Borrower Statistics: Bank Selection into Emergency Lending Facilities

1 Setup and Load Packages

library(dplyr)
library(knitr)
library(tidyverse)
library(lubridate)
library(kableExtra)
library(pander)

2 Directory Setup

# ============================================================================
# PATH SETUP
# ============================================================================
BASE_PATH <- "C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025"
DATA_PROC <- file.path(BASE_PATH, "01_data/processed")

# Output directories
OUTPUT_PATH <- file.path(BASE_PATH, "03_documentation/borrowing_t_stat")
TABLE_PATH <- file.path(OUTPUT_PATH, "tables")
FIG_PATH <- file.path(OUTPUT_PATH, "figures")

# Create directories if they don't exist
dir.create(TABLE_PATH, recursive = TRUE, showWarnings = FALSE)
dir.create(FIG_PATH, recursive = TRUE, showWarnings = FALSE)

cat("Output directories:\n")

## Output directories:

cat("  Tables:", TABLE_PATH, "\n")

##   Tables: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables

cat("  Figures:", FIG_PATH, "\n")

##   Figures: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/figures

3 Helper Functions

# ==============================================================================
# HELPER FUNCTIONS
# ==============================================================================

# Safe division to avoid Inf/NaN
safe_div <- function(num, denom) {
  result <- num / denom
  result[!is.finite(result)] <- NA_real_
  return(result)
}

# Format numbers with commas
fmt_num <- function(x, digits = 2) {
  formatC(x, format = "f", digits = digits, big.mark = ",")
}

# Calculate T-statistic for difference in means (Welch's t-test)
# Returns only t-statistic (for backward compatibility)
calc_tstat <- function(vals1, vals2) {
  # Remove NAs
  vals1 <- vals1[!is.na(vals1)]
  vals2 <- vals2[!is.na(vals2)]
  
  n1 <- length(vals1)
  n2 <- length(vals2)
  
  # Need at least 2 observations in each group
  if (n1 < 2 | n2 < 2) return(NA_real_)
  
  mean1 <- mean(vals1)
  mean2 <- mean(vals2)
  var1 <- var(vals1)
  var2 <- var(vals2)
  
  # Welch's t-statistic
  se <- sqrt(var1/n1 + var2/n2)
  if (se == 0 | !is.finite(se)) return(NA_real_)
  
  t_stat <- (mean1 - mean2) / se
  return(t_stat)
}

# ==============================================================================
# NEW: Calculate T-statistic with p-value and significance stars
# ==============================================================================
calc_tstat_with_stars <- function(vals1, vals2) {
  # Remove NAs
  vals1 <- vals1[!is.na(vals1)]
  vals2 <- vals2[!is.na(vals2)]
  
  n1 <- length(vals1)
  n2 <- length(vals2)
  
  # Need at least 2 observations in each group
  if (n1 < 2 | n2 < 2) {
    return(list(tstat = NA_real_, pval = NA_real_, stars = ""))
  }
  
  # Perform Welch's t-test
  test_result <- tryCatch({
    t.test(vals1, vals2, var.equal = FALSE)
  }, error = function(e) {
    return(NULL)
  })
  
  if (is.null(test_result)) {
    return(list(tstat = NA_real_, pval = NA_real_, stars = ""))
  }
  
  t_stat <- test_result$statistic
  p_val <- test_result$p.value
  
  # Determine significance stars
  stars <- ""
  if (!is.na(p_val)) {
    if (p_val < 0.01) {
      stars <- "***"
    } else if (p_val < 0.05) {
      stars <- "**"
    } else if (p_val < 0.10) {
      stars <- "*"
    }
  }
  
  return(list(tstat = as.numeric(t_stat), pval = p_val, stars = stars))
}

4 Period Definitions

# ==============================================================================
# PERIOD DEFINITIONS
# ==============================================================================

periods <- tribble(
  ~period_num, ~period_name, ~start_date, ~end_date, ~description,
  0, "Pre-BTFP",   "2023-03-01", "2023-03-10", "DW only available",
  1, "Acute",      "2023-03-13", "2023-05-01", "Peak crisis phase",
  2, "Post-Acute", "2023-05-02", "2023-10-31", "Stabilization",
  3, "Arbitrage",  "2023-11-01", "2024-01-24", "BTFP rate < IORB",
  4, "Wind-down",  "2024-01-25", "2024-03-11", "BTFP closing announced"
) %>% 
  mutate(across(c(start_date, end_date), as.Date))

# Key dates
BTFP_LAUNCH <- as.Date("2023-03-12")
BTFP_CLOSE <- as.Date("2024-03-11")
DW_DATA_END <- as.Date("2023-12-31")
BASELINE_DATE <- "2022Q4"

# Display periods
periods %>%
  kable(col.names = c("Period", "Name", "Start", "End", "Description"),
        caption = "Analysis Period Definitions") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Analysis Period Definitions

Period	Name	Start	End	Description
0	Pre-BTFP	2023-03-01	2023-03-10	DW only available
1	Acute	2023-03-13	2023-05-01	Peak crisis phase
2	Post-Acute	2023-05-02	2023-10-31	Stabilization
3	Arbitrage	2023-11-01	2024-01-24	BTFP rate < IORB
4	Wind-down	2024-01-25	2024-03-11	BTFP closing announced

5 Load Data

# ==============================================================================
# LOAD DATA
# ==============================================================================

cat("=== LOADING DATA ===\n")

## === LOADING DATA ===

call_q <- read_csv(file.path(DATA_PROC, "final_call_gsib.csv"), show_col_types = FALSE) %>%
  mutate(idrssd = as.character(idrssd))

btfp_loans_raw <- read_csv(file.path(DATA_PROC, "btfp_loan_bank_only.csv"), show_col_types = FALSE) %>%
  mutate(rssd_id = as.character(rssd_id), 
         btfp_loan_date = mdy(btfp_loan_date))

dw_loans_raw <- read_csv(file.path(DATA_PROC, "dw_loan_bank_2023.csv"), show_col_types = FALSE) %>%
  mutate(rssd_id = as.character(rssd_id), 
         dw_loan_date = ymd(dw_loan_date))

cat("Call Report:", nrow(call_q), "observations\n")

## Call Report: 75989 observations

cat("BTFP Loans:", nrow(btfp_loans_raw), "loans\n")

## BTFP Loans: 6734 loans

cat("DW Loans:", nrow(dw_loans_raw), "loans\n")

## DW Loans: 10008 loans

6 Define Exclusions

# ============================================================================
# DEFINE EXCLUDED BANKS AND GSIB IDENTIFICATION
# ============================================================================

# Identify GSIBs at baseline
gsib_banks <- call_q %>%
  filter(period == '2022Q4', gsib == 1) %>%
  pull(idrssd)

# Identify failed banks
failed_banks <- call_q %>%
  filter(period == '2022Q4', failed_bank == 1) %>%
  pull(idrssd)

# Exclude failed + GSIBs for main analysis
excluded_banks <- union(gsib_banks, failed_banks)

cat("=== EXCLUSIONS ===\n")

## === EXCLUSIONS ===

cat("G-SIBs:", length(gsib_banks), "\n")

## G-SIBs: 33

cat("Failed banks:", length(failed_banks), "\n")

## Failed banks: 8

cat("Total excluded:", length(excluded_banks), "\n")

## Total excluded: 41

7 Prepare Baseline Data (2022Q4)

cat("\n=== PREPARING BASELINE DATA (2022Q4) ===\n")

## 
## === PREPARING BASELINE DATA (2022Q4) ===

# Step 1: Filter to baseline quarter and exclude failed/GSIB banks

has_failed_bank <- "failed_bank" %in% names(call_q)
has_gsib <- "gsib" %in% names(call_q)


baseline_q <- call_q %>%
  filter(period == '2022Q4')

cat("Baseline observations (after exclusions):", nrow(baseline_q), "\n")

## Baseline observations (after exclusions): 4737

# Step 3: Calculate medians for cutoffs (BEFORE adding derived variables)
medians <- baseline_q %>%
  summarise(
    median_uninsured = median(uninsured_deposit_to_total_asset, na.rm = TRUE),
    median_mtm = median(mtm_loss_to_total_asset, na.rm = TRUE),
    q75_uninsured = quantile(uninsured_deposit_to_total_asset, 0.75, na.rm = TRUE),
    q75_mtm = quantile(mtm_loss_to_total_asset, 0.75, na.rm = TRUE)
  )

cat("Median uninsured ratio:", round(medians$median_uninsured, 2), "%\n")

## Median uninsured ratio: 21.97 %

cat("Median MTM loss ratio:", round(medians$median_mtm, 2), "%\n")

## Median MTM loss ratio: 5.26 %

cat("Q75 uninsured ratio:", round(medians$q75_uninsured, 2), "%\n")

## Q75 uninsured ratio: 30.27 %

cat("Q75 MTM loss ratio:", round(medians$q75_mtm, 2), "%\n")

## Q75 MTM loss ratio: 6.95 %

# Step 4: Add ALL derived variables to baseline
baseline_q <- baseline_q %>%
  mutate(
    # Market value adjustment: (Book Asset / MV Asset) - 1
    
    cet1_ratio = safe_div(cet1, total_asset) * 100,
    mv_asset = mm_asset,
    mv_adjustment = if_else(mv_asset == 0 | is.na(mv_asset), NA_real_, 
                            (total_asset / mv_asset) - 1),
    
    # IDCR: (MV_Asset - s*Uninsured - Insured) / Insured
    idcr_s50 = safe_div(mv_asset - 0.5 * uninsured_deposit - insured_deposit, insured_deposit),
    idcr_s100 = safe_div(mv_asset - 1.0 * uninsured_deposit - insured_deposit, insured_deposit),
    
    # Capital Insolvency: (Equity - s*Uninsured*MVAdj) / Assets
    cap_insolvency_s50 = safe_div((total_asset - total_liability) - 0.5 * uninsured_deposit * mv_adjustment, total_asset),
    cap_insolvency_s100 = safe_div((total_asset - total_liability) - 1.0 * uninsured_deposit * mv_adjustment, total_asset),
    
    # Adjusted Equity (book equity ratio - MTM loss ratio, both in %)
    adjusted_equity = book_equity_to_total_asset - mtm_loss_to_total_asset,
    adjusted_tier1 = tier1cap_to_total_asset - mtm_loss_to_total_asset,
    adjusted_cet1 = cet1_ratio - mtm_loss_to_total_asset,
    
    # MTM Insolvent dummy (adjusted equity < 0)
    mtm_insolvent = as.integer(adjusted_equity < 0),
    mtm_insolvent_tier1 = as.integer(adjusted_tier1 < 0),
    mtm_insolvent_cet1 = as.integer(adjusted_cet1 < 0),
    
    # Uninsured leverage (uninsured deposit / book equity)
    uninsured_leverage = uninsured_deposit_to_total_asset,
    liquidity_available = safe_div(cash + rerepo + fed_fund_sold, total_asset) * 100,
    
   
    
    # Run risk dummies
    # a. Solvent and Liquid (both uninsured and MTM below median)
    run_risk_1 = as.integer(
      uninsured_deposit_to_total_asset < medians$median_uninsured & 
        mtm_loss_to_total_asset < medians$median_mtm
    ),
    # b. Solvent and Illiquid
    run_risk_2 = as.integer(
      uninsured_deposit_to_total_asset > medians$median_uninsured & 
        mtm_loss_to_total_asset < medians$median_mtm
    ),
   # c. Insolvent and Liquid  
        run_risk_3 = as.integer(
      uninsured_deposit_to_total_asset < medians$median_uninsured & 
        mtm_loss_to_total_asset > medians$median_mtm
    ),
    # d. Insolvent and ILLiquid (both uninsured and MTM above median)  
        run_risk_4 = as.integer(
      uninsured_deposit_to_total_asset > medians$median_uninsured & 
        mtm_loss_to_total_asset > medians$median_mtm
    ),
    
    # IDCR insolvency dummies
    idcr_insolvent_s50 = as.integer(idcr_s50 < 0),
    idcr_insolvent_s100 = as.integer(idcr_s100 < 0),
    
    # Capital insolvency dummies
    cap_insolvent_s50 = as.integer(cap_insolvency_s50 < 0),
    cap_insolvent_s100 = as.integer(cap_insolvency_s100 < 0)
  ) %>%
  filter(
    gsib == 0 | is.na(gsib),
    failed_bank == 0 | is.na(failed_bank),
    !is.na(omo_eligible) & omo_eligible > 0
  )

cat("\nDerived variables added successfully.\n")

## 
## Derived variables added successfully.

cat("Final baseline observations:", nrow(baseline_q), "\n")

## Final baseline observations: 4292

8 Table Functions

# ==============================================================================
# TABLE FORMAT FUNCTIONS WITH T-STATISTICS AND SIGNIFICANCE STARS
# ==============================================================================

# Main function to create summary table with consistent format
# Format: Mean (Median) [T-stat]*** with N below each row
# T-stat compares each group to Non-Borrower group
# Significance: *** p<0.01, ** p<0.05, * p<0.10

create_summary_table_v2 <- function(data, group_var, group_levels = NULL, reference_group = "Non-Borrower") {
  
  # Define variable specifications
  var_specs <- tribble(
    ~var_name, ~var_label, ~is_dummy,
    "book_equity_to_total_asset", "Book Equity Ratio", FALSE,
    "loss_asset_htm_only", "MTM Loss (HTM)", FALSE,
    "mtm_loss_omo_eligible_to_total_asset", "MTM Loss (BTFP Elg.)", FALSE,
    "mtm_loss_to_total_asset", "MTM Loss (Total)", FALSE,
    "adjusted_equity", "Adjusted Equity", FALSE,
    "mtm_insolvent", "Adjusted Equity < 0", TRUE,
    "adjusted_tier1", "Adj. Tier1 Equity", FALSE,
    "mtm_insolvent_tier1", "Adj. Tier1 Equity < 0", TRUE,
    "adjusted_cet1", " Adj. Common equity tier1",FALSE, 
    "mtm_insolvent_cet1", "Adj. Common equity tier1 < 0", TRUE,
    "uninsured_leverage", "Uninsured Leverage", FALSE,
    "mtm_insolvent", "MTM Insolvent", TRUE,
    "run_risk_1", "Solvent and Liquid", TRUE,
    "run_risk_2", "Solvent and Illiquid", TRUE,
    "run_risk_3", "Insolvent and Liquid", TRUE,
    "run_risk_4", "Insolvent and Illiquid", TRUE,
    "idcr_insolvent_s50", "IDCR Insolvent (s=0.5)", TRUE,
    "idcr_insolvent_s100", "IDCR Insolvent (s=1.0)", TRUE,
    "cap_insolvent_s50", "Capital Insolvent (s=0.5)", TRUE,
    "cap_insolvent_s100", "Capital Insolvent (s=1.0)", TRUE,
    "liquidity_available", "Available Liquidity Ratio", FALSE,
    "change_uninsured_fwd_q", "Change Uninsured Deposits (Fwd Q)", FALSE,
    "change_insured_deposit_fwd_q", "Change Insured Deposits (Fwd Q)", FALSE,
    "abnormal_insured_outflow_10pct", "Abnormal Insured Outflow (10%)", TRUE,
    "abnormal_insured_outflow_5pct", "Abnormal Insured Outflow (5%)", TRUE,
    "abnormal_uninsured_outflow_10pct", "Abnormal Uninsured Outflow (10%)", TRUE,
    "abnormal_uninsured_outflow_5pct", "Abnormal Uninsured Outflow (5%)", TRUE
    
  )
  
  # Get unique groups
  if (is.null(group_levels)) {
    group_levels <- unique(data[[group_var]])
  }
  
  # Calculate N for each group
  n_by_group <- data %>%
    group_by(.data[[group_var]]) %>%
    summarise(N = n(), .groups = "drop")
  
  # Get reference group data for T-stat calculation
  ref_data <- data %>% filter(.data[[group_var]] == reference_group)
  
  # Initialize output list
  output_rows <- list()
  
  # Add N row first
  n_row <- c(Variable = "N", Row_Type = "count")
  for (g in group_levels) {
    n_val <- n_by_group %>% filter(.data[[group_var]] == g) %>% pull(N)
    n_row[g] <- as.character(if(length(n_val) == 0) 0 else n_val)
  }
  output_rows[[1]] <- n_row
  
  row_idx <- 2
  
  # Process each variable
  for (i in seq_len(nrow(var_specs))) {
    v <- var_specs$var_name[i]
    label <- var_specs$var_label[i]
    is_dummy <- var_specs$is_dummy[i]
    
    # Stats row: Mean (Median)
    stats_row <- c(Variable = label, Row_Type = "stats")
    # T-stat row (below stats)
    tstat_row <- c(Variable = "", Row_Type = "tstat")
    # N row (below t-stat)
    obs_row <- c(Variable = "", Row_Type = "n")
    
    # Get reference group values for T-stat
    ref_vals <- if (v %in% names(ref_data)) ref_data[[v]] else numeric(0)
    ref_vals <- ref_vals[!is.na(ref_vals)]
    
    for (g in group_levels) {
      group_data <- data %>% filter(.data[[group_var]] == g)
      
      # Check if variable exists
      if (!v %in% names(group_data)) {
        stats_row[g] <- "—"
        tstat_row[g] <- ""
        obs_row[g] <- "0"
        next
      }
      
      vals <- group_data[[v]]
      vals <- vals[!is.na(vals)]
      
      if (length(vals) == 0) {
        stats_row[g] <- "—"
        tstat_row[g] <- ""
        obs_row[g] <- "0"
      } else {
        mean_val <- mean(vals, na.rm = TRUE)
        median_val <- median(vals, na.rm = TRUE)
        n_val <- length(vals)
        
        if (is_dummy) {
          stats_row[g] <- sprintf("%.2f (%.2f)", mean_val * 100, median_val * 100)
          n_val <- sum(vals)  # Count of banks where dummy = 1
        } else {
          stats_row[g] <- sprintf("%.2f (%.2f)", mean_val, median_val)
          n_val <- length(vals)  # Total non-NA observations
        }
        
        # Calculate T-stat vs reference group (skip for reference group itself)
        if (g == reference_group) {
          tstat_row[g] <- ""
        } else {
          # Use the new function with stars
          tstat_result <- calc_tstat_with_stars(vals, ref_vals)
          if (!is.na(tstat_result$tstat) && is.finite(tstat_result$tstat)) {
            tstat_row[g] <- sprintf("[%.2f]%s", tstat_result$tstat, tstat_result$stars)
          } else {
            tstat_row[g] <- ""
          }
        }
        
        obs_row[g] <- as.character(n_val)
      }
    }
    
    output_rows[[row_idx]] <- stats_row
    output_rows[[row_idx + 1]] <- tstat_row
    output_rows[[row_idx + 2]] <- obs_row
    row_idx <- row_idx + 3
  }
  
  # Convert to data frame
  result_df <- bind_rows(lapply(output_rows, function(x) as_tibble_row(x)))
  
  return(result_df)
}

# ==============================================================================
# LATEX TABLE EXPORT FUNCTION (UPDATED FOR T-STATS WITH STARS)
# ==============================================================================

export_latex_table <- function(summary_df, filepath, caption, label, note = NULL) {
  
  # Get column names (excluding Variable and Row_Type)
  group_cols <- names(summary_df)[!names(summary_df) %in% c("Variable", "Row_Type")]
  n_groups <- length(group_cols)
  
  # Clean column names for display
  clean_cols <- gsub("_", " ", group_cols)
  clean_cols <- gsub("Non Borrower", "Non-Borrower", clean_cols)
  
  # Build LaTeX
  latex <- c(
    "\\begin{table}[htbp]",
    "\\centering",
    "\\small",
    sprintf("\\caption{%s}", caption),
    sprintf("\\label{%s}", label),
    "",
    sprintf("\\begin{tabular}{l%s}", paste(rep("c", n_groups), collapse = "")),
    "\\toprule",
    paste0(" & ", paste(clean_cols, collapse = " & "), " \\\\"),
    "\\midrule"
  )
  
  # Add rows
  for (i in seq_len(nrow(summary_df))) {
    row <- summary_df[i, ]
    var_name <- as.character(row$Variable)
    row_type <- as.character(row$Row_Type)
    vals <- as.character(row[group_cols])
    
    if (row_type == "count") {
      # N row - bold
      latex <- c(latex, 
                 sprintf("N & %s \\\\", paste(vals, collapse = " & ")),
                 "\\midrule")
    } else if (row_type == "stats" && var_name != "") {
      # Variable name row with statistics
      latex <- c(latex, sprintf("%s & %s \\\\", var_name, paste(vals, collapse = " & ")))
    } else if (row_type == "tstat") {
      # T-stat row (smaller font, in brackets) - stars are already included
      tstat_vals <- paste0("\\scriptsize{", vals, "}")
      latex <- c(latex, sprintf("& %s \\\\", paste(tstat_vals, collapse = " & ")))
    } else if (row_type == "n") {
      # N below stats (italics)
      n_vals <- paste0("\\textit{", vals, "}")
      latex <- c(latex, sprintf("& %s \\\\[0.3em]", paste(n_vals, collapse = " & ")))
    }
  }
  
  latex <- c(latex,
             "\\bottomrule",
             "\\end{tabular}",
             "")
  
  # Add note if provided (updated to include significance legend)
  if (!is.null(note)) {
    latex <- c(latex,
               "\\vspace{0.3em}",
               "\\begin{minipage}{0.95\\textwidth}",
               "\\footnotesize",
               sprintf("\\textit{Notes:} %s T-statistics in brackets compare each borrower group to Non-Borrowers using Welch's t-test. Significance: *** p$<$0.01, ** p$<$0.05, * p$<$0.10.", note),
               "\\end{minipage}")
  }
  
  latex <- c(latex, "\\end{table}")
  
  # Write to file
  writeLines(latex, filepath)
  cat("LaTeX table saved:", filepath, "\n")
  
  return(invisible(latex))
}

# ==============================================================================
# HTML TABLE EXPORT FUNCTION
# ==============================================================================

export_html_table <- function(summary_df, filepath, caption) {
  
  # Get column names (excluding Row_Type)
  display_cols <- names(summary_df)[names(summary_df) != "Row_Type"]
  
  # Clean column names
  clean_names <- gsub("_", " ", display_cols)
  clean_names <- gsub("Non Borrower", "Non-Borrower", clean_names)
  
  # Create display dataframe
  display_df <- summary_df %>%
    select(all_of(display_cols))
  
  # Generate HTML table
  html_table <- display_df %>%
    kable(format = "html", 
          col.names = clean_names,
          caption = caption,
          escape = FALSE) %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                  full_width = FALSE,
                  font_size = 12) %>%
    row_spec(1, bold = TRUE)  # Bold N row
  
  # Save to file
  save_kable(html_table, filepath)
  cat("HTML table saved:", filepath, "\n")
  
  return(invisible(html_table))
}

# ==============================================================================
# COMBINED EXPORT FUNCTION
# ==============================================================================

export_table <- function(summary_df, table_name, caption, label, note = NULL) {
  
  # LaTeX export
  latex_path <- file.path(TABLE_PATH, paste0(table_name, ".tex"))
  export_latex_table(summary_df, latex_path, caption, label, note)
  

  # HTML export
  html_path <- file.path(TABLE_PATH, paste0(table_name, ".html"))
  export_html_table(summary_df, html_path, caption)
  
  # CSV export (for reference)
  csv_path <- file.path(TABLE_PATH, paste0(table_name, ".csv"))
  write_csv(summary_df, csv_path)
  cat("CSV table saved:", csv_path, "\n")
  
  return(invisible(list(latex = latex_path, html = html_path, csv = csv_path)))
}

9 Table 1: March 10, 2023 - DW Borrowers vs Non-Borrowers

# ==============================================================================
# TABLE 1: March 10, 2023 Only - DW Borrowers vs Non-Borrowers
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE 1: March 10, 2023 - DW Borrowers vs Non-Borrowers\n")

## TABLE 1: March 10, 2023 - DW Borrowers vs Non-Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

dw_mar10 <- dw_loans_raw %>%
  filter(dw_loan_date == as.Date("2023-03-10")) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

cat("DW borrowers on March 10:", length(dw_mar10), "\n")

## DW borrowers on March 10: 52

table1_data <- baseline_q %>%
  mutate(
    borrower_type = if_else(idrssd %in% dw_mar10, "DW Borrower", "Non-Borrower")
  )

table1 <- create_summary_table_v2(table1_data, "borrower_type", 
                                   group_levels = c("DW Borrower", "Non-Borrower"))

# Display
table1 %>%
  select(-Row_Type) %>%
  kable(caption = "Table 1: Pre-Crisis Bank Characteristics - DW Borrowers vs Non-Borrowers (March 10, 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table 1: Pre-Crisis Bank Characteristics - DW Borrowers vs Non-Borrowers (March 10, 2023)

Variable	DW Borrower	Non-Borrower
N	47	4245
Book Equity Ratio	8.20 (8.45)	10.24 (8.85)
	[-4.93]***
	47	4245
MTM Loss (HTM)	0.23 (0.00)	0.20 (0.00)
	[0.28]
	47	4235
MTM Loss (BTFP Elg.)	0.89 (0.58)	0.68 (0.40)
	[1.72]*
	47	4235
MTM Loss (Total)	5.69 (5.66)	5.46 (5.31)
	[0.72]
	47	4235
Adjusted Equity	2.51 (2.44)	4.70 (3.64)
	[-3.70]***
	47	4235
Adjusted Equity < 0	23.40 (0.00)	19.22 (0.00)
	[0.67]
	11	814
Adj. Tier1 Equity	3.67 (2.90)	6.33 (5.06)
	[-6.67]***
	47	4235
Adj. Tier1 Equity < 0	4.26 (0.00)	3.31 (0.00)
	[0.32]
	2	140
Adj. Common equity tier1	3.66 (2.90)	6.28 (5.05)
	[-6.60]***
	47	4179
Adj. Common equity tier1 < 0	4.26 (0.00)	3.25 (0.00)
	[0.33]
	2	136
Uninsured Leverage	29.88 (28.54)	23.54 (22.22)
	[3.44]***
	47	4245
MTM Insolvent	23.40 (0.00)	19.22 (0.00)
	[0.67]
	11	814
Solvent and Liquid	10.64 (0.00)	22.42 (0.00)
	[-2.57]**
	5	951
Solvent and Illiquid	29.79 (0.00)	26.56 (0.00)
	[0.48]
	14	1126
Insolvent and Liquid	19.15 (0.00)	26.55 (0.00)
	[-1.27]
	9	1126
Insolvent and Illiquid	40.43 (0.00)	24.30 (0.00)
	[2.22]**
	19	1030
IDCR Insolvent (s=0.5)	2.13 (0.00)	4.23 (0.00)
	[-0.98]
	1	178
IDCR Insolvent (s=1.0)	34.04 (0.00)	28.40 (0.00)
	[0.80]
	16	1194
Capital Insolvent (s=0.5)	4.26 (0.00)	2.17 (0.00)
	[0.70]
	2	92
Capital Insolvent (s=1.0)	17.02 (0.00)	7.51 (0.00)
	[1.71]*
	8	318
Available Liquidity Ratio	3.27 (2.25)	9.36 (5.94)
	[-13.94]***
	47	4245
Change Uninsured Deposits (Fwd Q)	-13.11 (-11.89)	0.18 (-4.54)
	[-4.14]***
	47	4181
Change Insured Deposits (Fwd Q)	5.35 (2.14)	3.83 (1.48)
	[0.97]
	47	4187
Abnormal Insured Outflow (10%)	6.38 (0.00)	7.51 (0.00)
	[-0.31]
	3	319
Abnormal Insured Outflow (5%)	0.00 (0.00)	2.69 (0.00)
	[-10.82]***
	0	114
Abnormal Uninsured Outflow (10%)	42.55 (0.00)	21.25 (0.00)
	[2.91]***
	20	902
Abnormal Uninsured Outflow (5%)	25.53 (0.00)	9.07 (0.00)
	[2.55]**
	12	385

