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Research Training in the Biomedical, Behavioral, and Clinical Research Sciences
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:
U.S.-trained males
U.S.-trained females
Foreign-trained males
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.
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.
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.
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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FIGURE E-1 Total biomedical, behavioral, and clinical sciences workforces, 2006-2016, scenario 1.
SOURCE: NRC analysis.
FIGURE E-2 Total biomedical sciences workforce, 2006-2016.
SOURCE: NRC analysis.
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FIGURE E-3 Total behavioral sciences workforce, 2006-2016.
SOURCE: NRC analysis.
FIGURE E-4 Total clinical sciences workforce, 2006-2016.
SOURCE: NRC analysis.
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TABLE E-1 Biomedical Sciences Workforce Projections for All Scenarios
BIOMEDICAL
Scenario 1
Scenario 2
Scenario 3
2006
159,853
159,853
159,853
2007
162,950
164,598
162,926
2008
166,423
170,244
166,296
2009
170,339
177,046
169,995
2010
174,782
185,354
174,063
2011
179,854
195,662
178,543
2012
185,684
208,677
183,489
2013
192,437
225,425
188,959
2014
200,321
247,417
195,024
2015
209,607
276,908
201,764
2016
220,642
317,302
209,274
SOURCE: NRC analysis.
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
SOURCE: NRC analysis.
TABLE E-3 Clinical Sciences 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
SOURCE: NRC analysis.
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FIGURE E-5 Breakout of biomedical sciences workforce, 2006-2016, scenario 1.
SOURCE: NRC analysis.
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 analysis.
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FIGURE E-6 Breakout of behavioral sciences workforce, 2006-2016, scenario 1.
SOURCE: NRC 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
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: NRC analysis.
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FIGURE E-7 Breakout of clinical sciences workforce, 2006-2016, scenario 1.
SOURCE: NRC analysis.
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
SOURCE: NRC analysis.
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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.
FIGURE E-8 Breakout of biomedical sciences workforce, 2006-2016, scenario 2.
SOURCE: NRC analysis.
TABLE E-7 Breakout of Biomedical Sciences Workforce, 2006-2016, Scenario 2
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 analysis.
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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.
FIGURE E-9 Breakout of behavioral sciences workforce, 2006-2016, scenario 2.
SOURCE: NRC analysis.
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 analysis.
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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.
FIGURE E-10 Breakout of clinical sciences workforce, 2006-2016, scenario 2.
SOURCE: NRC 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
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
SOURCE: NRC analysis.
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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.
FIGURE E-11 Breakout of biomedical sciences workforce, 2006-2016, scenario 3.
SOURCE: NRC analysis.
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: NRC analysis.
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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.
FIGURE E-12 Breakout of behavioral sciences workforce, 2006-2016, scenario 3.
SOURCE: 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
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: NRC analysis.
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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.
FIGURE E-13 Breakout of clinical sciences workforce, 2006-2016, scenario 3.
SOURCE: NRC analysis.
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
SOURCE: NRC analysis.
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DESCRIPTION OF DATA USED FOR WORKFORCE PROJECTIONS
Table E-13 shows the data for U.S.-trained Ph.D.s. In Table E-13, the values in the rightmost columns are the average annual growth rates using the past 5 years of data (i.e., 2001 to 2006) and the past 7 years of data (i.e., 1999 to 2006). The numbers in these columns that are shaded gray are the annual growth rates used for those demographic groups in the workforce projections. To mitigate large changes, the smaller of the two annual growth rates is typically used, or the most reasonable value is used based on inspection.
TABLE E-13 Data for U.S.-Trained Ph.D.s
SOURCE: Data adapted from National Science Foundation Survey of Doctoral Recipients, 1995-2006.
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Table E-14 shows the data for foreign-trained Ph.D.s. It should be noted that information regarding foreign-trained Ph.D. students is not as well documented as the information for U.S.-trained Ph.D. students. In Table E-14, the values in the rightmost column are the average annual growth rates using the past 3 years of data (e.g., 2003 to 2006) because there are no data available for 2001. These are the annual growth rates used for the various foreign-trained Ph.D. groups in the workforce projections. Where there are “blanks” in the 2003 or 2006 data, values have been assumed to be the same as either the preceding data or the succeeding data. These cells are shaded gray and will show no growth between 2003 and 2006 because the same numbers are used for both years.
TABLE E-14 Data for Foreign-Trained Ph.D.s
SOURCE: Dara adopted from National Science Foundation Survey of College Graduates, 1995-2006.
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DESCRIPTION OF SYSTEM DYNAMICS MODELS
System dynamics (SD) is the application of feedback control systems principles and techniques to managerial, organizational, and socioeconomic problems. As such, the methodology seeks to bring together multiple views or aspects of the same problem under study and integrate them into a conceptual and meaningful whole. In fact, most difficulties to fully understanding complex issues arise from looking independently at various elements of an issue instead of considering pertinent interrelations. Consequently, optimization is sought for each separate element in the system, which inadvertently leads to sub-optimization of total system performance. With SD, it is possible to take hypotheses about the separate parts of a system, to combine them in a computer simulation model, and to learn both the “local” and “global” consequences of decisions and actions, as well as the impact of these decisions and actions on short-term and long-term performance. Most of the time, the impact on short-term and long-term performance are opposite: an action that looks positive in the short-term is often very detrimental in the long-term. Conversely, an action that produces favorable long-term performance must usually suffer poor performance in the short-term.
