11 K-12 Spending & ESSER Funds

$3.4266 billion Total spent through FY23 from ARP and ESSER Funds. - 1.694 billion spent in 2023 (ARP = ESSER III) but that money arrived in 2022 (=> Fiscal Gap Implications!)
$2.34 Billion of ARP Emergency Relief Funds (aka ESSER III) spent in 2022 and 2023 combined
583 million for ESSER II in 2021
376 million with ESSER I in 2021 and 128 million in 2020.
ISBE received around an additional $7.9 billion total from ESSER compared to its normal federal funding because of COVID
At end of State’s FY23, $3.43/$7.9 billion = ~ 50% spent
As of Dec.7th, it has spent 74.1% of these funds (“ESSER Spending Dashboard” 2023)
640 million allocated for FY24 (according to CPS, I think they have different fiscal years though…)

Code

#|
rev_temp <- rev_temp %>% 
  mutate(
    rev_type = ifelse(rev_type=="57" & agency=="478" & (source=="0618"|source=="2364"|source=="0660"|source=="1552"| source=="2306"| source=="2076"|source=="0676"|source=="0692"), "58", rev_type),
    rev_type_name = ifelse(rev_type=="58", "Federal Medicaid Reimbursements", rev_type_name),
    rev_type = ifelse(rev_type=="57" & agency=="494", "59", rev_type),
    rev_type_name = ifelse(rev_type=="59", "Federal Transportation", rev_type_name),
    rev_type_name = ifelse(rev_type=="57", "Federal - Other", rev_type_name),
    rev_type = ifelse(rev_type=="6", "06", rev_type),
    rev_type = ifelse(rev_type=="9", "09", rev_type)) 

rev_temp %>% 
  filter(rev_type == "57") %>% 
  group_by(fy, rev_type, rev_type_name) %>% 
  summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% 
  ggplot(aes(x=fy, y=receipts, color=rev_type_name)) +
  geom_recessions(xformat = "numeric",text = FALSE)+
    geom_smooth(aes(x=fy, y = receipts), method = lm, se = FALSE, lty = "dashed", color = "red", alpha = .5, lwd = .5) + 

  geom_line(aes(x=fy, y=receipts,color=rev_type_name), lwd = 1.5) +
  theme_classic() +
  scale_y_continuous(labels = comma)+
  scale_x_continuous(expand = c(0,0)) +
  labs(title = "Other Federal Revenue
       with federal stimulus funds", 
       caption = "These values include stimulus funds after the Great Recession and the COVID pandemic response.",
       y = "Billions of Dollars", x = "") + 
  theme(legend.position = "bottom", legend.title = element_blank()  )
rev_temp %>% 
  filter(rev_type == "57") %>% 
  filter(source_name_AWM != "FEDERAL STIMULUS PACKAGE" & source_name_AWM != "STATE CURE") %>%
  group_by(fy, rev_type, rev_type_name) %>% 
  summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% 
  ggplot(aes(x=fy, y=receipts, color=rev_type_name) )+
  geom_recessions(xformat = "numeric",text = FALSE)+
  geom_line(aes(x=fy, y=receipts, color=rev_type_name), lwd = 1.5) +
  geom_smooth(aes(x=fy, y = receipts), method = lm, se = FALSE, lty = "dashed", color = "red", alpha = .5, lwd = .5) + 
  theme_classic() +
  scale_y_continuous(limits = c(0,20), labels = comma)+
  scale_x_continuous(expand = c(0,0)) +
  labs(title = "Federal to State Transfers: All Other Fed Rev 
       without Federal Stimulus $", 
       caption = "These values do NOT include stimulus funds after the Great Recession and the COVID pandemic response.",
       y = "Billions of Dollars", x = "") + 
  theme(legend.position = "bottom", legend.title = element_blank()  )

Code

rev_temp %>% 
  filter(rev_type == "57" #fund_name == "SBE FEDERAL DEPT OF EDUCATION"
         ) %>% 
  group_by(fy) %>% 
  summarise(receipts = sum(receipts, na.rm = TRUE)) %>%
  mutate(adj_receipts = case_when(
    fy==2020 ~ receipts - 677964975, # ESSER 1 from CARES
    fy==2022 ~ receipts - 7353697183, # ARPA ESSER3 + CRRSA ESSER2 $ to ISBE
  )
         )