# Export
export_table(
  table1, 
  "table1_dw_mar10",
  caption = "Pre-Crisis Bank Characteristics: DW Borrowers vs Non-Borrowers (March 10, 2023)",
  label = "tab:dw_mar10",
  note = "Bank characteristics from 2022Q4 Call Reports. Values shown as Mean (Median) with N observations below. Continuous variables in percentage points. Dummy variables show proportion (\\%) with value = 1. DW borrowers identified as banks with Discount Window loans on March 10, 2023 only. Sample excludes G-SIBs and failed banks."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table1_dw_mar10.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table1_dw_mar10.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table1_dw_mar10.csv

10 Table 2: March 10-13, 2023 - DW Borrowers vs Non-Borrowers

# ==============================================================================
# TABLE 2: March 10-13, 2023 - DW Borrowers vs Non-Borrowers
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE 2: March 10-13, 2023 - DW Borrowers vs Non-Borrowers\n")

## TABLE 2: March 10-13, 2023 - DW Borrowers vs Non-Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

dw_mar10_13 <- dw_loans_raw %>%
  filter(dw_loan_date >= as.Date("2023-03-10") & dw_loan_date <= as.Date("2023-03-13")) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

cat("DW borrowers March 10-13:", length(dw_mar10_13), "\n")

## DW borrowers March 10-13: 98

table2_data <- baseline_q %>%
  mutate(
    borrower_type = if_else(idrssd %in% dw_mar10_13, "DW Borrower", "Non-Borrower")
  )

table2 <- create_summary_table_v2(table2_data, "borrower_type",
                                   group_levels = c("DW Borrower", "Non-Borrower"))

# Display
table2 %>%
  select(-Row_Type) %>%
  kable(caption = "Table 2: Pre-Crisis Bank Characteristics - DW Borrowers vs Non-Borrowers (March 10-13, 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table 2: Pre-Crisis Bank Characteristics - DW Borrowers vs Non-Borrowers (March 10-13, 2023)

Variable	DW Borrower	Non-Borrower
N	90	4202
Book Equity Ratio	8.55 (8.46)	10.26 (8.85)
	[-5.17]***
	90	4202
MTM Loss (HTM)	0.24 (0.00)	0.20 (0.00)
	[0.55]
	90	4192
MTM Loss (BTFP Elg.)	0.84 (0.55)	0.68 (0.40)
	[1.92]*
	90	4192
MTM Loss (Total)	5.54 (5.61)	5.47 (5.31)
	[0.34]
	90	4192
Adjusted Equity	3.01 (2.72)	4.71 (3.64)
	[-3.86]***
	90	4192
Adjusted Equity < 0	20.00 (0.00)	19.25 (0.00)
	[0.17]
	18	807
Adj. Tier1 Equity	4.03 (3.75)	6.35 (5.07)
	[-7.80]***
	90	4192
Adj. Tier1 Equity < 0	3.33 (0.00)	3.32 (0.00)
	[0.01]
	3	139
Adj. Common equity tier1	3.99 (3.82)	6.30 (5.06)
	[-7.99]***
	87	4139
Adj. Common equity tier1 < 0	3.45 (0.00)	3.26 (0.00)
	[0.09]
	3	135
Uninsured Leverage	32.99 (30.99)	23.41 (22.12)
	[6.81]***
	90	4202
MTM Insolvent	20.00 (0.00)	19.25 (0.00)
	[0.17]
	18	807
Solvent and Liquid	10.00 (0.00)	22.56 (0.00)
	[-3.87]***
	9	947
Solvent and Illiquid	33.33 (0.00)	26.45 (0.00)
	[1.36]
	30	1110
Insolvent and Liquid	12.22 (0.00)	26.77 (0.00)
	[-4.11]***
	11	1124
Insolvent and Illiquid	44.44 (0.00)	24.05 (0.00)
	[3.84]***
	40	1009
IDCR Insolvent (s=0.5)	5.56 (0.00)	4.18 (0.00)
	[0.56]
	5	174
IDCR Insolvent (s=1.0)	34.44 (0.00)	28.33 (0.00)
	[1.20]
	31	1179
Capital Insolvent (s=0.5)	5.56 (0.00)	2.12 (0.00)
	[1.41]
	5	89
Capital Insolvent (s=1.0)	16.67 (0.00)	7.42 (0.00)
	[2.33]**
	15	311
Available Liquidity Ratio	4.30 (2.86)	9.40 (5.97)
	[-10.23]***
	90	4202
Change Uninsured Deposits (Fwd Q)	-10.14 (-8.93)	0.25 (-4.45)
	[-3.97]***
	90	4138
Change Insured Deposits (Fwd Q)	8.03 (2.39)	3.76 (1.45)
	[1.48]
	90	4144
Abnormal Insured Outflow (10%)	5.56 (0.00)	7.54 (0.00)
	[-0.81]
	5	317
Abnormal Insured Outflow (5%)	1.11 (0.00)	2.69 (0.00)
	[-1.39]
	1	113
Abnormal Uninsured Outflow (10%)	41.11 (0.00)	21.06 (0.00)
	[3.82]***
	37	885
Abnormal Uninsured Outflow (5%)	22.22 (0.00)	8.97 (0.00)
	[2.99]***
	20	377

# Export
export_table(
  table2, 
  "table2_dw_mar10_13",
  caption = "Pre-Crisis Bank Characteristics: DW Borrowers vs Non-Borrowers (March 10--13, 2023)",
  label = "tab:dw_mar10_13",
  note = "Bank characteristics from 2022Q4 Call Reports. Values shown as Mean (Median) with N observations below. DW borrowers identified as banks with Discount Window loans between March 10--13, 2023. Sample excludes G-SIBs and failed banks."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table2_dw_mar10_13.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table2_dw_mar10_13.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table2_dw_mar10_13.csv

11 Table 3: March 9-14, 2023 - DW Borrowers vs Non-Borrowers

# ==============================================================================
# TABLE 3: March 9-14, 2023 - DW Borrowers vs Non-Borrowers
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE 3: March 9-14, 2023 - DW Borrowers vs Non-Borrowers\n")

## TABLE 3: March 9-14, 2023 - DW Borrowers vs Non-Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

dw_mar9_14 <- dw_loans_raw %>%
  filter(dw_loan_date >= as.Date("2023-03-09") & dw_loan_date <= as.Date("2023-03-14")) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

cat("DW borrowers March 9-14:", length(dw_mar9_14), "\n")

## DW borrowers March 9-14: 137

table3_data <- baseline_q %>%
  mutate(
    borrower_type = if_else(idrssd %in% dw_mar9_14, "DW Borrower", "Non-Borrower")
  )

table3 <- create_summary_table_v2(table3_data, "borrower_type",
                                   group_levels = c("DW Borrower", "Non-Borrower"))

# Display
table3 %>%
  select(-Row_Type) %>%
  kable(caption = "Table 3: Pre-Crisis Bank Characteristics - DW Borrowers vs Non-Borrowers (March 9-14, 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table 3: Pre-Crisis Bank Characteristics - DW Borrowers vs Non-Borrowers (March 9-14, 2023)

Variable	DW Borrower	Non-Borrower
N	124	4168
Book Equity Ratio	9.01 (8.64)	10.26 (8.85)
	[-3.59]***
	124	4168
MTM Loss (HTM)	0.19 (0.00)	0.20 (0.00)
	[-0.25]
	124	4158
MTM Loss (BTFP Elg.)	0.77 (0.52)	0.68 (0.40)
	[1.30]
	124	4158
MTM Loss (Total)	5.50 (5.58)	5.47 (5.31)
	[0.17]
	124	4158
Adjusted Equity	3.51 (2.91)	4.71 (3.64)
	[-2.72]***
	124	4158
Adjusted Equity < 0	20.16 (0.00)	19.24 (0.00)
	[0.25]
	25	800
Adj. Tier1 Equity	4.58 (3.98)	6.35 (5.07)
	[-5.05]***
	124	4158
Adj. Tier1 Equity < 0	3.23 (0.00)	3.32 (0.00)
	[-0.06]
	4	138
Adj. Common equity tier1	4.56 (3.98)	6.30 (5.06)
	[-4.95]***
	121	4105
Adj. Common equity tier1 < 0	3.31 (0.00)	3.26 (0.00)
	[0.03]
	4	134
Uninsured Leverage	31.30 (29.52)	23.38 (22.08)
	[6.50]***
	124	4168
MTM Insolvent	20.16 (0.00)	19.24 (0.00)
	[0.25]
	25	800
Solvent and Liquid	11.29 (0.00)	22.62 (0.00)
	[-3.87]***
	14	942
Solvent and Illiquid	33.87 (0.00)	26.38 (0.00)
	[1.73]*
	42	1098
Insolvent and Liquid	14.52 (0.00)	26.83 (0.00)
	[-3.79]***
	18	1117
Insolvent and Illiquid	40.32 (0.00)	24.00 (0.00)
	[3.65]***
	50	999
IDCR Insolvent (s=0.5)	5.65 (0.00)	4.17 (0.00)
	[0.70]
	7	172
IDCR Insolvent (s=1.0)	35.48 (0.00)	28.25 (0.00)
	[1.65]
	44	1166
Capital Insolvent (s=0.5)	4.84 (0.00)	2.12 (0.00)
	[1.40]
	6	88
Capital Insolvent (s=1.0)	15.32 (0.00)	7.38 (0.00)
	[2.43]**
	19	307
Available Liquidity Ratio	5.31 (3.31)	9.42 (6.00)
	[-7.31]***
	124	4168
Change Uninsured Deposits (Fwd Q)	-7.45 (-8.04)	0.26 (-4.46)
	[-2.97]***
	124	4104
Change Insured Deposits (Fwd Q)	7.31 (2.63)	3.74 (1.44)
	[1.62]
	124	4110
Abnormal Insured Outflow (10%)	6.45 (0.00)	7.53 (0.00)
	[-0.48]
	8	314
Abnormal Insured Outflow (5%)	2.42 (0.00)	2.66 (0.00)
	[-0.17]
	3	111
Abnormal Uninsured Outflow (10%)	37.10 (0.00)	21.02 (0.00)
	[3.65]***
	46	876
Abnormal Uninsured Outflow (5%)	18.55 (0.00)	8.97 (0.00)
	[2.71]***
	23	374

# Export
export_table(
  table3, 
  "table3_dw_mar9_14",
  caption = "Pre-Crisis Bank Characteristics: DW Borrowers vs Non-Borrowers (March 9--14, 2023)",
  label = "tab:dw_mar9_14",
  note = "Bank characteristics from 2022Q4 Call Reports. Values shown as Mean (Median) with N observations below. DW borrowers identified as banks with Discount Window loans between March 9--14, 2023. Sample excludes G-SIBs and failed banks."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table3_dw_mar9_14.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table3_dw_mar9_14.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table3_dw_mar9_14.csv

12 Table 4: Acute Period - BTFP, DW, Both, Non-Borrowers

# ==============================================================================
# TABLE 4: Acute Period (March 13 - May 1, 2023)
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE 4: Acute Period - BTFP, DW, Both, Non-Borrowers\n")

## TABLE 4: Acute Period - BTFP, DW, Both, Non-Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

acute_start <- as.Date("2023-03-13")
acute_end <- as.Date("2023-05-01")

btfp_acute <- btfp_loans_raw %>%
  filter(btfp_loan_date >= acute_start & btfp_loan_date <= acute_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

dw_acute <- dw_loans_raw %>%
  filter(dw_loan_date >= acute_start & dw_loan_date <= acute_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

cat("BTFP borrowers (Acute):", length(btfp_acute), "\n")

## BTFP borrowers (Acute): 492

cat("DW borrowers (Acute):", length(dw_acute), "\n")

## DW borrowers (Acute): 424

cat("Both:", length(intersect(btfp_acute, dw_acute)), "\n")

## Both: 103

table4_data <- baseline_q %>%
  mutate(
    btfp_user = idrssd %in% btfp_acute,
    dw_user = idrssd %in% dw_acute,
    borrower_type = case_when(
      btfp_user & dw_user ~ "Both",
      btfp_user & !dw_user ~ "BTFP Only",
      !btfp_user & dw_user ~ "DW Only",
      TRUE ~ "Non-Borrower"
    )
  )

table4 <- create_summary_table_v2(table4_data, "borrower_type",
                                   group_levels = c("BTFP Only", "DW Only", "Both", "Non-Borrower"))

# Display
table4 %>%
  select(-Row_Type) %>%
  kable(caption = "Table 4: Pre-Crisis Bank Characteristics by Borrower Type (Acute Period: March 13 - May 1, 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table 4: Pre-Crisis Bank Characteristics by Borrower Type (Acute Period: March 13 - May 1, 2023)

Variable	BTFP Only	DW Only	Both	Non-Borrower
N	368	299	94	3531
Book Equity Ratio	8.18 (8.08)	9.05 (8.82)	8.19 (8.23)	10.59 (8.96)
	[-10.58]***	[-6.25]***	[-7.48]***
	368	299	94	3531
MTM Loss (HTM)	0.21 (0.00)	0.22 (0.00)	0.32 (0.00)	0.20 (0.00)
	[0.37]	[0.64]	[1.64]
	368	299	94	3521
MTM Loss (BTFP Elg.)	0.88 (0.61)	0.74 (0.48)	0.92 (0.61)	0.65 (0.38)
	[4.32]***	[1.85]*	[2.68]***
	368	299	94	3521
MTM Loss (Total)	6.18 (6.13)	5.75 (5.67)	5.86 (5.75)	5.36 (5.18)
	[7.42]***	[3.19]***	[2.60]**
	368	299	94	3521
Adjusted Equity	2.00 (2.03)	3.30 (3.01)	2.33 (2.70)	5.13 (3.89)
	[-11.32]***	[-6.16]***	[-6.43]***
	368	299	94	3521
Adjusted Equity < 0	30.43 (0.00)	19.06 (0.00)	21.28 (0.00)	18.06 (0.00)
	[4.97]***	[0.42]	[0.75]
	112	57	20	636
Adj. Tier1 Equity	4.13 (3.77)	4.71 (4.15)	3.76 (3.70)	6.73 (5.31)
	[-11.55]***	[-8.16]***	[-9.80]***
	368	299	94	3521
Adj. Tier1 Equity < 0	4.62 (0.00)	3.34 (0.00)	5.32 (0.00)	3.12 (0.00)
	[1.32]	[0.20]	[0.94]
	17	10	5	110
Adj. Common equity tier1	4.11 (3.77)	4.71 (4.19)	3.65 (3.58)	6.67 (5.29)
	[-11.42]***	[-8.03]***	[-9.79]***
	361	293	88	3484
Adj. Common equity tier1 < 0	4.71 (0.00)	2.73 (0.00)	5.68 (0.00)	3.10 (0.00)
	[1.39]	[-0.37]	[1.03]
	17	8	5	108
Uninsured Leverage	26.21 (25.09)	26.85 (25.28)	32.63 (30.81)	22.83 (21.44)
	[5.33]***	[5.78]***	[6.67]***
	368	299	94	3531
MTM Insolvent	30.43 (0.00)	19.06 (0.00)	21.28 (0.00)	18.06 (0.00)
	[4.97]***	[0.42]	[0.75]
	112	57	20	636
Solvent and Liquid	11.41 (0.00)	11.37 (0.00)	7.45 (0.00)	24.75 (0.00)
	[-7.36]***	[-6.77]***	[-6.14]***
	42	34	7	873
Solvent and Illiquid	22.01 (0.00)	29.10 (0.00)	30.85 (0.00)	26.75 (0.00)
	[-2.07]**	[0.86]	[0.85]
	81	87	29	943
Insolvent and Liquid	26.09 (0.00)	24.75 (0.00)	12.77 (0.00)	27.02 (0.00)
	[-0.39]	[-0.87]	[-4.03]***
	96	74	12	953
Insolvent and Illiquid	40.49 (0.00)	34.78 (0.00)	48.94 (0.00)	21.28 (0.00)
	[7.24]***	[4.75]***	[5.29]***
	149	104	46	750
IDCR Insolvent (s=0.5)	3.53 (0.00)	7.02 (0.00)	3.19 (0.00)	4.07 (0.00)
	[-0.53]	[1.95]*	[-0.47]
	13	21	3	142
IDCR Insolvent (s=1.0)	29.08 (0.00)	33.11 (0.00)	24.47 (0.00)	28.11 (0.00)
	[0.39]	[1.77]*	[-0.81]
	107	99	23	981
Capital Insolvent (s=0.5)	1.63 (0.00)	3.68 (0.00)	7.45 (0.00)	1.99 (0.00)
	[-0.51]	[1.52]	[2.00]**
	6	11	7	70
Capital Insolvent (s=1.0)	9.78 (0.00)	11.71 (0.00)	11.70 (0.00)	6.93 (0.00)
	[1.77]*	[2.50]**	[1.42]
	36	35	11	244
Available Liquidity Ratio	5.15 (3.63)	6.97 (4.15)	4.97 (2.85)	10.04 (6.53)
	[-15.65]***	[-6.79]***	[-8.39]***
	368	299	94	3531
Change Uninsured Deposits (Fwd Q)	-6.40 (-7.40)	-4.28 (-5.30)	-6.38 (-6.48)	1.26 (-4.09)
	[-3.01]***	[-2.15]**	[-2.74]***
	367	299	94	3468
Change Insured Deposits (Fwd Q)	3.58 (1.54)	4.25 (2.10)	6.92 (1.90)	3.76 (1.40)
	[-0.21]	[0.58]	[1.12]
	368	299	94	3473
Abnormal Insured Outflow (10%)	11.96 (0.00)	4.68 (0.00)	5.32 (0.00)	7.34 (0.00)
	[2.64]***	[-2.04]**	[-0.85]
	44	14	5	259
Abnormal Insured Outflow (5%)	4.35 (0.00)	1.00 (0.00)	3.19 (0.00)	2.61 (0.00)
	[1.59]	[-2.52]**	[0.32]
	16	3	3	92
Abnormal Uninsured Outflow (10%)	26.90 (0.00)	27.76 (0.00)	34.04 (0.00)	20.05 (0.00)
	[2.84]***	[2.88]***	[2.82]***
	99	83	32	708
Abnormal Uninsured Outflow (5%)	12.50 (0.00)	12.37 (0.00)	14.89 (0.00)	8.50 (0.00)
	[2.24]**	[1.97]**	[1.72]*
	46	37	14	300

# Export
export_table(
  table4, 
  "table4_acute_period",
  caption = "Pre-Crisis Bank Characteristics by Borrower Type: Acute Crisis Period",
  label = "tab:acute_period",
  note = "Bank characteristics from 2022Q4 Call Reports. Acute crisis period: March 13 -- May 1, 2023. Banks classified by facility usage during this period. Sample excludes G-SIBs and failed banks."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table4_acute_period.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table4_acute_period.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table4_acute_period.csv

13 Table 5A: Q1 2023 - All Facilities (10% Threshold)

# ==============================================================================
# TABLE 5A: Q1 2023 (Jan 1 - Mar 31) - BTFP, DW, FHLB (10% Threshold)
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE 5A: Q1 2023 - BTFP, DW, FHLB (10%), Combinations, Non-Borrowers\n")

## TABLE 5A: Q1 2023 - BTFP, DW, FHLB (10%), Combinations, Non-Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

q1_start <- as.Date("2023-01-01")
q1_end <- as.Date("2023-03-31")

btfp_q1 <- btfp_loans_raw %>%
  filter(btfp_loan_date >= q1_start & btfp_loan_date <= q1_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

dw_q1 <- dw_loans_raw %>%
  filter(dw_loan_date >= q1_start & dw_loan_date <= q1_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

# Define FHLB borrowers using 10% threshold (banks with abnormal FHLB advances in 2023Q1)
fhlb_borrowers_10pct <- baseline_q %>%
  filter(!is.na(abnormal_fhlb_borrowing_10pct) & abnormal_fhlb_borrowing_10pct > 0) %>%
  pull(idrssd)

cat("BTFP borrowers (Q1 2023):", length(btfp_q1), "\n")

## BTFP borrowers (Q1 2023): 302

cat("DW borrowers (Q1 2023):", length(dw_q1), "\n")

## DW borrowers (Q1 2023): 479

cat("FHLB borrowers (Q1 2023, 10%):", length(fhlb_borrowers_10pct), "\n")

## FHLB borrowers (Q1 2023, 10%): 302

table5a_data <- baseline_q %>%
  mutate(
    btfp_user = idrssd %in% btfp_q1,
    dw_user = idrssd %in% dw_q1,
    fhlb_user = idrssd %in% fhlb_borrowers_10pct,
    borrower_type = case_when(
      btfp_user & dw_user & fhlb_user ~ "All Three",
      btfp_user & dw_user & !fhlb_user ~ "BTFP+DW",
      btfp_user & !dw_user & fhlb_user ~ "BTFP+FHLB",
      !btfp_user & dw_user & fhlb_user ~ "DW+FHLB",
      btfp_user & !dw_user & !fhlb_user ~ "BTFP Only",
      !btfp_user & dw_user & !fhlb_user ~ "DW Only",
      !btfp_user & !dw_user & fhlb_user ~ "FHLB Only",
      TRUE ~ "Non-Borrower"
    )
  )

table5a <- create_summary_table_v2(table5a_data, "borrower_type",
                                   group_levels = c("BTFP Only", "DW Only", "FHLB Only", 
                                                    "BTFP+DW", "BTFP+FHLB", "DW+FHLB", 
                                                    "All Three", "Non-Borrower"))

# Display
table5a %>%
  select(-Row_Type) %>%
  kable(caption = "Table 5A: Pre-Crisis Bank Characteristics by Borrower Type (Q1 2023: Jan 1 - Mar 31) - 10% FHLB Threshold") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  scroll_box(width = "100%")

Table 5A: Pre-Crisis Bank Characteristics by Borrower Type (Q1 2023: Jan 1 - Mar 31) - 10% FHLB Threshold

Variable	BTFP Only	DW Only	FHLB Only	BTFP+DW	BTFP+FHLB	DW+FHLB	All Three	Non-Borrower
N	199	346	260	60	12	21	9	3385
Book Equity Ratio	8.19 (8.06)	8.85 (8.58)	9.82 (9.28)	8.01 (8.09)	8.89 (8.36)	11.00 (10.55)	9.36 (9.47)	10.55 (8.90)
	[-8.78]***	[-6.62]***	[-2.78]***	[-5.75]***	[-1.80]*	[0.68]	[-1.75]
	199	346	260	60	12	21	9	3385
MTM Loss (HTM)	0.18 (0.00)	0.24 (0.00)	0.14 (0.00)	0.38 (0.00)	0.15 (0.00)	0.12 (0.00)	0.08 (0.00)	0.20 (0.00)
	[-0.55]	[0.95]	[-2.33]**	[1.62]	[-0.81]	[-1.37]	[-2.33]**
	199	346	260	60	12	21	9	3375
MTM Loss (BTFP Elg.)	0.81 (0.53)	0.85 (0.55)	0.53 (0.28)	0.98 (0.65)	0.77 (0.87)	0.46 (0.23)	0.54 (0.34)	0.66 (0.39)
	[2.61]***	[3.53]***	[-3.19]***	[2.43]**	[0.75]	[-1.39]	[-0.80]
	199	346	260	60	12	21	9	3375
MTM Loss (Total)	6.09 (6.03)	5.66 (5.64)	5.69 (5.26)	5.94 (5.82)	6.37 (6.54)	5.13 (5.07)	6.10 (5.66)	5.38 (5.23)
	[5.05]***	[2.33]**	[2.13]**	[2.34]**	[1.96]*	[-0.65]	[0.79]
	199	346	260	60	12	21	9	3375
Adjusted Equity	2.11 (2.08)	3.19 (2.91)	4.13 (4.12)	2.06 (2.27)	2.52 (1.53)	5.87 (6.33)	3.26 (3.04)	5.07 (3.79)
	[-8.88]***	[-6.00]***	[-2.93]***	[-5.22]***	[-1.96]*	[0.94]	[-1.33]
	199	346	260	60	12	21	9	3375
Adjusted Equity < 0	29.65 (0.00)	19.36 (0.00)	14.23 (0.00)	28.33 (0.00)	33.33 (0.00)	4.76 (0.00)	33.33 (0.00)	18.87 (0.00)
	[3.25]***	[0.22]	[-2.04]**	[1.60]	[1.02]	[-2.93]***	[0.87]
	59	67	37	17	4	1	3	637
Adj. Tier1 Equity	4.16 (3.82)	4.72 (4.21)	5.32 (5.01)	3.63 (3.28)	4.13 (3.49)	5.61 (5.57)	3.31 (4.04)	6.73 (5.24)
	[-9.62]***	[-7.55]***	[-5.29]***	[-7.48]***	[-3.69]***	[-1.70]	[-3.27]**
	199	346	260	60	12	21	9	3375
Adj. Tier1 Equity < 0	4.52 (0.00)	4.62 (0.00)	5.00 (0.00)	6.67 (0.00)	0.00 (0.00)	0.00 (0.00)	22.22 (0.00)	2.90 (0.00)
	[1.08]	[1.47]	[1.51]	[1.15]	[-10.04]***	[-10.04]***	[1.31]
	9	16	13	4	0	0	2	98
Adj. Common equity tier1	4.13 (3.82)	4.80 (4.26)	5.31 (5.01)	3.54 (3.18)	3.83 (3.21)	5.61 (5.57)	3.31 (4.04)	6.66 (5.21)
	[-9.41]***	[-6.99]***	[-5.06]***	[-7.28]***	[-4.06]***	[-1.59]	[-3.20]**
	193	333	258	57	11	21	9	3344
Adj. Common equity tier1 < 0	4.66 (0.00)	3.30 (0.00)	5.04 (0.00)	7.02 (0.00)	0.00 (0.00)	0.00 (0.00)	22.22 (0.00)	2.96 (0.00)
	[1.10]	[0.33]	[1.49]	[1.18]	[-10.10]***	[-10.10]***	[1.31]
	9	11	13	4	0	0	2	99
Uninsured Leverage	26.66 (24.86)	27.17 (25.28)	23.71 (22.82)	32.21 (29.91)	31.75 (28.72)	30.18 (30.05)	28.58 (31.00)	22.82 (21.42)
	[4.82]***	[6.27]***	[1.26]	[5.13]***	[2.73]**	[2.62]**	[1.26]
	199	346	260	60	12	21	9	3385
MTM Insolvent	29.65 (0.00)	19.36 (0.00)	14.23 (0.00)	28.33 (0.00)	33.33 (0.00)	4.76 (0.00)	33.33 (0.00)	18.87 (0.00)
	[3.25]***	[0.22]	[-2.04]**	[1.60]	[1.02]	[-2.93]***	[0.87]
	59	67	37	17	4	1	3	637
Solvent and Liquid	10.55 (0.00)	10.98 (0.00)	19.62 (0.00)	5.00 (0.00)	0.00 (0.00)	14.29 (0.00)	0.00 (0.00)	24.84 (0.00)
	[-6.20]***	[-7.53]***	[-2.03]**	[-6.77]***	[-33.43]***	[-1.34]	[-33.43]***
	21	38	51	3	0	3	0	840
Solvent and Illiquid	23.12 (0.00)	30.92 (0.00)	30.38 (0.00)	30.00 (0.00)	25.00 (0.00)	42.86 (0.00)	44.44 (0.00)	25.87 (0.00)
	[-0.89]	[1.95]*	[1.53]	[0.69]	[-0.07]	[1.53]	[1.06]
	46	107	79	18	3	9	4	874
Insolvent and Liquid	24.12 (0.00)	26.30 (0.00)	26.92 (0.00)	11.67 (0.00)	16.67 (0.00)	14.29 (0.00)	33.33 (0.00)	26.94 (0.00)
	[-0.90]	[-0.26]	[-0.01]	[-3.60]***	[-0.91]	[-1.61]	[0.38]
	48	91	70	7	2	3	3	911
Insolvent and Illiquid	42.21 (0.00)	31.79 (0.00)	23.08 (0.00)	53.33 (100.00)	58.33 (100.00)	28.57 (0.00)	22.22 (0.00)	22.14 (0.00)
	[5.60]***	[3.70]***	[0.35]	[4.77]***	[2.43]**	[0.64]	[0.01]
	84	110	60	32	7	6	2	748
IDCR Insolvent (s=0.5)	4.02 (0.00)	5.49 (0.00)	4.62 (0.00)	3.33 (0.00)	0.00 (0.00)	4.76 (0.00)	11.11 (0.00)	4.07 (0.00)
	[-0.03]	[1.12]	[0.41]	[-0.31]	[-11.90]***	[0.15]	[0.63]
	8	19	12	2	0	1	1	136
IDCR Insolvent (s=1.0)	28.14 (0.00)	29.77 (0.00)	27.31 (0.00)	21.67 (0.00)	8.33 (0.00)	14.29 (0.00)	33.33 (0.00)	28.71 (0.00)
	[-0.17]	[0.41]	[-0.49]	[-1.30]	[-2.43]**	[-1.83]*	[0.28]
	56	103	71	13	1	3	3	960
Capital Insolvent (s=0.5)	2.01 (0.00)	2.89 (0.00)	3.08 (0.00)	6.67 (0.00)	0.00 (0.00)	4.76 (0.00)	11.11 (0.00)	1.96 (0.00)
	[0.05]	[1.00]	[1.02]	[1.45]	[-8.20]***	[0.59]	[0.82]
	4	10	8	4	0	1	1	66
Capital Insolvent (s=1.0)	12.06 (0.00)	11.56 (0.00)	7.31 (0.00)	8.33 (0.00)	0.00 (0.00)	4.76 (0.00)	11.11 (0.00)	6.99 (0.00)
	[2.15]**	[2.57]**	[0.19]	[0.37]	[-15.93]***	[-0.47]	[0.37]
	24	40	19	5	0	1	1	236
Available Liquidity Ratio	5.20 (3.79)	6.47 (3.75)	5.43 (4.31)	4.02 (2.59)	4.30 (3.76)	4.95 (3.49)	2.87 (2.22)	10.28 (6.70)
	[-12.76]***	[-9.19]***	[-16.41]***	[-8.54]***	[-6.25]***	[-6.11]***	[-12.18]***
	199	346	260	60	12	21	9	3385
Change Uninsured Deposits (Fwd Q)	-6.23 (-6.25)	-5.34 (-5.75)	-6.08 (-7.06)	-9.13 (-8.59)	-10.34 (-12.20)	-3.51 (-4.59)	-17.22 (-12.22)	1.71 (-3.71)
	[-2.96]***	[-2.67]***	[-2.85]***	[-3.76]***	[-2.43]**	[-1.54]	[-2.78]**
	199	344	260	60	12	21	9	3323
Change Insured Deposits (Fwd Q)	3.31 (1.97)	4.62 (2.13)	1.92 (0.96)	8.70 (2.08)	6.32 (0.94)	4.53 (3.66)	9.39 (7.84)	3.84 (1.42)
	[-0.56]	[0.89]	[-2.27]**	[1.14]	[0.45]	[0.44]	[2.14]*
	199	344	260	60	12	21	9	3329
Abnormal Insured Outflow (10%)	13.57 (0.00)	4.62 (0.00)	9.62 (0.00)	8.33 (0.00)	8.33 (0.00)	0.00 (0.00)	0.00 (0.00)	7.33 (0.00)
	[2.52]**	[-2.22]**	[1.21]	[0.28]	[0.12]	[-16.36]***	[-16.36]***
	27	16	25	5	1	0	0	248
Abnormal Insured Outflow (5%)	5.03 (0.00)	1.45 (0.00)	2.69 (0.00)	1.67 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	2.69 (0.00)
	[1.48]	[-1.78]*	[0.00]	[-0.60]	[-9.67]***	[-9.67]***	[-9.67]***
	10	5	7	1	0	0	0	91
Abnormal Uninsured Outflow (10%)	25.13 (0.00)	29.19 (0.00)	29.23 (0.00)	43.33 (0.00)	41.67 (0.00)	38.10 (0.00)	55.56 (100.00)	19.23 (0.00)
	[1.87]*	[3.92]***	[3.44]***	[3.72]***	[1.51]	[1.73]*	[2.07]*
	50	101	76	26	5	8	5	651
Abnormal Uninsured Outflow (5%)	8.54 (0.00)	11.85 (0.00)	12.69 (0.00)	25.00 (0.00)	33.33 (0.00)	9.52 (0.00)	22.22 (0.00)	8.36 (0.00)
	[0.09]	[1.93]*	[2.04]**	[2.94]***	[1.76]	[0.18]	[0.94]
	17	41	33	15	4	2	2	283

