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Get compensation appraisal

Source: R/get-appraisal.R
get_appraisal.Rd

Wrapper function for generating entire compensation appraisal for a given nonprofit and a specified search criteria.

The Appraisal Process Vignette contains detailed examples on how the appraisal is generated. The Compensator Vignette contains a few simple examples of how to use this function.

Usage

get_appraisal(org, search.criteria)

Arguments

org

output from get_org_values()

search.criteria

a list with the following items:

  • type.org: vector of the types of organization you want to include. Options are RG, AA, MT, PA, RP, MS, MM, and/or NS.

  • broad.category: vector of broad categories you wish to include in returned data set Options are ART, EDU, ENV, HEL, HMS, IFA, PSB, REL, MMB, UNU, UNI, and/or HOS

  • major.group vector: of major groups you wish to include in returned data set. Options are A-Z.

  • division: vector of divisions you wish to include. Divisions exist entirely inside major groups. We suggest you do not use this parameter if you have more than one item in major.group. Options are 0, 2, 3, ..., 9 (1 is not an option.

  • subdivision: vector of subdivision you wish to include. Subdivisions exist entirely inside divisions. We suggest you do not use this parameter if you have more than one item in division. Options are 0 - 9.

  • univ: TRUE of FALSE. Are universities to be included?

  • hosp: TRUE of FALSE, Are hospitals to be included?

  • location.type: vector of "metro", "suburban", "town", and/or "rural" for which city types to include

  • state: vector of 2 letter state abbreviations to be included

  • total.expense: vector of c(min,max) of range of total expenses to be included

Value

A list with 3 objects

  1. suggested.salary: a numeric of the suggested CEO compensation for the input organization

  2. suggested.range: vector of minimum and maximum suggested salary range

  3. reference.set: a data frame of all nonprofits used to calculated the suggested CEO compensation with the following variables:

  • EIN: IRS Employer Identification Number

  • form.year: IRS filing year from which this nonprofits information was obtained

  • name: Name of the nonprofit

  • total.employee: Total number of employees at the nonprofit

  • gross.receipts: Gross receipts reported for the year

  • total.assests: Total assets reported for the year

  • total.expense: Total expenses reported for the year

  • ceo.compensation: Total CEO compensation reported for the year

  • gender: Imputed gender of the CEO

  • zip5: 5 digit zip code of where the nonprofit is located

  • state: Two letter abbreviation of the state where the nonprofit is located

  • location.type: Either "metro" or "rural" for type of location the nonprofit is in

  • ntee: Original ntee code

  • broad.category, major.group, type.org, two.digit, two.digit.s, tens, ones, us.state, univ, hosp: Parts of the dissagregated NTEE code. See ... for details.

  • log.expense.dist: Total Expense distance between the nonprofit and the reference organization

  • mission.dist: Mission distance between the nonprofit and the reference organization

  • geo.dist: Geographic distance between the nonprofit and the reference organization

  • total.dist: Total distance between the nonprofit and the reference organization

  • rank: Ranking of all nonprofits that match the reference set from closest to farthest from the reference organization.

  • dist.std: standardized total distance

  • weight: weight used to calculated weighted average of suggested.salary

  • residual.percent: the residual of that observation as a percent of the expected salary.

  • fitted.values: is suggested.salary(1+residual.percent)

See ... Vignette for details on how these values are calculated.

Examples


if (FALSE) {
# Step 1: Create list of organization characteristics 
org <- get_org_values(
         state = "FL",
         location.type = "metro",
         total.expense = 1000000,
         ntee = "P20")
search.criteria <-
  list(
    type.org = "RG",
    broad.category = "HMS",
    major.group = c("M", "N", "O", "P"),
    division = NA,
    subdivision = NA,
    univ = FALSE,
    hosp = FALSE,
    location.type = c("metro", "suburban"),
    state = c("FL", "PR", "GA", "SC", "MS", "TN", "AL"),
    total.expense = c(100000, 10000000)
  )
       
#Step 3: Get appraisal
appraisal <- get_appraisal(org, search.criteria) 

appraisal$suggested.salary
appraisal$suggested.range
reference.set <- appraisal$reference.set
View(reference.set)
}