Income Segregation and Relative Deprivation: ACS Data

Income Inequality

To investigate income inequality over time, we consider the American Community Survey (ACS). The ACS provides data at the county level from 2005-2018. However, we will also consider national and state-level estimates. The main variable of interest is Total Household Income, although Age is used to filter out individuals under 15, and Group Quarters Status to filter out individuals living in group quarters. I also replace missing values and code negative incomes as 0.

# clean the acs data
acs_data <- acs_data %>%
  mutate(hh_inc = na_if(HHINCOME, 9999999) %>%
                     replace(HHINCOME < 0, 0) %>% # # setting bottom code to 0 for now
                     replace(AGE < 15 | GQ == 2, NA)) # only consider population 15 yrs and older

print("ACS Data Summary - All Years (2005-2018), All Counties")

## [1] "ACS Data Summary - All Years (2005-2018), All Counties"

summary(acs_data)

##       YEAR          SAMPLE           SERIAL           CBSERIAL        
##  Min.   :2000   Min.   :200004   Min.   :      1   Min.   :1.000e+00  
##  1st Qu.:2007   1st Qu.:200701   1st Qu.: 275696   1st Qu.:4.207e+05  
##  Median :2011   Median :201101   Median : 596335   Median :8.414e+05  
##  Mean   :2011   Mean   :201066   Mean   : 633023   Mean   :2.996e+11  
##  3rd Qu.:2015   3rd Qu.:201501   3rd Qu.: 983824   3rd Qu.:1.262e+06  
##  Max.   :2018   Max.   :201801   Max.   :1410976   Max.   :2.018e+12  
##                                                    NA's   :5027734    
##       HHWT           CLUSTER             STATEFIP       COUNTYFIP      
##  Min.   :   1.0   Min.   :2.000e+12   Min.   : 1.00   Min.   :  0      
##  1st Qu.:  58.0   1st Qu.:2.007e+12   1st Qu.:12.00   1st Qu.:  0      
##  Median :  83.0   Median :2.011e+12   Median :27.00   Median : 13      
##  Mean   : 114.5   Mean   :2.011e+12   Mean   :27.68   Mean   : 47      
##  3rd Qu.: 133.0   3rd Qu.:2.015e+12   3rd Qu.:41.00   3rd Qu.: 67      
##  Max.   :4331.0   Max.   :2.018e+12   Max.   :56.00   Max.   :810      
##                                                       NA's   :5027734  
##       PUMA             STRATA              GQ           HHINCOME      
##  Min.   :  100     Min.   :      1   Min.   :1.000   Min.   : -39996  
##  1st Qu.:  803     1st Qu.:  50056   1st Qu.:1.000   1st Qu.:  34500  
##  Median : 1906     Median : 160045   Median :1.000   Median :  64500  
##  Mean   : 3456     Mean   : 309526   Mean   :1.085   Mean   : 415963  
##  3rd Qu.: 3704     3rd Qu.: 350042   3rd Qu.:1.000   3rd Qu.: 110000  
##  Max.   :77777     Max.   :7777722   Max.   :5.000   Max.   :9999999  
##  NA's   :5027734                                                      
##      PERNUM           PERWT             AGE            hh_inc        
##  Min.   : 1.000   Min.   :   1.0   Min.   : 0.00   Min.   :      0   
##  1st Qu.: 1.000   1st Qu.:  59.0   1st Qu.:19.00   1st Qu.:  33400   
##  Median : 2.000   Median :  86.0   Median :40.00   Median :  62000   
##  Mean   : 2.104   Mean   : 119.8   Mean   :39.96   Mean   :  82860   
##  3rd Qu.: 3.000   3rd Qu.: 140.0   3rd Qu.:58.00   3rd Qu.: 103400   
##  Max.   :20.000   Max.   :5419.0   Max.   :97.00   Max.   :3563100   
##                                                    NA's   :10424104

First, we look at the number of counties with full information available for each state.

# state level
df.state <- acs_data %>%
  filter(PERNUM == 1  &
           STATEFIP <= 56) %>% # restrict to 50 states + DC  
  group_by(YEAR, STATEFIP) %>%
  summarise(mean = weighted.mean(hh_inc, na.rm = T, w = HHWT), 
            median = weighted.median(hh_inc, na.rm = T, w = HHWT),
            gini = weighted.gini(hh_inc[!is.na(hh_inc)], 
                                 HHWT[!is.na(hh_inc)])$Gini,
            n_hh = sum(PERNUM*HHWT)/1000000) %>%
  group_by(STATEFIP) %>%
  mutate(avg_n_hh = mean(n_hh)) %>% # average n_hh over time
  ungroup() %>%
  mutate(rank_n_hh = dense_rank(desc(avg_n_hh))) # rank states by size

# county level
df.county <- acs_data %>%
  filter(PERNUM == 1  & !is.na(hh_inc) &
           !is.na(COUNTYFIP) & COUNTYFIP != 0) %>%
  mutate(State = paste0(as_factor(STATEFIP), " (", STATEFIP, ")"),
         STATEFIP = paste0(
            strrep("0",(2  - nchar(as.character(STATEFIP)))),
            as.character(STATEFIP)),
         COUNTYFIP = paste0(
            strrep("0",(3  - nchar(as.character(COUNTYFIP)))),
            as.character(COUNTYFIP)),
         FIP = paste(STATEFIP, COUNTYFIP)) %>%
  group_by(YEAR, FIP) %>%
  summarise(State = first(State),
            mean = weighted.mean(hh_inc, na.rm = T, w = HHWT), 
            median = weighted.median(hh_inc, na.rm = T, w = HHWT),
            gini = weighted.gini(hh_inc[!is.na(hh_inc)], 
                                 HHWT[!is.na(hh_inc)])$Gini,
            n_hh = sum(PERNUM*HHWT)/1000000) %>%
  group_by(FIP)

For now, we consider counties that encompass at least one PUMA; in other words, counties of at least 100,000 people. There were 472 of these counties in the U.S. for the pre-2012 period. Because the ACS can be aggregated to the PUMA level every year, and there was a PUMA boundary change in 2012, there are 453 counties with full data in every year. As an alternative measure, I’ve included the simple count of the number of counties in each file, for which there are 331 counties with full data. In addition to containing more estimated counties, the PUMA method should provide more accurately weighted results.

# add number of years variable
df.county <- df.county %>%
  group_by(FIP) %>% 
  mutate(n_years = n()) %>%
  ungroup()

# look at counties with all data for each year

df.county.long <- df.county %>% 
  mutate(all_years = c(n_years == 14)*1) %>%
  filter(all_years == 1) %>% 
  group_by(State) %>%
  summarise(`n (county variable estimates)` = n()/14)

# use test data, and compute the whole thing on Sherlock
acs_puma <- acs_data %>%
  filter(PERNUM == 1 & YEAR > 2004) %>%
  mutate(State = paste0(as_factor(STATEFIP), " (", STATEFIP, ")"),
    PUMA2k = paste0(
      strrep("0",(5  - nchar(as.character(PUMA)))),
      as.character(PUMA)),
    STATEFIP = paste0(
    strrep("0",(2  - nchar(as.character(STATEFIP)))),
    as.character(STATEFIP)),
    PUMA_ID = paste0(STATEFIP, PUMA2k)) %>%
  dplyr::select(-c(COUNTYFIP))


### reconstruct weights for county level estimates
acs_data_county_pre2012 <- acs_puma %>%
  filter(YEAR < 2012) %>%    # pre 2012 PUMA boundaries
  left_join(dplyr::select(county_to_puma_pre2012, 
                          COUNTYFIP, n_pumas, PUMA_ID, STATEFIP, FIP, 
                          contains_puma, wgt_puma, afact))

acs_data_county_post2012 <- acs_puma %>% # post 2012 PUMA boundaries
  filter(YEAR > 2011) %>%
  left_join(dplyr::select(county_to_puma_post2012, 
                          COUNTYFIP, n_pumas, PUMA_ID, STATEFIP, FIP, 
                          contains_puma, wgt_puma, afact))

acs_data_county <- bind_rows(acs_data_county_pre2012, acs_data_county_post2012) %>%
  mutate(HHWT_new = HHWT/n_pumas) %>%
  filter(!is.na(n_pumas)) %>%
  group_by(FIP) %>%
  mutate(n_years = length(unique(YEAR))) %>%
  filter(n_years == 14)



df.puma <- acs_data_county  %>% 
  dplyr::select(YEAR, FIP, State) %>% 
  distinct() %>%
  group_by(FIP) %>%
  mutate(n_years = length(unique(YEAR)),
         all_years = c(n_years == 14)*1) %>%
  filter(all_years == 1 & !duplicated(FIP)) %>% 
  group_by(State) %>%
  summarise(`n (PUMA estimates)` = n())

df.county.long <- read.csv("df.county.long.csv") %>%
  dplyr::select(-X)
df.puma <- read.csv("df.puma.csv") %>%
  dplyr::select(-X)

puma_county_table <- full_join(df.puma, df.county.long) %>% 
  rename("Counties (PUMA estimates)" = "n..PUMA.estimates.",
         "Counties (ACS county var)" = "n..county.variable.estimates.") %>%
 add_row(State = "Total", `Counties (PUMA estimates)` = sum(df.puma$n..PUMA.estimates.), 
         `Counties (ACS county var)` = sum(df.county.long$n..county.variable.estimates.))

## Joining, by = "State"

## Warning: Column `State` joining factors with different levels, coercing to
## character vector

kable(puma_county_table, caption = paste("Number of counties with complete data in each state (2005-2018), measured using aggregated county variable and ACS PUMAs")) %>%
  kable_styling() %>%
  scroll_box(width = "800px", height = "500px")

Number of counties with complete data in each state (2005-2018), measured using aggregated county variable and ACS PUMAs
State	Counties (PUMA estimates)	Counties (ACS county var)
Alabama (1)	10	7
Alaska (2)	1	1
Arizona (4)	5	5
Arkansas (5)	2	3
California (6)	34	34
Colorado (8)	11	NA
Connecticut (9)	8	8
Delaware (10)	3	3
District of Columbia (11)	1	1
Florida (12)	30	24
Georgia (13)	11	9
Hawaii (15)	2	2
Idaho (16)	1	NA
Illinois (17)	18	9
Indiana (18)	13	10
Iowa (19)	5	4
Kansas (20)	4	2
Kentucky (21)	3	3
Louisiana (22)	8	3
Maine (23)	3	2
Maryland (24)	11	11
Massachusetts (25)	10	1
Michigan (26)	15	12
Minnesota (27)	7	6
Mississippi (28)	4	3
Missouri (29)	7	5
Montana (30)	1	NA
Nebraska (31)	3	2
Nevada (32)	2	2
New Hampshire (33)	3	NA
New Jersey (34)	19	16
New Mexico (35)	3	1
New York (36)	25	19
North Carolina (37)	19	11
North Dakota (38)	1	1
Ohio (39)	25	18
Oklahoma (40)	3	NA
Oregon (41)	8	5
Pennsylvania (42)	28	19
Rhode Island (44)	3	3
South Carolina (45)	7	4
Tennessee (47)	10	6
Texas (48)	31	26
Utah (49)	4	4
Virginia (51)	12	9
Washington (53)	9	9
Wisconsin (55)	10	8
Total	453	331

To examine variation across states and counties, it is necessary to consider the complex survey design of the ACS. Looking at states and counties, we must consider survey strata and household weights. To aggregate PUMAs into county estimates, I re-weight households according to the share of the county that the PUMA contributes. If a county just has one PUMA, then the household weights are unchanged, but for counties with multiple PUMAs, households are downweighted in proportion to the overlap between PUMA and county.

