Appendix D

Data Tables

Table 1: Science, Engineering & Health researcher numbers in US, over time

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_resnums = pd.read_csv("./data/Researcher_numbers_over_time.csv")

# Create the table.
Markdown(tabulate(
  df_resnums, 
  headers=[ "Year", "Graduate Student", "Postdoc",  "Non-Faculty Researcher",],
  showindex=False,
))

Table 1: Science, Engineering & Health researcher numbers in US, over time

Year	Graduate Student	Postdoc	Non-Faculty Researcher
1979	357578	18101	2687
1980	367078	18399	3260
1981	375130	19634	3559
1982	382291	19363	4026
1983	390432	20712	4896
1984	394670	21535	5042
1985	404021	22387	5103
1986	415520	23721	4846
1987	421497	24881	4597
1988	424523	26123	4869
1989	434478	27932	4908
1990	452113	29565	5255
1991	471212	30865	5478
1992	493522	32747	5482
1993	504304	34322	6001
1994	504399	36377	6209
1995	499640	35926	6534
1996	494079	37107	6604
1997	487208	38481	6722
1998	485627	40086	7100
1999	493256	40800	7573
2000	493311	43115	7879
2001	509607	43311	7531
2002	540404	45034	7906
2003	567121	46728	8473
2004	574463	47240	9075
2005	582226	48555	9527
2006	597643	49343	10814
2007	619499	50840	10752
2008	631489	54164	13747
2009	631645	57805	14059
2010	632652	63439	21345
2011	626820	62639	21498
2012	627243	62851	21908
2013	633010	61942	22465
2014	666586	63593	23706
2015	685397	63861	25292
2016	684825	64712	25747
2017	649112	64733	28180
2018	668307	64783	29284
2019	690117	66247	30349
2020	697813	65681	29661

Corresponding graph: Science, Engineering & Health researcher numbers in US, over time

Source:

• Table 1-1, Graduate students, postdoctoral appointees, and doctorate-holding nonfaculty researchers in science, engineering, and health: 1975–2020. From the NSF, “Survey of Graduate Students and Postdoctorates in Science and Engineering: Fall 2020”. https://ncses.nsf.gov/pubs/nsf22319

Table 2: Percentage of Science, Engineering & Health researchers in US who are on temporary visas, over time

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_visanums = pd.read_csv("./data/Researcher_temporary-visa-holder_percentage_over_time.csv")

# Create the table.
Markdown(tabulate(
  df_visanums, 
  headers=[ "Year", "Graduate Student", "Postdoc",],
  showindex=False,
))

Table 2: Percentage of Science, Engineering & Health researchers in US who are on temporary visas, over time

Year	Graduate Student	Postdoc
1980	13.7	35.4
1981	14.5	37.1
1982	17.7	37.4
1983	18.8	36.3
1984	19	37.1
1985	19.8	39.6
1986	21	40.8
1987	21.9	43.2
1988	22.9	44.8
1989	23.5	46.8
1990	23.7	48.9
1991	24	51
1992	23.1	51.8
1993	21.9	51.3
1994	21.1	50.6
1995	20.6	49.5
1996	20.8	50.4
1997	21.3	50.8
1998	22	50.8
1999	23.4	53.7
2000	26	54.9
2001	27.6	57.6
2002	28.3	56.3
2003	27.3	57.9
2004	26.3	57.3
2005	25.3	55.7
2006	25.3	57.1
2007	25.7	56.5
2008	26.6	54
2009	27.2	53.1
2010	27.5	52.5
2011	28.1	52.6
2012	29.3	52.5
2013	31.1	52.3
2014	34.1	52.7
2015	35.5	55
2016	36.3	53.9
2017	35.8	53.5
2018	34.4	54.3
2019	33.9	55.5
2020	30.2	54.5

Corresponding graph: Percentage of Science, Engineering & Health researchers in US who are on temporary visas, over time

Source:

