Executive Summary

Maps and Tables

Maryland with 2018 Population

PUMA NAME Percent
00100 Allegany & Garrett Counties–Cumberland City PUMA, Maryland 1.69
00200 Washington County–Hagerstown City PUMA, Maryland 2.50
00301 Frederick County (Outside Greater Frederick City) PUMA, Maryland 2.13
00302 Frederick County (Central)–Greater Frederick City PUMA, Maryland 2.01
00400 Carroll County PUMA, Maryland 2.79
00501 Baltimore County (Outer) PUMA, Maryland 2.00
00502 Baltimore County–Randallstown (East), Owings Mills, Milford Mill & Reisterstown PUMA; Maryland 2.20
00503 Baltimore County–Pikesville (South), Lochearn, Cockeysville & Mays Chapel PUMA; Maryland 1.97
00504 Baltimore County–Towson (East & Central), Parkville & Carney PUMA; Maryland 1.98
00505 Baltimore County–Perry Hall, Middle River & Rosedale PUMA; Maryland 1.87
00506 Baltimore County–Dundalk, Essex & Edgemere PUMA; Maryland 1.88
00507 Baltimore County–Catonsville, Woodlawn & Arbutus PUMA; Maryland 1.89
00601 Harford County (North & West)–Bel Air Town, Fallston & Jarrettsville PUMA; Maryland 2.28
00602 Harford County (South & East)–Aberdeen & Havre de Grace Cities PUMA, Maryland 1.90
00700 Cecil County PUMA, Maryland 1.71
00801 Baltimore City–Sandtown-Winchester, Ashburton & Mount Washington PUMA; Maryland 2.26
00802 Baltimore City–Guilford, Roland Park & Druid Lake PUMA; Maryland 1.85
00803 Baltimore City–Frankford, Belair-Edison & Loch Raven PUMA; Maryland 2.03
00804 Baltimore City–Inner Harbor, Canton & Bayview PUMA; Maryland 2.16
00805 Baltimore City–Irvington, Ten Hills & Cherry Hill PUMA; Maryland 1.94
00901 Howard County (West)–Columbia (West) & Ellicott City (Northwest) PUMA, Maryland 2.27
00902 Howard County (East)–Columbia (East), Ellicott City (Southeast) & Elkridge PUMA; Maryland 2.98
01001 Montgomery County (North & West)–Olney, Damascus, Clarksburg & Darnestown PUMA; Maryland 2.34
01002 Montgomery County (West Central)–Germantown & Montgomery Village PUMA, Maryland 2.30
01003 Montgomery County (Central)–Rockville, Gaithersburg Cities & North Potomac PUMA; Maryland 3.17
01004 Montgomery County (South)–Bethesda, Potomac & North Bethesda PUMA; Maryland 3.12
01005 Montgomery County (East Central)–Wheaton, Aspen Hill & Glenmont PUMA; Maryland 2.40
01006 Montgomery County (East)–Fairland, Calverton, White Oak & Burtonsville PUMA; Maryland 2.04
01007 Montgomery County (Southeast)–Takoma Park City & Silver Spring PUMA, Maryland 1.95
01101 Prince George’s County (Northwest)–College Park City & Langley Park PUMA, Maryland 2.01
01102 Prince George’s County (North)–Laurel, Greenbelt (North & East) Cities & Beltsville PUMA; Maryland 2.29
01103 Prince George’s County (Northwest)–New Carrollton & Hyattsville (Southeast) Cities PUMA, Maryland 1.84
01104 Prince George’s County (Central)–Seat Pleasant City, Capitol Heights Town & Landover PUMA; Maryland 1.92
01105 Prince George’s County (East)–Bowie City, Kettering, Largo, Mitchellville & Lanham PUMA; Maryland 3.00
01106 Prince George’s County (South)–Clinton, Fort Washington (South), Rosaryville & Croom PUMA; Maryland 2.11
01107 Prince George’s County (Southwest)–Oxon Hill, Hillcrest Heights & Temple Hills PUMA; Maryland 1.91
01201 Anne Arundel County (Northwest)–Severn, Odenton, Crofton, Maryland City & Fort Meade PUMA; Maryland 2.87
01202 Anne Arundel County (North)–Glen Burnie, Pasadena, Ferndale & Brooklyn Park PUMA; Maryland 1.94
01203 Anne Arundel County (Central)–Severna Park, Arnold & Lake Shore PUMA; Maryland 2.30
01204 Anne Arundel County (Southeast)–Annapolis City, Parole, Annapolis Neck & Edgewater PUMA; Maryland 2.35
01300 Queen Anne’s, Talbot, Caroline, Dorchester & Kent Counties PUMA; Maryland 2.85
01400 Wicomico, Worcester & Somerset Counties–Salisbury City PUMA; Maryland 2.99
01500 St. Mary’s & Calvert Counties PUMA, Maryland 3.37
01600 Charles County–La Plata Town & Waldorf PUMA, Maryland 2.63