The following displays county-level Gini variance estimates and standard errors.

#############################################
### State and county variance estimates #####
#############################################



county_state_gini <- data.frame(matrix(nrow = 0, ncol = 9))
names(county_state_gini) <- c("hh_inc", "se", "gini.moe", "STATEFIP",
                              "YEAR", "LEVEL", 
                              "COUNTYFIP", "FIP", "se_squared")

########### STATE LEVEL ##################

for(i in c("06", "12", "36", "42", "48")){
### estimate gini coefficients and variance using complex survey design
des_acs <- svydesign(ids = ~CLUSTER, weights = ~HHWT , strata = ~STRATA,
                     data = acs_data %>%
                       filter(!is.na(hh_inc) & STATEFIP == i ) %>%
                       dplyr::select(CLUSTER, HHWT, STRATA, YEAR, STATEFIP, PUMA, hh_inc,
                                     State))
des_acs <- convey_prep(des_acs)

svygini( ~hh_inc , design = des_acs )

puma_gini <- svyby(~hh_inc, by = ~interaction(YEAR, STATEFIP, PUMA), design = des_acs, FUN = svygini, deff = FALSE)


state_gini <- svyby(~hh_inc, by = ~interaction(YEAR, STATEFIP), design = des_acs, FUN = svygini, deff = FALSE)

state_gini <- state_gini %>%
  mutate(gini.moe = paste0(round(hh_inc, 4), " (", round(se, 4), ")"),
         STATEFIP = substr(`interaction(YEAR, STATEFIP)`, 6, 7),
         YEAR = substr(`interaction(YEAR, STATEFIP)`, 0, 4),
         LEVEL = "State")

########### COUNTY LEVEL ###################

# if a county = puma, weights won't change
# otherwise, pumas that only make up a portion of the county will be downweighted


des_acs <- svydesign(ids = ~CLUSTER, weights = ~HHWT_new , strata = ~STRATA,
                     data = acs_data_county %>%
                       filter(!is.na(hh_inc) & STATEFIP == i ) %>%
                       dplyr::select(CLUSTER, HHWT_new, STRATA, YEAR, STATEFIP, PUMA, hh_inc, FIP))
des_acs <- convey_prep(des_acs)

county_gini <- svyby(~hh_inc, by = ~interaction(YEAR, FIP), design = des_acs, FUN = svygini, deff = FALSE)

county_gini <- county_gini %>%
  mutate(gini.moe = paste0(round(hh_inc, 4), " (", round(se, 4), ")"),
         STATEFIP = substr(`interaction(YEAR, FIP)`, 6, 7),
         COUNTYFIP = substr(`interaction(YEAR, FIP)`, 8, 12),
         YEAR = substr(`interaction(YEAR, FIP)`, 0, 4), 
         LEVEL = "County")

####### join county and state estimates
county_state_gini <- bind_rows(county_state_gini, 
                               dplyr::select(state_gini, -starts_with("interact")),
                               dplyr::select(county_gini, -starts_with("interact"))) %>%
  mutate(FIP = paste0(STATEFIP, COUNTYFIP),
         se_squared = se^2)

}

# run this to give acs_data correct state labels
acs_data <- acs_data %>%
  filter(PERNUM == 1 & YEAR > 2004) %>%
  mutate(State = paste0(as_factor(STATEFIP), " (",
                        STATEFIP,
    ")"),
    STATEFIP = paste0(
    strrep("0",(2  - nchar(as.character(STATEFIP)))),
    as.character(STATEFIP)),
    COUNTYFIP = paste0(
      strrep("0",(5  - nchar(as.character(COUNTYFIP)))),
      as.character(COUNTYFIP)),
    PUMA2k = paste0(
      strrep("0",(5  - nchar(as.character(PUMA)))),
      as.character(PUMA)),
    PUMA_ID = paste0(STATEFIP, PUMA2k))

county_state_gini <- read.csv("county_state_gini.csv") %>%
  dplyr::select(-X) %>%
  mutate(STATEFIP = paste0(
    strrep("0",(2  - nchar(as.character(STATEFIP)))),
    as.character(STATEFIP)),
    COUNTYFIP = paste0(
      strrep("0",(5  - nchar(as.character(COUNTYFIP)))),
      as.character(COUNTYFIP)))

# run second loop to output ses
for(i in c("06", "12", "36", "42", "48")){
df.state.county.wide <- county_state_gini %>%
  filter(STATEFIP == i & YEAR > 2004) %>%
  dplyr::select(YEAR, se, FIP, LEVEL) %>%
  mutate(FIP = replace(as.character(FIP), LEVEL == "State", 
                       first(acs_data$State[acs_data$STATEFIP == i]))) %>%
  dplyr::select(YEAR, se, FIP) %>%
  group_by(YEAR) %>%
  pivot_wider(names_from = FIP, values_from = se) 

print(kable(df.state.county.wide, caption = "Gini standard errors for the five most populous states and their counties with complete data (2005-2018)") %>%
  kable_styling() %>%
  scroll_box(width = "900px", height = "500px"))

}

Gini standard errors for the five most populous states and their counties with complete data (2005-2018)
YEAR	California (6)	06 001	06 007	06 013	06 019	06 023	06 025	06 029	06 031	06 037	06 039	06 041	06 047	06 053	06 055	06 059	06 061	06 065	06 067	06 071	06 073	06 075	06 077	06 079	06 081	06 083	06 085	06 087	06 089	06 095	06 097	06 099	06 107	06 111	06 113
2005	0.0012740	0.0054515	0.0133535	0.0068103	0.0078690	0.0214774	0.0149719	0.0085691	0.0223949	0.0027418	0.0185695	0.0093834	0.0100812	0.0100403	0.0355828	0.0040214	0.0103301	0.0055803	0.0053614	0.0058026	0.0043449	0.0071242	0.0096846	0.0126033	0.0075346	0.0094608	0.0048545	0.0119476	0.0158566	0.0113981	0.0093915	0.0155113	0.0116248	0.0076507	0.0133710
2006	0.0011427	0.0050571	0.0136716	0.0061503	0.0082622	0.0148980	0.0176217	0.0072815	0.0150458	0.0022688	0.0261542	0.0108265	0.0109414	0.0113129	0.0176629	0.0037656	0.0093901	0.0044738	0.0052987	0.0050874	0.0039852	0.0068134	0.0082875	0.0148402	0.0068167	0.0085341	0.0047340	0.0153573	0.0162342	0.0094840	0.0096817	0.0108671	0.0108618	0.0068706	0.0136389
2007	0.0011499	0.0049602	0.0127861	0.0058032	0.0074939	0.0184915	0.0211461	0.0086895	0.0154131	0.0023919	0.0233848	0.0104048	0.0123371	0.0106778	0.0160700	0.0036045	0.0093557	0.0049638	0.0050376	0.0046770	0.0035923	0.0066061	0.0109453	0.0131139	0.0070105	0.0087175	0.0045919	0.0123562	0.0170246	0.0086156	0.0099080	0.0124755	0.0112678	0.0070515	0.0127005
2008	0.0011414	0.0046603	0.0120554	0.0060890	0.0072744	0.0228068	0.0183933	0.0073202	0.0120794	0.0023616	0.0214920	0.0111503	0.0108737	0.0099347	0.0220692	0.0037400	0.0106523	0.0045212	0.0046826	0.0053766	0.0036985	0.0065255	0.0082167	0.0116757	0.0066860	0.0081811	0.0045019	0.0120514	0.0154390	0.0093357	0.0089506	0.0084466	0.0103624	0.0072888	0.0152555
2009	0.0010907	0.0048942	0.0118198	0.0062165	0.0075870	0.0183510	0.0157328	0.0066870	0.0211389	0.0022227	0.0214232	0.0092548	0.0132170	0.0091937	0.0135149	0.0035556	0.0135917	0.0046674	0.0050034	0.0047840	0.0036188	0.0062455	0.0078469	0.0120823	0.0064239	0.0086285	0.0042729	0.0115750	0.0178031	0.0088805	0.0079449	0.0085604	0.0099176	0.0066292	0.0126803
2010	0.0010522	0.0045855	0.0190929	0.0058410	0.0072824	0.0186685	0.0142770	0.0064794	0.0145117	0.0021225	0.0248944	0.0094045	0.0137759	0.0098383	0.0182063	0.0034270	0.0100626	0.0043850	0.0048215	0.0045321	0.0035918	0.0060544	0.0077548	0.0137738	0.0063025	0.0079262	0.0043950	0.0107317	0.0141520	0.0089841	0.0081151	0.0099358	0.0096990	0.0064536	0.0137064
2011	0.0011797	0.0048963	0.0148667	0.0083103	0.0071229	0.0174437	0.0164094	0.0070919	0.0234851	0.0023494	0.0199978	0.0109757	0.0116251	0.0101873	0.0162115	0.0036797	0.0113365	0.0052229	0.0056536	0.0056200	0.0037888	0.0066044	0.0083494	0.0125914	0.0065618	0.0079601	0.0045854	0.0108174	0.0126585	0.0100967	0.0090906	0.0091367	0.0084676	0.0074336	0.0132007
2012	0.0011265	0.0048387	0.0151235	0.0056087	0.0084491	0.0156837	0.0188390	0.0088509	0.0236514	0.0021825	0.0201696	0.0103078	0.0129587	0.0087301	0.0144554	0.0039420	0.0086612	0.0044419	0.0058187	0.0053496	0.0039370	0.0067492	0.0122286	0.0153579	0.0069301	0.0101238	0.0044887	0.0129352	0.0119564	0.0094358	0.0088342	0.0092812	0.0092804	0.0075146	0.0154586
2013	0.0011442	0.0045707	0.0140653	0.0057363	0.0079912	0.0166344	0.0163271	0.0083064	0.0202160	0.0022601	0.0208065	0.0097701	0.0115420	0.0096592	0.0137974	0.0037958	0.0098725	0.0048239	0.0059854	0.0051900	0.0043145	0.0074922	0.0098383	0.0126398	0.0068164	0.0098441	0.0042072	0.0125804	0.0149247	0.0086983	0.0084329	0.0098632	0.0120908	0.0068325	0.0135798
2014	0.0011600	0.0051349	0.0164144	0.0063330	0.0078271	0.0153609	0.0209405	0.0087990	0.0183121	0.0022877	0.0193896	0.0099446	0.0228956	0.0102019	0.0170682	0.0041941	0.0102111	0.0048399	0.0070647	0.0051297	0.0039646	0.0065169	0.0085005	0.0129784	0.0067250	0.0113974	0.0043826	0.0126870	0.0127442	0.0084450	0.0092816	0.0129651	0.0134931	0.0069581	0.0138386
2015	0.0011187	0.0045560	0.0160279	0.0061100	0.0074857	0.0179509	0.0138931	0.0099185	0.0167272	0.0022343	0.0217464	0.0105312	0.0135426	0.0113331	0.0132922	0.0037947	0.0099026	0.0044340	0.0056722	0.0050739	0.0038856	0.0063339	0.0081455	0.0122768	0.0066291	0.0101502	0.0044716	0.0139796	0.0113839	0.0092540	0.0090549	0.0100596	0.0112387	0.0070835	0.0133472
2016	0.0011292	0.0045090	0.0161929	0.0058814	0.0080134	0.0310538	0.0221368	0.0094286	0.0171482	0.0022423	0.0203389	0.0098747	0.0166007	0.0100245	0.0152251	0.0041279	0.0108912	0.0045691	0.0053080	0.0050099	0.0040751	0.0063503	0.0110637	0.0127326	0.0065090	0.0096432	0.0043210	0.0126956	0.0142247	0.0093159	0.0091344	0.0097380	0.0151351	0.0068554	0.0160161
2017	0.0010991	0.0049055	0.0149172	0.0058890	0.0081178	0.0223547	0.0202160	0.0078719	0.0151898	0.0022121	0.0174288	0.0106974	0.0150546	0.0091988	0.0129694	0.0039129	0.0098401	0.0046290	0.0051330	0.0052472	0.0039606	0.0060835	0.0083273	0.0118902	0.0060710	0.0101308	0.0042111	0.0125590	0.0145139	0.0086102	0.0089684	0.0092535	0.0110020	0.0082758	0.0129002
2018	0.0011469	0.0046826	0.0149418	0.0058616	0.0070496	0.0174533	0.0205664	0.0092818	0.0178445	0.0023065	0.0204278	0.0103004	0.0139488	0.0104013	0.0128470	0.0040430	0.0096618	0.0055314	0.0055049	0.0064368	0.0040536	0.0062918	0.0086100	0.0135776	0.0064054	0.0101015	0.0043983	0.0118142	0.0118694	0.0081711	0.0094083	0.0142134	0.0144586	0.0070279	0.0131858

