Elite-Embededdness/ta2_pe_stats.Rmd at master · HigherEdData/Elite-Embededdness · GitHub

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---
title: "Table A.2 - PE firm statistics"
output: html_document
---

Load libraries
```{r warning=FALSE}
library(tidyverse)
library(haven)
library(table1)
```


Load data
```{r}
preqin <- read_dta("preqin_sandp_trustee_ties.dta")
```


Modify the dataset
```{r}
preqin_updated <- preqin %>%
  mutate(strength = ifelse(is.na(strength), 0, strength),
         Non_finance_business = ifelse(is.na(Non_finance_business), 0, Non_finance_business)) %>%
  mutate(All_other_ties = strength-Non_finance_business,
         excess_irr = net_irr_pcent - sp500_return_pct) %>%
  mutate(fund_type = ifelse(fund_type== "Early Stage: Seed", "Early_Stage",
                     ifelse(fund_type== "Early Stage: Start-up", "Early_Stage",
                     ifelse(fund_type== "Early Stage", "Early_Stage",
                     ifelse(fund_type== "Direct Secondaries", "Secondaries",
                     ifelse(fund_type== "Co-Investment Multi-Manager", "Coinvestment",
                     ifelse(fund_type== "Co-investment", "Coinvestment",
                     ifelse(fund_type== "Fund of Funds", "Fund_of_Funds",
                     ifelse(fund_type== "Venture (General)", "Venture",
                     ifelse(fund_type== "Expansion / Late Stage", "Expansion", fund_type)))))))))) %>%
  filter(!is.na(net_irr_pcent), !is.na(fundvaluemnusd)) %>%
  filter(fund_focus=="US"|fund_focus=="Europe" | fund_focus == "Diversified Multi-Re")
```


Table A.2: Private equity fund types and fund focus by share of capital invested
```{r}
my.render.cont <- function(x) {
    with(stats.apply.rounding(stats.default(x), digits=4), c("",
        "Mean (SD)"=sprintf("%s (%s)", MEAN, SD)))
}

# Fund Focus for all PE firms
preqin_fund_focus <- preqin_updated %>%
  mutate(fund_focus = ifelse(fund_focus=="Diversified Multi-Re","Diversified",fund_focus)) %>%
  group_by(firmid, year, fund_focus) %>%
  summarise(fund_size = sum(fundvaluemnusd, na.rm = TRUE)) %>%
  mutate(fund_size_pct = round(fund_size*100/sum(fund_size, na.rm = TRUE),2)) %>%
  select(firmid, year, fund_focus, fund_size_pct) %>%
  pivot_wider(names_from = fund_focus, values_from = c(fund_size_pct)) %>%
  replace(is.na(.), 0) %>%
  group_by(firmid) %>%
  mutate(count = n()) %>%
  filter(count>1)

table1(~ Europe + US + Diversified, data = preqin_fund_focus, render.continuous=my.render.cont)

# Fund focus for PE firms with trustee ties
preqin_fund_focus_top60 <- preqin_updated %>%
  mutate(fund_focus = ifelse(fund_focus=="Diversified Multi-Re","Diversified",fund_focus)) %>%
  group_by(firmid, year, fund_focus, strength) %>%
  summarise(fund_size = sum(fundvaluemnusd, na.rm = TRUE)) %>%
  mutate(fund_size_pct = round(fund_size*100/sum(fund_size, na.rm = TRUE),2)) %>%
  select(firmid, year, fund_focus, fund_size_pct, strength) %>%
  group_by(firmid) %>%
  mutate(max_strength = max(strength, na.rm = TRUE)) %>%
  filter(max_strength > 0) %>%
  mutate(count = n()) %>%
  filter(count>1) %>%
  pivot_wider(names_from = fund_focus, values_from = c(fund_size_pct)) %>%
  replace(is.na(.), 0)

table1(~ Europe + US + Diversified, data = preqin_fund_focus_top60, render.continuous=my.render.cont)

# Fund type for all PE firms
preqin_fund_type <- preqin_updated %>%
  group_by(firmid, year, fund_type) %>%
  summarise(fund_size = sum(fundvaluemnusd, na.rm = TRUE)) %>%
  mutate(fund_size_pct = round(fund_size*100/sum(fund_size, na.rm = TRUE),2)) %>%
  select(firmid, year, fund_type, fund_size_pct) %>%
  pivot_wider(names_from = fund_type, values_from = c(fund_size_pct)) %>%
  replace(is.na(.), 0) %>%
  group_by(firmid) %>%
  mutate(count = n()) %>%
  filter(count>1)

table1(~ Buyout + Growth + Fund_of_Funds + Venture + Coinvestment + Expansion + Secondaries + Early_Stage + Balanced + Turnaround, data = preqin_fund_type, render.continuous=my.render.cont)

# Fund type for PE firms with trustee ties
preqin_fund_type_top60 <- preqin_updated %>%
  group_by(firmid, year, fund_type, strength) %>%
  summarise(fund_size = sum(fundvaluemnusd, na.rm = TRUE)) %>%
  mutate(fund_size_pct = round(fund_size*100/sum(fund_size, na.rm = TRUE),2)) %>%
  select(firmid, year, fund_type, fund_size_pct, strength) %>%
  group_by(firmid) %>%
  mutate(max_strength = max(strength, na.rm = TRUE)) %>%
  filter(max_strength > 0) %>%
  mutate(count = n()) %>%
  filter(count>1) %>%
  pivot_wider(names_from = fund_type, values_from = c(fund_size_pct)) %>%
  replace(is.na(.), 0)%>%
  group_by(firmid) %>%
  mutate(count = n()) %>%
  filter(count>1)

table1(~ Buyout + Growth + Fund_of_Funds + Venture + Coinvestment + Expansion + Secondaries + Early_Stage + Balanced, data = preqin_fund_type_top60, render.continuous=my.render.cont)
```