An Explanation of a Methodological Change in How We Categorize Some Pension Spending
Fiscal Futures’ conceptual framework requires categorizing expenditures according to the function they support rather than the budgetary funds through which they are funneled. Because of this, past reports counted most pension spending as part of the expenditures for the employees of the relevant agency. For example, pension spending that went as benefits to human service employees was counted as spending on human services. However, because most teachers are not state employees, in past years, spending to support teacher pensions was treated differently. We accounted for this spending as a separate expenditure item rather than including it as part of state spending on K-12 education.
On reflection, we decided to treat these pension payments in parallel with other pension spending and, therefore, include them as part of K-12 education spending. In order to maintain consistency across years we have recalculated all previous years using this methodology.
However, we thought that readers also might be interested in total pension spending during current and past years. We have added a separate table showing this information but emphasize that pension spending is already incorporated in other spending categories and should not be added to the total shown in Table 1 of the report.
Aggregate expenditures: Save tax refunds as negative revenue. Code refunds to match the rev_type codes (02=income taxes, 03 = corporate income taxes, 06=sales tax, 09=motor fuel tax, 24=insurance taxes and fees, 35 = all other tax refunds).
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tax_refund_long <- exp_temp %>%# fund != "0401" # removes State Trust Fundsfilter(fund !="0401"& (object =="9900"| object=="9910"|object=="9921"|object=="9923"|object=="9925")) %>%# keeps these objects which represent revenue, insurance, treasurer,and financial and professional reg tax refundsmutate(refund =case_when( object =="9900"~"FY23_Rebates", fund=="0278"& sequence =="00"~"02", # for income tax refund fund=="0278"& sequence =="01"~"03", # tax administration and enforcement and tax operations become corporate income tax refund fund =="0278"& sequence =="02"~"02", object=="9921"~"21", # inheritance tax and estate tax refund appropriation object=="9923"~"09", # motor fuel tax refunds obj_seq_type =="99250055"~"06", # sales tax refund fund=="0378"& object=="9925"~"24", # insurance privilege tax refund (fund=="0001"& object=="9925") | (object=="9925"& fund =="0384"& fy ==2023) ~"35", # all other taxes# fund=="0001" & object=="9925" ~ "35", # all other taxes T ~"CHECK")) # if none of the items above apply to the observations, then code them as CHECK exp_temp <-left_join(exp_temp, tax_refund_long) %>%mutate(refund =ifelse(is.na(refund),"not refund", as.character(refund)))tax_refund <- tax_refund_long %>%group_by(refund, fy)%>%summarize(refund_amount =sum(expenditure, na.rm =TRUE)/1000000) %>%pivot_wider(names_from = refund, values_from = refund_amount, names_prefix ="ref_") %>%mutate_all(~replace_na(.,0)) %>%arrange(fy)# # tax_refund %>% # pivot_longer(c(ref_06:ref_35, ref_FY23_Rebates), names_to = "Refund Type", values_to = "Amount") %>%# ggplot()+ # theme_classic()+# geom_line(aes(x=fy,y=Amount, group = `Refund Type`, color = `Refund Type`))+# labs(title = "Refund Types", # caption = "Refunds are excluded from Expenditure totals and instead subtracted from Revenue totals") + # labs(title = "Tax refunds", # caption = "Rev_type codes: 02=income taxes, 03=corporate income taxes, 06=sales tax, 09=motor fuel tax, # 24=insurance taxes and fees, 35 = all other tax refunds." )# remove the items we recoded in tax_refund_long# exp_temp <- exp_temp %>% filter(refund == "not refund")
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# manually adds the abatements as expenditure item and keeps on expenditure side.# otherwise ignored since it is in fund 0278 and exp_temp <- exp_temp %>%mutate(in_ff =ifelse(object ==9900, 1, in_ff))
2.1.2 Pension Expenditures
State pension contributions for TRS and SURS are largely captured with object=4431. (State payments into pension fund). State payments to the following pension systems:
New POB bond in 2019: Accelerated Bond Fund paid benefits in advance as lump sum
State Employee Retirement System (SERS) Agency 589 –> SERS Agency 589 - Note: Object 4431 does not have SERS expenditures in it. Those are only in object 116X objects
State University Retirement System (SURS) Agency 693 –> University Education (Group = 960)
General Assembly Retirement System (GARS) –> Legislative (Group 910)
There are also “Other Post-Employment Benefits” (OPEBs). Expenditure object 4430 is for retirement benefits.
While it is good to know the overall cost of pensions for the state, if you want to know the true cost of providing services, pension and other benefit costs should be included in the department that is paying employees to provide those services.
Change in pension coding in chunk below:
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exp_temp <- exp_temp %>%arrange(fund) %>%mutate(pension =case_when( ## Commented out line below:# (object=="4431") ~ 1, # 4431 = easy to find pension payments INTO fund (object=="1298"&# Purchase of Investments, Normally excluded (fy==2010| fy==2011) & (fund=="0477"| fund=="0479"| fund=="0481")) ~3, #judges retirement OUT of fund# state borrowed money from pension funds to pay for core services during 2010 and 2011. # used to fill budget gap and push problems to the future. fund =="0319"~4, # pension stabilization fundTRUE~0) )
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# special accounting of pension obligation bond (POB)-funded contributions to JRS, SERS, GARS, TRS exp_temp <- exp_temp %>%# change object for 2010 and 2011, retirement expenditures were bond proceeds and would have been excludedmutate(object =ifelse((pension >0& in_ff =="0"), "4431", object)) %>%# changes weird teacher & judge retirement system pensions object to normal pension object 4431mutate(pension =ifelse(pension >0& in_ff =="0", 6, pension)) %>%# coded as 6 if it was supposed to be excluded. mutate(in_ff =ifelse(pension >0, "1", in_ff))# all other pensions objects codes get agency code 901 for State Pension Contributionsexp_temp <- exp_temp %>%mutate(agency =ifelse(pension >0, "901", as.character(agency)),agency_name =ifelse(agency =="901", "State Pension Contributions", as.character(agency_name)))
Can also be thought of past commitments vs current contributions. Cost of past commitments in the form of Pension benefits paid out,
Current Employees vs Retired Employees
Current Employees: - Group Insurance Benefits
Retired Employees: - Deferred Compensation
- Medicare Retirees and Survivors of State of Illinois Employees Group Insurance Program (SEGIP)
- Part of Medicare
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exp_temp|>filter(fy==2024) |>filter((appr_org=="01"| appr_org =="65"| appr_org=="88") & (object=="4900"| object=="4400") )|>group_by(agency, agency_name) |># separates CHIP from health and human services and saves it as Medicaidsummarize(expenditure =sum(expenditure))
Drop all cash transfers between funds, statutory transfers, and purchases of investments from expenditure data.
# always check to make sure you aren't accidentally dropping something of interest.exp_temp <-anti_join(exp_temp, transfers_drop)
2.1.3 State employee healthcare costs
Commented out line of code that seperates healthcare costs. This should keep healthcare costs in the agency, similar to the change that was made for pensions.
exp_temp |>filter(org_name =="BUREAU OF BENEFITS") |>group_by(fy, agency) |>summarize(expenditure =sum(expenditure) ) |>ggplot() +geom_line(aes(x=fy, y=expenditure, group= agency, color = agency)) +scale_y_continuous(labels = scales::dollar) +labs(title="Bureau of Benefits Expenditures")
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exp_temp |>filter(org_name =="BUREAU OF BENEFITS") |>group_by(fy, object) |>summarize(expenditure =sum(expenditure) ) |>ggplot() +geom_line(aes(x=fy, y=expenditure, group= object, color = object)) +scale_y_continuous(labels = scales::dollar) +labs(title="Bureau of Benefits Expenditures")
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#if observation is a group insurance contribution, then the expenditure amount is set to $0 (essentially dropped from analysis)# pretend eehc is named group_insurance_contribution or something like that# eehc coded as zero implies that it is group insurance# if eehc=0, then expenditures are coded as zero for group insurance to avoid double counting costsexp_temp <- exp_temp %>%mutate(eehc =ifelse(# group insurance contributions for 1998-2005 and 2013-present fund =="0001"& (object =="1180"| object =="1900") & agency =="416"& appr_org=="20", 0, 1) )%>%mutate(eehc =ifelse(# group insurance contributions for 2006-2012 fund =="0001"& object =="1180"& agency =="478"& appr_org=="80", 0, eehc) )%>%# group insurance contributions from road fund# coded with 1900 for some reason??mutate(eehc =ifelse( fund =="0011"& object =="1900"& agency =="416"& appr_org=="20", 0, eehc) ) %>%mutate(expenditure =ifelse(eehc=="0", 0, expenditure)) %>%mutate(agency =case_when(## turns specific items into State Employee Healthcare (agency=904) fund=="0907"& (agency=="416"& appr_org=="20") ~"904", # central management Bureau of benefits using health insurance reserve fund=="0907"& (agency=="478"& appr_org=="80") ~"904", # agency = 478: healthcare & family services using health insurance reserve - stopped using this in 2012TRUE~as.character(agency))) %>%mutate(agency_name =ifelse( agency =="904", "STATE EMPLOYEE HEALTHCARE", as.character(agency_name)),in_ff =ifelse(agency =="904", 1, in_ff),group =ifelse(agency =="904", "904", as.character(agency))) # creates group variable# Default group = agency numberhealthcare_costs <- exp_temp %>%filter(group =="904")
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exp_temp <- exp_temp %>%mutate(agency =case_when(fund=="0515"& object=="4470"& type=="08"~"971", # income tax to local governments fund=="0515"& object=="4491"& type=="08"& sequence=="00"~"971", # object is shared revenue payments fund=="0802"& object=="4491"~"972", #pprt transfer fund=="0515"& object=="4491"& type=="08"& sequence=="01"~"976", #gst to local fund=="0627"& object=="4472"~"976" , # public transportation fund but no observations exist fund=="0648"& object=="4472"~"976", # downstate public transportation, but doesn't exist fund=="0515"& object=="4470"& type=="00"~"976", # object 4470 is grants to local governments object=="4491"& (fund=="0188"|fund=="0189") ~"976", fund=="0187"& object=="4470"~"976", fund=="0186"& object=="4470"~"976", object=="4491"& (fund=="0413"|fund=="0414"|fund=="0415") ~"975", #mft to local fund =="0952"~"975", # Added Sept 29 2022 AWM. Transportation Renewal MFTTRUE~as.character(agency)),agency_name =case_when(agency =="971"~"INCOME TAX 1/10 TO LOCAL", agency =="972"~"PPRT TRANSFER TO LOCAL", agency =="975"~"MFT TO LOCAL", agency =="976"~"GST TO LOCAL",TRUE~as.character(agency_name)),group =ifelse(agency>"970"& agency <"977", as.character(agency), as.character(group)))
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transfers_long <- exp_temp %>%filter(group =="971"|group =="972"| group =="975"| group =="976")transfers <- transfers_long %>%group_by(fy, group ) %>%summarize(sum_expenditure =sum(expenditure)/1000000) %>%pivot_wider(names_from ="group", values_from ="sum_expenditure", names_prefix ="exp_" )exp_temp <-anti_join(exp_temp, transfers_long)dropped_inff_0 <- exp_temp %>%filter(in_ff ==0)exp_temp <- exp_temp %>%filter(in_ff ==1) # drops in_ff = 0 funds AFTER dealing with net-revenue above
exp_temp <- exp_temp %>%#mutate(agency = as.numeric(agency) ) %>%# arrange(agency)%>%mutate(group =case_when( agency>"100"& agency<"200"~"910", # legislative agency =="528"| (agency>"200"& agency<"300") ~"920", # judicial####################################################### Not used if we are not separating pension costs!!# pension > 0 ~ "901", # pensions## New CODE: April 23rd, 2025: agency =="593"~"959", # TRS becomes part of K-12 costs agency =="594"~"959", # TRS agency =="589"~"948", # SERS becomes part of "Other Agencies" agency =="693"~"960", # SURS becomes part of group 960 agency =="275"~"920", # JRS becomes part of group 920 agency =="131"~"910", # GARS becomes part of Group 910###################################################### (agency>"309"& agency<"400") ~"930", # elected officers: Governor, lt gov, attorney general, sec. of state, comptroller, treasurer agency =="586"~"959", # create new K-12 group agency=="402"| agency=="418"| agency=="478"| agency=="444"| agency=="482"~as.character(agency), # aging, CFS, HFS, human services, public health T ~as.character(group)) ) %>%mutate(group =case_when( agency=="478"& (appr_org=="01"| appr_org =="65"| appr_org=="88") & (object=="4900"| object=="4400") ~"945", # separates CHIP from health and human services and saves it as Medicaid agency =="586"& fund =="0355"~"945", # 586 (Board of Edu) has special education which is part of medicaid# OLD CODE: agency == "586" & appr_org == "18" ~ "945", # Spec. Edu Medicaid Matching agency=="425"| agency=="466"| agency=="546"| agency=="569"| agency=="578"| agency=="583"| agency=="591"| agency=="592"| agency=="493"| agency=="588"~"941", # public safety & Corrections agency=="420"| agency=="494"| agency=="406"| agency=="557"~as.character(agency), # econ devt & infra, tollway agency=="511"| agency=="554"| agency=="574"| agency=="598"~"946", # Capital improvement agency=="422"| agency=="532"~as.character(agency), # environment & nat. resources agency=="440"| agency=="446"| agency=="524"| agency=="563"~"944", # business regulation agency=="492"~"492", # revenue agency =="416"~"416", # central management services agency=="448"& fy >2016~"416", #add DoIT to central management T ~as.character(group))) %>%mutate(group =case_when(# agency=="684" | agency=="691" ~ as.character(agency), # moved under higher education in next line. 11/28/2022 AWM agency=="692"| agency =="693"| agency=="695"| agency =="684"|agency =="691"| (agency>"599"& agency<"677") ~"960", # higher education agency=="427"~as.character(agency), # employment security############################ # Leaving these agencies as their own agency number for now. Had been coded to "Other departments" Group 948# - GOMB (507) # - Human Rights (442) # - Illinois Power Agency (445) # - Labor (452) # - State Lottery (458) # - Veteran's Affairs (497) agency=="507"| agency=="442"| agency=="445"| agency=="452"|agency=="458"| agency=="497"~as.character(agency), # Were included within "other departments"# agency=="507"| agency=="442" | agency=="445" | agency=="452" |agency=="458" | agency=="497" ~ "948", # other departments###########################################3# other boards & Commissions agency=="503"| agency=="509"| agency=="510"| agency=="565"|agency=="517"| agency=="525"| agency=="526"| agency=="529"| agency=="537"| agency=="541"| agency=="542"| agency=="548"| agency=="555"| agency=="558"| agency=="559"| agency=="562"| agency=="564"| agency=="568"| agency=="579"| agency=="580"| agency=="587"| agency=="590"| agency=="527"| agency=="585"| agency=="567"| agency=="571"| agency=="575"| agency=="540"| agency=="576"| agency=="564"| agency=="534"| agency=="520"| agency=="506"| agency =="533"~"949", # # Other Departments# agency=="131" |# # agency=="275" | #JRS# # agency=="589" | #SERS# # agency=="593"| # TRS# # agency=="594"| # Also TRS# # agency=="693" #SURS# ~ "948", T ~as.character(group))) %>%mutate(group_name =case_when( group =="416"~"Central Management", group =="442"~"Human Rights", group =="445"~"Illinois Power Agency", group =="452"~"Labor", group =="458"~"State Lottery", group =="489"~"SERS", group =="478"~"Healthcare and Family Services", group =="482"~"Public Health", group =="497"~"Veteran's Affairs", group =="507"~"GOMB", group =="900"~"NOT IN FRAME", group =="901"~"STATE PENSION CONTRIBUTION", group =="903"~"DEBT SERVICE", group =="910"~"LEGISLATIVE" , group =="920"~"JUDICIAL" , group =="930"~"ELECTED OFFICERS" , group =="940"~"OTHER HEALTH-RELATED", group =="941"~"PUBLIC SAFETY" , group =="942"~"ECON DEVT & INFRASTRUCTURE" , group =="943"~"CENTRAL SERVICES", group =="944"~"BUS & PROFESSION REGULATION" , group =="945"~"MEDICAID" , group =="946"~"CAPITAL IMPROVEMENT" , group =="948"~"OTHER DEPARTMENTS" , group =="949"~"OTHER BOARDS & COMMISSIONS" , group =="959"~"K-12 EDUCATION" , group =="960"~"UNIVERSITY EDUCATION" , group == agency ~as.character(agency_name),TRUE~"Check name"),year = fy)exp_temp %>%filter(group_name =="Check name")
All expenditures recoded but not aggregated: Allows for inspection of individual expenditures within larger categories. This stage of the data is extremely useful for investigating how individual items have been coded before they are aggregated into larger categories.
2.2 Modify Revenue data
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# recodes old agency numbers to consistent agency numberrev_temp <- rev_temp %>%mutate(agency =case_when( (agency=="438"| agency=="475"|agency =="505") ~"440",# financial institution & professional regulation &# banks and real estate --> coded as financial and professional reg agency =="473"~"588", # nuclear safety moved into IEMA (agency =="531"| agency =="577") ~"532", # coded as EPA (agency =="556"| agency =="538") ~"406", # coded as agriculture agency =="560"~"592", # IL finance authority (fire trucks and agriculture stuff)to state fire marshal agency =="570"& fund =="0011"~"494", # city of Chicago road fund to transportationTRUE~ (as.character(agency))))
Insurance premiums for employees is coded below but it is NOT used in the fiscal futures model. Employee and employer premiums are considered rev_51 and dropped from analysis in later step.