# Export
export_table(
  table5a, 
  "table5a_q1_2023_10pct",
  caption = "Pre-Crisis Bank Characteristics by Borrower Type: Q1 2023 (10\\% FHLB Threshold)",
  label = "tab:q1_2023_10pct",
  note = "Bank characteristics from 2022Q4 Call Reports. Q1 2023: January 1 -- March 31, 2023. FHLB borrowers identified as banks with abnormal FHLB advances at the 90th percentile. Sample excludes G-SIBs and failed banks."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table5a_q1_2023_10pct.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table5a_q1_2023_10pct.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table5a_q1_2023_10pct.csv

14 Table 5B: Q1 2023 - All Facilities (5% Threshold)

# ==============================================================================
# TABLE 5B: Q1 2023 (Jan 1 - Mar 31) - BTFP, DW, FHLB (5% Threshold)
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE 5B: Q1 2023 - BTFP, DW, FHLB (5%), Combinations, Non-Borrowers\n")

## TABLE 5B: Q1 2023 - BTFP, DW, FHLB (5%), Combinations, Non-Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

# Define FHLB borrowers using 5% threshold (banks with abnormal FHLB advances in 2023Q1)
fhlb_borrowers_5pct <- baseline_q %>%
  filter(!is.na(abnormal_fhlb_borrowing_5pct) & abnormal_fhlb_borrowing_5pct > 0) %>%
  pull(idrssd)

cat("BTFP borrowers (Q1 2023):", length(btfp_q1), "\n")

## BTFP borrowers (Q1 2023): 302

cat("DW borrowers (Q1 2023):", length(dw_q1), "\n")

## DW borrowers (Q1 2023): 479

cat("FHLB borrowers (Q1 2023, 5%):", length(fhlb_borrowers_5pct), "\n")

## FHLB borrowers (Q1 2023, 5%): 221

table5b_data <- baseline_q %>%
  mutate(
    btfp_user = idrssd %in% btfp_q1,
    dw_user = idrssd %in% dw_q1,
    fhlb_user = idrssd %in% fhlb_borrowers_5pct,
    borrower_type = case_when(
      btfp_user & dw_user & fhlb_user ~ "All Three",
      btfp_user & dw_user & !fhlb_user ~ "BTFP+DW",
      btfp_user & !dw_user & fhlb_user ~ "BTFP+FHLB",
      !btfp_user & dw_user & fhlb_user ~ "DW+FHLB",
      btfp_user & !dw_user & !fhlb_user ~ "BTFP Only",
      !btfp_user & dw_user & !fhlb_user ~ "DW Only",
      !btfp_user & !dw_user & fhlb_user ~ "FHLB Only",
      TRUE ~ "Non-Borrower"
    )
  )

table5b <- create_summary_table_v2(table5b_data, "borrower_type",
                                   group_levels = c("BTFP Only", "DW Only", "FHLB Only", 
                                                    "BTFP+DW", "BTFP+FHLB", "DW+FHLB", 
                                                    "All Three", "Non-Borrower"))

# Display
table5b %>%
  select(-Row_Type) %>%
  kable(caption = "Table 5B: Pre-Crisis Bank Characteristics by Borrower Type (Q1 2023: Jan 1 - Mar 31) - 5% FHLB Threshold") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  scroll_box(width = "100%")

Table 5B: Pre-Crisis Bank Characteristics by Borrower Type (Q1 2023: Jan 1 - Mar 31) - 5% FHLB Threshold

Variable	BTFP Only	DW Only	FHLB Only	BTFP+DW	BTFP+FHLB	DW+FHLB	All Three	Non-Borrower
N	202	352	192	64	9	15	5	3453
Book Equity Ratio	8.18 (8.04)	8.88 (8.61)	9.80 (9.36)	8.01 (7.93)	9.42 (8.67)	11.24 (10.89)	10.44 (9.62)	10.54 (8.90)
	[-8.89]***	[-6.56]***	[-2.66]***	[-6.04]***	[-1.06]	[0.82]	[-0.11]
	202	352	192	64	9	15	5	3453
MTM Loss (HTM)	0.17 (0.00)	0.24 (0.00)	0.16 (0.00)	0.36 (0.00)	0.18 (0.01)	0.17 (0.04)	0.11 (0.00)	0.20 (0.00)
	[-0.55]	[0.91]	[-1.29]	[1.53]	[-0.21]	[-0.42]	[-1.03]
	202	352	192	64	9	15	5	3443
MTM Loss (BTFP Elg.)	0.82 (0.54)	0.84 (0.53)	0.52 (0.28)	0.96 (0.65)	0.64 (0.52)	0.57 (0.37)	0.35 (0.23)	0.66 (0.38)
	[2.78]***	[3.40]***	[-2.91]***	[2.47]**	[-0.12]	[-0.47]	[-2.27]*
	202	352	192	64	9	15	5	3443
MTM Loss (Total)	6.08 (6.04)	5.66 (5.64)	5.60 (5.14)	5.93 (5.78)	6.59 (6.26)	5.00 (5.07)	6.35 (6.57)	5.39 (5.23)
	[4.97]***	[2.23]**	[1.27]	[2.35]**	[2.11]*	[-1.14]	[0.61]
	202	352	192	64	9	15	5	3443
Adjusted Equity	2.10 (2.06)	3.22 (2.92)	4.20 (4.30)	2.07 (2.39)	2.82 (1.53)	6.24 (6.33)	4.09 (3.04)	5.04 (3.79)
	[-8.92]***	[-5.92]***	[-2.42]**	[-5.46]***	[-1.46]	[1.19]	[-0.40]
	202	352	192	64	9	15	5	3443
Adjusted Equity < 0	29.70 (0.00)	19.32 (0.00)	12.50 (0.00)	28.12 (0.00)	33.33 (0.00)	0.00 (0.00)	40.00 (0.00)	18.88 (0.00)
	[3.29]***	[0.20]	[-2.57]**	[1.62]	[0.87]	[-28.30]***	[0.86]
	60	68	24	18	3	0	2	650
Adj. Tier1 Equity	4.16 (3.81)	4.73 (4.24)	5.25 (5.01)	3.59 (3.28)	4.12 (3.78)	5.85 (5.39)	3.59 (4.63)	6.71 (5.23)
	[-9.66]***	[-7.56]***	[-5.16]***	[-7.89]***	[-3.17]**	[-1.09]	[-1.71]
	202	352	192	64	9	15	5	3443
Adj. Tier1 Equity < 0	4.46 (0.00)	4.55 (0.00)	4.69 (0.00)	6.25 (0.00)	0.00 (0.00)	0.00 (0.00)	40.00 (0.00)	2.96 (0.00)
	[1.01]	[1.38]	[1.11]	[1.07]	[-10.25]***	[-10.25]***	[1.51]
	9	16	9	4	0	0	2	102
Adj. Common equity tier1	4.11 (3.80)	4.81 (4.29)	5.24 (5.00)	3.50 (3.18)	4.12 (3.78)	5.84 (5.39)	3.59 (4.63)	6.63 (5.21)
	[-9.50]***	[-6.99]***	[-4.96]***	[-7.71]***	[-3.09]**	[-1.01]	[-1.67]
	195	339	191	61	9	15	5	3411
Adj. Common equity tier1 < 0	4.62 (0.00)	3.24 (0.00)	4.71 (0.00)	6.56 (0.00)	0.00 (0.00)	0.00 (0.00)	40.00 (0.00)	3.02 (0.00)
	[1.04]	[0.22]	[1.08]	[1.10]	[-10.30]***	[-10.30]***	[1.51]
	9	11	9	4	0	0	2	103
Uninsured Leverage	26.81 (25.02)	27.21 (25.31)	24.65 (23.17)	32.36 (30.12)	30.11 (26.15)	30.56 (30.05)	23.81 (22.44)	22.79 (21.40)
	[5.06]***	[6.43]***	[2.33]**	[5.44]***	[1.95]*	[2.28]**	[0.18]
	202	352	192	64	9	15	5	3453
MTM Insolvent	29.70 (0.00)	19.32 (0.00)	12.50 (0.00)	28.12 (0.00)	33.33 (0.00)	0.00 (0.00)	40.00 (0.00)	18.88 (0.00)
	[3.29]***	[0.20]	[-2.57]**	[1.62]	[0.87]	[-28.30]***	[0.86]
	60	68	24	18	3	0	2	650
Solvent and Liquid	10.40 (0.00)	10.80 (0.00)	19.27 (0.00)	4.69 (0.00)	0.00 (0.00)	20.00 (0.00)	0.00 (0.00)	24.76 (0.00)
	[-6.31]***	[-7.71]***	[-1.86]*	[-7.27]***	[-33.69]***	[-0.44]	[-33.69]***
	21	38	37	3	0	3	0	854
Solvent and Illiquid	23.27 (0.00)	31.25 (0.00)	33.33 (0.00)	31.25 (0.00)	22.22 (0.00)	40.00 (0.00)	40.00 (0.00)	25.79 (0.00)
	[-0.82]	[2.11]**	[2.16]**	[0.93]	[-0.24]	[1.08]	[0.58]
	47	110	64	20	2	6	2	889
Insolvent and Liquid	23.76 (0.00)	26.42 (0.00)	23.44 (0.00)	12.50 (0.00)	22.22 (0.00)	6.67 (0.00)	40.00 (0.00)	27.14 (0.00)
	[-1.09]	[-0.29]	[-1.17]	[-3.46]***	[-0.33]	[-3.05]***	[0.52]
	48	93	45	8	2	1	2	936
Insolvent and Illiquid	42.57 (0.00)	31.53 (0.00)	23.96 (0.00)	51.56 (100.00)	55.56 (100.00)	33.33 (0.00)	20.00 (0.00)	22.11 (0.00)
	[5.75]***	[3.66]***	[0.58]	[4.65]***	[1.90]*	[0.89]	[-0.11]
	86	111	46	33	5	5	1	762
IDCR Insolvent (s=0.5)	3.96 (0.00)	5.68 (0.00)	3.65 (0.00)	3.12 (0.00)	0.00 (0.00)	0.00 (0.00)	20.00 (0.00)	4.13 (0.00)
	[-0.12]	[1.21]	[-0.35]	[-0.45]	[-12.13]***	[-12.13]***	[0.79]
	8	20	7	2	0	0	1	141
IDCR Insolvent (s=1.0)	28.22 (0.00)	29.83 (0.00)	27.60 (0.00)	21.88 (0.00)	0.00 (0.00)	6.67 (0.00)	40.00 (0.00)	28.66 (0.00)
	[-0.14]	[0.46]	[-0.32]	[-1.29]	[-37.02]***	[-3.28]***	[0.46]
	57	105	53	14	0	1	2	978
Capital Insolvent (s=0.5)	1.98 (0.00)	3.12 (0.00)	3.12 (0.00)	6.25 (0.00)	0.00 (0.00)	0.00 (0.00)	20.00 (0.00)	1.98 (0.00)
	[0.01]	[1.20]	[0.90]	[1.40]	[-8.33]***	[-8.33]***	[0.90]
	4	11	6	4	0	0	1	68
Capital Insolvent (s=1.0)	11.88 (0.00)	11.65 (0.00)	7.81 (0.00)	7.81 (0.00)	0.00 (0.00)	0.00 (0.00)	20.00 (0.00)	6.97 (0.00)
	[2.11]**	[2.65]***	[0.42]	[0.25]	[-16.06]***	[-16.06]***	[0.65]
	24	41	15	5	0	0	1	240
Available Liquidity Ratio	5.20 (3.81)	6.41 (3.70)	5.35 (4.38)	3.92 (2.49)	3.95 (3.59)	5.66 (4.04)	3.24 (3.59)	10.19 (6.66)
	[-12.67]***	[-9.25]***	[-15.40]***	[-9.04]***	[-6.73]***	[-3.94]***	[-9.94]***
	202	352	192	64	9	15	5	3453
Change Uninsured Deposits (Fwd Q)	-6.13 (-6.27)	-5.29 (-5.75)	-6.10 (-6.21)	-10.11 (-8.59)	-14.10 (-13.96)	-3.95 (-4.59)	-11.14 (-12.22)	1.56 (-3.89)
	[-2.91]***	[-2.64]***	[-2.83]***	[-3.97]***	[-4.72]***	[-1.51]	[-3.57]***
	202	350	192	64	9	15	5	3391
Change Insured Deposits (Fwd Q)	3.59 (1.86)	4.62 (2.19)	1.49 (0.78)	8.98 (2.15)	1.07 (0.75)	4.47 (3.66)	6.42 (4.81)	3.82 (1.43)
	[-0.24]	[0.92]	[-2.94]***	[1.29]	[-1.49]	[0.33]	[0.80]
	202	350	192	64	9	15	5	3397
Abnormal Insured Outflow (10%)	13.37 (0.00)	4.55 (0.00)	8.33 (0.00)	7.81 (0.00)	11.11 (0.00)	0.00 (0.00)	0.00 (0.00)	7.44 (0.00)
	[2.43]**	[-2.42]**	[0.43]	[0.11]	[0.33]	[-16.66]***	[-16.66]***
	27	16	16	5	1	0	0	257
Abnormal Insured Outflow (5%)	4.95 (0.00)	1.42 (0.00)	2.08 (0.00)	1.56 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	2.72 (0.00)
	[1.43]	[-1.89]*	[-0.60]	[-0.73]	[-9.83]***	[-9.83]***	[-9.83]***
	10	5	4	1	0	0	0	94
Abnormal Uninsured Outflow (10%)	25.25 (0.00)	29.26 (0.00)	30.21 (0.00)	43.75 (0.00)	44.44 (0.00)	40.00 (0.00)	60.00 (100.00)	19.37 (0.00)
	[1.87]*	[3.92]***	[3.20]***	[3.88]***	[1.43]	[1.57]	[1.66]
	51	103	58	28	4	6	3	669
Abnormal Uninsured Outflow (5%)	8.91 (0.00)	11.93 (0.00)	14.06 (0.00)	25.00 (0.00)	33.33 (0.00)	6.67 (0.00)	20.00 (0.00)	8.37 (0.00)
	[0.26]	[1.99]**	[2.22]**	[3.04]***	[1.50]	[-0.25]	[0.58]
	18	42	27	16	3	1	1	289

# Export
export_table(
  table5b, 
  "table5b_q1_2023_5pct",
  caption = "Pre-Crisis Bank Characteristics by Borrower Type: Q1 2023 (5\\% FHLB Threshold)",
  label = "tab:q1_2023_5pct",
  note = "Bank characteristics from 2022Q4 Call Reports. Q1 2023: January 1 -- March 31, 2023. FHLB borrowers identified as banks with abnormal FHLB advances at the 95th percentile. Sample excludes G-SIBs and failed banks."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table5b_q1_2023_5pct.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table5b_q1_2023_5pct.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table5b_q1_2023_5pct.csv

15 Table 6: Arbitrage Period - BTFP, DW, Both, Non-Borrowers

# ==============================================================================
# TABLE 6: Arbitrage Period (Nov 1, 2023 - Jan 24, 2024)
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE 6: Arbitrage Period - BTFP, DW, Both, Non-Borrowers\n")

## TABLE 6: Arbitrage Period - BTFP, DW, Both, Non-Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

arb_start <- as.Date("2023-11-01")
arb_end <- as.Date("2024-01-24")

btfp_arb <- btfp_loans_raw %>%
  filter(btfp_loan_date >= arb_start & btfp_loan_date <= arb_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

# Note: DW data ends 2023-12-31, so only partial coverage
dw_arb <- dw_loans_raw %>%
  filter(dw_loan_date >= arb_start & dw_loan_date <= min(arb_end, DW_DATA_END)) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

cat("BTFP borrowers (Arbitrage):", length(btfp_arb), "\n")

## BTFP borrowers (Arbitrage): 801

cat("DW borrowers (Arbitrage, partial):", length(dw_arb), "\n")

## DW borrowers (Arbitrage, partial): 481

cat("Note: DW data ends", as.character(DW_DATA_END), "\n")

## Note: DW data ends 2023-12-31

table6_data <- baseline_q %>%
  mutate(
    btfp_user = idrssd %in% btfp_arb,
    dw_user = idrssd %in% dw_arb,
    borrower_type = case_when(
      btfp_user & dw_user ~ "Both",
      btfp_user & !dw_user ~ "BTFP Only",
      !btfp_user & dw_user ~ "DW Only",
      TRUE ~ "Non-Borrower"
    )
  )

table6 <- create_summary_table_v2(table6_data, "borrower_type",
                                   group_levels = c("BTFP Only", "DW Only", "Both", "Non-Borrower"))

# Display
table6 %>%
  select(-Row_Type) %>%
  kable(caption = "Table 6: Pre-Crisis Bank Characteristics by Borrower Type (Arbitrage Period: Nov 1, 2023 - Jan 24, 2024)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table 6: Pre-Crisis Bank Characteristics by Borrower Type (Arbitrage Period: Nov 1, 2023 - Jan 24, 2024)

Variable	BTFP Only	DW Only	Both	Non-Borrower
N	623	302	143	3224
Book Equity Ratio	8.59 (8.31)	9.27 (9.17)	8.15 (8.08)	10.72 (9.01)
	[-9.58]***	[-5.74]***	[-8.93]***
	623	302	143	3224
MTM Loss (HTM)	0.25 (0.00)	0.17 (0.00)	0.21 (0.00)	0.20 (0.00)
	[1.37]	[-1.29]	[0.17]
	623	302	143	3214
MTM Loss (BTFP Elg.)	0.87 (0.60)	0.69 (0.51)	0.76 (0.49)	0.64 (0.36)
	[6.02]***	[1.15]	[1.74]*
	623	302	143	3214
MTM Loss (Total)	6.17 (5.93)	5.36 (5.22)	6.13 (6.12)	5.31 (5.16)
	[9.76]***	[0.38]	[4.53]***
	623	302	143	3214
Adjusted Equity	2.41 (2.28)	3.91 (3.75)	2.02 (1.93)	5.30 (3.93)
	[-11.47]***	[-4.44]***	[-8.39]***
	623	302	143	3214
Adjusted Equity < 0	26.97 (0.00)	15.56 (0.00)	32.17 (0.00)	17.55 (0.00)
	[4.95]***	[-0.90]	[3.68]***
	168	47	46	564
Adj. Tier1 Equity	4.48 (4.07)	5.23 (4.91)	3.71 (3.66)	6.87 (5.32)
	[-11.09]***	[-6.18]***	[-10.92]***
	623	302	143	3214
Adj. Tier1 Equity < 0	5.62 (0.00)	3.97 (0.00)	7.69 (0.00)	2.61 (0.00)
	[3.11]***	[1.17]	[2.25]**
	35	12	11	84
Adj. Common equity tier1	4.46 (4.07)	5.27 (4.93)	3.66 (3.63)	6.80 (5.31)
	[-10.99]***	[-5.77]***	[-10.75]***
	619	290	137	3180
Adj. Common equity tier1 < 0	5.82 (0.00)	3.79 (0.00)	8.03 (0.00)	2.52 (0.00)
	[3.36]***	[1.10]	[2.35]**
	36	11	11	80
Uninsured Leverage	25.43 (23.98)	26.41 (25.24)	28.22 (26.52)	22.79 (21.42)
	[5.35]***	[4.72]***	[5.19]***
	623	302	143	3224
MTM Insolvent	26.97 (0.00)	15.56 (0.00)	32.17 (0.00)	17.55 (0.00)
	[4.95]***	[-0.90]	[3.68]***
	168	47	46	564
Solvent and Liquid	11.88 (0.00)	18.21 (0.00)	8.39 (0.00)	25.31 (0.00)
	[-8.92]***	[-3.02]***	[-6.91]***
	74	55	12	815
Solvent and Illiquid	22.47 (0.00)	32.78 (0.00)	31.47 (0.00)	26.60 (0.00)
	[-2.24]**	[2.20]**	[1.22]
	140	99	45	856
Insolvent and Liquid	29.70 (0.00)	22.52 (0.00)	24.48 (0.00)	26.30 (0.00)
	[1.70]*	[-1.50]	[-0.50]
	185	68	35	847
Insolvent and Illiquid	35.96 (0.00)	26.49 (0.00)	35.66 (0.00)	21.57 (0.00)
	[7.00]***	[1.86]*	[3.45]***
	224	80	51	694
IDCR Insolvent (s=0.5)	4.82 (0.00)	3.97 (0.00)	2.80 (0.00)	4.18 (0.00)
	[0.69]	[-0.17]	[-0.97]
	30	12	4	133
IDCR Insolvent (s=1.0)	32.10 (0.00)	29.47 (0.00)	32.17 (0.00)	27.49 (0.00)
	[2.27]**	[0.72]	[1.17]
	200	89	46	875
Capital Insolvent (s=0.5)	1.77 (0.00)	4.97 (0.00)	5.59 (0.00)	1.87 (0.00)
	[-0.17]	[2.43]**	[1.92]*
	11	15	8	60
Capital Insolvent (s=1.0)	8.99 (0.00)	11.26 (0.00)	13.29 (0.00)	6.75 (0.00)
	[1.82]*	[2.40]**	[2.27]**
	56	34	19	217
Available Liquidity Ratio	5.33 (3.74)	7.07 (4.63)	4.96 (3.44)	10.47 (6.86)
	[-18.84]***	[-7.13]***	[-13.84]***
	623	302	143	3224
Change Uninsured Deposits (Fwd Q)	-1.15 (-5.82)	-5.39 (-6.07)	-2.18 (-3.91)	0.88 (-4.09)
	[-0.48]	[-2.26]**	[-1.08]
	623	301	143	3161
Change Insured Deposits (Fwd Q)	3.72 (1.65)	5.93 (2.87)	4.14 (1.81)	3.66 (1.31)
	[0.06]	[2.16]**	[0.39]
	623	302	143	3166
Abnormal Insured Outflow (10%)	7.38 (0.00)	4.97 (0.00)	7.69 (0.00)	7.75 (0.00)
	[-0.32]	[-2.08]**	[-0.03]
	46	15	11	250
Abnormal Insured Outflow (5%)	2.41 (0.00)	1.99 (0.00)	2.80 (0.00)	2.76 (0.00)
	[-0.52]	[-0.91]	[0.03]
	15	6	4	89
Abnormal Uninsured Outflow (10%)	26.97 (0.00)	27.48 (0.00)	23.78 (0.00)	19.76 (0.00)
	[3.77]***	[2.90]***	[1.10]
	168	83	34	637
Abnormal Uninsured Outflow (5%)	12.68 (0.00)	11.59 (0.00)	9.79 (0.00)	8.34 (0.00)
	[3.05]***	[1.70]*	[0.57]
	79	35	14	269

# Export
export_table(
  table6, 
  "table6_arbitrage_period",
  caption = "Pre-Crisis Bank Characteristics by Borrower Type: Arbitrage Period",
  label = "tab:arbitrage_period",
  note = "Bank characteristics from 2022Q4 Call Reports. Arbitrage period: November 1, 2023 -- January 24, 2024 (BTFP rate below IORB). Note: DW data available only through December 31, 2023. Sample excludes G-SIBs and failed banks."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table6_arbitrage_period.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table6_arbitrage_period.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table6_arbitrage_period.csv

# ==============================================================================
# TABLE 6b: Arbitrage Period (Nov 1, 2023 - Jan 24, 2024)
# UPDATED: Uses 2023Q4 baseline instead of 2022Q4
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE 6b: Arbitrage Period - BTFP, DW, Both, Non-Borrowers\n")