Figure 11.1

Code

rev_temp %>% 
  filter(fund_name == "SBE FEDERAL DEPT OF EDUCATION") %>% 
  group_by(fy, rev_type, rev_type_name) %>% 
  summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% 
  ggplot(aes(x=fy, y=receipts, color=rev_type_name)) +
  geom_recessions(xformat = "numeric",text = FALSE)+
    geom_smooth(aes(x=fy, y = receipts), method = lm, se = FALSE, lty = "dashed", color = "red", alpha = .5, lwd = .5) + 
  geom_line(aes(x=fy, y=receipts,color=rev_type_name), lwd = 1.5) +
  theme_classic() +
  scale_y_continuous(limits = c(0,4), labels = comma)+
  scale_x_continuous(expand = c(0,0)) +
  labs(title = "K-12 Federal Revenue (all revenue received)", 
       caption = "All funds received by the SBE fund",
       y = "Billions of Dollars", x = "") + 
  theme(legend.position = "bottom", legend.title = element_blank()  )



rev_temp %>% 
  filter(rev_type == "57" & fund_name == "SBE FEDERAL DEPT OF EDUCATION") %>% 
  filter(source_name_AWM != "FEDERAL STIMULUS PACKAGE") %>%
  group_by(fy, rev_type, rev_type_name) %>% 
  summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% 
  ggplot(aes(x=fy, y=receipts, color=rev_type_name) )+
  geom_recessions(xformat = "numeric",text = FALSE)+
  geom_line(aes(x=fy, y=receipts, color=rev_type_name), lwd = 1.5) +
  geom_smooth(aes(x=fy, y = receipts), method = lm, se = FALSE, lty = "dashed", color = "red", alpha = .5, lwd = .5) + 
  theme_classic() +
  scale_y_continuous(limits= c(0,4),labels = comma)+
  scale_x_continuous(expand = c(0,0)) +
  labs(title = "Federal Revenue for K-12", 
       caption = "These values do NOT include stimulus funds after the Great Recession and the COVID pandemic response.",
       y = "Billions of Dollars", x = "") + 
  theme(legend.position = "bottom", legend.title = element_blank()  )

Code

K12rev <- rev_temp %>% 
  filter( fund_name == "SBE FEDERAL DEPT OF EDUCATION" &  agency_name == "STATE BOARD OF EDUCATION") %>%
  group_by(fy) %>% 
  summarize(receipts =round(sum(receipts)/1000000)) %>% 
  arrange(-receipts)

K12exp <- exp_temp %>% 
  filter( fund_name == "SBE FEDERAL DEPT OF EDUCATION" & agency_name == "STATE BOARD OF EDUCATION") %>%
  group_by(fy) %>% 
  summarize(expenditure =round(sum(expenditure)/1000000)) %>% 
  arrange(-expenditure)

ggplot() +
  geom_line(data = K12rev, aes(x=fy, y = receipts), lty = 1 ) +
  geom_line(data = K12exp, aes(x=fy, y = expenditure), lty = 2) +
  scale_x_continuous(expand = c(0,0))

# looks at ISBE federal funding after 2018
# gives a sort of base comparison of funding before pandemic
# ESSER revenue sources had multiple source names so lots of scrolling and CTRL-F searching was done
rev_temp %>% 
  filter(
  #rev_type == "57" & 
           fy > 2018 & fund_name == "SBE FEDERAL DEPT OF EDUCATION") %>%
  group_by(source_name_AWM , fund_name, fy) %>% 
  summarize(receipts =round(sum(receipts)/1000000)) %>% 
  arrange(-receipts)

