Metric Construction

Definition & Interpretation

\[Assets\: to\: Revenues\:Ratio = \frac{Total\: Assets}{Total \: Revenues} \]

This metric measures how many dollars of assets it has per dollar of annual revenues (how many dollars of revenue have been converted into assets in the past or that year for the current year).

Generally, this ratio should be higher for organizations with large inventories of developments or large endowments.

Variables

Note: This data is available only for all organizations.

  • Numerator: Total Assets

    • On 990: Part X, Line 16B
      • SOI PC EXTRACTS: totassetsend
    • On EZ: Pt II, Line 25B
      • SOI PC EXTRACTS: totassetsend

  • Denominator: Total Revenues

    • On 990: Part VIII, Line 12A -SOI PC EXTRACTS: totrevenue

    • On EZ: Part I, Line 9 -SOI PC EXTRACTS: totrevnue 



# TEMPORARY VARIABLES 
total_assets  <- core$totassetsend
total_revenues <- core$totrevenue

# can't divide by zero
total_revenues[ total_revenues == 0 ] <- NA

# SAVE RESULTS 
core$asset_rev_ratio <-  total_assets / total_revenues
                     
# summary( core$asset_rev_ratio )

Standardize Scales

Check high and low values to see what makes sense.

x.05 <- quantile( core$asset_rev_ratio, 0.05, na.rm=T )
x.95 <- quantile( core$asset_rev_ratio, 0.95, na.rm=T )

ggplot( core, aes(x = asset_rev_ratio ) ) +  
  geom_density( alpha = 0.5) + 
  xlim( x.05, x.95 ) 

core2 <- core

# proportion of values that are negative
#mean( core2$asset_rev_ratio < 0, na.rm=T ) 
#core2$asset_rev_ratio[ core2$asset_rev_ratio < 0 ] <- 0

# proption of values above 200% 
#mean( core2$asset_rev_ratio > 50, na.rm=T ) 
#core2$asset_rev_ratio[ core2$asset_rev_ratio > 50 ] <- 50



x.05 <- quantile( core$asset_rev_ratio, 0.05, na.rm=T )
x.95 <- quantile( core$asset_rev_ratio, 0.95, na.rm=T )

core2 <- core

# proportion of values that are negative
# mean( core2$der < 0, na.rm=T ) 

# proption of values above 1% 
# mean( core2$der > 5, na.rm=T ) 

# WINSORIZATION AT 5th and 95th PERCENTILES

core2$asset_rev_ratio[ core2$asset_rev_ratio < x.05 ] <- x.05
core2$asset_rev_ratio[ core2$asset_rev_ratio > x.95 ] <- x.95

Metric Scope

Tax data is available for full 990 filers and 990EZ filers (organizations with Gross receipts < $200,000 and Total assets < $500,000).

The data have been capped to those with values between 5% and 95% of the normal distribution to cut off outliers and exempt organizations with zero profitability (though negative values are allowed still).

Descriptive Statistics

Note: All monetary variables have been converted to thousands of dollars.


core2 %>%
  mutate( # asset_rev_ratio = asset_rev_ratio * 10000,
    totrevenue = totrevenue / 1000,
    totfuncexpns = totfuncexpns / 1000, 
    lndbldgsequipend = lndbldgsequipend / 1000,
    totassetsend = totassetsend / 1000,
    totliabend = totliabend / 1000,
    totnetassetend = totnetassetend / 1000 ) %>% 
  select( STATE,  NTEE1, NTMAJ12, 
          asset_rev_ratio, 
          AGE, 
          totrevenue, totfuncexpns, 
          lndbldgsequipend, totassetsend, 
          totnetassetend, totliabend ) %>%

  stargazer( type = s.type, 
             digits=2, 
             summary.stat = c("min","p25","median",
                              "mean","p75","max", "sd"),
             covariate.labels = c("Asset to Revenue Ratio", "Age", 
                                  "Revenue ($1k)", "Expenses($1k)", 
                                  "Buildings ($1k)", "Total Assets ($1k)",
                                  "Net Assets ($1k)", "Liabiliies ($1k)"))
Statistic Min Pctl(25) Median Mean Pctl(75) Max St. Dev.
Asset to Revenue Ratio 0.18 0.79 2.54 4.78 5.98 22.82 5.90
Age 3 22 30 32.04 41 95 14.75
Revenue (1k) -5,376.77 258.90 909.40 4,521.71 3,672.25 408,932.00 14,285.64
Expenses(1k) 0.00 263.50 840.06 4,192.08 3,327.50 382,666.50 13,465.77
Buildings (1k) -4.48 79.14 824.25 3,504.47 2,868.50 513,508.80 13,210.06
Total Assets (1k) -7,552.11 777.90 2,446.11 9,261.85 7,477.25 672,021.00 27,038.89
Net Assets (1k) -178,869.70 155.67 1,093.86 4,553.27 4,078.70 531,067.70 15,470.31
Liabiliies (1k) -2,707.10 115.44 815.58 4,708.51 3,133.16 705,623.10 18,721.86

