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Earned Income Dependency Ratio

Source: R/get-earned-income-ratio.R
get_earned_income_ratio.Rd

Share of total revenue derived from earned (non-donation) income sources.

Formula:

eidr = earned_revenue / total_revenue

earned_revenue = program_service_revenue + membership_dues
                 + royalties + other_revenue

Definitional Range

Bounded [0, 1]. Organizations heavily reliant on philanthropy show values near zero; fee-based service providers may show values above 0.90.

Benchmarks and rules of thumb

  • No universal threshold – context matters: an advocacy organization is expected to show a low ratio; a hospital is expected to show a high one.

  • A low ratio combined with high donation dependence indicates philanthropic concentration risk.

Calculated For: 990 + 990EZ filers.

Usage

get_earned_income_ratio( df,
  program_service_rev = "F9_08_REV_PROG_TOT_TOT",
  membership_dues     = "F9_08_REV_CONTR_MEMBSHIP_DUE",
  royalties           = "F9_08_REV_OTH_ROY_TOT",
  other_revenue       = "F9_08_REV_MISC_OTH_TOT",
  total_revenue       = "F9_08_REV_TOT_TOT",
  numerator = NULL, denominator = NULL, winsorize = 0.98 ,
  range     = "zo",
  sanitize  = TRUE,
  summarize = FALSE )

Arguments

df

A data.frame containing the fields required for computing the metric.

program_service_rev

Program service revenue.

membership_dues

Membership dues and assessments.

royalties

Royalties.

other_revenue

Other miscellaneous revenue.

total_revenue

Total revenue.

numerator

Optional. A pre-aggregated column for earned revenue. Cannot be combined with the individual component arguments.

denominator

Optional. A pre-aggregated column for total revenue. Cannot be combined with total_revenue.

winsorize

The winsorization value (between 0 and 1), defaults to 0.98, which winsorizes at the 1st and 99th percentiles.

range

Character string specifying the theoretical range of the ratio, used to set winsorization bounds. Default "zo". Options: "np" (negative to positive), "zp" (zero to positive), "zo" (zero to one), "nz" (negative to zero), or a custom "lo;hi" pair (e.g. "0;10").

sanitize

Logical (default TRUE). If TRUE, NA values in the financial input columns are imputed to zero before the ratio is computed, respecting form scope: Part X and VIII/IX fields (990 only) are imputed only for 990 filers; Part I summary fields (990 + 990EZ) are imputed for all filers. The returned dataframe always contains the original unmodified input columns.

summarize

Logical. If TRUE, prints a summary() of the results and plots density curves for all four output columns (raw, winsorized, z-score, percentile). Defaults to FALSE.

Value

Object of class data.frame: the original dataframe appended with four new columns:

- `earned_income`   - earned income dependency ratio (raw)
- `earned_income_w` - winsorized version
- `earned_income_z` - standardized z-score (based on winsorized values)
- `earned_income_p` - percentile rank (1-100)

Details

Primary uses and key insights

The earned income dependency ratio measures the combined share of revenue from program services, membership dues, royalties, and miscellaneous revenue - the sources that flow from the organization's own activities rather than from voluntary contributions. It is the revenue-side complement to get_self_sufficiency_ratio(), which compares program revenue to total expenses.

Organizations with high earned income ratios are often considered more financially resilient because earned revenue is tied to service delivery rather than donor preferences, though it also creates exposure to market competition and customer retention challenges.

Formula variations and their sources

(Program service revenue + membership dues + royalties + other miscellaneous revenue) / total revenue (Part VIII lines 2g + 1b + 5 + 11d-11e / line 12A). This broad definition of earned income follows several studies (Young 2007). A narrower version uses only program service revenue (see get_revenue_programs_ratio()).

Canonical citations

  • Young, D.R. (2007). Financing Nonprofits. AltaMira Press.

  • Weisbrod, B.A. (1998). The nonprofit mission and its financing. Journal of Policy Analysis and Management, 17(2), 165-174.

  • Chang, C.F. & Tuckman, H.P. (1994). Revenue diversification among nonprofits. VOLUNTAS, 5(3), 273-290.

Variables used:

  • F9_08_REV_PROG_TOT_TOT: Program service revenue (program_service_rev)

  • F9_08_REV_CONTR_MEMBSHIP_DUE: Membership dues (membership_dues)

  • F9_08_REV_OTH_ROY_TOT: Royalties (royalties)

  • F9_08_REV_MISC_OTH_TOT: Other miscellaneous revenue (other_revenue)

  • F9_08_REV_TOT_TOT: Total revenue (total_revenue)

Examples

library( fiscal )
data( dat10k )

d <- get_earned_income_ratio( df = dat10k )
#>    :: Total revenue equal to zero :: 74 case(s) replaced with NaN
head( d[ , c( "earned_income", "earned_income_w", "earned_income_z", "earned_income_p" ) ] )
#>    earned_income earned_income_w earned_income_z earned_income_p
#>            <num>           <num>           <num>           <int>
#> 1:            NA              NA              NA              NA
#> 2:     0.0000000       0.0000000       -3.088972               1
#> 3:     0.0214227       0.0214227       -1.365449              37
#> 4:            NA              NA              NA              NA
#> 5:     0.0000000       0.0000000       -3.088972               1
#> 6:            NA              NA              NA              NA