diff --git a/README.md b/README.md index 7b2c056..f26c37e 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,7 @@ # The Evolving Nonprofit Sector Data Infrastructure -Demo scripts for the data workshop at ARNOVA Conference 2024: - -WORKFLOW EXAMPLE +A demonstration of reproducible workflows with nonprofit data for the ARNOVA Conference 2024. --- @@ -29,8 +27,8 @@ If you are having problems downloading packages try: - [NODC on GitHub](https://github.com/Nonprofit-Open-Data-Collective) - [List of Nonprofit Packages](https://nonprofit-open-data-collective.github.io/tools/) -### R Packages +### R Packages ```r package.list <- @@ -45,12 +43,22 @@ install.packages( package.list ) ### Workflow Demo +WORKFLOW EXAMPLE + +1. Build tables using the irs990efile package. +1. Standardize names of the leadership team present in Part VII (the compensation tables) using the peopleparser package. +1. Standardize titles in Part VII using the titleclassifier package. +1. Identify CEO transition years within the data. +1. Build a financial dataset by combining the following 990 Parts: revenues, expenses, and assets. +1. Generate common financial operating ratios using the fiscal package. +1. Add NTEE codes and standardized address fields from the Unified BMF. +1. Merge financial tables and compensation tables into a single table for the analysis. + ```r +# REPRODUCIBLE WORKFLOWS library( dplyr ) library( data.table ) -library( peopleparser ) -library( titleclassifier ) nodc <- "https://raw.githubusercontent.com/Nonprofit-Open-Data-Collective/" repo <- "arnova-2024/refs/heads/main/" @@ -65,6 +73,11 @@ df <- merge( hd.2016, p1.2016, by="URL", all=TRUE ) df <- merge( df, bmf, by="EIN2", all.x=TRUE ) ``` +### NCCS CORE Panel Downloads: + +Sector in Brief Dashboard + + ### This repo: diff --git a/draft/Glass_Cliff_Toy_Example.Rmd b/draft/Glass_Cliff_Toy_Example.Rmd deleted file mode 100644 index 7f2eaae..0000000 --- a/draft/Glass_Cliff_Toy_Example.Rmd +++ /dev/null @@ -1,508 +0,0 @@ ---- -title: "Glass Cliff Toy Example" -author: "Tiana Marrese" -date: "2024-11-19" -output: html_document ---- - - - -# Nonprofits and the Glass Cliff -## A Research Exploration - -This script will present several new components of the Nonprofit Open Data Collective through a toy example investigating the "Glass Cliff phenomenon" or the idea the women are more likely to be appointed to precarious positions of power relative to their male counterparts. We will specifically highlight the integrated data environment and the numerous resources available for research on the third sector. - - -### Important Resources - -[Data Catalog](https://nccs.urban.org/nccs/catalogs/catalog-efile.html) -\ -[Data Dictionary](https://nonprofit-open-data-collective.github.io/irs990efile/data-dictionary/data-dictionary.html) -\ -[Open Data Collective Landing Page](https://nonprofit-open-data-collective.github.io/tools/) - -\ - - -```{r setup, results='hide', message = F} - - -#devtools::install_github( "Nonprofit-Open-Data-Collective/peopleparser" ) -#devtools::install_github( 'nonprofit-open-data-collective/titleclassifier' ) -#devtools::install_github('nonprofit-open-data-collective/fiscal') - -library(dplyr) -library(utils) -library(peopleparser) -library(titleclassifier) -library( fiscal ) -library(tidyr) -library(RecordLinkage) -library(stringr) -library(reshape2) -library(ggplot2) -library(ggrepel) - -#Change working directory to personal path -setwd("~/Tiana_School_Work/PhD/Dissertation/Glass_Cliff_Paper") - -``` - - - - - - -### Getting Main Data - -Processed IRS 990 Efile data are housed in the [data catalog](https://nccs.urban.org/nccs/catalogs/catalog-efile.html) with the respective [data dictionaries ](https://nonprofit-open-data-collective.github.io/irs990efile/data-dictionary/data-dictionary.html). You can find further information about sources, processing, and final parsing on these sites . -\ -\ -For our toy example, we are particularly interested in the part of the 990 form that collects information about the board members in a nonprofit. This is known as Part VII data. We will begin by pulling two years of this data. -\ -\ -There are two main ways to access the data. You can download the data locally to your computer from the data catalog and read it into your R environment or you can use pre-built functions to read it directly in R. We present examples of both below. -\ - -```{r} - -#Reading in locally store data -df2010 <- read.csv( "Coding/Data/PartVII/PartVII-2010.csv" ) -df2011 <- read.csv( "Coding/Data/PartVII/PartVII-2011.csv" ) - - - -#Pre-built functions: -nodc <- "https://raw.githubusercontent.com/Nonprofit-Open-Data-Collective/" -repo <- "arnova-2024/refs/heads/main/" -file <- "functions.R" -source( paste0( nodc, repo, file ) ) - - -df2010.fun <- get_partvii(2010) -df2011.fun <- get_partvii(2011) - - - -``` - - -\ -\ - -### Preparing Main Data - -The main variables of interest in these data are the board member names and their titles. We will create a data frame that contains both years of our data then we will utilize pre-built packages from the [Nonprofit Open Data Repositories](https://github.com/orgs/Nonprofit-Open-Data-Collective/repositories) to clean these variables further (the packages are `peopleparser` and `titleclassifier`). Eventually, pre-cleaned versions of the data will be available for download but for now we highlight the steps to utilize these packages directly. -\ - -```{r, results = 'hide'} - -#First, we want to combine the two years of data -#For simplicity, we will only work with the data pulled locally. - -#We need to: -#Remove "X" columns from the dfs that have this -#Also remove "average hours for related org (COMP_DTK_AVE_HOUR_WEEK_RL)" since this isn't present in all of them -#You could alternatively add a column of this variable to the dfs that don't have this, we don't this var so we will remove it - -df2011 <- df2011 %>% select(-c( "X", "F9_07_COMP_DTK_AVE_HOUR_WEEK_RL")) - -#Let's put them all together -df <- rbind(df2010, df2011) - - -#The packages to clean the name and title variables can take a bit to run. So before initiating them, we can make some assumptions about the observations we want. We are specifically interested in locating paid CEOs of NP. We will assume these positions must make more than $1 and work more than 15 hours - -df_sub <- df %>% filter(!is.na(F9_07_COMP_DTK_COMP_ORG) & - F9_07_COMP_DTK_COMP_ORG > 1 & - !is.na(F9_07_COMP_DTK_AVE_HOUR_WEEK) & - F9_07_COMP_DTK_AVE_HOUR_WEEK > 15 - ) - -#For simplicity and to limit run time, we will take a sample of 10 people from above and perform the functions provided by peopleparse and titleclassifier. You can comment out the next line if you would like to do the entire df (but be aware that it will take a bit of time to run) - -df_sub <- df_sub[1:10,] -#write.csv(df_sub, "PARTVII_10employees_2009_2010.csv") - -``` - - -### Utilizing NP Open Data Collective Packages - -The Nonprofit Open Data Collection provides tools in addition to dataframes that can be used to clean, process, and extend the data. We highlight two below. `peopleparser` provides name standarization and gender coding while `titleclassifier` standardized titles of the 990 form. Examples of their usage with a subset of 10 nonprofit employees from the Part VII 990 data are below. -\ - -#### PeopleParser - -```{r, message = F} - - -df_sub <- read.csv(file = "PARTVII_10employees_2009_2010.csv") - - -#################################### -#Peopleparser -#https://github.com/Nonprofit-Open-Data-Collective/peopleparser -#################################### - -#Now let's use peopleparser to clean the names. This function can take a bit to run so let's further limit our df to only unique names -df_unique_names <- df_sub %>% select(F9_07_COMP_DTK_NAME_PERS) %>% - distinct() - - -#Now doing people parser -df_names <- parse.names(df_unique_names$F9_07_COMP_DTK_NAME_PERS) - -#We can left_join these names back onto the df -df_sub <- df_sub %>% left_join(df_names, by = c("F9_07_COMP_DTK_NAME_PERS" = "name")) - - -#write.csv(df_sub, file = "PARTVII_10employees_2009_2010_names.csv") - -``` - - -#### TitleClassifier - -```{r, message = F} - -df_sub <- read.csv(file = "PARTVII_10employees_2009_2010_names.csv") - -#################################### -#Titleclassifier -#https://github.com/Nonprofit-Open-Data-Collective/titleclassifier -#################################### - -#Since this function also takes a bit of time, we will narrow down the titles to their unique occurrences based on the vars input to the function -df_unique_titles <- df_sub %>% - distinct(F9_07_COMP_DTK_TITLE, - FORMTYPE, - F9_07_COMP_DTK_AVE_HOUR_WEEK, - F9_07_COMP_DTK_COMP_ORG, - F9_07_COMP_DTK_EMPL_BEN, - F9_07_COMP_DTK_COMP_RLTD, - F9_07_COMP_DTK_COMP_OTH, - F9_07_COMP_DTK_POS_INDIV_TRUST_X, - F9_07_COMP_DTK_POS_INST_TRUST_X, - F9_07_COMP_DTK_POS_OFF_X, - F9_07_COMP_DTK_POS_KEY_EMPL_X, - F9_07_COMP_DTK_POS_HIGH_COMP_X, - F9_07_COMP_DTK_POS_FORMER_X, - F9_07_COMP_DTK_NAME_PERS , .keep_all = T) - - -#Running the title classifier now -df_titles <- df_unique_titles %>% - standardize_df() %>% - remove_dates() %>% - standardize_conj() %>% - split_titles() %>% - standardize_spelling() %>% - gen_status_codes() %>% - standardize_titles() %>% - categorize_titles() - - - - -#Note: the title classifier seems to only keep unique rows by a person's name, not each occurrence they have. So columns like hours worked and pay by individual are not completely correct. But the total info about the org should be fine to keep. Will need to eventually revisit assumptions made in the package - - -#now we join back to df_sub -#df_titles may have multiple rows for people that list "x/y" in their title -#So we want to do a full join where we keep all rows from both -df_sub_names_titles <- df_sub %>% full_join(df_titles, by = c("EIN"= "ein", - "F9_07_COMP_DTK_NAME_PERS" = "dtk.name", - "F9_07_COMP_DTK_TITLE" = "title.raw"), - relationship = "many-to-many") - - -#write.csv(df_sub_names_titles, "PARTVII_10employees_2009_2010_names_titles.csv") - -``` - - - -### Glass Cliff Analysis - -There are several data cleaning and preparation steps that need to be performed on the base data before our analysis. These are beyond the scope of this toy example. Instead, we provide a cleaned subset of the data. We provide two dataframes of 1000 and 10 unique nonprofits that experienced at least one CEO transition between 2009 and 2019. It should be noted that it is possible for these organizations to experience more than one transition during this time frame. As such, we have a total of 1067 transitions. We can analyze various facets of these transitions. -\ - -```{r} - -ceo_trans_10 <- read.csv("toy_CEO_trans_10EIN.csv" ) -ceo_trans_1000 <- read.csv("toy_CEO_trans_1000EIN.csv") - -``` - - -#### Financial Data Joining -The first thing we want to do with these data are to add financial information about the orgs during each respective year. We can again pull this info from the open data collective. This step takes about 10 minutes to run so we will comment out the lines and run similar steps with the data of 10 nonprofits and pre-subset financial dataframes. -\ - -```{r, results = 'hide'} - - -summary <- read.csv("F9-P01-T00-SUMMARY-SAMPLE-10.csv") -revenue <- read.csv("F9-P08-T00-REVENUE-SAMPLE-10.csv") -expenses <- read.csv("F9-P09-T00-EXPENSES-SAMPLE-10.csv") - - -#let's join everything together: - -ceo_trans_10_fncl <- ceo_trans_10 %>% left_join(summary, by = c("TAXYR" = "TAX_YEAR", - "EIN" = "ORG_EIN")) - -ceo_trans_10_fncl <- ceo_trans_10_fncl %>% left_join(revenue, by = c("TAXYR" = "TAX_YEAR", - "EIN" = "ORG_EIN")) - -ceo_trans_10_fncl <- ceo_trans_10_fncl %>% left_join(expenses, by = c("TAXYR" = "TAX_YEAR", - "EIN" = "ORG_EIN")) - -write.csv(ceo_trans_10_fncl, "CEO_trans_10EIN_fncl.csv" ) - - -# summary2009 <- get_table( "F9-P01-T00-SUMMARY", year=2009 ) -# summary2010 <- get_table( "F9-P01-T00-SUMMARY", year=2010 ) -# summary2011 <- get_table( "F9-P01-T00-SUMMARY", year=2011 ) -# summary2012 <- get_table( "F9-P01-T00-SUMMARY", year=2012 ) -# summary2013 <- get_table( "F9-P01-T00-SUMMARY", year=2013 ) -# summary2014 <- get_table( "F9-P01-T00-SUMMARY", year=2014 ) -# summary2015 <- get_table( "F9-P01-T00-SUMMARY", year=2015 ) -# summary2016 <- get_table( "F9-P01-T00-SUMMARY", year=2016 ) -# summary2017 <- get_table( "F9-P01-T00-SUMMARY", year=2017 ) -# summary2018 <- get_table( "F9-P01-T00-SUMMARY", year=2018 ) -# summary2019 <- get_table( "F9-P01-T00-SUMMARY", year=2019 ) -# -# revenue2009 <- get_table( "F9-P08-T00-REVENUE", year=2009 ) -# revenue2010 <- get_table( "F9-P08-T00-REVENUE", year=2010 ) -# revenue2011 <- get_table( "F9-P08-T00-REVENUE", year=2011 ) -# revenue2012 <- get_table( "F9-P08-T00-REVENUE", year=2012 ) -# revenue2013 <- get_table( "F9-P08-T00-REVENUE", year=2013 ) -# revenue2014 <- get_table( "F9-P08-T00-REVENUE", year=2014 ) -# revenue2015 <- get_table( "F9-P08-T00-REVENUE", year=2015 ) -# revenue2016 <- get_table( "F9-P08-T00-REVENUE", year=2016 ) -# revenue2017 <- get_table( "F9-P08-T00-REVENUE", year=2017 ) -# revenue2018 <- get_table( "F9-P08-T00-REVENUE", year=2018 ) -# revenue2019 <- get_table( "F9-P08-T00-REVENUE", year=2019 ) -# -# -# expenses2009 <- get_table( "F9-P09-T00-EXPENSES", year=2009 ) -# expenses2010 <- get_table( "F9-P09-T00-EXPENSES", year=2010 ) -# expenses2011 <- get_table( "F9-P09-T00-EXPENSES", year=2011 ) -# expenses2012 <- get_table( "F9-P09-T00-EXPENSES", year=2012 ) -# expenses2013 <- get_table( "F9-P09-T00-EXPENSES", year=2013 ) -# expenses2014 <- get_table( "F9-P09-T00-EXPENSES", year=2014 ) -# expenses2015 <- get_table( "F9-P09-T00-EXPENSES", year=2015 ) -# expenses2016 <- get_table( "F9-P09-T00-EXPENSES", year=2016 ) -# expenses2017 <- get_table( "F9-P09-T00-EXPENSES", year=2017 ) -# expenses2018 <- get_table( "F9-P09-T00-EXPENSES", year=2018 ) -# expenses2019 <- get_table( "F9-P09-T00-EXPENSES", year=2019 ) -# -# -# #Let's put them all together -# summary <- rbind(as.data.frame(summary2009), as.data.frame(summary2010),as.data.frame(summary2011), -# as.data.frame(summary2012),as.data.frame(summary2013),as.data.frame(summary2014), -# as.data.frame(summary2015),as.data.frame(summary2016),as.data.frame(summary2017), -# as.data.frame(summary2018),as.data.frame(summary2019)) -# -# revenue <- rbind(as.data.frame(revenue2009),as.data.frame(revenue2010),as.data.frame(revenue2011), -# as.data.frame(revenue2012),as.data.frame(revenue2013),as.data.frame(revenue2014), -# as.data.frame(revenue2015),as.data.frame(revenue2016),as.data.frame(revenue2017), -# as.data.frame(revenue2018),as.data.frame(revenue2019)) -# -# expenses <- rbind(as.data.frame(expenses2009),as.data.frame(expenses2010),as.data.frame(expenses2011), -# as.data.frame(expenses2012),as.data.frame(expenses2013),as.data.frame(expenses2014), -# as.data.frame(expenses2015),as.data.frame(expenses2016),as.data.frame(expenses2017), -# as.data.frame(expenses2018),as.data.frame(expenses2019)) -# -# -# -# -# #Orgs can return various versions of their tax forms each year -# #For now, we will keep only the first observation, true analysis will need to revisit this decision -# summary <- summary %>% group_by(ORG_EIN, TAX_YEAR) %>% -# filter(row_number()==1) -# -# revenue <- revenue %>% group_by(ORG_EIN, TAX_YEAR) %>% -# filter(row_number()==1) -# -# expenses <- expenses %>% group_by(ORG_EIN, TAX_YEAR) %>% -# filter(row_number()==1) -# -# -# -# #Saving environment space by removing the other dfs -# remove(summary2009, summary2010, summary2011, summary2012, summary2013, summary2014, -# summary2015, summary2016, summary2017, summary2018, summary2019 ) -# remove(expenses2009, expenses2010, expenses2011, expenses2012, expenses2013, expenses2014, -# expenses2015, expenses2016, expenses2017, expenses2018, expenses2019 ) -# remove(balance2009, balance2010, balance2011, balance2012, balance2013, balance2014, -# balance2015, balance2016, balance2017, balance2018, balance2019 ) -# #Let's join everything together -# -# -# ceo_trans_1000_fncl <- ceo_trans_1000 %>% left_join(summary, by = c("TAXYR" = "TAX_YEAR", -# "EIN" = "ORG_EIN")) -# -# ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% left_join(expenses, by = c("TAXYR" = "TAX_YEAR", -# "EIN" = "ORG_EIN")) -# -# ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% left_join(revenue, by = c("TAXYR" = "TAX_YEAR", -# "EIN" = "ORG_EIN")) -# -# -# write.csv(ceo_trans_1000_fncl, "toy_CEO_trans_1000EIN_fncl.csv") - - -``` - - - - -#### Summary Statistics of Transitions - -Now we're ready for some analyses! We are most interested in understanding transitions by their gendering. That is to say that we want to know if there are differences in observable firm dimensions for nonprofits that experience a CEO transition from male to male relative to the other permutations of male to female, female to male, and female to female. Let's start by looking at org differences by these transition types. We will use the 1000 EIN observations for this analysis. We have provided this dataset with the financial data attached (this was accomplished by running the commented lines above). For simplicity, we will only consider a few firm dimensions such as employee size and total assets. - - - -```{r} - -#This df -ceo_trans_1000_fncl <- read.csv("toy_CEO_trans_1000EIN_fncl.csv") - -#First need to denote transitions -ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% - arrange(EIN, transition_no, TAXYR)%>% #for each org, transition, and year, - group_by(EIN, transition_no) %>% - mutate(period = row_number())%>% #let's denote which period the row is relative to (i.e., how many years before /after a transition) - ungroup() - -#Let's make this variable a bit more readable -ceo_trans_1000_fncl$period <- paste0("T", ceo_trans_1000_fncl$period-3) -ceo_trans_1000_fncl$period <- factor(ceo_trans_1000_fncl$period, levels = c("T-2", "T-1", "T0" , "T1", "T2")) - - -#Need to get transition type too -#We want to know if the transition is M>M, M>F, F>M, or F>M -#We will first create a string of the CEO gender throughout the 5 year period -#The we will create a column that keeps the first and last gender (this is the gender transition) -ceo_trans_1000_fncl<- ceo_trans_1000_fncl %>% - group_by(EIN, transition_no)%>% - mutate(trans_tot = str_c(unlist(strsplit(paste(gender, collapse = ""), "")), collapse = "") - ) %>% - mutate( - trans_type = str_sub(trans_tot, 1, 1) %>% - str_c(str_sub(trans_tot, nchar(trans_tot), nchar(trans_tot))) # Combine 1st and last letter - - ) - -#We want this variable to be a factor -ceo_trans_1000_fncl$trans_type <- as.factor(ceo_trans_1000_fncl$trans_type) - - - -#We're going to get firm attributes by transition type -#Let's consider the attributes at t-1 -#We'll consider basic variables like number of employees, total assets, etc. - -#Note that further data cleaning is necessary for final presentation due to NA values input into some of these columns. For now, we will just remove the nas and continue with our analysis - -temp <- ceo_trans_1000_fncl %>% group_by(trans_type)%>% - filter(period == "T-1") %>% - summarize(count = n(), - num_emp_av =mean(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T), - num_emp_sd = sd(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T), - num_vol_av = mean(F9_01_ACT_GVRN_VOL_TOT, na.rm = T), - num_vol_sd = sd(F9_01_ACT_GVRN_VOL_TOT, na.rm = T), - total_exp_av = mean(F9_01_EXP_TOT_PY, na.rm = T), - total_exp_sd = sd(F9_01_EXP_TOT_PY, na.rm = T), - total_assets_av = mean(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T), - total_assets_sd = sd(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T)) - -temp - - -``` -\ -There are 349 transitions from male to male (MM) transitions, 253 male to female transitions (MF), 160 female to male transitions (FM), and 305 female to female transitions (FF) in our dataset. FF orgs have the smallest number of employees, on average while MF have the highest. It should be noted that the standard deviation for number of employees is pretty high, suggesting a wide range of nonprofits. MM nonprofits have the highest total expeness on average and the highest total assets. Again, the SDs are wide, suggesting large variation across observations. - - - - - - -#### Financial Measures by Transitions - -The Glass Cliff Phenomenon hypothesizes that women are chosen for positions of power when these positions are more precarious. One way to denote a precarious position is by the firm's financial performance; poor financial performance suggests more precariousness. We acknowledge that financial performance is comprised of several different dimensions and it is sometimes hard to arrive at clean conclusions about "poorly performing nonprofits." For demonstration purposes we will specifically focus on the financial performance metric of post-depreciation profitability margin (podpm). This is defined as an income measure that determines a firm's profitability after incorporating non-cash expenses. Higher values of this metric are generally desirable because the indicate that an org is not lost its revenue to expenses. We will use the package `fiscal` from the Open Data Collective to calculate our variable of interest. The default parameters of the respective functions are already built for the 990 naming conventions so usage is pretty straight forward! -\ - -```{r, message = F, warning = F} - - - -#Make sure to run the code chunk about to ensure your df_long_fncl has all the necessary variables - -#site: https://github.com/Nonprofit-Open-Data-Collective/fiscal/tree/main/R -ceo_trans_1000_fncl <- get_podpm(ceo_trans_1000_fncl) - - - -#Let's plot these measures -plot_temp <- ceo_trans_1000_fncl %>% - group_by(trans_type, period)%>% - summarize(median_podpm =median(podpm, na.rm = T)) - - -ggplot(data = plot_temp, aes(x = period, y = median_podpm, group = trans_type, color = trans_type)) + - geom_line(linewidth = 1.5)+ - geom_text_repel(aes(label = round(median_podpm,3)), size = 5, nudge_x = -0.07, nudge_y = 0.001, segment.size = 0, segment.color = NA)+ - theme_bw( ) + -# theme(text = element_text(size = 24))+ - scale_x_discrete(labels = c("T-2", "T-1", "Transition", "T+1", "T+2"))+ - labs(color = "Transition") + - xlab("Period")+ - ylab("Median Post-Depreciation Profitability Margin") + - ggtitle("Median Post-Depreciation Profitability Margin by Transition Type") + - geom_vline(xintercept = 3, linetype = "dashed") - - - -################################################ -#Now doing density plots of these respective vars -################################################ - -#Let's compare the MM density to MF density in the t-1 period for the vats - -ceo_trans_1000_fncl_MM <- ceo_trans_1000_fncl %>% filter(trans_type == "MM" & - period == "T-1") -ceo_trans_1000_fncl_MF <- ceo_trans_1000_fncl %>% filter(trans_type == "MF"& - period == "T-1") - - -ggplot() + - geom_density(data = ceo_trans_1000_fncl_MM, aes(x = podpm, fill = "lightblue"), alpha = 0.5) + - geom_density(data = ceo_trans_1000_fncl_MF, aes(x = podpm, fill = "pink"), alpha = 0.5) + - xlim(-1,1)+ - theme_bw()+ -# theme(text = element_text(size = 24))+ - scale_fill_manual(name = "Transition", values = c('lightblue', 'pink'), labels = c("pink" = "MF" , "lightblue" = "MM") ) + - xlab("Post-Depreciation Profitability Margin")+ - ylab("Density") + - ggtitle("Density ofPost-Depreciation Profitability Margin by Transition at t-1") - - -``` - - -The glass cliff phenomenon suggest that financial precarious NPs (seen by a significant drop in their financials between T-2 and T-1) would more likely hire a female to the CEO position. We start by looking at the PODPM variable over the periods. MM organizations start with the highest median profitability margin but also experience the steepest drop between T-2 and T-1. We see a similar slope for FM organizations although the starting point is the lowest in the entire group. MF transitions do not appear to be preceded by steep changes in profitability. If the glass cliff hypothesis were true, we would expect the observed trajectory of FM but would not expect the other firms to have similar metrics around their transitions. This graph calls for further analysis on other financial measures to determine whether FM firms display a transition during significantly more precarious times than their counterparts. - -\ -\ -As a final understanding of the data, we look at the distribution of PODPM at T-1. We consider the two most relevant groups of MM and MF. We see that the distribution of financial variables tends to be extremely similar to both types of transitions, again failing to provide strong motivation for a glass cliff phenomenon. -\ -\ - - - - diff --git a/draft/Glass_Cliff_Toy_Example.html b/draft/Glass_Cliff_Toy_Example.html deleted file mode 100644 index 32d205c..0000000 --- a/draft/Glass_Cliff_Toy_Example.html +++ /dev/null @@ -1,934 +0,0 @@ - - - - - - - - - - - - - - - -Glass Cliff Toy Example - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Nonprofits and the Glass Cliff