Gini standard errors for the five most populous states and their counties with complete data (2005-2018)
YEAR	Florida (12)	12 001	12 009	12 011	12 015	12 019	12 021	12 031	12 033	12 053	12 057	12 069	12 071	12 073	12 081	12 083	12 085	12 086	12 091	12 095	12 097	12 099	12 101	12 103	12 105	12 109	12 111	12 113	12 115	12 117	12 127
2005	0.0016341	0.0124953	0.0078932	0.0047356	0.0149164	0.0136468	0.0105818	0.0080551	0.0110517	0.0159171	0.0066160	0.0113479	0.0080041	0.0124286	0.0114750	0.0106374	0.0157276	0.0050123	0.0143009	0.0068583	0.0130564	0.0054520	0.0098317	0.0071541	0.0110385	0.0149242	0.0162404	0.0158257	0.0092973	0.0086153	0.0103289
2006	0.0015906	0.0134263	0.0102127	0.0050599	0.0173296	0.0112849	0.0094080	0.0071676	0.0136290	0.0138853	0.0064951	0.0100858	0.0081989	0.0106187	0.0094829	0.0107600	0.0158735	0.0045896	0.0145489	0.0071731	0.0138847	0.0048687	0.0088909	0.0063180	0.0074701	0.0161911	0.0113046	0.0157465	0.0096094	0.0091329	0.0149573
2007	0.0015849	0.0130440	0.0078452	0.0049550	0.0181710	0.0167268	0.0109689	0.0076714	0.0121367	0.0153279	0.0059354	0.0110404	0.0080491	0.0099306	0.0095103	0.0105866	0.0150424	0.0045881	0.0164700	0.0070636	0.0152188	0.0055702	0.0093877	0.0066882	0.0089296	0.0125498	0.0114384	0.0239719	0.0090589	0.0091939	0.0104859
2008	0.0015254	0.0116658	0.0074355	0.0045374	0.0153929	0.0136436	0.0095768	0.0075408	0.0103857	0.0127734	0.0059771	0.0101994	0.0079408	0.0121513	0.0109144	0.0170210	0.0123705	0.0044323	0.0113876	0.0059894	0.0126591	0.0051784	0.0087848	0.0065255	0.0096591	0.0134022	0.0104701	0.0147598	0.0099112	0.0077728	0.0092766
2009	0.0014901	0.0138441	0.0077666	0.0049825	0.0143703	0.0138355	0.0099483	0.0065742	0.0104065	0.0155718	0.0056844	0.0110125	0.0075296	0.0116019	0.0095886	0.0122070	0.0133487	0.0047279	0.0114456	0.0063349	0.0116817	0.0048092	0.0089327	0.0062153	0.0082521	0.0130066	0.0127926	0.0152246	0.0090382	0.0080963	0.0090222
2010	0.0014865	0.0138245	0.0079933	0.0047016	0.0128478	0.0129045	0.0100678	0.0067584	0.0106171	0.0150164	0.0057937	0.0095224	0.0075992	0.0125237	0.0113705	0.0106979	0.0161696	0.0043905	0.0125527	0.0059473	0.0127269	0.0051282	0.0079704	0.0058690	0.0087149	0.0157308	0.0130145	0.0160881	0.0094055	0.0079205	0.0087076
2011	0.0016499	0.0122077	0.0079975	0.0049656	0.0294757	0.0344552	0.0125291	0.0072060	0.0124754	0.0135696	0.0058956	0.0127779	0.0083250	0.0121010	0.0115207	0.0138505	0.0162229	0.0047169	0.0130350	0.0066330	0.0151491	0.0048126	0.0118803	0.0056476	0.0079166	0.0162590	0.0178495	0.0228622	0.0087080	0.0097716	0.0103996
2012	0.0015956	0.0155357	0.0082262	0.0052093	0.0156371	0.0159451	0.0110659	0.0077707	0.0111019	0.0138570	0.0058684	0.0126139	0.0083039	0.0111966	0.0122290	0.0136554	0.0146144	0.0045377	0.0123390	0.0067165	0.0153895	0.0052847	0.0093902	0.0058769	0.0083284	0.0133719	0.0139004	0.0248417	0.0095314	0.0093742	0.0085928
2013	0.0015789	0.0134597	0.0076280	0.0052231	0.0147284	0.0221755	0.0103982	0.0070928	0.0166741	0.0125716	0.0057621	0.0116588	0.0077419	0.0114424	0.0124129	0.0107706	0.0143374	0.0046393	0.0140836	0.0066552	0.0164659	0.0048720	0.0079749	0.0066896	0.0081099	0.0117967	0.0160969	0.0174296	0.0095696	0.0097195	0.0088328
2014	0.0015471	0.0129251	0.0090382	0.0051129	0.0142111	0.0179484	0.0093912	0.0074851	0.0113768	0.0150685	0.0053945	0.0136344	0.0074430	0.0114856	0.0100229	0.0132204	0.0164520	0.0042903	0.0140710	0.0071153	0.0137135	0.0049925	0.0080660	0.0063562	0.0083454	0.0131079	0.0138630	0.0178245	0.0095996	0.0109542	0.0081309
2015	0.0016633	0.0128694	0.0086599	0.0054163	0.0170776	0.0196200	0.0096466	0.0085704	0.0116738	0.0171051	0.0055309	0.0107420	0.0079053	0.0126110	0.0100465	0.0123616	0.0166944	0.0047802	0.0136492	0.0099108	0.0152029	0.0051256	0.0083625	0.0059599	0.0089657	0.0120102	0.0156928	0.0178580	0.0095256	0.0090077	0.0091142
2016	0.0015543	0.0149351	0.0073538	0.0053229	0.0160879	0.0139638	0.0094727	0.0073563	0.0116083	0.0122838	0.0053155	0.0106849	0.0077745	0.0111386	0.0107695	0.0160260	0.0163799	0.0043811	0.0132593	0.0073721	0.0131074	0.0050675	0.0096983	0.0056604	0.0089403	0.0123206	0.0154899	0.0149021	0.0097869	0.0099113	0.0081561
2017	0.0015809	0.0144891	0.0082390	0.0057151	0.0144813	0.0173863	0.0087934	0.0080025	0.0104695	0.0136944	0.0057245	0.0100213	0.0089470	0.0138348	0.0105942	0.0124260	0.0172775	0.0043014	0.0127030	0.0069769	0.0146696	0.0051294	0.0079471	0.0058730	0.0084194	0.0112313	0.0132260	0.0159329	0.0090840	0.0108240	0.0081641
2018	0.0015908	0.0136409	0.0073930	0.0054801	0.0174427	0.0137969	0.0108349	0.0075838	0.0110052	0.0177204	0.0053778	0.0131792	0.0084698	0.0125231	0.0109207	0.0148290	0.0140675	0.0044778	0.0133372	0.0073552	0.0143843	0.0052090	0.0081548	0.0059524	0.0090496	0.0108996	0.0152550	0.0234287	0.0091880	0.0094101	0.0086107