## TABLE 6b: Arbitrage Period - BTFP, DW, Both, Non-Borrowers

cat("Using 2023Q4 as baseline\n")

## Using 2023Q4 as baseline

cat(strrep("=", 60), "\n")

## ============================================================

# Create 2023Q4 baseline data
baseline_q4_2023 <- call_q %>%
  filter(period == '2023Q4') %>%
  filter(
    gsib == 0 | is.na(gsib),
    failed_bank == 0 | is.na(failed_bank),
    !is.na(omo_eligible) & omo_eligible > 0
  )



# Step 3: Calculate medians for cutoffs (BEFORE adding derived variables)
medians_q4 <- baseline_q4_2023 %>%
  summarise(
    median_uninsured = median(uninsured_deposit_to_total_asset, na.rm = TRUE),
    median_mtm = median(mtm_loss_to_total_asset, na.rm = TRUE),
    q75_uninsured = quantile(uninsured_deposit_to_total_asset, 0.75, na.rm = TRUE),
    q75_mtm = quantile(mtm_loss_to_total_asset, 0.75, na.rm = TRUE)
  )

cat("Median uninsured ratio:", round(medians_q4$median_uninsured, 2), "%\n")

## Median uninsured ratio: 19.94 %

cat("Median MTM loss ratio:", round(medians_q4$median_mtm, 2), "%\n")

## Median MTM loss ratio: 5.13 %

cat("Q75 uninsured ratio:", round(medians_q4$q75_uninsured, 2), "%\n")

## Q75 uninsured ratio: 27.17 %

cat("Q75 MTM loss ratio:", round(medians_q4$q75_mtm, 2), "%\n")

## Q75 MTM loss ratio: 6.81 %

# Step 4: Add ALL derived variables to baseline
baseline_q4_2023 <- baseline_q4_2023 %>%
  mutate(
    # Market value adjustment: (Book Asset / MV Asset) - 1
    
    cet1_ratio = safe_div(cet1, total_asset) * 100,
    mv_asset = mm_asset,
    mv_adjustment = if_else(mv_asset == 0 | is.na(mv_asset), NA_real_, 
                            (total_asset / mv_asset) - 1),
    
    # IDCR: (MV_Asset - s*Uninsured - Insured) / Insured
    idcr_s50 = safe_div(mv_asset - 0.5 * uninsured_deposit - insured_deposit, insured_deposit),
    idcr_s100 = safe_div(mv_asset - 1.0 * uninsured_deposit - insured_deposit, insured_deposit),
    
    # Capital Insolvency: (Equity - s*Uninsured*MVAdj) / Assets
    cap_insolvency_s50 = safe_div((total_asset - total_liability) - 0.5 * uninsured_deposit * mv_adjustment, total_asset),
    cap_insolvency_s100 = safe_div((total_asset - total_liability) - 1.0 * uninsured_deposit * mv_adjustment, total_asset),
    
    # Adjusted Equity (book equity ratio - MTM loss ratio, both in %)
    adjusted_equity = book_equity_to_total_asset - mtm_loss_to_total_asset,
    adjusted_tier1 = tier1cap_to_total_asset - mtm_loss_to_total_asset,
    adjusted_cet1 = cet1_ratio - mtm_loss_to_total_asset,
    
    # MTM Insolvent dummy (adjusted equity < 0)
    mtm_insolvent = as.integer(adjusted_equity < 0),
    mtm_insolvent_tier1 = as.integer(adjusted_tier1 < 0),
    mtm_insolvent_cet1 = as.integer(adjusted_cet1 < 0),
    
    adjusted_book_value = mm_asset - total_liability,
    
    mv_insolvent = as.integer(adjusted_book_value < 0),
    
    # Uninsured leverage (uninsured deposit / book equity)
    uninsured_leverage = uninsured_deposit_to_total_asset,
    liquidity_available = safe_div(cash + rerepo + fed_fund_sold, total_asset) * 100,
    
   
    
    # Run risk dummies
    # a. Solvent and Liquid (both uninsured and MTM below median)
    run_risk_1 = as.integer(
      uninsured_deposit_to_total_asset < medians_q4$median_uninsured & 
        mtm_loss_to_total_asset < medians_q4$median_mtm
    ),
    # b. Solvent and Illiquid
    run_risk_2 = as.integer(
      uninsured_deposit_to_total_asset > medians_q4$median_uninsured & 
        mtm_loss_to_total_asset < medians_q4$median_mtm
    ),
   # c. Insolvent and Liquid  
        run_risk_3 = as.integer(
      uninsured_deposit_to_total_asset < medians_q4$median_uninsured & 
        mtm_loss_to_total_asset > medians_q4$median_mtm
    ),
    # d. Insolvent and ILLiquid (both uninsured and MTM above median)  
        run_risk_4 = as.integer(
      uninsured_deposit_to_total_asset > medians_q4$median_uninsured & 
        mtm_loss_to_total_asset > medians_q4$median_mtm
    ),
    
    # IDCR insolvency dummies
    idcr_insolvent_s50 = as.integer(idcr_s50 < 0),
    idcr_insolvent_s100 = as.integer(idcr_s100 < 0),
    
    # Capital insolvency dummies
    cap_insolvent_s50 = as.integer(cap_insolvency_s50 < 0),
    cap_insolvent_s100 = as.integer(cap_insolvency_s100 < 0)
  ) %>%
  filter(
    gsib == 0 | is.na(gsib),
    failed_bank == 0 | is.na(failed_bank),
    !is.na(omo_eligible) & omo_eligible > 0)

cat("2023Q4 baseline observations:", nrow(baseline_q4_2023), "\n")

## 2023Q4 baseline observations: 4197

arb_start <- as.Date("2023-11-01")
arb_end <- as.Date("2024-01-24")

btfp_arb <- btfp_loans_raw %>%
  filter(btfp_loan_date >= arb_start & btfp_loan_date <= arb_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

# Note: DW data ends 2023-12-31, so only partial coverage
dw_arb <- dw_loans_raw %>%
  filter(dw_loan_date >= arb_start & dw_loan_date <= min(arb_end, DW_DATA_END)) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

cat("BTFP borrowers (Arbitrage):", length(btfp_arb), "\n")

## BTFP borrowers (Arbitrage): 801

cat("DW borrowers (Arbitrage, partial):", length(dw_arb), "\n")

## DW borrowers (Arbitrage, partial): 481

cat("Note: DW data ends", as.character(DW_DATA_END), "\n")

## Note: DW data ends 2023-12-31

table6b_data <- baseline_q4_2023 %>%
  mutate(
    btfp_user = idrssd %in% btfp_arb,
    dw_user = idrssd %in% dw_arb,
    borrower_type = case_when(
      btfp_user & dw_user ~ "Both",
      btfp_user & !dw_user ~ "BTFP Only",
      !btfp_user & dw_user ~ "DW Only",
      TRUE ~ "Non-Borrower"
    )
  )

table6b <- create_summary_table_v2(table6b_data, "borrower_type",
                                   group_levels = c("BTFP Only", "DW Only", "Both", "Non-Borrower"))

# Display
table6b %>%
  select(-Row_Type) %>%
  kable(caption = "Table 6b: Bank Characteristics by Borrower Type (Arbitrage Period: Nov 1, 2023 - Jan 24, 2024) - 2023Q4 Baseline") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table 6b: Bank Characteristics by Borrower Type (Arbitrage Period: Nov 1, 2023 - Jan 24, 2024) - 2023Q4 Baseline

Variable	BTFP Only	DW Only	Both	Non-Borrower
N	632	302	141	3122
Book Equity Ratio	9.06 (8.74)	9.84 (9.75)	8.75 (8.65)	11.46 (9.56)
	[-11.05]***	[-6.57]***	[-9.96]***
	632	302	141	3122
MTM Loss (HTM)	0.24 (0.00)	0.16 (0.00)	0.21 (0.00)	0.20 (0.00)
	[1.25]	[-1.40]	[0.17]
	632	302	141	3104
MTM Loss (BTFP Elg.)	0.84 (0.56)	0.66 (0.49)	0.75 (0.48)	0.63 (0.35)
	[5.61]***	[0.86]	[1.79]*
	632	302	141	3104
MTM Loss (Total)	5.92 (5.69)	5.17 (5.06)	5.86 (5.76)	5.16 (4.99)
	[8.61]***	[0.13]	[3.93]***
	632	302	141	3104
Adjusted Equity	3.14 (3.12)	4.66 (4.57)	2.89 (2.94)	6.11 (4.69)
	[-12.19]***	[-4.75]***	[-8.64]***
	632	302	141	3104
Adjusted Equity < 0	20.41 (0.00)	11.26 (0.00)	22.70 (0.00)	12.21 (0.00)
	[4.80]***	[-0.50]	[2.92]***
	129	34	32	379
Adj. Tier1 Equity	4.71 (4.40)	5.61 (5.24)	4.12 (3.97)	7.26 (5.71)
	[-12.25]***	[-6.36]***	[-10.83]***
	632	302	141	3104
Adj. Tier1 Equity < 0	3.32 (0.00)	3.64 (0.00)	7.09 (0.00)	2.16 (0.00)
	[1.53]	[1.34]	[2.26]**
	21	11	10	67
Adj. Common equity tier1	4.68 (4.38)	5.59 (5.25)	4.06 (3.87)	7.19 (5.69)
	[-12.15]***	[-6.17]***	[-10.63]***
	627	288	135	3072
Adj. Common equity tier1 < 0	3.51 (0.00)	3.47 (0.00)	7.41 (0.00)	2.15 (0.00)
	[1.74]*	[1.19]	[2.31]**
	22	10	10	66
Uninsured Leverage	22.06 (20.68)	22.63 (21.30)	24.60 (23.23)	20.84 (19.45)
	[2.71]***	[2.71]***	[3.84]***
	632	302	141	3122
MTM Insolvent	20.41 (0.00)	11.26 (0.00)	22.70 (0.00)	12.21 (0.00)
	[4.80]***	[-0.50]	[2.92]***
	129	34	32	379
Solvent and Liquid	15.82 (0.00)	21.85 (0.00)	14.18 (0.00)	25.50 (0.00)
	[-5.87]***	[-1.45]	[-3.71]***
	100	66	20	794
Solvent and Illiquid	22.94 (0.00)	29.80 (0.00)	26.95 (0.00)	26.86 (0.00)
	[-2.12]**	[1.07]	[0.02]
	145	90	38	836
Insolvent and Liquid	31.01 (0.00)	21.85 (0.00)	26.24 (0.00)	26.04 (0.00)
	[2.48]**	[-1.67]*	[0.05]
	196	66	37	811
Insolvent and Illiquid	30.06 (0.00)	26.49 (0.00)	32.62 (0.00)	21.27 (0.00)
	[4.47]***	[1.97]**	[2.82]***
	190	80	46	662
IDCR Insolvent (s=0.5)	10.92 (0.00)	15.56 (0.00)	10.64 (0.00)	11.64 (0.00)
	[-0.53]	[1.81]*	[-0.38]
	69	47	15	358
IDCR Insolvent (s=1.0)	34.81 (0.00)	37.75 (0.00)	41.13 (0.00)	36.36 (0.00)
	[-0.74]	[0.48]	[1.12]
	220	114	58	1118
Capital Insolvent (s=0.5)	3.80 (0.00)	5.63 (0.00)	5.67 (0.00)	3.41 (0.00)
	[0.46]	[1.62]	[1.14]
	24	17	8	106
Capital Insolvent (s=1.0)	12.03 (0.00)	12.91 (0.00)	17.73 (0.00)	10.76 (0.00)
	[0.90]	[1.07]	[2.13]**
	76	39	25	334
Available Liquidity Ratio	5.77 (4.25)	6.75 (4.95)	5.18 (4.11)	10.12 (7.25)
	[-16.63]***	[-7.98]***	[-13.57]***
	632	302	141	3122
Change Uninsured Deposits (Fwd Q)	0.81 (0.31)	0.62 (0.71)	2.17 (1.39)	20.41 (-0.31)
	[-1.27]	[-1.29]	[-1.19]
	629	301	141	3073
Change Insured Deposits (Fwd Q)	1.99 (1.09)	2.30 (1.74)	2.02 (1.00)	1.83 (1.23)
	[0.57]	[1.26]	[0.37]
	629	301	141	3076
Abnormal Insured Outflow (10%)	8.86 (0.00)	7.28 (0.00)	10.64 (0.00)	7.85 (0.00)
	[0.82]	[-0.36]	[1.05]
	56	22	15	245
Abnormal Insured Outflow (5%)	5.06 (0.00)	3.97 (0.00)	5.67 (0.00)	4.93 (0.00)
	[0.14]	[-0.81]	[0.37]
	32	12	8	154
Abnormal Uninsured Outflow (10%)	11.23 (0.00)	9.93 (0.00)	4.96 (0.00)	10.95 (0.00)
	[0.20]	[-0.56]	[-3.12]***
	71	30	7	342
Abnormal Uninsured Outflow (5%)	6.49 (0.00)	5.63 (0.00)	3.55 (0.00)	6.21 (0.00)
	[0.26]	[-0.42]	[-1.65]
	41	17	5	194

# Export
export_table(
  table6b, 
  "table6b_arbitrage_period_2023q4",
  caption = "Bank Characteristics by Borrower Type: Arbitrage Period (2023Q4 Baseline)",
  label = "tab:arbitrage_period_2023q4",
  note = "Bank characteristics from 2023Q4 Call Reports. Arbitrage period: November 1, 2023 -- January 24, 2024 (BTFP rate below IORB). Note: DW data available only through December 31, 2023. Sample excludes G-SIBs and failed banks."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table6b_arbitrage_period_2023q4.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table6b_arbitrage_period_2023q4.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table6b_arbitrage_period_2023q4.csv

16 Table 7: FHLB Borrower Characteristics (5% Threshold)

# ==============================================================================
# TABLE 7: FHLB Borrower Characteristics - 5% Threshold (Q1 2023)
# ==============================================================================

# First, identify banks with abnormal FHLB borrowing at 5% threshold
fhlb_borrower_5pct <- baseline_q %>%
  filter(!is.na(abnormal_fhlb_borrowing_5pct) & abnormal_fhlb_borrowing_5pct > 0) %>%
  pull(idrssd)

# Create FHLB summary table function with custom variables (WITH T-STATS)
create_fhlb_summary_table <- function(data, group_var, group_levels = NULL, reference_group = "Non-Borrower") {
  
  # Define variable specifications for FHLB table
  var_specs <- tribble(
    ~var_name, ~var_label, ~is_dummy,
    # Basic fundamentals
    "book_equity_to_total_asset", "Book Equity Ratio", FALSE,
    "loss_asset_htm_only", "MTM Loss (HTM)", FALSE,
    "mtm_loss_omo_eligible_to_total_asset", "MTM Loss (BTFP Elg.)", FALSE,
    "mtm_loss_to_total_asset", "MTM Loss (Total)", FALSE,
    "adjusted_equity", "Adjusted Equity", FALSE,
    "mtm_insolvent", "Adjusted Equity < 0", TRUE,
    "adjusted_tier1", "Adj. Tier1 Equity", FALSE,
    "mtm_insolvent_tier1", "Adj. Tier1 Equity < 0", TRUE,
    "adjusted_cet1", " Adj. Common equity tier1",FALSE, 
    "mtm_insolvent_cet1", "Adj. Common equity tier1 < 0", TRUE,
    "uninsured_leverage", "Uninsured Leverage", FALSE,
    "mtm_insolvent", "MTM Insolvent", TRUE,
    "run_risk_1", "Solvent and Liquid", TRUE,
    "run_risk_2", "Solvent and Illiquid", TRUE,
    "run_risk_3", "Insolvent and Liquid", TRUE,
    "run_risk_4", "Insolvent and Illiquid", TRUE,
    "idcr_insolvent_s50", "IDCR Insolvent (s=0.5)", TRUE,
    "idcr_insolvent_s100", "IDCR Insolvent (s=1.0)", TRUE,
    "cap_insolvent_s50", "Capital Insolvent (s=0.5)", TRUE,
    "cap_insolvent_s100", "Capital Insolvent (s=1.0)", TRUE,
    "liquidity_available", "Available Liquidity Ratio", FALSE,
    "change_uninsured_fwd_q", "Change Uninsured Deposits (Fwd Q)", FALSE,
    "change_insured_deposit_fwd_q", "Change Insured Deposits (Fwd Q)", FALSE,
    "abnormal_insured_outflow_10pct", "Abnormal Insured Outflow (10%)", TRUE,
    "abnormal_insured_outflow_5pct", "Abnormal Insured Outflow (5%)", TRUE,
    "abnormal_uninsured_outflow_10pct", "Abnormal Uninsured Outflow (10%)", TRUE,
    "abnormal_uninsured_outflow_5pct", "Abnormal Uninsured Outflow (5%)", TRUE
  )
  
  # Get unique groups
  if (is.null(group_levels)) {
    group_levels <- unique(data[[group_var]])
  }
  
  # Calculate N for each group
  n_by_group <- data %>%
    group_by(.data[[group_var]]) %>%
    summarise(N = n(), .groups = "drop")
  
  # Get reference group data for T-stat calculation
  ref_data <- data %>% filter(.data[[group_var]] == reference_group)
  
  # Initialize output list
  output_rows <- list()
  
  # Add N row first
  n_row <- c(Variable = "N", Row_Type = "count")
  for (g in group_levels) {
    n_val <- n_by_group %>% filter(.data[[group_var]] == g) %>% pull(N)
    n_row[g] <- as.character(if(length(n_val) == 0) 0 else n_val)
  }
  output_rows[[1]] <- n_row
  
  row_idx <- 2
  
  # Process each variable
  for (i in seq_len(nrow(var_specs))) {
    v <- var_specs$var_name[i]
    label <- var_specs$var_label[i]
    is_dummy <- var_specs$is_dummy[i]
    
    # Stats row: Mean (Median)
    stats_row <- c(Variable = label, Row_Type = "stats")
    # T-stat row (below stats)
    tstat_row <- c(Variable = "", Row_Type = "tstat")
    # N row (below t-stat)
    obs_row <- c(Variable = "", Row_Type = "n")
    
    # Get reference group values for T-stat
    ref_vals <- if (v %in% names(ref_data)) ref_data[[v]] else numeric(0)
    ref_vals <- ref_vals[!is.na(ref_vals)]
    
    for (g in group_levels) {
      group_data <- data %>% filter(.data[[group_var]] == g)
      
      # Check if variable exists
      if (!v %in% names(group_data)) {
        stats_row[g] <- "—"
        tstat_row[g] <- ""
        obs_row[g] <- "0"
        next
      }
      
      vals <- group_data[[v]]
      vals <- vals[!is.na(vals)]
      
      if (length(vals) == 0) {
        stats_row[g] <- "—"
        tstat_row[g] <- ""
        obs_row[g] <- "0"
      } else {
        mean_val <- mean(vals, na.rm = TRUE)
        median_val <- median(vals, na.rm = TRUE)
        
        if (is_dummy) {
          # For dummies: mean is proportion, show as percentage
          # N shows count of 1s (banks with dummy = 1)
          stats_row[g] <- sprintf("%.2f (%.2f)", mean_val * 100, median_val * 100)
          n_val <- sum(vals)  # Count of banks where dummy = 1
        } else if (v == "total_asset") {
          # For total assets: show in millions
          stats_row[g] <- sprintf("%.0f (%.0f)", mean_val / 1e6, median_val / 1e6)
          n_val <- length(vals)
        } else {
          stats_row[g] <- sprintf("%.2f (%.2f)", mean_val, median_val)
          n_val <- length(vals)  # Total non-NA observations
        }
        
        # Calculate T-stat vs reference group (skip for reference group itself)
        if (g == reference_group) {
          tstat_row[g] <- ""
        } else {
          t_stat <- calc_tstat(vals, ref_vals)
          if (!is.na(t_stat) && is.finite(t_stat)) {
            tstat_row[g] <- sprintf("[%.2f]", t_stat)
          } else {
            tstat_row[g] <- ""
          }
        }
        
        obs_row[g] <- as.character(n_val)
      }
    }
    
    output_rows[[row_idx]] <- stats_row
    output_rows[[row_idx + 1]] <- tstat_row
    output_rows[[row_idx + 2]] <- obs_row
    row_idx <- row_idx + 3
  }
  
  # Convert to data frame
  result_df <- bind_rows(lapply(output_rows, function(x) as_tibble_row(x)))
  
  return(result_df)
}

# Prepare data with FHLB variables (5% threshold)
table7_data <- baseline_q %>%
  mutate(
    # Categorize by FHLB usage (5% threshold)
    fhlb_user = idrssd %in% fhlb_borrower_5pct,
    btfp_user = idrssd %in% btfp_q1,
    dw_user = idrssd %in% dw_q1,
    borrower_type = case_when(
      fhlb_user & btfp_user & dw_user ~ "FHLB+BTFP+DW",
      fhlb_user & btfp_user & !dw_user ~ "FHLB+BTFP",
      fhlb_user & !btfp_user & dw_user ~ "FHLB+DW",
      fhlb_user & !btfp_user & !dw_user ~ "FHLB Only",
      !fhlb_user & (btfp_user | dw_user) ~ "Fed Only",
      TRUE ~ "Non-Borrower"
    )
  )

# Count by category
table7_data %>%
  count(borrower_type) %>%
  arrange(desc(n)) %>%
  pander(caption = "FHLB Borrower Breakdown (5% Threshold)")

FHLB Borrower Breakdown (5% Threshold)

borrower_type	n
Non-Borrower	3453
Fed Only	618
FHLB Only	192
FHLB+DW	15
FHLB+BTFP	9
FHLB+BTFP+DW	5

table7 <- create_fhlb_summary_table(table7_data, "borrower_type",
                                     group_levels = c("FHLB Only", "FHLB+BTFP", "FHLB+DW", 
                                                      "FHLB+BTFP+DW", "Fed Only", "Non-Borrower"))

# Display
table7 %>%
  select(-Row_Type) %>%
  kable(caption = "Table 7: Bank Characteristics by FHLB Borrower Type - 5% Threshold (Q1 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  scroll_box(width = "100%")

Table 7: Bank Characteristics by FHLB Borrower Type - 5% Threshold (Q1 2023)

Variable	FHLB Only	FHLB+BTFP	FHLB+DW	FHLB+BTFP+DW	Fed Only	Non-Borrower
N	192	9	15	5	618	3453
Book Equity Ratio	9.80 (9.36)	9.42 (8.67)	11.24 (10.89)	10.44 (9.62)	8.56 (8.36)	10.54 (8.90)
	[-2.66]	[-1.06]	[0.82]	[-0.11]	[-9.28]
	192	9	15	5	618	3453
MTM Loss (HTM)	0.16 (0.00)	0.18 (0.01)	0.17 (0.04)	0.11 (0.00)	0.23 (0.00)	0.20 (0.00)
	[-1.29]	[-0.21]	[-0.42]	[-1.03]	[0.85]
	192	9	15	5	618	3443
MTM Loss (BTFP Elg.)	0.52 (0.28)	0.64 (0.52)	0.57 (0.37)	0.35 (0.23)	0.84 (0.56)	0.66 (0.38)
	[-2.91]	[-0.12]	[-0.47]	[-2.27]	[4.74]
	192	9	15	5	618	3443
MTM Loss (Total)	5.60 (5.14)	6.59 (6.26)	5.00 (5.07)	6.35 (6.57)	5.83 (5.83)	5.39 (5.23)
	[1.27]	[2.11]	[-1.14]	[0.61]	[4.81]
	192	9	15	5	618	3443
Adjusted Equity	4.20 (4.30)	2.82 (1.53)	6.24 (6.33)	4.09 (3.04)	2.73 (2.47)	5.04 (3.79)
	[-2.42]	[-1.46]	[1.19]	[-0.40]	[-9.20]
	192	9	15	5	618	3443
Adjusted Equity < 0	12.50 (0.00)	33.33 (0.00)	0.00 (0.00)	40.00 (0.00)	23.62 (0.00)	18.88 (0.00)
	[-2.57]	[0.87]	[-28.30]	[0.86]	[2.59]
	24	3	0	2	146	650
Adj. Tier1 Equity	5.25 (5.01)	4.12 (3.78)	5.85 (5.39)	3.59 (4.63)	4.42 (3.99)	6.71 (5.23)
	[-5.16]	[-3.17]	[-1.09]	[-1.71]	[-10.67]
	192	9	15	5	618	3443
Adj. Tier1 Equity < 0	4.69 (0.00)	0.00 (0.00)	0.00 (0.00)	40.00 (0.00)	4.69 (0.00)	2.96 (0.00)
	[1.11]	[-10.25]	[-10.25]	[1.51]	[1.92]
	9	0	0	2	29	102
Adj. Common equity tier1	5.24 (5.00)	4.12 (3.78)	5.84 (5.39)	3.59 (4.63)	4.45 (4.00)	6.63 (5.21)
	[-4.96]	[-3.09]	[-1.01]	[-1.67]	[-10.26]
	191	9	15	5	595	3411
Adj. Common equity tier1 < 0	4.71 (0.00)	0.00 (0.00)	0.00 (0.00)	40.00 (0.00)	4.03 (0.00)	3.02 (0.00)
	[1.08]	[-10.30]	[-10.30]	[1.51]	[1.18]
	9	0	0	2	24	103
Uninsured Leverage	24.65 (23.17)	30.11 (26.15)	30.56 (30.05)	23.81 (22.44)	27.61 (25.53)	22.79 (21.40)
	[2.33]	[1.95]	[2.28]	[0.18]	[9.10]
	192	9	15	5	618	3453
MTM Insolvent	12.50 (0.00)	33.33 (0.00)	0.00 (0.00)	40.00 (0.00)	23.62 (0.00)	18.88 (0.00)
	[-2.57]	[0.87]	[-28.30]	[0.86]	[2.59]
	24	3	0	2	146	650
Solvent and Liquid	19.27 (0.00)	0.00 (0.00)	20.00 (0.00)	0.00 (0.00)	10.03 (0.00)	24.76 (0.00)
	[-1.86]	[-33.69]	[-0.44]	[-33.69]	[-10.41]
	37	0	3	0	62	854
Solvent and Illiquid	33.33 (0.00)	22.22 (0.00)	40.00 (0.00)	40.00 (0.00)	28.64 (0.00)	25.79 (0.00)
	[2.16]	[-0.24]	[1.08]	[0.58]	[1.45]
	64	2	6	2	177	889
Insolvent and Liquid	23.44 (0.00)	22.22 (0.00)	6.67 (0.00)	40.00 (0.00)	24.11 (0.00)	27.14 (0.00)
	[-1.17]	[-0.33]	[-3.05]	[0.52]	[-1.61]
	45	2	1	2	149	936
Insolvent and Illiquid	23.96 (0.00)	55.56 (100.00)	33.33 (0.00)	20.00 (0.00)	37.22 (0.00)	22.11 (0.00)
	[0.58]	[1.90]	[0.89]	[-0.11]	[7.30]
	46	5	5	1	230	762
IDCR Insolvent (s=0.5)	3.65 (0.00)	0.00 (0.00)	0.00 (0.00)	20.00 (0.00)	4.85 (0.00)	4.13 (0.00)
	[-0.35]	[-12.13]	[-12.13]	[0.79]	[0.78]
	7	0	0	1	30	141
IDCR Insolvent (s=1.0)	27.60 (0.00)	0.00 (0.00)	6.67 (0.00)	40.00 (0.00)	28.48 (0.00)	28.66 (0.00)
	[-0.32]	[-37.02]	[-3.28]	[0.46]	[-0.09]
	53	0	1	2	176	978
Capital Insolvent (s=0.5)	3.12 (0.00)	0.00 (0.00)	0.00 (0.00)	20.00 (0.00)	3.07 (0.00)	1.98 (0.00)
	[0.90]	[-8.33]	[-8.33]	[0.90]	[1.50]
	6	0	0	1	19	68
Capital Insolvent (s=1.0)	7.81 (0.00)	0.00 (0.00)	0.00 (0.00)	20.00 (0.00)	11.33 (0.00)	6.97 (0.00)
	[0.42]	[-16.06]	[-16.06]	[0.65]	[3.23]
	15	0	0	1	70	240
Available Liquidity Ratio	5.35 (4.38)	3.95 (3.59)	5.66 (4.04)	3.24 (3.59)	5.76 (3.57)	10.19 (6.66)
	[-15.40]	[-6.73]	[-3.94]	[-9.94]	[-14.39]
	192	9	15	5	618	3453
Change Uninsured Deposits (Fwd Q)	-6.10 (-6.21)	-14.10 (-13.96)	-3.95 (-4.59)	-11.14 (-12.22)	-6.06 (-6.30)	1.56 (-3.89)
	[-2.83]	[-4.72]	[-1.51]	[-3.57]	[-3.01]
	192	9	15	5	616	3391
Change Insured Deposits (Fwd Q)	1.49 (0.78)	1.07 (0.75)	4.47 (3.66)	6.42 (4.81)	4.73 (2.05)	3.82 (1.43)
	[-2.94]	[-1.49]	[0.33]	[0.80]	[1.03]
	192	9	15	5	616	3397
Abnormal Insured Outflow (10%)	8.33 (0.00)	11.11 (0.00)	0.00 (0.00)	0.00 (0.00)	7.77 (0.00)	7.44 (0.00)
	[0.43]	[0.33]	[-16.66]	[-16.66]	[0.28]
	16	1	0	0	48	257
Abnormal Insured Outflow (5%)	2.08 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	2.59 (0.00)	2.72 (0.00)
	[-0.60]	[-9.83]	[-9.83]	[-9.83]	[-0.19]
	4	0	0	0	16	94
Abnormal Uninsured Outflow (10%)	30.21 (0.00)	44.44 (0.00)	40.00 (0.00)	60.00 (100.00)	29.45 (0.00)	19.37 (0.00)
	[3.20]	[1.43]	[1.57]	[1.66]	[5.16]
	58	4	6	3	182	669
Abnormal Uninsured Outflow (5%)	14.06 (0.00)	33.33 (0.00)	6.67 (0.00)	20.00 (0.00)	12.30 (0.00)	8.37 (0.00)
	[2.22]	[1.50]	[-0.25]	[0.58]	[2.80]
	27	3	1	1	76	289