• Table 1-3a, Citizenship of graduate students and postdoctoral appointees in science, engineering, and health: 1980–2020. From the NSF, “Survey of Graduate Students and Postdoctorates in Science and Engineering: Fall 2020”. https://ncses.nsf.gov/pubs/nsf22319

Table 3: Annual mean wages for professional scientists in academic & non-academic roles in May 2021 (US$)

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_visanums = pd.read_csv("./data/salaries_acadVSnonacad2021.csv")

# Create the table.
Markdown(tabulate(
  df_visanums, 
  headers=[ "Discipline", "Academic (US$)", "Non-Academic (US$)",],
  showindex=False,
))

Table 3: Annual mean wages for professional scientists in academic & non-academic roles in May 2021 (US$)

Discipline	Academic (US$)	Non-Academic (US$)
Biochemist/biophysicist	66060	121310
Physical scientist	70580	130680
Life scientist	85770	107740
Physicist	104050	162240

Corresponding graph: Comparison of salaries for research scientist roles inside versus outside academia.

Sources:

• U.S. Bureau of Labor Statistics, “Occupational Employment and Wages, May 2021: 19-1021 Biochemists and Biophysicists” Accessed Dec 9, 2022 at https://www.bls.gov/oes/current/oes191021.htm

• U.S. Bureau of Labor Statistics, “Occupational Employment and Wages, May 2021: 19-2099 Physical Scientists, All Other” Accessed Dec 9, 2022 at https://www.bls.gov/oes/current/oes192099.htm

• U.S. Bureau of Labor Statistics, “Occupational Employment and Wages, May 2021: 19-1099 Life Scientists, All Other” Accessed Dec 9, 2022 at https://www.bls.gov/oes/current/oes191099.htm

• U.S. Bureau of Labor Statistics, “Occupational Employment and Wages, May 2021: 19-2012 Physicists” Accessed Dec 9, 2022 at https://www.bls.gov/oes/current/oes192012.htm

Table 4: NIH Postdoc Salaries

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_nihsals = pd.read_csv("./data/NIH_Postdoc_Salaries.csv")

# Create the table.
Markdown(tabulate(
  df_nihsals, 
  headers=[ "2001 NIH Goal (US$)", "2001 NIH Goal, inflation-adjusted for 2022 (US$)",  "NIH Actual Minimum in 2022 (US$)",],
  showindex=False,
))

Table 4: NIH Postdoc Salaries

2001 NIH Goal (US$)	2001 NIH Goal, inflation-adjusted for 2022 (US$)	NIH Actual Minimum in 2022 (US$)
45000	74403	54840

Corresponding graph: NIH Postdoc Salaries.

Sources:

• For 2001 NIH Goal: National Institutes of Health, (Mar 22, 2001) “NIH Statement in Response to the NAS Report: Addressing the Nation’s Changing Needs for Biomedical and Behavioral Scientists”. https://grants.nih.gov/grants/guide/notice-files/NOT-OD-01-027.html

• Inflation adjustment performed using the US Bureau of Labor Statistics Inflation Calculator. Accessed Jan 22, 2023 at https://www.bls.gov/data/inflation_calculator.htm

• For NIH Minimum Salary for fiscal year 2022: “Correction to Stipend Levels for Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2022” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-22-132.html

Table 5: Minimum Postdoc Salary Over Time

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_nihsals = pd.read_csv("./data/min_postdoc_salary_overtime.csv")

# Create the table.
Markdown(tabulate(
  df_nihsals, 
  headers=["Year",  "Date", "Stanford (US$)",   "NIH (US$)",    "Difference (US$)", "Stanford min as Percentage of NIH min (%)",],
  showindex=False,
))