Gini standard errors for the five most populous states and their counties with complete data (2005-2018)
YEAR	New York (36)	36 001	36 005	36 007	36 013	36 027	36 029	36 047	36 055	36 059	36 061	36 063	36 065	36 067	36 071	36 075	36 081	36 083	36 085	36 087	36 089	36 091	36 093	36 103	36 111	36 119
2005	0.0020670	0.0146037	0.0072189	0.0110915	0.0142006	0.0110932	0.0073823	0.0066563	0.0084142	0.0057286	0.0051669	0.0199559	0.0165928	0.0082598	0.0136928	0.0149806	0.0043644	0.0131109	0.0262467	0.0124505	0.0161731	0.0172323	0.0183906	0.0065483	0.0134123	0.0077932
2006	0.0017836	0.0102938	0.0059672	0.0118821	0.0149958	0.0122541	0.0069759	0.0047235	0.0069381	0.0059269	0.0048611	0.0145043	0.0084360	0.0082128	0.0111466	0.0128617	0.0042230	0.0122522	0.0116068	0.0109343	0.0133105	0.0111036	0.0314359	0.0058376	0.0125745	0.0059805
2007	0.0018176	0.0166129	0.0059482	0.0145597	0.0142268	0.0119461	0.0074916	0.0053140	0.0076048	0.0057977	0.0046325	0.0104821	0.0103888	0.0079735	0.0120882	0.0114532	0.0043343	0.0132716	0.0099766	0.0127846	0.0178110	0.0147604	0.0162990	0.0056825	0.0162113	0.0064349
2008	0.0018963	0.0120236	0.0066745	0.0157568	0.0130684	0.0122774	0.0070015	0.0052197	0.0077794	0.0055680	0.0047868	0.0113476	0.0104448	0.0084707	0.0111120	0.0170452	0.0048121	0.0126246	0.0086621	0.0120430	0.0145677	0.0116128	0.0130724	0.0057291	0.0143893	0.0067203
2009	0.0018881	0.0119805	0.0074268	0.0100512	0.0121487	0.0108121	0.0070344	0.0050551	0.0070296	0.0055323	0.0049110	0.0200211	0.0103964	0.0078374	0.0101927	0.0141152	0.0051057	0.0131089	0.0099043	0.0128212	0.0164704	0.0136098	0.0137179	0.0056041	0.0119900	0.0064721
2010	0.0017068	0.0105207	0.0055948	0.0106808	0.0127909	0.0138280	0.0067884	0.0049522	0.0081666	0.0053810	0.0044904	0.0134837	0.0083719	0.0080077	0.0100889	0.0130069	0.0047807	0.0138450	0.0110825	0.0107342	0.0298197	0.0117898	0.0132936	0.0057980	0.0111011	0.0060846
2011	0.0019164	0.0114309	0.0063306	0.0092119	0.0127057	0.0115495	0.0062093	0.0048303	0.0089418	0.0054491	0.0057323	0.0145723	0.0109417	0.0072590	0.0133493	0.0168974	0.0047643	0.0170633	0.0096641	0.0108112	0.0140134	0.0143551	0.0150166	0.0055701	0.0124763	0.0057445
2012	0.0018272	0.0123815	0.0065019	0.0091560	0.0238032	0.0129098	0.0082106	0.0043137	0.0074804	0.0052248	0.0058744	0.0142282	0.0114421	0.0083391	0.0114532	0.0170971	0.0042247	0.0147564	0.0113346	0.0108079	0.0194630	0.0157114	0.0174387	0.0054141	0.0163295	0.0059152
2013	0.0020184	0.0131596	0.0076760	0.0119180	0.0144075	0.0108738	0.0068209	0.0048928	0.0089139	0.0062836	0.0065471	0.0151118	0.0083818	0.0083927	0.0143637	0.0167807	0.0051344	0.0129765	0.0102441	0.0115877	0.0248648	0.0138659	0.0142245	0.0062167	0.0122518	0.0063668
2014	0.0018606	0.0155879	0.0061748	0.0121158	0.0185587	0.0114324	0.0075341	0.0046478	0.0084119	0.0057748	0.0057344	0.0131168	0.0109185	0.0097317	0.0113154	0.0133519	0.0048806	0.0229906	0.0111535	0.0129282	0.0174487	0.0136085	0.0183163	0.0052155	0.0218313	0.0061878
2015	0.0019800	0.0152232	0.0067426	0.0100298	0.0120018	0.0118187	0.0076694	0.0045339	0.0083912	0.0054447	0.0060795	0.0134624	0.0131143	0.0090682	0.0141691	0.0135997	0.0048397	0.0170044	0.0098045	0.0125311	0.0175567	0.0131348	0.0184568	0.0055196	0.0136846	0.0066144
2016	0.0018836	0.0121559	0.0061286	0.0100536	0.0114462	0.0119649	0.0075467	0.0044453	0.0077347	0.0051384	0.0054383	0.0130240	0.0122761	0.0090848	0.0113366	0.0152700	0.0044114	0.0140997	0.0110749	0.0111086	0.0196946	0.0131154	0.0150126	0.0058196	0.0127497	0.0058368
2017	0.0018453	0.0116857	0.0074248	0.0140146	0.0135326	0.0126039	0.0070987	0.0045181	0.0082513	0.0055741	0.0055957	0.0135993	0.0121400	0.0083222	0.0109442	0.0171453	0.0047343	0.0149880	0.0109197	0.0125648	0.0181880	0.0138356	0.0201960	0.0058810	0.0140307	0.0057039
2018	0.0018203	0.0122618	0.0070459	0.0138750	0.0157383	0.0115314	0.0071197	0.0047276	0.0069556	0.0055500	0.0053853	0.0177837	0.0100295	0.0090125	0.0102243	0.0160230	0.0044021	0.0138441	0.0109890	0.0129252	0.0138738	0.0182674	0.0142741	0.0060039	0.0124964	0.0059788

Gini standard errors for the five most populous states and their counties with complete data (2005-2018)
YEAR	Pennsylvania (42)	42 003	42 007	42 011	42 017	42 019	42 021	42 027	42 029	42 041	42 043	42 045	42 049	42 051	42 055	42 069	42 071	42 075	42 077	42 079	42 085	42 089	42 091	42 095	42 101	42 107	42 125	42 129	42 133
2005	0.0020278	0.0054590	0.0125522	0.0175199	0.0077296	0.0132326	0.0228382	0.0188312	0.0081322	0.0130742	0.0141609	0.0095529	0.0157435	0.0121519	0.0115515	0.0120994	0.0121233	0.0194747	0.0083672	0.0099873	0.0172786	0.0110768	0.0060415	0.0123013	0.0064199	0.0195516	0.0126323	0.0064298	0.0093570
2006	0.0018820	0.0056567	0.0116740	0.0117331	0.0073681	0.0127822	0.0152289	0.0190606	0.0078558	0.0117049	0.0109706	0.0080075	0.0151158	0.0094868	0.0132986	0.0107804	0.0090860	0.0157062	0.0073139	0.0102616	0.0169507	0.0109385	0.0060999	0.0111237	0.0062843	0.0110416	0.0119798	0.0063488	0.0088704
2007	0.0020100	0.0062745	0.0116049	0.0106594	0.0073305	0.0138282	0.0134389	0.0184546	0.0088317	0.0133358	0.0160202	0.0090101	0.0148676	0.0099627	0.0138018	0.0108625	0.0097750	0.0160592	0.0076961	0.0098813	0.0159568	0.0109044	0.0066102	0.0122081	0.0063783	0.0106788	0.0144284	0.0077962	0.0102218
2008	0.0019164	0.0057039	0.0117261	0.0112470	0.0069369	0.0137917	0.0130437	0.0180346	0.0079621	0.0104887	0.0135282	0.0093530	0.0115304	0.0128222	0.0109465	0.0133145	0.0103364	0.0144528	0.0072181	0.0115922	0.0150613	0.0087046	0.0063423	0.0108381	0.0063585	0.0122134	0.0107179	0.0070688	0.0094376
2009	0.0019900	0.0055740	0.0096963	0.0094204	0.0075853	0.0135738	0.0126014	0.0182283	0.0080398	0.0112587	0.0131020	0.0078952	0.0113508	0.0102473	0.0134602	0.0126109	0.0098091	0.0182118	0.0076905	0.0125745	0.0156194	0.0107412	0.0069467	0.0107779	0.0070371	0.0110084	0.0104933	0.0064608	0.0102222
2010	0.0018823	0.0057095	0.0098070	0.0111780	0.0077758	0.0102216	0.0144330	0.0144079	0.0086671	0.0101755	0.0124806	0.0075123	0.0116736	0.0106751	0.0098133	0.0132991	0.0088277	0.0167102	0.0078449	0.0102187	0.0146852	0.0091017	0.0061171	0.0117466	0.0062390	0.0106597	0.0131431	0.0064374	0.0090643
2011	0.0019788	0.0050322	0.0156541	0.0123813	0.0072474	0.0152811	0.0133573	0.0184409	0.0084066	0.0119419	0.0149649	0.0089902	0.0201415	0.0095925	0.0115726	0.0124308	0.0093753	0.0215619	0.0089976	0.0097557	0.0279146	0.0122759	0.0061683	0.0145006	0.0074394	0.0126246	0.0123902	0.0064562	0.0102237
2012	0.0019612	0.0055488	0.0124378	0.0107353	0.0083562	0.0138429	0.0168461	0.0194335	0.0088490	0.0128851	0.0158514	0.0093622	0.0124200	0.0145468	0.0107408	0.0129202	0.0092933	0.0239999	0.0087192	0.0100713	0.0135974	0.0164698	0.0068475	0.0137084	0.0066040	0.0123710	0.0112604	0.0100027	0.0115467
2013	0.0021158	0.0062267	0.0114336	0.0099301	0.0086222	0.0134071	0.0210154	0.0188161	0.0090419	0.0123480	0.0137858	0.0115935	0.0134312	0.0190613	0.0128606	0.0122585	0.0087573	0.0211676	0.0099577	0.0119971	0.0161384	0.0158094	0.0076193	0.0128598	0.0082081	0.0205573	0.0129412	0.0125929	0.0125626
2014	0.0019970	0.0065481	0.0150174	0.0091570	0.0093055	0.0138714	0.0188286	0.0219025	0.0079021	0.0115147	0.0144650	0.0088559	0.0127841	0.0162949	0.0134191	0.0123042	0.0110974	0.0152390	0.0097176	0.0087945	0.0211063	0.0175259	0.0068128	0.0134040	0.0067033	0.0132731	0.0146627	0.0109634	0.0105188
2015	0.0020098	0.0064647	0.0128056	0.0175892	0.0082000	0.0140402	0.0114930	0.0185579	0.0088221	0.0118190	0.0124819	0.0083959	0.0124533	0.0169312	0.0137414	0.0134904	0.0101917	0.0186652	0.0093100	0.0123734	0.0238172	0.0157382	0.0068298	0.0108313	0.0064157	0.0190646	0.0131888	0.0102271	0.0095122
2016	0.0019317	0.0057002	0.0122138	0.0108631	0.0081621	0.0135765	0.0138597	0.0181977	0.0083993	0.0100393	0.0131667	0.0094865	0.0121980	0.0154462	0.0125830	0.0123486	0.0114294	0.0176358	0.0097802	0.0096348	0.0154508	0.0162534	0.0068026	0.0116943	0.0066579	0.0212093	0.0145777	0.0096155	0.0093657
2017	0.0020404	0.0065908	0.0115600	0.0135716	0.0078299	0.0143773	0.0160545	0.0206099	0.0089167	0.0112752	0.0147515	0.0097666	0.0139593	0.0155836	0.0125161	0.0141046	0.0091058	0.0156487	0.0097820	0.0143370	0.0157084	0.0200571	0.0068820	0.0134335	0.0068176	0.0178074	0.0121100	0.0114790	0.0104589
2018	0.0020143	0.0058238	0.0111005	0.0106215	0.0075858	0.0154213	0.0120732	0.0189173	0.0081114	0.0129738	0.0139005	0.0091746	0.0120300	0.0245573	0.0142585	0.0123140	0.0106139	0.0145928	0.0105386	0.0090610	0.0195054	0.0178912	0.0071347	0.0125527	0.0072812	0.0172949	0.0120737	0.0112648	0.0112981