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A Research Exploration

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This script will present several new components of the Nonprofit Open -Data Collective through a toy example investigating the “Glass Cliff -phenomenon” or the idea the women are more likely to be appointed to -precarious positions of power relative to their male counterparts. We -will specifically highlight the integrated data environment and the -numerous resources available for research on the third sector.

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Important Resources

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Data -Catalog
-Data -Dictionary
-Open -Data Collective Landing Page

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#devtools::install_github( "Nonprofit-Open-Data-Collective/peopleparser" )
-#devtools::install_github( 'nonprofit-open-data-collective/titleclassifier' )
-#devtools::install_github('nonprofit-open-data-collective/fiscal')
-
-library(dplyr)
-library(utils)
-library(peopleparser)
-library(titleclassifier)
-library( fiscal )
-library(tidyr)
-library(RecordLinkage)
-library(stringr)
-library(reshape2)
-library(ggplot2)
-library(ggrepel)
-
-#Change working directory to personal path
-setwd("~/Tiana_School_Work/PhD/Dissertation/Glass_Cliff_Paper")
-
-
-

Getting Main Data

-

Processed IRS 990 Efile data are housed in the data -catalog with the respective data -dictionaries. You can find further information about sources, -processing, and final parsing on these sites .
-
-For our toy example, we are particularly interested in the part of the -990 form that collects information about the board members in a -nonprofit. This is known as Part VII data. We will begin by pulling two -years of this data.
-
-There are two main ways to access the data. You can download the data -locally to your computer from the data catalog and read it into your R -environment or you can use pre-built functions to read it directly in R. -We present examples of both below.
-

-
#Reading in locally store data
-df2010 <- read.csv( "Coding/Data/PartVII/PartVII-2010.csv" )
-df2011 <- read.csv( "Coding/Data/PartVII/PartVII-2011.csv" )
-
-
-
-#Pre-built functions:
-nodc <- "https://raw.githubusercontent.com/Nonprofit-Open-Data-Collective/"
-repo <- "arnova-2024/refs/heads/main/"
-file <- "functions.R"
-source( paste0( nodc, repo, file ) )
-
-
-df2010.fun <- get_partvii(2010)
-df2011.fun <- get_partvii(2011)
-


-
-

-
-
-

Preparing Main Data

-

The main variables of interest in these data are the board member -names and their titles. We will create a data frame that contains both -years of our data then we will utilize pre-built packages from the Nonprofit -Open Data Repositories to clean these variables further (the -packages are peopleparser and -titleclassifier). Eventually, pre-cleaned versions of the -data will be available for download but for now we highlight the steps -to utilize these packages directly.
-

-
#First, we want to combine the two years of data
-#For simplicity, we will only work with the data pulled locally. 
-
-#We need to:
-#Remove "X" columns from the dfs that have this
-#Also remove "average hours for related org (COMP_DTK_AVE_HOUR_WEEK_RL)" since this isn't present in all of them
-#You could alternatively add a column of this variable to the dfs that don't have this, we don't this var so we will remove it
-
-df2011 <- df2011 %>% select(-c( "X", "F9_07_COMP_DTK_AVE_HOUR_WEEK_RL"))
-
-#Let's put them all together
-df <- rbind(df2010, df2011)
-
-
-#The packages to clean the name and title variables can take a bit to run. So before initiating them, we can make some assumptions about the observations we want. We are specifically interested in locating paid CEOs of NP. We will assume these positions must make more than $1 and work more than 15 hours
-
-df_sub <- df %>% filter(!is.na(F9_07_COMP_DTK_COMP_ORG) &
-                      F9_07_COMP_DTK_COMP_ORG > 1  &
-                      !is.na(F9_07_COMP_DTK_AVE_HOUR_WEEK) &
-                      F9_07_COMP_DTK_AVE_HOUR_WEEK > 15
-                      )
-
-#For simplicity and to limit run time, we will take a sample of 10 people from above and perform the functions provided by peopleparse and titleclassifier. You can comment out the next line if you would like to do the entire df (but be aware that it will take a bit of time to run)
-
-df_sub <- df_sub[1:10,]
-#write.csv(df_sub, "PARTVII_10employees_2009_2010.csv")
-
-
-

Utilizing NP Open Data Collective Packages

-

The Nonprofit Open Data Collection provides tools in addition to -dataframes that can be used to clean, process, and extend the data. We -highlight two below. peopleparser provides name -standarization and gender coding while titleclassifier -standardized titles of the 990 form. Examples of their usage with a -subset of 10 nonprofit employees from the Part VII 990 data are -below.
-

-
-

PeopleParser

-
df_sub <- read.csv(file = "PARTVII_10employees_2009_2010.csv")
-
-
-####################################
-#Peopleparser 
-#https://github.com/Nonprofit-Open-Data-Collective/peopleparser
-####################################
-
-#Now let's use peopleparser to clean the names. This function can take a bit to run so let's further limit our df to only unique names
-df_unique_names <- df_sub %>% select(F9_07_COMP_DTK_NAME_PERS) %>%
-  distinct()
-
-
-#Now doing people parser
-df_names <- parse.names(df_unique_names$F9_07_COMP_DTK_NAME_PERS)
-
-#We can left_join  these names back onto the df
-df_sub <- df_sub %>% left_join(df_names, by = c("F9_07_COMP_DTK_NAME_PERS" = "name"))
-
-
-#write.csv(df_sub, file = "PARTVII_10employees_2009_2010_names.csv")
-
-
-

TitleClassifier

-
df_sub <- read.csv(file = "PARTVII_10employees_2009_2010_names.csv")
-
-####################################
-#Titleclassifier 
-#https://github.com/Nonprofit-Open-Data-Collective/titleclassifier
-####################################
-
-#Since this function also takes a bit of time, we will narrow down the titles to their unique occurrences based on the vars input to the function
-df_unique_titles <- df_sub %>% 
-  distinct(F9_07_COMP_DTK_TITLE,
-             FORMTYPE,
-             F9_07_COMP_DTK_AVE_HOUR_WEEK,  
-             F9_07_COMP_DTK_COMP_ORG, 
-             F9_07_COMP_DTK_EMPL_BEN, 
-             F9_07_COMP_DTK_COMP_RLTD, 
-             F9_07_COMP_DTK_COMP_OTH,
-             F9_07_COMP_DTK_POS_INDIV_TRUST_X, 
-             F9_07_COMP_DTK_POS_INST_TRUST_X, 
-             F9_07_COMP_DTK_POS_OFF_X, 
-             F9_07_COMP_DTK_POS_KEY_EMPL_X, 
-             F9_07_COMP_DTK_POS_HIGH_COMP_X, 
-             F9_07_COMP_DTK_POS_FORMER_X,
-             F9_07_COMP_DTK_NAME_PERS  , .keep_all = T)
-
-
-#Running the title classifier now
-df_titles <-  df_unique_titles %>% 
-  standardize_df() %>% 
-  remove_dates() %>% 
-  standardize_conj() %>% 
-  split_titles() %>% 
-  standardize_spelling() %>% 
-  gen_status_codes() %>% 
-  standardize_titles() %>%
-  categorize_titles()
-
## ✔ standardize df step complete
-## ✔ remove dates step complete
-## ✔ standardize conjunctions step complete
-## ✔ split titles step complete
-## ✔ standardize spelling step complete
-
## ✔ generate status codes step complete
-## ✔ standardize titles step complete
-## ✔ categorize titles step complete
-
#Note: the title classifier seems to only keep unique rows by a person's name, not each occurrence they have. So columns like hours worked and pay by individual are not completely correct. But the total info about the org should be fine to keep. Will need to eventually revisit assumptions made in the package
-
-
-#now we join back to df_sub
-#df_titles may have multiple rows for people that list "x/y" in their title
-#So we want to do a full join where we keep all rows from both
-df_sub_names_titles <- df_sub %>% full_join(df_titles, by = c("EIN"= "ein",
-                                                     "F9_07_COMP_DTK_NAME_PERS" = "dtk.name",
-                                                      "F9_07_COMP_DTK_TITLE" = "title.raw"),
-                                            relationship = "many-to-many")
-
-
-#write.csv(df_sub_names_titles, "PARTVII_10employees_2009_2010_names_titles.csv")
-
-
-
-