0120 = ins prem-option life
0120 = ins prem-optional life/univ
0347 = optional health - HMO
0348 = optional health - dental
0349 = optional health - univ/local SI
0350 = optional health - univ/local
0351 = optional health - retirement
0352 = optional health - retirement SI
0353 = optional health - retire/dental
0354 = optional health - retirement hmo
2199-2209 = various HMOs, dental, health plans from Health Insurance Reserve (fund)
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#collect optional insurance premiums to fund 0907 for use in eehc expenditure rev_temp <- rev_temp %>%mutate(employee_premiums =ifelse(fund=="0907"& (source=="0120"| source=="0121"| (source>"0345"& source<"0357")|(source>"2199"& source<"2209")), 1, 0),# adds more rev_type codesrev_type =case_when( fund =="0427"~"12", # pub utility tax fund =="0742"| fund =="0473"~"24", # insurance and fees fund =="0976"~"36",# receipts from rev producing fund =="0392"|fund =="0723"~"39", # licenses and fees fund =="0656"~"78", #all other rev sourcesTRUE~as.character(rev_type)))# if not mentioned, then rev_type as it was# # optional insurance premiums = employee insurance premiums# emp_premium <- rev_temp %>%# group_by(fy, employee_premiums) %>%# summarize(employee_premiums_sum = sum(receipts)/1000000) %>%# filter(employee_premiums == 1) %>%# rename(year = fy) %>% # select(-employee_premiums)emp_premium_long <- rev_temp %>%filter(employee_premiums ==1)# 381 observations have employee premiums == 1# drops employee premiums from revenue# rev_temp <- rev_temp %>% filter(employee_premiums != 1)# should be dropped in next step since rev_type = 51
Note: In FY21, employee premiums were subtracted from state healthcare costs on the expenditure side to calculate a “Net Healthcare Cost” but that methodology has been discontinued. Totals were practically unchanged: revenue from employee premiums is also very small.
2.2.2 Transfers in and Out:
Funds that hold and disperse local taxes or fees are dropped from the analysis. Then other excluded revenue types are also dropped.
Drops Blank, Student Fees, Retirement contributions, proceeds/investments, bond issue proceeds, interagency receipts, cook IGT, Prior year refunds:
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rev_temp <- rev_temp %>%filter(in_ff ==1) %>%mutate(local =ifelse(is.na(local), 0, local)) %>%# drops all revenue observations that were coded as "local == 1"filter(local !=1)# 1175 doesnt exist?in_from_out <-c("0847", "0867", "1175", "1176", "1177", "1178", "1181", "1182", "1582", "1592", "1745", "1982", "2174", "2264")# what does this actually include:# all are items with rev_type = 75 originally. in_out_df <- rev_temp %>%mutate(infromout =ifelse(source %in% in_from_out, 1, 0)) %>%filter(infromout ==1)rev_temp <- rev_temp %>%mutate(rev_type_new =ifelse(source %in% in_from_out, "76", rev_type))# if source contains any of the codes in in_from_out, code them as 76 (all other rev).# I end up excluding rev_76 in later steps
Are the 4 smallest categories from past years still the 4 smallest categories? Check it each year.
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# revenue types to dropdrop_type <-c("32", "45", "51", "66", "72", "75", "76", "79", "98", "99")# drops Blank, Student Fees, Retirement contributions, proceeds/investments,# bond issue proceeds, interagency receipts, cook IGT, Prior year refunds.rev_temp <- rev_temp %>%filter(!rev_type_new %in% drop_type)# keep observations that do not have a revenue type mentioned in drop_typetable(rev_temp$rev_type_new)
# combines smallest 4 categories to to "Other"# they were the 4 smallest in past years, are they still the 4 smallest? # rev_temp <- rev_temp %>% # mutate(rev_type_new = ifelse(rev_type=="30" | rev_type=="60" | rev_type=="63" ,# ## | rev_type=="76", # "78", rev_type_new))#table(rev_temp$rev_type_new) # check workrm(rev_1998_2022)rm(exp_1998_2022)write_csv(exp_temp, "data/exp_fy24_pensionrecode.csv")write_csv(rev_temp, "data/rev_fy24_pensionrecode.csv")
2.3 Pivoting and Merging
Local Government Transfers (exp_970) should be on the expenditure side
2.3.1 Revenues
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ff_rev <- rev_temp %>%group_by(rev_type_new, fy) %>%summarize(sum_receipts =sum(receipts, na.rm=TRUE)/1000000 ) %>%pivot_wider(names_from ="rev_type_new", values_from ="sum_receipts", names_prefix ="rev_")# ff_rev<- left_join(ff_rev, tax_refund)#ff_rev <- left_join(ff_rev, pension2_fy22, by=c("fy" = "year"))#ff_rev <- left_join(ff_rev, eehc2_amt) ff_rev <-mutate_all(ff_rev, ~replace_na(.,0))# # ff_rev <- ff_rev %>%# mutate(rev_02 = rev_02 - ref_02,# rev_03 = rev_03 - ref_03,# rev_06 = rev_06 - ref_06,# rev_09 = rev_09 - ref_09,# rev_21 = rev_21 - ref_21,# rev_24 = rev_24 - ref_24,# rev_35 = rev_35 - ref_35# # # rev_78new = rev_78 #+ pension_amt #+ eehc# ) %>% # select(-c(ref_02:ref_35, rev_99, rev_NA, rev_76# #, ref_CHECK#, pension_amt , rev_76,# # , eehc# ))# # ff_rev#noproblem <- c(0) # if ref_CHECK = $0, then there is no problem. :) # # if((sum(ff_rev$ref_CHECK) == 0 )){# # ff_rev <- ff_rev %>%# # mutate(rev_02 = rev_02 - ref_02,# rev_03 = rev_03 - ref_03,# rev_06 = rev_06 - ref_06,# rev_09 = rev_09 - ref_09,# rev_21 = rev_21 - ref_21,# rev_24 = rev_24 - ref_24,# rev_35 = rev_35 - ref_35# ) %>% # select(-c(ref_02:ref_35, rev_99, rev_76, ref_CHECK )) # }else{"You have a problem! Check what revenue items did not have rev codes (causing it to be coded as rev_NA) or the check if there were refunds that were not assigned revenue codes (tax_refunds_long objects)"}ff_rev %>%mutate_all(., ~round(.,digits=0))
Since I already pivot_wider()ed the table in the previous code chunk, I now change each column’s name by using rename() to set new variable names. Ideally the final dataframe would have both the variable name and the variable label but I have not done that yet.
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aggregate_rev_labels <- ff_rev %>%rename("INDIVIDUAL INCOME TAXES, gross of local, net of refunds"= rev_02,"CORPORATE INCOME TAXES, gross of PPRT, net of refunds"= rev_03,"SALES TAXES, gross of local share"= rev_06 ,"MOTOR FUEL TAX, gross of local share, net of refunds"= rev_09 ,"PUBLIC UTILITY TAXES, gross of PPRT"= rev_12,"CIGARETTE TAXES"= rev_15 ,"LIQUOR GALLONAGE TAXES"= rev_18,"INHERITANCE TAX"= rev_21,"INSURANCE TAXES&FEES&LICENSES, net of refunds"= rev_24 ,"CORP FRANCHISE TAXES & FEES"= rev_27,"HORSE RACING TAXES & FEES"= rev_30, # in Other"MEDICAL PROVIDER ASSESSMENTS"= rev_31 ,# "GARNISHMENT-LEVIES " = rev_32 , # dropped"LOTTERY RECEIPTS"= rev_33 ,"OTHER TAXES"= rev_35,"RECEIPTS FROM REVENUE PRODUCNG"= rev_36, "LICENSES, FEES & REGISTRATIONS"= rev_39 ,"MOTOR VEHICLE AND OPERATORS"= rev_42 ,# "STUDENT FEES-UNIVERSITIES" = rev_45, # dropped"RIVERBOAT WAGERING TAXES"= rev_48 ,# "RETIREMENT CONTRIBUTIONS " = rev_51, # dropped"GIFTS AND BEQUESTS"= rev_54, "FEDERAL OTHER"= rev_57 ,"FEDERAL MEDICAID"= rev_58, "FEDERAL TRANSPORTATION"= rev_59 ,"OTHER GRANTS AND CONTRACTS"= rev_60, #other"INVESTMENT INCOME"= rev_63, # other# "PROCEEDS,INVESTMENT MATURITIES" = rev_66 , #dropped# "BOND ISSUE PROCEEDS" = rev_72, #dropped# "INTER-AGENCY RECEIPTS" = rev_75, #dropped# "TRANSFER IN FROM OUT FUNDS" = rev_76, # dropped"ALL OTHER SOURCES"= rev_78,# "COOK COUNTY IGT" = rev_79, #dropped# "PRIOR YEAR REFUNDS" = rev_98 #dropped ) aggregate_rev_labels %>%mutate_all(., ~round(., digits =0))
Table 2.2: Aggregated Revenue Categories ($ Millions), with old labels
2.3.2 Expenditures
Create exp_970 for all local government transfers (exp_971 + exp_972 + exp_975 + exp_976).
Table 2.4: Final Expenditure Categories, with Fiscal Futures Grouped Expenditure Categories
3 Graphs and Tables
Create total revenues and total expenditures only:
after aggregating expenditures and revenues, pivoting wider, then I want to drop the columns that I no longer want and then pivot_longer(). After pivoting_longer() and creating rev_long and exp_long, expenditures and revenues are in the same format and can be combined together for the totals and gap each year.
Each year, you need to increase the cagr value by 1. The value should be the (current year - 1998). For FY23, this is 2023-1998 = 25. So all cagr values that were 24 will be changed to 25.
Code
# This works for one variable at a timecagr_max <-calc_cagr(exp_long, 26) %>%# group_by(Category) %>%summarize(cagr_max =round(sum(cagr*100, na.rm =TRUE), 2))# doesn't need to be changed since it is just pre-covid cagr_precovid <- exp_long %>%filter(Year <=2019) %>%calc_cagr(21) %>%summarize(cagr_21 =round(sum(cagr*100, na.rm =TRUE), 2))# Update year in the filter() and summarize() commands to current year.cagr_10 <-calc_cagr(exp_long, 10) %>%filter(Year == current_year) %>%summarize(cagr_10 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_5 <-calc_cagr(exp_long, 5) %>%filter(Year == current_year) %>%summarize(cagr_5 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_3 <-calc_cagr(exp_long, 3) %>%filter(Year == current_year) %>%summarize(cagr_3 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_2 <-calc_cagr(exp_long, 2) %>%filter(Year == current_year) %>%summarize(cagr_2 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_1 <-calc_cagr(exp_long, 1) %>%filter(Year == current_year) %>%summarize(cagr_1 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))CAGR_expenditures_summary_tot <-data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_max ) %>%select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>%rename("Expenditure Category"= Category_name, "1 Year CAGR"= cagr_1, "2 Year CAGR"= cagr_2, "3 Year CAGR"= cagr_3, "5 Year CAGR"= cagr_5, "10 Year CAGR"= cagr_10, "26 Year CAGR"= cagr_max )totalrow <-which(grepl("Total", CAGR_expenditures_summary_tot$`Expenditure Category`))CAGR_expenditures_summary_tot <-move_to_last(CAGR_expenditures_summary_tot, totalrow) lastrow =nrow(CAGR_expenditures_summary_tot)CAGR_expenditures_summary_tot %>%kbl(caption ="CAGR Calculations for All Expenditure Categories" , row.names=FALSE) %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")
Table 4.1: Expenditure Category CAGRs with Total CAGR (Ordered Alphabetically)
Table 4.2: Revenue Category CAGRs with Total CAGR (Ordered Alphabetically)
CAGR Calculations for All Revenue Sources (Ordered Alphabetical)
Revenue Category
1 Year CAGR
2 Year CAGR
3 Year CAGR
5 Year CAGR
10 Year CAGR
26 Year CAGR
Cigarette Taxes
-9.80
-8.27
-8.26
-1.65
-1.93
1.64
Corp Franchise Taxes & Fees
-9.97
-3.05
-14.03
-3.85
-0.01
2.10
Corporate Income Taxes
-20.82
-9.01
11.48
15.79
6.47
5.92
Federal Medicaid
5.80
5.95
6.79
12.12
9.19
7.40
Federal Other
-3.60
-26.44
4.43
12.03
4.96
4.04
Federal Transportation
11.66
13.46
-0.27
11.62
3.11
4.08
Gifts And Bequests
16.60
14.87
17.76
17.03
15.15
11.69
Horse Racing Taxes & Fees
1.29
-4.28
-3.80
3.40
1.35
-6.31
Individual Income Taxes
8.17
1.76
4.52
6.05
5.10
5.48
Inheritance Tax
24.82
1.98
11.73
10.07
8.54
3.60
Insurance Taxes&Fees&Licenses
0.14
4.27
1.55
4.84
3.82
6.40
Investment Income
68.70
291.96
130.49
34.80
37.68
6.00
Licenses, Fees & Registrations
14.12
11.66
6.05
14.82
7.04
8.18
Liquor Gallonage Taxes
-1.63
-1.39
-0.10
0.92
1.07
6.74
Lottery Receipts
3.06
7.38
2.66
3.89
1.86
2.55
Medical Provider Assessments
6.19
7.83
4.46
12.81
6.05
8.32
Motor Fuel Tax
9.84
5.67
5.79
15.88
8.11
3.02
Motor Vehicle And Operators
2.51
1.24
-1.09
0.47
0.81
3.06
Other Grants And Contracts
50.29
196.09
78.81
62.80
36.17
-0.16
Other Taxes
4.07
8.44
24.44
13.59
13.67
7.75
Public Utility Taxes
-0.26
0.97
1.68
-0.48
-0.10
0.72
Receipts From Revenue Producing
16.36
12.35
9.14
3.07
4.40
5.62
Riverboat Wagering Taxes
3.44
6.36
26.93
-3.08
-2.74
2.10
Sales Taxes
1.25
3.03
5.73
5.69
4.42
3.22
All Other Sources
-0.88
12.15
21.08
16.19
7.37
6.70
Total
3.00
-0.40
6.54
9.05
5.84
5.04
Update all years in mutate() commands so that they all go up by 1:
Code
revenue_change2 <- rev_long %>%filter(Year >= past_year) %>%pivot_wider(names_from = Year , values_from = Dollars, names_prefix ="Dollars_") %>%mutate("FY Current FY ($ billions)"=round(Dollars_2024/1000, digits =2),"FY Past FY ($ billions)"=round(Dollars_2023/1000, digits =2),"1-Year Change"=round(((Dollars_2024 -Dollars_2023)/Dollars_2023*100), digits =2)) %>%left_join(CAGR_revenue_summary_tot, by =c("Category_name"="Revenue Category")) %>%arrange(-`FY Current FY ($ billions)`)%>%# filter(Category_name != "NA") %>%#select(-c(Dollars_2021, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%rename( "26-Year CAGR"=`26 Year CAGR`, "Revenue Category"= Category_name ) %>%select(-c(Dollars_2023, Dollars_2024, `1 Year CAGR`:`10 Year CAGR`)) allother_row <-which(grepl("All Other", revenue_change2$`Revenue Category`))revenue_change2 <-move_to_last(revenue_change2, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", revenue_change2$`Revenue Category`))revenue_change2 <-move_to_last(revenue_change2, totalrow) # Move "Total" to last rowlastrow =nrow(revenue_change2)revenue_change2 %>%filter(!is.na(`Revenue Category`)) %>%kbl(caption ="Table 1. Yearly Change in Revenue", row.names =FALSE) %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")
Table 1. Yearly Change in Revenue
Revenue Category
FY Current FY ($ billions)
FY Past FY ($ billions)
1-Year Change
26-Year CAGR
Individual Income Taxes
27.38
25.31
8.17
5.48
Federal Medicaid
21.38
20.21
5.80
7.40
Sales Taxes
16.43
16.22
1.25
3.22
Federal Other
10.49
10.88
-3.60
4.04
Corporate Income Taxes
8.30
10.48
-20.82
5.92
Medical Provider Assessments
4.34
4.09
6.19
8.32
Receipts From Revenue Producing
3.01
2.59
16.36
5.62
Motor Fuel Tax
2.82
2.57
9.84
3.02
Gifts And Bequests
2.45
2.10
16.60
11.69
Federal Transportation
2.36
2.11
11.66
4.08
Licenses, Fees & Registrations
2.35
2.06
14.12
8.18
Other Taxes
1.70
1.64
4.07
7.75
Motor Vehicle And Operators
1.64
1.60
2.51
3.06
Lottery Receipts
1.61
1.56
3.06
2.55
Public Utility Taxes
1.44
1.44
-0.26
0.72
Investment Income
1.26
0.75
68.70
6.00
Cigarette Taxes
0.71
0.78
-9.80
1.64
Insurance Taxes&Fees&Licenses
0.66
0.66
0.14
6.40
Inheritance Tax
0.63
0.50
24.82
3.60
Riverboat Wagering Taxes
0.36
0.35
3.44
2.10
Liquor Gallonage Taxes
0.31
0.32
-1.63
6.74
Corp Franchise Taxes & Fees
0.21
0.23
-9.97
2.10
Horse Racing Taxes & Fees
0.01
0.01
1.29
-6.31
Other Grants And Contracts
0.00
0.00
50.29
-0.16
All Other Sources
3.28
3.31
-0.88
6.70
Total
115.13
111.77
3.00
5.04
Code
expenditure_change2 <- exp_long %>%filter(Year >= past_year) %>%pivot_wider(names_from = Year , values_from = Dollars, names_prefix ="Dollars_") %>%mutate("FY 2024 ($ billions)"=round(Dollars_2024/1000, digits =2),"FY 2023 ($ billions)"=round(Dollars_2023/1000, digits =2),"1-Year Change"=round((Dollars_2024 -Dollars_2023)/Dollars_2023*100, digits =2) )%>%left_join(CAGR_expenditures_summary_tot, by =c("Category_name"="Expenditure Category")) %>%arrange(-`FY 2024 ($ billions)`)%>%select(-c(Dollars_2024, Dollars_2023, `1 Year CAGR`:`10 Year CAGR`)) %>%rename( "26-Year CAGR"=`26 Year CAGR`, "Expenditure Category"= Category_name )allother_row <-which(grepl("All Other", expenditure_change2$`Expenditure Category`))expenditure_change2 <-move_to_last(expenditure_change2, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", expenditure_change2$`Expenditure Category`))expenditure_change2 <-move_to_last(expenditure_change2, totalrow) # Move "Total" to last rowlastrow =nrow(expenditure_change2)expenditure_change2 %>%filter(!is.na(`Expenditure Category`)) %>%kbl(caption ="Table 2. Yearly Change in Expenditures - All FF Categories, Ordered from Largest to Smallest Expenditure Amount", row.names =FALSE) %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")
Table 2. Yearly Change in Expenditures - All FF Categories, Ordered from Largest to Smallest Expenditure Amount
Expenditure Category
FY 2024 ($ billions)
FY 2023 ($ billions)
1-Year Change
26-Year CAGR
Medicaid
33.44
32.41
3.19
7.27
K-12 Education
21.71
21.05
3.10
5.35
Human Services
10.47
8.83
18.54
3.84
Local Govt Revenue Sharing
9.66
10.90
-11.43
4.01
Transportation
5.91
5.29
11.68
4.30
University Education
5.02
4.67
7.44
3.07
State Employee Healthcare
3.16
3.00
5.34
5.80
Debt Service
2.29
1.95
17.27
6.15
Public Safety
2.18
1.74
25.27
6.38
Tollway
1.98
1.89
4.38
6.69
Other Departments
1.86
1.82
2.46
22.60
Corrections
1.83
1.70
7.26
2.40
Children And Family Services
1.79
1.58
13.71
1.25
Community Development
1.61
1.50
7.49
4.89
Aging
1.54
1.38
11.77
7.87
Central Management
1.40
1.36
2.41
4.67
Revenue
1.39
3.11
-55.49
4.51
Elected Officers
1.20
1.07
12.19
4.14
State Lottery
1.00
1.40
-28.31
4.93
Environmental Protect Agency
0.88
0.62
42.97
4.10
Capital Improvement
0.83
0.65
27.05
4.61
Judicial
0.79
0.75
5.21
4.05
Public Health
0.73
0.77
-4.75
5.99
Healthcare & Fam Ser Net Of Medicaid
0.46
0.43
8.50
5.58
Natural Resources
0.38
0.32
18.54
2.54
Other Boards & Commissions
0.35
0.33
6.75
5.17
Employment Security
0.27
0.27
-1.89
1.48
Bus & Profession Regulation
0.25
0.23
9.70
1.94
Legislative
0.24
0.24
1.88
5.34
Veterans' Affairs
0.15
0.14
13.42
3.91
Agriculture
0.11
0.09
22.39
1.57
Il Power Agency
0.05
0.04
46.08
0.00
Gomb
0.03
0.01
206.53
-0.80
Human Rights
0.02
0.01
11.46
2.27
Labor
0.02
0.01
24.51
4.28
State Pension Contribution
0.00
0.40
-100.00
0.00
Total
115.00
111.97
2.71
5.14
4.1 Summary Tables - Largest Categories
The 10 largest revenue sources and 15 largest expenditure sources remain separate categories and all other smaller sources/expenditures are combined into “All Other Revenues (Expenditures)”. These condensed tables are typically used in the Fiscal Futures articles. They were manually created in past years but this hopefully automates the process a bit until final formatting stages.