Table 5: Minimum Postdoc Salary Over Time

Year	Date	Stanford (US$)	NIH (US$)	Difference (US$)	Stanford min as Percentage of NIH min (%)
2000	1-Sep-00	nan	26916	nan	nan
2001	1-Sep-01	nan	28260	nan	nan
2002	1-Sep-02	nan	31092	nan	nan
2003	1-Sep-03	nan	34200	nan	nan
2004	1-Sep-04	nan	35568	nan	nan
2005	1-Sep-05	nan	35568	nan	nan
2006	1-Sep-06	nan	36996	nan	nan
2007	1-Sep-07	nan	36996	nan	nan
2008	1-Sep-08	nan	36996	nan	nan
2009	1-Sep-09	nan	37368	nan	nan
2010	1-Sep-10	nan	37740	nan	nan
2011	1-Sep-11	nan	38496	nan	nan
2012	1-Sep-12	nan	39264	nan	nan
2013	1-Sep-13	nan	39264	nan	nan
2014	1-Sep-14	44310	42000	2310	106
2015	1-Sep-15	50000	42840	7160	117
2016	1-Sep-16	53406	43692	9714	122
2017	1-Sep-17	53406	47484	5922	113
2018	1-Sep-18	60000	48432	11568	124
2019	1-Sep-19	63618	50004	13614	127
2020	1-Sep-20	64268	52704	11564	122
2021	1-Sep-21	65568	53760	11808	122
2022	1-Sep-22	68238	54840	13398	125

Corresponding graph: Comparison of Stanford Postdoc Salary and National Institutes of Health (NIH) Postdoc Salary since 2012. Minimum Postdoc Salaries from Stanford and the NIH.

Source for Stanford Minimum Salaries:

• OPA website (https://postdocs.stanford.edu/funding-rates-and-guidelines) and email correspondence.

Sources for NIH Salaries:

• For fiscal year 2022: “Correction to Stipend Levels for Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2022” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-22-132.html

• For fiscal year 2021: “Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2021” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-049.html

• For fiscal year 2020: “Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2020” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-20-070.html

• For fiscal year 2019: “Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2019” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-19-036.html

• For fiscal year 2018: “Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2018” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-18-175.html

• For fiscal year 2017: “Ruth L. Kirschstein National Research Service Awards (NRSA) Postdoctoral Stipends, Training Related Expenses, Institutional Allowance, and Tuition/Fees Effective for Fiscal Year 2017” https://grants.nih.gov/grants/guide/notice-files/not-od-17-003.html

• For fiscal year 2016: “Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2016” https://grants.nih.gov/grants/guide/notice-files/not-od-16-047.html

• For fiscal year 2015: “Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2015” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-048.html

• For fiscal year 2014: “Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2014” https://grants.nih.gov/grants/guide/notice-files/not-od-14-046.html

• For fiscal year 2013: “NIH Fiscal Policy for Grant Awards – FY 2013” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-13-064.html

• For fiscal year 2012: “Ruth L. Kirschstein National Research Service Award (NRSA) Stipends, Tuition/Fees and Other Budgetary Levels Effective for Fiscal Year 2012” https://grants.nih.gov/grants/guide/notice-files/NOT-OD-12-033.html

Table 6a: Average postdoc incomes by location, absolute & adjusted for local cost of living, 2016, all US states

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_nihsals = pd.read_csv("./data/Salaries_byLocation_USstates.csv")

# Create the table.
Markdown(tabulate(
  df_nihsals, 
  headers=[ "State", "Absolute Income ($)", "Income Adjusted for Local Cost of Living ($)",],
  showindex=False,
))

Table 6: Average postdoc incomes by location, absolute & adjusted for local cost of living, 2016, all US states

State	Absolute Income ($)	Income Adjusted for Local Cost of Living ($)
Alabama	42960	48394
Alaska	48750	37305
Arkansas	42030	45127
Arizona	45748	48305
California	48576	32036
Colorado	47184	42778
Connecticut	45776	36975
Delaware	45179	41988
District of Columbia	49947	33521
Florida	44394	45283
Georgia	43362	43933
Hawaii	54375	28603
Idaho	45313	51668
Illinois	45889	39032
Indiana	45655	49669
Iowa	44938	46935
Kansas	43422	47075
Kentucky	39125	43049
Louisiana	45012	46491
Maine	45500	40661
Maryland	49901	39862
Massachusetts	47908	33058
Michigan	45321	45011
Minnesota	43996	42254
Mississippi	41650	48922
Missouri	44388	48509
Montana	47850	47517
Nebraska	42132	46043
Nevada	42313	40520
New Hampshire	43975	41369
New Jersey	48026	39398
New Mexico	56736	59848
New York	48356	26027
North Carolina	44986	47325
North Dakota	43313	45005
Ohio	44869	49493
Oklahoma	44333	52660
Oregon	45900	42015
Pennsylvania	45361	43685
Rhode Island	47266	38743
South Carolina	44494	44244
South Dakota	41250	43977
Tennessee	47787	54644
Texas	44766	47202
Utah	44959	48033
Vermont	40667	48183
Virginia	44880	46967
Washington	47508	33181
West Virginia	39875	41710
Wisconsin	44424	41791
Wyoming	53750	58679