Demographic Detail

Age in 2020, Adjusted from 2018 Census Estimates

PUMA Children Adolescents Adults Seniors
00100 745 541 1759 565
00200 1197 649 3248 826
00301 1821 665 3609 780
00302 1296 697 3134 601
00400 2229 936 4689 1129
00501 1450 562 3179 907
00502 1552 818 3404 788
00503 1284 575 2925 826
00504 1366 961 2837 769
00505 1161 612 2944 583
00506 821 515 2186 491
00507 1038 709 2800 624
00601 1818 719 3695 982
00602 1195 635 2783 501
00700 1082 544 2410 554
00801 741 619 2213 631
00802 833 997 2384 535
00803 866 755 2609 473
00804 543 1060 2756 288
00805 596 638 2185 375
00901 2021 652 3962 1074
00902 2376 1012 5045 850
01001 2200 727 3973 938
01002 1492 673 3659 452
01003 2108 895 4929 1190
01004 2771 776 5076 1752
01005 1362 555 3187 896
01006 1123 634 3007 948
01007 1100 588 3078 636
01101 903 1289 1782 299
01102 1362 782 3374 665
01103 703 529 2375 505
01104 793 554 2668 657
01105 1992 995 5125 1417
01106 1262 633 3198 895
01107 753 585 2604 691
01201 1992 916 4409 684
01202 910 661 2624 609
01203 1956 717 3856 933
01204 1247 570 3489 1129
01300 1391 739 3627 1256
01400 1273 1031 3170 1137
01500 2263 1177 4932 1147
01600 1919 957 3947 847
  • Children: under 17 years
  • Adolescents: 17-24 years
  • Adults: 25-64 years
  • Seniors: 65 or more years

Citizenship, Sex and Ethnicity

PUMA Noncitizens Citizens Female Male White Black Asian Other
00100 34 3576 1851 1759 3377 142 29 62
00200 91 5829 2973 2947 5280 339 133 168
00301 121 6754 3483 3392 6085 257 284 249
00302 289 5439 2914 2814 4260 732 360 376
00400 69 8914 4482 4501 8372 201 138 272
00501 113 5985 3075 3023 4963 728 208 199
00502 431 6131 3569 2993 1919 4140 280 223
00503 315 5295 2951 2659 3567 1484 421 138
00504 164 5769 3176 2757 4150 1247 337 199
00505 246 5054 2790 2510 3423 1225 435 217
00506 115 3898 2027 1986 2986 770 69 188
00507 241 4930 2732 2439 3156 1426 424 165
00601 71 7143 3618 3596 6604 244 160 206
00602 84 5030 2638 2476 3578 1086 155 295
00700 22 4568 2279 2311 4146 208 59 177
00801 176 4028 2371 1833 827 3222 72 83
00802 262 4487 2481 2268 2568 1650 320 211
00803 82 4621 2632 2071 1029 3516 38 120
00804 287 4360 2368 2279 2763 1418 203 263
00805 113 3681 2078 1716 1497 2058 93 146
00901 464 7245 3895 3814 5176 809 1300 424
00902 611 8672 4793 4490 5224 2081 1510 468
01001 377 7461 3914 3924 5151 787 1351 549
01002 853 5423 3166 3110 2994 1668 1074 540
01003 1222 7900 4736 4386 5224 990 2163 745
01004 1178 9197 5299 5076 7923 503 1491 458
01005 695 5305 3075 2925 3242 1127 581 1050
01006 598 5114 3001 2711 2071 2147 850 644
01007 581 4821 2775 2627 3175 1217 412 598
01101 752 3521 2026 2247 1556 1372 394 951
01102 551 5632 3161 3022 1729 3312 550 592
01103 637 3475 2118 1994 1122 2151 135 704
01104 206 4466 2584 2088 152 4259 41 220
01105 444 9085 5077 4452 1639 7062 330 498
01106 198 5790 3212 2776 656 4697 226 409
01107 234 4399 2540 2093 346 3832 151 304
01201 291 7710 4133 3868 4824 2178 497 502
01202 138 4666 2445 2359 3658 722 135 289
01203 107 7355 3712 3750 6382 559 198 323
01204 121 6314 3286 3149 5529 558 96 252
01300 129 6884 3611 3402 6068 665 56 224
01400 189 6422 3496 3115 5054 1253 140 164
01500 94 9425 4848 4671 7991 954 211 363
01600 110 7560 4057 3613 3587 3389 224 470

Methodology

Data Sources

Demographics

The average monthly employment for 2018 and the May 2020 employment were derived from the U.S. Bureau of Labor Statistics Local Area Unemployment Statistics. The difference between the two estimates represents the loss of persons employed in 2018 versus May 2020. May 2020 was the latest month for which data was available at the time of this report.

Employment, insurance coverage, age, sex, citizenship status and ethnicity were derived from the U.S. Census Bureau American Community Survey 2018 Public Use Microdata Area files.

The Census Bureau and Bureau of Labor Statistics estimates of 2018 employment agree closely, to within 0.5%. The difference appears due to differences in reporting periods used.

Geography

Geographic data was obtained from U.S. Census Bureau TIGER files.

Data Transformation

At the request of the client, the American Community Survey data was obtained through the ipumsr compilation.