take ff_rev and ff_exp data frames, which were in wide format, pivot them longer and mutate the Category_name variable to nicer labels. Keep largest categories separate and aggregate the rest.
You need to manually comment out the categories that are not the largest each year. Check and compare to the previous years largest categories!
Code
exp_totals <- ff_exp %>%rowwise() %>%mutate(exp_TOTALS =sum(across(exp_402:exp_970), na.rm=TRUE)) # creates total column toorev_totals <- ff_rev %>%rowwise() %>%mutate(rev_TOTALS =sum(across(rev_02:rev_78), na.rm=TRUE))rev_long_majorcats <-pivot_longer(rev_totals, rev_02:rev_TOTALS, names_to =c("type","Category"), values_to ="Dollars", names_sep ="_") %>%rename(Year = fy) %>%mutate(Category_name =case_when( Category =="02"~"Income Tax" , Category =="03"~"Corporate Income Tax" , Category =="06"~"Sales Tax" , Category =="09"~"Motor Fuel Taxes" ,# Category == "12" ~ "PUBLIC UTILITY TAXES, gross of PPRT" ,# Category == "15" ~ "CIGARETTE TAXES" ,# Category == "18" ~ "LIQUOR GALLONAGE TAXES" ,# Category == "21" ~ "INHERITANCE TAX" ,# Category == "24" ~ "INSURANCE TAXES&FEES&LICENSES, net of refunds " ,# Category == "27" ~ "CORP FRANCHISE TAXES & FEES" ,# Category == "30" ~ "HORSE RACING TAXES & FEES", # in Other Category =="31"~"Medical Provider Assessments" ,# Category == "32" ~ "GARNISHMENT-LEVIES" , # dropped# Category == "33" ~ "LOTTERY RECEIPTS" ,# Category == "35" ~ "OTHER TAXES" , Category =="36"~"Receipts from Revenue Producing", Category =="39"~"Licenses, Fees, Registration" ,# Category == "42" ~ "MOTOR VEHICLE AND OPERATORS" ,# Category == "45" ~ "STUDENT FEES-UNIVERSITIES", # dropped# Category == "48" ~ "RIVERBOAT WAGERING TAXES" ,# Category == "51" ~ "RETIREMENT CONTRIBUTIONS" , # dropped# Category == "54" ~ "GIFTS AND BEQUESTS", Category =="57"~"Federal Other" , Category =="58"~"Federal Medicaid Reimbursements", Category =="59"~"Federal Transportation" ,# Category == "60" ~ "OTHER GRANTS AND CONTRACTS", #other# Category == "63" ~ "INVESTMENT INCOME", # other# Category == "66" ~ "PROCEEDS,INVESTMENT MATURITIES" , #dropped# Category == "72" ~ "BOND ISSUE PROCEEDS", #dropped# Category == "75" ~ "INTER-AGENCY RECEIPTS ", #dropped# Category == "76" ~ "TRANSFER IN FROM OUT FUNDS", #other# Category == "78new" ~ "ALL OTHER SOURCES" ,# Category == "79" ~ "COOK COUNTY IGT", #dropped# Category == "98" ~ "PRIOR YEAR REFUNDS", #dropped Category =="TOTALS"~"Total Revenue", T ~"All Other Sources **"# any other Category number that was not specifically referenced is combined into Other Revenue Sources ) ) %>%select(-type, -Category) %>%# drop extra columns type and Category numbergroup_by(Year, Category_name) %>%summarise(Dollars=round(sum(Dollars),digits=2)) # revenue_wide # not actually in wide format yet. # has 10 largest rev sources separate and combined all others to Other in long data format. # creates wide version of table where each revenue source is a columnrevenue_wide_majorcats <- rev_long_majorcats %>%pivot_wider(names_from = Category_name, values_from = Dollars) %>%relocate("All Other Sources **", .after =last_col()) %>%relocate("Total Revenue", .after =last_col()) exp_long_majorcats <-pivot_longer(exp_totals, exp_402:exp_TOTALS , names_to =c("type", "Category"), values_to ="Dollars", names_sep ="_") %>%rename(Year = fy ) %>%mutate(Category_name =case_when(# Category == "402" ~ "Aging" ,# Category == "406" ~ "AGRICULTURE", # Category == "416" ~ "Central Management", Category =="418"~"Children & Family Services", Category =="420"~"Community Development",# Category == "422" ~ "NATURAL RESOURCES" , Category =="426"~"Corrections",# Category == "427" ~ "EMPLOYMENT SECURITY" , Category =="444"~"Human Services" ,# Category == "478" ~ "HEALTHCARE & FAM SER NET OF MEDICAID", # Category == "482" ~ "PUBLIC HEALTH", Category =="492"~"Revenue", Category =="494"~"Transportation" ,# Category == "532" ~ "ENVIRONMENTAL PROTECT AGENCY" , Category =="557"~"Tollway" ,# Category == "684" ~ "IL COMMUNITY COLLEGE BOARD", # Category == "691" ~ "IL STUDENT ASSISTANCE COMM" ,# Category == "900" ~ "NOT IN FRAME",# Category == "901" ~ "State Pension Contribution", Category =="903"~"Debt Service", Category =="904"~"State Employee Healthcare",# Category == "910" ~ "LEGISLATIVE" ,# Category == "920" ~ "JUDICIAL" ,# Category == "930" ~ "Elected Officers" , # Category == "940" ~ "OTHER HEALTH-RELATED", Category =="941"~"Public Safety" ,# Category == "942" ~ "ECON DEVT & INFRASTRUCTURE" ,# Category == "943" ~ "CENTRAL SERVICES",# Category == "944" ~ "BUS & PROFESSION REGULATION" , Category =="945"~"Medicaid" ,# Category == "946" ~ "Capital Improvement" , # Category == "948" ~ "OTHER DEPARTMENTS" ,# Category == "949" ~ "OTHER BOARDS & COMMISSIONS" , Category =="959"~"K-12 Education" , Category =="960"~"University Education", Category =="970"~"Local Govt Revenue Sharing", Category =="TOTALS"~"Total Expenditures", T ~"All Other Expenditures **") ) %>%select(-type, -Category) %>%group_by(Year, Category_name) %>%summarise(Dollars=round(sum(Dollars),digits=2))expenditure_wide_majorcats <- exp_long_majorcats %>%pivot_wider(names_from = Category_name, values_from = Dollars) %>%relocate("All Other Expenditures **", .after =last_col()) %>%relocate("Total Expenditures", .after =last_col())# CAGR values for largest expenditure categories and combined All Other Expenditures# function for calculating the CAGRcalc_cagr <-function(df, n) { df <- exp_long_majorcats %>%#select(-type) %>%arrange(Category_name, Year) %>%group_by(Category_name) %>%mutate(cagr = ((`Dollars`/lag(`Dollars`, n)) ^ (1/ n)) -1,cagr =ifelse(is.na(cagr), 0, cagr))return(df)}# This works for one variable at a timecagr_max <-calc_cagr(exp_long_majorcats, 26) %>%# group_by(Category) %>%summarize(cagr_max =round(sum(cagr*100, na.rm =TRUE), 2))cagr23_precovid <- exp_long_majorcats %>%filter(Year <=2019) %>%calc_cagr(21) %>%summarize(cagr_21 =round(sum(cagr*100, na.rm =TRUE), 2))cagr_10 <-calc_cagr(exp_long_majorcats, 10) %>%filter(Year == current_year) %>%summarize(cagr_10 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_5 <-calc_cagr(exp_long_majorcats, 5) %>%filter(Year == current_year) %>%summarize(cagr_5 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_3 <-calc_cagr(exp_long_majorcats, 3) %>%filter(Year == current_year) %>%summarize(cagr_3 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_2 <-calc_cagr(exp_long_majorcats, 2) %>%filter(Year == current_year) %>%summarize(cagr_2 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_1 <-calc_cagr(exp_long_majorcats, 1) %>%filter(Year == current_year) %>%summarize(cagr_1 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))CAGR_expenditures_majorcats_tot <-data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_max) %>%select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>%rename("Expenditure Category"= Category_name, "1 Year CAGR"= cagr_1, "2 Year CAGR"= cagr_2, "3 Year CAGR"= cagr_3, "5 Year CAGR"= cagr_5, "10 Year CAGR"= cagr_10,"26-Year CAGR"= cagr_max )allother_row <-which(grepl("All Other", CAGR_expenditures_majorcats_tot$`Expenditure Category`))CAGR_expenditures_majorcats_tot <-move_to_last(CAGR_expenditures_majorcats_tot, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", CAGR_expenditures_majorcats_tot$`Expenditure Category`))CAGR_expenditures_majorcats_tot <-move_to_last(CAGR_expenditures_majorcats_tot, totalrow) # Move "Total" to last rowlastrow =nrow(CAGR_expenditures_majorcats_tot)CAGR_expenditures_majorcats_tot%>%kbl(caption ="CAGR Calculations for Largest Expenditure Categories" , row.names=FALSE) %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")# Yearly change for Top 13 largest expenditure categoriesexpenditure_change_majorcats <- exp_long_majorcats %>%filter(Year >= past_year) %>%pivot_wider(names_from = Year , values_from = Dollars, names_prefix ="Dollars_") %>%mutate("FY 2024 ($ Billions)"=round(Dollars_2024/1000, digits =2),"FY 2023 ($ Billions)"=round(Dollars_2023/1000, digits =2),"1-Year Change"=percent((Dollars_2024 -Dollars_2023)/Dollars_2023, accuracy = .01) ) %>%left_join(CAGR_expenditures_majorcats_tot, by =c("Category_name"="Expenditure Category")) %>%arrange(-`FY 2024 ($ Billions)`)%>%mutate(`26-Year CAGR`=percent(`26-Year CAGR`/100, accuracy=.1)) %>%select(-c(Dollars_2024, Dollars_2023, `1 Year CAGR`:`10 Year CAGR`)) %>%rename( "26-Year CAGR"=`26-Year CAGR`, "Expenditure Category"= Category_name )allother_row <-which(grepl("All Other", expenditure_change_majorcats$`Expenditure Category`))expenditure_change_majorcats <-move_to_last(expenditure_change_majorcats, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", expenditure_change_majorcats$`Expenditure Category`))expenditure_change_majorcats <-move_to_last(expenditure_change_majorcats, totalrow) # Move "Total" to last rowlastrow =nrow(expenditure_change_majorcats)expenditure_change_majorcats %>%kbl(caption ="Yearly Change in Expenditures", row.names =FALSE, align ="l") %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")
Table 4.3: Largest Expenditure Categories with CAGRs
CAGR Calculations for Largest Expenditure Categories
Expenditure Category
1 Year CAGR
2 Year CAGR
3 Year CAGR
5 Year CAGR
10 Year CAGR
26-Year CAGR
Children & Family Services
13.71
19.69
11.25
9.24
4.74
1.25
Community Development
7.49
7.60
-1.32
22.82
4.35
4.89
Corrections
7.26
10.32
4.26
2.92
3.04
2.40
Debt Service
17.27
6.71
4.14
2.20
1.77
6.15
Human Services
18.54
19.93
16.91
13.23
6.40
3.84
K-12 Education
3.10
5.52
6.95
7.18
5.73
5.35
Local Govt Revenue Sharing
-11.43
-3.40
10.39
8.17
4.82
4.01
Medicaid
3.19
8.03
8.37
11.94
8.21
7.27
Public Safety
25.27
12.58
2.80
16.59
10.05
6.38
Revenue
-55.49
-13.87
-6.81
17.48
10.85
4.51
State Employee Healthcare
5.34
2.59
3.15
0.07
1.88
5.80
Tollway
4.38
-3.02
0.23
2.45
3.34
6.69
Transportation
11.68
13.73
3.19
11.39
2.83
4.30
University Education
7.44
7.16
6.07
6.19
2.57
3.07
All Other Expenditures **
1.96
9.41
0.00
0.00
0.00
0.00
Total Expenditures
2.71
7.20
7.38
9.10
5.56
5.14
Yearly Change in Expenditures
Expenditure Category
FY 2024 ($ Billions)
FY 2023 ($ Billions)
1-Year Change
26-Year CAGR
Medicaid
33.44
32.41
3.19%
7.3%
K-12 Education
21.71
21.05
3.10%
5.4%
Human Services
10.47
8.83
18.54%
3.8%
Local Govt Revenue Sharing
9.66
10.90
-11.43%
4.0%
Transportation
5.91
5.29
11.68%
4.3%
University Education
5.02
4.67
7.44%
3.1%
State Employee Healthcare
3.16
3.00
5.34%
5.8%
Debt Service
2.29
1.95
17.27%
6.2%
Public Safety
2.18
1.74
25.27%
6.4%
Tollway
1.98
1.89
4.38%
6.7%
Corrections
1.83
1.70
7.26%
2.4%
Children & Family Services
1.79
1.58
13.71%
1.2%
Community Development
1.61
1.50
7.49%
4.9%
Revenue
1.39
3.11
-55.49%
4.5%
All Other Expenditures **
12.58
12.34
1.96%
0.0%
Total Expenditures
115.00
111.97
2.71%
5.1%
Top 10 revenue sources CAGRs and Yearly Change Tables:
Code
##### Top 10 revenue CAGRs: ####calc_cagr <-function(df, n) { df <- rev_long_majorcats %>%arrange(Category_name, Year) %>%group_by(Category_name) %>%mutate(cagr = ((Dollars /lag(Dollars, n)) ^ (1/ n)) -1,cag =ifelse(is.na(cagr), 0 , cagr))return(df)}# This works for one variable at a timecagr_max <-calc_cagr(rev_long_majorcats, 26) %>%# group_by(Category) %>%summarize(cagr_max =round(sum(cagr*100, na.rm =TRUE), 2))cagr_10 <-calc_cagr(rev_long_majorcats, 10) %>%filter(Year == current_year) %>%summarize(cagr_10 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_5 <-calc_cagr(rev_long_majorcats, 5) %>%filter(Year == current_year) %>%summarize(cagr_5 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_3 <-calc_cagr(rev_long_majorcats, 3) %>%filter(Year == current_year) %>%summarize(cagr_3 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_2 <-calc_cagr(rev_long_majorcats, 2) %>%filter(Year == current_year) %>%summarize(cagr_2 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2))) cagr_1 <-calc_cagr(rev_long_majorcats, 1) %>%filter(Year == current_year) %>%summarize(cagr_1 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))CAGR_revenue_majorcats_tot <-data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_max) %>%select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>%rename("Revenue Category"= Category_name, "1 Year CAGR"= cagr_1, "2 Year CAGR"= cagr_2, "3 Year CAGR"= cagr_3, "5 Year CAGR"= cagr_5, "10 Year CAGR"= cagr_10, "26-Year CAGR"= cagr_max )allother_row <-which(grepl("All Other", CAGR_revenue_majorcats_tot$`Revenue Category`))CAGR_revenue_majorcats_tot <-move_to_last(CAGR_revenue_majorcats_tot, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", CAGR_revenue_majorcats_tot$`Revenue Category`))CAGR_revenue_majorcats_tot <-move_to_last(CAGR_revenue_majorcats_tot, totalrow) # Move "Total" to last rowlastrow =nrow(CAGR_revenue_majorcats_tot)CAGR_revenue_majorcats_tot %>%kbl(caption ="CAGR Calculations for Largest Revenue Sources", row.names =FALSE) %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")
Table 4.4: Top 10 Revenue Sources with CAGRs
CAGR Calculations for Largest Revenue Sources
Revenue Category
1 Year CAGR
2 Year CAGR
3 Year CAGR
5 Year CAGR
10 Year CAGR
26-Year CAGR
Corporate Income Tax
-20.82
-9.01
11.48
15.79
6.47
5.92
Federal Medicaid Reimbursements
5.80
5.95
6.79
12.12
9.19
7.40
Federal Other
-3.60
-26.44
4.43
12.03
4.96
4.04
Federal Transportation
11.66
13.46
-0.27
11.62
3.11
4.08
Income Tax
8.17
1.76
4.52
6.05
5.10
5.48
Licenses, Fees, Registration
14.12
11.66
6.05
14.82
7.04
8.18
Medical Provider Assessments
6.19
7.83
4.46
12.81
6.05
8.32
Motor Fuel Taxes
9.84
5.67
5.79
15.88
8.11
3.02
Receipts from Revenue Producing
16.36
12.35
9.14
3.07
4.40
5.62
Sales Tax
1.25
3.03
5.73
5.69
4.42
3.22
All Other Sources **
6.67
10.50
11.58
8.31
5.42
4.39
Total Revenue
3.00
-0.40
6.54
9.05
5.84
5.04
Code
###### Yearly change summary table for Top 10 Revenues #####revenue_change_majorcats <- rev_long_majorcats %>%#select(-c(Category)) %>%filter(Year >= past_year) %>%pivot_wider(names_from = Year , values_from = Dollars, names_prefix ="Dollars_") %>%mutate("FY 2024 ($ billions)"=round(Dollars_2024/1000, digits =1),"FY 2023 ($ billions)"=round(Dollars_2023/1000, digits =1),"1-Year Change"=percent(((Dollars_2024 -Dollars_2023)/Dollars_2023), accuracy = .1)) %>%left_join(CAGR_revenue_majorcats_tot, by =c("Category_name"="Revenue Category")) %>%arrange(-`FY 2024 ($ billions)`)%>%#select(-c(Dollars_2021, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%mutate("26-Year Change"=percent(`26-Year CAGR`/100, accuracy=.1)) %>%rename("Revenue Category"= Category_name ) %>%select(-c(Dollars_2024, Dollars_2023, `1 Year CAGR`:`26-Year CAGR`)) allother_row <-which(grepl("All Other", revenue_change_majorcats$`Revenue Category`))revenue_change_majorcats <-move_to_last(revenue_change_majorcats, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", revenue_change_majorcats$`Revenue Category`))revenue_change_majorcats <-move_to_last(revenue_change_majorcats, totalrow) # Move "Total" to last rowlastrow =nrow(revenue_change_majorcats)revenue_change_majorcats%>%kbl(caption ="Yearly Change in Revenue for Main Revenue Sources", row.names =FALSE, align ="l") %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")
Table 4.5: Top 10 Revenue Sources with CAGRs
Yearly Change in Revenue for Main Revenue Sources
Revenue Category
FY 2024 ($ billions)
FY 2023 ($ billions)
1-Year Change
26-Year Change
Income Tax
27.4
25.3
8.2%
5.5%
Federal Medicaid Reimbursements
21.4
20.2
5.8%
7.4%
Sales Tax
16.4
16.2
1.3%
3.2%
Federal Other
10.5
10.9
-3.6%
4.0%
Corporate Income Tax
8.3
10.5
-20.8%
5.9%
Medical Provider Assessments
4.3
4.1
6.2%
8.3%
Receipts from Revenue Producing
3.0
2.6
16.4%
5.6%
Motor Fuel Taxes
2.8
2.6
9.8%
3.0%
Federal Transportation
2.4
2.1
11.7%
4.1%
Licenses, Fees, Registration
2.4
2.1
14.1%
8.2%
All Other Sources **
16.3
15.3
6.7%
4.4%
Total Revenue
115.1
111.8
3.0%
5.0%
Export summary file with Totals
Code
#install.packages("openxlsx")library(openxlsx)dataset_names <-list('Aggregate Revenues'= revenue_wide2, 'Aggregate Expenditures'= expenditure_wide2, 'Table 1'= revenue_change_majorcats, #Top categories with yearly change, 23 yr cagr'Table 2'= expenditure_change_majorcats,'Table 1a. AllCats'= revenue_change2,'Table 2a. AllCats'= expenditure_change2,'CAGR Rev-MajorCats'= CAGR_revenue_majorcats_tot, # Categories Match Table 1 in paper'CAGR Exp-MajorCats'= CAGR_expenditures_majorcats_tot, # 'Table 1-AllCats' = expenditure_change_allcats, # All Categories by Year# 'Table 2-AllCats' = revenue_change_allcats,# 'CAGR_Revenue-AllCats' = CAGR_revenue_summary_tot, # 'CAGR_Expenditures-AllCats' = CAGR_expenditures_summary_tot, 'Fiscal Gap'= year_totals, # Total Revenue, Expenditure, and Fiscal gap per year'aggregated_totals_long'= aggregated_totals_long # all data in long format. Good for creating pivot tables in Excel )write.xlsx(dataset_names, file =paste0("data/FY", current_year, " Files/summary_file_FY24_PensionChange_2025_04_23.xlsx"))
Saves main items in one excel file named summary_file.xlsx. Delete eval=FALSE to run on local computer.