Corresponding graph: Comparison of 2016 postdoc salaries in absolute dollars and normalized to cost of living, ordered from lowest to highest based on adjusted salary.

Source:

• Table S6 in McConnell, Westerman, Pierre, Heckler, Schwartz, (Dec 18, 2018) “Research: United States National Postdoc Survey results and the interaction of gender, career choice and mentor impact” eLife. https://doi.org/10.7554/eLife.40189

Table 6b: Average postdoc incomes by location, absolute & adjusted for local cost of living, 2016, US counties with more than 50 respondents

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_nihsals = pd.read_csv("./data/Salaries_byLocation_Counties.csv")

# Create the table.
Markdown(tabulate(
  df_nihsals, 
  headers=[ "County", "Absolute Income ($)",    "Income Adjusted for Local Cost of Living ($)",],
  showindex=False,
))

Table 7: Average postdoc incomes by location, absolute & adjusted for local cost of living, 2016, US counties with more than 50 respondents

County	Absolute Income ($)	Income Adjusted for Local Cost of Living ($)
Maricopa, AZ	46230	47709
Pima, AZ	45237	48642
Alameda, CA	48646	32780
Los Angeles, CA	52427	38302
Yolo, CA	46183	39711
San Diego, CA	45368	31462
San Francisco, CA	47490	26800
Santa Clara, CA	52723	23653
Denver, CO	44993	40792
New Haven, CT	45758	36932
Alachua, FL	43460	44392
Clarke, GA	41619	42168
DeKalb, GA	45104	45699
Cook, IL	45611	38840
Tippecanoe, IN	42806	49487
Johnson, IA	45083	46718
Douglas, KS	42815	46588
Baltimore City, MD	46718	40448
Montgomery, MD	53631	39993
Prince George’s, MD	50331	37532
Middlesex, MA	48433	34325
Suffolk, MA	47454	32085
Ingham, MI	44165	47901
Washtenaw, MI	45757	43745
Hennepin, MN	43955	41663
Boone, MO	43030	45343
St. Louis, MO	44916	49741
Los Alamos, NM	60000	63291
Monroe, NY	47311	46797
New York, NY	50811	22304
Orange, NC	45461	47404
Franklin, OH	43978	48919
Hamilton, OH	46443	50981
Benton, OR	46996	46030
Jackson, OR	45777	44836
Multnomah, OR	45426	35378
Allegheny, PA	44561	47480
Philadelphia, PA	46293	39066
Charleston, SC	45634	43921
Dallas, TX	44910	44776
Harris, TX	44896	45441
McLennan, TX	45446	51527
Salt Lake, UT	44873	47941
King, WA	47374	32694

Corresponding graph: Comparison of 2016 postdoc salaries in absolute dollars and normalized to cost of living, ordered from lowest to highest based on adjusted salary.