What proportion of orgs have asset to revenue ratios equal to zero?

prop.zero <- mean( core2$asset_rev_ratio == 0, na.rm=T )

In the sample, 0 percent of the organizations have asset to revenue ratios equal to zero, meaning they have no assets. These organizations are dropped from subsequent graphs to keep the visualizations clean. The interpretation of the graphics should be the distributions of asset to revenue ratios for organizations that have positive or negative values.

###
### ADD QUANTILES
###
###   function create_quantiles() defined in r-functions.R

core2$exp.q   <- create_quantiles( var=core2$totfuncexpns,   n.groups=5 )
core2$rev.q   <- create_quantiles( var=core2$totrevenue,     n.groups=5 )
core2$asset.q <- create_quantiles( var=core2$totnetassetend, n.groups=5 )
core2$age.q   <- create_quantiles( var=core2$AGE,            n.groups=5 )
core2$land.q <- create_quantiles(var=core2$lndbldgsequipend,            n.groups=5 )

Assets to Revenue Density

min.x <- min( core2$asset_rev_ratio, na.rm=T )
max.x <- max( core2$asset_rev_ratio, na.rm=T )

ggplot( core2, aes(x = asset_rev_ratio )) +  
  geom_density( alpha = 0.5 ) + 
  xlim( min.x, max.x  ) +
  xlab( variable.label ) +
  theme( axis.title.y=element_blank(),
         axis.text.y=element_blank(), 
         axis.ticks.y=element_blank() )

Assets to Revenue by NTEE Major Code

core3 <- core2 %>% filter( ! is.na(NTEE1) )
table( core3$NTEE1) %>% sort(decreasing=TRUE) %>% kable()
Var1 Freq
Housing 2837
Community Development 1585
Human Services 1102 

t <- table( factor(core3$NTEE1) ) 
df <- data.frame( x=Inf, y=Inf, 
                  N=paste0( "N=", as.character(t) ), 
                  NTEE1=names(t) )

ggplot( core3, aes( x=asset_rev_ratio ) ) + 
  geom_density( alpha = 0.5) + 
  # xlim( -0.1, 1 ) +
  labs( title="Nonprofit Subsectors" ) + 
  xlab( variable.label ) + 
  facet_wrap( ~ NTEE1, nrow=1 ) + 
    theme_minimal( base_size = 15 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank(),
           strip.text = element_text( face="bold") ) +  # size=20 
  geom_text( data=df, 
             aes(x, y, label=N ), 
             hjust=2, vjust=3, 
             color="gray60", size=6 )

Assets to Revenue by Region

table( core2$Region) %>% kable()
Var1 Freq
Midwest 1444
Northeast 1368
South 1610
West 1088 
t <- table( factor(core2$Region) ) 
df <- data.frame( x=Inf, y=Inf, 
                  N=paste0( "N=", as.character(t) ), 
                  Region=names(t) )

core2 %>% 
  filter( ! is.na(Region) ) %>% 
  ggplot( aes(asset_rev_ratio) )  + 
    geom_density( alpha = 0.5 ) + 
    xlab( "Census Regions" ) + 
    ylab( variable.label ) +
    facet_wrap( ~ Region, nrow=3 ) + 
    theme_minimal( base_size = 22 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() ) + 
    geom_text( data=df, 
             aes(x, y, label=N ), 
             hjust=2, vjust=3, 
             color="gray60", size=6 )

table( core2$Division ) %>% kable()
Var1 Freq
East North Central 1038
East South Central 289
Middle Atlantic 904
Mountain 303
New England 464
Pacific 785
South Atlantic 900
West North Central 406
West South Central 421 
t <- table( factor(core2$Division) ) 
df <- data.frame( x=Inf, y=Inf, 
                  N=paste0( "N=", as.character(t) ), 
                  Division=names(t) )

core2 %>% 
  filter( ! is.na(Division) ) %>% 
  ggplot( aes(asset_rev_ratio) )  + 
    geom_density( alpha = 0.5 ) + 
    xlab( "Census Sub-Regions (10)" ) + 
    ylab( variable.label ) +
    facet_wrap( ~ Division, nrow=3 ) + 
    theme_minimal( base_size = 22 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() ) + 
    geom_text( data=df, 
             aes(x, y, label=N ), 
             hjust=2, vjust=3, 
             color="gray60", size=6 )

