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step-03-calculate-effects.Rmd
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step-03-calculate-effects.Rmd
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---
title: "Step 03 - Create Tables"
author: "Jesse Lecy"
output:
html_document:
theme: readable
df_print: paged
highlight: zenburn
toc: true
self_contained: false
number_sections: false
css: clean.css
---
```{r, echo=F, warning=FALSE, message=FALSE}
library( foreign )
library( memisc )
library( knitr )
library( dplyr )
library( xtable)
library( stargazer)
library( broom)
library( pander)
library( plotly )
# library( ReporteRs ) # no longer available
library( officer ) # replacement for ReporteRs
```
# MATH OF EFFECT SIZE
Here we define effect size as the increase or decrease in probability of professionalization when a specific covariate is present.
Typically we look at the median time to success/failure (professionalization) as the average effect, and then compare all effects to the average. In this case, we don't have a long enough panel to calculate the median time to success, so we have a choice:
* Use the model that fits
* Compare cases at age=5 since this is the last data point we have.
$$ h_j = \frac{1}{1 + e^{-(a_1D_1 + a_2D_2 + ... + a_jD_j + X\beta ) }} $$
The baseline hazard rate then for any particular time j is calculated as:
$$ h_j = \frac{1}{1 + e^{-(a_jD_j ) }} $$
And the effect size of a binary covariate at that period would be:
$$ h_j = \frac{1}{1 + e^{-(a_jD_j + \beta_1 \cdot ProfessionalFundraising) }} $$
Where you compare the case where ProfFundraising=1 to the case where ProfFundraising=0.
For continuous covariates, like HHI, you would compare the case where the covariate equals the mean, to a case where the covariate is at the mean plus a standard deviation, or conversely a case at the 25th percentile (low HHI) to 75th percentile (high HHI).
The effect in these cases will represent the change in probability of professionalization as a result of the covariate.
# LOAD DATA
```{r, warning=FALSE }
dat <- readRDS( "./Data/CompleteHazardSpells.rds" )
# copy original dataset
dat.original <- dat
```
# BASELINE CASE
## Baseline Model
```{r}
m.all <- glm( formula= prof ~ D1 + D2 + D3 + D4 + D5 + D6 + Accrual + GovtMoneyRat + HHI +
UNAgrand + FixedCostRat + SurplusRat_ndrop_w892 + EqRat_w_K +
ProfFundFeeYes - 1,
data=dat, family=binomial("logit") )
m.all %>% tidy %>% kable( digits=3 )
```
## Create Baseline Function
```{r}
# df is data frame of specific subsector
# mod is the model that was run
createBaseline <- function( df, mod )
{
# only IVs in the model
td <- df[ , c("Accrual","GovtMoneyRat","HHI","UNAgrand","FixedCostRat",
"SurplusRat_ndrop_w892","EqRat_w_K","ProfFundFeeYes") ]
period1 <- c( c(1,0,0,0,0,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period2 <- c( c(0,1,0,0,0,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period3 <- c( c(0,0,1,0,0,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period4 <- c( c(0,0,0,1,0,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period5 <- c( c(0,0,0,0,1,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period6 <- c( c(0,0,0,0,0,1), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
# period1[12] <- median( dat$SurplusRat_ndrop_w892, na.rm=T )
# period2[12] <- median( dat$SurplusRat_ndrop_w892, na.rm=T )
# period3[12] <- median( dat$SurplusRat_ndrop_w892, na.rm=T )
# period4[12] <- median( dat$SurplusRat_ndrop_w892, na.rm=T )
# period5[12] <- median( dat$SurplusRat_ndrop_w892, na.rm=T )
# period6[12] <- median( dat$SurplusRat_ndrop_w892, na.rm=T )
# period1 <- c(1,0,0,0,0,0, 0,0,0.54,0,0,-0.49853,1.9,0)
# period2 <- c(0,1,0,0,0,0, 0,0,0.54,0,0,-0.49853,1.9,0)
# period3 <- c(0,0,1,0,0,0, 0,0,0.54,0,0,-0.49853,1.9,0)
# period4 <- c(0,0,0,1,0,0, 0,0,0.54,0,0,-0.49853,1.9,0)
# period5 <- c(0,0,0,0,1,0, 0,0,0.54,0,0,-0.49853,1.9,0)
# period6 <- c(0,0,0,0,0,1, 0,0,0.54,0,0,-0.49853,1.9,0)
h1 <- ( 1 / ( 1 + exp(-( sum(period1 * mod$coefficients) )) ) )
