Health Insurance Coverage for full-time workers in California

Under the ACA, employers with 50 or more full-time employees (or the equivalent in part-time employees) must provide health insurance to 95% of their full-time employees or pay a penalty; However, the penalty has been repealed.
Health Insurance Coverage as a Voluntary Benefit Many smaller companies offer health insurance as a benefit, even if they aren’t required to by law. In fact, the majority of Americans have health insurance coverage through an employer.
A study by the Urban Institute reported that 83.1% of all workers were offered health insurance through an employer in the first quarter of 2016.

Analysis of Census data, American Community Servey 2018.

TidyCensus Package was used to extract data for the following Census variables.

C27013_002 Worked Full-Time, Year-Round
C27013_003 With Private Health Insurance
C27013_004 No Private Health Insurance
Yes.Percentage = insurance population / full-time population
No.Percentage = no-insurance population / full-time population

Var<-c('Total workers' ="C27013_002",'insurance'="C27013_003",'noInsurance'="C27013_004")


ca_hc <- get_acs(geography = "county",
                   variables = Var,
                   year = 2018,
                   survey = "acs5",
                 state = "06",   output = 'wide',
                   geometry = F)

## Getting data from the 2014-2018 5-year ACS

## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

ca_hc$NAME <- gsub("County, California","",ca_hc$NAME)


 library(DT)
ca_hc1 <- ca_hc[,c(2,3,5,7)]

ca_hc1 %>%
  kable(col.names = c("County","Total workers","Worker with insurance","Worker without insurance")) %>%
 kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

County	Total workers	Worker with insurance	Worker without insurance
Lake	12988	9463	3525
Mariposa	3996	3378	618
Yuba	18844	15132	3712
Contra Costa	354027	316881	37146
Lassen	5951	5419	532
Santa Barbara	127283	99819	27464
Sonoma	148791	127901	20890
Imperial	34998	26811	8187
Mono	5030	4062	968
Alameda	574651	508949	65702
Sacramento	454965	388329	66636
Napa	43043	38330	4713
Monterey	111282	87544	23738
Sierra	666	555	111
San Diego	1023860	871282	152578
Yolo	59393	51851	7542
Humboldt	33106	25609	7497
Alpine	164	130	34
Mendocino	20179	14969	5210
Santa Cruz	74671	64480	10191
Los Angeles	3239505	2501304	738201
Riverside	653172	512200	140972
Santa Clara	680592	616714	63878
Marin	76321	69472	6849
Siskiyou	9410	7470	1940
Shasta	43932	35764	8168
Del Norte	5235	4242	993
San Luis Obispo	76508	64363	12145
Glenn	6965	5293	1672
Butte	52811	43095	9716
Plumas	4076	3582	494
Kern	221166	163649	57517
Orange	1031753	871956	159797
Calaveras	10492	9219	1273
Sutter	24290	18657	5633
San Mateo	274325	247732	26593
Tuolumne	12234	10726	1508
San Joaquin	199543	159646	39897
Amador	8573	7185	1388
Merced	64682	47911	16771
Modoc	1990	1399	591
Inyo	5001	4182	819
Stanislaus	144263	116117	28146
Tehama	15228	11756	3472
San Benito	17709	14268	3441
El Dorado	50557	45237	5320
San Francisco	354503	327759	26744
Tulare	110136	77379	32757
Nevada	23987	19853	4134
Madera	35029	25486	9543
Trinity	2400	1660	740
San Bernardino	595736	465626	130110
Placer	112608	102362	10246
Solano	129853	114466	15387
Colusa	5917	4388	1529
Kings	35675	27828	7847
Fresno	244726	187515	57211
Ventura	261095	218516	42579

ca_hc1 %>%
  ggplot(aes(x = insuranceE, y = reorder(NAME,insuranceE))) +
  geom_point(color="darkred")+
  labs(x="Insurance Coverage Count",y="County",title = "Full-time workers by county")

State Assembly District

hc_ad <- get_acs(geography = "state legislative district (lower chamber)",
                   variables = Var,
                   year = 2018,
                   survey = "acs5",
                 state = "06",   output = 'wide',
                   geometry = F)

## Getting data from the 2014-2018 5-year ACS

hc_ad$NAME <- gsub("2018","",hc_ad$NAME)
hc_ad$District <-  as.numeric(gsub("\\D", "", hc_ad$NAME))
hc_ad$Yes.Percent <- hc_ad$insuranceE/hc_ad$`Total workersE`
hc_ad$No.Percent <- 1- hc_ad$Yes.Percent

 library(DT)
hc_ad1 <- hc_ad[,c(9,10,11,7)]