Glass Cliff Analysis

-

There are several data cleaning and preparation steps that need to be -performed on the base data before our analysis. These are beyond the -scope of this toy example. Instead, we provide a cleaned subset of the -data. We provide two dataframes of 1000 and 10 unique nonprofits that -experienced at least one CEO transition between 2009 and 2019. It should -be noted that it is possible for these organizations to experience more -than one transition during this time frame. As such, we have a total of -1067 transitions. We can analyze various facets of these -transitions.
-

-
ceo_trans_10    <- read.csv("toy_CEO_trans_10EIN.csv" )
-ceo_trans_1000 <- read.csv("toy_CEO_trans_1000EIN.csv")
-
-

Financial Data Joining

-

The first thing we want to do with these data are to add financial -information about the orgs during each respective year. We can again -pull this info from the open data collective. This step takes about 10 -minutes to run so we will comment out the lines and run similar steps -with the data of 10 nonprofits and pre-subset financial -dataframes.
-

-
summary <- read.csv("F9-P01-T00-SUMMARY-SAMPLE-10.csv")
-revenue <- read.csv("F9-P08-T00-REVENUE-SAMPLE-10.csv")
-expenses <- read.csv("F9-P09-T00-EXPENSES-SAMPLE-10.csv")
-
-
-#let's join everything together:
-
-ceo_trans_10_fncl <- ceo_trans_10 %>% left_join(summary, by = c("TAXYR" = "TAX_YEAR",
-                                                               "EIN" = "ORG_EIN"))
-
-ceo_trans_10_fncl <- ceo_trans_10_fncl %>% left_join(revenue, by = c("TAXYR" = "TAX_YEAR",
-                                                               "EIN" = "ORG_EIN"))
-
-ceo_trans_10_fncl <- ceo_trans_10_fncl %>% left_join(expenses, by = c("TAXYR" = "TAX_YEAR",
-                                                               "EIN" = "ORG_EIN"))
-
-write.csv(ceo_trans_10_fncl, "CEO_trans_10EIN_fncl.csv" )
-
-
-# summary2009 <- get_table( "F9-P01-T00-SUMMARY", year=2009 )
-# summary2010 <- get_table( "F9-P01-T00-SUMMARY", year=2010 )
-# summary2011 <- get_table( "F9-P01-T00-SUMMARY", year=2011 )
-# summary2012 <- get_table( "F9-P01-T00-SUMMARY", year=2012 )
-# summary2013 <- get_table( "F9-P01-T00-SUMMARY", year=2013 )
-# summary2014 <- get_table( "F9-P01-T00-SUMMARY", year=2014 )
-# summary2015 <- get_table( "F9-P01-T00-SUMMARY", year=2015 )
-# summary2016 <- get_table( "F9-P01-T00-SUMMARY", year=2016 )
-# summary2017 <- get_table( "F9-P01-T00-SUMMARY", year=2017 )
-# summary2018 <- get_table( "F9-P01-T00-SUMMARY", year=2018 )
-# summary2019 <- get_table( "F9-P01-T00-SUMMARY", year=2019 )
-# 
-# revenue2009 <- get_table( "F9-P08-T00-REVENUE", year=2009 )
-# revenue2010 <- get_table( "F9-P08-T00-REVENUE", year=2010 )
-# revenue2011 <- get_table( "F9-P08-T00-REVENUE", year=2011 )
-# revenue2012 <- get_table( "F9-P08-T00-REVENUE", year=2012 )
-# revenue2013 <- get_table( "F9-P08-T00-REVENUE", year=2013 )
-# revenue2014 <- get_table( "F9-P08-T00-REVENUE", year=2014 )
-# revenue2015 <- get_table( "F9-P08-T00-REVENUE", year=2015 )
-# revenue2016 <- get_table( "F9-P08-T00-REVENUE", year=2016 )
-# revenue2017 <- get_table( "F9-P08-T00-REVENUE", year=2017 )
-# revenue2018 <- get_table( "F9-P08-T00-REVENUE", year=2018 )
-# revenue2019 <- get_table( "F9-P08-T00-REVENUE", year=2019 )
-# 
-# 
-# expenses2009 <- get_table( "F9-P09-T00-EXPENSES", year=2009 )
-# expenses2010 <- get_table( "F9-P09-T00-EXPENSES", year=2010 )
-# expenses2011 <- get_table( "F9-P09-T00-EXPENSES", year=2011 )
-# expenses2012 <- get_table( "F9-P09-T00-EXPENSES", year=2012 )
-# expenses2013 <- get_table( "F9-P09-T00-EXPENSES", year=2013 )
-# expenses2014 <- get_table( "F9-P09-T00-EXPENSES", year=2014 )
-# expenses2015 <- get_table( "F9-P09-T00-EXPENSES", year=2015 )
-# expenses2016 <- get_table( "F9-P09-T00-EXPENSES", year=2016 )
-# expenses2017 <- get_table( "F9-P09-T00-EXPENSES", year=2017 )
-# expenses2018 <- get_table( "F9-P09-T00-EXPENSES", year=2018 )
-# expenses2019 <- get_table( "F9-P09-T00-EXPENSES", year=2019 )
-# 
-# 
-# #Let's put them all together
-# summary <- rbind(as.data.frame(summary2009), as.data.frame(summary2010),as.data.frame(summary2011),
-#                  as.data.frame(summary2012),as.data.frame(summary2013),as.data.frame(summary2014),
-#                  as.data.frame(summary2015),as.data.frame(summary2016),as.data.frame(summary2017),
-#                  as.data.frame(summary2018),as.data.frame(summary2019))
-# 
-# revenue <- rbind(as.data.frame(revenue2009),as.data.frame(revenue2010),as.data.frame(revenue2011),
-#                  as.data.frame(revenue2012),as.data.frame(revenue2013),as.data.frame(revenue2014),
-#                  as.data.frame(revenue2015),as.data.frame(revenue2016),as.data.frame(revenue2017),
-#                  as.data.frame(revenue2018),as.data.frame(revenue2019))
-# 
-# expenses <- rbind(as.data.frame(expenses2009),as.data.frame(expenses2010),as.data.frame(expenses2011),
-#                   as.data.frame(expenses2012),as.data.frame(expenses2013),as.data.frame(expenses2014),
-#                   as.data.frame(expenses2015),as.data.frame(expenses2016),as.data.frame(expenses2017),
-#                   as.data.frame(expenses2018),as.data.frame(expenses2019))
-# 
-# 
-# 
-# 
-# #Orgs can return various versions of their tax forms each year
-# #For now, we will keep only the first observation, true analysis will need to revisit this decision
-# summary <- summary %>% group_by(ORG_EIN, TAX_YEAR) %>%
-#   filter(row_number()==1)
-# 
-# revenue <- revenue %>% group_by(ORG_EIN, TAX_YEAR) %>%
-#   filter(row_number()==1)
-# 
-# expenses <- expenses %>% group_by(ORG_EIN, TAX_YEAR) %>%
-#   filter(row_number()==1)
-# 
-# 
-# 
-# #Saving environment space by removing the other dfs
-# remove(summary2009, summary2010, summary2011, summary2012, summary2013, summary2014,
-#      summary2015, summary2016, summary2017, summary2018, summary2019 )
-# remove(expenses2009, expenses2010, expenses2011, expenses2012, expenses2013, expenses2014,
-#      expenses2015, expenses2016, expenses2017, expenses2018, expenses2019 )
-# remove(balance2009, balance2010, balance2011, balance2012, balance2013, balance2014,
-#      balance2015, balance2016, balance2017, balance2018, balance2019 )
-# #Let's join everything together
-# 
-# 
-# ceo_trans_1000_fncl <- ceo_trans_1000 %>% left_join(summary, by = c("TAXYR" = "TAX_YEAR",
-#                                                               "EIN" = "ORG_EIN"))
-# 
-# ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% left_join(expenses, by = c("TAXYR" = "TAX_YEAR",
-#                                                                    "EIN" = "ORG_EIN"))
-# 
-# ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% left_join(revenue, by = c("TAXYR" = "TAX_YEAR",
-#                                                                    "EIN" = "ORG_EIN"))
-# 
-# 
-# write.csv(ceo_trans_1000_fncl, "toy_CEO_trans_1000EIN_fncl.csv")
-
-
-

Summary Statistics of Transitions

-

Now we’re ready for some analyses! We are most interested in -understanding transitions by their gendering. That is to say that we -want to know if there are differences in observable firm dimensions for -nonprofits that experience a CEO transition from male to male relative -to the other permutations of male to female, female to male, and female -to female. Let’s start by looking at org differences by these transition -types. We will use the 1000 EIN observations for this analysis. We have -provided this dataset with the financial data attached (this was -accomplished by running the commented lines above). For simplicity, we -will only consider a few firm dimensions such as employee size and total -assets.

-
#This df 
-ceo_trans_1000_fncl <- read.csv("toy_CEO_trans_1000EIN_fncl.csv")
-
-#First need to denote transitions 
-ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>%
-  arrange(EIN, transition_no, TAXYR)%>% #for each org, transition, and year, 
-  group_by(EIN, transition_no) %>%       
-  mutate(period = row_number())%>%       #let's denote which period the row is relative to (i.e., how many years before /after a transition)
-  ungroup()
-
-#Let's make this variable a bit more readable
-ceo_trans_1000_fncl$period <- paste0("T", ceo_trans_1000_fncl$period-3)
-ceo_trans_1000_fncl$period <- factor(ceo_trans_1000_fncl$period, levels = c("T-2", "T-1", "T0" , "T1", "T2"))
-
-
-#Need to get transition type too
-#We want to know if the transition is M>M, M>F, F>M, or F>M
-#We will first create a string of the CEO gender throughout the 5 year period
-#The we will create a column that keeps the first and last gender (this is the gender transition)
-ceo_trans_1000_fncl<- ceo_trans_1000_fncl %>%
-  group_by(EIN, transition_no)%>%
-  mutate(trans_tot = str_c(unlist(strsplit(paste(gender, collapse = ""), "")), collapse = "")
-  ) %>%
-  mutate(
-    trans_type = str_sub(trans_tot, 1, 1) %>%
-      str_c(str_sub(trans_tot, nchar(trans_tot), nchar(trans_tot)))  # Combine 1st and last letter
-    
-  )
-
-#We want this variable to be a factor
-ceo_trans_1000_fncl$trans_type <- as.factor(ceo_trans_1000_fncl$trans_type)
-
-
-
-#We're going to get firm attributes by transition type
-#Let's consider the attributes at t-1
-#We'll consider basic variables like number of employees, total assets, etc.
-
-#Note that further data cleaning is necessary for final presentation due to NA values input into some of these columns. For now, we will just remove the nas and continue with our analysis
-
-temp <- ceo_trans_1000_fncl %>% group_by(trans_type)%>%
-  filter(period == "T-1") %>%
-  summarize(count = n(),
-    num_emp_av =mean(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T),
-            num_emp_sd = sd(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T),
-            num_vol_av = mean(F9_01_ACT_GVRN_VOL_TOT, na.rm = T),
-            num_vol_sd = sd(F9_01_ACT_GVRN_VOL_TOT, na.rm = T),
-            total_exp_av = mean(F9_01_EXP_TOT_PY, na.rm = T),
-            total_exp_sd = sd(F9_01_EXP_TOT_PY, na.rm = T),
-            total_assets_av = mean(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T),
-            total_assets_sd = sd(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T))
-
-temp
-
## # A tibble: 4 × 10
-##   trans_type count num_emp_av num_emp_sd num_vol_av num_vol_sd total_exp_av
-##   <fct>      <int>      <dbl>      <dbl>      <dbl>      <dbl>        <dbl>
-## 1 FF           305       92.0       306.       226.      1045.     6045988.
-## 2 FM           160      265.        606.       257.      1058.    19338303 
-## 3 MF           253      318.        850.       195.       533.    25018166.
-## 4 MM           349      312.        835.       321.      1109.    32994239.
-## # ℹ 3 more variables: total_exp_sd <dbl>, total_assets_av <dbl>,
-## #   total_assets_sd <dbl>
-


-There are 349 transitions from male to male (MM) transitions, 253 male -to female transitions (MF), 160 female to male transitions (FM), and 305 -female to female transitions (FF) in our dataset. FF orgs have the -smallest number of employees, on average while MF have the highest. It -should be noted that the standard deviation for number of employees is -pretty high, suggesting a wide range of nonprofits. MM nonprofits have -the highest total expeness on average and the highest total assets. -Again, the SDs are wide, suggesting large variation across -observations.

-
-
-

Financial Measures by Transitions

-

The Glass Cliff Phenomenon hypothesizes that women are chosen for -positions of power when these positions are more precarious. One way to -denote a precarious position is by the firm’s financial performance; -poor financial performance suggests more precariousness. We acknowledge -that financial performance is comprised of several different dimensions -and it is sometimes hard to arrive at clean conclusions about “poorly -performing nonprofits.” For demonstration purposes we will specifically -focus on the financial performance metric of post-depreciation -profitability margin (podpm). This is defined as an income measure that -determines a firm’s profitability after incorporating non-cash expenses. -Higher values of this metric are generally desirable because the -indicate that an org is not lost its revenue to expenses. We will use -the package fiscal from the Open Data Collective to -calculate our variable of interest. The default parameters of the -respective functions are already built for the 990 naming conventions so -usage is pretty straight forward!
-

-
#Make sure to run the code chunk about to ensure your df_long_fncl has all the necessary variables 
-
-#site: https://github.com/Nonprofit-Open-Data-Collective/fiscal/tree/main/R
-ceo_trans_1000_fncl <- get_podpm(ceo_trans_1000_fncl)
-
## [1] "Revenues cannot be equal to zero: 0 cases have been replaced with NA."
-##      podpm              podpm.w            podpm.n            podpm.p      
-##  Min.   :-619.4798   Min.   :-0.64627   Min.   :-4.04004   Min.   :  1.00  
-##  1st Qu.:  -0.0327   1st Qu.:-0.03269   1st Qu.:-0.32851   1st Qu.: 25.00  
-##  Median :   0.0194   Median : 0.01937   Median :-0.01358   Median : 50.00  
-##  Mean   :  -0.0964   Mean   : 0.02162   Mean   : 0.00000   Mean   : 50.29  
-##  3rd Qu.:   0.0809   3rd Qu.: 0.08092   3rd Qu.: 0.35867   3rd Qu.: 75.00  
-##  Max.   :  39.8564   Max.   : 0.57166   Max.   : 3.32717   Max.   :100.00  
-##  NA's   :203         NA's   :203        NA's   :203        NA's   :203
-

-
#Let's plot these measures
-plot_temp <- ceo_trans_1000_fncl %>%
-  group_by(trans_type, period)%>%
-  summarize(median_podpm =median(podpm, na.rm = T))
-
-
-ggplot(data = plot_temp, aes(x = period, y = median_podpm, group = trans_type, color = trans_type)) + 
-  geom_line(linewidth = 1.5)+ 
-  geom_text_repel(aes(label = round(median_podpm,3)), size = 5, nudge_x = -0.07, nudge_y = 0.001, segment.size = 0, segment.color = NA)+
-  theme_bw( ) + 
-#  theme(text = element_text(size = 24))+
-  scale_x_discrete(labels = c("T-2", "T-1", "Transition", "T+1", "T+2"))+
-  labs(color = "Transition") +
-  xlab("Period")+
-  ylab("Median Post-Depreciation Profitability Margin") +
-  ggtitle("Median Post-Depreciation Profitability Margin by Transition Type") +
-  geom_vline(xintercept = 3, linetype = "dashed")
-

-
################################################
-#Now doing density plots of these respective vars
-################################################
-
-#Let's compare the MM density to MF density in the t-1 period for the vats
-
-ceo_trans_1000_fncl_MM <- ceo_trans_1000_fncl %>% filter(trans_type == "MM" &
-                                             period == "T-1")
-ceo_trans_1000_fncl_MF <- ceo_trans_1000_fncl %>% filter(trans_type == "MF"&
-                                             period == "T-1")
-
-
-ggplot() + 
-  geom_density(data = ceo_trans_1000_fncl_MM,  aes(x = podpm, fill = "lightblue"), alpha = 0.5) +
-  geom_density(data = ceo_trans_1000_fncl_MF,  aes(x = podpm, fill = "pink"), alpha = 0.5) +
-  xlim(-1,1)+
-  theme_bw()+
-#  theme(text = element_text(size = 24))+
-  scale_fill_manual(name = "Transition", values = c('lightblue', 'pink'), labels = c("pink" = "MF" ,  "lightblue" = "MM") ) +
-  xlab("Post-Depreciation Profitability Margin")+
-  ylab("Density") +
-  ggtitle("Density ofPost-Depreciation Profitability Margin by Transition at t-1")
-

-

The glass cliff phenomenon suggest that financial precarious NPs -(seen by a significant drop in their financials between T-2 and T-1) -would more likely hire a female to the CEO position. We start by looking -at the PODPM variable over the periods. MM organizations start with the -highest median profitability margin but also experience the steepest -drop between T-2 and T-1. We see a similar slope for FM organizations -although the starting point is the lowest in the entire group. MF -transitions do not appear to be preceded by steep changes in -profitability. If the glass cliff hypothesis were true, we would expect -the observed trajectory of FM but would not expect the other firms to -have similar metrics around their transitions. This graph calls for -further analysis on other financial measures to determine whether FM -firms display a transition during significantly more precarious times -than their counterparts.

