unemp, Seeking Work Males Unemp, Not Seeking, Not Retired 154 154 0.0% Males Retired 456 192 296 95 -22.6% Males Postdoc Females Emp, in S&E 779 947 992 768 1513 32.3% Females Emp, out of S&E 257 234 71 1260 817 -11.7% Females Unemp, Seeking Work Females Unemp, Not Seeking, Not Retired 89 108 154 14.2% Females Retired 60 65 156 71 71 0.0% Females Postdoc SOURCE: Dara National Science Foundation Survey ey of College Graduates, SOURCE: adopted from National Science Foundation Surv of College Graduates, 1995-2006. 1995-2006 Table E-15: Ph.D. Data Used in Scenario 3

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 APPENDIX E deSCriPtioN of SyStem dyNamiCS modelS a sanity check on assumptions and are more rigorous than mental models or spreadsheets, allow for analysis of a wider System dynamics (SD) is the application of feedback range of issues, and identify the actions that are most effec- control systems principles and techniques to managerial, tive (and least effective) for improving performance. Third, organizational, and socioeconomic problems. As such, the communication is more effective because the approach is methodology seeks to bring together multiple views or graphical (the connections are easily seen and understood), aspects of the same problem under study and integrate them logical (the results can be traced back to their root causes), into a conceptual and meaningful whole. In fact, most dif- and experiential (we learn best by doing and simulation is a ficulties to fully understanding complex issues arise from good substitute for the real world). looking independently at various elements of an issue instead In SD models, a “stock” and “flow” methodology is used of considering pertinent interrelations. Consequently, opti- in which stocks represent accumulations of “things” (e.g., mization is sought for each separate element in the system, people, inventory), and flows are the movement of these which inadvertently leads to sub-optimization of total system “things” into, out of, and between stocks (Figure E-14). performance. With SD, it is possible to take hypotheses about For Scenario 1 (moderate risk) and Scenario 2 (high risk), a the separate parts of a system, to combine them in a computer very basic SD model was used in which the stocks represent simulation model, and to learn both the “local” and “global” groups of people in the following categories (which were consequences of decisions and actions, as well as the impact established based on available data): of these decisions and actions on short-term and long-term performance. Most of the time, the impact on short-term and • In Science and Engineering (S&E)—The number of long-term performance are opposite: an action that looks people employed in science and engineering positions (not positive in the short-term is often very detrimental in the considered postdoctorates). long-term. Conversely, an action that produces favorable • Out of S&E—The number of people employed in areas long-term performance must usually suffer poor performance other than science and engineering. in the short-term. • Unemp Seeking Work—The number of people cur- SD extends modeling methods traditionally associated rently unemployed but are seeking work. with engineering design and feedback control theory into the • Unemp Not Seeking Work—The number of people arena of policy evaluation and management decision making. currently unemployed but not seeking work, but are not The following characteristics distinguish SD models from retired. traditional decision support methodologies: • Retired—The number of people currently retired. • Postdoctorate—The number of people employed as • Its building blocks are feedback loops; postdoctorates. • It can accommodate non-linear relationships among variables; The total number of people considered in the “work- • It enforces causality; force” is the sum of all people that are not retired. Thus, the • It can include delays; workforce for any particular demographic group (e.g., U.S.- • It can model “soft” variables; trained males in biomedical science) is the following: • It can model management policies; and • It presents a dynamic environment for decision Workforce = In S&E + Out of S&E + Unemp Seeking Work analysis. + Unemp Not Seeking Work + Postdoctorate These characteristics are important because they allow The flows in and out of the stocks (e.g., In , Out ) are SD models to capture the key structural relationships that based on growth rates determined from the data for the spe- define a social system. The structure, in turn, produces the cific demographic group and shown earlier in Tables E-13 dynamic behavior of interest. The resulting simulation mir- and E-14. If the growth rate is greater than zero (i.e., posi- rors reality because the underlying model structure includes tive), then people are added to the stock through the In flow. the appropriate feedback loops, causality, delays, and other If the growth rate is less than zero (i.e., negative), then people relationships. SD models include real-world causal logic, are removed from the stock through the Out flow. The amount which allows someone to trace through the model to see why of people that are added or removed is based on the percent- things happen the way they do. age growth rate multiplied by the current number of people The SD modeling and simulation approach is differ- in the stock. For example, if 100 people were in a stock and ent from traditional statistical approaches in several ways. the growth rate is 5 percent, then 5 people would be added First, the models are more realistic because they capture to the stock during that simulation step. cause-and-effect linkages, feedback loops, delays, non-linear Figure E-14 below shows this stock-and-flow diagram relationships, and management policies. Second, the simula - for the U.S.-trained males in biomedical science. This exact tions are more accurate and reliable because they provide same model structure is used for all other demographic

