Personal Income during COVID19 Pandemic
Joe Long
10/30/2021
Consumer Sentiment as general trends.
- UMCSENT: University of Michigan Consumer Sentiment. Index 1966:Q1=100,Not Seasonally Adjusted
senti <-
"UMCSENT" %>%
tq_get(get = "economic.data", from = "2015-01-01") %>%
rename(count = price) ## Registered S3 method overwritten by 'tune':
## method from
## required_pkgs.model_spec parsnip senti %>%
ggplot(aes(x = date, y = count)) +
geom_line(color = "firebrick2",size=.8) +
labs(
x = "",
y = "",
title = "Consumer Sentiment Index per University of Michigan",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("Monthly from {min(senti$date)} through {max(senti$date)}")
) +
theme_economist()
- Personal Consumption Expenditures (PCE)
- Billions of Dollars, Not Seasonally Adjusted
indicator <-fredr_series_observations(series_id = "PCE",
observation_start = as.Date("2017-01-01"))
tail(indicator,n=24) %>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1:3, T, color = "blue" ) | date | series_id | value | realtime_start | realtime_end |
|---|---|---|---|---|
| 2019-10-01 | PCE | 14608 | 2021-10-31 | 2021-10-31 |
| 2019-11-01 | PCE | 14668 | 2021-10-31 | 2021-10-31 |
| 2019-12-01 | PCE | 14686 | 2021-10-31 | 2021-10-31 |
| 2020-01-01 | PCE | 14770 | 2021-10-31 | 2021-10-31 |
| 2020-02-01 | PCE | 14785 | 2021-10-31 | 2021-10-31 |
| 2020-03-01 | PCE | 13762 | 2021-10-31 | 2021-10-31 |
| 2020-04-01 | PCE | 12022 | 2021-10-31 | 2021-10-31 |
| 2020-05-01 | PCE | 13058 | 2021-10-31 | 2021-10-31 |
| 2020-06-01 | PCE | 13889 | 2021-10-31 | 2021-10-31 |
| 2020-07-01 | PCE | 14129 | 2021-10-31 | 2021-10-31 |
| 2020-08-01 | PCE | 14270 | 2021-10-31 | 2021-10-31 |
| 2020-09-01 | PCE | 14482 | 2021-10-31 | 2021-10-31 |
| 2020-10-01 | PCE | 14546 | 2021-10-31 | 2021-10-31 |
| 2020-11-01 | PCE | 14467 | 2021-10-31 | 2021-10-31 |
| 2020-12-01 | PCE | 14390 | 2021-10-31 | 2021-10-31 |
| 2021-01-01 | PCE | 14858 | 2021-10-31 | 2021-10-31 |
| 2021-02-01 | PCE | 14700 | 2021-10-31 | 2021-10-31 |
| 2021-03-01 | PCE | 15459 | 2021-10-31 | 2021-10-31 |
| 2021-04-01 | PCE | 15619 | 2021-10-31 | 2021-10-31 |
| 2021-05-01 | PCE | 15624 | 2021-10-31 | 2021-10-31 |
| 2021-06-01 | PCE | 15802 | 2021-10-31 | 2021-10-31 |
| 2021-07-01 | PCE | 15812 | 2021-10-31 | 2021-10-31 |
| 2021-08-01 | PCE | 15967 | 2021-10-31 | 2021-10-31 |
| 2021-09-01 | PCE | 16060 | 2021-10-31 | 2021-10-31 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
geom_line(color = "red4",size=.8) +
labs(
y = "Billions of Dollars",
x = "Monthly",
title = "Personal Consumption Expenditures (PCE)",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")) +
theme_economist()
Real Personal Consumption Expenditures: Services (PCESC96)
indicator <-fredr_series_observations(series_id = "PCESC96",
observation_start = as.Date("2017-01-01"))
tail(indicator,n=24) %>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(3, T, color = "red" ) | date | series_id | value | realtime_start | realtime_end |
|---|---|---|---|---|
| 2019-10-01 | PCESC96 | 8492 | 2021-10-31 | 2021-10-31 |