h2 <- ( 1 / ( 1 + exp(-( sum(period2 * mod$coefficients) )) ) )
h3 <- ( 1 / ( 1 + exp(-( sum(period3 * mod$coefficients) )) ) )
h4 <- ( 1 / ( 1 + exp(-( sum(period4 * mod$coefficients) )) ) )
h5 <- ( 1 / ( 1 + exp(-( sum(period5 * mod$coefficients) )) ) )
h6 <- ( 1 / ( 1 + exp(-( sum(period6 * mod$coefficients) )) ) )
hrc <- data.frame( age=1:6, hazard.rate=round(c(h1,h2,h3,h4,h5,h6),4) )
src <- cumprod( (1-hrc$hazard.rate))
hrc <- cbind( hrc, survival.rate=src )
hrc
}
```
## Baseline Rates
```{r, results='asis'}
hrc <- createBaseline( df=dat.original, mod=m.all )
# dput( hrc )
# hrt <- calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 )
# hrc %>% pander
saveRDS( hrc, "./Data/BaselineVitalRates.rds" )
stargazer( hrc, summary=FALSE, type="html", out="./Results/BaseVitalRates.doc" )
```
## Calc Effect Function
```{r}
# prof ~ D1 + D2 + D3 + D4 + D5 + D6 + Accrual + GovtMoneyRat +
# HHI + UNAgrand + FixedCostRat + SurplusRat_ndrop_w892 + EqRat_w_K +
# ProfFundFeeYes - 1
# CALC EFFECTS AT 25th and 75th percentiles, or 0 to 1 for binary
# Accrual - 0 to 1
# GovMoneyRat - 0 to 0.5
# HHI 0.54 to 0.99
# UNAgrand 0 to 44.8
# FixedCostRat 0 to 0.06221
# SurplusRat_ndrop_w892 -0.49853 to 0.14060
# EqRat_w_K 1.9 to 24912
# ProfFundFeeYes 0 to 1
calc.effect <- function( model, df, variable.name, quant )
{
td <- df[ , c("Accrual","GovtMoneyRat","HHI","UNAgrand","FixedCostRat",
"SurplusRat_ndrop_w892","EqRat_w_K","ProfFundFeeYes") ]
period1 <- c( c(1,0,0,0,0,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period2 <- c( c(0,1,0,0,0,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period3 <- c( c(0,0,1,0,0,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period4 <- c( c(0,0,0,1,0,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period5 <- c( c(0,0,0,0,1,0), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
period6 <- c( c(0,0,0,0,0,1), unlist( lapply( td, quantile, 0.50, na.rm=T ) ) )
coef.names <- c("D1","D2","D3","D4","D5","D6","Accrual","GovtMoneyRat",
"HHI","UNAgrand","FixedCostRat","SurplusRat_ndrop_w892",
"EqRat_w_K","ProfFundFeeYes" )
this.one <- which( coef.names == variable.name )
new.value <- quantile( df[,variable.name], quant, na.rm=T )
period1[ this.one ] <- new.value
period2[ this.one ] <- new.value
period3[ this.one ] <- new.value
period4[ this.one ] <- new.value
period5[ this.one ] <- new.value
period6[ this.one ] <- new.value
h1 <- ( 1 / ( 1 + exp(-( sum(period1 * model$coefficients) )) ) )
h2 <- ( 1 / ( 1 + exp(-( sum(period2 * model$coefficients) )) ) )
h3 <- ( 1 / ( 1 + exp(-( sum(period3 * model$coefficients) )) ) )
h4 <- ( 1 / ( 1 + exp(-( sum(period4 * model$coefficients) )) ) )
h5 <- ( 1 / ( 1 + exp(-( sum(period5 * model$coefficients) )) ) )
h6 <- ( 1 / ( 1 + exp(-( sum(period6 * model$coefficients) )) ) )
hrt <- data.frame( age=1:6, hazard.rate=round( c(h1,h2,h3,h4,h5,h6), 4) )
srt <- cumprod( (1-hrt$hazard.rate))
hrt <- cbind( hrt, survival.rate=srt )
hrt
}
```
## Example Effect
```{r}
# "ACCRUAL"
hrt <- calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 )
saveRDS( hrt, "./Data/AdjustedVitalRates.rds" )
# dput( hrt )
```
## Examine Effect Sizes of Each Variable
```{r}
# prof ~ D1 + D2 + D3 + D4 + D5 + D6 + Accrual + GovtMoneyRat +
# HHI + UNAgrand + FixedCostRat + SurplusRat_ndrop_w892 + EqRat_w_K +
# ProfFundFeeYes - 1
# CALC EFFECTS AT 25th and 75th percentiles, or 0 to 1 for binary
# Accrual - 0 to 1
# GovMoneyRat - 0 to 0.5
# HHI 0.54 to 0.99
# UNAgrand 0 to 44.8
# FixedCostRat 0 to 0.06221
# SurplusRat_ndrop_w892 -0.49853 to 0.14060
# EqRat_w_K 1.9 to 24912
# ProfFundFeeYes 0 to 1
# "BASELINE MODEL"
hrc %>% kable( caption="BASELINE MODEL" )
# "ACCRUAL"
quantile( dat$Accrual, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 ) %>% kable( caption="ACCRUAL" )
