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Debt to Asset Ratio

Definition

\[DAR = \frac{Total \: Liabilities}{Total \: Assets}\]

Notes: Variables should be equivalent for 990 and EZ filers in the IRS data.

Variables

Total liabilities, EOY

• On 990: Part X, line 26B
  • SOI PC EXTRACTS: totliabend
    
  • NCCS Core:
    
• On EZ: Part II, line 26B

Denominator: Total assets, EOY

• On 990: Part X, line 16B
  
• On EZ: Part II, line 25B

Tabulation

# can't divide by zero
zero.these <- core$totassetsend == 0 
core$totassetsend[ core$totassetsend == 0 ] <- NA 

core$dar <- core$totliabend / core$totassetsend

Standardize Scales

Check high and low values to see what makes sense.

A ratio below 0 is odd because it means assets are negative and liabilities are positive.

A ratio above 1 might be realistic, but not more meaningful than knowing the ratio is at least one, which means the org has liabilities that as as large or larger than assets. Practically, a ratio of 1 or 2 probably mean the same thing for an organization (they are in trouble).

ggplot( core, aes(x = dar )) +  
  geom_density( alpha = 0.5, fill="blue" ) + 
  xlim( -1, 2 ) 

core$dar[ core$dar < 0 ] <- 0
core$dar[ core$dar >  1 ] <-  1

Metric Scope

Score describes whether the metric applies to all 990 filers, or only the full 990 filers (if the variable is not included on the 990-EZ form).

Tax data is available for both full 990 and 990-EZ filers, so this metric describes all orgs.

Scope codes:

• PZ: Both 990 and 990-EZ filers
  
• PC: Only full 990 (public charity or PC) filers only
  
• EZ: Only 990-EZ filers

Descriptive Statistics

Put Debt to Asset Ratio on a scale of -10,000 to +20,000.

Convert everything else to thousands of dollars.

# rescale DAR where 1 = 10,000 

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

  stargazer( type = s.type, 
             digits=0, 
             summary.stat = c("min","p25","median",
                              "mean","p75","max", "sd") )

Statistic	Min	Pctl(25)	Median	Mean	Pctl(75)	Max	St. Dev.
AGE	3	22	30	32	41	95	15
dar	0	996	3,466	4,226	7,188	10,000	3,518
totrevenue	-5,377	259	909	4,522	3,672	408,932	14,286
totfuncexpns	0	263	840	4,192	3,328	382,667	13,466
lndbldgsequipend	-4	79	824	3,504	2,868	513,509	13,210
totassetsend	-7,552	788	2,459	9,292	7,492	672,021	27,078
totliabend	-2,707	115	816	4,709	3,133	705,623	18,722
totnetassetend	-178,870	156	1,094	4,553	4,079	531,068	15,470

What proportion of orgs have a DAR of zero (no outstanding liabilities)?

prop.zero <- mean( core$dar == 0, na.rm=T )

In the sample, 5 percent of the organizations have a DAR of zero, meaning they carried no debt. These organizations are dropped from subsequent graphs to keep the visualizations clean. The interpretation of the graphics should be the distributions of Debt to Asset Ratio for organizations that carry debt.

Filter out cases with DAR=0 because they dominate the graphics otherwise.

# drop cases where DAR=0
core2 <-  core 
core2$dar[ core2$dar == 0 ] <- NA

Create quantile groups:

###
### 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 )

DAR Density

ggplot( core2, aes(x = dar )) +  
  geom_density( alpha = 0.5, fill="darkorange" ) + 
  xlim( -0.1, 1 ) +
  xlab( variable.label ) +
  theme( axis.title.y=element_blank(),
         axis.text.y=element_blank(), 
         axis.ticks.y=element_blank() )

---

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DAR 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=dar ) ) + 
  geom_density( alpha = 0.5, fill="blue" ) + 
  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 )

---

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DAR by Region

table( core$Region) %>% kable()

Var1	Freq
Midwest	1444
Northeast	1368
South	1610
West	1088

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

core2 %>% 
  filter( ! is.na(Region) ) %>% 
  ggplot( aes(dar) )  + 
    geom_density( alpha = 0.5, fill="blue" ) + 
    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( core$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(core3$Division) ) 
df <- data.frame( x=Inf, y=Inf, 
                  N=paste0( "N=", as.character(t) ), 
                  Division=names(t) )

core2 %>% 
  filter( ! is.na(Division) ) %>% 
  ggplot( aes(dar) )  + 
    geom_density( alpha = 0.5, fill="blue" ) + 
    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 ) 

DAR by Nonprofit Size (Expenses)

ggplot( core2, aes(x = totfuncexpns )) +  
  geom_density( alpha = 0.5, fill="darkorange" ) + 
  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$totfuncexpns), core3$dar, 
       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(dar) )  + 
    geom_density( alpha = 0.5, fill="blue" ) + 
    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() )

DAR by Nonprofit Size (Revenue)

ggplot( core2, aes(x = totrevenue )) +  
  geom_density( alpha = 0.5, fill="darkorange" ) + 
  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$totrevenue), core3$dar, 
       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(dar) )  + 
    geom_density( alpha = 0.5, fill="blue" ) + 
    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() )

DAR by Nonprofit Size (Net Assets)

ggplot( core2, aes(x = totnetassetend )) +  
  geom_density( alpha = 0.5, fill="darkorange" ) + 
  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$totnetassetend), core3$dar, 
       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(dar) )  + 
    geom_density( alpha = 0.5, fill="blue" ) + 
    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$totassetsend), core3$dar, 
       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, fill="darkorange" ) + 
  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(dar) )  + 
    geom_density( alpha = 0.5, fill="blue" ) + 
    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() )

DAR by Nonprofit Age

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

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

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

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

core2 %>% 
  filter( ! is.na(age.q) ) %>% 
  ggplot( aes(dar) )  + 
    geom_density( alpha = 0.5, fill="blue" ) + 
    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() )

DAR 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$lndbldgsequipend), core3$dar, 
       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(dar) )  + 
    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.dar <- select( core, ein, tax_pd, dar )
saveRDS( core.dar, "03-data-ratios/m-01-dept-to-asset-ratio.rds" )
write.csv( core.dar, "03-data-ratios/m-01-dept-to-asset-ratio.csv" )

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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

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