-


-
-As a final understanding of the data, we look at the distribution of -PODPM at T-1. We consider the two most relevant groups of MM and MF. We -see that the distribution of financial variables tends to be extremely -similar to both types of transitions, again failing to provide strong -motivation for a glass cliff phenomenon.  
-

-
-
-
-
- - - - -
- - - - - - - - - - - - - - - diff --git a/draft/glass-cliff-workflow.html b/draft/glass-cliff-workflow.html deleted file mode 100644 index d0a485d..0000000 --- a/draft/glass-cliff-workflow.html +++ /dev/null @@ -1,3598 +0,0 @@ - - - - - - - - - - - - - - - -Nonprofit CEO Transition Dynamics - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - -
- -
- -
-

A Workflow Demonstration

-

This script will present several new components of the Nonprofit Open -Data Collective data environment using an example motivated by the -“Glass Cliff”, or the idea the women are more likely to be appointed to -precarious positions of power relative to their male counterparts.

-

We specifically want to highlight the value of an integrated data -environment developed around the EFILE Database, one of the emerging -research centerpieces in nonprofit scholarship. This tutorial -demonstrates a reproducible data engineering workflow that follows FAIR Data -Guidelines. We will answer the research question using a dataset -created through the following 8 Steps:

-

-
    -
  1. Build tables using the irs990efile package.
    2. -
  2. Standardize names of the leadership team present in Part VII (the -compensation tables) using the peopleparser -package.
    3. -
  3. Standardize titles in Part VII using the -titleclassifier package.
    4. -
  4. Identify CEO transition years within the data.
    5. -
  5. Build a financial dataset by combining the following 990 Parts: -revenues, expenses, and assets.
    6. -
  6. Generate common financial operating ratios using the -fiscal package.
    7. -
  7. Add NTEE codes and standardized address fields from the Unified -BMF.
    8. -
  8. Merge financial tables and compensation tables into a single table -for the analysis.
    9. -
-

We are using the term “integrated data environment” here to mean:

- -

These things allow us to create more expressive and intuitive data -steps that anyone can replicate. It also allows researchers to create -custom tools for refining or analyzing data, as demonstrated by the -packages used in this workflow.

-
-
-

Glass Cliff Research

-
-

The glass cliff is a hypothesized phenomenon in which women are more -likely to break the “glass ceiling” (i.e. achieve leadership roles in -business and government) during periods of crisis or downturn when the -risk of failure is highest.

-
-

We know from previous studies that male CEOs are put in charge of -large, thriving nonprofits while women are more likely to be hired to -lead smaller social services organizations.

-

Here we explore the question of whether female CEOs are more likely -to be hired when the current leader is struggling.

-

-

Grasse, N. J., Heidbreder, B., Kukla-Acevedo, S. A., & Lecy, -J. D. (2024). Some Good News, More Bad News: Two Decades of the Gender -Pay Gap for Nonprofit Directors and Chief Financial Officers. Review of -Public Personnel Administration, 0734371X241248854.

-
-
-

Data and Software

-
-

Efile Data

-

Processed IRS 990 Efile data are housed in the NCCS Data Catalog. You -can find further information about sources, processing, and variable -descriptions here:

- -
-
-

R Packages

-

There is a growing library of nonprofit tools:

- -

We will be using the following packages for the demo:

-
devtools::install_github( 'Nonprofit-Open-Data-Collective/peopleparser' )
-devtools::install_github( 'nonprofit-open-data-collective/titleclassifier' )
-devtools::install_github( 'nonprofit-open-data-collective/fiscal')
-devtools::install_github( 'nonprofit-open-data-collective/irs990efile')
-
library( tidyverse )
-library( pander )
-library( data.table )
-
-# nonprofit data packages 
-library( peopleparser )
-library( titleclassifier )
-library( fiscal )
-
-# helper functions for the demo: 
-nodc <- "https://raw.githubusercontent.com/Nonprofit-Open-Data-Collective/"
-repo <- "arnova-2024/refs/heads/main/"
-file <- "functions.R"
-source( paste0( nodc, repo, file ) )
-
-
-
-

STEP 1: Building Efile Tables from Scratch

-

-

This step builds Efile tables by loading raw XML efile returns from -the Data Commons and using the irs990efile package to -parse XML files into rectangular CSV tables.

-

This code creates data for a sample of 100 nonprofits from 2018:

-
library( irs990efile )
-
-index <- build_index( tax.years=2018 )
-
-index100 <-
-  index %>% 
-  filter( FormType %in% c("990","990EZ") ) %>%
-  sample_n( 100 )
-
-TABLES <- c( "F9-P00-T00-HEADER",
-             "F9-P01-T00-SUMMARY",
-             "F9-P08-T00-REVENUE",
-             "F9-P09-T00-EXPENSES",
-             "F9-P11-T00-ASSETS" )
-
-URLS <- index100$URL
-
-build_tables( urls=URLS, year=2018, table.names=TABLES  )
-

Typically you would not need to replicate this step since it is a -computationally-intensive process. It can take a couple of days to build -the full efile database. It is much easier to pull existing CSV files -from NCCS:

-

EFILE -DATA CATALOG.

-
-
-

STEP 2: Parse DTK Names

-

-
root    <- paste0( nodc, repo )
-fn      <- "data/PART-VII-SAMPLE-10.CSV"
-url     <- paste0( root, fn )
-partvii <- read.csv( url )
- - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table continues below
NAMETAXYRFORMTYPEF9_07_COMP_DTK_NAME_PERS
President and Fellows of Middlebury College2009990Ronald Liebowitz
President and Fellows of Middlebury College2009990Frederick M Fritz
President and Fellows of Middlebury College2009990Marna C Whittington
President and Fellows of Middlebury College2009990Kendrick R Wilson III
President and Fellows of Middlebury College2009990S Carolyn Ramos
President and Fellows of Middlebury College2009990Roxanne M Leighton
- ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
F9_07_COMP_DTK_TITLEF9_07_COMP_DTK_AVE_HOUR_WEEKF9_07_COMP_DTK_COMP_ORG
President40409609
Chair50
Vice Chair50
Vice Chair50
Alumni Trustee10
Charter Trustee10
-

Now let’s use peopleparser to clean the names. Names do not arrive -standardized in any specific format:

-
## RITA WARNOCK  ;;  A HERBERT SCHWARTZ MD  ;;  CAROLINE W KNOWLES
-
## KARA FARACLAS  ;;  Richard T Scanlon  ;;  ALEXANDER G LYLE
-
## Sunder Ramaswamy  ;;  JANETTE JOHNSON  ;;  DAVID MCGRAW
-
## Charles M Gately  ;;  James Edward Virtue  ;;  JUSTIN VAN EPPS
-
## William F Truscott  ;;  TERRI DAVIDSON  ;;  Stephen Donadio
-

The peopleparser package will remove nuisance text -and split the name string into 5 parts:

- -

And add a predicted gender label (M/F/U) plus confidence level once -it is able to identify the individual’s first name:

-
peopleparser::parse.name( "(1) Rev Kendrick R Wilson III" )
-
## [1] "REV|KENDRICK|R|WILSON|III||M|100"
-
peopleparser::parse.name( "Doctor Wilson III, Kendrick R" )
-
## [1] "DOCTOR|KENDRICK|R|WILSON|III||M|100"
-
# gender prediction is based on first names
-peopleparser::parse.name( "Wilson, K R Until June 2008" )
-
## [1] "|K|R|WILSON|||U|0.0"
-
nm <- partvii[[ "F9_07_COMP_DTK_NAME_PERS" ]] %>% unique()
-nm.parsed <- peopleparser::parse.names( nm )
- - ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table continues below
namesalutationfirst_namemiddle_namelast_name
Ronald LiebowitzRONALDLIEBOWITZ
Frederick M FritzFREDERICKMFRITZ
Marna C WhittingtonMARNACWHITTINGTON
Kendrick R Wilson IIIKENDRICKRWILSON
S Carolyn RamosSCAROLYNRAMOS
Roxanne M LeightonROXANNEMLEIGHTON
- ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
suffixstatusgendergender_confidence
M99.7
M100
F100
IIIM100
F100
F100
-

Join parsed names back to the original data frame.

-
# CORE R VERSION OF A TABLE JOIN 
-partvii <- 
-  partvii %>% 
-  merge( nm.parsed, 
-         by.x="F9_07_COMP_DTK_NAME_PERS", by.y="name", 
-         all.x=T )
-
# TIDYVERSE VERSION OF A TABLE JOIN
-partvii <- 
-  partvii %>% 
-  left_join( nm.parsed, 
-             by=c( "F9_07_COMP_DTK_NAME_PERS" = "name" ) )
-
write.csv( partvii, "data/PART-VII-SAMPLE-10-PARSED-NAMES.CSV", row.names=F )
-
-
-

STEP 3: Standardize Titles

-

-
root    <- paste0( nodc, repo )
-fn      <- "data/PART-VII-SAMPLE-10-PARSED-NAMES.CSV"
-url     <- paste0( root, fn )
-partvii <- read.csv( url )
-
# steps from titleclassifier package 
-
-titles <-  
-  partvii %>% 
-  standardize_df() %>% 
-  remove_dates() %>% 
-  standardize_conj() %>% 
-  split_titles() %>% 
-  standardize_spelling() %>% 
-  gen_status_codes() %>% 
-  standardize_titles() %>%
-  categorize_titles()
-
## ✔ standardize df step complete
-## ✔ remove dates step complete
-## ✔ standardize conjunctions step complete
-## ✔ split titles step complete
-## ✔ standardize spelling step complete
-
## ✔ generate status codes step complete
-## ✔ standardize titles step complete
-## ✔ categorize titles step complete
- ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
dtk.nametitle.rawtitle.mult.xtitle.order
KATHRYN DUPREEEXECUTIVE DIRECTOR01
LINDA GRIMMACADEMY DIRECTOR01
ARLENE KAYEDIRECTOR OF CHILDREN’S BEHAVIORAL SERVICES01
TACIE LOWEDIRECTOR OF INDIVIDUAL & FAMILY SUPPORT01
CARL J CASPERCHAIR01
THOMAS IGOETREASURER/SECRETARY12
THOMAS IGOETREASURER/SECRETARY11
LARRY WOODDIRECTOR01
STEPHEN SIMONSONRESIDENTIAL DIRECTOR01
ARLENE KAYECBD DIRECTOR01
-

Title processing steps from the package:

- - ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table continues below
 title.rawtitle.v4
150EXECUTIVE DIRECTOR/FSE DIRECTOREXECUTIVE DIRECTOR
151EXECUTIVE DIRECTOR/FSE DIRECTORFSE DIRECTOR
152DIRECTORDIRECTOR
153DIRECTORDIRECTOR
154DIRECTORDIRECTOR
155DIRECTOR, FLUTIE FDN. AND FSEDIRECTOR AND FLUTIE FDN AND FSE
156CO-CHAIR/DIRECTORCO-CHAIR
157DIRECTORDIRECTOR
158CO-CHAIR/DIRECTORDIRECTOR
159DIRECTORDIRECTOR
160DIRECTORDIRECTOR
161DIRECTORDIRECTOR
162EXECUTIVE DIRECTOREXECUTIVE DIRECTOR
163CLERK/DIRECTORCLERK
164CLERK/DIRECTORDIRECTOR
165DIRECTORDIRECTOR
- ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 title.v7title.standard
150CEOCEO
151FSE DIRECTORNA
152DIRECTORDIRECTOR
153DIRECTORDIRECTOR
154DIRECTORDIRECTOR
155DIRECTOR AND FLUTIE FDN AND FSENA
156CO-CHAIRBOARD PRESIDENT
157DIRECTORDIRECTOR
158DIRECTORDIRECTOR
159DIRECTORDIRECTOR
160DIRECTORDIRECTOR
161DIRECTORDIRECTOR
162CEOCEO
163CLERKCLERK
164DIRECTORDIRECTOR
165DIRECTORDIRECTOR
-

Pay and hours tabulated relative to other employees in the same -org:

- - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table continues below
 dtk.nametitle.standardtot.hours
15KATHRYN DUPEECEO37.5
16JOHN BALDINODIRECTOR OF FACILITIES37.5
17JOSEPH SPITERIGENERAL MANAGER37.5
18TACIE LOWENA37.5
19THOMAS IGOEBOARD TREASURER0.5
20KARA FARACLASBOARD VICE PRESIDENT0.5
21JAMES MCPARTLAND PHDDIRECTOR0.5
22JANETTE JOHNSONDIRECTOR0.5
23STEPHEN WIZNERDIRECTOR0.5
- ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 hours.ranktot.comppay.maxpay.rank
1511760771881303
1611419831881307
1711415381881308
18112032718813018
192018813025
202018813025
212018813025
222018813025
232018813025
-
partvii <- 
-  merge( partvii, titles, 
-         by.x=c("EIN","TAXYR"), by.y=c("ein","taxyr"),
-         all.x=TRUE )
-
write.csv( partvii, "data/PART-VII-SAMPLE-10-PARSED-NAMES-TITLES.CSV", row.names=F )
-
-
-

STEP 4: Identify CEO Transitions

-

We are omitting this step because it is outside the scope of the -workflow. All that you need to know is leadership transitions are -identified by isolating CEOs and finding periods where the individuals -change. Transitions are labeled as:

- - -------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
EINTAXYRperiodCEO1CEO2trans_type
100242452013T-2JOHN PORTERNAMF
100242452014T-1JOHN PORTERNAMF
100242452015T0JOHN PORTERNAMF
100242452016T1DEB NEUMANNAMF
100242452017T2DEB NEUMANNAMF
101961942014T-2NORMAND DUBREUILNAMM
101961942015T-1NORMAND DUBREUILNAMM
101961942016T0NORMAND DUBREUILCOLE TUCKERMM
101961942017T1COLE TUCKERNAMM
101961942018T2COLE TUCKERNAMM
102066032010T-2DONNA STECKINONAFM
102066032011T-1DONNA STECKINONAFM
102066032012T0KERRY WOODNAFM
102066032013T1KERRY WOODNAFM
102066032014T2KERRY WOODNAFM
102066032012T-2KERRY WOODNAMF
102066032013T-1KERRY WOODNAMF
102066032014T0KERRY WOODNAMF
102066032015T1JENNIFER HOGANNAMF
102066032016T2JENNIFER HOGANNAMF
102114812014T-2STACY SCHAFFERNAFF
102114812015T-1STACY SCHAFFERNAFF
102114812016T0STACY SCHAFFERBETHANY BILODEAUFF
102114812017T1BETHANY BILODEAUNAFF
102114812018T2BETHANY BILODEAUNAFF
102114842009T-2KENT ULERYNAMM
102114842010T-1KENT ULERYNAMM
102114842011T0ROBERT GROVE-MARKWOODNAMM
102114842012T1ROBERT GROVE-MARKWOODNAMM
102114842013T2ROBERT GROVE-MARKWOODNAMM
102114972012T-2WILLIAM ADAMSNAMM
102114972013T-1WILLIAM ADAMSNAMM
102114972014T0DAVID GREENEWILLIAM ADAMSMM
102114972015T1DAVID GREENENAMM
102114972016T2DAVID GREENENAMM
102115092009T-2DANIEL KUNKLENAMM
102115092010T-1DANIEL KUNKLENAMM
102115092011T0DANIEL KUNKLENAMM
102115092012T1MATTHEW RUBYNAMM
102115092013T2MATTHEW RUBYNAMM
-
-
-