# "GovtMoneyRat"
quantile( dat$GovtMoneyRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
calc.effect( model=m.all, df=dat, variable.name="GovtMoneyRat", quant=0.99 ) %>% kable( caption="GovtMoneyRat" )
# "HHI"
quantile( dat$HHI, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
calc.effect( model=m.all, df=dat, variable.name="HHI", quant=0.99 ) %>% kable( caption="HHI" )
# "UNAgrand"
quantile( dat$UNAgrand, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
calc.effect( model=m.all, df=dat, variable.name="UNAgrand", quant=0.99 ) %>% kable( caption="UNAgrand" )
# "FixedCostRat"
quantile( dat$FixedCostRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
calc.effect( model=m.all, df=dat, variable.name="FixedCostRat", quant=0.99 ) %>% kable( caption="FixedCostRat" )
# "SurplusRat_ndrop_w892"
quantile( dat$SurplusRat_ndrop_w892, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
calc.effect( model=m.all, df=dat, variable.name="SurplusRat_ndrop_w892", quant=0.99 ) %>% kable( caption="SURPLUS RATIO" )
#"EqRat_w_K"
quantile( dat$EqRat_w_K, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
calc.effect( model=m.all, df=dat, variable.name="EqRat_w_K", quant=0.99 ) %>% kable( caption="EQUITY RATIO" )
# "ProfFundFeeYes"
quantile( dat$ProfFundFeeYes, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
calc.effect( model=m.all, df=dat, variable.name="ProfFundFeeYes", quant=1 ) %>% kable( caption="PROF FUNDRAISING?" )
```
## Plot of Effects
```{r}
# "BASELINE MODEL"
# hrc %>% kable( caption="BASELINE MODEL" )
# "ACCRUAL"
quantile( dat$Accrual, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.accrual <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 )[,"survival.rate"]
# "GovMoneyRat"
quantile( dat$GovtMoneyRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.gov <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="GovtMoneyRat", quant=0.99 )[,"survival.rate"]
# "HHI"
quantile( dat$HHI, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.hhi <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="HHI", quant=0.99 )[,"survival.rate"]
# "UNAgrand"
quantile( dat$UNAgrand, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.una <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="UNAgrand", quant=0.99 )[,"survival.rate"]
# "FixedCostRat"
quantile( dat$FixedCostRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fcr <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="FixedCostRat", quant=0.99 )[,"survival.rate"]
# "SurplusRat_ndrop_w892"
quantile( dat$SurplusRat_ndrop_w892, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.surp <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="SurplusRat_ndrop_w892", quant=0.99 )[,"survival.rate"]
#"EqRat_w_K"
quantile( dat$EqRat_w_K, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.eqrat <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="EqRat_w_K", quant=0.99 )[,"survival.rate"]
# "ProfFundFeeYes"
quantile( dat$ProfFundFeeYes, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fundfee <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="ProfFundFeeYes", quant=1 )[,"survival.rate"]
plot( 1:6, rep(0,6), type="n", ylim=c(-0.1,0.5), bty="n",
ylab="Increase in Prob. of Professionalization", xlab="Time in Years")
abline( h=0, lty=2, col="red" )
points( 1:6, effect.accrual, type="b", pch=19, cex=0.5 )
points( 1:6, effect.gov, type="b", pch=19, cex=0.5 )
points( 1:6, effect.hhi, type="b", pch=19, cex=0.5 )
points( 1:6, effect.una, type="b", pch=19, cex=0.5 )
points( 1:6, effect.fcr, type="b", pch=19, cex=0.5 )
points( 1:6, effect.surp, type="b", pch=19, cex=0.5 )
points( 1:6, effect.eqrat, type="b", pch=19, cex=0.5 )
points( 1:6, effect.fundfee, type="b", pch=19, cex=0.5 )
Effect.Size <- data.frame(
prob.increase=c( effect.accrual[6], effect.gov[6], effect.hhi[6], effect.una[6],
effect.fcr[6], effect.surp[6], effect.eqrat[6], effect.fundfee[6] ),
var.name=c("Accrual Accounting","Government Revenue","Revenue Concentration",