Gini standard errors for the five most populous states and their counties with complete data (2005-2018)
YEAR	Texas (48)	48 027	48 029	48 039	48 041	48 061	48 085	48 113	48 121	48 135	48 139	48 141	48 157	48 167	48 201	48 215	48 245	48 251	48 303	48 309	48 329	48 339	48 355	48 375	48 381	48 423	48 439	48 441	48 453	48 479	48 485	48 491
2005	0.0015008	0.0097598	0.0059808	0.0155011	0.0167579	0.0144960	0.0071509	0.0047349	0.0086073	0.0187451	0.0138733	0.0086147	0.0079127	0.0113345	0.0039757	0.0108120	0.0128731	0.0139037	0.0121899	0.0143825	0.0157278	0.0090525	0.0125223	0.0216024	0.0194417	0.0151754	0.0042780	0.0141975	0.0060762	0.0154110	0.0149642	0.0084103
2006	0.0014567	0.0131066	0.0050773	0.0143166	0.0162170	0.0113399	0.0075697	0.0048353	0.0078879	0.0183872	0.0153351	0.0092036	0.0078507	0.0109745	0.0036627	0.0094095	0.0166690	0.0199327	0.0120888	0.0143673	0.0183975	0.0095703	0.0103662	0.0170302	0.0183612	0.0170160	0.0040660	0.0133233	0.0059814	0.0138634	0.0245756	0.0089157
2007	0.0013992	0.0114400	0.0052649	0.0112623	0.0192079	0.0134969	0.0066061	0.0046292	0.0074506	0.0195722	0.0198720	0.0083862	0.0074687	0.0112823	0.0034753	0.0094913	0.0123343	0.0177860	0.0160833	0.0137359	0.0183878	0.0086395	0.0108049	0.0190389	0.0198377	0.0166813	0.0039306	0.0287371	0.0054269	0.0139252	0.0205104	0.0080413
2008	0.0014089	0.0119883	0.0056248	0.0102305	0.0163828	0.0106543	0.0073401	0.0045948	0.0081470	0.0330773	0.0139228	0.0085976	0.0076573	0.0119603	0.0035169	0.0095364	0.0115555	0.0178598	0.0148915	0.0134472	0.0160792	0.0079544	0.0129742	0.0192104	0.0217886	0.0126017	0.0040735	0.0189048	0.0054696	0.0141712	0.0194779	0.0078612
2009	0.0013785	0.0111546	0.0055821	0.0113547	0.0144226	0.0116114	0.0068233	0.0044775	0.0074931	0.0197473	0.0186000	0.0079863	0.0073862	0.0107012	0.0034678	0.0097536	0.0120768	0.0271981	0.0126407	0.0152922	0.0194854	0.0078496	0.0089433	0.0176819	0.0174843	0.0171616	0.0037993	0.0174642	0.0059285	0.0144523	0.0198772	0.0076765
2010	0.0012696	0.0105210	0.0048345	0.0118737	0.0153442	0.0117173	0.0062211	0.0041777	0.0066441	0.0210219	0.0178633	0.0079571	0.0072077	0.0097562	0.0031699	0.0099591	0.0110905	0.0203340	0.0108907	0.0106003	0.0192587	0.0075095	0.0097807	0.0138128	0.0166727	0.0148526	0.0035745	0.0226591	0.0049857	0.0137460	0.0194701	0.0074553
2011	0.0015192	0.0106200	0.0066170	0.0138692	0.0183032	0.0119110	0.0082726	0.0050251	0.0095332	0.0162682	0.0156148	0.0093180	0.0093666	0.0109142	0.0036775	0.0097749	0.0169745	0.0186481	0.0150825	0.0134015	0.0156225	0.0114670	0.0121888	0.0169213	0.0194535	0.0206691	0.0043150	0.0223719	0.0057438	0.0140865	0.0148485	0.0091346
2012	0.0014644	0.0114634	0.0056752	0.0134787	0.0151961	0.0136392	0.0073493	0.0041781	0.0066550	0.0156039	0.0162296	0.0108349	0.0089884	0.0107088	0.0036594	0.0128734	0.0143389	0.0145126	0.0132507	0.0160062	0.0236811	0.0137048	0.0117788	0.0153812	0.0175874	0.0178313	0.0054032	0.0276396	0.0066656	0.0124808	0.0165238	0.0081198
2013	0.0014298	0.0163212	0.0050889	0.0101707	0.0159511	0.0128398	0.0068492	0.0045859	0.0071527	0.0178353	0.0150504	0.0095840	0.0089195	0.0113914	0.0035624	0.0109203	0.0143913	0.0158068	0.0141100	0.0137932	0.0190406	0.0110372	0.0112908	0.0386299	0.0257420	0.0132558	0.0057787	0.0169312	0.0062603	0.0132441	0.0175957	0.0084002
2014	0.0013878	0.0121673	0.0050560	0.0147965	0.0192857	0.0099823	0.0061246	0.0042803	0.0071287	0.0198991	0.0110505	0.0078605	0.0084261	0.0114797	0.0034199	0.0127966	0.0122847	0.0165844	0.0131145	0.0123558	0.0174441	0.0101347	0.0114480	0.0215077	0.0171926	0.0170459	0.0051507	0.0210663	0.0060422	0.0177155	0.0201234	0.0088164
2015	0.0014195	0.0119680	0.0051271	0.0141307	0.0151061	0.0104192	0.0063400	0.0044822	0.0072104	0.0163019	0.0154441	0.0077492	0.0097720	0.0145371	0.0034952	0.0106938	0.0140938	0.0163392	0.0119181	0.0137689	0.0192897	0.0116883	0.0129690	0.0248409	0.0172062	0.0164827	0.0049584	0.0178326	0.0061072	0.0163949	0.0163218	0.0087267
2016	0.0013751	0.0148898	0.0050642	0.0130128	0.0149788	0.0148543	0.0061440	0.0041713	0.0061148	0.0239232	0.0149875	0.0087783	0.0092858	0.0105414	0.0037068	0.0103190	0.0121707	0.0221473	0.0139313	0.0142544	0.0178936	0.0107451	0.0111644	0.0195212	0.0177811	0.0138439	0.0045331	0.0175757	0.0057733	0.0129173	0.0174278	0.0083697
2017	0.0014595	0.0112806	0.0050704	0.0207059	0.0154701	0.0117344	0.0063753	0.0041248	0.0066689	0.0219342	0.0128177	0.0080393	0.0086046	0.0116878	0.0037650	0.0092472	0.0138044	0.0131301	0.0117880	0.0130284	0.0188575	0.0149400	0.0100521	0.0191269	0.0153215	0.0135235	0.0049930	0.0154372	0.0066638	0.0131800	0.0193253	0.0079726
2018	0.0013717	0.0113240	0.0051520	0.0138984	0.0142449	0.0110294	0.0061962	0.0042549	0.0064950	0.0178975	0.0128197	0.0076692	0.0091028	0.0139766	0.0033791	0.0096787	0.0141291	0.0141373	0.0137758	0.0124982	0.0203657	0.0101394	0.0151138	0.0227757	0.0158462	0.0158906	0.0047909	0.0151325	0.0060794	0.0174833	0.0186168	0.0074358

Next, we observe the gini coefficients for the five most populous states (and all counties within).

# and a third loop to output ginis
for(i in c("06", "12", "36", "42", "48")){
df.state.county.wide <- county_state_gini %>%
  filter(STATEFIP == i & YEAR > 2004) %>%
  dplyr::select(YEAR, hh_inc, FIP, LEVEL) %>%
  mutate(FIP = replace(as.character(FIP), LEVEL == "State", 
                       first(acs_data$State[acs_data$STATEFIP == i]))) %>%
  dplyr::select(YEAR, hh_inc, FIP) %>%
  group_by(YEAR) %>%
  pivot_wider(names_from = FIP, values_from = hh_inc) 

print(kable(df.state.county.wide, caption = "Gini values for the five most populous states and their counties with complete data (2005-2018)") %>%
  kable_styling() %>%
  scroll_box(width = "900px", height = "500px"))