| 2019-11-01 | PCESC96 | 8518 | 2021-10-31 | 2021-10-31 |
| 2019-12-01 | PCESC96 | 8508 | 2021-10-31 | 2021-10-31 |
| 2020-01-01 | PCESC96 | 8544 | 2021-10-31 | 2021-10-31 |
| 2020-02-01 | PCESC96 | 8550 | 2021-10-31 | 2021-10-31 |
| 2020-03-01 | PCESC96 | 7759 | 2021-10-31 | 2021-10-31 |
| 2020-04-01 | PCESC96 | 6814 | 2021-10-31 | 2021-10-31 |
| 2020-05-01 | PCESC96 | 7206 | 2021-10-31 | 2021-10-31 |
| 2020-06-01 | PCESC96 | 7632 | 2021-10-31 | 2021-10-31 |
| 2020-07-01 | PCESC96 | 7733 | 2021-10-31 | 2021-10-31 |
| 2020-08-01 | PCESC96 | 7817 | 2021-10-31 | 2021-10-31 |
| 2020-09-01 | PCESC96 | 7895 | 2021-10-31 | 2021-10-31 |
| 2020-10-01 | PCESC96 | 7935 | 2021-10-31 | 2021-10-31 |
| 2020-11-01 | PCESC96 | 7917 | 2021-10-31 | 2021-10-31 |
| 2020-12-01 | PCESC96 | 7899 | 2021-10-31 | 2021-10-31 |
| 2021-01-01 | PCESC96 | 7962 | 2021-10-31 | 2021-10-31 |
| 2021-02-01 | PCESC96 | 7939 | 2021-10-31 | 2021-10-31 |
| 2021-03-01 | PCESC96 | 8080 | 2021-10-31 | 2021-10-31 |
| 2021-04-01 | PCESC96 | 8157 | 2021-10-31 | 2021-10-31 |
| 2021-05-01 | PCESC96 | 8214 | 2021-10-31 | 2021-10-31 |
| 2021-06-01 | PCESC96 | 8272 | 2021-10-31 | 2021-10-31 |
| 2021-07-01 | PCESC96 | 8336 | 2021-10-31 | 2021-10-31 |
| 2021-08-01 | PCESC96 | 8373 | 2021-10-31 | 2021-10-31 |
| 2021-09-01 | PCESC96 | 8404 | 2021-10-31 | 2021-10-31 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
geom_line(color = "red4",size=.8) +
labs(
y = "Billions of Dollars",
x = "Monthly",
title = "Real Personal Consumption Expenditures: Services (PCESC96)",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")) +
theme_economist()
Real personal consumption expenditures: Food (DFXARX1M020SBEA)
indicator <-fredr_series_observations(series_id = "DFXARX1M020SBEA",
observation_start = as.Date("2017-01-01"))
tail(indicator,n=24) %>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(3, T, color = "red" ) | date | series_id | value | realtime_start | realtime_end |
|---|---|---|---|---|
| 2019-10-01 | DFXARX1M020SBEA | 993 | 2021-10-31 | 2021-10-31 |
| 2019-11-01 | DFXARX1M020SBEA | 994 | 2021-10-31 | 2021-10-31 |
| 2019-12-01 | DFXARX1M020SBEA | 998 | 2021-10-31 | 2021-10-31 |
| 2020-01-01 | DFXARX1M020SBEA | 995 | 2021-10-31 | 2021-10-31 |
| 2020-02-01 | DFXARX1M020SBEA | 992 | 2021-10-31 | 2021-10-31 |
| 2020-03-01 | DFXARX1M020SBEA | 1213 | 2021-10-31 | 2021-10-31 |
| 2020-04-01 | DFXARX1M020SBEA | 1042 | 2021-10-31 | 2021-10-31 |
| 2020-05-01 | DFXARX1M020SBEA | 1067 | 2021-10-31 | 2021-10-31 |
| 2020-06-01 | DFXARX1M020SBEA | 1060 | 2021-10-31 | 2021-10-31 |
| 2020-07-01 | DFXARX1M020SBEA | 1074 | 2021-10-31 | 2021-10-31 |
| 2020-08-01 | DFXARX1M020SBEA | 1059 | 2021-10-31 | 2021-10-31 |
| 2020-09-01 | DFXARX1M020SBEA | 1068 | 2021-10-31 | 2021-10-31 |
| 2020-10-01 | DFXARX1M020SBEA | 1060 | 2021-10-31 | 2021-10-31 |
| 2020-11-01 | DFXARX1M020SBEA | 1067 | 2021-10-31 | 2021-10-31 |
| 2020-12-01 | DFXARX1M020SBEA | 1047 | 2021-10-31 | 2021-10-31 |
| 2021-01-01 | DFXARX1M020SBEA | 1104 | 2021-10-31 | 2021-10-31 |
| 2021-02-01 | DFXARX1M020SBEA | 1078 | 2021-10-31 | 2021-10-31 |