Source Code
# Pension Costs Included within Department Expenditures {#sec-all-code-pensionchange}**An Explanation of a Methodological Change in How We Categorize Some Pension Spending**Fiscal Futures’ conceptual framework requires categorizing expenditures according to the function they support rather than the budgetary funds through which they are funneled. Because of this, past reports counted most pension spending as part of the expenditures for the employees of the relevant agency. For example, pension spending that went as benefits to human service employees was counted as spending on human services. However, because most teachers are not state employees, in past years, spending to support teacher pensions was treated differently. We accounted for this spending as a separate expenditure item rather than including it as part of state spending on K-12 education.On reflection, we decided to treat these pension payments in parallel with other pension spending and, therefore, include them as part of K-12 education spending. In order to maintain consistency across years we have recalculated all previous years using this methodology.However, we thought that readers also might be interested in total pension spending during current and past years. We have added a separate table showing this information but emphasize that pension spending is already incorporated in other spending categories and should not be added to the total shown in Table 1 of the report.```{r setup, warning=FALSE, message=FALSE}knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)library(tidyverse)#library(haven)library(formatR)library(lubridate)#library(smooth)#library(forecast)library(scales)library(kableExtra)library(ggplot2)library(readxl)#library(data.table)#library(quantmod)#library(geofacet)library(janitor)library(cmapplot)theme_set(theme_classic() )move_to_last <- function(df, n) df[c(setdiff(seq_len(nrow(df)), n), n), ]current_year <- 2024 # fiscal year, not calendar yearpast_year=current_year-1rev_temp <- read_csv(paste0("../../Fiscal Futures IGPA/Fiscal-Future-Topics/data/FY", current_year, " Files/rev_temp.csv"))exp_temp <- read_csv(paste0("../../Fiscal Futures IGPA/Fiscal-Future-Topics/data/FY", current_year, " Files/exp_temp.csv"))``````{r include=FALSE}update_recessions <- function(url = NULL, quietly = FALSE){ # Use default URL if user does not override if (is_null(url) | missing(url)) { url <- "https://data.nber.org/data/cycles/business_cycle_dates.json" } # locally bind variable names start_char <- end_char <- start_date <- end_date <- ongoing <- index <- peak <- trough <- NULL return( # attempt to download and format recessions table tryCatch({ recessions <- jsonlite::fromJSON(url) %>% # drop first row trough dplyr::slice(-1) %>% # convert peaks and troughs... dplyr::mutate( # ...to R dates start_date = as.Date(peak), end_date = as.Date(trough), # ... and clean char strings start_char = format(start_date, "%b %Y"), end_char = format(end_date, "%b %Y")) %>% # confirm ascending and create row number dplyr::arrange(start_date) %>% mutate(index = row_number()) %>% mutate( # Flag unfinished recessions ongoing = case_when( is.na(end_date) & index == max(.$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()```## Modify Expenditure File### Tax refundsAggregate expenditures: Save tax refunds as negative revenue. Code refunds to match the rev_type codes (02=income taxes, 03 = corporate income taxes, 06=sales tax, 09=motor fuel tax, 24=insurance taxes and fees, 35 = all other tax refunds).```{r}#| label: fig-tax-refunds#| fig-cap: Tax Refundstax_refund_long <- exp_temp %>%# fund != "0401" # removes State Trust Fundsfilter(fund !="0401"& (object =="9900"| object=="9910"|object=="9921"|object=="9923"|object=="9925")) %>%# keeps these objects which represent revenue, insurance, treasurer,and financial and professional reg tax refundsmutate(refund =case_when( object =="9900"~"FY23_Rebates", fund=="0278"& sequence =="00"~"02", # for income tax refund fund=="0278"& sequence =="01"~"03", # tax administration and enforcement and tax operations become corporate income tax refund fund =="0278"& sequence =="02"~"02", object=="9921"~"21", # inheritance tax and estate tax refund appropriation object=="9923"~"09", # motor fuel tax refunds obj_seq_type =="99250055"~"06", # sales tax refund fund=="0378"& object=="9925"~"24", # insurance privilege tax refund (fund=="0001"& object=="9925") | (object=="9925"& fund =="0384"& fy ==2023) ~"35", # all other taxes# fund=="0001" & object=="9925" ~ "35", # all other taxes T ~"CHECK")) # if none of the items above apply to the observations, then code them as CHECK exp_temp <-left_join(exp_temp, tax_refund_long) %>%mutate(refund =ifelse(is.na(refund),"not refund", as.character(refund)))tax_refund <- tax_refund_long %>%group_by(refund, fy)%>%summarize(refund_amount =sum(expenditure, na.rm =TRUE)/1000000) %>%pivot_wider(names_from = refund, values_from = refund_amount, names_prefix ="ref_") %>%mutate_all(~replace_na(.,0)) %>%arrange(fy)# # tax_refund %>% # pivot_longer(c(ref_06:ref_35, ref_FY23_Rebates), names_to = "Refund Type", values_to = "Amount") %>%# ggplot()+ # theme_classic()+# geom_line(aes(x=fy,y=Amount, group = `Refund Type`, color = `Refund Type`))+# labs(title = "Refund Types", # caption = "Refunds are excluded from Expenditure totals and instead subtracted from Revenue totals") + # labs(title = "Tax refunds", # caption = "Rev_type codes: 02=income taxes, 03=corporate income taxes, 06=sales tax, 09=motor fuel tax, # 24=insurance taxes and fees, 35 = all other tax refunds." )# remove the items we recoded in tax_refund_long# exp_temp <- exp_temp %>% filter(refund == "not refund")``````{r}# manually adds the abatements as expenditure item and keeps on expenditure side.# otherwise ignored since it is in fund 0278 and exp_temp <- exp_temp %>%mutate(in_ff =ifelse(object ==9900, 1, in_ff))```### Pension ExpendituresState pension contributions for TRS and SURS are largely captured with object=4431. **(State payments into pension fund).** State payments to the following pension systems:- Teachers Retirement System (TRS) Agency 493 --> K-12 Education (Group = 959)- New POB bond in 2019: Accelerated Bond Fund paid benefits in advance as lump sum - State Employee Retirement System (SERS) Agency 589 --> SERS Agency 589 - _Note: Object 4431 does not have SERS expenditures in it. Those are only in object 116X objects_ - State University Retirement System (SURS) Agency 693 --> University Education (Group = 960) - Judges Retirement System (JRS) Agency 275 --> Judicial (Group = 920) - General Assembly Retirement System (GARS) --> Legislative (Group 910) There are also "Other Post-Employment Benefits" (OPEBs). Expenditure object 4430 is for retirement benefits.While it is good to know the overall cost of pensions for the state, if you want to know the true cost of providing services, pension and other benefit costs should be included in the department that is paying employees to provide those services. > Change in pension coding in chunk below:```{r fig-pensions, fig.cap="Pensions"}exp_temp <- exp_temp %>% arrange(fund) %>% mutate(pension = case_when( ## Commented out line below: # (object=="4431") ~ 1, # 4431 = easy to find pension payments INTO fund (object=="1298" & # Purchase of Investments, Normally excluded (fy==2010 | fy==2011) & (fund=="0477" | fund=="0479" | fund=="0481")) ~ 3, #judges retirement OUT of fund # state borrowed money from pension funds to pay for core services during 2010 and 2011. # used to fill budget gap and push problems to the future. fund == "0319" ~ 4, # pension stabilization fund TRUE ~ 0) )``````{r fig-pensions-POB, fig.cap="Pension Expenditures"}# special accounting of pension obligation bond (POB)-funded contributions to JRS, SERS, GARS, TRS exp_temp <- exp_temp %>% # change object for 2010 and 2011, retirement expenditures were bond proceeds and would have been excluded mutate(object = ifelse((pension > 0 & in_ff == "0"), "4431", object)) %>% # changes weird teacher & judge retirement system pensions object to normal pension object 4431 mutate(pension = ifelse(pension > 0 & in_ff == "0", 6, pension)) %>% # coded as 6 if it was supposed to be excluded. mutate(in_ff = ifelse(pension > 0, "1", in_ff))# all other pensions objects codes get agency code 901 for State Pension Contributionsexp_temp <- exp_temp %>% mutate(agency = ifelse(pension > 0, "901", as.character(agency)), agency_name = ifelse(agency == "901", "State Pension Contributions", as.character(agency_name)))```Can also be thought of past commitments vs current contributions.Cost of past commitments in the form of Pension benefits paid out, **Current Employees vs Retired Employees**Current Employees: - Group Insurance Benefits Retired Employees:- Deferred Compensation - Medicare Retirees and Survivors of State of Illinois Employees Group Insurance Program (SEGIP) - Part of Medicare ```{r}exp_temp|>filter(fy==2024) |>filter((appr_org=="01"| appr_org =="65"| appr_org=="88") & (object=="4900"| object=="4400") )|>group_by(agency, agency_name) |># separates CHIP from health and human services and saves it as Medicaidsummarize(expenditure =sum(expenditure))```Drop all cash transfers between funds, statutory transfers, and purchases of investments from expenditure data.```{r drop-transfers}transfers_drop <- exp_temp %>% filter( agency == "799" | # statutory transfers object == "1993" | # interfund cash transfers object == "1298") # purchase of investmentstransfers_drop # items being dropped, # always check to make sure you aren't accidentally dropping something of interest.exp_temp <- anti_join(exp_temp, transfers_drop)```### State employee healthcare costs> Commented out line of code that seperates healthcare costs. This should keep healthcare costs in the agency, similar to the change that was made for pensions. agency=="507"| agency=="442" | agency=="445" | agency=="452" |agency=="458" | agency=="497" ~ "948", # other departmentsIt looks like healthcare costs shift to "Other Departments" which includes : - GOMB (507) - Human Rights (442) - Illinois Power Agency (445) - Labor (452) - State Lottery (458) - Veteran's Affairs (497) ```{r}exp_temp |>filter(org_name =="BUREAU OF BENEFITS") |>group_by(fy, agency) |>summarize(expenditure =sum(expenditure) ) |>pivot_wider(names_from ="fy", values_from ="expenditure")exp_temp |>filter(org_name =="BUREAU OF BENEFITS") |>group_by(fy, agency) |>summarize(expenditure =sum(expenditure) ) |>ggplot() +geom_line(aes(x=fy, y=expenditure, group= agency, color = agency)) +scale_y_continuous(labels = scales::dollar) +labs(title="Bureau of Benefits Expenditures")exp_temp |>filter(org_name =="BUREAU OF BENEFITS") |>group_by(fy, object) |>summarize(expenditure =sum(expenditure) ) |>ggplot() +geom_line(aes(x=fy, y=expenditure, group= object, color = object)) +scale_y_continuous(labels = scales::dollar) +labs(title="Bureau of Benefits Expenditures")``````{r eehc1}#if observation is a group insurance contribution, then the expenditure amount is set to $0 (essentially dropped from analysis)# pretend eehc is named group_insurance_contribution or something like that# eehc coded as zero implies that it is group insurance# if eehc=0, then expenditures are coded as zero for group insurance to avoid double counting costsexp_temp <- exp_temp %>% mutate(eehc = ifelse( # group insurance contributions for 1998-2005 and 2013-present fund == "0001" & (object == "1180" | object =="1900") & agency == "416" & appr_org=="20", 0, 1) )%>% mutate(eehc = ifelse( # group insurance contributions for 2006-2012 fund == "0001" & object == "1180" & agency == "478" & appr_org=="80", 0, eehc) )%>% # group insurance contributions from road fund # coded with 1900 for some reason?? mutate(eehc = ifelse( fund == "0011" & object == "1900" & agency == "416" & appr_org=="20", 0, eehc) ) %>% mutate(expenditure = ifelse(eehc=="0", 0, expenditure)) %>% mutate(agency = case_when( ## turns specific items into State Employee Healthcare (agency=904) fund=="0907" & (agency=="416" & appr_org=="20") ~ "904", # central management Bureau of benefits using health insurance reserve fund=="0907" & (agency=="478" & appr_org=="80") ~ "904", # agency = 478: healthcare & family services using health insurance reserve - stopped using this in 2012 TRUE ~ as.character(agency))) %>% mutate(agency_name = ifelse( agency == "904", "STATE EMPLOYEE HEALTHCARE", as.character(agency_name)), in_ff = ifelse(agency == "904", 1, in_ff), group = ifelse(agency == "904", "904", as.character(agency))) # creates group variable# Default group = agency numberhealthcare_costs <- exp_temp %>% filter(group == "904")``````{r transfers-to-local}exp_temp <- exp_temp %>% mutate( agency = case_when(fund=="0515" & object=="4470" & type=="08" ~ "971", # income tax to local governments fund=="0515" & object=="4491" & type=="08" & sequence=="00" ~ "971", # object is shared revenue payments fund=="0802" & object=="4491" ~ "972", #pprt transfer fund=="0515" & object=="4491" & type=="08" & sequence=="01" ~ "976", #gst to local fund=="0627" & object=="4472"~ "976" , # public transportation fund but no observations exist fund=="0648" & object=="4472" ~ "976", # downstate public transportation, but doesn't exist fund=="0515" & object=="4470" & type=="00" ~ "976", # object 4470 is grants to local governments object=="4491" & (fund=="0188"|fund=="0189") ~ "976", fund=="0187" & object=="4470" ~ "976", fund=="0186" & object=="4470" ~ "976", object=="4491" & (fund=="0413"|fund=="0414"|fund=="0415") ~ "975", #mft to local fund == "0952"~ "975", # Added Sept 29 2022 AWM. Transportation Renewal MFT TRUE ~ as.character(agency)), agency_name = case_when(agency == "971"~ "INCOME TAX 1/10 TO LOCAL", agency == "972" ~ "PPRT TRANSFER TO LOCAL", agency == "975" ~ "MFT TO LOCAL", agency == "976" ~ "GST TO LOCAL", TRUE~as.character(agency_name)), group = ifelse(agency>"970" & agency < "977", as.character(agency), as.character(group)))``````{r fig-drop-local-transfers, fig.cap="Drop Transfers from State to Local Governments"}transfers_long <- exp_temp %>% filter(group == "971" |group == "972" | group == "975" | group == "976")transfers <- transfers_long %>% group_by(fy, group ) %>% summarize(sum_expenditure = sum(expenditure)/1000000) %>% pivot_wider(names_from = "group", values_from = "sum_expenditure", names_prefix = "exp_" )exp_temp <- anti_join(exp_temp, transfers_long)dropped_inff_0 <- exp_temp %>% filter(in_ff == 0)exp_temp <- exp_temp %>% filter(in_ff == 1) # drops in_ff = 0 funds AFTER dealing with net-revenue above``````{r debt-service}debt_drop <- exp_temp %>% filter(object == "8841" | object == "8811") # escrow OR principle#debt_drop %>% group_by(fy) %>% summarize(sum = sum(expenditure)) %>% arrange(-fy)debt_keep <- exp_temp %>% filter(fund != "0455" & (object == "8813" | object == "8800" )) # examine the debt costs we want to include#debt_keep %>% group_by(fy) %>% summarize(sum = sum(expenditure)) %>% arrange(-fy) exp_temp <- anti_join(exp_temp, debt_drop) exp_temp <- anti_join(exp_temp, debt_keep)debt_keep <- debt_keep %>% mutate( agency = ifelse(fund != "0455" & (object == "8813" | object == "8800"), "903", as.character(agency)), group = ifelse(fund != "0455" & (object == "8813" | object == "8800"), "903", as.character(group)), in_ff = ifelse(group == "903", 1, as.character(in_ff)))debt_keep_yearly <- debt_keep %>% group_by(fy, group) %>% summarize(debt_cost = sum(expenditure,na.rm=TRUE)/1000000) %>% select(-group)```### Add Other Fiscal Future group codes> Commented out line that creates Other departments from a few agencies. Keeping them seperate to see where healthcare costs go. agency=="507"| agency=="442" | agency=="445" | agency=="452" |agency=="458" | agency=="497" ~ "948", # other departments```{r group-codes}exp_temp <- exp_temp %>% #mutate(agency = as.numeric(agency) ) %>% # arrange(agency)%>% mutate( group = case_when( agency>"100"& agency<"200" ~ "910", # legislative agency == "528" | (agency>"200" & agency<"300") ~ "920", # judicial ###################################################### # Not used if we are not separating pension costs!! # pension > 0 ~ "901", # pensions ## New CODE: April 23rd, 2025: agency == "593" ~ "959", # TRS becomes part of K-12 costs agency == "594" ~ "959", # TRS agency == "589" ~ "948", # SERS becomes part of "Other Agencies" agency == "693" ~ "960", # SURS becomes part of group 960 agency == "275" ~ "920", # JRS becomes part of group 920 agency == "131" ~ "910", # GARS becomes part of Group 910 ###################################################### (agency>"309" & agency<"400") ~ "930", # elected officers: Governor, lt gov, attorney general, sec. of state, comptroller, treasurer agency == "586" ~ "959", # create new K-12 group agency=="402" | agency=="418" | agency=="478" | agency=="444" | agency=="482" ~ as.character(agency), # aging, CFS, HFS, human services, public health T ~ as.character(group)) ) %>% mutate(group = case_when( agency=="478" & (appr_org=="01" | appr_org == "65" | appr_org=="88") & (object=="4900" | object=="4400") ~ "945", # separates CHIP from health and human services and saves it as Medicaid agency == "586" & fund == "0355" ~ "945", # 586 (Board of Edu) has special education which is part of medicaid # OLD CODE: agency == "586" & appr_org == "18" ~ "945", # Spec. Edu Medicaid Matching agency=="425" | agency=="466" | agency=="546" | agency=="569" | agency=="578" | agency=="583" | agency=="591" | agency=="592" | agency=="493" | agency=="588" ~ "941", # public safety & Corrections agency=="420" | agency=="494" | agency=="406" | agency=="557" ~ as.character(agency), # econ devt & infra, tollway agency=="511" | agency=="554" | agency=="574" | agency=="598" ~ "946", # Capital improvement agency=="422" | agency=="532" ~ as.character(agency), # environment & nat. resources agency=="440" | agency=="446" | agency=="524" | agency=="563" ~ "944", # business regulation agency=="492" ~ "492", # revenue agency == "416" ~ "416", # central management services agency=="448" & fy > 2016 ~ "416", #add DoIT to central management T ~ as.character(group))) %>% mutate(group = case_when( # agency=="684" | agency=="691" ~ as.character(agency), # moved under higher education in next line. 11/28/2022 AWM agency=="692" | agency == "693" | agency=="695" | agency == "684" |agency == "691" | (agency>"599" & agency<"677") ~ "960", # higher education agency=="427" ~ as.character(agency), # employment security############################ # Leaving these agencies as their own agency number for now. Had been coded to "Other departments" Group 948# - GOMB (507) # - Human Rights (442) # - Illinois Power Agency (445) # - Labor (452) # - State Lottery (458) # - Veteran's Affairs (497) agency=="507" | agency=="442" | agency=="445" | agency=="452" |agency=="458" | agency=="497" ~ as.character(agency), # Were included within "other departments" # agency=="507"| agency=="442" | agency=="445" | agency=="452" |agency=="458" | agency=="497" ~ "948", # other departments###########################################3# other boards & Commissions agency=="503" | agency=="509" | agency=="510" | agency=="565" |agency=="517" | agency=="525" | agency=="526" | agency=="529" | agency=="537" | agency=="541" | agency=="542" | agency=="548" | agency=="555" | agency=="558" | agency=="559" | agency=="562" | agency=="564" | agency=="568" | agency=="579" | agency=="580" | agency=="587" | agency=="590" | agency=="527" | agency=="585" | agency=="567" | agency=="571" | agency=="575" | agency=="540" | agency=="576" | agency=="564" | agency=="534" | agency=="520" | agency=="506" | agency == "533" ~ "949", # # Other Departments # agency=="131" | # # agency=="275" | #JRS # # agency=="589" | #SERS # # agency=="593"| # TRS # # agency=="594"| # Also TRS # # agency=="693" #SURS # ~ "948", T ~ as.character(group))) %>% mutate(group_name = case_when( group == "416" ~ "Central Management", group == "442" ~ "Human Rights", group == "445" ~ "Illinois Power Agency", group == "452" ~ "Labor", group == "458" ~ "State Lottery", group == "489" ~ "SERS", group == "478" ~ "Healthcare and Family Services", group == "482" ~ "Public Health", group == "497" ~ "Veteran's Affairs", group == "507" ~ "GOMB", group == "900" ~ "NOT IN FRAME", group == "901" ~ "STATE PENSION CONTRIBUTION", group == "903" ~ "DEBT SERVICE", group == "910" ~ "LEGISLATIVE" , group == "920" ~ "JUDICIAL" , group == "930" ~ "ELECTED OFFICERS" , group == "940" ~ "OTHER HEALTH-RELATED", group == "941" ~ "PUBLIC SAFETY" , group == "942" ~ "ECON DEVT & INFRASTRUCTURE" , group == "943" ~ "CENTRAL SERVICES", group == "944" ~ "BUS & PROFESSION REGULATION" , group == "945" ~ "MEDICAID" , group == "946" ~ "CAPITAL IMPROVEMENT" , group == "948" ~ "OTHER DEPARTMENTS" , group == "949" ~ "OTHER BOARDS & COMMISSIONS" , group == "959" ~ "K-12 EDUCATION" , group == "960" ~ "UNIVERSITY EDUCATION" , group == agency ~ as.character(agency_name), TRUE ~ "Check name"), year = fy)exp_temp %>% filter(group_name == "Check name")# write_csv(exp_temp, "data/FY2024 Files/all_expenditures_recoded_pensionrecode.csv")```::: callout-importantAll expenditures recoded but not aggregated: Allows for inspection of individual expenditures within larger categories. This stage of the data is extremely useful for investigating how individual items have been coded before they are aggregated into larger categories.:::## Modify Revenue data```{r rev-recode-agencies, warning = FALSE, message=FALSE}# recodes old agency numbers to consistent agency numberrev_temp <- rev_temp %>% mutate(agency = case_when( (agency=="438"| agency=="475" |agency == "505") ~ "440", # financial institution & professional regulation & # banks and real estate --> coded as financial and professional reg agency == "473" ~ "588", # nuclear safety moved into IEMA (agency =="531" | agency =="577") ~ "532", # coded as EPA (agency =="556" | agency == "538") ~ "406", # coded as agriculture agency == "560" ~ "592", # IL finance authority (fire trucks and agriculture stuff)to state fire marshal agency == "570" & fund == "0011" ~ "494", # city of Chicago road fund to transportation TRUE ~ (as.character(agency)))) ``````{r fig-create-rev-federal-transfers}#rev_temp <- rev_temp %>% filter(in_ff==1)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)) ``````{r coding-stimuluspackage-dollars}rev_temp <- rev_temp %>% mutate(covid_dollars = ifelse(source_name_AWM == "FEDERAL STIMULUS PACKAGE",1,0))rev_temp %>% filter(source_name_AWM == "FEDERAL STIMULUS PACKAGE") %>% group_by(fy) %>% summarize(Received = sum(receipts))```### Health Insurance Premiums from EmployeesInsurance premiums for employees is coded below but it is NOT used in the fiscal futures model. Employee and employer premiums are considered rev_51 and dropped from analysis in later step.- 0120 = ins prem-option life- 0120 = ins prem-optional life/univ- 0347 = optional health - HMO- 0348 = optional health - dental- 0349 = optional health - univ/local SI- 0350 = optional health - univ/local- 0351 = optional health - retirement- 0352 = optional health - retirement SI- 0353 = optional health - retire/dental- 0354 = optional health - retirement hmo- 2199-2209 = various HMOs, dental, health plans from Health Insurance Reserve (fund)```{r insurance-premiums}#collect optional insurance premiums to fund 0907 for use in eehc expenditure rev_temp <- rev_temp %>% mutate( employee_premiums = ifelse(fund=="0907" & (source=="0120"| source=="0121"| (source>"0345" & source<"0357")|(source>"2199" & source<"2209")), 1, 0), # adds more rev_type codes rev_type = case_when( fund =="0427" ~ "12", # pub utility tax fund == "0742" | fund == "0473" ~ "24", # insurance and fees fund == "0976" ~ "36",# receipts from rev producing fund == "0392" |fund == "0723" ~ "39", # licenses and fees fund == "0656" ~ "78", #all other rev sources TRUE ~ as.character(rev_type)))# if not mentioned, then rev_type as it was# # optional insurance premiums = employee insurance premiums# emp_premium <- rev_temp %>%# group_by(fy, employee_premiums) %>%# summarize(employee_premiums_sum = sum(receipts)/1000000) %>%# filter(employee_premiums == 1) %>%# rename(year = fy) %>% # select(-employee_premiums)emp_premium_long <- rev_temp %>% filter(employee_premiums == 1)# 381 observations have employee premiums == 1# drops employee premiums from revenue# rev_temp <- rev_temp %>% filter(employee_premiums != 1)# should be dropped in next step since rev_type = 51```*Note: In FY21, employee premiums were subtracted from state healthcare costs on the expenditure side to calculate a "Net Healthcare Cost" but that methodology has been discontinued. Totals were practically unchanged: revenue from employee premiums is also very small.*### Transfers in and Out:Funds that hold and disperse local taxes or fees are dropped from the analysis. Then other excluded revenue types are also dropped.Drops Blank, Student Fees, Retirement contributions, proceeds/investments, bond issue proceeds, interagency receipts, cook IGT, Prior year refunds:```{r}rev_temp <- rev_temp %>%filter(in_ff ==1) %>%mutate(local =ifelse(is.na(local), 0, local)) %>%# drops all revenue observations that were coded as "local == 1"filter(local !=1)# 1175 doesnt exist?in_from_out <-c("0847", "0867", "1175", "1176", "1177", "1178", "1181", "1182", "1582", "1592", "1745", "1982", "2174", "2264")# what does this actually include:# all are items with rev_type = 75 originally. in_out_df <- rev_temp %>%mutate(infromout =ifelse(source %in% in_from_out, 1, 0)) %>%filter(infromout ==1)rev_temp <- rev_temp %>%mutate(rev_type_new =ifelse(source %in% in_from_out, "76", rev_type))# if source contains any of the codes in in_from_out, code them as 76 (all other rev).# I end up excluding rev_76 in later steps```> Are the 4 smallest categories from past years still the 4 smallest categories? Check it each year.```{r droprevtypes}# revenue types to dropdrop_type <- c("32", "45", "51", "66", "72", "75", "76", "79", "98", "99")# drops Blank, Student Fees, Retirement contributions, proceeds/investments,# bond issue proceeds, interagency receipts, cook IGT, Prior year refunds.rev_temp <- rev_temp %>% filter(!rev_type_new %in% drop_type)# keep observations that do not have a revenue type mentioned in drop_typetable(rev_temp$rev_type_new)rev_temp %>% group_by(fy, rev_type_new) %>% summarize(total_reciepts = sum(receipts)/1000000) %>% pivot_wider(names_from = rev_type_new, values_from = total_reciepts, names_prefix = "rev_") # combines smallest 4 categories to to "Other"# they were the 4 smallest in past years, are they still the 4 smallest? # rev_temp <- rev_temp %>% # mutate(rev_type_new = ifelse(rev_type=="30" | rev_type=="60" | rev_type=="63" ,# ## | rev_type=="76", # "78", rev_type_new))#table(rev_temp$rev_type_new) # check workrm(rev_1998_2022)rm(exp_1998_2022)write_csv(exp_temp, "data/exp_fy24_pensionrecode.csv")write_csv(rev_temp, "data/rev_fy24_pensionrecode.csv")```## Pivoting and Merging- Local Government Transfers (exp_970) should be on the expenditure side### Revenues<!--- If there are NA rev types, it will cause the code to break when binding dataframes together later --->```{r code--check, include=FALSE}rev_temp %>% filter(is.na(rev_type))``````{r}#| label: tbl-final-ffrev-table#| tbl-cap: "Pivoted Revenue Table ($ Millions) - Intermediate Step"ff_rev <- rev_temp %>%group_by(rev_type_new, fy) %>%summarize(sum_receipts =sum(receipts, na.rm=TRUE)/1000000 ) %>%pivot_wider(names_from ="rev_type_new", values_from ="sum_receipts", names_prefix ="rev_")# ff_rev<- left_join(ff_rev, tax_refund)#ff_rev <- left_join(ff_rev, pension2_fy22, by=c("fy" = "year"))#ff_rev <- left_join(ff_rev, eehc2_amt) ff_rev <-mutate_all(ff_rev, ~replace_na(.,0))# # ff_rev <- ff_rev %>%# mutate(rev_02 = rev_02 - ref_02,# rev_03 = rev_03 - ref_03,# rev_06 = rev_06 - ref_06,# rev_09 = rev_09 - ref_09,# rev_21 = rev_21 - ref_21,# rev_24 = rev_24 - ref_24,# rev_35 = rev_35 - ref_35# # # rev_78new = rev_78 #+ pension_amt #+ eehc# ) %>% # select(-c(ref_02:ref_35, rev_99, rev_NA, rev_76# #, ref_CHECK#, pension_amt , rev_76,# # , eehc# ))# # ff_rev#noproblem <- c(0) # if ref_CHECK = $0, then there is no problem. :) # # if((sum(ff_rev$ref_CHECK) == 0 )){# # ff_rev <- ff_rev %>%# # mutate(rev_02 = rev_02 - ref_02,# rev_03 = rev_03 - ref_03,# rev_06 = rev_06 - ref_06,# rev_09 = rev_09 - ref_09,# rev_21 = rev_21 - ref_21,# rev_24 = rev_24 - ref_24,# rev_35 = rev_35 - ref_35# ) %>% # select(-c(ref_02:ref_35, rev_99, rev_76, ref_CHECK )) # }else{"You have a problem! Check what revenue items did not have rev codes (causing it to be coded as rev_NA) or the check if there were refunds that were not assigned revenue codes (tax_refunds_long objects)"}ff_rev %>%mutate_all(., ~round(.,digits=0))```Since I already pivot_wider()ed the table in the previous code chunk, I now change each column's name by using rename() to set new variable names. Ideally the final dataframe would have both the variable name and the variable label but I have not done that yet.```{r}#| label: tbl-labeled-final-ffrev-table#| tbl-cap: Aggregated Revenue Categories ($ Millions), with old labelsaggregate_rev_labels <- ff_rev %>%rename("INDIVIDUAL INCOME TAXES, gross of local, net of refunds"= rev_02,"CORPORATE INCOME TAXES, gross of PPRT, net of refunds"= rev_03,"SALES TAXES, gross of local share"= rev_06 ,"MOTOR FUEL TAX, gross of local share, net of refunds"= rev_09 ,"PUBLIC UTILITY TAXES, gross of PPRT"= rev_12,"CIGARETTE TAXES"= rev_15 ,"LIQUOR GALLONAGE TAXES"= rev_18,"INHERITANCE TAX"= rev_21,"INSURANCE TAXES&FEES&LICENSES, net of refunds"= rev_24 ,"CORP FRANCHISE TAXES & FEES"= rev_27,"HORSE RACING TAXES & FEES"= rev_30, # in Other"MEDICAL PROVIDER ASSESSMENTS"= rev_31 ,# "GARNISHMENT-LEVIES " = rev_32 , # dropped"LOTTERY RECEIPTS"= rev_33 ,"OTHER TAXES"= rev_35,"RECEIPTS FROM REVENUE PRODUCNG"= rev_36, "LICENSES, FEES & REGISTRATIONS"= rev_39 ,"MOTOR VEHICLE AND OPERATORS"= rev_42 ,# "STUDENT FEES-UNIVERSITIES" = rev_45, # dropped"RIVERBOAT WAGERING TAXES"= rev_48 ,# "RETIREMENT CONTRIBUTIONS " = rev_51, # dropped"GIFTS AND BEQUESTS"= rev_54, "FEDERAL OTHER"= rev_57 ,"FEDERAL MEDICAID"= rev_58, "FEDERAL TRANSPORTATION"= rev_59 ,"OTHER GRANTS AND CONTRACTS"= rev_60, #other"INVESTMENT INCOME"= rev_63, # other# "PROCEEDS,INVESTMENT MATURITIES" = rev_66 , #dropped# "BOND ISSUE PROCEEDS" = rev_72, #dropped# "INTER-AGENCY RECEIPTS" = rev_75, #dropped# "TRANSFER IN FROM OUT FUNDS" = rev_76, # dropped"ALL OTHER SOURCES"= rev_78,# "COOK COUNTY IGT" = rev_79, #dropped# "PRIOR YEAR REFUNDS" = rev_98 #dropped ) aggregate_rev_labels %>%mutate_all(., ~round(., digits =0))```### ExpendituresCreate exp_970 for all local government transfers (exp_971 + exp_972 + exp_975 + exp_976).