Code

fedrev<- rev_temp %>% 
  filter(rev_type == "58" | rev_type == "59" | rev_type == "57") 

fedrev %>% 
  group_by(fy) %>% 
  summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% 
  ggplot() +
  geom_line(aes(x=fy, y=receipts)) +
  theme_classic() +
  scale_y_continuous(labels = comma)+
  labs(title = "All Federal Revenue w/ Fed. Stimulus Package", y = "Billions of Dollars", x = "") + 
  theme(legend.position = "bottom", legend.title = element_blank()  )+
  scale_y_continuous(limits = c(0,45))


fedrev %>% 
  filter(source_name_AWM != "FEDERAL STIMULUS PACKAGE") %>%
  group_by(fy) %>% 
  summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% 
  ggplot() +
  geom_line(aes(x=fy, y=receipts)) +
      theme_classic() +
  scale_y_continuous(labels = comma)+
  labs(title = "All Federal Rev EXCEPT Federal Stimulus Package", 
       y = "Billions of Dollars", x = "",
       caption = "Note: Dropping Federal Stimulus Package revenue only removes the $3.5 billion from FY20, $0.3 billion from FY21, and 
       $8.5 billion from FY22. There is still over $12 billion more in Federal Revenue compared to FY19.") + 
  theme(legend.position = "bottom", legend.title = element_blank()  ) +
  scale_y_continuous(limits = c(0,45))

## Looking at ESSER funds spent per year ## 

exp_temp %>% 
  filter(fy >2018 & fund_name == "SBE FEDERAL DEPT OF EDUCATION" & 
           agency_name == "STATE BOARD OF EDUCATION") %>% 
  group_by(wh_approp_name) %>% 
  summarize(expenditures = round(sum(expenditure)/1000000, digits = 2))

Table 11.1: All Appropriations made by State Board of Education - funds spent per state fiscal year

Key words and phrases from the appropriations above were used to identify appropriations that specifically used ESSER funds.

Code

K12_ESSER_words <- c("CRRSA","ESSER","EMER R", "EMR R", "CARES", "AMER R", "EMER ED")

K12 <- exp_temp %>% 
  filter(agency_name == "STATE BOARD OF EDUCATION") %>%
  mutate(ESSERfunds = case_when(
    str_detect(wh_approp_name, "CRRSA") ~ "ESSER",
    str_detect(wh_approp_name, "ESSER") ~ "ESSER",
    str_detect(wh_approp_name, "EMER R") ~ "ESSER",
    str_detect(wh_approp_name, "EMR R") ~ "ESSER",
    str_detect(wh_approp_name, "CARES") ~ "ESSER",
    str_detect(wh_approp_name, "AMER R") ~ "ESSER",
    str_detect(wh_approp_name, "EMER ED") ~ "ESSER",
    
    TRUE ~ 'not_esser')) %>%
  filter(ESSERfunds == "ESSER")

# amount of ESSER money spent total (all years summed together)

K12 %>% 
  summarize(esser_spent = sum(expenditure))

Table 11.2: Amount of ESSER money spent total (all years summed together)

# grouped by fiscal year
K12 %>% 
  group_by(fy) %>% 
  summarize(esser_spent = sum(expenditure))

Table 11.3: ESSER spent each year

# Grouped by fiscal year and appropriation description
K12 %>% 
  group_by(fy, wh_approp_name) %>% 
  summarize(esser_spent = sum(expenditure))