"Unrestricted Net Assets","Fixed Cost Ratios","Surplus Ratio",
"Equity Ratio","Professional Fundraising")
)
Effect.Size
this.order <- order( Effect.Size$prob.increase ) # set one order for all graphs
ES.all <- Effect.Size[ this.order , ]
Effect.Size <- arrange( Effect.Size, prob.increase )
dotchart( Effect.Size$prob.increase, labels=Effect.Size$var.name, pch=19, bty="n" )
```
```{r, echo=F}
# p <- plot_ly( Effect.Size , x=prob.increase, y=var.name, mode = "markers", marker=list(color="gray", size=20) )
#
# layout(
# title = "Effect Size of Each Study Variable",
# xaxis = list(title = "Increase in Probability of Professionalization"),
# yaxis = list(title = ""),
# margin = list(l = 200)
# )
#
# p
```
# ARTS
```{r}
dat <- dat.original[ dat.original$Subsector2 == "Arts" , ]
# > unique( as.character(dat$Subsector2) )
# [1] "Edu(Exclu Higher)" "Human Svcs"
# [3] "Other"
# [5] "Public" "Health"
# [7] "Higher Edu or Hospitals"
m.arts <- glm( formula= prof ~ D1 + D2 + D3 + D4 + D5 + D6 + Accrual + GovtMoneyRat + HHI +
UNAgrand + FixedCostRat + SurplusRat_ndrop_w892 + EqRat_w_K +
ProfFundFeeYes - 1,
data=dat, family=binomial("logit") )
m.arts %>% tidy %>% kable( digits=3 )
# "BASELINE MODEL"
hrc <- createBaseline( df=dat, mod=m.arts )
hrc %>% pander
# "ACCRUAL"
quantile( dat$Accrual, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.accrual <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 )[,"survival.rate"]
# "GovMoneyRat"
quantile( dat$GovtMoneyRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.gov <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="GovtMoneyRat", quant=0.99 )[,"survival.rate"]
# "HHI"
quantile( dat$HHI, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.hhi <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="HHI", quant=0.99 )[,"survival.rate"]
# "UNAgrand"
quantile( dat$UNAgrand, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.una <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="UNAgrand", quant=0.99 )[,"survival.rate"]
# "FixedCostRat"
quantile( dat$FixedCostRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fcr <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="FixedCostRat", quant=0.99 )[,"survival.rate"]
# "SurplusRat_ndrop_w892"
quantile( dat$SurplusRat_ndrop_w892, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.surp <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="SurplusRat_ndrop_w892", quant=0.99 )[,"survival.rate"]
#"EqRat_w_K"
quantile( dat$EqRat_w_K, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.eqrat <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="EqRat_w_K", quant=0.99 )[,"survival.rate"]
# "ProfFundFeeYes"
quantile( dat$ProfFundFeeYes, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fundfee <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="ProfFundFeeYes", quant=1 )[,"survival.rate"]
Effect.Size <- data.frame(
prob.increase=c( effect.accrual[6], effect.gov[6], effect.hhi[6], effect.una[6],
effect.fcr[6], effect.surp[6], effect.eqrat[6], effect.fundfee[6] ),
var.name=c("Accrual Accounting","Government Revenue","Revenue Concentration",
"Unrestricted Net Assets","Fixed Cost Ratios","Surplus Ratio",
"Equity Ratio","Professional Fundraising")
)
Effect.Size
ES.arts <- Effect.Size[ this.order , ]
Effect.Size <- arrange( Effect.Size, prob.increase )
#dotchart( Effect.Size$prob.increase, labels=Effect.Size$var.name, pch=19, bty="n" )
# p <- plot_ly( Effect.Size , x=prob.increase, y=var.name, mode = "markers", marker=list(color="gray", size=20) )
#
# layout(
# title = "Effect Size of Each Study Variable",
# xaxis = list(title = "Increase in Probability of Professionalization"),
# yaxis = list(title = ""),
# margin = list(l = 200)
# )
# p
```
# HEALTH
```{r}
dat <- dat.original[ dat.original$Subsector2 == "Health" , ]
