2020 Business Formation in Some States During COVID Pandemic

Business Applications for California (BUSAPPWNSACA)

• The core business applications series that corresponds to a subset of all EIN applications. BA includes all applications for an EIN, except for applications for tax liens, estates, trusts, or certain financial filings, applications with no state-county geocodes, applications from certain agricultural, public entities, and applications in certain industries (e.g. private households, civic and social organizations).

symbol <-fredr_series_observations(series_id = "BUSAPPWNSACA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	11670
789	2021-02-13	11650
790	2021-02-20	10180
791	2021-02-27	11610
792	2021-03-06	11760
793	2021-03-13	12560

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for California",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue1",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for California _ Short Term Trend",
       caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}")
    ) +
    theme_economist()

Business Applications from Corporations for California (CBUSAPPWNSACA)

• Business Applications from Corporations (CBA): A subset of High-Propensity Business Applications (HBA) that contains all applications that come from a corporation or a personal service corporation.

symbol <-fredr_series_observations(series_id = "CBUSAPPWNSACA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	2790
789	2021-02-13	2930
790	2021-02-20	2450
791	2021-02-27	2930
792	2021-03-06	3470
793	2021-03-13	3380

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Corporate Business Applications for California",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Corporate Business Applications for California _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for the United States (BUSAPPWNSAUS)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSAUS", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	122400
789	2021-02-13	118510
790	2021-02-20	101990
791	2021-02-27	115130
792	2021-03-06	119060
793	2021-03-13	120920

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for United States",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for United states _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve.\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for Oregon (BBUSAPPWNSAOR)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSAOR", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	1200
789	2021-02-13	1080
790	2021-02-20	880
791	2021-02-27	1100
792	2021-03-06	1080
793	2021-03-13	1080

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for OREGON",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
       caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for OREGON _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for New York (BUSAPPWNSANY)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSANY", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	6460
789	2021-02-13	6550
790	2021-02-20	5650
791	2021-02-27	6250
792	2021-03-06	6860
793	2021-03-13	6910

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for New York State",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for New York State _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for Arizona (BUSAPPWNSAAZ)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSAAZ", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	2360
789	2021-02-13	2440
790	2021-02-20	2120
791	2021-02-27	2320
792	2021-03-06	2390
793	2021-03-13	2280

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for ARIZONA",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for ARIZONA _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for TEXAS (BUSAPPWNSATX)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSATX", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	12190
789	2021-02-13	10890
790	2021-02-20	5760
791	2021-02-27	8980
792	2021-03-06	9890
793	2021-03-13	10670

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for TEXAS",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for TEXAS _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for FLORIDA (BUSAPPWNSAFL)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSAFL", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	13320
789	2021-02-13	15320
790	2021-02-20	14630
791	2021-02-27	14960
792	2021-03-06	13980
793	2021-03-13	13820

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for FLORIDA",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for FLORIDA _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for OKLAHOMA (BUSAPPWNSAOK)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSAOK", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	1440
789	2021-02-13	1190
790	2021-02-20	830
791	2021-02-27	1150
792	2021-03-06	1220
793	2021-03-13	1280

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for OKLAHOMA",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for OKLAHOMA _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for COLORADO (BUSAPPWNSACO)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSACO", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	2460
789	2021-02-13	2450
790	2021-02-20	2160
791	2021-02-27	2350
792	2021-03-06	2390
793	2021-03-13	2470

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for COLORADO",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for COLORADO _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for GEORGIA (BUSAPPWNSAGA)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSAGA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	7940
789	2021-02-13	7110
790	2021-02-20	7220
791	2021-02-27	7630
792	2021-03-06	7750
793	2021-03-13	7340

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for GEORGIA",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for GEORGIA _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

Business Applications for ILLINOIS (BUSAPPWNSAIL)

symbol <-fredr_series_observations(series_id = "BUSAPPWNSAIL", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
788	2021-02-06	4860
789	2021-02-13	4500
790	2021-02-20	3860
791	2021-02-27	4570
792	2021-03-06	4840
793	2021-03-13	4730

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Weekly",
      y = "Count",
      title = "Business Applications for ILLINOIS",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "",
      y = "Count",
      title = "Business Applications for ILLINOIS _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED Federal Reserve\nIllustration by @JoeLongSanDiego") +
    theme_economist()