Assets to Revenue by Nonprofit Size (Expenses)

ggplot( core2, aes(x = totfuncexpns )) +  
  geom_density( alpha = 0.5 ) + 
  xlim( quantile(core2$totfuncexpns, c(0.02,0.98), na.rm=T ) )

core2$totfuncexpns[ core2$totfuncexpns < 1 ] <- 1
# core2$totfuncexpns[ is.na(core2$totfuncexpns) ] <- 1

if( nrow(core2) > 10000 )
{
  core3 <- sample_n( core2, 10000 )
} else
{
  core3 <- core2
}

jplot( log10(core3$totfuncexpns), core3$asset_rev_ratio, 
       xlab="Nonprofit Size (logged Expenses)", 
       ylab=variable.label,
       xaxt="n", xlim=c(3,10) )
axis( side=1, 
      at=c(3,4,5,6,7,8,9,10), 
      labels=c("1k","10k","100k","1m","10m","100m","1b","10b") )

core2 %>% 
  filter( ! is.na(exp.q) ) %>% 
  ggplot( aes(asset_rev_ratio) )  + 
    geom_density( alpha = 0.5) + 
    labs( title="Nonprofit Size (logged expenses)" ) + 
    xlab( variable.label ) +
    facet_wrap( ~ exp.q, nrow=3 ) + 
    theme_minimal( base_size = 22 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

Assets to Revenue by Nonprofit Size (Revenue)

ggplot( core2, aes(x = totrevenue )) +  
  geom_density( alpha = 0.5 ) + 
  xlim( quantile(core2$totrevenue, c(0.02,0.98), na.rm=T ) ) + 
  theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

core2$totrevenue[ core2$totrevenue < 1 ] <- 1

if( nrow(core2) > 10000 )
{
  core3 <- sample_n( core2, 10000 )
} else
{
  core3 <- core2
}

jplot( log10(core3$totrevenue), core3$asset_rev_ratio, 
       xlab="Nonprofit Size (logged Revenue)", 
       ylab=variable.label,
       xaxt="n", xlim=c(3,10) )
axis( side=1, 
      at=c(3,4,5,6,7,8,9,10), 
      labels=c("1k","10k","100k","1m","10m","100m","1b","10b") )

core2 %>% 
  filter( ! is.na(rev.q) ) %>% 
  ggplot( aes(asset_rev_ratio) )  + 
    geom_density( alpha = 0.5 ) + 
    labs( title="Nonprofit Size (logged revenues)" ) + 
    xlab( variable.label ) +
    facet_wrap( ~ rev.q, nrow=3 ) + 
    theme_minimal( base_size = 22 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

Assets to Revenue by Nonprofit Size (Net Assets)

ggplot( core2, aes(x = totnetassetend )) +  
  geom_density( alpha = 0.5) + 
  xlim( quantile(core2$totnetassetend, c(0.02,0.98), na.rm=T ) ) + 
  xlab( "Net Assets" ) +
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

core2$totnetassetend[ core2$totnetassetend < 1 ] <- NA

if( nrow(core2) > 10000 )
{
  core3 <- sample_n( core2, 10000 )
} else
{
  core3 <- core2
}

jplot( log10(core3$totnetassetend), core3$asset_rev_ratio, 
       xlab="Nonprofit Size (logged Net Assets)", 
       ylab=variable.label,
       xaxt="n", xlim=c(3,10) )
axis( side=1, 
      at=c(3,4,5,6,7,8,9,10), 
      labels=c("1k","10k","100k","1m","10m","100m","1b","10b") )

core2$totnetassetend[ core2$totnetassetend < 1 ] <- NA
core2$asset.q <- create_quantiles( var=core2$totnetassetend, n.groups=5 )

core2 %>% 
  filter( ! is.na(asset.q) ) %>% 
  ggplot( aes(asset_rev_ratio) )  + 
    geom_density( alpha = 0.5 ) + 
    labs( title="Nonprofit Size (logged net assets, if assets > 0)" ) + 
    xlab( variable.label ) + 
    ylab( "" ) + 
    facet_wrap( ~ asset.q, nrow=3 ) + 
    theme_minimal( base_size = 22 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