# Export (silently)
invisible(export_table(
  table7, 
  "table7_fhlb_borrowers_5pct",
  caption = "Bank Characteristics by FHLB Borrower Type: Q1 2023 (5\\% Threshold)",
  label = "tab:fhlb_borrowers_5pct",
  note = "Bank characteristics from 2022Q4 Call Reports. FHLB borrowers identified as banks with abnormal FHLB advances at the 95th percentile in 2023Q1. Abnormal FHLB borrowing is measured using Z-scores calculated as (current quarter change $-$ mean) / std, where mean and standard deviation are computed from the prior 8 quarters (2021Q1--2022Q4). Abnormal FHLB (5\\%) indicates Z $>$ 1.65 (top 5 percentile). Fed Only = BTFP and/or DW but no abnormal FHLB increase. Sample excludes G-SIBs and failed banks."
))

LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table7_fhlb_borrowers_5pct.tex HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table7_fhlb_borrowers_5pct.html CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table7_fhlb_borrowers_5pct.csv

17 Table 8: FHLB Borrower Characteristics (10% Threshold)

# ==============================================================================
# TABLE 8: FHLB Borrower Characteristics - 10% Threshold (Q1 2023)
# ==============================================================================

# Identify banks with abnormal FHLB borrowing at 10% threshold
fhlb_borrower_10pct <- baseline_q %>%
  filter(!is.na(abnormal_fhlb_borrowing_10pct) & abnormal_fhlb_borrowing_10pct > 0) %>%
  pull(idrssd)

# Prepare data with FHLB variables (10% threshold)
table8_data <- baseline_q %>%
  mutate(
    # Categorize by FHLB usage (10% threshold)
    fhlb_user = idrssd %in% fhlb_borrower_10pct,
    btfp_user = idrssd %in% btfp_q1,
    dw_user = idrssd %in% dw_q1,
    borrower_type = case_when(
      fhlb_user & btfp_user & dw_user ~ "FHLB+BTFP+DW",
      fhlb_user & btfp_user & !dw_user ~ "FHLB+BTFP",
      fhlb_user & !btfp_user & dw_user ~ "FHLB+DW",
      fhlb_user & !btfp_user & !dw_user ~ "FHLB Only",
      !fhlb_user & (btfp_user | dw_user) ~ "Fed Only",
      TRUE ~ "Non-Borrower"
    )
  )

# Count by category
table8_data %>%
  count(borrower_type) %>%
  arrange(desc(n)) %>%
  pander(caption = "FHLB Borrower Breakdown (10% Threshold)")

FHLB Borrower Breakdown (10% Threshold)

borrower_type	n
Non-Borrower	3385
Fed Only	605
FHLB Only	260
FHLB+DW	21
FHLB+BTFP	12
FHLB+BTFP+DW	9

table8 <- create_fhlb_summary_table(table8_data, "borrower_type",
                                     group_levels = c("FHLB Only", "FHLB+BTFP", "FHLB+DW", 
                                                      "FHLB+BTFP+DW", "Fed Only", "Non-Borrower"))

# Display
table8 %>%
  select(-Row_Type) %>%
  kable(caption = "Table 8: Bank Characteristics by FHLB Borrower Type - 10% Threshold (Q1 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  scroll_box(width = "100%")

Table 8: Bank Characteristics by FHLB Borrower Type - 10% Threshold (Q1 2023)

Variable	FHLB Only	FHLB+BTFP	FHLB+DW	FHLB+BTFP+DW	Fed Only	Non-Borrower
N	260	12	21	9	605	3385
Book Equity Ratio	9.82 (9.28)	8.89 (8.36)	11.00 (10.55)	9.36 (9.47)	8.55 (8.35)	10.55 (8.90)
	[-2.78]	[-1.80]	[0.68]	[-1.75]	[-9.22]
	260	12	21	9	605	3385
MTM Loss (HTM)	0.14 (0.00)	0.15 (0.00)	0.12 (0.00)	0.08 (0.00)	0.23 (0.00)	0.20 (0.00)
	[-2.33]	[-0.81]	[-1.37]	[-2.33]	[0.90]
	260	12	21	9	605	3375
MTM Loss (BTFP Elg.)	0.53 (0.28)	0.77 (0.87)	0.46 (0.23)	0.54 (0.34)	0.85 (0.56)	0.66 (0.39)
	[-3.19]	[0.75]	[-1.39]	[-0.80]	[4.74]
	260	12	21	9	605	3375
MTM Loss (Total)	5.69 (5.26)	6.37 (6.54)	5.13 (5.07)	6.10 (5.66)	5.83 (5.83)	5.38 (5.23)
	[2.13]	[1.96]	[-0.65]	[0.79]	[4.92]
	260	12	21	9	605	3375
Adjusted Equity	4.13 (4.12)	2.52 (1.53)	5.87 (6.33)	3.26 (3.04)	2.72 (2.44)	5.07 (3.79)
	[-2.93]	[-1.96]	[0.94]	[-1.33]	[-9.19]
	260	12	21	9	605	3375
Adjusted Equity < 0	14.23 (0.00)	33.33 (0.00)	4.76 (0.00)	33.33 (0.00)	23.64 (0.00)	18.87 (0.00)
	[-2.04]	[1.02]	[-2.93]	[0.87]	[2.57]
	37	4	1	3	143	637
Adj. Tier1 Equity	5.32 (5.01)	4.13 (3.49)	5.61 (5.57)	3.31 (4.04)	4.43 (3.99)	6.73 (5.24)
	[-5.29]	[-3.69]	[-1.70]	[-3.27]	[-10.58]
	260	12	21	9	605	3375
Adj. Tier1 Equity < 0	5.00 (0.00)	0.00 (0.00)	0.00 (0.00)	22.22 (0.00)	4.79 (0.00)	2.90 (0.00)
	[1.51]	[-10.04]	[-10.04]	[1.31]	[2.06]
	13	0	0	2	29	98
Adj. Common equity tier1	5.31 (5.01)	3.83 (3.21)	5.61 (5.57)	3.31 (4.04)	4.46 (4.00)	6.66 (5.21)
	[-5.06]	[-4.06]	[-1.59]	[-3.20]	[-10.15]
	258	11	21	9	583	3344
Adj. Common equity tier1 < 0	5.04 (0.00)	0.00 (0.00)	0.00 (0.00)	22.22 (0.00)	4.12 (0.00)	2.96 (0.00)
	[1.49]	[-10.10]	[-10.10]	[1.31]	[1.32]
	13	0	0	2	24	99
Uninsured Leverage	23.71 (22.82)	31.75 (28.72)	30.18 (30.05)	28.58 (31.00)	27.51 (25.38)	22.82 (21.42)
	[1.26]	[2.73]	[2.62]	[1.26]	[8.75]
	260	12	21	9	605	3385
MTM Insolvent	14.23 (0.00)	33.33 (0.00)	4.76 (0.00)	33.33 (0.00)	23.64 (0.00)	18.87 (0.00)
	[-2.04]	[1.02]	[-2.93]	[0.87]	[2.57]
	37	4	1	3	143	637
Solvent and Liquid	19.62 (0.00)	0.00 (0.00)	14.29 (0.00)	0.00 (0.00)	10.25 (0.00)	24.84 (0.00)
	[-2.03]	[-33.43]	[-1.34]	[-33.43]	[-10.13]
	51	0	3	0	62	840
Solvent and Illiquid	30.38 (0.00)	25.00 (0.00)	42.86 (0.00)	44.44 (0.00)	28.26 (0.00)	25.87 (0.00)
	[1.53]	[-0.07]	[1.53]	[1.06]	[1.21]
	79	3	9	4	171	874
Insolvent and Liquid	26.92 (0.00)	16.67 (0.00)	14.29 (0.00)	33.33 (0.00)	24.13 (0.00)	26.94 (0.00)
	[-0.01]	[-0.91]	[-1.61]	[0.38]	[-1.48]
	70	2	3	3	146	911
Insolvent and Illiquid	23.08 (0.00)	58.33 (100.00)	28.57 (0.00)	22.22 (0.00)	37.36 (0.00)	22.14 (0.00)
	[0.35]	[2.43]	[0.64]	[0.01]	[7.27]
	60	7	6	2	226	748
IDCR Insolvent (s=0.5)	4.62 (0.00)	0.00 (0.00)	4.76 (0.00)	11.11 (0.00)	4.79 (0.00)	4.07 (0.00)
	[0.41]	[-11.90]	[0.15]	[0.63]	[0.78]
	12	0	1	1	29	136
IDCR Insolvent (s=1.0)	27.31 (0.00)	8.33 (0.00)	14.29 (0.00)	33.33 (0.00)	28.43 (0.00)	28.71 (0.00)
	[-0.49]	[-2.43]	[-1.83]	[0.28]	[-0.14]
	71	1	3	3	172	960
Capital Insolvent (s=0.5)	3.08 (0.00)	0.00 (0.00)	4.76 (0.00)	11.11 (0.00)	2.98 (0.00)	1.96 (0.00)
	[1.02]	[-8.20]	[0.59]	[0.82]	[1.39]
	8	0	1	1	18	66
Capital Insolvent (s=1.0)	7.31 (0.00)	0.00 (0.00)	4.76 (0.00)	11.11 (0.00)	11.40 (0.00)	6.99 (0.00)
	[0.19]	[-15.93]	[-0.47]	[0.37]	[3.23]
	19	0	1	1	69	236
Available Liquidity Ratio	5.43 (4.31)	4.30 (3.76)	4.95 (3.49)	2.87 (2.22)	5.81 (3.57)	10.28 (6.70)
	[-16.41]	[-6.25]	[-6.11]	[-12.18]	[-14.28]
	260	12	21	9	605	3385
Change Uninsured Deposits (Fwd Q)	-6.08 (-7.06)	-10.34 (-12.20)	-3.51 (-4.59)	-17.22 (-12.22)	-6.01 (-6.29)	1.71 (-3.71)
	[-2.85]	[-2.43]	[-1.54]	[-2.78]	[-3.00]
	260	12	21	9	603	3323
Change Insured Deposits (Fwd Q)	1.92 (0.96)	6.32 (0.94)	4.53 (3.66)	9.39 (7.84)	4.59 (2.02)	3.84 (1.42)
	[-2.27]	[0.45]	[0.44]	[2.14]	[0.84]
	260	12	21	9	603	3329
Abnormal Insured Outflow (10%)	9.62 (0.00)	8.33 (0.00)	0.00 (0.00)	0.00 (0.00)	7.93 (0.00)	7.33 (0.00)
	[1.21]	[0.12]	[-16.36]	[-16.36]	[0.51]
	25	1	0	0	48	248
Abnormal Insured Outflow (5%)	2.69 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	2.64 (0.00)	2.69 (0.00)
	[0.00]	[-9.67]	[-9.67]	[-9.67]	[-0.06]
	7	0	0	0	16	91
Abnormal Uninsured Outflow (10%)	29.23 (0.00)	41.67 (0.00)	38.10 (0.00)	55.56 (100.00)	29.26 (0.00)	19.23 (0.00)
	[3.44]	[1.51]	[1.73]	[2.07]	[5.09]
	76	5	8	5	177	651
Abnormal Uninsured Outflow (5%)	12.69 (0.00)	33.33 (0.00)	9.52 (0.00)	22.22 (0.00)	12.07 (0.00)	8.36 (0.00)
	[2.04]	[1.76]	[0.18]	[0.94]	[2.63]
	33	4	2	2	73	283

# Export (silently)
invisible(export_table(
  table8, 
  "table8_fhlb_borrowers_10pct",
  caption = "Bank Characteristics by FHLB Borrower Type: Q1 2023 (10\\% Threshold)",
  label = "tab:fhlb_borrowers_10pct",
  note = "Bank characteristics from 2022Q4 Call Reports. FHLB borrowers identified as banks with abnormal FHLB advances at the 90th percentile in 2023Q1. Abnormal FHLB borrowing is measured using Z-scores calculated as (current quarter change $-$ mean) / std, where mean and standard deviation are computed from the prior 8 quarters (2021Q1--2022Q4). Abnormal FHLB (10\\%) indicates Z $>$ 1.28 (top 10 percentile). Fed Only = BTFP and/or DW but no abnormal FHLB increase. Sample excludes G-SIBs and failed banks."
))

LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table8_fhlb_borrowers_10pct.tex HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table8_fhlb_borrowers_10pct.html CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table8_fhlb_borrowers_10pct.csv

18 Summary Statistics

# ==============================================================================
# SUMMARY OF BORROWER COUNTS
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("SUMMARY OF BORROWER COUNTS\n")

## SUMMARY OF BORROWER COUNTS

cat(strrep("=", 60), "\n")

## ============================================================

summary_counts <- tribble(
  ~Table, ~Period, ~Baseline, ~Description, ~N_Borrowers, ~N_Total,
  "Table 1", "March 10, 2023", "2022Q4", "DW only", 
    sum(baseline_q$idrssd %in% dw_mar10), nrow(baseline_q),
  "Table 2", "March 10-13, 2023", "2022Q4", "DW only", 
    sum(baseline_q$idrssd %in% dw_mar10_13), nrow(baseline_q),
  "Table 3", "March 9-14, 2023", "2022Q4", "DW only", 
    sum(baseline_q$idrssd %in% dw_mar9_14), nrow(baseline_q),
  "Table 4", "Acute (Mar 13 - May 1)", "2022Q4", "BTFP + DW", 
    sum(baseline_q$idrssd %in% union(btfp_acute, dw_acute)), nrow(baseline_q),
  "Table 5A", "Q1 2023 (Jan-Mar)", "2022Q4", "BTFP + DW + FHLB (10%)", 
    sum(baseline_q$idrssd %in% union(union(btfp_q1, dw_q1), fhlb_borrowers_10pct)), nrow(baseline_q),
  "Table 5B", "Q1 2023 (Jan-Mar)", "2022Q4", "BTFP + DW + FHLB (5%)", 
    sum(baseline_q$idrssd %in% union(union(btfp_q1, dw_q1), fhlb_borrowers_5pct)), nrow(baseline_q),
  "Table 6", "Arbitrage (Nov 1 - Jan 24)", "2022Q4", "BTFP + DW", 
    sum(baseline_q$idrssd %in% union(btfp_arb, dw_arb)), nrow(baseline_q),
  "Table 6b", "Arbitrage (Nov 1 - Jan 24)", "2023Q4", "BTFP + DW", 
    sum(baseline_q4_2023$idrssd %in% union(btfp_arb, dw_arb)), nrow(baseline_q4_2023),
  "Table 7", "Q1 2023", "2022Q4", "FHLB focus (5%)", 
    sum(baseline_q$idrssd %in% fhlb_borrower_5pct), nrow(baseline_q),
  "Table 8", "Q1 2023", "2022Q4", "FHLB focus (10%)", 
    sum(baseline_q$idrssd %in% fhlb_borrower_10pct), nrow(baseline_q)
)

summary_counts %>%
  kable(caption = "Summary of Borrower Counts Across Tables") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Summary of Borrower Counts Across Tables

Table	Period	Baseline	Description	N_Borrowers	N_Total
Table 1	March 10, 2023	2022Q4	DW only	47	4292
Table 2	March 10-13, 2023	2022Q4	DW only	90	4292
Table 3	March 9-14, 2023	2022Q4	DW only	124	4292
Table 4	Acute (Mar 13 - May 1)	2022Q4	BTFP + DW	761	4292
Table 5A	Q1 2023 (Jan-Mar)	2022Q4	BTFP + DW + FHLB (10%)	907	4292
Table 5B	Q1 2023 (Jan-Mar)	2022Q4	BTFP + DW + FHLB (5%)	839	4292
Table 6	Arbitrage (Nov 1 - Jan 24)	2022Q4	BTFP + DW	1068	4292
Table 6b	Arbitrage (Nov 1 - Jan 24)	2023Q4	BTFP + DW	1075	4197
Table 7	Q1 2023	2022Q4	FHLB focus (5%)	221	4292
Table 8	Q1 2023	2022Q4	FHLB focus (10%)	302	4292

# Save summary
write_csv(summary_counts, file.path(TABLE_PATH, "summary_borrower_counts.csv"))

cat("\n=== ALL TABLES EXPORTED ===\n")

## 
## === ALL TABLES EXPORTED ===

cat("Output directory:", TABLE_PATH, "\n")

## Output directory: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables

cat("Files created:\n")

## Files created:

list.files(TABLE_PATH, pattern = "\\.(tex|html|csv)$") %>%
  paste("  -", .) %>%
  cat(sep = "\n")

##   - borrower_size_insolvency_2024q1.tex
##   - borrower_size_insolvency_acute.tex
##   - insolvency_size_2024q1.tex
##   - insolvency_size_acute.tex
##   - summary_borrower_counts.csv
##   - table_beta1_dw_mar10.csv
##   - table_beta1_dw_mar10.html
##   - table_beta1_dw_mar10.tex
##   - table_beta2_dw_mar10_13.csv
##   - table_beta2_dw_mar10_13.html
##   - table_beta2_dw_mar10_13.tex
##   - table_beta3_acute.csv
##   - table_beta3_acute.html
##   - table_beta3_acute.tex
##   - table_beta4_q1_2023.csv
##   - table_beta4_q1_2023.html
##   - table_beta4_q1_2023.tex
##   - table_beta5_arbitrage.csv
##   - table_beta5_arbitrage.html
##   - table_beta5_arbitrage.tex
##   - table1_dw_mar10.csv
##   - table1_dw_mar10.html
##   - table1_dw_mar10.tex
##   - table2_dw_mar10_13.csv
##   - table2_dw_mar10_13.html
##   - table2_dw_mar10_13.tex
##   - table3_dw_mar9_14.csv
##   - table3_dw_mar9_14.html
##   - table3_dw_mar9_14.tex
##   - table4_acute_period.csv
##   - table4_acute_period.html
##   - table4_acute_period.tex
##   - table5a_q1_2023_10pct.csv
##   - table5a_q1_2023_10pct.html
##   - table5a_q1_2023_10pct.tex
##   - table5b_q1_2023_5pct.csv
##   - table5b_q1_2023_5pct.html
##   - table5b_q1_2023_5pct.tex
##   - table6_arbitrage_period.csv
##   - table6_arbitrage_period.html
##   - table6_arbitrage_period.tex
##   - table6b_arbitrage_period_2023q4.csv
##   - table6b_arbitrage_period_2023q4.html
##   - table6b_arbitrage_period_2023q4.tex
##   - table7_fhlb_borrowers_5pct.csv
##   - table7_fhlb_borrowers_5pct.html
##   - table7_fhlb_borrowers_5pct.tex
##   - table8_fhlb_borrowers_10pct.csv
##   - table8_fhlb_borrowers_10pct.html
##   - table8_fhlb_borrowers_10pct.tex
##   - variable_definitions.csv
##   - variable_definitions.tex

# ==============================================================================
# DEPOSIT BETA ANALYSIS
# ==============================================================================

# Load deposit beta data
deposit_beta <- read_csv(file.path(DATA_PROC, "deposit_beta_yearly.csv"), show_col_types = FALSE) %>%
  mutate(rssd_id = as.character(rssd_id))

cat("Deposit beta observations:", nrow(deposit_beta), "\n")

## Deposit beta observations: 4781

# Merge with baseline data
baseline_q <- baseline_q %>%
  left_join(deposit_beta, by = c("idrssd" = "rssd_id"))

cat("Banks with deposit beta data:", sum(!is.na(baseline_q$beta_2022)), "\n")

## Banks with deposit beta data: 4221

# ==============================================================================
# DEPOSIT BETA SUMMARY TABLE FUNCTION
# ==============================================================================

create_deposit_beta_table <- function(data, group_var, group_levels = NULL, reference_group = "Non-Borrower") {
  
  # Define variable specifications for deposit beta
  var_specs <- tribble(
    ~var_name, ~var_label, ~is_dummy,
    # 2022 Betas (Pre-Crisis)
    "beta_2022", "Deposit Beta (2022)", FALSE,
    "rsquared_2022", "R-squared (2022)", FALSE,
    
    
    # 2023 Betas (Crisis Year)
    "beta_2023", "Deposit Beta (2023)", FALSE,
    "rsquared_2023", "R-squared (2023)", FALSE,
    
    
    # 2024 Betas (Post-Crisis)
    "beta_2024", "Deposit Beta (2024)", FALSE,
    "rsquared_2024", "R-squared (2024)", FALSE,
   
    
    # Beta Changes
    "beta_change_2022_2023", "Δ Beta (2022→2023)", FALSE,
    "beta_change_2023_2024", "Δ Beta (2023→2024)", FALSE,
    "beta_change_2022_2024", "Δ Beta (2022→2024)", FALSE
  )
  
  # Get unique groups
  if (is.null(group_levels)) {
    group_levels <- unique(data[[group_var]])
  }
  
  # Calculate N for each group (with beta data)
  n_by_group <- data %>%
    filter(!is.na(beta_2022)) %>%
    group_by(.data[[group_var]]) %>%
    summarise(N = n(), .groups = "drop")
  
  # Get reference group data for T-stat calculation
  ref_data <- data %>% filter(.data[[group_var]] == reference_group)
  
  # Initialize output list
  output_rows <- list()
  
  # Add N row first
  n_row <- c(Variable = "N (with beta data)", Row_Type = "count")
  for (g in group_levels) {
    n_val <- n_by_group %>% filter(.data[[group_var]] == g) %>% pull(N)
    n_row[g] <- as.character(if(length(n_val) == 0) 0 else n_val)
  }
  output_rows[[1]] <- n_row
  
  row_idx <- 2
  
  # Process each variable
  for (i in seq_len(nrow(var_specs))) {
    v <- var_specs$var_name[i]
    label <- var_specs$var_label[i]
    is_dummy <- var_specs$is_dummy[i]
    
    # Stats row: Mean (Median)
    stats_row <- c(Variable = label, Row_Type = "stats")
    # T-stat row
    tstat_row <- c(Variable = "", Row_Type = "tstat")
    # N row
    obs_row <- c(Variable = "", Row_Type = "n")
    
    # Get reference group values
    ref_vals <- if (v %in% names(ref_data)) ref_data[[v]] else numeric(0)
    ref_vals <- ref_vals[!is.na(ref_vals)]
    
    for (g in group_levels) {
      group_data <- data %>% filter(.data[[group_var]] == g)
      
      if (!v %in% names(group_data)) {
        stats_row[g] <- "—"
        tstat_row[g] <- ""
        obs_row[g] <- "0"
        next
      }
      
      vals <- group_data[[v]]
      vals <- vals[!is.na(vals)]
      
      if (length(vals) == 0) {
        stats_row[g] <- "—"
        tstat_row[g] <- ""
        obs_row[g] <- "0"
      } else {
        mean_val <- mean(vals, na.rm = TRUE)
        median_val <- median(vals, na.rm = TRUE)
        n_val <- length(vals)
        
        stats_row[g] <- sprintf("%.3f (%.3f)", mean_val, median_val)
        
        if (g == reference_group) {
          tstat_row[g] <- ""
        } else {
          tstat_result <- calc_tstat_with_stars(vals, ref_vals)
          if (!is.na(tstat_result$tstat) && is.finite(tstat_result$tstat)) {
            tstat_row[g] <- sprintf("[%.2f]%s", tstat_result$tstat, tstat_result$stars)
          } else {
            tstat_row[g] <- ""
          }
        }
        
        obs_row[g] <- as.character(n_val)
      }
    }
    
    output_rows[[row_idx]] <- stats_row
    output_rows[[row_idx + 1]] <- tstat_row
    output_rows[[row_idx + 2]] <- obs_row
    row_idx <- row_idx + 3
  }
  
  result_df <- bind_rows(lapply(output_rows, function(x) as_tibble_row(x)))
  return(result_df)
}

# ==============================================================================
# CREATE BETA CHANGE VARIABLES
# ==============================================================================

baseline_q <- baseline_q %>%
  mutate(
    beta_change_2022_2023 = beta_2023 - beta_2022,
    beta_change_2023_2024 = beta_2024 - beta_2023,
    beta_change_2022_2024 = beta_2024 - beta_2022
  )

# ==============================================================================
# TABLE B1: Deposit Beta - March 10, 2023 DW Borrowers
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE B1: Deposit Beta - March 10, 2023 DW Borrowers\n")

## TABLE B1: Deposit Beta - March 10, 2023 DW Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

table_beta1_data <- baseline_q %>%
  mutate(
    borrower_type = if_else(idrssd %in% dw_mar10, "DW Borrower", "Non-Borrower")
  )

table_beta1 <- create_deposit_beta_table(table_beta1_data, "borrower_type",
                                          group_levels = c("DW Borrower", "Non-Borrower"))

table_beta1 %>%
  select(-Row_Type) %>%
  kable(caption = "Table B1: Deposit Beta - DW Borrowers vs Non-Borrowers (March 10, 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table B1: Deposit Beta - DW Borrowers vs Non-Borrowers (March 10, 2023)