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 APPENDIX E U.S. BIO Male Grow th Rate 1 In S & E 1 In 1 Out 1 Grow th Rate 2 Out of S & E 1 In 2 Out 2 Grow th Rate 3 Total Employed 1 Unemp Seeking Work 1 In 3 Out 3 Total Other 1 Grow th Rate 4 Adj Factor 1 Unemp Not Seeking Work 1 Out 4 In 4 US BIO Male Work force Grow th Rate 5 Foreign BIO Work force Retired 1 Total BIO Work force Out 5 In 5 Grow th Rate 6 Postdoc 1 US BIO Work force Out 6 In 6 FIGURE E-14 Model for U.S.-trained males in biomedical science for scenarios 1 and 2. groups (e.g., U.S.-trained females in biomedical science, workforce. (At this point, because the data for Ph.D. students foreign-trained males in clinical science, etc.). However, is aggregate, the workforce is represented as aggregate to different data are used to initialize the model based on which maintain consistency, as opposed to multiple portions of the specific demographic group is being modeled. workforce as in Scenarios 1 and 2 and in Figure E-14.) The E-14.eps For Scenario 3, a slightly different stock-and-flow struc- inclusion of the supply pipeline in Scenario 3 is the reason ture is used that includes more of the “supply pipeline” that this scenario is considered low risk. Adding the Ph.D. (Figure E-15). For each demographic group, a stock of Ph.D. student pool produces limits to the growth of the following students is also included that precedes the stock for the entire workforce, which is more realistic than letting the workforce

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 APPENDIX E U.S. BIO Female PhD 4 Work force 4 Enter Work force 4 Leave Work force 4 Enter PhD 4 Avg Work Length 3 Avg Grow th Rate 4 Avg Grad Length 3 FIGURE E-15 Model for U.S.-trained females in biomedical science for scenario 3. E-15.eps continue to grow (or shrink) at its current pace. Conse - Table E-15 shows the data used for the Ph.D. pipeline quently, the workforce projection numbers are lower for all model. The values in the rightmost columns are the average three major workforces (i.e., biomedical science, clinical annual growth rates using the past 5 years of data (i.e., 2001 science, and behavioral science). to 2006), as highlighted by the gray shaded cells. The 5-year In the pipeline model for each demographic group, the average annual growth rates are the ones used in the Scenario model starts with the number of Ph.D. students and uses the 3 model for the growth of the Ph.D. student population. growth rate for Ph.D. students to determine how many Ph.D. It should be noted that the pipeline model is not com- students enter the Ph.D. pool. The Ag Grad Length then plete. Additional stocks could precede the Ph.D. pool (e.g., determines how quickly students move through the Ph.D. undergraduate students, K-12 students, etc.) to represent the pool to enter the workforce. For the purposes of this analysis, full pipeline of students progressing up to employment in the average graduation time is assumed to be 7 years. Thus, the workforce. In addition, based on detailed data for the 1/7th of the Ph.D. pool enters the workforce each year. For the Ph.D. pool, several pipeline models could be used to show Workforce, the Ag Work Length determines how many people the movement through the pipelines for the fields of science, retire or move out of the workforce each year. For the purposes engineering, etc. in addition to the separation of male/female of this analysis, the average time that someone spends in the and U.S./foreign. workforce is assumed to be 50 years. Thus, 1/50th of the people leave the workforce each year of the simulation.

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 APPENDIX E TABLE E-15 Ph.D. Data Used in Scenario 3 Doctorates by Year, Citizenship and Gender FEMALES 1999 2000 2001 2002 2003 2004 2005 2006 Annual Avg Growth Last 5 yrs Biomedical Sciences Citizens 1528 1683 1670 1596 1639 1738 1830 1897 2.7% Permanent Residents 218 176 182 182 151 126 142 185 0.3% Temporary Residents 442 532 476 480 549 600 754 897 17.7% Unknown 17 7 5 4 20 29 35 16 44.0% Clinical Sciences Citizens 650 762 713 762 797 826 827 860 4.1% Permanent Residents 34 41 43 42 33 48 52 54 5.1% Temporary Residents 133 144 140 172 149 180 207 214 10.6% Unknown 7 9 4 8 12 11 22 9 25.0% Behavioral Sciences Citizens 2487 2523 2317 2250 2301 2240 2260 2325 0.1% Permanent Residents 81 79 72 69 63 69 85 88 4.4% Temporary Residents 124 159 142 152 186 190 187 206 9.0% Unknown 6 2 6 5 6 18 12 13 23.3% MALES 1999 2000 2001 2002 2003 2004 2005 2006 Annual Avg Growth Last 5 yrs Biomedical Sciences Citizens 1910 1937 1965 1974 1913 1990 2018 2074 1.1% Permanent Residents 259 188 155 134 114 102 111 118 -4.8% Temporary Residents 782 804 725 742 788 805 889 995 7.4% Unknown 30 4 17 7 15 20 27 26 10.6% Clinical Sciences Citizens 275 307 298 299 316 307 326 322 1.6% Permanent Residents 47 33 32 21 27 20 29 32 0.0% Temporary Residents 117 131 150 131 154 155 167 174 3.2% Unknown 8 4 8 7 8 8 7 12 10.0% Behavioral Sciences Citizens 1310 1296 1200 1158 1138 1201 1110 1049 -2.5% Permanent Residents 38 56 42 47 33 28 31 25 -8.1% Temporary Residents 141 151 133 113 135 142 163 153 3.0% Unknown 7 4 5 2 11 9 5 5 0.0%

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