Table 11.4: ESSER items grouped by fiscal year and appropriation description

# K-12 Spending & ESSER Funds ```{r warning=FALSE, message=FALSE, echo=FALSE} library(tidyverse) library(haven) library(formatR) library(lubridate) library(smooth) library(forecast) library(scales) library(kableExtra) library(ggplot2) library(readxl) library(tidyverse) library(data.table) library(quantmod) library(geofacet) library(janitor) library(cmapplot) theme_set(theme_classic()) knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE) exp_temp <- read_csv("./data/exp_temp.csv") %>% filter(agency != "799") exp_temp <- exp_temp %>% mutate(fund_name = fund_name_ab) rev_temp <- read_csv("./data/rev_temp.csv") %>% filter(agency != "799" & rev_type != "75") rev_temp <- rev_temp %>% mutate(fund_name = fund_name_ab) ``` ```{r include=FALSE} update_recessions <- function(url = NULL, quietly = FALSE){ # Use default URL if user does not override if (is_null(url) | missing(url)) { url <- "https://data.nber.org/data/cycles/business_cycle_dates.json" } # locally bind variable names start_char <- end_char <- start_date <- end_date <- ongoing <- index <- peak <- trough <- NULL return( # attempt to download and format recessions table tryCatch({ recessions <- jsonlite::fromJSON(url) %>% # drop first row trough dplyr::slice(-1) %>% # convert peaks and troughs... dplyr::mutate( # ...to R dates start_date = as.Date(peak), end_date = as.Date(trough), # ... and clean char strings start_char = format(start_date, "%b %Y"), end_char = format(end_date, "%b %Y")) %>% # confirm ascending and create row number dplyr::arrange(start_date) %>% mutate(index = row_number()) %>% mutate( # Flag unfinished recessions ongoing = case_when( is.na(end_date) & index == max(.$index) ~ T, TRUE ~ F), # set ongoing recession to arbitrary future date end_date = case_when( ongoing ~ as.Date("2200-01-01"), TRUE ~ end_date), # mark ongoing recession in char field end_char = case_when( ongoing ~ "Ongoing", TRUE ~ end_char) ) %>% # clean up select(start_char, end_char, start_date, end_date, ongoing) if (!quietly) {message("Successfully fetched from NBER")} # Return recessions recessions }, error = function(cond){ if (!quietly) message("WARNING: Fetch or processing failed. `NULL` returned.") return(NULL) } ) ) } #recess_table <- list(build_recessions(update)) recessions <- update_recessions() ``` - \$3.4266 billion Total spent through FY23 from ARP and ESSER Funds. - 1.694 billion spent in 2023 (ARP = ESSER III) but that money arrived in 2022 (=\> Fiscal Gap Implications!) - \$2.34 Billion of ARP Emergency Relief Funds (aka ESSER III) spent in 2022 and 2023 combined - 583 million for ESSER II in 2021 - 376 million with ESSER I in 2021 and 128 million in 2020. - ISBE received around an additional \$7.9 billion total from ESSER compared to its normal federal funding because of COVID - At end of State's FY23, \$3.43/\$7.9 billion = \~ 50% spent - As of Dec.7th, it has spent 74.1% of these funds ("ESSER Spending Dashboard" 2023) - 640 million allocated for FY24 (according to CPS, I think they have different fiscal years though...) ```{r} #| layout-ncol: 2 #| column: screen #| rev_temp <- rev_temp %>% mutate( rev_type = ifelse(rev_type=="57" & agency=="478" & (source=="0618"|source=="2364"|source=="0660"|source=="1552"| source=="2306"| source=="2076"|source=="0676"|source=="0692"), "58", rev_type), rev_type_name = ifelse(rev_type=="58", "Federal Medicaid Reimbursements", rev_type_name), rev_type = ifelse(rev_type=="57" & agency=="494", "59", rev_type), rev_type_name = ifelse(rev_type=="59", "Federal Transportation", rev_type_name), rev_type_name = ifelse(rev_type=="57", "Federal - Other", rev_type_name), rev_type = ifelse(rev_type=="6", "06", rev_type), rev_type = ifelse(rev_type=="9", "09", rev_type)) rev_temp %>% filter(rev_type == "57") %>% group_by(fy, rev_type, rev_type_name) %>% summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% ggplot(aes(x=fy, y=receipts, color=rev_type_name)) + geom_recessions(xformat = "numeric",text = FALSE)+ geom_smooth(aes(x=fy, y = receipts), method = lm, se = FALSE, lty = "dashed", color = "red", alpha = .5, lwd = .5) + geom_line(aes(x=fy, y=receipts,color=rev_type_name), lwd = 1.5) + theme_classic() + scale_y_continuous(labels = comma)+ scale_x_continuous(expand = c(0,0)) + labs(title = "Other Federal Revenue with federal stimulus funds", caption = "These values include stimulus funds after the Great Recession and the COVID pandemic response.", y = "Billions of Dollars", x = "") + theme(legend.position = "bottom", legend.title = element_blank() ) rev_temp %>% filter(rev_type == "57") %>% filter(source_name_AWM != "FEDERAL STIMULUS PACKAGE" & source_name_AWM != "STATE CURE") %>% group_by(fy, rev_type, rev_type_name) %>% summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% ggplot(aes(x=fy, y=receipts, color=rev_type_name) )+ geom_recessions(xformat = "numeric",text = FALSE)+ geom_line(aes(x=fy, y=receipts, color=rev_type_name), lwd = 1.5) + geom_smooth(aes(x=fy, y = receipts), method = lm, se = FALSE, lty = "dashed", color = "red", alpha = .5, lwd = .5) + theme_classic() + scale_y_continuous(limits = c(0,20), labels = comma)+ scale_x_continuous(expand = c(0,0)) + labs(title = "Federal to State Transfers: All Other Fed Rev without Federal Stimulus $", caption = "These values do NOT include stimulus funds after the Great Recession and the COVID pandemic response.", y = "Billions of Dollars", x = "") + theme(legend.position = "bottom", legend.title = element_blank() ) ``` ```{r} #| label: fig-k12-federal-revenue rev_temp %>% filter(rev_type == "57" #fund_name == "SBE FEDERAL DEPT OF EDUCATION" ) %>% group_by(fy) %>% summarise(receipts = sum(receipts, na.rm = TRUE)) %>% mutate(adj_receipts = case_when( fy==2020 ~ receipts - 677964975, # ESSER 1 from CARES fy==2022 ~ receipts - 7353697183, # ARPA ESSER3 + CRRSA ESSER2 $ to ISBE ) ) rev_temp %>% filter(fund_name == "SBE FEDERAL DEPT OF EDUCATION") %>% group_by(fy, rev_type, rev_type_name) %>% summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% ggplot(aes(x=fy, y=receipts, color=rev_type_name)) + geom_recessions(xformat = "numeric",text = FALSE)+ geom_smooth(aes(x=fy, y = receipts), method = lm, se = FALSE, lty = "dashed", color = "red", alpha = .5, lwd = .5) + geom_line(aes(x=fy, y=receipts,color=rev_type_name), lwd = 1.5) + theme_classic() + scale_y_continuous(limits = c(0,4), labels = comma)+ scale_x_continuous(expand = c(0,0)) + labs(title = "K-12 Federal Revenue (all revenue received)", caption = "All funds received by the SBE fund", y = "Billions of Dollars", x = "") + theme(legend.position = "bottom", legend.title = element_blank() ) rev_temp %>% filter(rev_type == "57" & fund_name == "SBE FEDERAL DEPT OF EDUCATION") %>% filter(source_name_AWM != "FEDERAL STIMULUS PACKAGE") %>% group_by(fy, rev_type, rev_type_name) %>% summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% ggplot(aes(x=fy, y=receipts, color=rev_type_name) )+ geom_recessions(xformat = "numeric",text = FALSE)+ geom_line(aes(x=fy, y=receipts, color=rev_type_name), lwd = 1.5) + geom_smooth(aes(x=fy, y = receipts), method = lm, se = FALSE, lty = "dashed", color = "red", alpha = .5, lwd = .5) + theme_classic() + scale_y_continuous(limits= c(0,4),labels = comma)+ scale_x_continuous(expand = c(0,0)) + labs(title = "Federal Revenue for K-12", caption = "These values do NOT include stimulus funds after the Great Recession and the COVID pandemic response.", y = "Billions of Dollars", x = "") + theme(legend.position = "bottom", legend.title = element_blank() ) ``` ```{r} K12rev <- rev_temp %>% filter( fund_name == "SBE FEDERAL DEPT OF EDUCATION" & agency_name == "STATE BOARD OF EDUCATION") %>% group_by(fy) %>% summarize(receipts =round(sum(receipts)/1000000)) %>% arrange(-receipts) K12exp <- exp_temp %>% filter( fund_name == "SBE FEDERAL DEPT OF EDUCATION" & agency_name == "STATE BOARD OF