Total Assets for Comparison

core2$totassetsend[ core2$totassetsend < 1 ] <- NA
core2$tot.asset.q <- create_quantiles( var=core2$totassetsend, n.groups=5 )

if( nrow(core2) > 10000 )
{
  core3 <- sample_n( core2, 10000 )
} else
{
  core3 <- core2
}

jplot( log10(core3$totassetsend), core3$asset_rev_ratio, 
       xlab="Nonprofit Size (logged Total Assets)", 
       ylab=variable.label,
       xaxt="n", xlim=c(3,10) )
axis( side=1, 
      at=c(3,4,5,6,7,8,9,10), 
      labels=c("1k","10k","100k","1m","10m","100m","1b","10b") )

ggplot( core2, aes(x = totassetsend )) +  
  geom_density( alpha = 0.5) + 
  xlim( quantile(core2$totassetsend, c(0.02,0.98), na.rm=T ) ) + 
  xlab( "Net Assets" ) +
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

core2 %>% 
  filter( ! is.na(tot.asset.q) ) %>% 
  ggplot( aes(asset_rev_ratio) )  + 
    geom_density( alpha = 0.5 ) + 
    xlab( "Nonprofit Size (logged total assets, if assets > 0)" ) + 
    ylab( variable.label ) +
    facet_wrap( ~ tot.asset.q, nrow=3 ) + 
    theme_minimal( base_size = 22 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

Assets to Revenue by Nonprofit Age

ggplot( core2, aes(x = AGE )) +  
  geom_density( alpha = 0.5 )  

core2$AGE[ core2$AGE < 1 ] <- NA

if( nrow(core2) > 10000 )
{
  core3 <- sample_n( core2, 10000 )
} else
{
  core3 <- core2
}

jplot( core3$AGE, core3$asset_rev_ratio, 
       xlab="Nonprofit Age", 
       ylab=variable.label ) 

core2 %>% 
  filter( ! is.na(age.q) ) %>% 
  ggplot( aes(asset_rev_ratio) )  + 
    geom_density( alpha = 0.5 ) + 
    labs( title="Nonprofit Age" ) + 
    xlab( variable.label ) +
    ylab( "" ) +
    facet_wrap( ~ age.q, nrow=3 ) + 
    theme_minimal( base_size = 22 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

Assets to Revenue by Land and Building Value

ggplot( core2, aes(x = lndbldgsequipend )) +  
  geom_density( alpha = 0.5 )  

core2$lndbldgsequipend[ core2$lndbldgsequipend < 1 ] <- NA
if( nrow(core2) > 10000 )
{
  core3 <- sample_n( core2, 10000 )
} else
{
  core3 <- core2


jplot( log10(core3$lndbldgsequipend), core3$asset_rev_ratio, 
       xlab="Land and Building Value (logged)", 
       ylab=variable.label,
       xaxt="n", xlim=c(3,10) )
axis( side=1, 
      at=c(3,4,5,6,7,8,9,10), 
      labels=c("1k","10k","100k","1m","10m","100m","1b","10b") )
}

core2 %>% 
  filter( ! is.na(land.q) ) %>% 
  ggplot( aes(asset_rev_ratio) )  + 
    geom_density( alpha = 0.5 ) + 
    labs( title="Land and Building Value" ) + 
    xlab( variable.label ) +
    ylab( "" ) +
    facet_wrap( ~ land.q, nrow=3 ) + 
    theme_minimal( base_size = 22 )  + 
    theme( axis.title.y=element_blank(),
           axis.text.y=element_blank(), 
           axis.ticks.y=element_blank() )

Save Metrics

core.asset_rev_ratio <- select( core, ein, tax_pd, asset_rev_ratio )
saveRDS( core.asset_rev_ratio, "03-data-ratios/m-15-asset-revenue-ratio.rds" )
write.csv( core.asset_rev_ratio, "03-data-ratios/m-15-asset-revenue-ratio.csv" )









Metrics generated by Jesse Lecy and Nathan Dietz
Content licensed under Creative Commons: (CC BY-NC-SA 4.0)
Source code is available on GitHub

The work is supported by the
National Center for Charitable Statistics
at the Urban Institute