| 2021-03-01 | DFXARX1M020SBEA | 1127 | 2021-10-31 | 2021-10-31 |
| 2021-04-01 | DFXARX1M020SBEA | 1117 | 2021-10-31 | 2021-10-31 |
| 2021-05-01 | DFXARX1M020SBEA | 1106 | 2021-10-31 | 2021-10-31 |
| 2021-06-01 | DFXARX1M020SBEA | 1113 | 2021-10-31 | 2021-10-31 |
| 2021-07-01 | DFXARX1M020SBEA | 1095 | 2021-10-31 | 2021-10-31 |
| 2021-08-01 | DFXARX1M020SBEA | 1121 | 2021-10-31 | 2021-10-31 |
| 2021-09-01 | DFXARX1M020SBEA | 1120 | 2021-10-31 | 2021-10-31 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
geom_line(color = "red4",size=.8) +
labs(
y = "Billions of Chained 2012 Dollars",
x = "Monthly",
title = "Real personal consumption expenditures: Food (DFXARX1M020SBEA)",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")) +
theme_economist()
Real Personal Consumption Expenditures: Durable Goods (PCEDGC96)
indicator <-fredr_series_observations(series_id = "PCEDGC96",
observation_start = as.Date("2017-01-01"))
tail(indicator[,c(1,3)],n=24) %>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(1:2, T, color = "blue" ) | date | value |
|---|---|
| 2019-10-01 | 1776 |
| 2019-11-01 | 1802 |
| 2019-12-01 | 1806 |
| 2020-01-01 | 1816 |
| 2020-02-01 | 1811 |
| 2020-03-01 | 1588 |
| 2020-04-01 | 1420 |
| 2020-05-01 | 1810 |
| 2020-06-01 | 1965 |
| 2020-07-01 | 2016 |
| 2020-08-01 | 2026 |
| 2020-09-01 | 2050 |
| 2020-10-01 | 2075 |
| 2020-11-01 | 2044 |
| 2020-12-01 | 1990 |
| 2021-01-01 | 2210 |
| 2021-02-01 | 2117 |
| 2021-03-01 | 2434 |
| 2021-04-01 | 2421 |
| 2021-05-01 | 2279 |
| 2021-06-01 | 2248 |
| 2021-07-01 | 2158 |
| 2021-08-01 | 2147 |
| 2021-09-01 | 2137 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
geom_line(color = "red4",size=.8) +
labs(
y = "Billions of Chained 2012 Dollars",
x = "Monthly",
title = "Real Personal Consumption Expenditures: Durable Goods (PCEDGC96)",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")) +
theme_economist()
Personal current transfer receipts: Government social benefits to persons (A063RC1)
indicator <-fredr_series_observations(series_id = "A063RC1",
observation_start = as.Date("2017-01-01"))
tail(indicator) %>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "red" ) | date | series_id | value | realtime_start | realtime_end |
|---|---|---|---|---|
| 2021-04-01 | A063RC1 | 4656 | 2021-10-31 | 2021-10-31 |
| 2021-05-01 | A063RC1 | 4109 | 2021-10-31 | 2021-10-31 |
| 2021-06-01 | A063RC1 | 4009 | 2021-10-31 | 2021-10-31 |
| 2021-07-01 | A063RC1 | 4125 | 2021-10-31 | 2021-10-31 |
| 2021-08-01 | A063RC1 | 4144 | 2021-10-31 | 2021-10-31 |
| 2021-09-01 | A063RC1 | 3847 | 2021-10-31 | 2021-10-31 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
geom_line(color = "red4",size=.8) +
labs(
y = "Billions of Dollars",
x = "Monthly",
title = "Government social benefits to persons ",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")) +
theme_economist()
Employment during COVID19 pandemic
- All Employees, Total Nonfarm (PAYEMS)
- excluding proprietors, private household employees, unpaid volunteers, farm employees, self-employed.