STEP 5: Compile Financials

-

-

We will first build a financials table by selecting the relevant 990 -parts, then combining them:

-
##   JOINING ONE TO ONE TABLES
-
-root <- paste0( nodc, repo )
-
-fn1  <- "data/F9-P00-T00-HEADER-SAMPLE-10.CSV"
-fn2  <- "data/F9-P01-T00-SUMMARY-SAMPLE-10.CSV"
-fn3  <- "data/F9-P08-T00-REVENUE-SAMPLE-10.CSV"
-fn4  <- "data/F9-P09-T00-EXPENSES-SAMPLE-10.CSV"
-fn5  <- "data/F9-P10-T00-BALANCE-SHEET-SAMPLE-10.CSV"
-
-d1 <- read.csv( paste0( root, fn1 )  )
-d2 <- read.csv( paste0( root, fn2 )  )
-d3 <- read.csv( paste0( root, fn3 )  )
-d4 <- read.csv( paste0( root, fn4 )  )
-d5 <- read.csv( paste0( root, fn5 )  )
-

The efile one-to-one tables all share the same IDs:

-
intersect( names(d1), names(d2) )
-
## [1] "OBJECTID"       "URL"            "RETURN_VERSION" "ORG_EIN"       
-## [5] "ORG_NAME_L1"    "ORG_NAME_L2"    "RETURN_TYPE"    "TAX_YEAR"      
-## [9] "EIN2"
-

Which makes merging files easy:

-
df <- merge( d1, d2 )
-df <- merge( df, d3 )
-df <- merge( df, d4 )
-df <- merge( df, d5 )
-
write.csv( df, "data/FINANCIALS.CSV" )
-

Note that dataset dimensions should not change for one-to-one -merges:

-
dim(d1)
-
## [1] 107  55
-
dim(d2)
-
## [1] 107  50
-
dim(df)
-
## [1] 107 376
-

See the appendix for the code used to compile the five tables above -for the sample.

-
-
-

STEP 6: Add Financial Operating Ratios

-

-
root <- paste0( nodc, repo )
-fn   <- "data/FINANCIALS.CSV"
-df   <- read.csv( paste0( root, fn )  )
- ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
F9_01_EXP_TOT_PYF9_01_EXP_REV_LESS_EXP_CYF9_01_EXP_REV_LESS_EXP_PY
32176085692231606
870793-194147-257955
6925984263531852
11765651519017403144
8708339-558773-308774
-

The fiscal package contains the following financial -ratios:

-
df <- get_aer( df )     #   Assets to Revenues Ratio
-df <- get_arr( df )     #   Assets to Revenues Ratio
-df <- get_cr( df )      #   Current Ratio
-df <- get_dar( df )     #   Debt to Asset Ratio
-df <- get_der( df )     #   Debt to Equity Ratio
-df <- get_dgdr( df )    #   Donation/Grant Dependence Ratio
-df <- get_dmr( df )     #   Debt Management Ratio
-df <- get_doch( df )    #   Days of Operating Cash on Hand
-df <- get_doci( df )    #   Days of Operating Cash and Investments
-df <- get_eidr( df )    #   Earned Income Dependence Ratio
-df <- get_er( df )      #   Equity Ratio
-df <- get_ggr( df )     #   Government Grants Ratio
-df <- get_iidr( df )    #   Investment Income Dependence Ratio
-df <- get_lar( df )     #   Lands to Assets Ratio
-df <- get_moch( df )    #   Months of Operating Cash on Hand
-df <- get_or( df )      #   Operating Margin
-df <- get_per( df )     #   Program Efficiency Ratio
-df <- get_podpm( df )   #   Post-Depreciation Profitability Margin
-df <- get_predpm( df )  #   Pre-Depreciation Profitability Margin
-df <- get_qr( df )      #   Quick Ratio
-df <- get_ssr( df )     #   Self Sufficiency Ratio
-df <- get_stdr( df )    #   Short Term Debt Ratio
-

We will add the post-depreciation profitability margin and the quick -ratio to the data:

-
df <- get_podpm( df )
-
## [1] "Revenues cannot be equal to zero: 0 cases have been replaced with NA."
-##      podpm             podpm.w            podpm.n           podpm.p  
-##  Min.   :-0.50950   Min.   :-0.46216   Min.   :-3.3121   Min.   : 1  
-##  1st Qu.:-0.07950   1st Qu.:-0.07950   1st Qu.:-0.4535   1st Qu.:25  
-##  Median : 0.01107   Median : 0.01107   Median : 0.2230   Median :49  
-##  Mean   :-0.01858   Mean   :-0.01879   Mean   : 0.0000   Mean   :49  
-##  3rd Qu.: 0.05557   3rd Qu.: 0.05557   3rd Qu.: 0.5555   3rd Qu.:73  
-##  Max.   : 0.38204   Max.   : 0.31425   Max.   : 2.4878   Max.   :97  
-##  NA's   :10         NA's   :10         NA's   :10        NA's   :10
-

-
df <- get_dgdr( df )
-
## [1] "Net assets cannot be zero: 0 cases have been replaced with NA."
-##       dgdr             dgdr.w            dgdr.n            dgdr.p  
-##  Min.   :0.04165   Min.   :0.04638   Min.   :-1.6821   Min.   : 1  
-##  1st Qu.:0.42370   1st Qu.:0.42370   1st Qu.:-0.4077   1st Qu.:10  
-##  Median :0.61054   Median :0.61054   Median : 0.2233   Median :19  
-##  Mean   :0.54480   Mean   :0.54442   Mean   : 0.0000   Mean   :19  
-##  3rd Qu.:0.80102   3rd Qu.:0.80102   3rd Qu.: 0.8667   3rd Qu.:28  
-##  Max.   :0.92779   Max.   :0.90902   Max.   : 1.2314   Max.   :37  
-##  NA's   :70        NA's   :70        NA's   :70        NA's   :70
-

-

For details on the definitions and calculation of the ratios try:

-
help( get_dgdr )
-
write.csv( df, "data/FINANCIALS-W-RATIOS.CSV", row.names=F )
-
-
-

STEP 7: Add BMF Fields

-

The Business Master File contains -important information not available on 990 forms such as organizational -NTEE codes. In addition, the NCCS BMF files contain standardized -geographies and other useful information.

-

The BMF rows for our sample have been precompiled:

-
root <- paste0( nodc, repo )
-fn   <- "data/bmf_unified_10.csv"
-bmf  <- read.csv( paste0( root, fn ) )
-
root <- paste0( nodc, repo )
-fn   <- "data/FINANCIALS-W-RATIOS.CSV"
-df   <- read.csv( paste0( root, fn )  )
-
df <- merge( df, bmf, by="EIN2" )
-
write.csv( df, "data/FINANCIALS-W-RATIOS-PLUS-BMF.CSV", row.names=F )
-
-
-

STEP 8: Combine CEO Panel and Financials

-

Add this step? Or just add a few sentences explaining it.

-
-
-

Glass Cliff Examples

-

Add your examples here….

-

-


-
-




-
-
-

Appendix

-
-

Step 5

-

The demo files above (a sample of 10 nonprofits), read directly from -the demo repo on GitHub, were compiled using the following code:

-
EIN2_10 <- 
-c("EIN-02-0240383", "EIN-03-0179298", 
-  "EIN-04-2104310", "EIN-04-2259692", 
-  "EIN-04-2592472", "EIN-04-2596491", 
-  "EIN-04-3266589", "EIN-04-3543134", 
-  "EIN-05-0258941", "EIN-06-0840436" )
-
-tables <- 
-c( "F9-P00-T00-HEADER",
-"F9-P01-T00-SUMMARY",
-"F9-P08-T00-REVENUE",
-"F9-P09-T00-EXPENSES",
-"F9-P10-T00-BALANCE-SHEET")
-
-for( i in tables )
-{
-  for( j in 2009:2020 )
-  { 
-    df <- NULL
-    try( df <- get_table( i, j ) )
-    if( is.null(df) ){ next }
-    sub <- dplyr::filter( df$EIN2 %in% EIN2_10 )
-    fn <- paste0( i, "-", j, "-SAMPLE-10.CSV" )
-    write.csv( sub, fn, row.names=F, na="" )
-  }
-}
-
-# COMBINE ALL YEARS TO SINGLE FILE
-
-root <- "https://raw.githubusercontent.com/Nonprofit-Open-Data-Collective/arnova-2024/refs/heads/main/data/"
-
-for( i in tables )
-{
-  d.list <- list()
-  for( j in 2009:2020 )
-  {
-    fn <- paste0( i, "-", j, "-SAMPLE-10.CSV" )
-    url <- paste0( root, fn )
-    df <- read.csv( url, colClasses = "character" ) 
-    d.list[[ as.character(j) ]] <- df
-  }
-  dd <- dplyr::bind_rows( d.list )
-  filename <- paste0( i, "-SAMPLE-10.CSV" )
-  write.csv( dd,  filename, row.names=F, na="" )
-}
-
-# [1] "F9-P00-T00-HEADER-SAMPLE-10.CSV"       
-# [2] "F9-P01-T00-SUMMARY-SAMPLE-10.CSV"      
-# [3] "F9-P08-T00-REVENUE-SAMPLE-10.CSV"      
-# [4] "F9-P09-T00-EXPENSES-SAMPLE-10.CSV"     
-# [5] "F9-P10-T00-BALANCE-SHEET-SAMPLE-10.CSV"
-
-

For our toy example, we are particularly interested in the part of -the 990 form that collects information about the board members in a -nonprofit. This is known as Part VII data. We will begin by pulling two -years of this data.

-

There are two main ways to access the data. You can download the data -locally to your computer from the data catalog and read it into your R -environment or you can use pre-built functions to read it directly in R. -We present examples of both below.

-
# Reading in locally store data:
-df2010 <- read.csv( "Coding/Data/PartVII/PartVII-2010.csv" )
-df2011 <- read.csv( "Coding/Data/PartVII/PartVII-2011.csv" )
-
-# Pull from NCCS using helper functions: 
-df2010.fun <- get_partvii(2010)
-df2011.fun <- get_partvii(2011)
-
-
-

Glass Cliff Analysis

-

There are several data cleaning and preparation steps that need to be -performed on the base data before our analysis. These are beyond the -scope of this toy example. Instead, we provide a cleaned subset of the -data. We provide two dataframes of 1000 and 10 unique nonprofits that -experienced at least one CEO transition between 2009 and 2019. It should -be noted that it is possible for these organizations to experience more -than one transition during this time frame. As such, we have a total of -1067 transitions. We can analyze various facets of these -transitions.
-

-
ceo_trans_10    <- read.csv("toy_CEO_trans_10EIN.csv" )
-ceo_trans_1000 <- read.csv("toy_CEO_trans_1000EIN.csv")
-
-

Financial Data Joining

-

The first thing we want to do with these data are to add financial -information about the orgs during each respective year. We can again -pull this info from the open data collective. This step takes about 10 -minutes to run so we will comment out the lines and run similar steps -with the data of 10 nonprofits and pre-subset financial -dataframes.
-

-
summary <- read.csv("F9-P01-T00-SUMMARY-SAMPLE-10.csv")
-revenue <- read.csv("F9-P08-T00-REVENUE-SAMPLE-10.csv")
-expenses <- read.csv("F9-P09-T00-EXPENSES-SAMPLE-10.csv")
-
-
-#let's join everything together:
-
-ceo_trans_10_fncl <- ceo_trans_10 %>% left_join(summary, by = c("TAXYR" = "TAX_YEAR",
-                                                               "EIN" = "ORG_EIN"))
-
-ceo_trans_10_fncl <- ceo_trans_10_fncl %>% left_join(revenue, by = c("TAXYR" = "TAX_YEAR",
-                                                               "EIN" = "ORG_EIN"))
-
-ceo_trans_10_fncl <- ceo_trans_10_fncl %>% left_join(expenses, by = c("TAXYR" = "TAX_YEAR",
-                                                               "EIN" = "ORG_EIN"))
-
-write.csv(ceo_trans_10_fncl, "CEO_trans_10EIN_fncl.csv" )
-
-
-# summary2009 <- get_table( "F9-P01-T00-SUMMARY", year=2009 )
-# summary2010 <- get_table( "F9-P01-T00-SUMMARY", year=2010 )
-# summary2011 <- get_table( "F9-P01-T00-SUMMARY", year=2011 )
-# summary2012 <- get_table( "F9-P01-T00-SUMMARY", year=2012 )
-# summary2013 <- get_table( "F9-P01-T00-SUMMARY", year=2013 )
-# summary2014 <- get_table( "F9-P01-T00-SUMMARY", year=2014 )
-# summary2015 <- get_table( "F9-P01-T00-SUMMARY", year=2015 )
-# summary2016 <- get_table( "F9-P01-T00-SUMMARY", year=2016 )
-# summary2017 <- get_table( "F9-P01-T00-SUMMARY", year=2017 )
-# summary2018 <- get_table( "F9-P01-T00-SUMMARY", year=2018 )
-# summary2019 <- get_table( "F9-P01-T00-SUMMARY", year=2019 )
-# 
-# revenue2009 <- get_table( "F9-P08-T00-REVENUE", year=2009 )
-# revenue2010 <- get_table( "F9-P08-T00-REVENUE", year=2010 )
-# revenue2011 <- get_table( "F9-P08-T00-REVENUE", year=2011 )
-# revenue2012 <- get_table( "F9-P08-T00-REVENUE", year=2012 )
-# revenue2013 <- get_table( "F9-P08-T00-REVENUE", year=2013 )
-# revenue2014 <- get_table( "F9-P08-T00-REVENUE", year=2014 )
-# revenue2015 <- get_table( "F9-P08-T00-REVENUE", year=2015 )
-# revenue2016 <- get_table( "F9-P08-T00-REVENUE", year=2016 )
-# revenue2017 <- get_table( "F9-P08-T00-REVENUE", year=2017 )
-# revenue2018 <- get_table( "F9-P08-T00-REVENUE", year=2018 )
-# revenue2019 <- get_table( "F9-P08-T00-REVENUE", year=2019 )
-# 
-# 
-# expenses2009 <- get_table( "F9-P09-T00-EXPENSES", year=2009 )
-# expenses2010 <- get_table( "F9-P09-T00-EXPENSES", year=2010 )
-# expenses2011 <- get_table( "F9-P09-T00-EXPENSES", year=2011 )
-# expenses2012 <- get_table( "F9-P09-T00-EXPENSES", year=2012 )
-# expenses2013 <- get_table( "F9-P09-T00-EXPENSES", year=2013 )
-# expenses2014 <- get_table( "F9-P09-T00-EXPENSES", year=2014 )
-# expenses2015 <- get_table( "F9-P09-T00-EXPENSES", year=2015 )
-# expenses2016 <- get_table( "F9-P09-T00-EXPENSES", year=2016 )
-# expenses2017 <- get_table( "F9-P09-T00-EXPENSES", year=2017 )
-# expenses2018 <- get_table( "F9-P09-T00-EXPENSES", year=2018 )
-# expenses2019 <- get_table( "F9-P09-T00-EXPENSES", year=2019 )
-# 
-# 
-# #Let's put them all together
-# summary <- rbind(as.data.frame(summary2009), as.data.frame(summary2010),as.data.frame(summary2011),
-#                  as.data.frame(summary2012),as.data.frame(summary2013),as.data.frame(summary2014),
-#                  as.data.frame(summary2015),as.data.frame(summary2016),as.data.frame(summary2017),
-#                  as.data.frame(summary2018),as.data.frame(summary2019))
-# 
-# revenue <- rbind(as.data.frame(revenue2009),as.data.frame(revenue2010),as.data.frame(revenue2011),
-#                  as.data.frame(revenue2012),as.data.frame(revenue2013),as.data.frame(revenue2014),
-#                  as.data.frame(revenue2015),as.data.frame(revenue2016),as.data.frame(revenue2017),
-#                  as.data.frame(revenue2018),as.data.frame(revenue2019))
-# 
-# expenses <- rbind(as.data.frame(expenses2009),as.data.frame(expenses2010),as.data.frame(expenses2011),
-#                   as.data.frame(expenses2012),as.data.frame(expenses2013),as.data.frame(expenses2014),
-#                   as.data.frame(expenses2015),as.data.frame(expenses2016),as.data.frame(expenses2017),
-#                   as.data.frame(expenses2018),as.data.frame(expenses2019))
-# 
-# 
-# 
-# 
-# #Orgs can return various versions of their tax forms each year
-# #For now, we will keep only the first observation, true analysis will need to revisit this decision
-# summary <- summary %>% group_by(ORG_EIN, TAX_YEAR) %>%
-#   filter(row_number()==1)
-# 
-# revenue <- revenue %>% group_by(ORG_EIN, TAX_YEAR) %>%
-#   filter(row_number()==1)
-# 
-# expenses <- expenses %>% group_by(ORG_EIN, TAX_YEAR) %>%
-#   filter(row_number()==1)
-# 
-# 
-# 
-# #Saving environment space by removing the other dfs
-# remove(summary2009, summary2010, summary2011, summary2012, summary2013, summary2014,
-#      summary2015, summary2016, summary2017, summary2018, summary2019 )
-# remove(expenses2009, expenses2010, expenses2011, expenses2012, expenses2013, expenses2014,
-#      expenses2015, expenses2016, expenses2017, expenses2018, expenses2019 )
-# remove(balance2009, balance2010, balance2011, balance2012, balance2013, balance2014,
-#      balance2015, balance2016, balance2017, balance2018, balance2019 )
-# #Let's join everything together
-# 
-# 
-# ceo_trans_1000_fncl <- ceo_trans_1000 %>% left_join(summary, by = c("TAXYR" = "TAX_YEAR",
-#                                                               "EIN" = "ORG_EIN"))
-# 
-# ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% left_join(expenses, by = c("TAXYR" = "TAX_YEAR",
-#                                                                    "EIN" = "ORG_EIN"))
-# 
-# ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% left_join(revenue, by = c("TAXYR" = "TAX_YEAR",
-#                                                                    "EIN" = "ORG_EIN"))
-# 
-# 
-# write.csv(ceo_trans_1000_fncl, "toy_CEO_trans_1000EIN_fncl.csv")
-
-
-