Source:

• Table S6 in McConnell, Westerman, Pierre, Heckler, Schwartz, (Dec 18, 2018) “Research: United States National Postdoc Survey results and the interaction of gender, career choice and mentor impact” eLife. https://doi.org/10.7554/eLife.40189

Table 7: Postdoc Salary by Years Experience, Over Time

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_nihsals = pd.read_csv("./data/Salaries_Min_byYrsExperience.csv")

# Create the table.
Markdown(tabulate(
  df_nihsals, 
  headers=[ "Date", "Stanford (PhD + 0-5 yrs)", "Stanford (PhD + 6+ yrs)",  "NIH (PhD + 0 yrs)",    "NIH (PhD + 1 yr)", "NIH (PhD + 2 yrs)",    "NIH (PhD + 3 yrs)",    "NIH (PhD + 4 yrs)",    "NIH (PhD + 5 yrs)",    "NIH (PhD + 6 yrs)",    "NIH (PhD + 7 yrs)",],
  showindex=False,
))

Table 8: Postdoc Salary by Years Experience, Over Time

Date	Stanford (PhD + 0-5 yrs)	Stanford (PhD + 6+ yrs)	NIH (PhD + 0 yrs)	NIH (PhD + 1 yr)	NIH (PhD + 2 yrs)	NIH (PhD + 3 yrs)	NIH (PhD + 4 yrs)	NIH (PhD + 5 yrs)	NIH (PhD + 6 yrs)	NIH (PhD + 7 yrs)
1-Sep-2000	nan	nan	26916	nan	nan	nan	nan	nan	nan	nan
1-Sep-2001	nan	nan	28260	nan	nan	nan	nan	nan	nan	nan
1-Sep-2002	nan	nan	31092	nan	nan	nan	nan	nan	nan	nan
1-Sep-2003	nan	nan	34200	nan	nan	nan	nan	nan	nan	nan
1-Sep-2004	nan	nan	35568	nan	nan	nan	nan	nan	nan	nan
1-Sep-2005	nan	nan	35568	nan	nan	nan	nan	nan	nan	nan
1-Sep-2006	nan	nan	36996	nan	nan	nan	nan	nan	nan	nan
1-Sep-2007	nan	nan	36996	nan	nan	nan	nan	nan	nan	nan
1-Sep-2008	nan	nan	36996	nan	nan	nan	nan	nan	nan	nan
1-Sep-2009	nan	nan	37368	nan	nan	nan	nan	nan	nan	nan
1-Sep-2010	nan	nan	37740	nan	nan	nan	nan	nan	nan	nan
1-Sep-2011	nan	nan	38496	nan	nan	nan	nan	nan	nan	nan
1-Sep-2012	nan	nan	39264	41364	44340	46092	47820	49884	51582	54180
1-Sep-2013	nan	nan	39264	41364	44340	46092	47820	49884	51582	54180
1-Sep-2014	44310	nan	42000	43680	45432	47244	49128	51096	53148	55272
1-Sep-2015	50000	nan	42840	44556	46344	48192	50112	52116	54216	56376
1-Sep-2016	53406	nan	43692	45444	47268	49152	51120	53160	55296	57504
1-Sep-2017	53406	nan	47484	47844	48216	50316	52140	54228	56400	58560
1-Sep-2018	60000	nan	48432	48804	49188	51324	53184	55308	57528	59736
1-Sep-2019	63618	nan	50004	50376	50760	52896	54756	56880	59100	61308
1-Sep-2020	64268	64268	52704	53076	53460	55596	57456	59580	61800	64008
1-Sep-2021	65568	66888	53760	54144	54540	56712	58608	60780	63036	65292
1-Sep-2022	68238	69695	54840	55224	55632	57852	59784	61992	64296	66600

Corresponding graph: Comparison of Stanford Postdoc Salary and National Institutes of Health (NIH) Postdoc Salary since 2012. Postdoc Salaries by Number of Years Experience post-PhD.

Source for Stanford Minimum Salaries:

• OPA website (https://postdocs.stanford.edu/funding-rates-and-guidelines) and email correspondence.

Sources for NIH Salaries:

• As for Table 5.