Variable	DW Borrower	Non-Borrower
N (with beta data)	47	4174
Deposit Beta (2022)	0.314 (0.246)	0.213 (0.170)
	[2.38]**
	47	4174
R-squared (2022)	0.676 (0.746)	0.609 (0.619)
	[1.75]*
	47	4174
Deposit Beta (2023)	0.251 (0.232)	0.132 (0.103)
	[2.60]**
	47	4109
R-squared (2023)	0.462 (0.462)	0.430 (0.403)
	[0.64]
	47	4109
Deposit Beta (2024)	0.336 (0.315)	0.277 (0.265)
	[1.34]
	46	4025
R-squared (2024)	0.534 (0.651)	0.542 (0.592)
	[-0.16]
	46	4025
Δ Beta (2022→2023)	-0.063 (-0.056)	-0.081 (-0.077)
	[0.45]
	47	4085
Δ Beta (2023→2024)	0.093 (0.117)	0.147 (0.153)
	[-0.91]
	46	4010
Δ Beta (2022→2024)	0.022 (0.025)	0.066 (0.072)
	[-0.81]
	46	3994

export_table(
  table_beta1,
  "table_beta1_dw_mar10",
  caption = "Deposit Beta: DW Borrowers vs Non-Borrowers (March 10, 2023)",
  label = "tab:beta_dw_mar10",
  note = "Deposit beta measures interest rate sensitivity of deposits. Higher beta indicates more rate-sensitive deposits. Beta estimated from quarterly deposit growth regressed on Fed Funds rate changes."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta1_dw_mar10.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta1_dw_mar10.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta1_dw_mar10.csv

# ==============================================================================
# TABLE B2: Deposit Beta - March 10-13, 2023 DW Borrowers
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE B2: Deposit Beta - March 10-13, 2023 DW Borrowers\n")

## TABLE B2: Deposit Beta - March 10-13, 2023 DW Borrowers

cat(strrep("=", 60), "\n")

## ============================================================

table_beta2_data <- baseline_q %>%
  mutate(
    borrower_type = if_else(idrssd %in% dw_mar10_13, "DW Borrower", "Non-Borrower")
  )

table_beta2 <- create_deposit_beta_table(table_beta2_data, "borrower_type",
                                          group_levels = c("DW Borrower", "Non-Borrower"))

table_beta2 %>%
  select(-Row_Type) %>%
  kable(caption = "Table B2: Deposit Beta - DW Borrowers vs Non-Borrowers (March 10-13, 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table B2: Deposit Beta - DW Borrowers vs Non-Borrowers (March 10-13, 2023)

Variable	DW Borrower	Non-Borrower
N (with beta data)	90	4131
Deposit Beta (2022)	0.309 (0.271)	0.212 (0.169)
	[3.85]***
	90	4131
R-squared (2022)	0.686 (0.720)	0.608 (0.617)
	[3.10]***
	90	4131
Deposit Beta (2023)	0.267 (0.218)	0.131 (0.102)
	[4.34]***
	90	4066
R-squared (2023)	0.457 (0.460)	0.429 (0.402)
	[0.75]
	90	4066
Deposit Beta (2024)	0.356 (0.348)	0.276 (0.264)
	[2.38]**
	87	3984
R-squared (2024)	0.560 (0.686)	0.542 (0.591)
	[0.49]
	87	3984
Δ Beta (2022→2023)	-0.043 (-0.059)	-0.081 (-0.077)
	[1.47]
	90	4042
Δ Beta (2023→2024)	0.093 (0.109)	0.148 (0.153)
	[-1.37]
	87	3969
Δ Beta (2022→2024)	0.045 (0.044)	0.066 (0.072)
	[-0.54]
	87	3953

export_table(
  table_beta2,
  "table_beta2_dw_mar10_13",
  caption = "Deposit Beta: DW Borrowers vs Non-Borrowers (March 10--13, 2023)",
  label = "tab:beta_dw_mar10_13",
  note = "Deposit beta measures interest rate sensitivity of deposits. Higher beta indicates more rate-sensitive deposits."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta2_dw_mar10_13.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta2_dw_mar10_13.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta2_dw_mar10_13.csv

# ==============================================================================
# TABLE B3: Deposit Beta - Acute Period by Facility Choice
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE B3: Deposit Beta - Acute Period by Facility Choice\n")

## TABLE B3: Deposit Beta - Acute Period by Facility Choice

cat(strrep("=", 60), "\n")

## ============================================================

table_beta3_data <- baseline_q %>%
  mutate(
    btfp_user = idrssd %in% btfp_acute,
    dw_user = idrssd %in% dw_acute,
    borrower_type = case_when(
      btfp_user & dw_user ~ "Both",
      btfp_user & !dw_user ~ "BTFP Only",
      !btfp_user & dw_user ~ "DW Only",
      TRUE ~ "Non-Borrower"
    )
  )

table_beta3 <- create_deposit_beta_table(table_beta3_data, "borrower_type",
                                          group_levels = c("BTFP Only", "DW Only", "Both", "Non-Borrower"))

table_beta3 %>%
  select(-Row_Type) %>%
  kable(caption = "Table B3: Deposit Beta by Facility Choice (Acute Period: March 13 - May 1, 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table B3: Deposit Beta by Facility Choice (Acute Period: March 13 - May 1, 2023)

Variable	BTFP Only	DW Only	Both	Non-Borrower
N (with beta data)	365	298	92	3466
Deposit Beta (2022)	0.241 (0.212)	0.243 (0.212)	0.313 (0.269)	0.206 (0.163)
	[3.51]***	[3.16]***	[4.27]***
	365	298	92	3466
R-squared (2022)	0.612 (0.617)	0.647 (0.642)	0.691 (0.719)	0.604 (0.615)
	[0.65]	[3.00]***	[3.75]***
	365	298	92	3466
Deposit Beta (2023)	0.144 (0.130)	0.172 (0.142)	0.220 (0.172)	0.127 (0.095)
	[1.05]	[2.49]**	[2.98]***
	366	294	93	3403
R-squared (2023)	0.417 (0.387)	0.421 (0.373)	0.446 (0.452)	0.432 (0.406)
	[-0.89]	[-0.55]	[0.41]
	366	294	93	3403
Deposit Beta (2024)	0.284 (0.280)	0.311 (0.302)	0.334 (0.293)	0.272 (0.259)
	[0.68]	[1.77]*	[1.82]*
	361	284	92	3334
R-squared (2024)	0.553 (0.580)	0.552 (0.665)	0.609 (0.700)	0.539 (0.588)
	[0.81]	[0.63]	[2.00]**
	361	284	92	3334
Δ Beta (2022→2023)	-0.093 (-0.086)	-0.073 (-0.071)	-0.093 (-0.104)	-0.079 (-0.075)
	[-0.92]	[0.38]	[-0.44]
	364	294	92	3382
Δ Beta (2023→2024)	0.143 (0.152)	0.148 (0.161)	0.123 (0.159)	0.147 (0.152)
	[-0.18]	[0.03]	[-0.65]
	360	283	91	3322
Δ Beta (2022→2024)	0.046 (0.061)	0.073 (0.073)	0.028 (0.025)	0.068 (0.074)
	[-1.16]	[0.26]	[-1.20]
	358	283	90	3309

export_table(
  table_beta3,
  "table_beta3_acute",
  caption = "Deposit Beta by Facility Choice: Acute Crisis Period",
  label = "tab:beta_acute",
  note = "Deposit beta measures interest rate sensitivity of deposits. Acute crisis period: March 13 -- May 1, 2023. Banks classified by facility usage during this period."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta3_acute.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta3_acute.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta3_acute.csv

# ==============================================================================
# TABLE B4: Deposit Beta - Q1 2023 All Facilities
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE B4: Deposit Beta - Q1 2023 All Facilities\n")

## TABLE B4: Deposit Beta - Q1 2023 All Facilities

cat(strrep("=", 60), "\n")

## ============================================================

table_beta4_data <- baseline_q %>%
  mutate(
    btfp_user = idrssd %in% btfp_q1,
    dw_user = idrssd %in% dw_q1,
    fhlb_user = idrssd %in% fhlb_borrowers_10pct,
    borrower_type = case_when(
      btfp_user & dw_user & fhlb_user ~ "All Three",
      btfp_user & dw_user & !fhlb_user ~ "BTFP+DW",
      btfp_user & !dw_user & fhlb_user ~ "BTFP+FHLB",
      !btfp_user & dw_user & fhlb_user ~ "DW+FHLB",
      btfp_user & !dw_user & !fhlb_user ~ "BTFP Only",
      !btfp_user & dw_user & !fhlb_user ~ "DW Only",
      !btfp_user & !dw_user & fhlb_user ~ "FHLB Only",
      TRUE ~ "Non-Borrower"
    )
  )

table_beta4 <- create_deposit_beta_table(table_beta4_data, "borrower_type",
                                          group_levels = c("BTFP Only", "DW Only", "FHLB Only",
                                                           "BTFP+DW", "BTFP+FHLB", "DW+FHLB",
                                                           "All Three", "Non-Borrower"))

table_beta4 %>%
  select(-Row_Type) %>%
  kable(caption = "Table B4: Deposit Beta by Facility Choice (Q1 2023)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  scroll_box(width = "100%")

Table B4: Deposit Beta by Facility Choice (Q1 2023)

Variable	BTFP Only	DW Only	FHLB Only	BTFP+DW	BTFP+FHLB	DW+FHLB	All Three	Non-Borrower
N (with beta data)	198	344	258	59	12	21	9	3320
Deposit Beta (2022)	0.267 (0.237)	0.248 (0.219)	0.265 (0.225)	0.291 (0.272)	0.277 (0.273)	0.322 (0.269)	0.422 (0.334)	0.201 (0.158)
	[4.76]***	[4.16]***	[4.94]***	[3.48]***	[1.74]	[3.23]***	[2.02]*
	198	344	258	59	12	21	9	3320
R-squared (2022)	0.624 (0.624)	0.649 (0.669)	0.644 (0.653)	0.678 (0.672)	0.696 (0.679)	0.717 (0.742)	0.808 (0.794)	0.599 (0.606)
	[1.46]	[3.65]***	[2.81]***	[2.61]**	[1.59]	[2.50]**	[3.92]***
	198	344	258	59	12	21	9	3320
Deposit Beta (2023)	0.184 (0.182)	0.190 (0.182)	0.134 (0.118)	0.197 (0.167)	0.073 (0.057)	0.304 (0.220)	0.365 (0.318)	0.122 (0.089)
	[2.57]**	[4.30]***	[0.66]	[2.23]**	[-0.82]	[2.14]**	[1.75]
	198	335	254	59	12	21	9	3268
R-squared (2023)	0.458 (0.447)	0.436 (0.412)	0.407 (0.387)	0.405 (0.373)	0.312 (0.202)	0.559 (0.482)	0.597 (0.553)	0.429 (0.399)
	[1.31]	[0.38]	[-1.09]	[-0.53]	[-1.43]	[2.05]*	[2.23]*
	198	335	254	59	12	21	9	3268
Deposit Beta (2024)	0.294 (0.298)	0.302 (0.280)	0.302 (0.295)	0.273 (0.258)	0.406 (0.414)	0.396 (0.414)	0.466 (0.481)	0.270 (0.257)
	[1.06]	[1.75]*	[1.54]	[0.05]	[2.09]*	[2.57]**	[2.44]**
	194	326	249	59	12	20	9	3202
R-squared (2024)	0.549 (0.599)	0.545 (0.639)	0.547 (0.590)	0.557 (0.653)	0.721 (0.775)	0.710 (0.793)	0.653 (0.763)	0.539 (0.584)
	[0.41]	[0.29]	[0.35]	[0.41]	[2.20]*	[2.69]**	[1.18]
	194	326	249	59	12	20	9	3202
Δ Beta (2022→2023)	-0.082 (-0.053)	-0.062 (-0.062)	-0.128 (-0.107)	-0.094 (-0.107)	-0.204 (-0.164)	-0.018 (-0.034)	-0.057 (-0.045)	-0.078 (-0.077)
	[-0.16]	[1.19]	[-2.69]***	[-0.49]	[-2.09]*	[0.71]	[0.17]
	197	335	254	59	12	21	9	3245
Δ Beta (2023→2024)	0.105 (0.131)	0.122 (0.133)	0.168 (0.172)	0.112 (0.120)	0.333 (0.348)	0.083 (0.141)	0.101 (0.025)	0.150 (0.153)
	[-1.53]	[-1.48]	[0.72]	[-0.89]	[2.32]**	[-0.82]	[-0.34]
	194	324	249	58	12	20	9	3190
Δ Beta (2022→2024)	0.024 (0.060)	0.060 (0.061)	0.042 (0.057)	0.017 (0.013)	0.129 (0.145)	0.072 (0.094)	0.044 (0.018)	0.071 (0.074)
	[-1.82]*	[-0.65]	[-1.32]	[-1.31]	[0.86]	[0.02]	[-0.26]
	193	324	249	58	12	20	9	3175

export_table(
  table_beta4,
  "table_beta4_q1_2023",
  caption = "Deposit Beta by Facility Choice: Q1 2023",
  label = "tab:beta_q1_2023",
  note = "Deposit beta measures interest rate sensitivity of deposits. Q1 2023: January 1 -- March 31, 2023. FHLB borrowers identified at 10\\% threshold."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta4_q1_2023.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta4_q1_2023.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta4_q1_2023.csv

# ==============================================================================
# TABLE B5: Deposit Beta - Arbitrage Period
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("TABLE B5: Deposit Beta - Arbitrage Period\n")

## TABLE B5: Deposit Beta - Arbitrage Period

cat(strrep("=", 60), "\n")

## ============================================================

table_beta5_data <- baseline_q %>%
  mutate(
    btfp_user = idrssd %in% btfp_arb,
    dw_user = idrssd %in% dw_arb,
    borrower_type = case_when(
      btfp_user & dw_user ~ "Both",
      btfp_user & !dw_user ~ "BTFP Only",
      !btfp_user & dw_user ~ "DW Only",
      TRUE ~ "Non-Borrower"
    )
  )

table_beta5 <- create_deposit_beta_table(table_beta5_data, "borrower_type",
                                          group_levels = c("BTFP Only", "DW Only", "Both", "Non-Borrower"))

table_beta5 %>%
  select(-Row_Type) %>%
  kable(caption = "Table B5: Deposit Beta by Facility Choice (Arbitrage Period: Nov 1, 2023 - Jan 24, 2024)") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Table B5: Deposit Beta by Facility Choice (Arbitrage Period: Nov 1, 2023 - Jan 24, 2024)

Variable	BTFP Only	DW Only	Both	Non-Borrower
N (with beta data)	619	300	141	3161
Deposit Beta (2022)	0.225 (0.193)	0.259 (0.209)	0.247 (0.224)	0.206 (0.162)
	[2.48]**	[3.84]***	[2.67]***
	619	300	141	3161
R-squared (2022)	0.621 (0.626)	0.624 (0.645)	0.665 (0.681)	0.603 (0.613)
	[1.74]*	[1.41]	[3.25]***
	619	300	141	3161
Deposit Beta (2023)	0.145 (0.117)	0.174 (0.134)	0.165 (0.130)	0.126 (0.097)
	[1.46]	[2.56]**	[1.67]*
	618	301	141	3096
R-squared (2023)	0.439 (0.413)	0.427 (0.388)	0.462 (0.481)	0.427 (0.400)
	[0.81]	[-0.03]	[1.19]
	618	301	141	3096
Deposit Beta (2024)	0.259 (0.267)	0.303 (0.314)	0.265 (0.258)	0.279 (0.260)
	[-1.29]	[1.06]	[-0.55]
	617	296	141	3017
R-squared (2024)	0.554 (0.607)	0.586 (0.682)	0.526 (0.569)	0.536 (0.582)
	[1.23]	[2.48]**	[-0.36]
	617	296	141	3017
Δ Beta (2022→2023)	-0.081 (-0.080)	-0.085 (-0.087)	-0.079 (-0.091)	-0.080 (-0.074)
	[-0.10]	[-0.27]	[0.03]
	617	300	140	3075
Δ Beta (2023→2024)	0.128 (0.156)	0.134 (0.131)	0.109 (0.125)	0.153 (0.157)
	[-1.51]	[-0.68]	[-1.43]
	612	295	140	3009
Δ Beta (2022→2024)	0.042 (0.057)	0.052 (0.074)	0.023 (0.043)	0.074 (0.074)
	[-2.10]**	[-0.90]	[-1.97]*
	613	294	139	2994

export_table(
  table_beta5,
  "table_beta5_arbitrage",
  caption = "Deposit Beta by Facility Choice: Arbitrage Period",
  label = "tab:beta_arbitrage",
  note = "Deposit beta measures interest rate sensitivity of deposits. Arbitrage period: November 1, 2023 -- January 24, 2024 (BTFP rate below IORB)."
)

## LaTeX table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta5_arbitrage.tex 
## HTML table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta5_arbitrage.html 
## CSV table saved: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/table_beta5_arbitrage.csv

# ==============================================================================
# DEPOSIT BETA SUMMARY
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("DEPOSIT BETA SUMMARY\n")

## DEPOSIT BETA SUMMARY

cat(strrep("=", 60), "\n")

## ============================================================

# Summary by acute period facility choice
cat("\n--- Deposit Beta by Facility Choice (Acute Period) ---\n")

## 
## --- Deposit Beta by Facility Choice (Acute Period) ---

table_beta3_data %>%
  filter(!is.na(beta_2022)) %>%
  group_by(borrower_type) %>%
  summarise(
    N = n(),
    Beta_2022_Mean = round(mean(beta_2022, na.rm = TRUE), 3),
    Beta_2023_Mean = round(mean(beta_2023, na.rm = TRUE), 3),
    Beta_2024_Mean = round(mean(beta_2024, na.rm = TRUE), 3),
    Delta_22_23 = round(mean(beta_change_2022_2023, na.rm = TRUE), 3),
    Delta_23_24 = round(mean(beta_change_2023_2024, na.rm = TRUE), 3),
    .groups = "drop"
  ) %>%
  pander(caption = "Deposit Beta Evolution by Facility Choice")

Deposit Beta Evolution by Facility Choice (continued below)

borrower_type	N	Beta_2022_Mean	Beta_2023_Mean	Beta_2024_Mean
BTFP Only	365	0.241	0.148	0.289
Both	92	0.313	0.22	0.342
DW Only	298	0.243	0.172	0.318
Non-Borrower	3466	0.206	0.126	0.274

Delta_22_23	Delta_23_24
-0.093	0.139
-0.093	0.12
-0.073	0.148
-0.079	0.147

# T-test: BTFP Only vs DW Only
cat("\n--- T-Tests: BTFP Only vs DW Only ---\n")

## 
## --- T-Tests: BTFP Only vs DW Only ---

btfp_data <- table_beta3_data %>% filter(borrower_type == "BTFP Only")
dw_data <- table_beta3_data %>% filter(borrower_type == "DW Only")

for (v in c("beta_2022", "beta_2023", "beta_2024", "beta_change_2022_2023")) {
  result <- calc_tstat_with_stars(btfp_data[[v]], dw_data[[v]])
  cat(sprintf("%s: t = %.3f%s (p = %.4f)\n", v, result$tstat, result$stars, result$pval))
}

## beta_2022: t = -0.110 (p = 0.9121)
## beta_2023: t = -1.198 (p = 0.2314)
## beta_2024: t = -1.008 (p = 0.3141)
## beta_change_2022_2023: t = -0.947 (p = 0.3439)

19 Variable Definitions

# ==============================================================================
# VARIABLE DEFINITIONS TABLE
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("VARIABLE DEFINITIONS\n")

## VARIABLE DEFINITIONS

cat(strrep("=", 60), "\n")

## ============================================================

variable_definitions <- tribble(
  ~Category, ~Variable, ~Definition, ~Source,
  
  # Solvency Measures
  "Solvency", "Book Equity Ratio", "Total equity / Total assets (×100)", "Call Report",
  "Solvency", "MTM Loss (HTM)", "Mark-to-market loss on held-to-maturity securities / Total assets (×100)", "Call Report",
  "Solvency", "MTM Loss (BTFP Elg.)", "Mark-to-market loss on BTFP-eligible securities (Treasury, Agency, MBS) / Total assets (×100)", "Call Report",
  "Solvency", "MTM Loss (Total)", "Total mark-to-market loss on all securities / Total assets (×100)", "Call Report",
  "Solvency", "Adjusted Equity", "Book equity ratio − MTM loss ratio (percentage points)", "Derived",
  "Solvency", "Adjusted Equity < 0", "Indicator = 1 if adjusted equity < 0", "Derived",
  "Solvency", "Adj. Tier1 Equity", "Tier 1 capital ratio − MTM loss ratio (percentage points)", "Derived",
  "Solvency", "Adj. Tier1 Equity < 0", "Indicator = 1 if adjusted Tier 1 equity < 0", "Derived",
  "Solvency", "Adj. CET1", "Common Equity Tier 1 ratio − MTM loss ratio (percentage points)", "Derived",
  "Solvency", "Adj. CET1 < 0", "Indicator = 1 if adjusted CET1 < 0", "Derived",
  
  # Liquidity Measures
  "Liquidity", "Uninsured Leverage", "Uninsured deposits / Total assets (×100)", "Call Report",
  "Liquidity", "Available Liquidity Ratio", "(Cash + Reverse repo + Fed funds sold) / Total assets (×100)", "Call Report",
  
  # Run Risk Categories
  "Run Risk", "Solvent and Liquid", "Indicator = 1 if uninsured ratio < median AND MTM loss < median", "Derived",
  "Run Risk", "Solvent and Illiquid", "Indicator = 1 if uninsured ratio > median AND MTM loss < median", "Derived",
  "Run Risk", "Insolvent and Liquid", "Indicator = 1 if uninsured ratio < median AND MTM loss > median", "Derived",
  "Run Risk", "Insolvent and Illiquid", "Indicator = 1 if uninsured ratio > median AND MTM loss > median", "Derived",
  
  # IDCR Measures
  "IDCR", "IDCR Insolvent (s=0.5)", "Indicator = 1 if (MV_Asset − 0.5×Uninsured − Insured)/Insured < 0", "Derived",
  "IDCR", "IDCR Insolvent (s=1.0)", "Indicator = 1 if (MV_Asset − Uninsured − Insured)/Insured < 0", "Derived",
  
  # Capital Insolvency
  "Capital", "Capital Insolvent (s=0.5)", "Indicator = 1 if (Equity − 0.5×Uninsured×MV_Adj)/Assets < 0", "Derived",
  "Capital", "Capital Insolvent (s=1.0)", "Indicator = 1 if (Equity − Uninsured×MV_Adj)/Assets < 0", "Derived",
  
  # Deposit Flow Measures
  "Deposit Flows", "Change Uninsured Deposits (Fwd Q)", "Percentage change in uninsured deposits from baseline to next quarter", "Call Report",
  "Deposit Flows", "Change Insured Deposits (Fwd Q)", "Percentage change in insured deposits from baseline to next quarter", "Call Report",
  "Deposit Flows", "Abnormal Insured Outflow (10%)", "Indicator = 1 if insured deposit outflow Z-score > 1.28 (top 10%)", "Derived",
  "Deposit Flows", "Abnormal Insured Outflow (5%)", "Indicator = 1 if insured deposit outflow Z-score > 1.65 (top 5%)", "Derived",
  "Deposit Flows", "Abnormal Uninsured Outflow (10%)", "Indicator = 1 if uninsured deposit outflow Z-score > 1.28 (top 10%)", "Derived",
  "Deposit Flows", "Abnormal Uninsured Outflow (5%)", "Indicator = 1 if uninsured deposit outflow Z-score > 1.65 (top 5%)", "Derived",
  
  # Deposit Beta Measures
  "Deposit Beta", "Deposit Beta (2022)", "Interest rate sensitivity of deposits estimated from 2022 data", "Estimated",
  "Deposit Beta", "Deposit Beta (2023)", "Interest rate sensitivity of deposits estimated from 2023 data", "Estimated",
  "Deposit Beta", "Deposit Beta (2024)", "Interest rate sensitivity of deposits estimated from 2024 data", "Estimated",
  "Deposit Beta", "R-squared", "Goodness of fit from deposit beta regression", "Estimated",
  "Deposit Beta", "Δ Beta (2022→2023)", "Change in deposit beta from 2022 to 2023", "Derived",
  "Deposit Beta", "Δ Beta (2023→2024)", "Change in deposit beta from 2023 to 2024", "Derived",
  "Deposit Beta", "Δ Beta (2022→2024)", "Change in deposit beta from 2022 to 2024", "Derived",
  
  # FHLB Measures
  "FHLB", "Abnormal FHLB (10%)", "Indicator = 1 if FHLB borrowing Z-score > 1.28 (top 10%)", "Derived",
  "FHLB", "Abnormal FHLB (5%)", "Indicator = 1 if FHLB borrowing Z-score > 1.65 (top 5%)", "Derived",
  
  # Borrower Classifications
  "Classification", "BTFP Borrower", "Bank that borrowed from Bank Term Funding Program during specified period", "BTFP Data",
  "Classification", "DW Borrower", "Bank that borrowed from Discount Window during specified period", "DW Data",
  "Classification", "FHLB Borrower", "Bank with abnormal FHLB advances above specified threshold", "Call Report",
  "Classification", "Non-Borrower", "Bank that did not borrow from any emergency facility during specified period", "Derived"
)

# Display variable definitions
variable_definitions %>%
  kable(caption = "Table: Variable Definitions",
        col.names = c("Category", "Variable", "Definition", "Source")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE) %>%
  collapse_rows(columns = 1, valign = "top")

Table: Variable Definitions

Category	Variable	Definition	Source
Solvency	Book Equity Ratio	Total equity / Total assets (×100)	Call Report
	MTM Loss (HTM)	Mark-to-market loss on held-to-maturity securities / Total assets (×100)	Call Report
	MTM Loss (BTFP Elg.)	Mark-to-market loss on BTFP-eligible securities (Treasury, Agency, MBS) / Total assets (×100)	Call Report
	MTM Loss (Total)	Total mark-to-market loss on all securities / Total assets (×100)	Call Report
	Adjusted Equity	Book equity ratio − MTM loss ratio (percentage points)	Derived
	Adjusted Equity < 0	Indicator = 1 if adjusted equity < 0	Derived
	Adj. Tier1 Equity	Tier 1 capital ratio − MTM loss ratio (percentage points)	Derived
	Adj. Tier1 Equity < 0	Indicator = 1 if adjusted Tier 1 equity < 0	Derived
	Adj. CET1	Common Equity Tier 1 ratio − MTM loss ratio (percentage points)	Derived
	Adj. CET1 < 0	Indicator = 1 if adjusted CET1 < 0	Derived
Liquidity	Uninsured Leverage	Uninsured deposits / Total assets (×100)	Call Report
	Available Liquidity Ratio	(Cash + Reverse repo + Fed funds sold) / Total assets (×100)	Call Report
Run Risk	Solvent and Liquid	Indicator = 1 if uninsured ratio < median AND MTM loss < median	Derived
	Solvent and Illiquid	Indicator = 1 if uninsured ratio > median AND MTM loss < median	Derived
	Insolvent and Liquid	Indicator = 1 if uninsured ratio < median AND MTM loss > median	Derived
	Insolvent and Illiquid	Indicator = 1 if uninsured ratio > median AND MTM loss > median	Derived
IDCR	IDCR Insolvent (s=0.5)	Indicator = 1 if (MV_Asset − 0.5×Uninsured − Insured)/Insured < 0	Derived
	IDCR Insolvent (s=1.0)	Indicator = 1 if (MV_Asset − Uninsured − Insured)/Insured < 0	Derived
Capital	Capital Insolvent (s=0.5)	Indicator = 1 if (Equity − 0.5×Uninsured×MV_Adj)/Assets < 0	Derived
	Capital Insolvent (s=1.0)	Indicator = 1 if (Equity − Uninsured×MV_Adj)/Assets < 0	Derived
Deposit Flows	Change Uninsured Deposits (Fwd Q)	Percentage change in uninsured deposits from baseline to next quarter	Call Report
	Change Insured Deposits (Fwd Q)	Percentage change in insured deposits from baseline to next quarter	Call Report
	Abnormal Insured Outflow (10%)	Indicator = 1 if insured deposit outflow Z-score > 1.28 (top 10%)	Derived
	Abnormal Insured Outflow (5%)	Indicator = 1 if insured deposit outflow Z-score > 1.65 (top 5%)	Derived
	Abnormal Uninsured Outflow (10%)	Indicator = 1 if uninsured deposit outflow Z-score > 1.28 (top 10%)	Derived
	Abnormal Uninsured Outflow (5%)	Indicator = 1 if uninsured deposit outflow Z-score > 1.65 (top 5%)	Derived
Deposit Beta	Deposit Beta (2022)	Interest rate sensitivity of deposits estimated from 2022 data	Estimated
	Deposit Beta (2023)	Interest rate sensitivity of deposits estimated from 2023 data	Estimated
	Deposit Beta (2024)	Interest rate sensitivity of deposits estimated from 2024 data	Estimated
	R-squared	Goodness of fit from deposit beta regression	Estimated
	Δ Beta (2022→2023)	Change in deposit beta from 2022 to 2023	Derived
	Δ Beta (2023→2024)	Change in deposit beta from 2023 to 2024	Derived
	Δ Beta (2022→2024)	Change in deposit beta from 2022 to 2024	Derived
FHLB	Abnormal FHLB (10%)	Indicator = 1 if FHLB borrowing Z-score > 1.28 (top 10%)	Derived
	Abnormal FHLB (5%)	Indicator = 1 if FHLB borrowing Z-score > 1.65 (top 5%)	Derived
Classification	BTFP Borrower	Bank that borrowed from Bank Term Funding Program during specified period	BTFP Data
	DW Borrower	Bank that borrowed from Discount Window during specified period	DW Data
	FHLB Borrower	Bank with abnormal FHLB advances above specified threshold	Call Report
	Non-Borrower	Bank that did not borrow from any emergency facility during specified period	Derived