Assumptions

  • Census data is definitive
  • Bureau of Labor Statistics is definitive
  • No material errors were introduced through the ipums compilation
  • The proper unit of analysis is an employer provided insurance policy providing comprehensive health coverage.
  • The Census definition of employer provided coverage conforms.
  • A beneficiary of an employer provided insurance may be the related employee or an eligible co-insured.
  • Every covered beneficiary who was employed in 2018 and was unemployed in May 2020 lost coverage and associated beneficiaries also lost coverage.
  • Among persons who lost coverage, some have one or more other policies that provide equivalent coverage.
  • Veterans Administration coverage is not equivalent coverage except in cases of 100% service-related disability, an unknown number.
  • Private insurance is assumed to lapse as unemployment compensation benefits lapse and savings are depleted.
  • In the case of TRICARE, Medicaid, Medicare and Indian Health Service coverage, it is assumed that the loss of employment related coverage will have, at most, adverse effects arising from higher net out-of-pocket costs. Persons in that situation are excluded from the present analysis.
  • All persons who became eligible for Medicare to May 2020 have obtained coverage.
  • In the absence of data indicating which of the estimated 3,750,320 persons having insurance related coverage in 2018, 9.47% of the lost coverage is assigned to the group lacking any form of coverage other than employment related, in the ratio that 3,516,852 bears to 3,750,320, 9.47%, or 337,194.
  • Accordingly, a random sample without replacement of 337,194 was taken from 3,516,852 individuals at risk of loss of all comprehensive health care insurnace coverage.

Out of Scope

An allocation of job losses based on the 2018 survey occupation classification in conjunction with the Bureau of Labor Statistics Community Employment Survey data of business establishments was considered and rejected.

The Community Employment Survey tabulates jobs by occupation as reported by employer. The employer has no necessary information whether an employee has another job. The reporting does not depend on whether an employee is a resident of Maryland or another state. Thus, the resident employee based reporting of the American Community Survey could not be reconciled without multiplying assumptions.

Data Limitations

Citizenship

Census identification of non-citizen status does not distinguish among permanent residents, unexpired visa holders, persons present pending administrative determination of residence eligibility and persons subject to summary deportation.

Ethnicity

The Census file relied upon did not provide detail on Hispanic population. Chinese, Japanese, other Asian and Pacific Islander classified persons were consolidated in this report as Asian.

Source Code

Github repository access on request to technocrat@ technocratic.io

Credits

Kris Eberwein (2019). blscrapeR: An API Wrapper for
the Bureau of Labor Statistics (BLS). R package
version 3.2.0.
https://CRAN.R-project.org/package=blscrapeR

Max Kuhn (2020). caret: Classification and Regression
Training. R package version 6.0-86.
https://CRAN.R-project.org/package=caret

Hadley Wickham, Romain François, Lionel Henry and
Kirill Müller (2020). dplyr: A Grammar of Data
Manipulation. R package version 1.0.0.
https://CRAN.R-project.org/package=dplyr

Hiroaki Yutani (2020). ggsflabel: Labels for 'sf' with
'ggplot2'. R package version 0.0.1.
https://yutannihilation.github.io/ggsflabel/

Hadley Wickham and Evan Miller (2020). haven: Import
and Export 'SPSS', 'Stata' and 'SAS' Files. R package
version 2.3.1.
https://CRAN.R-project.org/package=haven

Kirill Müller (2017). here: A Simpler Way to Find Your
Files. R package version 0.1.
https://CRAN.R-project.org/package=here

Greg Freedman Ellis and Derek Burk (2020). ipumsr:
Read 'IPUMS' Extract Files. R package version 0.4.5.
https://CRAN.R-project.org/package=ipumsr

Sam Firke (2020). janitor: Simple Tools for Examining
and Cleaning Dirty Data. R package version 2.0.1.
https://CRAN.R-project.org/package=janitor

Hao Zhu (2019). kableExtra: Construct Complex Table
with 'kable' and Pipe Syntax. R package version 1.1.0.
https://CRAN.R-project.org/package=kableExtra

Garrett Grolemund, Hadley Wickham (2011). Dates and
Times Made Easy with lubridate. Journal of Statistical
Software, 40(3), 1-25. URL
http://www.jstatsoft.org/v40/i03/.

Hadley Wickham, Jim Hester and Romain Francois (2018).
readr: Read Rectangular Text Data. R package version
1.3.1. https://CRAN.R-project.org/package=readr

R Core Team (2020). R: A language and environment for
statistical computing. R Foundation for Statistical
Computing, Vienna, Austria. URL
https://www.R-project.org/

Pebesma, E., 2018. Simple Features for R: Standardized
Support for Spatial Vector Data. The R Journal 10 (1),
439-446, https://doi.org/10.32614/RJ-2018-009

Kirill Müller and Hadley Wickham (2020). tibble:
Simple Data Frames. R package version 3.0.3.
https://CRAN.R-project.org/package=tibble

Kyle Walker (2020). tidycensus: Load US Census
Boundary and Attribute Data as 'tidyverse' and
'sf'-Ready Data Frames. R package version 0.9.9.5.
https://CRAN.R-project.org/package=tidycensus