EDUCATION") %>% group_by(fy) %>% summarize(expenditure =round(sum(expenditure)/1000000)) %>% arrange(-expenditure) ggplot() + geom_line(data = K12rev, aes(x=fy, y = receipts), lty = 1 ) + geom_line(data = K12exp, aes(x=fy, y = expenditure), lty = 2) + scale_x_continuous(expand = c(0,0)) ``` ```{r} #| collapse: false #| code-fold: false # looks at ISBE federal funding after 2018 # gives a sort of base comparison of funding before pandemic # ESSER revenue sources had multiple source names so lots of scrolling and CTRL-F searching was done rev_temp %>% filter( #rev_type == "57" & fy > 2018 & fund_name == "SBE FEDERAL DEPT OF EDUCATION") %>% group_by(source_name_AWM , fund_name, fy) %>% summarize(receipts =round(sum(receipts)/1000000)) %>% arrange(-receipts) ``` ```{r} #| fig-cap: "All Federal Revenue" #| layout-ncol: 2 #| fig-show: hold #| fig-column: screen fedrev<- rev_temp %>% filter(rev_type == "58" | rev_type == "59" | rev_type == "57") fedrev %>% group_by(fy) %>% summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% ggplot() + geom_line(aes(x=fy, y=receipts)) + theme_classic() + scale_y_continuous(labels = comma)+ labs(title = "All Federal Revenue w/ Fed. Stimulus Package", y = "Billions of Dollars", x = "") + theme(legend.position = "bottom", legend.title = element_blank() )+ scale_y_continuous(limits = c(0,45)) fedrev %>% filter(source_name_AWM != "FEDERAL STIMULUS PACKAGE") %>% group_by(fy) %>% summarise(receipts = sum(receipts, na.rm = TRUE)/1000000000) %>% ggplot() + geom_line(aes(x=fy, y=receipts)) + theme_classic() + scale_y_continuous(labels = comma)+ labs(title = "All Federal Rev EXCEPT Federal Stimulus Package", y = "Billions of Dollars", x = "", caption = "Note: Dropping Federal Stimulus Package revenue only removes the $3.5 billion from FY20, $0.3 billion from FY21, and $8.5 billion from FY22. There is still over $12 billion more in Federal Revenue compared to FY19.") + theme(legend.position = "bottom", legend.title = element_blank() ) + scale_y_continuous(limits = c(0,45)) ``` ```{r} #| label: tbl-esserperyear #| code-fold: false #| tbl-cap: "All Appropriations made by State Board of Education - funds spent per state fiscal year" ## Looking at ESSER funds spent per year ## exp_temp %>% filter(fy >2018 & fund_name == "SBE FEDERAL DEPT OF EDUCATION" & agency_name == "STATE BOARD OF EDUCATION") %>% group_by(wh_approp_name) %>% summarize(expenditures = round(sum(expenditure)/1000000, digits = 2)) ``` Key words and phrases from the appropriations above were used to identify appropriations that specifically used ESSER funds. ```{r} K12_ESSER_words <- c("CRRSA","ESSER","EMER R", "EMR R", "CARES", "AMER R", "EMER ED") K12 <- exp_temp %>% filter(agency_name == "STATE BOARD OF EDUCATION") %>% mutate(ESSERfunds = case_when( str_detect(wh_approp_name, "CRRSA") ~ "ESSER", str_detect(wh_approp_name, "ESSER") ~ "ESSER", str_detect(wh_approp_name, "EMER R") ~ "ESSER", str_detect(wh_approp_name, "EMR R") ~ "ESSER", str_detect(wh_approp_name, "CARES") ~ "ESSER", str_detect(wh_approp_name, "AMER R") ~ "ESSER", str_detect(wh_approp_name, "EMER ED") ~ "ESSER", TRUE ~ 'not_esser')) %>% filter(ESSERfunds == "ESSER") ``` ```{r} #| tbl-column: margin #| label: tbl-table3 #| tbl-cap: "Amount of ESSER money spent total (all years summed together)" #| collapse: false #| code-fold: false # amount of ESSER money spent total (all years summed together) K12 %>% summarize(esser_spent = sum(expenditure)) ``` ```{r} #| label: tbl-fig3 #| code-fold: false #| collapse: false #| tbl-column: margin #| tbl-cap: "ESSER spent each year" # grouped by fiscal year K12 %>% group_by(fy) %>% summarize(esser_spent = sum(expenditure)) ``` ```{r} #| collapse: false #| label: tbl-table4 #| code-fold: false #| tbl-cap: ESSER items grouped by fiscal year and appropriation description # Grouped by fiscal year and appropriation description K12 %>% group_by(fy, wh_approp_name) %>% summarize(esser_spent = sum(expenditure)) ```