# > unique( as.character(dat$Subsector2) )
# [1] "Edu(Exclu Higher)" "Human Svcs"
# [3] "Other"
# [5] "Public"
# [7] "Higher Edu or Hospitals"
m.health <- glm( formula= prof ~ D1 + D2 + D3 + D4 + D5 + D6 + Accrual + GovtMoneyRat + HHI +
UNAgrand + FixedCostRat + SurplusRat_ndrop_w892 + EqRat_w_K +
ProfFundFeeYes - 1,
data=dat, family=binomial("logit") )
m.health %>% tidy %>% kable( digits=3 )
# "BASELINE MODEL"
hrc <- createBaseline( df=dat, mod=m.health )
hrc %>% pander
# "ACCRUAL"
quantile( dat$Accrual, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.accrual <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 )[,"survival.rate"]
# "GovMoneyRat"
quantile( dat$GovtMoneyRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.gov <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="GovtMoneyRat", quant=0.99 )[,"survival.rate"]
# "HHI"
quantile( dat$HHI, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.hhi <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="HHI", quant=0.99 )[,"survival.rate"]
# "UNAgrand"
quantile( dat$UNAgrand, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.una <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="UNAgrand", quant=0.99 )[,"survival.rate"]
# "FixedCostRat"
quantile( dat$FixedCostRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fcr <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="FixedCostRat", quant=0.99 )[,"survival.rate"]
# "SurplusRat_ndrop_w892"
quantile( dat$SurplusRat_ndrop_w892, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.surp <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="SurplusRat_ndrop_w892", quant=0.99 )[,"survival.rate"]
#"EqRat_w_K"
quantile( dat$EqRat_w_K, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.eqrat <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="EqRat_w_K", quant=0.99 )[,"survival.rate"]
# "ProfFundFeeYes"
quantile( dat$ProfFundFeeYes, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fundfee <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="ProfFundFeeYes", quant=1 )[,"survival.rate"]
Effect.Size <- data.frame(
prob.increase=c( effect.accrual[6], effect.gov[6], effect.hhi[6], effect.una[6],
effect.fcr[6], effect.surp[6], effect.eqrat[6], effect.fundfee[6] ),
var.name=c("Accrual Accounting","Government Revenue","Revenue Concentration",
"Unrestricted Net Assets","Fixed Cost Ratios","Surplus Ratio",
"Equity Ratio","Professional Fundraising")
)
Effect.Size
ES.health <- Effect.Size[ this.order , ]
Effect.Size <- arrange( Effect.Size, prob.increase )
#dotchart( Effect.Size$prob.increase, labels=Effect.Size$var.name, pch=19, bty="n" )
# p <- plot_ly( Effect.Size , x=prob.increase, y=var.name, mode = "markers", marker=list(color="gray", size=20) )
#
# layout(
# title = "Effect Size of Each Study Variable",
# xaxis = list(title = "Increase in Probability of Professionalization"),
# yaxis = list(title = ""),
# margin = list(l = 200)
# )
# p
```
# HUMAN SERVICES
```{r}
dat <- dat.original[ dat.original$Subsector2 == "Human Svcs" , ]
# > unique( as.character(dat$Subsector2) )
# [1] "Edu(Exclu Higher)"
# [3] "Other"
# [5] "Public"
# [7] "Higher Edu or Hospitals"
m.hs <- glm( formula= prof ~ D1 + D2 + D3 + D4 + D5 + D6 + Accrual + GovtMoneyRat + HHI +
UNAgrand + FixedCostRat + SurplusRat_ndrop_w892 + EqRat_w_K +
ProfFundFeeYes - 1,
data=dat, family=binomial("logit") )
m.hs %>% tidy %>% kable( digits=3 )
# "BASELINE MODEL"
hrc <- createBaseline( df=dat, mod=m.hs )
hrc %>% pander
# "ACCRUAL"
quantile( dat$Accrual, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.accrual <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 )[,"survival.rate"]
# "GovMoneyRat"
quantile( dat$GovtMoneyRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.gov <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="GovtMoneyRat", quant=0.99 )[,"survival.rate"]
# "HHI"
quantile( dat$HHI, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.hhi <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="HHI", quant=0.99 )[,"survival.rate"]