```{r}#| label: tbl-ffexp-notlabeled#| tbl-cap: "Pivoted Expenditure Categories ($ Millions)"ff_exp <- exp_temp %>%group_by(fy, group) %>%summarize(sum_expenditures =sum(expenditure, na.rm=TRUE)/1000000 ) %>%pivot_wider(names_from ="group", values_from ="sum_expenditures", names_prefix ="exp_")%>%left_join(debt_keep_yearly) %>%rename(exp_903 = debt_cost) %>%# left_join(healthcare_costs_yearly) %>%# join state employee healthcare and subtract employee premiums# left_join(emp_premium, by = c("fy" = "year")) %>%# mutate(exp_904_new = (`healthcare_cost` - `employee_premiums_sum`)) %>% # state employee healthcare premiums# left_join(retirement_contributions) %>%# mutate(exp_901_new = exp_901 - contributions/1000000) %>% #employee pension contributions# join local transfers and create exp_970left_join(transfers) %>%mutate(exp_970 = exp_971 + exp_972 + exp_975 + exp_976) ff_exp<- ff_exp %>%select(-c(exp_971:exp_976)) # drop unwanted columnsff_exp # not labeled``````{r}#| label: tbl-final-table-with-labels#| tbl-cap: "Final Expenditure Categories, with Fiscal Futures Grouped Expenditure Categories"# exp_temp %>%# group_by(year, group) %>%# summarize(sum_expenditure = round(sum(expenditure)/1000000)) %>%# arrange(year) %>%# pivot_wider(names_from = "group", values_from = "sum_expenditure")# aggregate_exp_labeled <- exp_temp %>%group_by(year, group_name) %>%summarize(sum_expenditure =sum(expenditure)/1000000) %>%arrange(year) %>%pivot_wider(names_from ="group_name", values_from ="sum_expenditure")aggregate_exp_labeled %>%mutate_all(., ~round(., digits =0))```# Graphs and Tables {#sec-graphs-and-tables}Create total revenues and total expenditures only:- after aggregating expenditures and revenues, pivoting wider, then I want to drop the columns that I no longer want and then pivot_longer(). After pivoting_longer() and creating `rev_long` and `exp_long`, expenditures and revenues are in the same format and can be combined together for the totals and gap each year.```{r}#| label: tbl-rev-and-exp-totals-long#| tbl-cap: Long Version of Data that has Revenue and Expenditures in One Dataframe#| tbl-cap-location: toprev_long <-pivot_longer(ff_rev, rev_02:rev_78, names_to =c("type","Category"), values_to ="Dollars", names_sep ="_") %>%rename(Year = fy) %>%mutate(Category_name =case_when( Category =="02"~"INDIVIDUAL INCOME TAXES" , Category =="03"~"CORPORATE INCOME TAXES" , Category =="06"~"SALES TAXES" , Category =="09"~"MOTOR FUEL TAX" , Category =="12"~"PUBLIC UTILITY TAXES" , Category =="15"~"CIGARETTE TAXES" , Category =="18"~"LIQUOR GALLONAGE TAXES" , Category =="21"~"INHERITANCE TAX" , Category =="24"~"INSURANCE TAXES&FEES&LICENSES" , Category =="27"~"CORP FRANCHISE TAXES & FEES" , Category =="30"~"HORSE RACING TAXES & FEES", # in Other Category =="31"~"MEDICAL PROVIDER ASSESSMENTS" , Category =="32"~"GARNISHMENT-LEVIES" , # dropped Category =="33"~"LOTTERY RECEIPTS" , Category =="35"~"OTHER TAXES" , Category =="36"~"RECEIPTS FROM REVENUE PRODUCING", Category =="39"~"LICENSES, FEES & REGISTRATIONS" , Category =="42"~"MOTOR VEHICLE AND OPERATORS" , Category =="45"~"STUDENT FEES-UNIVERSITIES", # dropped Category =="48"~"RIVERBOAT WAGERING TAXES" , Category =="51"~"RETIREMENT CONTRIBUTIONS" , # dropped Category =="54"~"GIFTS AND BEQUESTS", Category =="57"~"FEDERAL OTHER" , Category =="58"~"FEDERAL MEDICAID", Category =="59"~"FEDERAL TRANSPORTATION" , Category =="60"~"OTHER GRANTS AND CONTRACTS", #other Category =="63"~"INVESTMENT INCOME", # other Category =="66"~"PROCEEDS,INVESTMENT MATURITIES" , #dropped Category =="72"~"BOND ISSUE PROCEEDS", #dropped Category =="75"~"INTER-AGENCY RECEIPTS ", #dropped Category =="76"~"TRANSFER IN FROM OUT FUNDS", #other Category =="78"~"ALL OTHER SOURCES" , Category =="79"~"COOK COUNTY IGT", #dropped Category =="98"~"PRIOR YEAR REFUNDS", #dropped T ~"Check Me!" ) )%>%mutate(Category_name =str_to_title(Category_name))exp_long <-pivot_longer(ff_exp, exp_402:exp_970 , names_to =c("type", "Category"), values_to ="Dollars", names_sep ="_") %>%rename(Year = fy ) %>%mutate(Category_name =case_when( Category =="131"~"GARS", Category =="275"~"JRS", Category =="402"~"AGING" , Category =="406"~"AGRICULTURE", Category =="416"~"CENTRAL MANAGEMENT", Category =="418"~"CHILDREN AND FAMILY SERVICES", Category =="420"~"COMMERCE AND ECONOMIC OPPORTUNITY", Category =="422"~"NATURAL RESOURCES" , Category =="426"~"CORRECTIONS", Category =="427"~"EMPLOYMENT SECURITY" , Category =="442"~"Human Rights" , Category =="444"~"Human Services" , Category =="445"~"IL Power Agency" , Category =="448"~"Innovation and Technology", # AWM added fy2022 Category =="452"~"Labor" , Category =="458"~"State Lottery" , Category =="478"~"FAMILY SERVICES net Medicaid", Category =="482"~"PUBLIC HEALTH", Category =="492"~"REVENUE", Category =="493"~"Teacher Retirmeent System (TRS)", Category =="494"~"TRANSPORTATION" , Category =="489"~"SERS", Category =="507"~"GOMB", Category =="497"~"VETERNS' AFFAIRS" , Category =="532"~"ENVIRONMENTAL PROTECT AGENCY" , Category =="557"~"IL STATE TOLL HIGHWAY AUTH" , Category =="589"~"State Employment Retirement System (SERS)", Category =="684"~"IL COMMUNITY COLLEGE BOARD", Category =="691"~"IL STUDENT ASSISTANCE COMM" , Category =="693"~"SURS", Category =="900"~"NOT IN FRAME", Category =="901"~"STATE PENSION CONTRIBUTION", Category =="903"~"DEBT SERVICE", Category =="904"~"State Employee Healthcare", Category =="910"~"LEGISLATIVE" , Category =="920"~"JUDICIAL" , Category =="930"~"ELECTED OFFICERS" , Category =="940"~"OTHER HEALTH-RELATED", Category =="941"~"PUBLIC SAFETY" , Category =="942"~"ECON DEVT & INFRASTRUCTURE" , Category =="943"~"CENTRAL SERVICES", Category =="944"~"BUS & PROFESSION REGULATION" , Category =="945"~"MEDICAID" , Category =="946"~"CAPITAL IMPROVEMENT" , Category =="948"~"OTHER DEPARTMENTS" , Category =="949"~"OTHER BOARDS & COMMISSIONS" , Category =="959"~"K-12 EDUCATION" , Category =="960"~"UNIVERSITY EDUCATION", Category =="970"~"Local Govt Transfers", T ~"CHECK ME!") ) %>%mutate(Category_name =str_to_title(Category_name))#write_csv(exp_long, paste0("data/FY", current_year, "_Files/expenditures_recoded_long_FY_pensionchange", current_year, ".csv"))#write_csv(rev_long, paste0("data/FY", current_year,, "Files/revenue_recoded_long_FY_pensionchange", current_year, ".csv"))# combine revenue and expenditures into one data frameaggregated_totals_long <-rbind(rev_long, exp_long)aggregated_totals_long %>%mutate(`Dollars (Millions)`=round(Dollars, digits =0)) %>%select(-Dollars) %>%select(Year, Category_name, `Dollars (Millions)`, type, Category)#write_csv(aggregated_totals_long, paste0("data/FY", current_year, "aggregated_totals", current_year, ".csv"))``````{r}#| label: tbl-year-totals-table-withgap#| tbl-cap-location: topyear_totals <- aggregated_totals_long %>%group_by(type, Year) %>%summarize(Dollars =sum(Dollars, na.rm =TRUE)) %>%pivot_wider(names_from ="type", values_from = Dollars) %>%rename(Expenditures = exp,Revenue = rev) %>%mutate(`Fiscal Gap`= Revenue - Expenditures)# %>% arrange(desc(Year))# creates variable for the Gap each yearyear_totals %>%mutate_all(., ~round(., digits =0)) %>%kbl(caption ="Fiscal Gap for each Fiscal Year ($ Millions)") %>%kable_styling(bootstrap_options =c("striped")) %>%kable_classic() %>%add_footnote(c("Values include State CURE dollars (SLFRF)") )```Graphs made from `aggregated_totals_long` dataframe.### Fiscal Gap Graph```{r}#| label: fig-fiscal-gap#| fig-cap: "Fiscal Gap Comparison"#| fig-subcap: #| - "Fiscal Gap With Trend Lines"#| - "Fiscal Gap Without Trend Lines"#| fig-cap-location: top## Adjust x and y coordinates to move placement of textannotation <-data.frame(x =c(2004, 2017, 2019),y =c(60, 50, 5), label =c("Expenditures","Revenue", "Fiscal Gap"))annotation_nums <-data.frame(x =c(2022, 2022, 2023),y =c(91, 120, -5), label =c( year_totals$Expenditures[year_totals$Year==current_year]/1000, year_totals$Revenue[year_totals$Year==current_year]/1000, year_totals$`Fiscal Gap`[year_totals$Year==current_year]/1000))## Dashed line versions for expenditures: fiscal_gap <-ggplot(data = year_totals, aes(x=Year, y = Revenue/1000)) +geom_recessions(text =FALSE, update = recessions)+# geom_smooth adds regression line, graphed first so it appears behind line graphgeom_smooth(aes(x = Year, y = Revenue/1000), color ="gray", alpha =0.7, method ="lm", se =FALSE) +# scale_linetype_manual(values="dashed")+geom_smooth(aes(x = Year, y = Expenditures/1000), color ="rosybrown2", linetype ="dotted", method ="lm", se =FALSE, alpha =0.7) +# line graph of revenue and expendituresgeom_line(aes(x = Year, y = Revenue/1000), color ="Black", size=1) +geom_line(aes(x = Year, y = Expenditures/1000, linetype ="dashed"), color ="red", lwd=1) +geom_line(aes(x = Year, y = (`Fiscal Gap`/1000)), color ="darkgray", lwd =1) +geom_hline(yintercept =0) +geom_text(data = annotation, aes(x=x, y=y, label=label,parse =TRUE))+# labelstheme_classic() +theme(legend.position ="bottom", legend.title =element_blank())+scale_linetype_manual(values =c("dashed", "dashed")) +scale_x_continuous(expand =c(0,0)) +# scale_y_continuous(labels = comma)+xlab("Year") +ylab("Billions of Dollars") +ggtitle(paste0("Illinois Expenditures and Revenue Totals, 1998-",current_year))fiscal_gap# annotation_billions <- data.frame(# x = c(2004, 2017, 2019),# y = c(60, 50, 5), # label = c("Expenditures","Revenue", "Fiscal Gap"))fiscal_gap2 <-ggplot(data = year_totals, aes(x=Year, y = Revenue/1000)) +geom_recessions(text =FALSE, update_recessions = recessions)+geom_line(aes(x = Year, y = Revenue/1000, color ="Revenue"), lwd =1, label ="Revenue") +geom_line(aes(x = Year, y = Expenditures/1000, color ="Expenditures"), linetype ="dotted", lwd =1, label ="Expenditures") +geom_line(aes(x = Year, y = (`Fiscal Gap`/1000)), color ="darkgray", lwd=1) +geom_text(data = annotation, aes(x=x, y=y, label=label)) +## Word locations and textgeom_text(data = annotation_nums, aes(x = x, y = y, label = scales::dollar(label, accuracy =0.01L)), size =3) +## Number locations and texttheme_classic() +theme(legend.position ="bottom", legend.title =element_blank()) +scale_color_manual(values =c("Revenue"="black", "Expenditures"="red")) +geom_hline(yintercept =0) +scale_y_continuous(#labels = comma, limits =c(-12, 120), breaks =c(-10, 20, 40, 60, 80, 100, 120), minor_breaks =c(-10, 0, 10, 30, 50, 70, 90, 110))+scale_x_continuous(expand =c(0,0), limits =c(1998, current_year) ) +# scale_color_manual(values = c("red" = "Expenditures", "black" = "Revenue")) + xlab("Year") +ylab("Billions of Dollars") +ggtitle(paste0("Illinois Expenditures and Revenue Totals, 1998-",current_year))fiscal_gap2``````{r FIGURE1-EXPORT}#| include: falsefiscal_gap2 <- ggplot(data = year_totals, aes(x=Year, y = Revenue/1000)) + geom_recessions(text = FALSE, update = recessions)+ geom_line(aes(x = Year, y = Revenue/1000, color = "Revenue"), lty = "solid" , lwd = 1) + geom_line(aes(x = Year, y = Expenditures/1000, color = "Expenditures"), lty = "dashed" , lwd = .7 ) + geom_line(aes(x = Year, y = (`Fiscal Gap`/1000), color = "Fiscal Gap"), lwd=1) + geom_text(data = annotation, aes(x=x, y=y, label=label))+ geom_text(data = annotation_nums, aes(x = x+1, y = y, label = scales::dollar(label, accuracy=0.01L)), size = 3) + theme_classic() +theme( legend.position = "bottom", legend.title = element_blank(), panel.background = element_rect(fill='transparent'), #transparent panel bg plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg panel.grid.major = element_blank(), #remove major gridlines panel.grid.minor = element_blank(), #remove minor gridlines legend.background = element_rect(fill='transparent'), #transparent legend bg)+#scale_linetype_manual(values = c( "dashed", "solid", "solid" )) +#scale_linetype_manual(labels =c("Expenditures", "Revenue")) + geom_hline(yintercept = 0) + scale_color_manual(values = c( "Expenditures" = "red", "Revenue" = "black", "Fiscal Gap" = "darkgray"), guide = guide_legend(override.aes = list(linetype = c( "dashed", "solid", "solid"))) ) + scale_y_continuous(limits = c(-12, 120), breaks = c(-10, 20, 40, 60, 80, 100, 120))+ scale_x_continuous(expand = c(0,0), limits = c(1998, current_year), breaks = c(1998, 2000, 2005, 2010, 2015, 2020, current_year) ) + xlab("Year") + ylab("Billions of Dollars") fiscal_gap2# ggsave(plot = fiscal_gap2, filename= "./paper-figures/Figure1-fiscalgap.eps", width = 6, height = 4)# ggsave(plot = fiscal_gap2, filename= "./paper-figures/Figure1-fiscalgap.png", width = 6, height = 4)```**Expenditure and revenue amounts in billions of dollars:**```{r}#| label: fig-bar-graphs-fy23#| fig-cap: FY23 Totals#| fig-subcap: #| - "FY24 Expenditures"#| - "FY24 Revenue Sources"exp_long %>%filter(Year == current_year) %>%arrange(desc(`Dollars`)) %>%ggplot() +geom_col(aes(x =fct_reorder(Category_name, `Dollars`), y = (`Dollars`/1000), fill ="red"))+coord_flip() +theme_classic()+theme(legend.position ="none") +labs(title =paste0("Expenditures for ", current_year))+xlab("Expenditure Categories") +ylab("Billions of Dollars") rev_long %>%filter(Year == current_year) %>%arrange(desc(`Dollars`)) %>%ggplot() +geom_col(aes(x =fct_reorder(Category_name, `Dollars`), y = (`Dollars`/1000)))+coord_flip() +theme_classic() +theme(legend.position ="none") +labs(title =paste0("Revenue for ", current_year))+xlab("Revenue Categories") +ylab("Billions of Dollars") ```**Expenditure and revenues when focusing on largest categories and combining others into "All Other Expenditures(Revenues)":**```{r}#| label: fig-bargraphs-largest-expandrev-cats#| fig-cap: #| fig-subcap: #| - "Largest Expenditures for FY2024"#| - "Largest Revenue Sources for FY2024"exp_long %>%filter( Year == current_year) %>%mutate(rank =rank(Dollars),Category_name =ifelse(rank >13, Category_name, 'All Other Expenditures')) %>%# select(-c(Year, Dollars, rank)) %>%arrange(desc(Dollars)) %>%ggplot() +geom_col(aes(x =fct_reorder(Category_name, `Dollars`), y =`Dollars`/1000), fill ="rosybrown2") +coord_flip() +theme_classic() +labs(title =paste0("Expenditures for ", current_year))+xlab("") +ylab("Billions of Dollars")rev_long %>%filter( Year == current_year) %>%mutate(rank =rank(Dollars),Category_name =ifelse(rank >10, Category_name, 'All Other Sources')) %>%arrange(desc(Dollars)) %>%ggplot() +geom_col(aes(x =fct_reorder(Category_name, `Dollars`/1000), y =`Dollars`/1000), fill ="dark gray")+coord_flip() +theme_classic() +labs(title =paste0("Revenue for ", current_year)) +xlab("") +ylab("Billions of Dollars")```### Top 3 Revenues {#sec-top-3-revenues}```{r}#| label: fig-top3-revenues#| fig-cap: Top 3 Revenue Sources (Own-Source Revenues only)#| fig-cap-location: topannotation <-data.frame(x =c(2013, 2018, 2013),y =c(16, 10, 5), label =c("Individual Income Tax", "Sales Tax", "Corporate Income Tax"))top3 <- rev_long %>%filter(Category =="02"| Category =="03"| Category =="06")top3 <-ggplot(data = top3, aes(x=Year, y=Dollars/1000))+geom_recessions(text =FALSE, update = recessions)+geom_line(aes(x=Year, y=Dollars/1000, color = Category_name)) +geom_text(data = annotation, aes(x=x, y=y, label=label)) +theme_classic() +scale_x_continuous(expand =c(0,0)) +scale_y_continuous(labels = comma) +scale_linetype_manual(values =c("dotted", "dashed", "solid")) +theme(legend.position ="none") +labs(title ="Top 3 Own Source Revenues", subtitle ="Individual Income Taxes, Sales Tax, and Corporate income taxes",y ="Billions of Nominal Dollars") top3```### Own Source and Fed Transfers {#sec-own-source-and-fed-transfers}```{r}#| label: tbl-ownsource-and-fed-revenue#| tbl-cap: "Own Source and Federal Revenue"#| tbl-location: marginownsource_rev <- rev_long %>%filter(!Category %in%c("57", "58", "59")) %>%group_by(Year) %>%summarize(Dollars =sum(Dollars))ownsource_rev# ownsource_rev %>% # ggplot()+geom_line(aes(x=Year, y=Dollars)) + # labs(title = "Own Source Revenues", subtitle = "Total own source revenue", y = "Millions of Dollars")``````{r}fed_rev <- ff_rev %>%select(fy, rev_57, rev_58, rev_59) %>%mutate(fed_total = rev_57+rev_58+rev_59)annotation <-data.frame(x =c(2013, 2015),y =c(50, 25), label =c("Own Source Revenue", "Federal Revenue"))annotation_nums <-data.frame(x =c(2023, 2023),y =c(50, 25), label =c("Own Source Revenue", "Federal Revenue"))ownsource_graph <-ggplot(ownsource_rev, aes(x=Year, y=Dollars/1000)) +geom_recessions(text =FALSE, update_recessions = recessions)+geom_line(data = ownsource_rev, aes(x=Year, y=Dollars/1000), color ="Red") +geom_line(data = fed_rev, aes(x=fy, y=fed_total/1000), color ="Black") +geom_text(data = annotation, aes(x=x, y=y, label=label))+scale_y_continuous(labels = comma)+scale_x_continuous(expand =c(0,0)) +theme(legend.position ="none") +theme_classic()+labs(title ="Own Source Revenue and Federal Revenue, 1998-2023", y ="Billions of Dollars")ownsource_graph# ggsave(plot = ownsource_graph, file = "Figure4.eps")``````{r FIGURE2-EXPORT, include = FALSE}# Same as above but doesn't have title. # Made for fy23 report and design team.annotation_nums <- data.frame( x = c(2022, 2022.5), y = c(80, 30), label = c("$?", "$?"))ownsource_graph <- ggplot(ownsource_rev, aes(x=Year, y=Dollars/1000)) + geom_recessions(text = FALSE, update = recessions)+ geom_line(data = ownsource_rev, aes(x=Year, y=Dollars/1000), color = "Red") + geom_line(data = fed_rev, aes(x=fy, y=fed_total/1000), color = "Black") + geom_text(data = annotation, aes(x=x, y=y, label=label))+ geom_text(data = annotation_nums, aes(x=x, y=y, label=label))+ scale_y_continuous(labels = comma)+ scale_x_continuous(expand = c(0,0), limits = c(1998, 2024)) + theme_classic()+ theme(legend.position = "none", panel.background = element_rect(fill='transparent'), #transparent panel bg plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg panel.grid.major = element_blank(), #remove major gridlines panel.grid.minor = element_blank(), #remove minor gridlines legend.background = element_rect(fill='transparent'), #transparent legend bg )+ labs( #title = "Own Source Revenue and Federal Revenue, 1998-2023", y = "Billions of Dollars")ownsource_graph# ggsave(plot = ownsource_graph, file = "./paper-figures/Figure2.eps", limitsize=FALSE)# ggsave(plot = ownsource_graph, file = "./paper-figures/Figure2.png", limitsize = FALSE)```# Change from Previous YearEach year, you will need to update the CAGR formulas! Change the filter() year.`calc_cagr` is a function created for calculating the CAGRs for different spans of time.```{r}exp_totals <- ff_exp %>%rowwise() %>%mutate(exp_TOTALS =sum(across(exp_402:exp_970), na.rm=TRUE))rev_totals <- ff_rev %>%rowwise() %>%mutate(rev_TOTALS =sum(across(rev_02:rev_78), na.rm=TRUE))rev_long <-pivot_longer(rev_totals, rev_02:rev_TOTALS, names_to =c("type","Category"), values_to ="Dollars", names_sep ="_") %>%rename(Year = fy) %>%mutate(Category_name =case_when( Category =="02"~"INDIVIDUAL INCOME TAXES" , Category =="03"~"CORPORATE INCOME TAXES" , Category =="06"~"SALES TAXES" , Category =="09"~"MOTOR FUEL TAX" , Category =="12"~"PUBLIC UTILITY TAXES" , Category =="15"~"CIGARETTE TAXES" , Category =="18"~"LIQUOR GALLONAGE TAXES" , Category =="21"~"INHERITANCE TAX" , Category =="24"~"INSURANCE TAXES&FEES&LICENSES" , Category =="27"~"CORP FRANCHISE TAXES & FEES" , Category =="30"~"HORSE RACING TAXES & FEES", # in Other Category =="31"~"MEDICAL PROVIDER ASSESSMENTS" , Category =="32"~"GARNISHMENT-LEVIES" , # dropped Category =="33"~"LOTTERY RECEIPTS" , Category =="35"~"OTHER TAXES" , Category =="36"~"RECEIPTS FROM REVENUE PRODUCING", Category =="39"~"LICENSES, FEES & REGISTRATIONS" , Category =="42"~"MOTOR VEHICLE AND OPERATORS" , Category =="45"~"STUDENT FEES-UNIVERSITIES", # dropped Category =="48"~"RIVERBOAT WAGERING TAXES" , Category =="51"~"RETIREMENT CONTRIBUTIONS" , # dropped Category =="54"~"GIFTS AND BEQUESTS", Category =="57"~"FEDERAL OTHER" , Category =="58"~"FEDERAL MEDICAID", Category =="59"~"FEDERAL TRANSPORTATION" , Category =="60"~"OTHER GRANTS AND CONTRACTS", #other Category =="63"~"INVESTMENT INCOME", # other Category =="66"~"PROCEEDS,INVESTMENT MATURITIES" , #dropped Category =="72"~"BOND ISSUE PROCEEDS", #dropped Category =="75"~"INTER-AGENCY RECEIPTS ", #dropped Category =="76"~"TRANSFER IN FROM OUT FUNDS", #other Category =="78"~"ALL OTHER SOURCES" , Category =="79"~"COOK COUNTY IGT", #dropped Category =="98"~"PRIOR YEAR REFUNDS", #dropped Category =="TOTALS"~"Total", T ~"CHECK ME" ) ) %>%select(-type, -Category) %>%# drop extra columns type and Category numbergroup_by(Year, Category_name) %>%summarise(Dollars=round(sum(Dollars),digits=2)) %>%mutate(Category_name =str_to_title(Category_name))# creates wide version of table where each revenue source is a columnrevenue_wide2 <- rev_long %>%pivot_wider(names_from = Category_name, values_from = Dollars) %>%# relocate("Other Revenue Sources **", .after = last_col()) %>%relocate("Total", .after =last_col())exp_long <-pivot_longer(exp_totals, exp_402:exp_TOTALS , names_to =c("type", "Category"), values_to ="Dollars", names_sep ="_") %>%rename(Year = fy ) %>%mutate(Category_name =case_when( Category =="402"~"AGING" , Category =="406"~"AGRICULTURE", Category =="416"~"Central Management", Category =="418"~"CHILDREN AND FAMILY SERVICES", Category =="420"~"Community Development", Category =="422"~"NATURAL RESOURCES" , Category =="426"~"CORRECTIONS", Category =="427"~"EMPLOYMENT SECURITY" , Category =="442"~"Human Rights" , Category =="444"~"Human Services" , Category =="445"~"IL Power Agency" , Category =="452"~"Labor" , Category =="458"~"State Lottery" , Category =="478"~"HEALTHCARE & FAM SER NET OF MEDICAID", Category =="482"~"PUBLIC HEALTH", Category =="492"~"REVENUE", Category =="494"~"Transportation" , Category =="497"~"Veterans' Affairs" , Category =="507"~"GOMB", Category =="532"~"ENVIRONMENTAL PROTECT AGENCY" , Category =="557"~"Tollway" , Category =="900"~"NOT IN FRAME", Category =="901"~"State Pension Contribution", Category =="903"~"Debt Service", Category =="904"~"State Employee Healthcare", Category =="910"~"LEGISLATIVE" , Category =="920"~"JUDICIAL" , Category =="930"~"ELECTED OFFICERS" , Category =="941"~"Public Safety" , Category =="942"~"ECON DEVT & INFRASTRUCTURE" , Category =="943"~"CENTRAL SERVICES", Category =="944"~"BUS & PROFESSION REGULATION" , Category =="945"~"Medicaid" , Category =="946"~"Capital Improvement" , Category =="948"~"OTHER DEPARTMENTS" , Category =="949"~"OTHER BOARDS & COMMISSIONS" , Category =="959"~"K-12 Education" , Category =="960"~"UNIVERSITY EDUCATION", Category =="970"~"Local Govt Revenue Sharing", Category =="TOTALS"~"Total", T ~"CHECK ME"# T ~ "All Other Expenditures **") )) %>%select(-type, -Category) %>%group_by(Year, Category_name) %>%summarise(Dollars=round(sum(Dollars),digits=2)) %>%mutate(Category_name =str_to_title(Category_name))expenditure_wide2 <- exp_long%>%pivot_wider(names_from = Category_name, values_from = Dollars) %>%relocate("Total", .after =last_col())# function for calculating the CAGRcalc_cagr <-function(df, n) { df <- exp_long %>%#select(-type) %>%arrange(Category_name, Year) %>%group_by(Category_name) %>%mutate(cagr = ((`Dollars`/lag(`Dollars`, n)) ^ (1/ n)) -1,cagr =ifelse(is.na(cagr), 0, cagr))return(df)}```Things to do when updating the code:- Each year, you need to increase the cagr value by 1. The value should be the (current year - 1998). For FY23, this is 2023-1998 = 25. So all cagr values that were 24 will be changed to 25.```{r }#| label: tbl-good-exp-CAGR-withTotals#| tbl-cap: Expenditure Category CAGRs with Total CAGR (Ordered Alphabetically)#| tbl-cap-location: top# This works for one variable at a timecagr_max <- calc_cagr(exp_long, 26) %>% # group_by(Category) %>% summarize(cagr_max = round(sum(cagr*100, na.rm = TRUE), 2))# doesn't need to be changed since it is just pre-covid cagr_precovid <- exp_long %>% filter(Year <= 2019) %>% calc_cagr(21) %>% summarize(cagr_21 = round(sum(cagr*100, na.rm = TRUE), 2))# Update year in the filter() and summarize() commands to current year.cagr_10 <- calc_cagr(exp_long, 10) %>% filter(Year == current_year) %>% summarize(cagr_10 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_5 <- calc_cagr(exp_long, 5) %>% filter(Year == current_year) %>% summarize(cagr_5 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_3 <- calc_cagr(exp_long, 3) %>% filter(Year == current_year) %>% summarize(cagr_3 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_2 <- calc_cagr(exp_long, 2) %>% filter(Year == current_year) %>% summarize(cagr_2 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_1 <- calc_cagr(exp_long, 1) %>% filter(Year == current_year) %>% summarize(cagr_1 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))CAGR_expenditures_summary_tot <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_max ) %>% select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% rename("Expenditure Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10, "26 Year CAGR" = cagr_max )totalrow <- which(grepl("Total", CAGR_expenditures_summary_tot$`Expenditure Category`))CAGR_expenditures_summary_tot <- move_to_last(CAGR_expenditures_summary_tot, totalrow) lastrow = nrow(CAGR_expenditures_summary_tot)CAGR_expenditures_summary_tot %>% kbl(caption = "CAGR Calculations for All Expenditure Categories" , row.names=FALSE) %>% kable_classic() %>% row_spec(lastrow, bold = T, color = "black", background = "gray")``````{r }#| label: tbl-RevCAGRs-allcats#| tbl-cap: Revenue Category CAGRs with Total CAGR (Ordered Alphabetically)#| tbl-cap-location: top# revenue version function:calc_cagr <- function(df, n) { df <- rev_long %>% arrange(Category_name, Year) %>% group_by(Category_name) %>% mutate(cagr = ((Dollars / lag(Dollars, n)) ^ (1 / n)) - 1, cagr = ifelse(is.na(cagr), 0, cagr)) return(df)}# This works for one variable at a timecagr_max <- calc_cagr(rev_long, 26) %>% # group_by(Category) %>% summarize(cagr_max = round(sum(cagr*100, na.rm = TRUE), 2))cagr_10 <- calc_cagr(rev_long, 10) %>% filter(Year == current_year) %>% summarize(cagr_10 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_5 <- calc_cagr(rev_long, 5) %>% filter(Year == current_year) %>% summarize(cagr_5 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_3 <- calc_cagr(rev_long, 3) %>% filter(Year == current_year) %>% summarize(cagr_3 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_2 <- calc_cagr(rev_long, 2) %>% filter(Year == current_year) %>% summarize(cagr_2 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2))) cagr_1 <- calc_cagr(rev_long, 1) %>% filter(Year == current_year) %>% summarize(cagr_1 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))CAGR_revenue_summary_tot <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_max) %>% select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% rename("Revenue Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10, "26 Year CAGR" = cagr_max )CAGR_revenue_summary_tot <- move_to_last(CAGR_revenue_summary_tot, 1)totalrow <- which(grepl("Total", CAGR_revenue_summary_tot$`Revenue Category`))CAGR_revenue_summary_tot <- move_to_last(CAGR_revenue_summary_tot, totalrow)lastrow = nrow(CAGR_revenue_summary_tot)CAGR_revenue_summary_tot %>% kbl(caption = "CAGR Calculations for All Revenue Sources (Ordered Alphabetical)", row.names = FALSE) %>% kable_classic() %>% row_spec(lastrow, bold = T, color = "black", background = "gray")``````{r include = FALSE}rm(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_max)```Update all years in mutate() commands so that they all go up by 1:```{r}revenue_change2 <- rev_long %>%filter(Year >= past_year) %>%pivot_wider(names_from = Year , values_from = Dollars, names_prefix ="Dollars_") %>%mutate("FY Current FY ($ billions)"=round(Dollars_2024/1000, digits =2),"FY Past FY ($ billions)"=round(Dollars_2023/1000, digits =2),"1-Year Change"=round(((Dollars_2024 -Dollars_2023)/Dollars_2023*100), digits =2)) %>%left_join(CAGR_revenue_summary_tot, by =c("Category_name"="Revenue Category")) %>%arrange(-`FY Current FY ($ billions)`)%>%# filter(Category_name != "NA") %>%#select(-c(Dollars_2021, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%rename( "26-Year CAGR"=`26 Year CAGR`, "Revenue Category"= Category_name ) %>%select(-c(Dollars_2023, Dollars_2024, `1 Year CAGR`:`10 Year CAGR`)) allother_row <-which(grepl("All Other", revenue_change2$`Revenue Category`))revenue_change2 <-move_to_last(revenue_change2, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", revenue_change2$`Revenue Category`))revenue_change2 <-move_to_last(revenue_change2, totalrow) # Move "Total" to last rowlastrow =nrow(revenue_change2)revenue_change2 %>%filter(!is.na(`Revenue Category`)) %>%kbl(caption ="Table 1. Yearly Change in Revenue", row.names =FALSE) %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")expenditure_change2 <- exp_long %>%filter(Year >= past_year) %>%pivot_wider(names_from = Year , values_from = Dollars, names_prefix ="Dollars_") %>%mutate("FY 2024 ($ billions)"=round(Dollars_2024/1000, digits =2),"FY 2023 ($ billions)"=round(Dollars_2023/1000, digits =2),"1-Year Change"=round((Dollars_2024 -Dollars_2023)/Dollars_2023*100, digits =2) )%>%left_join(CAGR_expenditures_summary_tot, by =c("Category_name"="Expenditure Category")) %>%arrange(-`FY 2024 ($ billions)`)%>%select(-c(Dollars_2024, Dollars_2023, `1 Year CAGR`:`10 Year CAGR`)) %>%rename( "26-Year CAGR"=`26 Year CAGR`, "Expenditure Category"= Category_name )allother_row <-which(grepl("All Other", expenditure_change2$`Expenditure Category`))expenditure_change2 <-move_to_last(expenditure_change2, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", expenditure_change2$`Expenditure Category`))expenditure_change2 <-move_to_last(expenditure_change2, totalrow) # Move "Total" to last rowlastrow =nrow(expenditure_change2)expenditure_change2 %>%filter(!is.na(`Expenditure Category`)) %>%kbl(caption ="Table 2. Yearly Change in Expenditures - All FF Categories, Ordered from Largest to Smallest Expenditure Amount", row.names =FALSE) %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")```## Summary Tables - Largest CategoriesThe 10 largest revenue sources and 15 largest expenditure sources remain separate categories and all other smaller sources/expenditures are combined into "All Other Revenues (Expenditures)". These condensed tables are typically used in the Fiscal Futures articles. They were manually created in past years but this hopefully automates the process a bit until final formatting stages.