Summary Statistics of Transitions

-

Now we’re ready for some analyses! We are most interested in -understanding transitions by their gendering. That is to say that we -want to know if there are differences in observable firm dimensions for -nonprofits that experience a CEO transition from male to male relative -to the other permutations of male to female, female to male, and female -to female. Let’s start by looking at org differences by these transition -types. We will use the 1000 EIN observations for this analysis. We have -provided this dataset with the financial data attached (this was -accomplished by running the commented lines above). For simplicity, we -will only consider a few firm dimensions such as employee size and total -assets.

-
#This df 
-ceo_trans_1000_fncl <- read.csv("toy_CEO_trans_1000EIN_fncl.csv")
-
-#First need to denote transitions 
-ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>%
-  arrange(EIN, transition_no, TAXYR)%>% #for each org, transition, and year, 
-  group_by(EIN, transition_no) %>%       
-  mutate(period = row_number())%>%       #let's denote which period the row is relative to (i.e., how many years before /after a transition)
-  ungroup()
-
-#Let's make this variable a bit more readable
-ceo_trans_1000_fncl$period <- paste0("T", ceo_trans_1000_fncl$period-3)
-ceo_trans_1000_fncl$period <- factor(ceo_trans_1000_fncl$period, levels = c("T-2", "T-1", "T0" , "T1", "T2"))
-
-
-#Need to get transition type too
-#We want to know if the transition is M>M, M>F, F>M, or F>M
-#We will first create a string of the CEO gender throughout the 5 year period
-#The we will create a column that keeps the first and last gender (this is the gender transition)
-ceo_trans_1000_fncl<- ceo_trans_1000_fncl %>%
-  group_by(EIN, transition_no)%>%
-  mutate(trans_tot = str_c(unlist(strsplit(paste(gender, collapse = ""), "")), collapse = "")
-  ) %>%
-  mutate(
-    trans_type = str_sub(trans_tot, 1, 1) %>%
-      str_c(str_sub(trans_tot, nchar(trans_tot), nchar(trans_tot)))  # Combine 1st and last letter
-    
-  )
-
-#We want this variable to be a factor
-ceo_trans_1000_fncl$trans_type <- as.factor(ceo_trans_1000_fncl$trans_type)
-
-
-
-#We're going to get firm attributes by transition type
-#Let's consider the attributes at t-1
-#We'll consider basic variables like number of employees, total assets, etc.
-
-#Note that further data cleaning is necessary for final presentation due to NA values input into some of these columns. For now, we will just remove the nas and continue with our analysis
-
-temp <- ceo_trans_1000_fncl %>% group_by(trans_type)%>%
-  filter(period == "T-1") %>%
-  summarize(count = n(),
-    num_emp_av =mean(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T),
-            num_emp_sd = sd(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T),
-            num_vol_av = mean(F9_01_ACT_GVRN_VOL_TOT, na.rm = T),
-            num_vol_sd = sd(F9_01_ACT_GVRN_VOL_TOT, na.rm = T),
-            total_exp_av = mean(F9_01_EXP_TOT_PY, na.rm = T),
-            total_exp_sd = sd(F9_01_EXP_TOT_PY, na.rm = T),
-            total_assets_av = mean(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T),
-            total_assets_sd = sd(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T))
-
-temp
-


-There are 349 transitions from male to male (MM) transitions, 253 male -to female transitions (MF), 160 female to male transitions (FM), and 305 -female to female transitions (FF) in our dataset. FF orgs have the -smallest number of employees, on average while MF have the highest. It -should be noted that the standard deviation for number of employees is -pretty high, suggesting a wide range of nonprofits. MM nonprofits have -the highest total expeness on average and the highest total assets. -Again, the SDs are wide, suggesting large variation across -observations.

-
-
-

Financial Measures by Transitions

-

The Glass Cliff Phenomenon hypothesizes that women are chosen for -positions of power when these positions are more precarious. One way to -denote a precarious position is by the firm’s financial performance; -poor financial performance suggests more precariousness. We acknowledge -that financial performance is comprised of several different dimensions -and it is sometimes hard to arrive at clean conclusions about “poorly -performing nonprofits.” For demonstration purposes we will specifically -focus on the financial performance metric of post-depreciation -profitability margin (podpm). This is defined as an income measure that -determines a firm’s profitability after incorporating non-cash expenses. -Higher values of this metric are generally desirable because the -indicate that an org is not lost its revenue to expenses. We will use -the package fiscal from the Open Data Collective to -calculate our variable of interest. The default parameters of the -respective functions are already built for the 990 naming conventions so -usage is pretty straight forward!
-

-
#Make sure to run the code chunk about to ensure your df_long_fncl has all the necessary variables 
-
-#site: https://github.com/Nonprofit-Open-Data-Collective/fiscal/tree/main/R
-ceo_trans_1000_fncl <- get_podpm(ceo_trans_1000_fncl)
-
-
-
-#Let's plot these measures
-plot_temp <- ceo_trans_1000_fncl %>%
-  group_by(trans_type, period)%>%
-  summarize(median_podpm =median(podpm, na.rm = T))
-
-
-ggplot(data = plot_temp, aes(x = period, y = median_podpm, group = trans_type, color = trans_type)) + 
-  geom_line(linewidth = 1.5)+ 
-  geom_text_repel(aes(label = round(median_podpm,3)), size = 5, nudge_x = -0.07, nudge_y = 0.001, segment.size = 0, segment.color = NA)+
-  theme_bw( ) + 
-#  theme(text = element_text(size = 24))+
-  scale_x_discrete(labels = c("T-2", "T-1", "Transition", "T+1", "T+2"))+
-  labs(color = "Transition") +
-  xlab("Period")+
-  ylab("Median Post-Depreciation Profitability Margin") +
-  ggtitle("Median Post-Depreciation Profitability Margin by Transition Type") +
-  geom_vline(xintercept = 3, linetype = "dashed")
-
-
-
-################################################
-#Now doing density plots of these respective vars
-################################################
-
-#Let's compare the MM density to MF density in the t-1 period for the vats
-
-ceo_trans_1000_fncl_MM <- ceo_trans_1000_fncl %>% filter(trans_type == "MM" &
-                                             period == "T-1")
-ceo_trans_1000_fncl_MF <- ceo_trans_1000_fncl %>% filter(trans_type == "MF"&
-                                             period == "T-1")
-
-
-ggplot() + 
-  geom_density(data = ceo_trans_1000_fncl_MM,  aes(x = podpm, fill = "lightblue"), alpha = 0.5) +
-  geom_density(data = ceo_trans_1000_fncl_MF,  aes(x = podpm, fill = "pink"), alpha = 0.5) +
-  xlim(-1,1)+
-  theme_bw()+
-#  theme(text = element_text(size = 24))+
-  scale_fill_manual(name = "Transition", values = c('lightblue', 'pink'), labels = c("pink" = "MF" ,  "lightblue" = "MM") ) +
-  xlab("Post-Depreciation Profitability Margin")+
-  ylab("Density") +
-  ggtitle("Density ofPost-Depreciation Profitability Margin by Transition at t-1")
-

The glass cliff phenomenon suggest that financial precarious NPs -(seen by a significant drop in their financials between T-2 and T-1) -would more likely hire a female to the CEO position. We start by looking -at the PODPM variable over the periods. MM organizations start with the -highest median profitability margin but also experience the steepest -drop between T-2 and T-1. We see a similar slope for FM organizations -although the starting point is the lowest in the entire group. MF -transitions do not appear to be preceded by steep changes in -profitability. If the glass cliff hypothesis were true, we would expect -the observed trajectory of FM but would not expect the other firms to -have similar metrics around their transitions. This graph calls for -further analysis on other financial measures to determine whether FM -firms display a transition during significantly more precarious times -than their counterparts.

-


-
-As a final understanding of the data, we look at the distribution of -PODPM at T-1. We consider the two most relevant groups of MM and MF. We -see that the distribution of financial variables tends to be extremely -similar to both types of transitions, again failing to provide strong -motivation for a glass cliff phenomenon.  
-