# "UNAgrand"
quantile( dat$UNAgrand, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.una <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="UNAgrand", quant=0.99 )[,"survival.rate"]
# "FixedCostRat"
quantile( dat$FixedCostRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fcr <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="FixedCostRat", quant=0.99 )[,"survival.rate"]
# "SurplusRat_ndrop_w892"
quantile( dat$SurplusRat_ndrop_w892, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.surp <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="SurplusRat_ndrop_w892", quant=0.99 )[,"survival.rate"]
#"EqRat_w_K"
quantile( dat$EqRat_w_K, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.eqrat <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="EqRat_w_K", quant=0.99 )[,"survival.rate"]
# "ProfFundFeeYes"
quantile( dat$ProfFundFeeYes, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fundfee <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="ProfFundFeeYes", quant=1 )[,"survival.rate"]
Effect.Size <- data.frame(
prob.increase=c( effect.accrual[6], effect.gov[6], effect.hhi[6], effect.una[6],
effect.fcr[6], effect.surp[6], effect.eqrat[6], effect.fundfee[6] ),
var.name=c("Accrual Accounting","Government Revenue","Revenue Concentration",
"Unrestricted Net Assets","Fixed Cost Ratios","Surplus Ratio",
"Equity Ratio","Professional Fundraising")
)
Effect.Size
ES.hs <- Effect.Size[ this.order , ]
Effect.Size <- arrange( Effect.Size, prob.increase )
#dotchart( Effect.Size$prob.increase, labels=Effect.Size$var.name, pch=19, bty="n" )
p <- plot_ly( Effect.Size , x=~prob.increase, y=~var.name, type="scatter",
mode="marker", marker=list(color="gray", size=20) ) %>%
layout(
title = "Effect Size of Each Study Variable",
xaxis = list(title = "Increase in Probability of Professionalization"),
yaxis = list(title = ""),
margin = list(l = 200)
)
p
```
# PUBLIC
```{r}
dat <- dat.original[ dat.original$Subsector2 == "Public" , ]
# > unique( as.character(dat$Subsector2) )
# [1] "Edu(Exclu Higher)"
# [3] "Other"
# [5]
# [7] "Higher Edu or Hospitals"
m.pub <- glm( formula= prof ~ D1 + D2 + D3 + D4 + D5 + D6 + Accrual + GovtMoneyRat + HHI +
UNAgrand + FixedCostRat + SurplusRat_ndrop_w892 + EqRat_w_K +
ProfFundFeeYes - 1,
data=dat, family=binomial("logit") )
m.pub %>% tidy %>% kable( digits=3 )
# "BASELINE MODEL"
hrc <- createBaseline( df=dat, mod=m.pub )
hrc %>% pander
# "ACCRUAL"
quantile( dat$Accrual, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.accrual <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 )[,"survival.rate"]
# "GovMoneyRat"
quantile( dat$GovtMoneyRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.gov <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="GovtMoneyRat", quant=0.99 )[,"survival.rate"]
# "HHI"
quantile( dat$HHI, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.hhi <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="HHI", quant=0.99 )[,"survival.rate"]
# "UNAgrand"
quantile( dat$UNAgrand, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.una <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="UNAgrand", quant=0.99 )[,"survival.rate"]
# "FixedCostRat"
quantile( dat$FixedCostRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fcr <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="FixedCostRat", quant=0.99 )[,"survival.rate"]
# "SurplusRat_ndrop_w892"
quantile( dat$SurplusRat_ndrop_w892, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.surp <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="SurplusRat_ndrop_w892", quant=0.99 )[,"survival.rate"]
#"EqRat_w_K"
quantile( dat$EqRat_w_K, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.eqrat <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="EqRat_w_K", quant=0.99 )[,"survival.rate"]
# "ProfFundFeeYes"
quantile( dat$ProfFundFeeYes, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fundfee <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="ProfFundFeeYes", quant=1 )[,"survival.rate"]