hc_ad1 %>%
  kable(col.names = c("Assembly District","Workers with health insurance (%)","Workers with no insurance (%)",
                      "Workers with no insurance (population)")) %>%
 kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Assembly District	Workers with health insurance (%)	Workers with no insurance (%)	Workers with no insurance (population)
1	0.8254050	0.1745950	19078
2	0.8165237	0.1834763	22252
3	0.7954750	0.2045250	23919
4	0.8561375	0.1438625	19215
5	0.8194776	0.1805224	20340
6	0.9261161	0.0738839	11510
7	0.8417734	0.1582266	24468
8	0.8392275	0.1607725	23408
9	0.8465593	0.1534407	22213
10	0.8825829	0.1174171	16818
11	0.8817377	0.1182623	17697
12	0.8417273	0.1582727	21932
13	0.7868420	0.2131580	28217
14	0.8828406	0.1171594	18499
15	0.8955335	0.1044665	16688
16	0.9618175	0.0381825	6599
17	0.9142308	0.0857692	18078
18	0.8340503	0.1659497	27774
19	0.9286591	0.0713409	13895
20	0.8712684	0.1287316	23131
21	0.7430293	0.2569707	30238
22	0.9179225	0.0820775	14743
23	0.8447627	0.1552373	22035
24	0.9193421	0.0806579	14345
25	0.9186022	0.0813978	15984
29	0.8887964	0.1112036	15631
26	0.7111496	0.2888504	33513
27	0.8363260	0.1636740	27455
28	0.9309155	0.0690845	11976
30	0.7867181	0.2132819	28728
31	0.6592383	0.3407617	35567
32	0.6369704	0.3630296	38781
33	0.7462700	0.2537300	28740
34	0.8211790	0.1788210	24592
35	0.7792942	0.2207058	30419
36	0.8321632	0.1678368	20942
37	0.8441101	0.1558899	22255
38	0.9056894	0.0943106	14884
39	0.7226508	0.2773492	41089
40	0.8171393	0.1828607	25275
41	0.8687720	0.1312280	20220
42	0.7934383	0.2065617	23736
43	0.8222667	0.1777333	28614
44	0.8185797	0.1814203	25907
45	0.8231579	0.1768421	29397
46	0.7447291	0.2552709	42994
47	0.7368349	0.2631651	38240
48	0.7813894	0.2186106	32740
49	0.7715200	0.2284800	33842
50	0.9087758	0.0912242	16240
51	0.6804672	0.3195328	47164
52	0.7426834	0.2573166	38202
53	0.5289708	0.4710292	77416
54	0.8257391	0.1742609	28274
55	0.8966484	0.1033516	16066
56	0.6735610	0.3264390	35985
57	0.8189931	0.1810069	28253
58	0.7643830	0.2356170	35839
59	0.5015663	0.4984337	65234
60	0.8136528	0.1863472	29602
61	0.7570046	0.2429954	34977
62	0.7911776	0.2088224	34037
63	0.7049028	0.2950972	41856
64	0.6752532	0.3247468	44855
65	0.8156634	0.1843366	27548
66	0.8989066	0.1010934	15940
67	0.8287244	0.1712756	24415
68	0.8894677	0.1105323	19350
69	0.6784223	0.3215777	50917
70	0.8184467	0.1815533	28912
71	0.8400728	0.1599272	22401
72	0.8282743	0.1717257	25368
73	0.9197608	0.0802392	12921
74	0.9021735	0.0978265	16163
75	0.8159242	0.1840758	28041
76	0.8596933	0.1403067	21164
77	0.9364043	0.0635957	10926
78	0.8985776	0.1014224	17648
79	0.8527384	0.1472616	23199
80	0.7191971	0.2808029	35489

hc_ad1 %>%
  ggplot(aes(x = Yes.Percent, y = reorder(District,Yes.Percent))) +
  geom_point(color="brown4")+
  labs(x="Full-time workers(percent)",y="Assembly District",title = "Insurance Coverage (%) by Assembly District")

State Senate District

hc_sd <- get_acs(geography = "state legislative district (upper chamber)",
                   variables = Var,
                   year = 2018,
                   survey = "acs5",
                 state = "06",   output = 'wide',
                   geometry = F)

## Getting data from the 2014-2018 5-year ACS

hc_sd$NAME <- gsub("2018","",hc_sd$NAME)
hc_sd$District <-  as.numeric(gsub("\\D", "", hc_sd$NAME))
hc_sd$Yes.Percent <- hc_sd$insuranceE/hc_sd$`Total workersE`
hc_sd$No.Percent <- 1- hc_sd$Yes.Percent

 library(DT)
hc_sd1 <- hc_sd[,c(9,10,11,7)] 