}

Gini values for the five most populous states and their counties with complete data (2005-2018)
YEAR	California (6)	06 001	06 007	06 013	06 019	06 023	06 025	06 029	06 031	06 037	06 039	06 041	06 047	06 053	06 055	06 059	06 061	06 065	06 067	06 071	06 073	06 075	06 077	06 079	06 081	06 083	06 085	06 087	06 089	06 095	06 097	06 099	06 107	06 111	06 113
2005	0.4688495	0.4600084	0.4693296	0.4518016	0.4629645	0.4981256	0.4444868	0.4659109	0.4388620	0.4886109	0.4319333	0.4817535	0.4288500	0.4137013	0.4742795	0.4478635	0.4287467	0.4347674	0.4335318	0.4314090	0.4531117	0.5095203	0.4405206	0.4487058	0.4672796	0.4716024	0.4487527	0.4642045	0.4652230	0.4073103	0.4354989	0.4417255	0.4594434	0.4256266	0.4690752
2006	0.4659449	0.4533915	0.4593564	0.4556141	0.4613390	0.4285828	0.4472958	0.4633151	0.4194478	0.4795318	0.4628110	0.4835599	0.4374922	0.4466979	0.4559186	0.4528923	0.4302261	0.4295251	0.4322828	0.4236631	0.4540580	0.4958170	0.4266270	0.4779187	0.4555934	0.4722235	0.4484879	0.4772073	0.4536273	0.4130535	0.4381782	0.4495390	0.4481694	0.4259507	0.4527010
2007	0.4684264	0.4633969	0.4646881	0.4553710	0.4508963	0.4728978	0.4860553	0.4553426	0.3975831	0.4864999	0.4558215	0.4925928	0.4510712	0.4414592	0.4531368	0.4482493	0.4165530	0.4441874	0.4296258	0.4080626	0.4481302	0.5104727	0.4367389	0.4825625	0.4668718	0.4673699	0.4542852	0.4807253	0.4637627	0.3946295	0.4481161	0.4260976	0.4546317	0.4344489	0.4476707
2008	0.4719104	0.4612263	0.4567151	0.4571620	0.4651864	0.4942224	0.4756133	0.4607322	0.3836865	0.4896901	0.4584449	0.4810876	0.4524725	0.4355678	0.4621941	0.4540090	0.4168261	0.4321829	0.4228312	0.4353211	0.4559497	0.5142507	0.4455179	0.4347210	0.4698642	0.4602363	0.4480798	0.4772528	0.4657044	0.3909162	0.4552264	0.4311252	0.4358678	0.4341013	0.4641305
2009	0.4657316	0.4509934	0.4455267	0.4532065	0.4680720	0.4710221	0.4857174	0.4523221	0.4526668	0.4800524	0.4445564	0.4794456	0.4475788	0.4452510	0.4525777	0.4426721	0.4214813	0.4325420	0.4367569	0.4340413	0.4499076	0.5092592	0.4309899	0.4417961	0.4610728	0.4545971	0.4405417	0.4654810	0.4737649	0.4088567	0.4362320	0.4280466	0.4509956	0.4384945	0.4423270
2010	0.4705484	0.4656242	0.4591436	0.4511216	0.4638967	0.4564071	0.4504561	0.4546661	0.4332999	0.4849236	0.4591518	0.4875849	0.4780941	0.4576450	0.4539576	0.4482628	0.4402106	0.4404826	0.4394830	0.4334163	0.4595836	0.4984088	0.4386349	0.4546128	0.4488918	0.4723160	0.4499297	0.4521081	0.4493004	0.3985707	0.4465222	0.4530629	0.4439856	0.4414485	0.4695190
2011	0.4800512	0.4689664	0.4597217	0.4791328	0.4590830	0.4746391	0.4921737	0.4686474	0.4453428	0.4934404	0.4173822	0.5178142	0.4661026	0.4465293	0.4645022	0.4571000	0.4306358	0.4426929	0.4513277	0.4465388	0.4638061	0.5062722	0.4390205	0.4704879	0.4732745	0.4748403	0.4682694	0.4609980	0.4490039	0.4169606	0.4520391	0.4570952	0.4516682	0.4448847	0.4955278
2012	0.4804002	0.4740329	0.4756612	0.4641789	0.4831112	0.4744519	0.4717316	0.4528605	0.4708766	0.4928553	0.4724416	0.4957750	0.4588677	0.4319663	0.4531631	0.4639941	0.4170678	0.4472857	0.4610391	0.4365578	0.4639181	0.5189773	0.4591852	0.4590456	0.4767504	0.4646764	0.4595516	0.4766736	0.4254869	0.4322672	0.4570831	0.4499312	0.4622329	0.4435069	0.4744565
2013	0.4885798	0.4767079	0.4771901	0.4754311	0.4848486	0.4441967	0.4536215	0.4615412	0.4464091	0.5017938	0.4626037	0.4956892	0.4414015	0.4532200	0.4398917	0.4634728	0.4418782	0.4549107	0.4602252	0.4440001	0.4771520	0.5197961	0.4432091	0.4705443	0.4779405	0.4976590	0.4736131	0.4744201	0.4593596	0.4304027	0.4568358	0.4545834	0.4640850	0.4438400	0.4863766
2014	0.4877625	0.4641238	0.4949444	0.4741075	0.4807281	0.4683069	0.4840842	0.4694475	0.4496374	0.5015643	0.4336232	0.5097884	0.4556687	0.4677857	0.4921048	0.4712234	0.4498677	0.4487908	0.4665331	0.4521358	0.4645393	0.5078094	0.4711749	0.4548097	0.4721138	0.4887553	0.4574792	0.4892232	0.4562048	0.4117592	0.4640654	0.4513535	0.4745341	0.4491206	0.5109478
2015	0.4862039	0.4643196	0.4715596	0.4701663	0.4774845	0.4831385	0.4648692	0.4697925	0.4383556	0.4958643	0.4678372	0.5181847	0.4842508	0.4606100	0.4969411	0.4704792	0.4378511	0.4461928	0.4639204	0.4438331	0.4556440	0.5146409	0.4456149	0.4712021	0.4786240	0.4915540	0.4620494	0.4864585	0.4397831	0.4346290	0.4622040	0.4447199	0.4611985	0.4328489	0.4857553
2016	0.4890393	0.4638182	0.5021067	0.4570486	0.4962598	0.5038995	0.4955330	0.4676076	0.4106691	0.5023502	0.4426404	0.4966436	0.4701099	0.4504972	0.4687144	0.4658893	0.4644552	0.4509287	0.4606335	0.4430028	0.4611441	0.5063213	0.4548273	0.4528563	0.4772331	0.4754951	0.4636248	0.5026764	0.4799965	0.4311393	0.4421137	0.4448360	0.4612613	0.4421128	0.5179207
2017	0.4849602	0.4582281	0.4988304	0.4696606	0.4775850	0.4969116	0.4861718	0.4560915	0.4005768	0.4938627	0.4185932	0.4932594	0.4741553	0.4259763	0.4474976	0.4634659	0.4544596	0.4611439	0.4525249	0.4329679	0.4641113	0.4949222	0.4648664	0.4474887	0.4741280	0.4778310	0.4592460	0.4883889	0.4841743	0.4158705	0.4502656	0.4248691	0.4738346	0.4661473	0.4767718
2018	0.4902577	0.4623903	0.4885775	0.4689327	0.4677859	0.4529017	0.4503910	0.4721752	0.4181179	0.4966632	0.4421622	0.4942438	0.4565573	0.4587346	0.4501693	0.4709220	0.4465403	0.4687636	0.4533983	0.4426217	0.4675397	0.5156970	0.4506309	0.4629561	0.4751741	0.4757864	0.4725581	0.4800568	0.4533743	0.4003965	0.4616910	0.4578807	0.5030043	0.4455922	0.4882056

Gini values for the five most populous states and their counties with complete data (2005-2018)
YEAR	Florida (12)	12 001	12 009	12 011	12 015	12 019	12 021	12 031	12 033	12 053	12 057	12 069	12 071	12 073	12 081	12 083	12 085	12 086	12 091	12 095	12 097	12 099	12 101	12 103	12 105	12 109	12 111	12 113	12 115	12 117	12 127
2005	0.4662982	0.5118769	0.4425923	0.4637335	0.4193052	0.4064510	0.4779923	0.4405434	0.4448977	0.4288754	0.4536399	0.4127313	0.4659357	0.4670590	0.4555716	0.4300024	0.4932174	0.5092883	0.4255207	0.4484364	0.4224587	0.4875420	0.4366655	0.4733890	0.4308633	0.4881225	0.4330576	0.4089550	0.4830103	0.4429291	0.4442411
2006	0.4651495	0.5297553	0.4432454	0.4689046	0.4298009	0.3525047	0.4919482	0.4503116	0.4628097	0.4120513	0.4625961	0.4294021	0.4472927	0.4789039	0.4374784	0.4222706	0.4950365	0.5009873	0.4300566	0.4467647	0.4074065	0.4767186	0.4317928	0.4731491	0.4279128	0.4747374	0.4237316	0.3995126	0.4745190	0.4650538	0.4760185
2007	0.4664357	0.5335513	0.4453654	0.4698043	0.4210614	0.4070258	0.5013921	0.4402847	0.4625081	0.4256266	0.4659203	0.4207409	0.4613912	0.4693113	0.4449901	0.4547946	0.4851095	0.4907658	0.4444262	0.4608681	0.3989338	0.5015597	0.4217385	0.4633786	0.4275182	0.4697974	0.4052052	0.4281749	0.4716362	0.4756168	0.4499165
2008	0.4696204	0.4954768	0.4379772	0.4684393	0.4449406	0.3875304	0.4954669	0.4465566	0.4412575	0.4117354	0.4666863	0.4155154	0.4660902	0.4839587	0.4711085	0.4478481	0.5061096	0.5100776	0.4002439	0.4513822	0.4000853	0.5046336	0.4382399	0.4638100	0.4380824	0.4708549	0.4096626	0.3893347	0.4797495	0.4625880	0.4324536
2009	0.4678980	0.5381370	0.4487076	0.4688408	0.4301714	0.3978394	0.4925088	0.4475715	0.4499198	0.4226020	0.4637788	0.4307675	0.4649876	0.5013551	0.4571237	0.4360289	0.5099350	0.4989076	0.4168002	0.4573484	0.4111763	0.4954794	0.4351878	0.4662737	0.4363534	0.4769571	0.4300592	0.4172970	0.4825632	0.4631780	0.4575252
2010	0.4719196	0.5230732	0.4478673	0.4772039	0.4388931	0.3887171	0.4964027	0.4462189	0.4701262	0.4283456	0.4767055	0.4126343	0.4710536	0.5033674	0.4635442	0.4267602	0.4885177	0.5094886	0.4206561	0.4620575	0.4093595	0.4910736	0.4338230	0.4733144	0.4302876	0.4958292	0.4465407	0.4367728	0.4644079	0.4604173	0.4512950
2011	0.4797610	0.5133793	0.4523250	0.4840054	0.4445324	0.4533889	0.5182418	0.4667940	0.4576707	0.4001654	0.4813351	0.4048630	0.4722473	0.5013382	0.4695368	0.4435791	0.5107082	0.5121869	0.4488407	0.4772738	0.4114087	0.4983746	0.4530790	0.4764571	0.4388881	0.4742000	0.4551199	0.4500045	0.4693735	0.4748199	0.4696828
2012	0.4802428	0.5397934	0.4623660	0.4773927	0.4185451	0.4149425	0.5137731	0.4683207	0.4342222	0.4139429	0.4883120	0.4441215	0.4699652	0.4747832	0.4790815	0.4451133	0.5345721	0.5161495	0.4394125	0.4770422	0.4249470	0.5022959	0.4460350	0.4765805	0.4346369	0.4968999	0.4521654	0.4327965	0.4703102	0.4392984	0.4604213
2013	0.4835584	0.5233949	0.4370689	0.4890974	0.4362170	0.4328662	0.5199170	0.4756142	0.4475018	0.4259529	0.4843019	0.4215340	0.4876790	0.4939968	0.4842717	0.4481741	0.5090109	0.5152077	0.4395406	0.4665674	0.4451325	0.5124860	0.4480487	0.4945432	0.4339544	0.5033527	0.4771856	0.4302915	0.4754283	0.4394691	0.4499070
2014	0.4820291	0.5197579	0.4487807	0.4762401	0.4569967	0.4187183	0.4978682	0.4763694	0.4228368	0.4314295	0.4747971	0.4596082	0.4687095	0.4821926	0.4600270	0.4358152	0.4994569	0.5209401	0.4310935	0.4873186	0.4257227	0.5042836	0.4395945	0.4918075	0.4378319	0.4883164	0.4330333	0.4479159	0.4881913	0.4684883	0.4535686
2015	0.4862335	0.5158600	0.4395528	0.4825052	0.4829386	0.4250703	0.5033641	0.4825310	0.4358850	0.4352802	0.4748685	0.4273104	0.4765823	0.4987816	0.4700147	0.4415520	0.4978626	0.5350054	0.4304847	0.4851314	0.4307348	0.5092302	0.4491935	0.4931334	0.4358018	0.5247812	0.4500077	0.4290336	0.4723121	0.4632568	0.4575149
2016	0.4827579	0.5047215	0.4566660	0.4894410	0.4711624	0.4241541	0.5100984	0.4578557	0.4482322	0.4216912	0.4835045	0.4311872	0.4714058	0.4838791	0.4774551	0.4445836	0.5201246	0.5188109	0.4384778	0.4770869	0.4015270	0.5045082	0.4705335	0.4716642	0.4476142	0.4967117	0.4330641	0.4092837	0.4911889	0.4627698	0.4565760
2017	0.4845617	0.5359378	0.4629217	0.4959323	0.4371400	0.4024221	0.5013880	0.4657501	0.4453914	0.4239931	0.4873184	0.4282982	0.4770757	0.4943618	0.4680303	0.4457908	0.5278597	0.5108721	0.4288672	0.4935956	0.4131539	0.5126031	0.4495387	0.4824008	0.4511187	0.4959636	0.4282956	0.4120416	0.4950742	0.4817529	0.4584483
2018	0.4866674	0.5315980	0.4388706	0.4895282	0.4644393	0.4094468	0.5151276	0.4777114	0.4590157	0.4564340	0.4843651	0.4394522	0.4877547	0.5092447	0.4726144	0.4673404	0.5281894	0.5124210	0.4544147	0.4816326	0.4296158	0.5219469	0.4500990	0.4840108	0.4378233	0.4869966	0.4452767	0.4509827	0.4825911	0.4511955	0.4588234