- This measure accounts for approximately 80 percent of the workers who contribute to Gross Domestic Product.
labor <-
"PAYEMS" %>%
tq_get(get = "economic.data", from = "2015-01-01") %>%
rename(count = price)
labor %>%
ggplot(aes(x = date, y = count)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
geom_line(color = "firebrick4",size=.8) +
labs(
x = "",
y = "",
title = "Total Labor Force",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("From {min(labor$date)} through {max(labor$date)}")
) +
theme_economist()
Total Unemployed (U6RATE)
- Total Unemployed, Plus All Persons Marginally Attached to the Labor Force, Plus Total Employed Part Time for Economic Reasons, as a Percent of the Civilian Labor Force Plus All Persons Marginally Attached to the Labor Force (U6RATE)
u6 <-
"U6RATE" %>%
tq_get(get = "economic.data", from = "2019-01-01") %>%
rename(count = price)
u6 %>%
ggplot(aes(x = date, y = count)) +
geom_line(color = "blue",size=1.2) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
labs(
x = "",
y = "Percent",
title = "Total Unemployed Rate (U6Rate)",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("From {min(u6$date)} through {max(u6$date)}")
) +
theme_economist()
Personal consumption expenditures: Food (DFXARC1M027SBEA)
indicator <-fredr_series_observations(series_id = "DFXARC1M027SBEA",
observation_start = as.Date("2017-01-01"))
tail(indicator) %>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "red" ) | date | series_id | value | realtime_start | realtime_end |
|---|---|---|---|---|
| 2021-04-01 | DFXARC1M027SBEA | 1223 | 2021-10-31 | 2021-10-31 |
| 2021-05-01 | DFXARC1M027SBEA | 1215 | 2021-10-31 | 2021-10-31 |
| 2021-06-01 | DFXARC1M027SBEA | 1232 | 2021-10-31 | 2021-10-31 |
| 2021-07-01 | DFXARC1M027SBEA | 1220 | 2021-10-31 | 2021-10-31 |
| 2021-08-01 | DFXARC1M027SBEA | 1254 | 2021-10-31 | 2021-10-31 |
| 2021-09-01 | DFXARC1M027SBEA | 1265 | 2021-10-31 | 2021-10-31 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
geom_line(color = "red4",size=.8) +
labs(
y = "Billions of Dollars",
x = "Monthly",
title = "Personal consumption expenditures: Food",
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")) +
theme_economist()
Consumer Price Index for All Urban Consumers: Meats, Poultry, Fish, and Eggs in U.S. City Average (CUSR0000SAF112)
mpfe <-
"CUSR0000SAF112" %>%
tq_get(get = "economic.data", from = "2019-01-01")
mpfe %>%
ggplot(aes(x = date, y = price)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-02-2021'), ymin = -Inf, ymax = Inf),
fill = "lightyellow", alpha = 0.02)+
geom_line(color = "gold4",size=.8) +
labs(
x = "",
y = "Index 1982-1984=100",
caption = "Index 1982-1984=100,Seasonally Adjusted\n
Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego",
title = "CPI : Meats, Poultry, Fish, and Eggs (CUSR0000SAF112)",
subtitle = str_glue("From {min(mpfe$date)} through {max(mpfe$date)}")
) +
