Metric Construction

Definition & Interpretation

\[Equity\:Ratio = \frac{Net \: Assets}{Total \: Assets} \]

This metric indicates how much of an organization’s assets are owned free and clear or how much equity it has in its total assets. Nonprofits with greater amounts of equity are more flexible in the face of financial shocks than organizations with comparatively lesser amounts of equity because they can (1) borrow money from capital markets and (2) convert those assets to cash to offset financial shocks.

High values in this indicator are generally better, as they show that an organization has substantial equity in its assets. Low or negative values indicate an organization has higher liabilities and is generally more leveraged and thus more vulnerable to shocks. However, low values also indicate that an organization may be investing more of its equity for growth.

Variables

Note: This data is available only for both 990EZ and full 990 filers.

  • Numerator: Net Assets

    • On 990: Part X, Line 33B
      • SOI PC EXTRACTS: totnetassetend
    • On EZ: Part I, Line 21
      • SOI PC EXTRACTS: networthend


  • Denominator: Total Assets

    • On 990: Part X, Line 16B -SOI PC EXTRACTS: totassetsend

    • On EZ: Part II, Line 25B -SOI PC EXTRACTS: totassetsend 



# TEMPORARY VARIABLES 
netassets  <- ( core$totnetassetend)
totassets <- ( core$totassetsend)

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

# SAVE RESULTS 
core$equity_ratio <-  netassets / totassets
                     
# summary( core$equity_ratio )

Standardize Scales

Check high and low values to see what makes sense.

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

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

core2 <- core

# proportion of values that are negative
mean( core2$equity_ratio < 0, na.rm=T ) 
## [1] 0.1373048
core2$equity_ratio[ core2$equity_ratio < 0 ] <- 0

# proption of values above 200% 
mean( core2$equity_ratio > 50, na.rm=T ) 
## [1] 0.0001816201
core2$equity_ratio[ core2$equity_ratio > 50 ] <- 50



x.05 <- quantile( core$equity_ratio, 0.05, na.rm=T )
x.95 <- quantile( core$equity_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$equity_ratio[ core2$equity_ratio < x.05 ] <- x.05
core2$equity_ratio[ core2$equity_ratio > x.95 ] <- x.95

Metric Scope

Tax data is available for full 990 filers, so this metric does not describe any organizations with Gross receipts < $200,000 and Total assets < $500,000. Some organizations with receipts or assets below those thresholds may have filed a full 990, but these would be exceptions.

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).

Reference

Any cited works here…

Descriptive Statistics

Convert all monetary variables to thousands of dollars.


core2 %>%
  mutate( equity_ratio = equity_ratio * 100,
    totrevenue = totrevenue / 1000,
    totfuncexpns = totfuncexpns / 1000, 
    lndbldgsequipend = lndbldgsequipend / 1000,
    totassetsend = totassetsend / 1000,
    totliabend = totliabend / 1000,
    totnetassetend = totnetassetend / 1000 ) %>% 
  select( STATE,  NTEE1, NTMAJ12, 
          equity_ratio, 
          AGE, 
          totrevenue, totfuncexpns, 
          lndbldgsequipend, totassetsend, 
          totnetassetend, totliabend ) %>%

  stargazer( type = s.type, 
             digits=0, 
             summary.stat = c("min","p25","median",
                              "mean","p75","max", "sd"),
             covariate.labels = c("Equity Ratio (x100)", "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.
Equity Ratio (x100) -63 27 65 52 90 100 46
Age 3 22 30 32 41 95 15
Revenue (1k) -5,377 259 909 4,522 3,672 408,932 14,286
Expenses(1k) 0 263 840 4,192 3,328 382,667 13,466
Buildings (1k) -4 79 824 3,504 2,868 513,509 13,210
Total Assets (1k) -7,552 778 2,446 9,262 7,477 672,021 27,039
Net Assets (1k) -178,870 156 1,094 4,553 4,079 531,068 15,470
Liabiliies (1k) -2,707 115 816 4,709 3,133 705,623 18,722

What proportion of orgs have equity ratios equal to zero?

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

In the sample, 0 percent of the organizations have equity ratios equal to zero, meaning they have no equity in any of their assets. These organizations are dropped from subsequent graphs to keep the visualizations clean. The interpretation of the graphics should be the distributions of equity 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 )

Equity Ratio, Capital Ratio Density

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

ggplot( core2, aes(x = equity_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() )

Equity Ratio, Capital Ratio 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=equity_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 )

Equity Ratio, Capital Ratio 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(equity_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(equity_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 )

Equity Ratio, Capital Ratio 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$totfuncexpns), core3$equity_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(equity_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() )

Equity Ratio, Capital Ratio 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$totrevenue), core3$equity_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(equity_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() )

Equity Ratio, Capital Ratio 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$totnetassetend), core3$equity_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(equity_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$totassetsend), core3$equity_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(equity_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() )

Equity Ratio, Capital Ratio 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$AGE, core3$equity_ratio, 
       xlab="Nonprofit Age", 
       ylab=variable.label ) 

core2 %>% 
  filter( ! is.na(age.q) ) %>% 
  ggplot( aes(equity_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() )

Equity Ratio, Capital Ratio 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$lndbldgsequipend), core3$equity_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(equity_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.equity_ratio <- select( core, ein, tax_pd, equity_ratio )
saveRDS( core.equity_ratio, "03-data-ratios/m-11-equity-ratio.rds" )
write.csv( core.equity_ratio, "03-data-ratios/m-11-equity-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