Table 8a: Area Median Income (AMI) Levels for Santa Clara County over time, 1-person household (US$)

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_AMI_HH1 = pd.read_csv("./data/AMI_over_time,_Santa_Clara,_HH1.csv")

# Remove years before 2012 (they are empty).
df_AMI_HH1_after2012 = df_AMI_HH1[df_AMI_HH1['Year'] >= 2012]

# Create the table.
Markdown(tabulate(
  df_AMI_HH1_after2012, 
  headers=[ "Year", "Moderate Income (120% AMI)",   "Median Income (AMI)",  "Low Income (80% AMI)", "Very Low Income (50% AMI)",    "Extremely Low Income (30% AMI)",   "Acutely Low Income (15% AMI)"],
  showindex=False,
))

Table 9: Area Median Income (AMI) Levels for Santa Clara County over time, 1-person household (US$)

Year	Moderate Income (120% AMI)	Median Income (AMI)	Low Income (80% AMI)	Very Low Income (50% AMI)	Extremely Low Income (30% AMI)	Acutely Low Income (15% AMI)
2012	88200	73500	53000	36750	22050	nan
2013	nan	nan	nan	nan	nan	nan
2014	88600	73850	59400	37150	22300	nan
2015	nan	nan	nan	nan	nan	nan
2016	89950	74950	59400	39100	23450	nan
2017	95150	79300	59400	41800	25100	nan
2018	105200	87650	66150	46550	27950	nan
2019	110400	92000	72750	51250	30750	nan
2020	118950	99100	78550	55300	33150	nan
2021	127100	105900	82450	58000	34800	15900
2022	141550	117950	92250	59000	35400	17700

Corresponding graph: Comparison of Stanford Minimum Postdoc Salary with Area Median Income (AMI) levels for Santa Clara County (the county in which Stanford is located).

Source:

• California Department of Housing and Community Development “State and Federal Income, Rent, and Loan/Value Limits.” Accessed Feb 10, 2023 at https://www.hcd.ca.gov/grants-and-funding/income-limits/state-and-federal-income-rent-and-loan-value-limits

Table 8b: Area Median Income (AMI) Levels for Santa Clara County over time, 2-person household (US$)

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_AMI_HH2 = pd.read_csv("./data/AMI_over_time,_Santa_Clara,_HH2.csv")

# Remove years before 2012 (they are empty).
df_AMI_HH2_after2012 = df_AMI_HH2[df_AMI_HH2['Year'] >= 2012]

# Create the table.
Markdown(tabulate(
  df_AMI_HH2_after2012, 
  headers=[ "Year", "Moderate Income (120% AMI)",   "Median Income (AMI)",  "Low Income (80% AMI)", "Very Low Income (50% AMI)",    "Extremely Low Income (30% AMI)",   "Acutely Low Income (15% AMI)"],
  showindex=False,
))

Table 10: Area Median Income (AMI) Levels for Santa Clara County over time, 2-person household (US$)

Year	Moderate Income (120% AMI)	Median Income (AMI)	Low Income (80% AMI)	Very Low Income (50% AMI)	Extremely Low Income (30% AMI)	Acutely Low Income (15% AMI)
2012	100800	84000	60600	42000	25200	nan
2014	101300	84400	67900	42450	25500	nan
2016	102800	85700	67900	44650	26800	nan
2017	108750	90650	67900	47800	28650	nan
2018	120200	100150	75600	53200	31950	nan
2019	126150	105100	83150	58550	35150	nan
2020	135900	113300	89750	63200	37900	nan
2021	145250	121050	94200	66300	39800	18150
2022	161750	134800	105400	67400	40450	20250

Corresponding graph: Comparison of Stanford Minimum Postdoc Salary with Area Median Income (AMI) levels for Santa Clara County (the county in which Stanford is located).