SD extends modeling methods traditionally associated with engineering design and feedback control theory into the arena of policy evaluation and management decision making. The following characteristics distinguish SD models from traditional decision support methodologies:
Its building blocks are feedback loops;
It can accommodate non-linear relationships among variables;
It enforces causality;
It can include delays;
It can model “soft” variables;
It can model management policies; and
It presents a dynamic environment for decision analysis.
These characteristics are important because they allow SD models to capture the key structural relationships that define a social system. The structure, in turn, produces the dynamic behavior of interest. The resulting simulation mirrors reality because the underlying model structure includes the appropriate feedback loops, causality, delays, and other relationships. SD models include real-world causal logic, which allows someone to trace through the model to see why things happen the way they do.
The SD modeling and simulation approach is different from traditional statistical approaches in several ways. First, the models are more realistic because they capture cause-and-effect linkages, feedback loops, delays, non-linear relationships, and management policies. Second, the simulations are more accurate and reliable because they provide a sanity check on assumptions and are more rigorous than mental models or spreadsheets, allow for analysis of a wider range of issues, and identify the actions that are most effective (and least effective) for improving performance. Third, communication is more effective because the approach is graphical (the connections are easily seen and understood), logical (the results can be traced back to their root causes), and experiential (we learn best by doing and simulation is a good substitute for the real world).
In SD models, a “stock” and “flow” methodology is used in which stocks represent accumulations of “things” (e.g., people, inventory), and flows are the movement of these “things” into, out of, and between stocks (Figure E-14). For Scenario 1 (moderate risk) and Scenario 2 (high risk), a very basic SD model was used in which the stocks represent groups of people in the following categories (which were established based on available data):
In Science and Engineering (S&E)—The number of people employed in science and engineering positions (not considered postdoctorates).
Out of S&E—The number of people employed in areas other than science and engineering.
Unemp Seeking Work—The number of people currently unemployed but are seeking work.
Unemp Not Seeking Work—The number of people currently unemployed but not seeking work, but are not retired.
Retired—The number of people currently retired.
Postdoctorate—The number of people employed as postdoctorates.
The total number of people considered in the “workforce” is the sum of all people that are not retired. Thus, the workforce for any particular demographic group (e.g., U.S.-trained males in biomedical science) is the following:
Workforce = In S&E + Out of S&E + Unemp Seeking Work + Unemp Not Seeking Work + Postdoctorate
The flows in and out of the stocks (e.g., In 1, Out 1) are based on growth rates determined from the data for the specific demographic group and shown earlier in Tables E-13 and E-14. If the growth rate is greater than zero (i.e., positive), then people are added to the stock through the In flow. If the growth rate is less than zero (i.e., negative), then people are removed from the stock through the Out flow. The amount of people that are added or removed is based on the percentage growth rate multiplied by the current number of people in the stock. For example, if 100 people were in a stock and the growth rate is 5 percent, then 5 people would be added to the stock during that simulation step.
Figure E-14 below shows this stock-and-flow diagram for the U.S.-trained males in biomedical science. This exact same model structure is used for all other demographic
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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, foreign-trained males in clinical science, etc.). However, different data are used to initialize the model based on which specific demographic group is being modeled.
For Scenario 3, a slightly different stock-and-flow structure is used that includes more of the “supply pipeline” (Figure E-15). For each demographic group, a stock of Ph.D. students is also included that precedes the stock for the entire workforce. (At this point, because the data for Ph.D. students is aggregate, the workforce is represented as aggregate to maintain consistency, as opposed to multiple portions of the workforce as in Scenarios 1 and 2 and in Figure E-14.) The inclusion of the supply pipeline in Scenario 3 is the reason that this scenario is considered low risk. Adding the Ph.D. student pool produces limits to the growth of the following workforce, which is more realistic than letting the workforce
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FIGURE E-15 Model for U.S.-trained females in biomedical science for scenario 3.
continue to grow (or shrink) at its current pace. Consequently, the workforce projection numbers are lower for all three major workforces (i.e., biomedical science, clinical science, and behavioral science).
In the pipeline model for each demographic group, the model starts with the number of Ph.D. students and uses the growth rate for Ph.D. students to determine how many Ph.D. students enter the Ph.D. pool. The Avg Grad Length then determines how quickly students move through the Ph.D. pool to enter the workforce. For the purposes of this analysis, the average graduation time is assumed to be 7 years. Thus, 1/7th of the Ph.D. pool enters the workforce each year. For the Workforce, the Avg Work Length determines how many people retire or move out of the workforce each year. For the purposes of this analysis, the average time that someone spends in the workforce is assumed to be 50 years. Thus, 1/50th of the people leave the workforce each year of the simulation.
Table E-15 shows the data used for the Ph.D. pipeline model. The values in the rightmost columns are the average annual growth rates using the past 5 years of data (i.e., 2001 to 2006), as highlighted by the gray shaded cells. The 5-year average annual growth rates are the ones used in the Scenario 3 model for the growth of the Ph.D. student population.
It should be noted that the pipeline model is not complete. Additional stocks could precede the Ph.D. pool (e.g., undergraduate students, K-12 students, etc.) to represent the full pipeline of students progressing up to employment in the workforce. In addition, based on detailed data for the Ph.D. pool, several pipeline models could be used to show the movement through the pipelines for the fields of science, engineering, etc. in addition to the separation of male/female and U.S./foreign.
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TABLE E-15 Ph.D. Data Used in Scenario 3
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