- take ff_rev and ff_exp data frames, which were in wide format, pivot them longer and mutate the Category_name variable to nicer labels. Keep largest categories separate and aggregate the rest.- You need to manually comment out the categories that are not the largest each year. Check and compare to the previous years largest categories!```{r }#| label: tbl-top-exp-CAGRs#| tbl-cap: Largest Expenditure Categories with CAGRsexp_totals <- ff_exp %>% rowwise() %>% mutate(exp_TOTALS = sum(across(exp_402:exp_970), na.rm=TRUE)) # creates total column toorev_totals <- ff_rev %>% rowwise() %>% mutate(rev_TOTALS = sum(across(rev_02:rev_78), na.rm=TRUE))rev_long_majorcats <- pivot_longer(rev_totals, rev_02:rev_TOTALS, names_to = c("type","Category"), values_to = "Dollars", names_sep = "_") %>% rename(Year = fy) %>% mutate(Category_name = case_when( Category == "02" ~ "Income Tax" , Category == "03" ~ "Corporate Income Tax" , Category == "06" ~ "Sales Tax" , Category == "09" ~ "Motor Fuel Taxes" , # Category == "12" ~ "PUBLIC UTILITY TAXES, gross of PPRT" , # Category == "15" ~ "CIGARETTE TAXES" , # Category == "18" ~ "LIQUOR GALLONAGE TAXES" , # Category == "21" ~ "INHERITANCE TAX" , # Category == "24" ~ "INSURANCE TAXES&FEES&LICENSES, net of refunds " , # Category == "27" ~ "CORP FRANCHISE TAXES & FEES" , # Category == "30" ~ "HORSE RACING TAXES & FEES", # in Other Category == "31" ~ "Medical Provider Assessments" , # Category == "32" ~ "GARNISHMENT-LEVIES" , # dropped # Category == "33" ~ "LOTTERY RECEIPTS" , # Category == "35" ~ "OTHER TAXES" , Category == "36" ~ "Receipts from Revenue Producing", Category == "39" ~ "Licenses, Fees, Registration" , # Category == "42" ~ "MOTOR VEHICLE AND OPERATORS" , # Category == "45" ~ "STUDENT FEES-UNIVERSITIES", # dropped # Category == "48" ~ "RIVERBOAT WAGERING TAXES" , # Category == "51" ~ "RETIREMENT CONTRIBUTIONS" , # dropped # Category == "54" ~ "GIFTS AND BEQUESTS", Category == "57" ~ "Federal Other" , Category == "58" ~ "Federal Medicaid Reimbursements", Category == "59" ~ "Federal Transportation" , # Category == "60" ~ "OTHER GRANTS AND CONTRACTS", #other # Category == "63" ~ "INVESTMENT INCOME", # other # Category == "66" ~ "PROCEEDS,INVESTMENT MATURITIES" , #dropped # Category == "72" ~ "BOND ISSUE PROCEEDS", #dropped # Category == "75" ~ "INTER-AGENCY RECEIPTS ", #dropped # Category == "76" ~ "TRANSFER IN FROM OUT FUNDS", #other # Category == "78new" ~ "ALL OTHER SOURCES" , # Category == "79" ~ "COOK COUNTY IGT", #dropped # Category == "98" ~ "PRIOR YEAR REFUNDS", #dropped Category == "TOTALS" ~ "Total Revenue", T ~ "All Other Sources **" # any other Category number that was not specifically referenced is combined into Other Revenue Sources ) ) %>% select(-type, -Category) %>% # drop extra columns type and Category number group_by(Year, Category_name) %>% summarise(Dollars= round(sum(Dollars),digits=2)) # revenue_wide # not actually in wide format yet. # has 10 largest rev sources separate and combined all others to Other in long data format. # creates wide version of table where each revenue source is a columnrevenue_wide_majorcats <- rev_long_majorcats %>% pivot_wider(names_from = Category_name, values_from = Dollars) %>% relocate("All Other Sources **", .after = last_col()) %>% relocate("Total Revenue", .after = last_col()) exp_long_majorcats <- pivot_longer(exp_totals, exp_402:exp_TOTALS , names_to = c("type", "Category"), values_to = "Dollars", names_sep = "_") %>% rename(Year = fy ) %>% mutate(Category_name = case_when( # Category == "402" ~ "Aging" , # Category == "406" ~ "AGRICULTURE", # Category == "416" ~ "Central Management", Category == "418" ~ "Children & Family Services", Category == "420" ~ "Community Development", # Category == "422" ~ "NATURAL RESOURCES" , Category == "426" ~ "Corrections", # Category == "427" ~ "EMPLOYMENT SECURITY" , Category == "444" ~ "Human Services" , # Category == "478" ~ "HEALTHCARE & FAM SER NET OF MEDICAID", # Category == "482" ~ "PUBLIC HEALTH", Category == "492" ~ "Revenue", Category == "494" ~ "Transportation" , # Category == "532" ~ "ENVIRONMENTAL PROTECT AGENCY" , Category == "557" ~ "Tollway" , # Category == "684" ~ "IL COMMUNITY COLLEGE BOARD", # Category == "691" ~ "IL STUDENT ASSISTANCE COMM" , # Category == "900" ~ "NOT IN FRAME", # Category == "901" ~ "State Pension Contribution", Category == "903" ~ "Debt Service", Category == "904" ~ "State Employee Healthcare", # Category == "910" ~ "LEGISLATIVE" , # Category == "920" ~ "JUDICIAL" , # Category == "930" ~ "Elected Officers" , # Category == "940" ~ "OTHER HEALTH-RELATED", Category == "941" ~ "Public Safety" , # Category == "942" ~ "ECON DEVT & INFRASTRUCTURE" , # Category == "943" ~ "CENTRAL SERVICES", # Category == "944" ~ "BUS & PROFESSION REGULATION" , Category == "945" ~ "Medicaid" , # Category == "946" ~ "Capital Improvement" , # Category == "948" ~ "OTHER DEPARTMENTS" , # Category == "949" ~ "OTHER BOARDS & COMMISSIONS" , Category == "959" ~ "K-12 Education" , Category == "960" ~ "University Education", Category == "970" ~ "Local Govt Revenue Sharing", Category == "TOTALS" ~ "Total Expenditures", T ~ "All Other Expenditures **") ) %>% select(-type, -Category) %>% group_by(Year, Category_name) %>% summarise(Dollars= round(sum(Dollars),digits=2))expenditure_wide_majorcats <- exp_long_majorcats %>% pivot_wider(names_from = Category_name, values_from = Dollars) %>% relocate("All Other Expenditures **", .after = last_col()) %>% relocate("Total Expenditures", .after = last_col())# CAGR values for largest expenditure categories and combined All Other Expenditures# function for calculating the CAGRcalc_cagr <- function(df, n) { df <- exp_long_majorcats %>% #select(-type) %>% arrange(Category_name, Year) %>% group_by(Category_name) %>% mutate(cagr = ((`Dollars` / lag(`Dollars`, n)) ^ (1 / n)) - 1, cagr = ifelse(is.na(cagr), 0, cagr)) return(df)}# This works for one variable at a timecagr_max <- calc_cagr(exp_long_majorcats, 26) %>% # group_by(Category) %>% summarize(cagr_max = round(sum(cagr*100, na.rm = TRUE), 2))cagr23_precovid <- exp_long_majorcats %>% filter(Year <= 2019) %>% calc_cagr(21) %>% summarize(cagr_21 = round(sum(cagr*100, na.rm = TRUE), 2))cagr_10 <- calc_cagr(exp_long_majorcats, 10) %>% filter(Year == current_year) %>% summarize(cagr_10 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_5 <- calc_cagr(exp_long_majorcats, 5) %>% filter(Year == current_year) %>% summarize(cagr_5 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_3 <- calc_cagr(exp_long_majorcats, 3) %>% filter(Year == current_year) %>% summarize(cagr_3 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_2 <- calc_cagr(exp_long_majorcats, 2) %>% filter(Year == current_year) %>% summarize(cagr_2 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))cagr_1 <- calc_cagr(exp_long_majorcats, 1) %>% filter(Year == current_year) %>% summarize(cagr_1 = case_when(Year == current_year ~ round(sum(cagr*100, na.rm = TRUE), 2)))CAGR_expenditures_majorcats_tot <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_max) %>% select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% rename("Expenditure Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10,"26-Year CAGR" = cagr_max )allother_row <- which(grepl("All Other", CAGR_expenditures_majorcats_tot$`Expenditure Category`))CAGR_expenditures_majorcats_tot <- move_to_last(CAGR_expenditures_majorcats_tot, allother_row) # Move "All Other" to 2nd to last rowtotalrow <- which(grepl("Total", CAGR_expenditures_majorcats_tot$`Expenditure Category`))CAGR_expenditures_majorcats_tot <- move_to_last(CAGR_expenditures_majorcats_tot, totalrow) # Move "Total" to last rowlastrow = nrow(CAGR_expenditures_majorcats_tot)CAGR_expenditures_majorcats_tot%>% kbl(caption = "CAGR Calculations for Largest Expenditure Categories" , row.names=FALSE) %>% kable_classic() %>% row_spec(lastrow, bold = T, color = "black", background = "gray")# Yearly change for Top 13 largest expenditure categoriesexpenditure_change_majorcats <- exp_long_majorcats %>% filter(Year >= past_year) %>% pivot_wider(names_from = Year , values_from = Dollars, names_prefix = "Dollars_") %>% mutate("FY 2024 ($ Billions)" = round(Dollars_2024/1000, digits = 2), "FY 2023 ($ Billions)" = round(Dollars_2023/1000, digits = 2), "1-Year Change" = percent((Dollars_2024 -Dollars_2023)/Dollars_2023, accuracy = .01) ) %>% left_join(CAGR_expenditures_majorcats_tot, by = c("Category_name" = "Expenditure Category")) %>% arrange(-`FY 2024 ($ Billions)`)%>% mutate(`26-Year CAGR` = percent(`26-Year CAGR`/100, accuracy=.1)) %>% select(-c(Dollars_2024, Dollars_2023, `1 Year CAGR`:`10 Year CAGR`)) %>% rename( "26-Year CAGR" = `26-Year CAGR`, "Expenditure Category" = Category_name )allother_row <- which(grepl("All Other", expenditure_change_majorcats$`Expenditure Category`))expenditure_change_majorcats <- move_to_last(expenditure_change_majorcats, allother_row) # Move "All Other" to 2nd to last rowtotalrow <- which(grepl("Total", expenditure_change_majorcats$`Expenditure Category`))expenditure_change_majorcats <- move_to_last(expenditure_change_majorcats, totalrow) # Move "Total" to last rowlastrow = nrow(expenditure_change_majorcats)expenditure_change_majorcats %>% kbl(caption = "Yearly Change in Expenditures", row.names = FALSE, align = "l") %>% kable_classic() %>% row_spec(lastrow, bold = T, color = "black", background = "gray")```Top 10 revenue sources CAGRs and Yearly Change Tables:```{r}#| label: tbl-top10-rev-CAGRs#| tbl-cap: Top 10 Revenue Sources with CAGRs#| tbl-cap-location: top##### Top 10 revenue CAGRs: ####calc_cagr <-function(df, n) { df <- rev_long_majorcats %>%arrange(Category_name, Year) %>%group_by(Category_name) %>%mutate(cagr = ((Dollars /lag(Dollars, n)) ^ (1/ n)) -1,cag =ifelse(is.na(cagr), 0 , cagr))return(df)}# This works for one variable at a timecagr_max <-calc_cagr(rev_long_majorcats, 26) %>%# group_by(Category) %>%summarize(cagr_max =round(sum(cagr*100, na.rm =TRUE), 2))cagr_10 <-calc_cagr(rev_long_majorcats, 10) %>%filter(Year == current_year) %>%summarize(cagr_10 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_5 <-calc_cagr(rev_long_majorcats, 5) %>%filter(Year == current_year) %>%summarize(cagr_5 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_3 <-calc_cagr(rev_long_majorcats, 3) %>%filter(Year == current_year) %>%summarize(cagr_3 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))cagr_2 <-calc_cagr(rev_long_majorcats, 2) %>%filter(Year == current_year) %>%summarize(cagr_2 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2))) cagr_1 <-calc_cagr(rev_long_majorcats, 1) %>%filter(Year == current_year) %>%summarize(cagr_1 =case_when(Year == current_year ~round(sum(cagr*100, na.rm =TRUE), 2)))CAGR_revenue_majorcats_tot <-data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_max) %>%select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>%rename("Revenue Category"= Category_name, "1 Year CAGR"= cagr_1, "2 Year CAGR"= cagr_2, "3 Year CAGR"= cagr_3, "5 Year CAGR"= cagr_5, "10 Year CAGR"= cagr_10, "26-Year CAGR"= cagr_max )allother_row <-which(grepl("All Other", CAGR_revenue_majorcats_tot$`Revenue Category`))CAGR_revenue_majorcats_tot <-move_to_last(CAGR_revenue_majorcats_tot, allother_row) # Move "All Other" to 2nd to last rowtotalrow <-which(grepl("Total", CAGR_revenue_majorcats_tot$`Revenue Category`))CAGR_revenue_majorcats_tot <-move_to_last(CAGR_revenue_majorcats_tot, totalrow) # Move "Total" to last rowlastrow =nrow(CAGR_revenue_majorcats_tot)CAGR_revenue_majorcats_tot %>%kbl(caption ="CAGR Calculations for Largest Revenue Sources", row.names =FALSE) %>%kable_classic() %>%row_spec(lastrow, bold = T, color ="black", background ="gray")``````{r }#| label: tbl-top10-rev-yearlychange#| tbl-cap: Top 10 Revenue Sources with CAGRs#| tbl-cap-location: top###### Yearly change summary table for Top 10 Revenues #####revenue_change_majorcats <- rev_long_majorcats %>% #select(-c(Category)) %>% filter(Year >= past_year) %>% pivot_wider(names_from = Year , values_from = Dollars, names_prefix = "Dollars_") %>% mutate( "FY 2024 ($ billions)" = round(Dollars_2024/1000, digits = 1), "FY 2023 ($ billions)" = round(Dollars_2023/1000, digits = 1), "1-Year Change" = percent(((Dollars_2024 -Dollars_2023)/Dollars_2023), accuracy = .1)) %>% left_join(CAGR_revenue_majorcats_tot, by = c("Category_name" = "Revenue Category")) %>% arrange(-`FY 2024 ($ billions)`)%>% #select(-c(Dollars_2021, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>% mutate("26-Year Change" = percent(`26-Year CAGR`/100, accuracy=.1)) %>% rename("Revenue Category" = Category_name ) %>% select(-c(Dollars_2024, Dollars_2023, `1 Year CAGR`:`26-Year CAGR`)) allother_row <- which(grepl("All Other", revenue_change_majorcats$`Revenue Category`))revenue_change_majorcats <- move_to_last(revenue_change_majorcats, allother_row) # Move "All Other" to 2nd to last rowtotalrow <- which(grepl("Total", revenue_change_majorcats$`Revenue Category`))revenue_change_majorcats <- move_to_last(revenue_change_majorcats, totalrow) # Move "Total" to last rowlastrow = nrow(revenue_change_majorcats)revenue_change_majorcats%>% kbl(caption = "Yearly Change in Revenue for Main Revenue Sources", row.names = FALSE, align = "l") %>% kable_classic() %>% row_spec(lastrow, bold = T, color = "black", background = "gray")```Export summary file with Totals```{r}#install.packages("openxlsx")library(openxlsx)dataset_names <-list('Aggregate Revenues'= revenue_wide2, 'Aggregate Expenditures'= expenditure_wide2, 'Table 1'= revenue_change_majorcats, #Top categories with yearly change, 23 yr cagr'Table 2'= expenditure_change_majorcats,'Table 1a. AllCats'= revenue_change2,'Table 2a. AllCats'= expenditure_change2,'CAGR Rev-MajorCats'= CAGR_revenue_majorcats_tot, # Categories Match Table 1 in paper'CAGR Exp-MajorCats'= CAGR_expenditures_majorcats_tot, # 'Table 1-AllCats' = expenditure_change_allcats, # All Categories by Year# 'Table 2-AllCats' = revenue_change_allcats,# 'CAGR_Revenue-AllCats' = CAGR_revenue_summary_tot, # 'CAGR_Expenditures-AllCats' = CAGR_expenditures_summary_tot, 'Fiscal Gap'= year_totals, # Total Revenue, Expenditure, and Fiscal gap per year'aggregated_totals_long'= aggregated_totals_long # all data in long format. Good for creating pivot tables in Excel )write.xlsx(dataset_names, file =paste0("data/FY", current_year, " Files/summary_file_FY24_PensionChange_2025_04_23.xlsx"))```Saves main items in one excel file named `summary_file.xlsx`. Delete `eval=FALSE` to run on local computer.