Source:

• California Department of Housing and Community Development “State and Federal Income, Rent, and Loan/Value Limits.” Accessed Feb 10, 2023 at https://www.hcd.ca.gov/grants-and-funding/income-limits/state-and-federal-income-rent-and-loan-value-limits

Table 8c: Area Median Income (AMI) Levels for Santa Clara County over time, 3-person household (US$)

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_AMI_HH3 = pd.read_csv("./data/AMI_over_time,_Santa_Clara,_HH3.csv")

# Remove years before 2012 (they are empty).
df_AMI_HH3_after2012 = df_AMI_HH3[df_AMI_HH3['Year'] >= 2012]

# Create the table.
Markdown(tabulate(
  df_AMI_HH3_after2012, 
  headers=[ "Year", "Moderate Income (120% AMI)",   "Median Income (AMI)",  "Low Income (80% AMI)", "Very Low Income (50% AMI)",    "Extremely Low Income (30% AMI)",   "Acutely Low Income (15% AMI)"],
  showindex=False,
))

Table 11: Area Median Income (AMI) Levels for Santa Clara County over time, 3-person household (US$)

Year	Moderate Income (120% AMI)	Median Income (AMI)	Low Income (80% AMI)	Very Low Income (50% AMI)	Extremely Low Income (30% AMI)	Acutely Low Income (15% AMI)
2012	113400	94500	68150	47250	28350	nan
2013	nan	nan	nan	nan	nan	nan
2014	113950	94950	76400	47750	28650	nan
2015	nan	nan	nan	nan	nan	nan
2016	115650	96400	76400	50250	30150	nan
2017	122350	101950	76400	53750	32250	nan
2018	135250	112700	85050	59850	35950	nan
2019	141950	118250	93550	65850	39550	nan
2020	152900	127450	100950	71100	42650	nan
2021	163400	136150	106000	74600	44750	20450
2022	182000	151650	118600	75850	45500	22750

Corresponding graph: Comparison of Stanford Minimum Postdoc Salary with Area Median Income (AMI) levels for Santa Clara County (the county in which Stanford is located).

Source:

• California Department of Housing and Community Development “State and Federal Income, Rent, and Loan/Value Limits.” Accessed Feb 10, 2023 at https://www.hcd.ca.gov/grants-and-funding/income-limits/state-and-federal-income-rent-and-loan-value-limits

Table 8d: Area Median Income (AMI) Levels for Santa Clara County over time, 4-person household (US$)

Code

# Import modules.
from IPython.display import Markdown
from tabulate import tabulate
import numpy as np
import pandas as pd

# Load datatable from csv file.
df_AMI_HH4 = pd.read_csv("./data/AMI_over_time,_Santa_Clara,_HH4.csv")

# Remove years before 2012 (they are empty).
df_AMI_HH4_after2012 = df_AMI_HH4[df_AMI_HH4['Year'] >= 2012]

# Create the table.
Markdown(tabulate(
  df_AMI_HH4_after2012, 
  headers=[ "Year", "Moderate Income (120% AMI)",   "Median Income (AMI)",  "Low Income (80% AMI)", "Very Low Income (50% AMI)",    "Extremely Low Income (30% AMI)",   "Acutely Low Income (15% AMI)"],
  showindex=False,
))

Table 12: Area Median Income (AMI) Levels for Santa Clara County over time, 4-person household (US$)

Year	Moderate Income (120% AMI)	Median Income (AMI)	Low Income (80% AMI)	Very Low Income (50% AMI)	Extremely Low Income (30% AMI)	Acutely Low Income (15% AMI)
2012	126000	105000	75700	52500	31500	nan
2013	nan	nan	nan	nan	nan	nan
2014	126600	105500	84900	53050	31850	nan
2015	nan	nan	nan	nan	nan	nan
2016	128500	107100	84900	55800	33500	nan
2017	135950	113300	84900	59700	35800	nan
2018	150250	125200	94450	66500	39900	nan
2019	157770	131400	103900	73150	43900	nan
2020	169900	141600	112150	78950	47350	nan
2021	181550	151300	117750	82850	49700	22700
2022	202200	168500	131750	84250	50550	25300

Corresponding graph: Comparison of Stanford Minimum Postdoc Salary with Area Median Income (AMI) levels for Santa Clara County (the county in which Stanford is located).

Source:

• California Department of Housing and Community Development “State and Federal Income, Rent, and Loan/Value Limits.” Accessed Feb 10, 2023 at https://www.hcd.ca.gov/grants-and-funding/income-limits/state-and-federal-income-rent-and-loan-value-limits