# Export to LaTeX
var_def_latex <- c(
  "\\begin{table}[htbp]",
  "\\centering",
  "\\caption{Variable Definitions}",
  "\\label{tab:variable_definitions}",
  "\\footnotesize",
  "\\begin{tabular}{p{2cm}p{3.5cm}p{7.5cm}p{2cm}}",
  "\\toprule",
  "Category & Variable & Definition & Source \\\\",
  "\\midrule"
)

current_category <- ""
for (i in seq_len(nrow(variable_definitions))) {
  row <- variable_definitions[i, ]
  cat_display <- if (row$Category != current_category) row$Category else ""
  current_category <- row$Category
  
  # Escape special LaTeX characters
  def_escaped <- gsub("%", "\\\\%", row$Definition)
  def_escaped <- gsub("×", "$\\\\times$", def_escaped)
  def_escaped <- gsub("−", "$-$", def_escaped)
  
  var_def_latex <- c(var_def_latex,
                     sprintf("%s & %s & %s & %s \\\\",
                             cat_display, row$Variable, def_escaped, row$Source))
  
  # Add midrule between categories
  if (i < nrow(variable_definitions) && variable_definitions$Category[i+1] != current_category) {
    var_def_latex <- c(var_def_latex, "\\midrule")
  }
}

var_def_latex <- c(var_def_latex,
                   "\\bottomrule",
                   "\\end{tabular}",
                   "\\vspace{0.3em}",
                   "\\begin{minipage}{0.95\\textwidth}",
                   "\\scriptsize",
                   "\\textit{Notes:} All ratios expressed as percentages unless otherwise noted. MTM = Mark-to-market. MV = Market value. IDCR = Implied Deposit Coverage Ratio following Jiang et al. (2023). Z-scores calculated as (current value $-$ historical mean) / historical standard deviation using prior 8 quarters (2021Q1--2022Q4). Deposit beta estimated from quarterly deposit growth regressed on Fed Funds rate changes.",
                   "\\end{minipage}",
                   "\\end{table}")

writeLines(var_def_latex, file.path(TABLE_PATH, "variable_definitions.tex"))
cat("\nVariable definitions table saved to:", file.path(TABLE_PATH, "variable_definitions.tex"), "\n")

## 
## Variable definitions table saved to: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/variable_definitions.tex

# Also save as CSV
write_csv(variable_definitions, file.path(TABLE_PATH, "variable_definitions.csv"))
cat("Variable definitions CSV saved to:", file.path(TABLE_PATH, "variable_definitions.csv"), "\n")

## Variable definitions CSV saved to: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/variable_definitions.csv

20 Statistical Notes

# ==============================================================================
# STATISTICAL NOTES
# ==============================================================================

cat("\n", strrep("=", 60), "\n")

## 
##  ============================================================

cat("STATISTICAL NOTES\n")

## STATISTICAL NOTES

cat(strrep("=", 60), "\n")

## ============================================================

statistical_notes <- tribble(
  ~Item, ~Description,
  "T-statistics", "Calculated using Welch's t-test (unequal variances) comparing each borrower group to Non-Borrowers",
  "Significance levels", "*** p<0.01, ** p<0.05, * p<0.10 (two-tailed tests)",
  "Table format", "Mean (Median) with [T-statistic]significance stars and N observations",
  "Dummy variables", "Shown as percentage of banks with indicator = 1; N shows count of such banks",
  "Continuous variables", "Shown as percentage points; N shows total non-missing observations",
  "Baseline period", "2022Q4 unless otherwise noted (Table 6b uses 2023Q4)",
  "Sample", "Excludes G-SIBs and failed banks; includes only banks with BTFP-eligible securities"
)

statistical_notes %>%
  kable(caption = "Statistical Notes",
        col.names = c("Item", "Description")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE)

Statistical Notes

Item	Description
T-statistics	Calculated using Welch’s t-test (unequal variances) comparing each borrower group to Non-Borrowers
Significance levels	*** p<0.01, ** p<0.05, * p<0.10 (two-tailed tests)
Table format	Mean (Median) with [T-statistic]significance stars and N observations
Dummy variables	Shown as percentage of banks with indicator = 1; N shows count of such banks
Continuous variables	Shown as percentage points; N shows total non-missing observations
Baseline period	2022Q4 unless otherwise noted (Table 6b uses 2023Q4)
Sample	Excludes G-SIBs and failed banks; includes only banks with BTFP-eligible securities

# ==============================================================================
# INSOLVENCY BY SIZE CATEGORY - TWO PERIODS
# ==============================================================================

cat("\n", strrep("=", 70), "\n")

## 
##  ======================================================================

cat("PREPARING INSOLVENCY DATA BY SIZE CATEGORY\n")

## PREPARING INSOLVENCY DATA BY SIZE CATEGORY

cat(strrep("=", 70), "\n\n")

## ======================================================================

# --------------------------------------------------------------------------
# SIZE CATEGORY DEFINITIONS (from LaTeX table)
# --------------------------------------------------------------------------

create_size_category <- function(assets_thousands) {
  # Convert thousands to millions for comparison
  assets_millions <- assets_thousands / 1000
  
  case_when(
    assets_millions >= 700000 ~ "$700B and Above",
    assets_millions >= 250000 ~ "$250B-$700B",
    assets_millions >= 100000 ~ "$100B-$250B",
    assets_millions >= 50000  ~ "$50B-$100B",
    assets_millions >= 20000  ~ "$20B-$50B",
    assets_millions >= 10000  ~ "$10B-$20B",
    assets_millions >= 5000   ~ "$5B-$10B",
    assets_millions >= 3000   ~ "$3B-$5B",
    assets_millions >= 1000   ~ "$1B-$3B",
    assets_millions >= 500    ~ "$500M-$1B",
    TRUE                      ~ "Below $500M"
  )
}

# Define factor levels to ensure proper ordering
size_levels <- c(
  "$700B and Above",
  "$250B-$700B",
  "$100B-$250B",
  "$50B-$100B",
  "$20B-$50B",
  "$10B-$20B",
  "$5B-$10B",
  "$3B-$5B",
  "$1B-$3B",
  "$500M-$1B",
  "Below $500M"
)

# --------------------------------------------------------------------------
# CALCULATE INSOLVENCY MEASURES FOR BASELINE (2022Q4)
# --------------------------------------------------------------------------

baseline_insolvency <- call_q %>%
  filter(period == '2022Q4') %>%
  filter(
    gsib == 0 | is.na(gsib),
    failed_bank == 0 | is.na(failed_bank),
    !is.na(omo_eligible) & omo_eligible > 0
  ) %>%
  mutate(
    # Size category (total_asset is in thousands)
    size_category = create_size_category(total_asset),
    size_category = factor(size_category, levels = size_levels),
    
    # All assets liquidated at market value
    adjusted_book_value = mm_asset - total_liability,
    mv_insolvent = as.integer(adjusted_book_value < 0),
    
    # Deposit run scenarios
    idcr_s50 = safe_div(mm_asset - 0.5 * uninsured_deposit - insured_deposit, insured_deposit),
    idcr_s100 = safe_div(mm_asset - 1.0 * uninsured_deposit - insured_deposit, insured_deposit),
    
    idcr_insolvent_s50 = as.integer(idcr_s50 < 0),
    idcr_insolvent_s100 = as.integer(idcr_s100 < 0)
  ) %>%
  select(idrssd, size_category, total_asset, 
         mv_insolvent, idcr_insolvent_s50, idcr_insolvent_s100)

cat("Baseline insolvency data prepared:\n")

## Baseline insolvency data prepared:

cat(sprintf("  Total banks: %d\n", nrow(baseline_insolvency)))

##   Total banks: 4292

cat(sprintf("  MV Insolvent: %d\n", sum(baseline_insolvency$mv_insolvent, na.rm = TRUE)))

##   MV Insolvent: 2015

cat(sprintf("  IDCR Insolvent (s=1.0): %d\n", sum(baseline_insolvency$idcr_insolvent_s100, na.rm = TRUE)))

##   IDCR Insolvent (s=1.0): 1210

cat(sprintf("  IDCR Insolvent (s=0.5): %d\n", sum(baseline_insolvency$idcr_insolvent_s50, na.rm = TRUE)))

##   IDCR Insolvent (s=0.5): 179

# --------------------------------------------------------------------------
# TABLE 1: INSOLVENCY BY SIZE - ACUTE PERIOD (March 13 - May 1, 2023)
# --------------------------------------------------------------------------

cat("\n", strrep("=", 70), "\n")

## 
##  ======================================================================

cat("TABLE 1: INSOLVENT BANKS BY SIZE - ACUTE PERIOD (March 13 - May 1, 2023)\n")

## TABLE 1: INSOLVENT BANKS BY SIZE - ACUTE PERIOD (March 13 - May 1, 2023)

cat(strrep("=", 70), "\n\n")

## ======================================================================

# Define acute period
acute_start <- as.Date("2023-03-13")
acute_end <- as.Date("2023-05-01")

# Identify BTFP borrowers in acute period
btfp_acute <- btfp_loans_raw %>%
  filter(btfp_loan_date >= acute_start & btfp_loan_date <= acute_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

# Identify DW borrowers in acute period
dw_acute <- dw_loans_raw %>%
  filter(dw_loan_date >= acute_start & dw_loan_date <= acute_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

cat(sprintf("BTFP borrowers in acute period: %d\n", length(btfp_acute)))

## BTFP borrowers in acute period: 492

cat(sprintf("DW borrowers in acute period: %d\n", length(dw_acute)))

## DW borrowers in acute period: 424

# Create summary table by size
table_acute <- baseline_insolvency %>%
  group_by(size_category) %>%
  summarise(
    total_banks = n(),
    mv_insolvent = sum(mv_insolvent, na.rm = TRUE),
    idcr_insolvent_s100 = sum(idcr_insolvent_s100, na.rm = TRUE),
    idcr_insolvent_s50 = sum(idcr_insolvent_s50, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(size_category)

# Calculate percentage (handle case where sum is 0)
total_s50 <- sum(table_acute$idcr_insolvent_s50)
if (total_s50 > 0) {
  table_acute <- table_acute %>%
    mutate(pct_s50 = 100 * idcr_insolvent_s50 / total_s50)
} else {
  table_acute <- table_acute %>%
    mutate(pct_s50 = 0)
}

# Display table
print(table_acute)

## # A tibble: 10 × 6
##    size_category total_banks mv_insolvent idcr_insolvent_s100 idcr_insolvent_s50
##    <fct>               <int>        <int>               <int>              <int>
##  1 $250B-$700B             5            2                   1                  0
##  2 $100B-$250B            13            8                   4                  1
##  3 $50B-$100B             15            8                   4                  1
##  4 $20B-$50B              45           18                   9                  3
##  5 $10B-$20B              57           26                  15                  4
##  6 $5B-$10B              112           62                  33                  9
##  7 $3B-$5B               114           66                  32                  5
##  8 $1B-$3B               562          328                 166                 28
##  9 $500M-$1B             717          384                 237                 31
## 10 Below $500M          2652         1113                 709                 97
## # ℹ 1 more variable: pct_s50 <dbl>

# Calculate totals row
totals_acute <- table_acute %>%
  summarise(
    size_category = "Total",
    total_banks = sum(total_banks),
    mv_insolvent = sum(mv_insolvent),
    idcr_insolvent_s100 = sum(idcr_insolvent_s100),
    idcr_insolvent_s50 = sum(idcr_insolvent_s50),
    pct_s50 = 100
  )

table_acute_with_totals <- bind_rows(table_acute, totals_acute)

# Format and display with kable
table_acute_with_totals %>%
  mutate(pct_s50 = sprintf("%.1f%%", pct_s50)) %>%
  kable(
    caption = "Table 1: Insolvent Banks by Size - Acute Period (March 13 - May 1, 2023)",
    col.names = c("Asset Size", "Total Banks", "All Assets Liquidated", 
                  "100% Uninsured Run", "50% Uninsured Run", "Percentage"),
    align = c("l", rep("r", 5)),
    format.args = list(big.mark = ",")
  ) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  row_spec(nrow(table_acute_with_totals), bold = TRUE)

Table 1: Insolvent Banks by Size - Acute Period (March 13 - May 1, 2023)

Asset Size	Total Banks	All Assets Liquidated	100% Uninsured Run	50% Uninsured Run	Percentage
$250B-$700B	5	2	1	0	0.0%
$100B-$250B	13	8	4	1	0.6%
$50B-$100B	15	8	4	1	0.6%
$20B-$50B	45	18	9	3	1.7%
$10B-$20B	57	26	15	4	2.2%
$5B-$10B	112	62	33	9	5.0%
$3B-$5B	114	66	32	5	2.8%
$1B-$3B	562	328	166	28	15.6%
$500M-$1B	717	384	237	31	17.3%
Below $500M	2,652	1,113	709	97	54.2%
Total	4,292	2,015	1,210	179	100.0%

# Export to LaTeX
latex_acute <- c(
  "\\begin{table}[htbp]",
  "\\centering",
  "\\caption{Insolvent Banks by Asset Size: Acute Period (March 13--May 1, 2023)}",
  "\\label{tab:insolvency_size_acute}",
  "\\footnotesize",
  "",
  "\\begin{minipage}{0.95\\textwidth}",
  "\\smallskip",
  "This table reports the distribution of insolvent banks across asset size categories during",
  "the acute crisis period (March 13--May 1, 2023). Column (1) shows the total number of banks in each size bin.",
  "Columns (2)--(4) report the number of insolvent banks under three scenarios: (i) full liquidation of all",
  "assets at mark-to-market values, (ii) 100\\% uninsured depositor run, and",
  "(iii) 50\\% uninsured depositor run. Column (5) reports the percentage of insolvent banks",
  "under the 50\\% uninsured depositor run scenario relative to the total number of insolvent",
  "banks in that scenario.",
  "\\end{minipage}",
  "",
  "\\medskip",
  "\\begin{tabular}{l*{5}{c}}",
  "\\hline\\hline",
  "& (1) & (2) & (3) & (4) & (5) \\\\",
  "Asset Size ",
  "& Total Banks ",
  "& All Assets ",
  "& 100\\% Uninsured",
  "& 50\\% Uninsured ",
  "& Percentage \\\\",
  "",
  "& Banks ",
  "& Liquidated ",
  "&Deposit Run ",
  "& Deposit Run ",
  "& 50\\% Uninsured run \\\\",
  "\\hline",
  ""
)

for (i in 1:nrow(table_acute)) {
  row <- table_acute[i, ]
  latex_acute <- c(
    latex_acute,
    sprintf("%s & %d & %d & %d & %d & %.1f\\%% \\\\",
            row$size_category,
            row$total_banks,
            row$mv_insolvent,
            row$idcr_insolvent_s100,
            row$idcr_insolvent_s50,
            row$pct_s50)
  )
}

latex_acute <- c(
  latex_acute,
  "\\hline",
  "",
  sprintf("Number of Banks & %d & %d & %d & %d & 100\\%% \\\\",
          totals_acute$total_banks,
          totals_acute$mv_insolvent,
          totals_acute$idcr_insolvent_s100,
          totals_acute$idcr_insolvent_s50),
  "\\hline\\hline",
  "\\end{tabular}",
  "",
  "\\begin{minipage}{0.95\\textwidth}",
  "\\smallskip",
  "\\footnotesize",
  "\\textit{Note:} Insolvency in Column (2) is defined as mark-to-market (MTM) asset values being",
  "insufficient to cover total non-equity liabilities. In Column (3), the assumption is that all",
  "Uninsured depositors run; insolvency occurs if remaining insured deposits cannot be repaid from",
  "MTM asset values. In Column (4), only 50\\% of uninsured deposits run. The final column shows the",
  "distribution of insolvent banks in Column (4) as a percentage of all insolvent banks under that",
  "scenario. All insolvency measures are based on 2022Q4 balance sheet data. Sample excludes G-SIBs,",
  "failed banks, and banks without BTFP-eligible securities.",
  "\\end{minipage}",
  "",
  "\\end{table}"
)

writeLines(latex_acute, file.path(TABLE_PATH, "insolvency_size_acute.tex"))
cat("\nAcute period table saved to:", file.path(TABLE_PATH, "insolvency_size_acute.tex"), "\n")

## 
## Acute period table saved to: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/insolvency_size_acute.tex

# --------------------------------------------------------------------------
# TABLE 2: INSOLVENCY BY SIZE - JANUARY 1 - MARCH 31, 2024
# Includes BTFP, DW, and FHLB (10% threshold) borrowers
# --------------------------------------------------------------------------

cat("\n", strrep("=", 70), "\n")

## 
##  ======================================================================

cat("TABLE 2: INSOLVENT BANKS BY SIZE - JAN 1 - MARCH 31, 2024\n")

## TABLE 2: INSOLVENT BANKS BY SIZE - JAN 1 - MARCH 31, 2024

cat("Includes BTFP, DW, and FHLB (10% threshold) borrowers\n")

## Includes BTFP, DW, and FHLB (10% threshold) borrowers

cat(strrep("=", 70), "\n\n")

## ======================================================================

# Define period
period_2024_start <- as.Date("2024-01-01")
period_2024_end <- as.Date("2024-03-31")

# Identify BTFP borrowers in 2024 period
btfp_2024 <- btfp_loans_raw %>%
  filter(btfp_loan_date >= period_2024_start & btfp_loan_date <= period_2024_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

# Identify DW borrowers in 2024 period (if data exists)
dw_2024 <- dw_loans_raw %>%
  filter(dw_loan_date >= period_2024_start & dw_loan_date <= period_2024_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

# Identify FHLB borrowers using 10% threshold from 2022Q4 baseline
# Note: FHLB status is from baseline_q, not from date-specific loans
fhlb_borrowers_10pct <- call_q %>%
  filter(period == '2022Q4') %>%
  filter(
    gsib == 0 | is.na(gsib),
    failed_bank == 0 | is.na(failed_bank),
    !is.na(omo_eligible) & omo_eligible > 0,
    !is.na(abnormal_fhlb_borrowing_10pct) & abnormal_fhlb_borrowing_10pct > 0
  ) %>%
  pull(idrssd)

cat(sprintf("BTFP borrowers in 2024 period: %d\n", length(btfp_2024)))

## BTFP borrowers in 2024 period: 717

cat(sprintf("DW borrowers in 2024 period: %d\n", length(dw_2024)))

## DW borrowers in 2024 period: 0

cat(sprintf("FHLB borrowers (10%% threshold): %d\n", length(fhlb_borrowers_10pct)))

## FHLB borrowers (10% threshold): 302

# Create summary table by size (same insolvency measures as acute period)
table_2024 <- baseline_insolvency %>%
  group_by(size_category) %>%
  summarise(
    total_banks = n(),
    mv_insolvent = sum(mv_insolvent, na.rm = TRUE),
    idcr_insolvent_s100 = sum(idcr_insolvent_s100, na.rm = TRUE),
    idcr_insolvent_s50 = sum(idcr_insolvent_s50, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(size_category)

# Calculate percentage (handle case where sum is 0)
total_s50_2024 <- sum(table_2024$idcr_insolvent_s50)
if (total_s50_2024 > 0) {
  table_2024 <- table_2024 %>%
    mutate(pct_s50 = 100 * idcr_insolvent_s50 / total_s50_2024)
} else {
  table_2024 <- table_2024 %>%
    mutate(pct_s50 = 0)
}

# Display table
print(table_2024)

## # A tibble: 10 × 6
##    size_category total_banks mv_insolvent idcr_insolvent_s100 idcr_insolvent_s50
##    <fct>               <int>        <int>               <int>              <int>
##  1 $250B-$700B             5            2                   1                  0
##  2 $100B-$250B            13            8                   4                  1
##  3 $50B-$100B             15            8                   4                  1
##  4 $20B-$50B              45           18                   9                  3
##  5 $10B-$20B              57           26                  15                  4
##  6 $5B-$10B              112           62                  33                  9
##  7 $3B-$5B               114           66                  32                  5
##  8 $1B-$3B               562          328                 166                 28
##  9 $500M-$1B             717          384                 237                 31
## 10 Below $500M          2652         1113                 709                 97
## # ℹ 1 more variable: pct_s50 <dbl>

# Calculate totals row
totals_2024 <- table_2024 %>%
  summarise(
    size_category = "Total",
    total_banks = sum(total_banks),
    mv_insolvent = sum(mv_insolvent),
    idcr_insolvent_s100 = sum(idcr_insolvent_s100),
    idcr_insolvent_s50 = sum(idcr_insolvent_s50),
    pct_s50 = 100
  )

table_2024_with_totals <- bind_rows(table_2024, totals_2024)

# Format and display with kable
table_2024_with_totals %>%
  mutate(pct_s50 = sprintf("%.1f%%", pct_s50)) %>%
  kable(
    caption = "Table 2: Insolvent Banks by Size - January 1 - March 31, 2024",
    col.names = c("Asset Size", "Total Banks", "All Assets Liquidated", 
                  "100% Uninsured Run", "50% Uninsured Run", "Percentage"),
    align = c("l", rep("r", 5)),
    format.args = list(big.mark = ",")
  ) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  row_spec(nrow(table_2024_with_totals), bold = TRUE)

Table 2: Insolvent Banks by Size - January 1 - March 31, 2024

Asset Size	Total Banks	All Assets Liquidated	100% Uninsured Run	50% Uninsured Run	Percentage
$250B-$700B	5	2	1	0	0.0%
$100B-$250B	13	8	4	1	0.6%
$50B-$100B	15	8	4	1	0.6%
$20B-$50B	45	18	9	3	1.7%
$10B-$20B	57	26	15	4	2.2%
$5B-$10B	112	62	33	9	5.0%
$3B-$5B	114	66	32	5	2.8%
$1B-$3B	562	328	166	28	15.6%
$500M-$1B	717	384	237	31	17.3%
Below $500M	2,652	1,113	709	97	54.2%
Total	4,292	2,015	1,210	179	100.0%

# Export to LaTeX
latex_2024 <- c(
  "\\begin{table}[htbp]",
  "\\centering",
  "\\caption{Insolvent Banks by Asset Size: January 1--March 31, 2024}",
  "\\label{tab:insolvency_size_2024q1}",
  "\\footnotesize",
  "",
  "\\begin{minipage}{0.95\\textwidth}",
  "\\smallskip",
  "This table reports the distribution of insolvent banks across asset size categories during",
  "January 1--March 31, 2024. Column (1) shows the total number of banks in each size bin.",
  "Columns (2)--(4) report the number of insolvent banks under three scenarios: (i) full liquidation of all",
  "assets at mark-to-market values, (ii) 100\\% uninsured depositor run, and",
  "(iii) 50\\% uninsured depositor run. Column (5) reports the percentage of insolvent banks",
  "under the 50\\% uninsured depositor run scenario relative to the total number of insolvent",
  "banks in that scenario.",
  "\\end{minipage}",
  "",
  "\\medskip",
  "\\begin{tabular}{l*{5}{c}}",
  "\\hline\\hline",
  "& (1) & (2) & (3) & (4) & (5) \\\\",
  "Asset Size ",
  "& Total Banks ",
  "& All Assets ",
  "& 100\\% Uninsured",
  "& 50\\% Uninsured ",
  "& Percentage \\\\",
  "",
  "& Banks ",
  "& Liquidated ",
  "&Deposit Run ",
  "& Deposit Run ",
  "& 50\\% Uninsured run \\\\",
  "\\hline",
  ""
)

for (i in 1:nrow(table_2024)) {
  row <- table_2024[i, ]
  latex_2024 <- c(
    latex_2024,
    sprintf("%s & %d & %d & %d & %d & %.1f\\%% \\\\",
            row$size_category,
            row$total_banks,
            row$mv_insolvent,
            row$idcr_insolvent_s100,
            row$idcr_insolvent_s50,
            row$pct_s50)
  )
}

latex_2024 <- c(
  latex_2024,
  "\\hline",
  "",
  sprintf("Number of Banks & %d & %d & %d & %d & 100\\%% \\\\",
          totals_2024$total_banks,
          totals_2024$mv_insolvent,
          totals_2024$idcr_insolvent_s100,
          totals_2024$idcr_insolvent_s50),
  "\\hline\\hline",
  "\\end{tabular}",
  "",
  "\\begin{minipage}{0.95\\textwidth}",
  "\\smallskip",
  "\\footnotesize",
  "\\textit{Note:} Insolvency in Column (2) is defined as mark-to-market (MTM) asset values being",
  "insufficient to cover total non-equity liabilities. In Column (3), the assumption is that all",
  "Uninsured depositors run; insolvency occurs if remaining insured deposits cannot be repaid from",
  "MTM asset values. In Column (4), only 50\\% of uninsured deposits run. The final column shows the",
  "distribution of insolvent banks in Column (4) as a percentage of all insolvent banks under that",
  "scenario. All insolvency measures are based on 2022Q4 balance sheet data. Sample excludes G-SIBs,",
  "failed banks, and banks without BTFP-eligible securities. This period includes both",
  "the Arbitrage period (through January 24) and Wind-down period (January 25 onwards).",
  "Borrower identification includes BTFP, DW, and FHLB users (10\\% threshold).",
  "\\end{minipage}",
  "",
  "\\end{table}"
)

writeLines(latex_2024, file.path(TABLE_PATH, "insolvency_size_2024q1.tex"))
cat("\n2024 period table saved to:", file.path(TABLE_PATH, "insolvency_size_2024q1.tex"), "\n")

## 
## 2024 period table saved to: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/insolvency_size_2024q1.tex

# --------------------------------------------------------------------------
# SUMMARY STATISTICS
# --------------------------------------------------------------------------

cat("\n", strrep("=", 70), "\n")

## 
##  ======================================================================

cat("SUMMARY STATISTICS\n")

## SUMMARY STATISTICS

cat(strrep("=", 70), "\n\n")

## ======================================================================

cat("--- Acute Period (March 13 - May 1, 2023) ---\n")

## --- Acute Period (March 13 - May 1, 2023) ---

cat(sprintf("Total banks: %d\n", totals_acute$total_banks))

## Total banks: 4292

cat(sprintf("MV Insolvent (All Assets Liquidated): %d (%.1f%%)\n", 
            totals_acute$mv_insolvent, 
            100 * totals_acute$mv_insolvent / totals_acute$total_banks))

## MV Insolvent (All Assets Liquidated): 2015 (46.9%)

cat(sprintf("IDCR Insolvent (100%% Uninsured Run): %d (%.1f%%)\n", 
            totals_acute$idcr_insolvent_s100, 
            100 * totals_acute$idcr_insolvent_s100 / totals_acute$total_banks))