Effect.Size <- data.frame(
prob.increase=c( effect.accrual[6], effect.gov[6], effect.hhi[6], effect.una[6],
effect.fcr[6], effect.surp[6], effect.eqrat[6], effect.fundfee[6] ),
var.name=c("Accrual Accounting","Government Revenue","Revenue Concentration",
"Unrestricted Net Assets","Fixed Cost Ratios","Surplus Ratio",
"Equity Ratio","Professional Fundraising")
)
Effect.Size
ES.pub <- Effect.Size[ this.order , ]
Effect.Size <- arrange( Effect.Size, prob.increase )
#dotchart( Effect.Size$prob.increase, labels=Effect.Size$var.name, pch=19, bty="n" )
# p <- plot_ly( Effect.Size , x=prob.increase, y=var.name, mode = "markers", marker=list(color="gray", size=20) )
#
# layout(
# title = "Effect Size of Each Study Variable",
# xaxis = list(title = "Increase in Probability of Professionalization"),
# yaxis = list(title = ""),
# margin = list(l = 200)
# )
# p
```
# EDUCATION
```{r}
dat <- dat.original[ dat.original$Subsector2 == "Edu(Exclu Higher)" , ]
# > unique( as.character(dat$Subsector2) )
# [1]
# [3] "Other"
# [5]
# [7] "Higher Edu or Hospitals"
m.edu <- glm( formula= prof ~ D1 + D2 + D3 + D4 + D5 + D6 + Accrual + GovtMoneyRat + HHI +
UNAgrand + FixedCostRat + SurplusRat_ndrop_w892 + EqRat_w_K +
ProfFundFeeYes - 1,
data=dat, family=binomial("logit") )
m.edu %>% tidy %>% kable( digits=3 )
# "BASELINE MODEL"
hrc <- createBaseline( df=dat, mod=m.edu )
hrc %>% pander
# "ACCRUAL"
quantile( dat$Accrual, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.accrual <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="Accrual", quant=1 )[,"survival.rate"]
# "GovMoneyRat"
quantile( dat$GovtMoneyRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.gov <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="GovtMoneyRat", quant=0.99 )[,"survival.rate"]
# "HHI"
quantile( dat$HHI, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.hhi <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="HHI", quant=0.99 )[,"survival.rate"]
# "UNAgrand"
quantile( dat$UNAgrand, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.una <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="UNAgrand", quant=0.99 )[,"survival.rate"]
# "FixedCostRat"
quantile( dat$FixedCostRat, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fcr <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="FixedCostRat", quant=0.99 )[,"survival.rate"]
# "SurplusRat_ndrop_w892"
quantile( dat$SurplusRat_ndrop_w892, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.surp <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="SurplusRat_ndrop_w892", quant=0.99 )[,"survival.rate"]
#"EqRat_w_K"
quantile( dat$EqRat_w_K, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.eqrat <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="EqRat_w_K", quant=0.99 )[,"survival.rate"]
# "ProfFundFeeYes"
quantile( dat$ProfFundFeeYes, c(0.05,0.25,0.5,0.75,0.95,0.99,1) )
effect.fundfee <- hrc$survival.rate - calc.effect( model=m.all, df=dat, variable.name="ProfFundFeeYes", quant=1 )[,"survival.rate"]
Effect.Size <- data.frame(
prob.increase=c( effect.accrual[6], effect.gov[6], effect.hhi[6], effect.una[6],
effect.fcr[6], effect.surp[6], effect.eqrat[6], effect.fundfee[6] ),
var.name=c("Accrual Accounting","Government Revenue","Revenue Concentration",
"Unrestricted Net Assets","Fixed Cost Ratios","Surplus Ratio",
"Equity Ratio","Professional Fundraising")
)
Effect.Size
ES.edu <- Effect.Size[ this.order , ]
Effect.Size <- arrange( Effect.Size, prob.increase )
#dotchart( Effect.Size$prob.increase, labels=Effect.Size$var.name, pch=19, bty="n" )
# p <- plot_ly( Effect.Size , x=prob.increase, y=var.name, mode = "markers", marker=list(color="gray", size=20) )
#
# layout(
# title = "Effect Size of Each Study Variable",
# xaxis = list(title = "Increase in Probability of Professionalization"),
# yaxis = list(title = ""),
# margin = list(l = 200)
# )
# p
```