Gini values for the five most populous states and their counties with complete data (2005-2018)
YEAR	New York (36)	36 001	36 005	36 007	36 013	36 027	36 029	36 047	36 055	36 059	36 061	36 063	36 065	36 067	36 071	36 075	36 081	36 083	36 085	36 087	36 089	36 091	36 093	36 103	36 111	36 119
2005	0.4952089	0.4636004	0.4944766	0.4334032	0.4394284	0.3991144	0.4534456	0.5021090	0.4591214	0.4393454	0.5844708	0.4334761	0.4292125	0.4484736	0.4165697	0.4015011	0.4222343	0.4203839	0.4566632	0.4312884	0.4296849	0.4186455	0.4371087	0.4156750	0.4286874	0.5193213
2006	0.4933160	0.4154548	0.4741583	0.4254948	0.4456231	0.4095887	0.4571647	0.5007483	0.4488030	0.4505856	0.5819396	0.4175359	0.4151725	0.4558830	0.3936826	0.4336323	0.4244628	0.4035475	0.4288735	0.4360937	0.3909200	0.3966098	0.4502688	0.4222432	0.4409213	0.5083998
2007	0.4990642	0.4543043	0.4825446	0.4415527	0.4128414	0.4193946	0.4688216	0.5046260	0.4470254	0.4554998	0.5890392	0.4319338	0.4270233	0.4434866	0.4251769	0.3974609	0.4254160	0.4142575	0.4252964	0.4445270	0.4264760	0.4196592	0.4455534	0.4123431	0.4445055	0.5140957
2008	0.5015716	0.4394749	0.4802949	0.4646020	0.4262885	0.4290449	0.4532329	0.5068437	0.4467250	0.4476000	0.5917408	0.4189567	0.4296621	0.4520421	0.4236763	0.4427094	0.4374843	0.4056611	0.4023192	0.4535358	0.4206390	0.3892106	0.4177031	0.4183202	0.4441906	0.5225276
2009	0.5017188	0.4520347	0.4815087	0.4377873	0.4306107	0.4170024	0.4753100	0.5103711	0.4451880	0.4462688	0.5856575	0.4513810	0.4271087	0.4382507	0.4153887	0.4221579	0.4392837	0.4176237	0.4460334	0.4604436	0.4103796	0.4160472	0.4073381	0.4220914	0.4437322	0.5171470
2010	0.4994055	0.4566402	0.4787023	0.4304483	0.4237741	0.4429048	0.4563717	0.5087707	0.4560508	0.4536962	0.5821564	0.4533061	0.4090606	0.4595392	0.4134734	0.4192820	0.4469182	0.4293731	0.4421371	0.4423814	0.4857305	0.4110259	0.4337462	0.4277964	0.4564143	0.5101878
2011	0.5004082	0.4290880	0.4895156	0.4347624	0.4210563	0.4335988	0.4575374	0.5137522	0.4665667	0.4488334	0.5831555	0.4410059	0.4461711	0.4476835	0.4515279	0.4284948	0.4499043	0.3975459	0.4335199	0.4189149	0.4291160	0.4146637	0.4379106	0.4144907	0.4680245	0.5102378
2012	0.4986531	0.4481928	0.5018262	0.4381747	0.4708753	0.4394293	0.4601301	0.5028657	0.4622822	0.4397484	0.5779766	0.4395097	0.4458852	0.4537907	0.4597981	0.4285933	0.4433792	0.4138516	0.4407377	0.4408582	0.4645344	0.4336004	0.4551437	0.4359706	0.4706230	0.5144550
2013	0.5095780	0.4820035	0.4890815	0.4509792	0.4207484	0.4241009	0.4575772	0.5226184	0.4725650	0.4634998	0.5816106	0.4329254	0.4363664	0.4703027	0.4538960	0.4309710	0.4573989	0.4263501	0.4638332	0.4704275	0.4471993	0.4303954	0.4421989	0.4423142	0.4512369	0.5202126
2014	0.5092288	0.4671211	0.4925597	0.4473312	0.4564552	0.4523699	0.4701968	0.5190189	0.4704774	0.4640429	0.5770308	0.4423736	0.4430590	0.4565157	0.4372260	0.4176862	0.4516253	0.4663811	0.4589623	0.4666029	0.4438501	0.4139258	0.4291102	0.4321750	0.4925886	0.5239092
2015	0.5131678	0.4782370	0.5092392	0.4536608	0.4260036	0.4371346	0.4653820	0.5250593	0.4637513	0.4655987	0.5900971	0.4568891	0.4541815	0.4600299	0.4398837	0.3986265	0.4537893	0.4464598	0.4460224	0.4736564	0.4527831	0.4012030	0.4550019	0.4489700	0.4597019	0.5314238
2016	0.5109897	0.4756381	0.4840266	0.4596503	0.4219298	0.4483799	0.4666238	0.5215532	0.4691171	0.4546564	0.5929511	0.4251799	0.4587225	0.4664794	0.4487449	0.4224121	0.4539125	0.4342771	0.4687947	0.4593342	0.4458786	0.4340051	0.4577582	0.4408335	0.4774217	0.5181250
2017	0.5133048	0.4585263	0.5133124	0.4613249	0.4420961	0.4556446	0.4587555	0.5280107	0.4749508	0.4649150	0.5815944	0.4352426	0.4460462	0.4689469	0.4194398	0.4096262	0.4502820	0.4410922	0.4706641	0.4690248	0.4390952	0.4250928	0.4357497	0.4448529	0.4552475	0.5288414
2018	0.5115166	0.4586359	0.5178579	0.4580445	0.4624724	0.4609191	0.4578533	0.5263126	0.4620414	0.4551944	0.5832261	0.4586955	0.4426298	0.4645254	0.4572869	0.4691333	0.4454277	0.3891778	0.4569851	0.4802346	0.4465775	0.4248937	0.4376227	0.4390459	0.4676449	0.5308130

Gini values for the five most populous states and their counties with complete data (2005-2018)
YEAR	Pennsylvania (42)	42 003	42 007	42 011	42 017	42 019	42 021	42 027	42 029	42 041	42 043	42 045	42 049	42 051	42 055	42 069	42 071	42 075	42 077	42 079	42 085	42 089	42 091	42 095	42 101	42 107	42 125	42 129	42 133
2005	0.4514464	0.4628893	0.4175965	0.4285021	0.4336194	0.4137252	0.4408415	0.4544228	0.4331070	0.3981661	0.4300449	0.4599362	0.4518661	0.4328183	0.3764680	0.4622920	0.3994881	0.4095328	0.4340878	0.4418967	0.4168608	0.4247556	0.4325562	0.4219897	0.4780928	0.4532393	0.4212786	0.4275313	0.3881848
2006	0.4531325	0.4646743	0.4186753	0.3940694	0.4306978	0.4140132	0.4514816	0.4809039	0.4448324	0.3985454	0.4162904	0.4375357	0.4437013	0.4155675	0.3983751	0.4365571	0.4194606	0.3931261	0.4283619	0.4330767	0.4608257	0.4163567	0.4483210	0.4128069	0.4940238	0.4131045	0.4387694	0.4330738	0.3857190
2007	0.4604500	0.4901279	0.4278759	0.4248793	0.4215968	0.4340146	0.4325465	0.4700314	0.4318911	0.4001154	0.4296373	0.4527188	0.4338203	0.4324953	0.4183791	0.4346120	0.4057239	0.3951102	0.4177353	0.4451885	0.4114385	0.4218796	0.4613664	0.4042869	0.4892843	0.4200111	0.4269116	0.4471107	0.4194152
2008	0.4540986	0.4660654	0.4301395	0.4150875	0.4227379	0.4233507	0.4268557	0.4675304	0.4387746	0.3953876	0.4421539	0.4654976	0.4449048	0.4419460	0.3772350	0.4570222	0.4106627	0.3724373	0.4171875	0.4412178	0.3937538	0.3931553	0.4504329	0.4089870	0.4775583	0.4265020	0.4293442	0.4395573	0.4076876
2009	0.4596238	0.4760931	0.4087658	0.4116150	0.4207478	0.4216736	0.4457349	0.4605155	0.4478703	0.3999423	0.4450967	0.4572358	0.4421353	0.4441238	0.4142162	0.4397418	0.4087798	0.4317204	0.4278674	0.4569466	0.4171907	0.4113183	0.4585344	0.4162262	0.5026828	0.4192561	0.4321888	0.4322287	0.4039127
2010	0.4592992	0.4728716	0.4316544	0.4341008	0.4367263	0.3962830	0.4235501	0.4485894	0.4335791	0.4062840	0.4461675	0.4538965	0.4491690	0.4356486	0.3859533	0.4461424	0.4158027	0.4181372	0.4360529	0.4594020	0.4294203	0.4150942	0.4424816	0.4242052	0.5065060	0.4311375	0.4461183	0.4367472	0.4075516
2011	0.4604582	0.4647336	0.4444540	0.4282273	0.4313488	0.4589037	0.4288394	0.4821385	0.4417610	0.4035702	0.4457770	0.4649789	0.4567813	0.4176904	0.3914474	0.4367494	0.3963248	0.4217361	0.4479998	0.4541137	0.4526434	0.4213366	0.4530066	0.4360835	0.5095452	0.4176211	0.4419125	0.4336836	0.4215794
2012	0.4641501	0.4657553	0.4387584	0.4521747	0.4391813	0.4375898	0.4556047	0.4648492	0.4558239	0.4339125	0.4497556	0.4785966	0.4526240	0.4493364	0.4038092	0.4506039	0.4090630	0.4159925	0.4194014	0.4652340	0.4118916	0.4110049	0.4615360	0.4236182	0.5084244	0.4048453	0.4394033	0.4398110	0.4232561
2013	0.4691303	0.4780590	0.4267015	0.4306433	0.4481572	0.4621339	0.4512283	0.4564970	0.4569790	0.4237828	0.4381689	0.4821518	0.4671380	0.4429662	0.3919405	0.4473172	0.4100015	0.4355367	0.4616397	0.4594845	0.4360970	0.4270553	0.4761837	0.4521006	0.5134508	0.4435501	0.4481330	0.4563585	0.4322294
2014	0.4673423	0.4815841	0.4558988	0.4296465	0.4399495	0.4547086	0.4741864	0.4952571	0.4483872	0.4143819	0.4624807	0.4780905	0.4602145	0.4519943	0.4220604	0.4566165	0.4251133	0.3843187	0.4474251	0.4390270	0.4257576	0.4042321	0.4615735	0.4551983	0.5040200	0.4175845	0.4609805	0.4528139	0.4177360
2015	0.4689562	0.4903643	0.4521031	0.4583402	0.4498200	0.4284430	0.4194093	0.4628520	0.4503371	0.4163272	0.4294598	0.4607405	0.4454759	0.4760602	0.3978500	0.4696347	0.4492984	0.4273085	0.4486471	0.4499324	0.4324267	0.4157166	0.4694264	0.4333849	0.5025395	0.4510916	0.4515572	0.4568148	0.3902636
2016	0.4672412	0.4745718	0.4494688	0.4383262	0.4444719	0.4339385	0.4262145	0.4933007	0.4531129	0.3939501	0.4357797	0.4762104	0.4483572	0.4342475	0.4193069	0.4627230	0.4295779	0.4100033	0.4585561	0.4555975	0.4301787	0.3915314	0.4589260	0.4108742	0.5170839	0.4260180	0.4715809	0.4425568	0.3948436
2017	0.4763948	0.4864003	0.4486887	0.4569592	0.4553929	0.4479544	0.4412837	0.4755407	0.4426722	0.4193100	0.4438242	0.4801469	0.4708041	0.4258338	0.3997618	0.4451309	0.4083487	0.4173470	0.4785429	0.4531486	0.4324168	0.4353773	0.4598905	0.4699987	0.5604323	0.4131257	0.4659292	0.4514598	0.4120939
2018	0.4716892	0.4858082	0.4247899	0.4377716	0.4437065	0.4685284	0.4292354	0.4789302	0.4483041	0.4303404	0.4679560	0.4925817	0.4614667	0.4502859	0.4075370	0.4277375	0.4132332	0.3971601	0.4569904	0.4258253	0.4494706	0.4231629	0.4583039	0.4394510	0.5306395	0.4544152	0.4546837	0.4434057	0.4381259