## IDCR Insolvent (100% Uninsured Run): 1210 (28.2%)

cat(sprintf("IDCR Insolvent (50%% Uninsured Run): %d (%.1f%%)\n\n", 
            totals_acute$idcr_insolvent_s50, 
            100 * totals_acute$idcr_insolvent_s50 / totals_acute$total_banks))

## IDCR Insolvent (50% Uninsured Run): 179 (4.2%)

cat("--- January 1 - March 31, 2024 ---\n")

## --- January 1 - March 31, 2024 ---

cat(sprintf("Total banks: %d\n", totals_2024$total_banks))

## Total banks: 4292

cat(sprintf("MV Insolvent (All Assets Liquidated): %d (%.1f%%)\n", 
            totals_2024$mv_insolvent, 
            100 * totals_2024$mv_insolvent / totals_2024$total_banks))

## MV Insolvent (All Assets Liquidated): 2015 (46.9%)

cat(sprintf("IDCR Insolvent (100%% Uninsured Run): %d (%.1f%%)\n", 
            totals_2024$idcr_insolvent_s100, 
            100 * totals_2024$idcr_insolvent_s100 / totals_2024$total_banks))

## IDCR Insolvent (100% Uninsured Run): 1210 (28.2%)

cat(sprintf("IDCR Insolvent (50%% Uninsured Run): %d (%.1f%%)\n", 
            totals_2024$idcr_insolvent_s50, 
            100 * totals_2024$idcr_insolvent_s50 / totals_2024$total_banks))

## IDCR Insolvent (50% Uninsured Run): 179 (4.2%)

cat("\n=== TABLES GENERATED SUCCESSFULLY ===\n")

## 
## === TABLES GENERATED SUCCESSFULLY ===

# ==============================================================================
# BORROWERS BY SIZE AND INSOLVENCY CATEGORY - TWO PERIODS
# ==============================================================================

cat("\n", strrep("=", 70), "\n")

## 
##  ======================================================================

cat("BORROWERS BY SIZE AND INSOLVENCY CATEGORY\n")

## BORROWERS BY SIZE AND INSOLVENCY CATEGORY

cat(strrep("=", 70), "\n\n")

## ======================================================================

# --------------------------------------------------------------------------
# SIZE CATEGORY DEFINITIONS (from LaTeX table)
# --------------------------------------------------------------------------

create_size_category <- function(assets_thousands) {
  # Convert thousands to millions for comparison
  assets_millions <- assets_thousands / 1000
  
  case_when(
    assets_millions >= 700000 ~ "$700B and Above",
    assets_millions >= 250000 ~ "$250B-$700B",
    assets_millions >= 100000 ~ "$100B-$250B",
    assets_millions >= 50000  ~ "$50B-$100B",
    assets_millions >= 20000  ~ "$20B-$50B",
    assets_millions >= 10000  ~ "$10B-$20B",
    assets_millions >= 5000   ~ "$5B-$10B",
    assets_millions >= 3000   ~ "$3B-$5B",
    assets_millions >= 1000   ~ "$1B-$3B",
    assets_millions >= 500    ~ "$500M-$1B",
    TRUE                      ~ "Below $500M"
  )
}

# Define factor levels to ensure proper ordering
size_levels <- c(
  "$700B and Above",
  "$250B-$700B",
  "$100B-$250B",
  "$50B-$100B",
  "$20B-$50B",
  "$10B-$20B",
  "$5B-$10B",
  "$3B-$5B",
  "$1B-$3B",
  "$500M-$1B",
  "Below $500M"
)

# --------------------------------------------------------------------------
# PREPARE BASELINE WITH INSOLVENCY MEASURES (2022Q4)
# --------------------------------------------------------------------------

baseline_insolvency <- call_q %>%
  filter(period == '2022Q4') %>%
  filter(
    gsib == 0 | is.na(gsib),
    failed_bank == 0 | is.na(failed_bank),
    !is.na(omo_eligible) & omo_eligible > 0
  ) %>%
  mutate(
    # Size category
    size_category = create_size_category(total_asset),
    size_category = factor(size_category, levels = size_levels),
    
    # Insolvency measures
    adjusted_book_value = mm_asset - total_liability,
    mv_insolvent = as.integer(adjusted_book_value < 0),
    
    idcr_s50 = safe_div(mm_asset - 0.5 * uninsured_deposit - insured_deposit, insured_deposit),
    idcr_s100 = safe_div(mm_asset - 1.0 * uninsured_deposit - insured_deposit, insured_deposit),
    
    idcr_insolvent_s50 = as.integer(idcr_s50 < 0),
    idcr_insolvent_s100 = as.integer(idcr_s100 < 0)
  )

cat("Baseline data prepared:\n")

## Baseline data prepared:

cat(sprintf("  Total banks: %d\n", nrow(baseline_insolvency)))

##   Total banks: 4292

cat(sprintf("  MV Insolvent: %d\n", sum(baseline_insolvency$mv_insolvent, na.rm = TRUE)))

##   MV Insolvent: 2015

cat(sprintf("  IDCR Insolvent (s=1.0): %d\n", sum(baseline_insolvency$idcr_insolvent_s100, na.rm = TRUE)))

##   IDCR Insolvent (s=1.0): 1210

cat(sprintf("  IDCR Insolvent (s=0.5): %d\n", sum(baseline_insolvency$idcr_insolvent_s50, na.rm = TRUE)))

##   IDCR Insolvent (s=0.5): 179

# --------------------------------------------------------------------------
# TABLE 1: ACUTE PERIOD (March 13 - May 1, 2023)
# --------------------------------------------------------------------------

cat("\n", strrep("=", 70), "\n")

## 
##  ======================================================================

cat("TABLE 1: BORROWERS BY SIZE AND INSOLVENCY - ACUTE PERIOD\n")

## TABLE 1: BORROWERS BY SIZE AND INSOLVENCY - ACUTE PERIOD

cat(strrep("=", 70), "\n\n")

## ======================================================================

# Define acute period
acute_start <- as.Date("2023-03-13")
acute_end <- as.Date("2023-05-01")

# Identify borrowers
btfp_acute <- btfp_loans_raw %>%
  filter(btfp_loan_date >= acute_start & btfp_loan_date <= acute_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

dw_acute <- dw_loans_raw %>%
  filter(dw_loan_date >= acute_start & dw_loan_date <= acute_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

cat(sprintf("BTFP borrowers: %d\n", length(btfp_acute)))

## BTFP borrowers: 492

cat(sprintf("DW borrowers: %d\n", length(dw_acute)))

## DW borrowers: 424

# Add borrower classification
acute_data <- baseline_insolvency %>%
  mutate(
    btfp_user = idrssd %in% btfp_acute,
    dw_user = idrssd %in% dw_acute,
    borrower_type = case_when(
      btfp_user & dw_user ~ "Both",
      btfp_user & !dw_user ~ "BTFP Only",
      !btfp_user & dw_user ~ "DW Only",
      TRUE ~ "Non-Borrower"
    ),
    borrower_type = factor(borrower_type, levels = c("BTFP Only", "DW Only", "Both", "Non-Borrower"))
  )

# Create summary tables by size, borrower type, and insolvency status

# Panel A: All Assets Liquidated (MV Insolvent)
table1a <- acute_data %>%
  group_by(size_category, borrower_type) %>%
  summarise(
    total = n(),
    insolvent = sum(mv_insolvent, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  pivot_wider(
    names_from = borrower_type,
    values_from = c(total, insolvent),
    values_fill = 0
  ) %>%
  arrange(size_category)

cat("\n--- Panel A: All Assets Liquidated (MV Insolvent) ---\n")

## 
## --- Panel A: All Assets Liquidated (MV Insolvent) ---

print(table1a)

## # A tibble: 10 × 9
##    size_category `total_BTFP Only` `total_DW Only` `total_Non-Borrower`
##    <fct>                     <int>           <int>                <int>
##  1 $250B-$700B                   1               1                    3
##  2 $100B-$250B                   2               2                    7
##  3 $50B-$100B                    4               1                    8
##  4 $20B-$50B                     7               4                   27
##  5 $10B-$20B                     6              16                   26
##  6 $5B-$10B                     14              17                   74
##  7 $3B-$5B                      13              25                   73
##  8 $1B-$3B                      78              70                  387
##  9 $500M-$1B                    81              59                  563
## 10 Below $500M                 162             104                 2363
## # ℹ 5 more variables: total_Both <int>, `insolvent_BTFP Only` <int>,
## #   `insolvent_DW Only` <int>, `insolvent_Non-Borrower` <int>,
## #   insolvent_Both <int>

# Panel B: 100% Uninsured Run (IDCR s=1.0)
table1b <- acute_data %>%
  group_by(size_category, borrower_type) %>%
  summarise(
    total = n(),
    insolvent = sum(idcr_insolvent_s100, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  pivot_wider(
    names_from = borrower_type,
    values_from = c(total, insolvent),
    values_fill = 0
  ) %>%
  arrange(size_category)

cat("\n--- Panel B: 100% Uninsured Run (IDCR s=1.0) ---\n")

## 
## --- Panel B: 100% Uninsured Run (IDCR s=1.0) ---

print(table1b)

## # A tibble: 10 × 9
##    size_category `total_BTFP Only` `total_DW Only` `total_Non-Borrower`
##    <fct>                     <int>           <int>                <int>
##  1 $250B-$700B                   1               1                    3
##  2 $100B-$250B                   2               2                    7
##  3 $50B-$100B                    4               1                    8
##  4 $20B-$50B                     7               4                   27
##  5 $10B-$20B                     6              16                   26
##  6 $5B-$10B                     14              17                   74
##  7 $3B-$5B                      13              25                   73
##  8 $1B-$3B                      78              70                  387
##  9 $500M-$1B                    81              59                  563
## 10 Below $500M                 162             104                 2363
## # ℹ 5 more variables: total_Both <int>, `insolvent_BTFP Only` <int>,
## #   `insolvent_DW Only` <int>, `insolvent_Non-Borrower` <int>,
## #   insolvent_Both <int>

# Panel C: 50% Uninsured Run (IDCR s=0.5)
table1c <- acute_data %>%
  group_by(size_category, borrower_type) %>%
  summarise(
    total = n(),
    insolvent = sum(idcr_insolvent_s50, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  pivot_wider(
    names_from = borrower_type,
    values_from = c(total, insolvent),
    values_fill = 0
  ) %>%
  arrange(size_category)

cat("\n--- Panel C: 50% Uninsured Run (IDCR s=0.5) ---\n")

## 
## --- Panel C: 50% Uninsured Run (IDCR s=0.5) ---

print(table1c)

## # A tibble: 10 × 9
##    size_category `total_BTFP Only` `total_DW Only` `total_Non-Borrower`
##    <fct>                     <int>           <int>                <int>
##  1 $250B-$700B                   1               1                    3
##  2 $100B-$250B                   2               2                    7
##  3 $50B-$100B                    4               1                    8
##  4 $20B-$50B                     7               4                   27
##  5 $10B-$20B                     6              16                   26
##  6 $5B-$10B                     14              17                   74
##  7 $3B-$5B                      13              25                   73
##  8 $1B-$3B                      78              70                  387
##  9 $500M-$1B                    81              59                  563
## 10 Below $500M                 162             104                 2363
## # ℹ 5 more variables: total_Both <int>, `insolvent_BTFP Only` <int>,
## #   `insolvent_DW Only` <int>, `insolvent_Non-Borrower` <int>,
## #   insolvent_Both <int>

# Create comprehensive table for export
table1_comprehensive <- acute_data %>%
  group_by(size_category, borrower_type) %>%
  summarise(
    N = n(),
    MV_Insolvent = sum(mv_insolvent, na.rm = TRUE),
    IDCR_s100_Insolvent = sum(idcr_insolvent_s100, na.rm = TRUE),
    IDCR_s50_Insolvent = sum(idcr_insolvent_s50, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(size_category, borrower_type)

# Display comprehensive table
cat("\n--- Comprehensive Table: All Insolvency Measures ---\n")

## 
## --- Comprehensive Table: All Insolvency Measures ---

table1_comprehensive %>%
  kable(
    caption = "Table 1: Borrowers by Size and Insolvency Status - Acute Period (March 13 - May 1, 2023)",
    col.names = c("Asset Size", "Borrower Type", "N", "MV Insolvent", "IDCR s=1.0", "IDCR s=0.5"),
    align = c("l", "l", rep("r", 4))
  ) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  collapse_rows(columns = 1, valign = "top")

Table 1: Borrowers by Size and Insolvency Status - Acute Period (March 13 - May 1, 2023)

Asset Size	Borrower Type	N	MV Insolvent	IDCR s=1.0	IDCR s=0.5
$250B-$700B	BTFP Only	1	1	0	0
	DW Only	1	0	0	0
	Non-Borrower	3	1	1	0
$100B-$250B	BTFP Only	2	0	0	0
	DW Only	2	2	1	0
	Both	2	1	0	0
	Non-Borrower	7	5	3	1
$50B-$100B	BTFP Only	4	1	0	0
	DW Only	1	0	0	0
	Both	2	1	1	0
	Non-Borrower	8	6	3	1
$20B-$50B	BTFP Only	7	5	1	1
	DW Only	4	2	1	0
	Both	7	3	2	1
	Non-Borrower	27	8	5	1
$10B-$20B	BTFP Only	6	4	1	0
	DW Only	16	7	4	1
	Both	9	4	2	0
	Non-Borrower	26	11	8	3
$5B-$10B	BTFP Only	14	10	6	2
	DW Only	17	11	8	2
	Both	7	7	2	1
	Non-Borrower	74	34	17	4
$3B-$5B	BTFP Only	13	7	2	0
	DW Only	25	14	8	3
	Both	3	2	1	0
	Non-Borrower	73	43	21	2
$1B-$3B	BTFP Only	78	54	26	3
	DW Only	70	39	22	7
	Both	27	18	6	1
	Non-Borrower	387	217	112	17
$500M-$1B	BTFP Only	81	48	24	3
	DW Only	59	36	21	4
	Both	14	9	3	0
	Non-Borrower	563	291	189	24
Below $500M	BTFP Only	162	94	47	4
	DW Only	104	56	34	4
	Both	23	14	6	0
	Non-Borrower	2363	949	622	89

# Export to LaTeX
latex_acute <- c(
  "\\begin{table}[htbp]",
  "\\centering",
  "\\caption{Borrowers by Asset Size and Insolvency Status: Acute Period (March 13--May 1, 2023)}",
  "\\label{tab:borrower_size_insolvency_acute}",
  "\\footnotesize",
  "\\begin{tabular}{llrrrr}",
  "\\hline\\hline",
  "Asset Size & Borrower Type & N & MV Insol. & IDCR s=1.0 & IDCR s=0.5 \\\\",
  "\\hline"
)

current_size <- ""
for (i in 1:nrow(table1_comprehensive)) {
  row <- table1_comprehensive[i, ]
  size_display <- if (row$size_category != current_size) as.character(row$size_category) else ""
  current_size <- row$size_category
  
  latex_acute <- c(
    latex_acute,
    sprintf("%s & %s & %d & %d & %d & %d \\\\",
            size_display,
            row$borrower_type,
            row$N,
            row$MV_Insolvent,
            row$IDCR_s100_Insolvent,
            row$IDCR_s50_Insolvent)
  )
  
  # Add midrule between size categories
  if (i < nrow(table1_comprehensive) && 
      table1_comprehensive$size_category[i+1] != current_size) {
    latex_acute <- c(latex_acute, "\\midrule")
  }
}

latex_acute <- c(
  latex_acute,
  "\\hline\\hline",
  "\\end{tabular}",
  "\\begin{minipage}{0.95\\textwidth}",
  "\\smallskip",
  "\\footnotesize",
  "\\textit{Note:} This table reports the number of banks by asset size, borrower type, and insolvency status",
  "during the acute crisis period (March 13--May 1, 2023). MV Insol. = Mark-to-market insolvency",
  "(adjusted book value < 0). IDCR s=1.0 = Insolvent under 100\\% uninsured depositor run.",
  "IDCR s=0.5 = Insolvent under 50\\% uninsured depositor run. All insolvency measures based on 2022Q4",
  "balance sheet data. Sample excludes G-SIBs, failed banks, and banks without BTFP-eligible securities.",
  "\\end{minipage}",
  "\\end{table}"
)

writeLines(latex_acute, file.path(TABLE_PATH, "borrower_size_insolvency_acute.tex"))
cat("\nAcute period table saved to:", file.path(TABLE_PATH, "borrower_size_insolvency_acute.tex"), "\n")

## 
## Acute period table saved to: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/borrower_size_insolvency_acute.tex

# --------------------------------------------------------------------------
# TABLE 2: JANUARY 1 - MARCH 31, 2024
# Includes BTFP, DW, and FHLB (10% threshold)
# --------------------------------------------------------------------------

cat("\n", strrep("=", 70), "\n")

## 
##  ======================================================================

cat("TABLE 2: BORROWERS BY SIZE AND INSOLVENCY - JAN 1 - MARCH 31, 2024\n")

## TABLE 2: BORROWERS BY SIZE AND INSOLVENCY - JAN 1 - MARCH 31, 2024

cat(strrep("=", 70), "\n\n")

## ======================================================================

# Define period
period_2024_start <- as.Date("2024-01-01")
period_2024_end <- as.Date("2024-03-31")

# Identify borrowers
btfp_2024 <- btfp_loans_raw %>%
  filter(btfp_loan_date >= period_2024_start & btfp_loan_date <= period_2024_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

dw_2024 <- dw_loans_raw %>%
  filter(dw_loan_date >= period_2024_start & dw_loan_date <= period_2024_end) %>%
  distinct(rssd_id) %>%
  pull(rssd_id)

fhlb_borrowers_10pct <- call_q %>%
  filter(period == '2022Q4') %>%
  filter(
    gsib == 0 | is.na(gsib),
    failed_bank == 0 | is.na(failed_bank),
    !is.na(omo_eligible) & omo_eligible > 0,
    !is.na(abnormal_fhlb_borrowing_10pct) & abnormal_fhlb_borrowing_10pct > 0
  ) %>%
  pull(idrssd)

cat(sprintf("BTFP borrowers: %d\n", length(btfp_2024)))

## BTFP borrowers: 717

cat(sprintf("DW borrowers: %d\n", length(dw_2024)))

## DW borrowers: 0

cat(sprintf("FHLB borrowers (10%% threshold): %d\n", length(fhlb_borrowers_10pct)))

## FHLB borrowers (10% threshold): 302

# Add borrower classification
data_2024 <- baseline_insolvency %>%
  mutate(
    btfp_user = idrssd %in% btfp_2024,
    dw_user = idrssd %in% dw_2024,
    fhlb_user = idrssd %in% fhlb_borrowers_10pct,
    borrower_type = case_when(
      btfp_user & dw_user & fhlb_user ~ "All Three",
      btfp_user & dw_user & !fhlb_user ~ "BTFP+DW",
      btfp_user & !dw_user & fhlb_user ~ "BTFP+FHLB",
      !btfp_user & dw_user & fhlb_user ~ "DW+FHLB",
      btfp_user & !dw_user & !fhlb_user ~ "BTFP Only",
      !btfp_user & dw_user & !fhlb_user ~ "DW Only",
      !btfp_user & !dw_user & fhlb_user ~ "FHLB Only",
      TRUE ~ "Non-Borrower"
    ),
    borrower_type = factor(borrower_type, 
                          levels = c("BTFP Only", "DW Only", "FHLB Only",
                                   "BTFP+DW", "BTFP+FHLB", "DW+FHLB",
                                   "All Three", "Non-Borrower"))
  )

# Create comprehensive table
table2_comprehensive <- data_2024 %>%
  group_by(size_category, borrower_type) %>%
  summarise(
    N = n(),
    MV_Insolvent = sum(mv_insolvent, na.rm = TRUE),
    IDCR_s100_Insolvent = sum(idcr_insolvent_s100, na.rm = TRUE),
    IDCR_s50_Insolvent = sum(idcr_insolvent_s50, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(size_category, borrower_type)

# Display comprehensive table
cat("\n--- Comprehensive Table: All Insolvency Measures ---\n")

## 
## --- Comprehensive Table: All Insolvency Measures ---

table2_comprehensive %>%
  kable(
    caption = "Table 2: Borrowers by Size and Insolvency Status - January 1 - March 31, 2024",
    col.names = c("Asset Size", "Borrower Type", "N", "MV Insolvent", "IDCR s=1.0", "IDCR s=0.5"),
    align = c("l", "l", rep("r", 4))
  ) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  collapse_rows(columns = 1, valign = "top") %>%
  scroll_box(width = "100%")

Table 2: Borrowers by Size and Insolvency Status - January 1 - March 31, 2024

Asset Size	Borrower Type	N	MV Insolvent	IDCR s=1.0	IDCR s=0.5
$250B-$700B	BTFP Only	1	0	0	0
	Non-Borrower	4	2	1	0
$100B-$250B	FHLB Only	1	0	0	0
	Non-Borrower	12	8	4	1
$50B-$100B	BTFP Only	1	0	0	0
	FHLB Only	2	1	1	0
	Non-Borrower	12	7	3	1
$20B-$50B	BTFP Only	10	5	3	1
	FHLB Only	7	4	1	0
	BTFP+FHLB	1	0	0	0
	Non-Borrower	27	9	5	2
$10B-$20B	BTFP Only	13	6	4	0
	FHLB Only	7	2	2	0
	BTFP+FHLB	2	1	0	0
	Non-Borrower	35	17	9	4
$5B-$10B	BTFP Only	29	15	7	2
	FHLB Only	11	4	4	1
	BTFP+FHLB	3	1	0	0
	Non-Borrower	69	42	22	6
$3B-$5B	BTFP Only	24	14	2	0
	FHLB Only	8	3	3	0
	BTFP+FHLB	2	1	0	0
	Non-Borrower	80	48	27	5
$1B-$3B	BTFP Only	124	77	37	4
	FHLB Only	31	14	9	3
	BTFP+FHLB	19	10	5	0
	Non-Borrower	388	227	115	21
$500M-$1B	BTFP Only	137	86	51	7
	FHLB Only	40	23	11	1
	BTFP+FHLB	17	8	6	2
	Non-Borrower	523	267	169	21
Below $500M	BTFP Only	281	157	92	11
	FHLB Only	131	49	30	5
	BTFP+FHLB	20	12	6	2
	Non-Borrower	2220	895	581	79

# Export to LaTeX
latex_2024 <- c(
  "\\begin{table}[htbp]",
  "\\centering",
  "\\caption{Borrowers by Asset Size and Insolvency Status: January 1--March 31, 2024}",
  "\\label{tab:borrower_size_insolvency_2024q1}",
  "\\footnotesize",
  "\\begin{tabular}{llrrrr}",
  "\\hline\\hline",
  "Asset Size & Borrower Type & N & MV Insol. & IDCR s=1.0 & IDCR s=0.5 \\\\",
  "\\hline"
)

current_size <- ""
for (i in 1:nrow(table2_comprehensive)) {
  row <- table2_comprehensive[i, ]
  size_display <- if (row$size_category != current_size) as.character(row$size_category) else ""
  current_size <- row$size_category
  
  latex_2024 <- c(
    latex_2024,
    sprintf("%s & %s & %d & %d & %d & %d \\\\",
            size_display,
            row$borrower_type,
            row$N,
            row$MV_Insolvent,
            row$IDCR_s100_Insolvent,
            row$IDCR_s50_Insolvent)
  )
  
  # Add midrule between size categories
  if (i < nrow(table2_comprehensive) && 
      table2_comprehensive$size_category[i+1] != current_size) {
    latex_2024 <- c(latex_2024, "\\midrule")
  }
}

latex_2024 <- c(
  latex_2024,
  "\\hline\\hline",
  "\\end{tabular}",
  "\\begin{minipage}{0.95\\textwidth}",
  "\\smallskip",
  "\\footnotesize",
  "\\textit{Note:} This table reports the number of banks by asset size, borrower type, and insolvency status",
  "during January 1--March 31, 2024. MV Insol. = Mark-to-market insolvency (adjusted book value < 0).",
  "IDCR s=1.0 = Insolvent under 100\\% uninsured depositor run. IDCR s=0.5 = Insolvent under 50\\% uninsured",
  "depositor run. All insolvency measures based on 2022Q4 balance sheet data. FHLB borrowers identified",
  "using 10\\% threshold (90th percentile). Sample excludes G-SIBs, failed banks, and banks without",
  "BTFP-eligible securities.",
  "\\end{minipage}",
  "\\end{table}"
)

writeLines(latex_2024, file.path(TABLE_PATH, "borrower_size_insolvency_2024q1.tex"))
cat("\n2024 period table saved to:", file.path(TABLE_PATH, "borrower_size_insolvency_2024q1.tex"), "\n")

## 
## 2024 period table saved to: C:/Users/mferdo2/OneDrive - Louisiana State University/Finance_PhD/DW_Stigma_paper/Liquidity_project_2025/03_documentation/borrowing_t_stat/tables/borrower_size_insolvency_2024q1.tex

# --------------------------------------------------------------------------
# SUMMARY STATISTICS
# --------------------------------------------------------------------------

cat("\n", strrep("=", 70), "\n")

## 
##  ======================================================================

cat("SUMMARY STATISTICS BY BORROWER TYPE AND INSOLVENCY\n")

## SUMMARY STATISTICS BY BORROWER TYPE AND INSOLVENCY

cat(strrep("=", 70), "\n\n")

## ======================================================================

cat("--- Acute Period ---\n")

## --- Acute Period ---

acute_summary <- table1_comprehensive %>%
  group_by(borrower_type) %>%
  summarise(
    Total = sum(N),
    MV_Insol = sum(MV_Insolvent),
    MV_Pct = 100 * MV_Insol / Total,
    IDCR_100 = sum(IDCR_s100_Insolvent),
    IDCR_100_Pct = 100 * IDCR_100 / Total,
    IDCR_50 = sum(IDCR_s50_Insolvent),
    IDCR_50_Pct = 100 * IDCR_50 / Total,
    .groups = "drop"
  )
print(acute_summary)

## # A tibble: 4 × 8
##   borrower_type Total MV_Insol MV_Pct IDCR_100 IDCR_100_Pct IDCR_50 IDCR_50_Pct
##   <fct>         <int>    <int>  <dbl>    <int>        <dbl>   <int>       <dbl>
## 1 BTFP Only       368      224   60.9      107         29.1      13        3.53
## 2 DW Only         299      167   55.9       99         33.1      21        7.02
## 3 Both             94       59   62.8       23         24.5       3        3.19
## 4 Non-Borrower   3531     1565   44.3      981         27.8     142        4.02

cat("\n--- January 1 - March 31, 2024 ---\n")

## 
## --- January 1 - March 31, 2024 ---

summary_2024 <- table2_comprehensive %>%
  group_by(borrower_type) %>%
  summarise(
    Total = sum(N),
    MV_Insol = sum(MV_Insolvent),
    MV_Pct = 100 * MV_Insol / Total,
    IDCR_100 = sum(IDCR_s100_Insolvent),
    IDCR_100_Pct = 100 * IDCR_100 / Total,
    IDCR_50 = sum(IDCR_s50_Insolvent),
    IDCR_50_Pct = 100 * IDCR_50 / Total,
    .groups = "drop"
  )
print(summary_2024)

## # A tibble: 4 × 8
##   borrower_type Total MV_Insol MV_Pct IDCR_100 IDCR_100_Pct IDCR_50 IDCR_50_Pct
##   <fct>         <int>    <int>  <dbl>    <int>        <dbl>   <int>       <dbl>
## 1 BTFP Only       620      360   58.1      196         31.6      25        4.03
## 2 FHLB Only       238      100   42.0       61         25.6      10        4.20
## 3 BTFP+FHLB        64       33   51.6       17         26.6       4        6.25
## 4 Non-Borrower   3370     1522   45.2      936         27.8     140        4.15

cat("\n=== TABLES GENERATED SUCCESSFULLY ===\n")

## 
## === TABLES GENERATED SUCCESSFULLY ===