- -
-
-
- - - - -
- - - - - - - - - - - - - - - diff --git a/draft/glass-cliff-workflow.rmd b/draft/glass-cliff-workflow.rmd deleted file mode 100644 index f4ee2be..0000000 --- a/draft/glass-cliff-workflow.rmd +++ /dev/null @@ -1,1050 +0,0 @@ ---- -title: "Nonprofit CEO Transition Dynamics" -author: - - Tiana Marrese, UPenn - - Jesse Lecy, ASU -date: "`r format(Sys.time(), '%d %B, %Y')`" -output: - html_document: - theme: readable - df_print: paged - highlight: tango - toc: yes - toc_float: no ---- - - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE, message=F, warning=F, fig.width = 10, fig.pos = 'H') -``` - - - - - -## A Workflow Demonstration - -This script will present several new components of the Nonprofit Open Data Collective data environment using an example motivated by the "Glass Cliff", or the idea the women are more likely to be appointed to precarious positions of power relative to their male counterparts. - -We specifically want to highlight the value of an integrated data environment developed around the EFILE Database, one of the emerging research centerpieces in nonprofit scholarship. This tutorial demonstrates a reproducible data engineering workflow that follows [FAIR Data Guidelines](https://www.nature.com/articles/sdata201618). We will answer the research question using a dataset created through the following 8 Steps: - - -![](workflow.png) - -1. Build tables using the **irs990efile** package. -2. Standardize names of the leadership team present in Part VII (the compensation tables) using the **peopleparser** package. -3. Standardize titles in Part VII using the **titleclassifier** package. -4. Identify CEO transition years within the data. -5. Build a financial dataset by combining the following 990 Parts: revenues, expenses, and assets. -6. Generate common financial operating ratios using the **fiscal** package. -7. Add NTEE codes and standardized address fields from the Unified BMF. -8. Merge financial tables and compensation tables into a single table for the analysis. - - -We are using the term "integrated data environment" here to mean: - -- consistent variable names -- consistent record IDs -- consistent metadata -- consistent definitions of "year" (tax year, not fiscal year or filing year) - -These things allow us to create more expressive and intuitive data steps that anyone can replicate. It also allows researchers to create custom tools for refining or analyzing data, as demonstrated by the packages used in this workflow. - - -## Glass Cliff Research - -> The glass cliff is a hypothesized phenomenon in which women are more likely to break the "glass ceiling" (i.e. achieve leadership roles in business and government) during periods of crisis or downturn when the risk of failure is highest. - -We know from previous studies that male CEOs are put in charge of large, thriving nonprofits while women are more likely to be hired to lead smaller social services organizations. - -Here we explore the question of whether female CEOs are more likely to be hired when the current leader is struggling. - -![](ceo-sorting.png) - -*Grasse, N. J., Heidbreder, B., Kukla-Acevedo, S. A., & Lecy, J. D. (2024). Some Good News, More Bad News: Two Decades of the Gender Pay Gap for Nonprofit Directors and Chief Financial Officers. Review of Public Personnel Administration, [0734371X241248854](https://journals.sagepub.com/doi/full/10.1177/0734371X241248854).* - -## Data and Software - -### Efile Data - -Processed IRS 990 Efile data are housed in the [NCCS Data Catalog](https://nccs.urban.org/nccs/catalogs). You can find further information about sources, processing, and variable descriptions here: - -- [Efile Data Catalog](https://nccs.urban.org/nccs/catalogs/catalog-efile.html) -- [Efile Data Dictionary](https://nonprofit-open-data-collective.github.io/irs990efile/data-dictionary/data-dictionary.html) - - -### R Packages - -There is a growing library of nonprofit tools: - -- [Open Data Collective Landing Page](https://nonprofit-open-data-collective.github.io/tools/) - - -We will be using the following packages for the demo: - -```{r, eval=F} -devtools::install_github( 'Nonprofit-Open-Data-Collective/peopleparser' ) -devtools::install_github( 'nonprofit-open-data-collective/titleclassifier' ) -devtools::install_github( 'nonprofit-open-data-collective/fiscal') -devtools::install_github( 'nonprofit-open-data-collective/irs990efile') -``` - - -```{r packages} -library( tidyverse ) -library( pander ) -library( data.table ) -library( RecordLinkage ) -library( reshape2 ) -library( ggrepel ) -library( utils ) - -# nonprofit data packages -library( peopleparser ) -library( titleclassifier ) -library( fiscal ) - -# helper functions for the demo: -nodc <- "https://raw.githubusercontent.com/Nonprofit-Open-Data-Collective/" -repo <- "arnova-2024/refs/heads/main/" -file <- "functions.R" -source( paste0( nodc, repo, file ) ) -``` - - - - - - - - - - -## STEP 1: Building Efile Tables from Scratch - -![](workflow.png) - -This step builds Efile tables by loading raw XML efile returns from the Data Commons and using the **irs990efile** package to parse XML files into rectangular CSV tables. - -This code creates data for a sample of 100 nonprofits from 2018: - -```{r, eval=F} -library( irs990efile ) - -index <- build_index( tax.years=2018 ) - -index100 <- - index %>% - filter( FormType %in% c("990","990EZ") ) %>% - sample_n( 100 ) - -TABLES <- c( "F9-P00-T00-HEADER", - "F9-P01-T00-SUMMARY", - "F9-P08-T00-REVENUE", - "F9-P09-T00-EXPENSES", - "F9-P11-T00-ASSETS" ) - -URLS <- index100$URL - -build_tables( urls=URLS, year=2018, table.names=TABLES ) -``` - -Typically you would not need to replicate this step since it is a computationally-intensive process. It can take a couple of days to build the full efile database. It is much easier to pull existing CSV files from NCCS: - -[**EFILE DATA CATALOG**](https://nccs.urban.org/nccs/catalogs/catalog-efile.html). - - - - - -## STEP 2: Parse DTK Names - -![](workflow.png) - - -```{r, message = F} -root <- paste0( nodc, repo ) -fn <- "data/PART-VII-SAMPLE-10.CSV" -url <- paste0( root, fn ) -partvii <- read.csv( url ) -``` - - -```{r, echo=F} -keep <- - c("NAME", "TAXYR", "FORMTYPE", - "F9_07_COMP_DTK_NAME_PERS", "F9_07_COMP_DTK_TITLE", - "F9_07_COMP_DTK_AVE_HOUR_WEEK", "F9_07_COMP_DTK_COMP_ORG") -partvii[keep] |> head() |> pander::pander() -``` - - -Now let's use peopleparser to clean the names. Names do not arrive standardized in any specific format: - -```{r, echo=F} -# names are not standardized in the raw data -partvii$F9_07_COMP_DTK_NAME_PERS %>% - sample( 3 ) %>% - paste0( collapse=" ;; " ) %>% - cat( sep="\n") - -partvii$F9_07_COMP_DTK_NAME_PERS %>% - sample( 3 ) %>% - paste0( collapse=" ;; " ) %>% - cat( sep="\n" ) - -partvii$F9_07_COMP_DTK_NAME_PERS %>% - sample( 3 ) %>% - paste0( collapse=" ;; " ) %>% - cat( sep="\n" ) - -partvii$F9_07_COMP_DTK_NAME_PERS %>% - sample( 3 ) %>% - paste0( collapse=" ;; " ) %>% - cat( sep="\n" ) - -partvii$F9_07_COMP_DTK_NAME_PERS %>% - sample( 3 ) %>% - paste0( collapse=" ;; " ) %>% - cat( sep="\n" ) -``` - - -The **peopleparser** package will remove nuisance text and split the name string into 5 parts: - -- prefix (titles) -- first name -- middle name -- last name -- suffix (jr, sr, i, ii, etc.) - -And add a predicted gender label (M/F/U) plus confidence level once it is able to identify the individual's first name: - -```{r} -peopleparser::parse.name( "(1) Rev Kendrick R Wilson III" ) -``` -```{r} -peopleparser::parse.name( "Doctor Wilson III, Kendrick R" ) -``` -```{r} -# gender prediction is based on first names -peopleparser::parse.name( "Wilson, K R Until June 2008" ) -``` - - -```{r} -nm <- partvii[[ "F9_07_COMP_DTK_NAME_PERS" ]] %>% unique() -nm.parsed <- peopleparser::parse.names( nm ) -``` - - -```{r, echo=F} -nm.parsed %>% head() %>% pander::pander() -``` - - -Join parsed names back to the original data frame. - -```{r} -# CORE R VERSION OF A TABLE JOIN -partvii <- - partvii %>% - merge( nm.parsed, - by.x="F9_07_COMP_DTK_NAME_PERS", by.y="name", - all.x=T ) -``` - -```{r, eval=F} -# TIDYVERSE VERSION OF A TABLE JOIN -partvii <- - partvii %>% - left_join( nm.parsed, - by=c( "F9_07_COMP_DTK_NAME_PERS" = "name" ) ) -``` - -```{r} -write.csv( partvii, "data/PART-VII-SAMPLE-10-PARSED-NAMES.CSV", row.names=F ) -``` - - -## STEP 3: Standardize Titles - -![](workflow.png) - -```{r, eval=F} -root <- paste0( nodc, repo ) -fn <- "data/PART-VII-SAMPLE-10-PARSED-NAMES.CSV" -url <- paste0( root, fn ) -partvii <- read.csv( url ) -``` - - -```{r, message = F} -# steps from titleclassifier package - -titles <- - partvii %>% - standardize_df() %>% - remove_dates() %>% - standardize_conj() %>% - split_titles() %>% - standardize_spelling() %>% - gen_status_codes() %>% - standardize_titles() %>% - categorize_titles() -``` - -```{r, echo=F} -titles <- dplyr::arrange( titles, desc(ein), desc(taxyr), desc(tot.comp.incl.rltd) ) - -keep1 <- -c( "dtk.name", "title.raw", - "title.mult.x", "title.order") - -titles[keep1] %>% head(10) %>% pander::pander() -``` - -Title processing steps from the package: - -```{r, echo=F} -keep2 <- -c( "title.raw","title.v4","title.v7", "title.standard" ) - -titles[ 150:165, keep2 ] %>% pander::pander() -``` - -Pay and hours tabulated relative to other employees in the same org: - -```{r, echo=F} -keep3 <- -c( "dtk.name", "title.standard", - "tot.hours", "hours.rank", - "tot.comp", "pay.max", "pay.rank" ) - -these <- titles$taxyr == 2019 & titles$ein == "60840436" -# titles[ these, keep3 ] %>% pander::pander() - -these <- titles$taxyr == 2018 & titles$ein == "60840436" -# titles[ these, keep3 ] %>% pander::pander() - -these <- titles$taxyr == 2017 & titles$ein == "60840436" -titles[ these, keep3 ] %>% pander::pander() -``` - - -```{r} -partvii <- - merge( partvii, titles, - by.x=c("EIN","TAXYR"), by.y=c("ein","taxyr"), - all.x=TRUE ) -``` - - -```{r} -write.csv( partvii, "data/PART-VII-SAMPLE-10-PARSED-NAMES-TITLES.CSV", row.names=F ) -``` - - - - -## STEP 4: Identify CEO Transitions - -We are omitting this step because it is outside the scope of the workflow. All that you need to know is leadership transitions are identified by isolating CEOs and finding periods where the individuals change. Transitions are labeled as: - -- MM: outgoing male ceo, incoming male ceo -- MF: outgoing male ceo, incoming female ceo -- FM: outgoing female ceo, incoming male ceo -- FF: outgoing female ceo, incoming female ceo - - -```{r, echo=F} -root <- paste0( nodc, repo ) -fn <- "data/toy_CEO_trans_1000EIN_fncl.csv" -url <- paste0( root, fn ) -ceo_trans_1000_fncl <- read.csv( url ) - -#This df -# ceo_trans_1000_fncl <- read.csv("toy_CEO_trans_1000EIN_fncl.csv") - -#First need to denote transitions -ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% - arrange(EIN, transition_no, TAXYR)%>% #for each org, transition, and year, - group_by(EIN, transition_no) %>% - mutate(period = row_number())%>% #let's denote which period the row is relative to (i.e., how many years before /after a transition) - ungroup() - -#Let's make this variable a bit more readable -ceo_trans_1000_fncl$period <- paste0("T", ceo_trans_1000_fncl$period-3) -ceo_trans_1000_fncl$period <- factor(ceo_trans_1000_fncl$period, levels = c("T-2", "T-1", "T0" , "T1", "T2")) - - -#Need to get transition type too -#We want to know if the transition is M>M, M>F, F>M, or F>M -#We will first create a string of the CEO gender throughout the 5 year period -#The we will create a column that keeps the first and last gender (this is the gender transition) -ceo_trans_1000_fncl<- ceo_trans_1000_fncl %>% - group_by(EIN, transition_no)%>% - mutate(trans_tot = str_c(unlist(strsplit(paste(gender, collapse = ""), "")), collapse = "") - ) %>% - mutate( - trans_type = str_sub(trans_tot, 1, 1) %>% - str_c(str_sub(trans_tot, nchar(trans_tot), nchar(trans_tot))) # Combine 1st and last letter - - ) - -#We want this variable to be a factor -ceo_trans_1000_fncl$trans_type <- as.factor(ceo_trans_1000_fncl$trans_type) - - -keep <- - c("EIN","TAXYR","period", "CEO1", "CEO2", "trans_type") - -ceo_trans_1000_fncl[ 1:40, keep ] %>% pander::pander() -``` - - -```{r, eval=F, echo=F} -#We're going to get firm attributes by transition type -#Let's consider the attributes at t-1 -#We'll consider basic variables like number of employees, total assets, etc. - -#Note that further data cleaning is necessary for final presentation due to NA values input into some of these columns. For now, we will just remove the nas and continue with our analysis - -temp <- ceo_trans_1000_fncl %>% group_by(trans_type)%>% - filter(period == "T-1") %>% - summarize(count = n(), - num_emp_av =mean(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T), - num_emp_sd = sd(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T), - num_vol_av = mean(F9_01_ACT_GVRN_VOL_TOT, na.rm = T), - num_vol_sd = sd(F9_01_ACT_GVRN_VOL_TOT, na.rm = T), - total_exp_av = mean(F9_01_EXP_TOT_PY, na.rm = T), - total_exp_sd = sd(F9_01_EXP_TOT_PY, na.rm = T), - total_assets_av = mean(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T), - total_assets_sd = sd(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T)) - -temp -``` - - - - - -## STEP 5: Compile Financials - -![](workflow.png) - -We will first build a financials table by selecting the relevant 990 parts, then combining them: - -```{r} -## JOINING ONE TO ONE TABLES - -root <- paste0( nodc, repo ) - -fn1 <- "data/F9-P00-T00-HEADER-SAMPLE-10.CSV" -fn2 <- "data/F9-P01-T00-SUMMARY-SAMPLE-10.CSV" -fn3 <- "data/F9-P08-T00-REVENUE-SAMPLE-10.CSV" -fn4 <- "data/F9-P09-T00-EXPENSES-SAMPLE-10.CSV" -fn5 <- "data/F9-P10-T00-BALANCE-SHEET-SAMPLE-10.CSV" - -d1 <- read.csv( paste0( root, fn1 ) ) -d2 <- read.csv( paste0( root, fn2 ) ) -d3 <- read.csv( paste0( root, fn3 ) ) -d4 <- read.csv( paste0( root, fn4 ) ) -d5 <- read.csv( paste0( root, fn5 ) ) -``` - -The efile one-to-one tables all share the same IDs: - -```{r} -intersect( names(d1), names(d2) ) -``` - -Which makes merging files easy: - -```{r} -df <- merge( d1, d2 ) -df <- merge( df, d3 ) -df <- merge( df, d4 ) -df <- merge( df, d5 ) -``` - - -```{r} -write.csv( df, "data/FINANCIALS.CSV" ) -``` - - -Note that dataset dimensions should not change for one-to-one merges: - -```{r} -dim(d1) -dim(d2) -dim(df) -``` - - - -See the appendix for the code used to compile the five tables above for the sample. - - - - -## STEP 6: Add Financial Operating Ratios - -![](workflow.png) - -```{r, eval=F} -root <- paste0( nodc, repo ) -fn <- "data/FINANCIALS.CSV" -df <- read.csv( paste0( root, fn ) ) -``` - -```{r, echo=F} -df[ 1:5, 88:90 ] %>% knitr::kable() -``` - -The **fiscal** package contains the following financial ratios: - -```{r, eval=F} -df <- get_aer( df ) # Assets to Revenues Ratio -df <- get_arr( df ) # Assets to Revenues Ratio -df <- get_cr( df ) # Current Ratio -df <- get_dar( df ) # Debt to Asset Ratio -df <- get_der( df ) # Debt to Equity Ratio -df <- get_dgdr( df ) # Donation/Grant Dependence Ratio -df <- get_dmr( df ) # Debt Management Ratio -df <- get_doch( df ) # Days of Operating Cash on Hand -df <- get_doci( df ) # Days of Operating Cash and Investments -df <- get_eidr( df ) # Earned Income Dependence Ratio -df <- get_er( df ) # Equity Ratio -df <- get_ggr( df ) # Government Grants Ratio -df <- get_iidr( df ) # Investment Income Dependence Ratio -df <- get_lar( df ) # Lands to Assets Ratio -df <- get_moch( df ) # Months of Operating Cash on Hand -df <- get_or( df ) # Operating Margin -df <- get_per( df ) # Program Efficiency Ratio -df <- get_podpm( df ) # Post-Depreciation Profitability Margin -df <- get_predpm( df ) # Pre-Depreciation Profitability Margin -df <- get_qr( df ) # Quick Ratio -df <- get_ssr( df ) # Self Sufficiency Ratio -df <- get_stdr( df ) # Short Term Debt Ratio -``` - -We will add the post-depreciation profitability margin and the quick ratio to the data: - -```{r} -df <- get_podpm( df ) -df <- get_dgdr( df ) -``` - -For details on the definitions and calculation of the ratios try: - -```{r, eval=F} -help( get_dgdr ) -``` - -```{r} -write.csv( df, "data/FINANCIALS-W-RATIOS.CSV", row.names=F ) -``` - - - - -## STEP 7: Add BMF Fields - -![](workflow.png) -The Business Master File contains important information not available on 990 forms such as organizational NTEE codes. In addition, the NCCS BMF files contain standardized geographies and other useful information. - -The BMF rows for our sample have been precompiled: - -```{r} -root <- paste0( nodc, repo ) -fn <- "data/bmf_unified_10.csv" -bmf <- read.csv( paste0( root, fn ) ) -``` - -```{r, eval=F} -root <- paste0( nodc, repo ) -fn <- "data/FINANCIALS-W-RATIOS.CSV" -df <- read.csv( paste0( root, fn ) ) -``` - -```{r} -df <- merge( df, bmf, by="EIN2" ) -``` - -```{r} -write.csv( df, "data/FINANCIALS-W-RATIOS-PLUS-BMF.CSV", row.names=F ) -``` - - - - - -## STEP 8: Combine CEO Panel and Financials - - - -Add this step? Or just add a few sentences explaining it. - - - -## Glass Cliff Examples - -Add your examples here.... - - -![](example.png) - - - - - - - - - -
