Elite-Embededdness/t2_network_stats.Rmd at master · HigherEdData/Elite-Embededdness · GitHub

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---
title: "Table 2 - Network statistics"
output: html_document
---

Load libraries
```{r warning=FALSE}
library(tidyverse)
library(haven)
```


Load data
```{r}
board_members <- read_dta("d_trustees_long_network_ties_preqinids.dta")
```


Modify the datasets
```{r}
#Limit observations to top 60 from 2003 to 2017
board_members <- board_members %>%
  filter(schooltype!="liberal arts") %>%
  filter(unitid!=232186 & unitid!=126818 & unitid!=209542 & unitid!=221759 & unitid!=102614 & unitid!=129020 & unitid!=181464) %>%
  filter(year > 2002)

# Create dummy variables for each type of board member
board_members <- board_members %>%
  mutate(Alt_finance_x = ifelse(employer_type_aggregated=="PE and hedge funds",1,0),
         Other_finance_x =  ifelse(employer_type_aggregated=="Other finance",1,0),
         Edu_sci_med_x =  ifelse(employer_type_aggregated=="Education science medicine",1,0),
         Nonfinance_business_x =  ifelse(employer_type_aggregated=="Non-financial business",1,0),
         Nonprofit_x =  ifelse(employer_type_aggregated=="Non-profits/philanthropy",1,0),
         Public_sector_x =  ifelse(employer_type_aggregated=="Public sector",1,0),
         Real_estate_x = ifelse(employer_type_aggregated=="Real estate",1,0),
         Private = ifelse(schooltype=="research",1,0),
         Public = ifelse(schooltype=="public",1,0))
```


Table 2: Weighted degree of network centrality for trustees’ economic organizations by industry
```{r}
board_members_organizations <- board_members %>%
  select(strength, employer_type_aggregated, year, firmname_new) %>%
  unique() %>%
  filter(employer_type_aggregated != "Unknown_sector")

degree <- board_members_organizations %>%
  filter(year == 2003 | year == 2017) %>%
  group_by(employer_type_aggregated, year) %>%
  summarise(mean = round(mean(strength),2),
            sd = round(sd(strength), 2),
            count = n()) %>%
  mutate(year = as.character(year)) %>%
  arrange(year, desc(mean))

degree_all <- board_members_organizations %>%
  group_by(employer_type_aggregated) %>%
  summarise(mean = round(mean(strength),2),
            sd = round(sd(strength), 2),
            count = n()) %>%
  mutate(year = "2003 to 2017") %>%
  arrange(desc(mean))

degree <- bind_rows(degree, degree_all)

degree <- degree %>%
  mutate(Mean_SD_Count = paste(mean, '\n', "(", sd, ")", '\n', "N = ", count, sep = "")) %>%
  select(employer_type_aggregated, Mean_SD_Count, year) %>%
  pivot_wider(names_from = year, values_from = Mean_SD_Count)


## Summary for all sectors
degree_x <- board_members_organizations %>%
  filter(year == 2003 | year == 2017) %>%
  group_by(year) %>%
  summarise(mean = round(mean(strength),2),
            sd = round(sd(strength), 2),
            count = n()) %>%
  mutate(year = as.character(year)) %>%
  arrange(year, desc(mean))

degree_all_x <- board_members_organizations %>%
  summarise(mean = round(mean(strength),2),
            sd = round(sd(strength), 2),
            count = n()) %>%
  mutate(year = "2003 to 2017") %>%
  arrange(desc(mean))

degree_x <- bind_rows(degree_x, degree_all_x)

degree_x <- degree_x %>%
  mutate(employer_type_aggregated = "All sectors") %>%
  mutate(Mean_SD_Count = paste(mean, '\n', "(", sd, ")", '\n', "N = ", count, sep = "")) %>%
  select(employer_type_aggregated, Mean_SD_Count, year) %>%
  pivot_wider(names_from = year, values_from = Mean_SD_Count)


degree <- bind_rows(degree, degree_x)

rm(degree_x, degree_all_x, degree_all)

write.csv(degree, file = "Table_2.csv")
```