theme_economist()
- Consumer Price Index for All Urban Consumers: All Items in U.S. City Average (CPIAUCSL)
- Consumer Price Index for All Urban Consumers: Food at Home in U.S. City Average (CUSR0000SAF11)
- Consumer Price Index for All Urban Consumers: Alcoholic Beverages at Home in U.S. City Average (CUSR0000SEFW)
cpi <-
"CPIAUCSL" %>%
tq_get(get = "economic.data", from = "2019-01-01") %>%
rename(count = price)
cpi %>%
ggplot(aes(x = date, y = count)) +
geom_line(color = "goldenrod4",size=.8) +
labs(
x = "",
y = "", caption = "Index 1982-1984=100,Seasonally Adjusted",
title = "Consumer Price Index: All Items in U.S. City Average (CPIAUCSL)",
subtitle = str_glue("Monthly from {min(cpi$date)} through {max(cpi$date)}")
) +
theme(plot.title = element_text(color="blue", size=14, face="bold"))
Consumer Price Index for All Urban Consumers: Food at Home in U.S. City Average (CUSR0000SAF11)
food <-
"CUSR0000SAF11" %>%
tq_get(get = "economic.data", from = "2019-01-01") %>%
rename(count = price)
food %>%
ggplot(aes(x = date, y = count)) +
geom_line(color = "goldenrod4",size=.8) +
labs(
x = "",
y = "", caption = "Index 1982-1984=100,Seasonally Adjusted",
title = "CPI: Food at Home in U.S. City Average (CUSR0000SAF11)",
subtitle = str_glue("Monthly from {min(food$date)} through {max(food$date)}")
) +
theme_economist()+
theme(plot.title = element_text(color="blue", size=15, face="bold"))
CPI Average Price Data, U.S. city average (AP) (Select from list below) Bacon, sliced, per lb. - APU0000704111 Bananas, per lb. - APU0000711211 Bread, white, pan, per lb. - APU0000702111 Chicken, fresh, whole, per lb. - APU0000706111 Coffee, 100%, ground roast, all sizes, per lb. - APU0000717311 Eggs, grade A, large, per doz. - APU0000708111 Flour, white, all purpose, per lb. - APU0000701111 Milk, fresh, whole, fortified, per gal. - APU0000709112 Oranges, navel, per lb. - APU0000711311 Rice, white, long grain, uncooked, per lb. - APU0000701312 Tomatoes, field grown, per lb. - APU0000712311 Electricity per KWH - APU000072610 Fuel oil #2 per gallon - APU000072511 Gasoline, all types, per gallon - APU00007471A Gasoline, unleaded regular, per gallon - APU000074714
library(blscrapeR)
library(tidyverse)
df <- bls_api(c("APU0000704111", "APU0000706111","APU0000708111","APU0000709112"),
startyear = 2019, endyear = 2020) %>%
spread(seriesID, value) %>% dateCast() %>%
rename(chicken=APU0000706111,egg=APU0000708111,beacon=APU0000704111,milk=APU0000709112)## REQUEST_SUCCEEDEDggplot(data = df, aes(x = date)) +
geom_line(aes(y = chicken, color = "chicken"),size=.8) +
geom_line(aes(y = egg, color = "egg"),size=.8) +
geom_line(aes(y = beacon, color = "beacon"),size=.8) +
geom_line(aes(y = milk, color = "milk"),size=.8) +
labs(title = "Food Prices During COVID19 Pandemic", y="Price", x="Date") +
theme_economist()+
theme(legend.position="top", plot.title = element_text(hjust = 0.5)) 