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- - -## Appendix - -### Step 5 - -The demo files above (a sample of 10 nonprofits), read directly from the demo repo on GitHub, were compiled using the following code: - -```{r, eval=F} -EIN2_10 <- -c("EIN-02-0240383", "EIN-03-0179298", - "EIN-04-2104310", "EIN-04-2259692", - "EIN-04-2592472", "EIN-04-2596491", - "EIN-04-3266589", "EIN-04-3543134", - "EIN-05-0258941", "EIN-06-0840436" ) - -tables <- -c( "F9-P00-T00-HEADER", -"F9-P01-T00-SUMMARY", -"F9-P08-T00-REVENUE", -"F9-P09-T00-EXPENSES", -"F9-P10-T00-BALANCE-SHEET") - -for( i in tables ) -{ - for( j in 2009:2020 ) - { - df <- NULL - try( df <- get_table( i, j ) ) - if( is.null(df) ){ next } - sub <- dplyr::filter( df$EIN2 %in% EIN2_10 ) - fn <- paste0( i, "-", j, "-SAMPLE-10.CSV" ) - write.csv( sub, fn, row.names=F, na="" ) - } -} - -# COMBINE ALL YEARS TO SINGLE FILE - -root <- "https://raw.githubusercontent.com/Nonprofit-Open-Data-Collective/arnova-2024/refs/heads/main/data/" - -for( i in tables ) -{ - d.list <- list() - for( j in 2009:2020 ) - { - fn <- paste0( i, "-", j, "-SAMPLE-10.CSV" ) - url <- paste0( root, fn ) - df <- read.csv( url, colClasses = "character" ) - d.list[[ as.character(j) ]] <- df - } - dd <- dplyr::bind_rows( d.list ) - filename <- paste0( i, "-SAMPLE-10.CSV" ) - write.csv( dd, filename, row.names=F, na="" ) -} - -# [1] "F9-P00-T00-HEADER-SAMPLE-10.CSV" -# [2] "F9-P01-T00-SUMMARY-SAMPLE-10.CSV" -# [3] "F9-P08-T00-REVENUE-SAMPLE-10.CSV" -# [4] "F9-P09-T00-EXPENSES-SAMPLE-10.CSV" -# [5] "F9-P10-T00-BALANCE-SHEET-SAMPLE-10.CSV" -``` - - - - ------------ - - -For our toy example, we are particularly interested in the part of the 990 form that collects information about the board members in a nonprofit. This is known as Part VII data. We will begin by pulling two years of this data. - -There are two main ways to access the data. You can download the data locally to your computer from the data catalog and read it into your R environment or you can use pre-built functions to read it directly in R. We present examples of both below. - -```{r, echo=F, eval=F} -#Change working directory to personal path -setwd("~/Tiana_School_Work/PhD/Dissertation/Glass_Cliff_Paper") -``` - - - -```{r, eval=F} -# Reading in locally store data: -df2010 <- read.csv( "Coding/Data/PartVII/PartVII-2010.csv" ) -df2011 <- read.csv( "Coding/Data/PartVII/PartVII-2011.csv" ) - -# Pull from NCCS using helper functions: -df2010.fun <- get_partvii(2010) -df2011.fun <- get_partvii(2011) -``` - - - -### Glass Cliff Analysis - -There are several data cleaning and preparation steps that need to be performed on the base data before our analysis. These are beyond the scope of this toy example. Instead, we provide a cleaned subset of the data. We provide two dataframes of 1000 and 10 unique nonprofits that experienced at least one CEO transition between 2009 and 2019. It should be noted that it is possible for these organizations to experience more than one transition during this time frame. As such, we have a total of 1067 transitions. We can analyze various facets of these transitions. -\ - -```{r, eval=F} - -ceo_trans_10 <- read.csv("toy_CEO_trans_10EIN.csv" ) -ceo_trans_1000 <- read.csv("toy_CEO_trans_1000EIN.csv") - -``` - - -#### Financial Data Joining -The first thing we want to do with these data are to add financial information about the orgs during each respective year. We can again pull this info from the open data collective. This step takes about 10 minutes to run so we will comment out the lines and run similar steps with the data of 10 nonprofits and pre-subset financial dataframes. -\ - -```{r, results = 'hide', eval=F} - - -summary <- read.csv("F9-P01-T00-SUMMARY-SAMPLE-10.csv") -revenue <- read.csv("F9-P08-T00-REVENUE-SAMPLE-10.csv") -expenses <- read.csv("F9-P09-T00-EXPENSES-SAMPLE-10.csv") - - -#let's join everything together: - -ceo_trans_10_fncl <- ceo_trans_10 %>% left_join(summary, by = c("TAXYR" = "TAX_YEAR", - "EIN" = "ORG_EIN")) - -ceo_trans_10_fncl <- ceo_trans_10_fncl %>% left_join(revenue, by = c("TAXYR" = "TAX_YEAR", - "EIN" = "ORG_EIN")) - -ceo_trans_10_fncl <- ceo_trans_10_fncl %>% left_join(expenses, by = c("TAXYR" = "TAX_YEAR", - "EIN" = "ORG_EIN")) - -write.csv(ceo_trans_10_fncl, "CEO_trans_10EIN_fncl.csv" ) - - -# summary2009 <- get_table( "F9-P01-T00-SUMMARY", year=2009 ) -# summary2010 <- get_table( "F9-P01-T00-SUMMARY", year=2010 ) -# summary2011 <- get_table( "F9-P01-T00-SUMMARY", year=2011 ) -# summary2012 <- get_table( "F9-P01-T00-SUMMARY", year=2012 ) -# summary2013 <- get_table( "F9-P01-T00-SUMMARY", year=2013 ) -# summary2014 <- get_table( "F9-P01-T00-SUMMARY", year=2014 ) -# summary2015 <- get_table( "F9-P01-T00-SUMMARY", year=2015 ) -# summary2016 <- get_table( "F9-P01-T00-SUMMARY", year=2016 ) -# summary2017 <- get_table( "F9-P01-T00-SUMMARY", year=2017 ) -# summary2018 <- get_table( "F9-P01-T00-SUMMARY", year=2018 ) -# summary2019 <- get_table( "F9-P01-T00-SUMMARY", year=2019 ) -# -# revenue2009 <- get_table( "F9-P08-T00-REVENUE", year=2009 ) -# revenue2010 <- get_table( "F9-P08-T00-REVENUE", year=2010 ) -# revenue2011 <- get_table( "F9-P08-T00-REVENUE", year=2011 ) -# revenue2012 <- get_table( "F9-P08-T00-REVENUE", year=2012 ) -# revenue2013 <- get_table( "F9-P08-T00-REVENUE", year=2013 ) -# revenue2014 <- get_table( "F9-P08-T00-REVENUE", year=2014 ) -# revenue2015 <- get_table( "F9-P08-T00-REVENUE", year=2015 ) -# revenue2016 <- get_table( "F9-P08-T00-REVENUE", year=2016 ) -# revenue2017 <- get_table( "F9-P08-T00-REVENUE", year=2017 ) -# revenue2018 <- get_table( "F9-P08-T00-REVENUE", year=2018 ) -# revenue2019 <- get_table( "F9-P08-T00-REVENUE", year=2019 ) -# -# -# expenses2009 <- get_table( "F9-P09-T00-EXPENSES", year=2009 ) -# expenses2010 <- get_table( "F9-P09-T00-EXPENSES", year=2010 ) -# expenses2011 <- get_table( "F9-P09-T00-EXPENSES", year=2011 ) -# expenses2012 <- get_table( "F9-P09-T00-EXPENSES", year=2012 ) -# expenses2013 <- get_table( "F9-P09-T00-EXPENSES", year=2013 ) -# expenses2014 <- get_table( "F9-P09-T00-EXPENSES", year=2014 ) -# expenses2015 <- get_table( "F9-P09-T00-EXPENSES", year=2015 ) -# expenses2016 <- get_table( "F9-P09-T00-EXPENSES", year=2016 ) -# expenses2017 <- get_table( "F9-P09-T00-EXPENSES", year=2017 ) -# expenses2018 <- get_table( "F9-P09-T00-EXPENSES", year=2018 ) -# expenses2019 <- get_table( "F9-P09-T00-EXPENSES", year=2019 ) -# -# -# #Let's put them all together -# summary <- rbind(as.data.frame(summary2009), as.data.frame(summary2010),as.data.frame(summary2011), -# as.data.frame(summary2012),as.data.frame(summary2013),as.data.frame(summary2014), -# as.data.frame(summary2015),as.data.frame(summary2016),as.data.frame(summary2017), -# as.data.frame(summary2018),as.data.frame(summary2019)) -# -# revenue <- rbind(as.data.frame(revenue2009),as.data.frame(revenue2010),as.data.frame(revenue2011), -# as.data.frame(revenue2012),as.data.frame(revenue2013),as.data.frame(revenue2014), -# as.data.frame(revenue2015),as.data.frame(revenue2016),as.data.frame(revenue2017), -# as.data.frame(revenue2018),as.data.frame(revenue2019)) -# -# expenses <- rbind(as.data.frame(expenses2009),as.data.frame(expenses2010),as.data.frame(expenses2011), -# as.data.frame(expenses2012),as.data.frame(expenses2013),as.data.frame(expenses2014), -# as.data.frame(expenses2015),as.data.frame(expenses2016),as.data.frame(expenses2017), -# as.data.frame(expenses2018),as.data.frame(expenses2019)) -# -# -# -# -# #Orgs can return various versions of their tax forms each year -# #For now, we will keep only the first observation, true analysis will need to revisit this decision -# summary <- summary %>% group_by(ORG_EIN, TAX_YEAR) %>% -# filter(row_number()==1) -# -# revenue <- revenue %>% group_by(ORG_EIN, TAX_YEAR) %>% -# filter(row_number()==1) -# -# expenses <- expenses %>% group_by(ORG_EIN, TAX_YEAR) %>% -# filter(row_number()==1) -# -# -# -# #Saving environment space by removing the other dfs -# remove(summary2009, summary2010, summary2011, summary2012, summary2013, summary2014, -# summary2015, summary2016, summary2017, summary2018, summary2019 ) -# remove(expenses2009, expenses2010, expenses2011, expenses2012, expenses2013, expenses2014, -# expenses2015, expenses2016, expenses2017, expenses2018, expenses2019 ) -# remove(balance2009, balance2010, balance2011, balance2012, balance2013, balance2014, -# balance2015, balance2016, balance2017, balance2018, balance2019 ) -# #Let's join everything together -# -# -# ceo_trans_1000_fncl <- ceo_trans_1000 %>% left_join(summary, by = c("TAXYR" = "TAX_YEAR", -# "EIN" = "ORG_EIN")) -# -# ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% left_join(expenses, by = c("TAXYR" = "TAX_YEAR", -# "EIN" = "ORG_EIN")) -# -# ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% left_join(revenue, by = c("TAXYR" = "TAX_YEAR", -# "EIN" = "ORG_EIN")) -# -# -# write.csv(ceo_trans_1000_fncl, "toy_CEO_trans_1000EIN_fncl.csv") - - -``` - - - - -#### Summary Statistics of Transitions - -Now we're ready for some analyses! We are most interested in understanding transitions by their gendering. That is to say that we want to know if there are differences in observable firm dimensions for nonprofits that experience a CEO transition from male to male relative to the other permutations of male to female, female to male, and female to female. Let's start by looking at org differences by these transition types. We will use the 1000 EIN observations for this analysis. We have provided this dataset with the financial data attached (this was accomplished by running the commented lines above). For simplicity, we will only consider a few firm dimensions such as employee size and total assets. - - - -```{r, eval=F} - -#This df -ceo_trans_1000_fncl <- read.csv("toy_CEO_trans_1000EIN_fncl.csv") - -#First need to denote transitions -ceo_trans_1000_fncl <- ceo_trans_1000_fncl %>% - arrange(EIN, transition_no, TAXYR)%>% #for each org, transition, and year, - group_by(EIN, transition_no) %>% - mutate(period = row_number())%>% #let's denote which period the row is relative to (i.e., how many years before /after a transition) - ungroup() - -#Let's make this variable a bit more readable -ceo_trans_1000_fncl$period <- paste0("T", ceo_trans_1000_fncl$period-3) -ceo_trans_1000_fncl$period <- factor(ceo_trans_1000_fncl$period, levels = c("T-2", "T-1", "T0" , "T1", "T2")) - - -#Need to get transition type too -#We want to know if the transition is M>M, M>F, F>M, or F>M -#We will first create a string of the CEO gender throughout the 5 year period -#The we will create a column that keeps the first and last gender (this is the gender transition) -ceo_trans_1000_fncl<- ceo_trans_1000_fncl %>% - group_by(EIN, transition_no)%>% - mutate(trans_tot = str_c(unlist(strsplit(paste(gender, collapse = ""), "")), collapse = "") - ) %>% - mutate( - trans_type = str_sub(trans_tot, 1, 1) %>% - str_c(str_sub(trans_tot, nchar(trans_tot), nchar(trans_tot))) # Combine 1st and last letter - - ) - -#We want this variable to be a factor -ceo_trans_1000_fncl$trans_type <- as.factor(ceo_trans_1000_fncl$trans_type) - - - -#We're going to get firm attributes by transition type -#Let's consider the attributes at t-1 -#We'll consider basic variables like number of employees, total assets, etc. - -#Note that further data cleaning is necessary for final presentation due to NA values input into some of these columns. For now, we will just remove the nas and continue with our analysis - -temp <- ceo_trans_1000_fncl %>% group_by(trans_type)%>% - filter(period == "T-1") %>% - summarize(count = n(), - num_emp_av =mean(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T), - num_emp_sd = sd(F9_01_ACT_GVRN_EMPL_TOT, na.rm = T), - num_vol_av = mean(F9_01_ACT_GVRN_VOL_TOT, na.rm = T), - num_vol_sd = sd(F9_01_ACT_GVRN_VOL_TOT, na.rm = T), - total_exp_av = mean(F9_01_EXP_TOT_PY, na.rm = T), - total_exp_sd = sd(F9_01_EXP_TOT_PY, na.rm = T), - total_assets_av = mean(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T), - total_assets_sd = sd(F9_01_NAFB_ASSET_TOT_BOY, na.rm = T)) - -temp - - -``` -\ -There are 349 transitions from male to male (MM) transitions, 253 male to female transitions (MF), 160 female to male transitions (FM), and 305 female to female transitions (FF) in our dataset. FF orgs have the smallest number of employees, on average while MF have the highest. It should be noted that the standard deviation for number of employees is pretty high, suggesting a wide range of nonprofits. MM nonprofits have the highest total expeness on average and the highest total assets. Again, the SDs are wide, suggesting large variation across observations. - - - - - - -#### Financial Measures by Transitions - -The Glass Cliff Phenomenon hypothesizes that women are chosen for positions of power when these positions are more precarious. One way to denote a precarious position is by the firm's financial performance; poor financial performance suggests more precariousness. We acknowledge that financial performance is comprised of several different dimensions and it is sometimes hard to arrive at clean conclusions about "poorly performing nonprofits." For demonstration purposes we will specifically focus on the financial performance metric of post-depreciation profitability margin (podpm). This is defined as an income measure that determines a firm's profitability after incorporating non-cash expenses. Higher values of this metric are generally desirable because the indicate that an org is not lost its revenue to expenses. We will use the package `fiscal` from the Open Data Collective to calculate our variable of interest. The default parameters of the respective functions are already built for the 990 naming conventions so usage is pretty straight forward! -\ - -```{r, message = F, warning = F, eval=F} - - - -#Make sure to run the code chunk about to ensure your df_long_fncl has all the necessary variables - -#site: https://github.com/Nonprofit-Open-Data-Collective/fiscal/tree/main/R -ceo_trans_1000_fncl <- get_podpm(ceo_trans_1000_fncl) - - - -#Let's plot these measures -plot_temp <- ceo_trans_1000_fncl %>% - group_by(trans_type, period)%>% - summarize(median_podpm =median(podpm, na.rm = T)) - - -ggplot(data = plot_temp, aes(x = period, y = median_podpm, group = trans_type, color = trans_type)) + - geom_line(linewidth = 1.5)+ - geom_text_repel(aes(label = round(median_podpm,3)), size = 5, nudge_x = -0.07, nudge_y = 0.001, segment.size = 0, segment.color = NA)+ - theme_bw( ) + -# theme(text = element_text(size = 24))+ - scale_x_discrete(labels = c("T-2", "T-1", "Transition", "T+1", "T+2"))+ - labs(color = "Transition") + - xlab("Period")+ - ylab("Median Post-Depreciation Profitability Margin") + - ggtitle("Median Post-Depreciation Profitability Margin by Transition Type") + - geom_vline(xintercept = 3, linetype = "dashed") - - - -################################################ -#Now doing density plots of these respective vars -################################################ - -#Let's compare the MM density to MF density in the t-1 period for the vats - -ceo_trans_1000_fncl_MM <- ceo_trans_1000_fncl %>% filter(trans_type == "MM" & - period == "T-1") -ceo_trans_1000_fncl_MF <- ceo_trans_1000_fncl %>% filter(trans_type == "MF"& - period == "T-1") - - -ggplot() + - geom_density(data = ceo_trans_1000_fncl_MM, aes(x = podpm, fill = "lightblue"), alpha = 0.5) + - geom_density(data = ceo_trans_1000_fncl_MF, aes(x = podpm, fill = "pink"), alpha = 0.5) + - xlim(-1,1)+ - theme_bw()+ -# theme(text = element_text(size = 24))+ - scale_fill_manual(name = "Transition", values = c('lightblue', 'pink'), labels = c("pink" = "MF" , "lightblue" = "MM") ) + - xlab("Post-Depreciation Profitability Margin")+ - ylab("Density") + - ggtitle("Density ofPost-Depreciation Profitability Margin by Transition at t-1") - - -``` - - -The glass cliff phenomenon suggest that financial precarious NPs (seen by a significant drop in their financials between T-2 and T-1) would more likely hire a female to the CEO position. We start by looking at the PODPM variable over the periods. MM organizations start with the highest median profitability margin but also experience the steepest drop between T-2 and T-1. We see a similar slope for FM organizations although the starting point is the lowest in the entire group. MF transitions do not appear to be preceded by steep changes in profitability. If the glass cliff hypothesis were true, we would expect the observed trajectory of FM but would not expect the other firms to have similar metrics around their transitions. This graph calls for further analysis on other financial measures to determine whether FM firms display a transition during significantly more precarious times than their counterparts. - -\ -\ -As a final understanding of the data, we look at the distribution of PODPM at T-1. We consider the two most relevant groups of MM and MF. We see that the distribution of financial variables tends to be extremely similar to both types of transitions, again failing to provide strong motivation for a glass cliff phenomenon. -\ -\ - - -```{css, echo=F} -body{ - font-family:system-ui,-apple-system,"Segoe UI",Roboto,Helvetica,Arial,sans-serif; - font-size:calc(1.5em + 0.25vw); - font-weight:300;line-height:1.65; - -webkit-font-smoothing:antialiased; - -moz-osx-font-smoothing:grayscale; - margin-left:20%; - margin-right:20%} - - -h1 { color: #25383C; margin-top: 60px; } - -h3, h4, strong { color: #6D7B8D; } - -h2 { font-size: 2em; - margin-bottom: 40px; - color: #726e6e; - margin-top: 140px;} - -img { margin-top: 40px; - margin-bottom: 50px;} - -#TOC { - margin-top: 60px; - font-size: 0.9em; -} - -a { color:#AF7817; } - - -.footer { - background-color:#726e6e; - height:340px; - color:white; - padding: 20px 3px 20px 3px; - margin:0px; - line-height: normal; -} - - - -.footer a{ color:#AF7817; text-decoration:bold !important; } - - - - table{ - border-spacing:1px; - margin-top:80px; - margin-bottom:100px !important; - margin-left: auto; - margin-right: auto; - align:center} - - - -td{ padding: 6px 10px 6px 10px; - font-size:calc(0.7em + 0.25vw); } - -th{ text-align: left; } -``` - - - -