hc_sd1 %>%
  kable(col.names = c("Senate District","Workers with health insurance (%)","Workers with no insurance (%)",
                      "Workers with no insurance (population)")) %>%
 kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Senate District	Workers with health insurance (%)	Workers with no insurance (%)	Workers with no insurance (population)
1	0.8796735	0.1203265	30632
2	0.8414843	0.1585157	40720
3	0.8888036	0.1111964	32169
4	0.8249725	0.1750275	46865
5	0.8096682	0.1903318	52401
6	0.8469084	0.1530916	45963
7	0.9170090	0.0829910	27176
8	0.8418905	0.1581095	41973
9	0.8642962	0.1357038	44413
10	0.8973608	0.1026392	38913
11	0.9213822	0.0786178	31763
12	0.7338233	0.2661767	62806
13	0.9180870	0.0819130	29298
14	0.6210836	0.3789164	77086
15	0.8892862	0.1107138	37404
16	0.8013036	0.1986964	49782
17	0.8547279	0.1452721	40793
18	0.7347011	0.2652989	83510
19	0.7841645	0.2158355	61299
20	0.7397905	0.2602095	76442
21	0.8202884	0.1797116	46493
22	0.7587701	0.2412299	72485
23	0.8066639	0.1933361	51155
24	0.6083441	0.3916559	123607
25	0.8604007	0.1395993	42844
26	0.9175928	0.0824072	28266
27	0.8708420	0.1291580	41010
28	0.7824765	0.2175235	58099
29	0.8501751	0.1498249	45988
33	0.6872341	0.3127659	91393
30	0.6887529	0.3112471	93080
31	0.7867237	0.2132763	64579
32	0.8140382	0.1859618	57817
34	0.7690820	0.2309180	70094
35	0.7421079	0.2578921	75460
36	0.8904197	0.1095803	34456
37	0.8959931	0.1040069	35551
38	0.8419639	0.1580361	48055
39	0.9072690	0.0927310	32353
40	0.7670287	0.2329713	58822

hc_sd1 %>%
  ggplot(aes(x = Yes.Percent, y = reorder(District,Yes.Percent))) +
  geom_point(color="blue")+
  labs(x="Full-time workers(percent)",y="State Senate District",title = "Insurance Coverage (%) by State Senate District")

Congressional District

hc_cd <- get_acs(geography = "congressional district",
                   variables = Var,
                   year = 2018,
                   survey = "acs5",
                 state = "06",   output = 'wide',
                   geometry = F)

## Getting data from the 2014-2018 5-year ACS

hc_cd$NAME <- gsub("116","",hc_cd$NAME)
hc_cd$District <-  as.numeric(gsub("\\D", "", hc_cd$NAME))
hc_cd$Yes.Percent <- hc_cd$insuranceE/hc_cd$`Total workersE`
hc_cd$No.Percent <- 1- hc_cd$Yes.Percent


hc_cd1 <- hc_cd[,c(9,10,11,7)]

hc_cd1 %>%
  kable(col.names = c("Congressional District","Workers with health insurance (%)","Workers with no insurance (%)",
                      "Workers with no insurance (population)")) %>%
 kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Congressional District	Workers with health insurance (%)	Workers with no insurance (%)	Workers with no insurance (population)
1	0.8212441	0.1787559	29415
3	0.8402418	0.1597582	31897
5	0.8745109	0.1254891	27708
6	0.8224284	0.1775716	40017
8	0.7662799	0.2337201	41210
10	0.8245619	0.1754381	36242
11	0.8792471	0.1207529	28080
13	0.8693659	0.1306341	32423
15	0.9058235	0.0941765	26302
16	0.6929580	0.3070420	51354
18	0.9290198	0.0709802	17979
20	0.8035462	0.1964538	38181
21	0.6458584	0.3541416	57196
23	0.8163261	0.1836739	35497
24	0.8054396	0.1945604	40281
26	0.8236538	0.1763462	38775
28	0.8177984	0.1822016	46486
29	0.6760152	0.3239848	73887
31	0.7985909	0.2014091	42967
33	0.9395299	0.0604701	14161
34	0.5961276	0.4038724	96036
36	0.7188631	0.2811369	49556
38	0.8163863	0.1836137	42885
39	0.8790473	0.1209527	28312
41	0.7502902	0.2497098	54214
42	0.8520810	0.1479190	34528
44	0.6881648	0.3118352	64761
46	0.7086604	0.2913396	69673
47	0.8311113	0.1688887	39359
49	0.8769995	0.1230005	27675
51	0.7141329	0.2858671	50513
52	0.9291067	0.0708933	18306
2	0.8534541	0.1465459	28303
4	0.8919318	0.1080682	21877
7	0.8867055	0.1132945	25685
9	0.8116928	0.1883072	38902
12	0.9271206	0.0728794	23089
14	0.9053513	0.0946487	25609
25	0.8708576	0.1291424	27797
17	0.9233506	0.0766494	22559
19	0.8651745	0.1348255	34931
22	0.8104969	0.1895031	38421
27	0.8353764	0.1646236	37992
30	0.8494416	0.1505584	39192
32	0.7690890	0.2309110	52360
35	0.7379301	0.2620699	58483
37	0.7736054	0.2263946	55442
40	0.5886638	0.4113362	88267
43	0.7603755	0.2396245	55423
45	0.9175238	0.0824762	21258
48	0.8785113	0.1214887	29267
50	0.8304093	0.1695907	38299
53	0.8658903	0.1341097	33983

hc_cd1$District <- as.numeric(hc_cd1$District)
hc_cd1 %>%
  ggplot(aes(x = Yes.Percent, y = reorder(District,Yes.Percent))) +
  geom_point(color="brown4")+
  labs(x="Full-time workers(percent)",y="Congressional District",title = "Insurance Coverage (%) by Congressional District")

Health Insurance Coverage for full-time workers in California

Joe Long

7/24/2020

Analysis of Census data, American Community Servey 2018.

State Assembly District

State Senate District

Congressional District