Gini values for the five most populous states and their counties with complete data (2005-2018)
YEAR	Texas (48)	48 027	48 029	48 039	48 041	48 061	48 085	48 113	48 121	48 135	48 139	48 141	48 157	48 167	48 201	48 215	48 245	48 251	48 303	48 309	48 329	48 339	48 355	48 375	48 381	48 423	48 439	48 441	48 453	48 479	48 485	48 491
2005	0.4725611	0.4062352	0.4668360	0.4455865	0.5457543	0.5289417	0.4273424	0.4821671	0.4276108	0.4633725	0.3786066	0.4740221	0.4419462	0.4376861	0.4807235	0.4933015	0.4548140	0.4173098	0.4724586	0.4648797	0.4725233	0.4717412	0.4485557	0.4835068	0.4410594	0.4850489	0.4428689	0.4279504	0.4853962	0.4732561	0.4173852	0.4415051
2006	0.4721527	0.4448196	0.4506713	0.4270277	0.5201798	0.4902727	0.4293180	0.4824448	0.4259355	0.4261447	0.4060170	0.4824156	0.4535291	0.4429791	0.4811909	0.4912886	0.5105645	0.4042981	0.4651207	0.4599087	0.5204884	0.4643900	0.4722764	0.4546914	0.4428624	0.4634870	0.4452919	0.4040034	0.4808771	0.4620501	0.5280957	0.4377457
2007	0.4717420	0.4126567	0.4614186	0.4199362	0.5409961	0.4916261	0.4032559	0.4885284	0.4150233	0.4631576	0.4214058	0.4867329	0.4478461	0.4643482	0.4876793	0.4810001	0.4709150	0.4124913	0.4683518	0.4719921	0.5126624	0.4646431	0.4635370	0.4626271	0.4616135	0.4789234	0.4423603	0.4417783	0.4745225	0.4566852	0.4497759	0.4223645
2008	0.4736132	0.4198571	0.4739939	0.4155224	0.5175674	0.4805647	0.4240719	0.4949053	0.4255393	0.4777250	0.4000137	0.4697591	0.4397188	0.4513986	0.4848759	0.5014227	0.4450167	0.4206920	0.4783361	0.4842460	0.4594670	0.4405884	0.4790118	0.4918107	0.4655813	0.4432575	0.4439937	0.4716392	0.4877434	0.4593149	0.4314318	0.4351893
2009	0.4741431	0.4397842	0.4667427	0.4057392	0.5380215	0.5057988	0.4339656	0.4932202	0.4220748	0.4507466	0.4117835	0.4619330	0.4516087	0.4602350	0.4841213	0.4958854	0.4751174	0.4437399	0.4739842	0.4846475	0.4890287	0.4612776	0.4580358	0.4515148	0.4259194	0.4992373	0.4474292	0.4326879	0.4830028	0.4451838	0.4530798	0.4110129
2010	0.4680554	0.4028374	0.4551174	0.4226638	0.5357907	0.4825340	0.4282524	0.4814411	0.4246279	0.4933587	0.4460716	0.4722556	0.4506407	0.4496728	0.4787602	0.4853723	0.4579012	0.4056721	0.4710248	0.4457113	0.4812277	0.4539858	0.4573440	0.4241250	0.4122847	0.4783565	0.4448904	0.4733599	0.4821228	0.4750433	0.4518809	0.4270860
2011	0.4753711	0.4205514	0.4647780	0.4313874	0.5449617	0.4902305	0.4251258	0.4870967	0.4172637	0.4292024	0.3996370	0.4700769	0.4376574	0.4457474	0.4928160	0.4911119	0.4836707	0.3906052	0.5021514	0.4880708	0.4617506	0.4724847	0.4742913	0.4708515	0.4750636	0.4999504	0.4482216	0.4706051	0.4916968	0.4399121	0.4258286	0.4407735
2012	0.4747593	0.4322034	0.4662438	0.4295114	0.5413956	0.4961087	0.4294158	0.4841338	0.4187328	0.4236378	0.4034737	0.4902515	0.4161310	0.4402575	0.4938911	0.5026877	0.4899427	0.3820759	0.4838235	0.4708990	0.4800470	0.4736719	0.4511267	0.4752309	0.4175149	0.4697975	0.4519454	0.5090438	0.4907758	0.4715126	0.4399136	0.3792024
2013	0.4791761	0.4271334	0.4616772	0.3994821	0.5284422	0.5001004	0.4339077	0.5050554	0.4280078	0.4539033	0.4201986	0.4719378	0.4414118	0.4683559	0.4949687	0.4905649	0.4965417	0.4141874	0.4839182	0.4805917	0.4730999	0.4620716	0.4484441	0.5198347	0.4382044	0.4551959	0.4636453	0.4420492	0.4820351	0.4901135	0.4642417	0.4095190
2014	0.4817846	0.4298030	0.4708206	0.4210176	0.5159521	0.4843012	0.4249283	0.4984778	0.4477246	0.4507571	0.4008264	0.4637352	0.4293677	0.4775389	0.4974368	0.5060825	0.4964237	0.4400655	0.4858896	0.4755632	0.4732104	0.4819724	0.4613422	0.4694182	0.4387824	0.4727916	0.4648894	0.4781956	0.4887124	0.4968575	0.4822031	0.4034334
2015	0.4818033	0.4487661	0.4624428	0.4456395	0.5167429	0.4882194	0.4293600	0.5008844	0.4374480	0.4406296	0.4190004	0.4407127	0.4409851	0.4823705	0.4956904	0.5057590	0.4639867	0.4318852	0.4709072	0.4799792	0.4658036	0.4906743	0.4830385	0.4759411	0.4375245	0.4736834	0.4687712	0.4761751	0.4954431	0.4843383	0.4456764	0.4035074
2016	0.4787102	0.4391662	0.4612196	0.4180360	0.5430912	0.4989984	0.4333602	0.4860391	0.4386316	0.4730189	0.4139604	0.4567746	0.4447311	0.4781717	0.4976582	0.4979466	0.4905667	0.4069081	0.5010610	0.4880409	0.4756049	0.4890675	0.4553910	0.4684209	0.4511856	0.4489812	0.4526118	0.4429837	0.4771805	0.5085240	0.4584048	0.4022186
2017	0.4763822	0.4241634	0.4651383	0.4350992	0.5066398	0.4897964	0.4298625	0.4840179	0.4321570	0.4140610	0.3974054	0.4634494	0.4361459	0.4585956	0.4964401	0.4910466	0.4821459	0.3733873	0.5058372	0.4710131	0.4313703	0.4831099	0.4723959	0.4663925	0.4258166	0.4671403	0.4528570	0.4573214	0.4758998	0.4610231	0.4906793	0.4054541
2018	0.4799454	0.4197029	0.4728724	0.4331879	0.5238015	0.4743535	0.4388566	0.4886704	0.4349589	0.4387302	0.3802584	0.4751484	0.4360878	0.4835420	0.4949773	0.4935264	0.5006952	0.4086477	0.4808871	0.4678293	0.4596877	0.4689415	0.4678044	0.5014638	0.4132385	0.4802610	0.4589642	0.4459782	0.4881870	0.4719877	0.4719481	0.3914748

Finally, we want to get a sense of trends at the state and county levels. Considering the restricted sample of the five most populous states (CA, FL, NY, PA, and TX), the following analysis describes the trends for the “average” state and county. The Hodrick-Prescott filter models the trend by obtaining filter weights \(\hat{\beta_{j}} = argmin E[(y_t - \hat{y_t}^2]\), where the filter, \(B(L)\), is a function of weights and a lag operator \(L\): \(B(L) = \sum_{j=-\infty}^{\infty}B_jL^j\), and \(L^kx_t = x_{t-k}\). The filter is used in the model \(y_t = B(L)x_t\) to predict time series outcomes.

####### join county and state estimates

ggplot(county_state_gini, aes(x = YEAR, y = hh_inc, color = LEVEL)) + 
  geom_point() + 
  facet_wrap(~LEVEL)  + 
  labs(title = "Gini estimates at the state and county levels, calculated using PUMA-constructed counties (2005-2018)")

county_state_ts <- county_state_gini %>%
  group_by(YEAR, LEVEL) %>%
  summarise(gini = mean(hh_inc))

state_ts <- ts(county_state_ts[county_state_ts$LEVEL == "State", "gini"], 
               start = 2005, end = 2018)
county_ts <- ts(county_state_ts[county_state_ts$LEVEL == "County", "gini"], 
               start = 2005, end = 2018)

state.hp <- hpfilter(state_ts)
county.hp <- hpfilter(county_ts)

plot(state.hp)

For the five most populous states, we notice a rise in inequality from close to 2008 through 2018. The total increase is roughly 0.02 points, which appears similar to the county level increase.

plot(county.hp)

At the county level, the trend is similar, but flattens in the latter years. Whereas the state level time-series peaks at 2018, the county level time-series peaks at 2014.

Income Segregation and Relative Deprivation: ACS Data

Tyler McDaniel / Florencia Torche

March 11, 2020

Income Inequality