Examining Funding Trends of Corporate and Public Pensions in the United States
Christian Kincaid
February 9, 2019
Abstract
Acknowledgements
Introduction
In the aftermath of the financial crisis, central banks around the world took extraordinary measures to ensure the flow of credit and stimulate economic growth. In the United States, the Federal Reserve instituted quantitative easing (QE), an expansionary monetary policy which involved large-scale asset purchases and a lowering of benchmark lending rates to zero. While unconventional, such actions were done in pursuit of the Federal Reserve’s dual mandate of price stability and full employment.
At the time of this writing, the U.S. is nearly ten years into a business expansion. Major financial institutions appear more stable than in 2008 thanks to improved lending standards, federally-administered stress tests, and more stringent liquidity requirements. But as savings accounts, asset prices, and employment levels have all improved markedly, a disparity has emerged in retirement plans: specifically, corporate pensions have recovered to a near fully-funded level, while a funding gap in public pensions persists. Simply put, public pensions have not participated in this economic recovery.
This paper analyzes the factors behind this phenomenon, taking central bank policy, demographic trends, and investment assumptions into account.
Identifying the Problem
library(readxl)
library(ggplot2)
library(maps)
library(dplyr)slg_CashFlow <- read_excel("C:/Users/Christian/Desktop/SURE Research Project/Y323RC1A027NBEA.xls", skip = 10)
colnames(slg_CashFlow) <- c("date", "cash_flow")
# head(slg_CashFlow)
# Cash Flow Plot
cash_flow_plot <- ggplot(slg_CashFlow, aes(x = date, y = cash_flow))
cash_flow_plot + geom_line(size = 1.8) +
labs(title = "State and Local Government Defined Benefit (DB) Plans",
subtitle = "Cash Flow, billions",
caption = "Source: U.S. Bureau of Economic Analysis",
x = "",
y = "")
# Asset/Liability Data - Isolate U.S. observations
state_pensions <- read.csv("C:/Users/Christian/Desktop/SURE Research Project/efa-state-pension-tables-annual-historical.csv")
colnames(state_pensions) <- c("date", "region", "assets", "liabilities", "funded_status","funded_ratio")
state_pensions <- state_pensions[,0:6]
head(state_pensions)## date region assets liabilities funded_status funded_ratio
## 1 2016 United States 3819272.00 8035858.00 -4216586.00 47.52787
## 2 2016 Alabama 36593.75 78591.45 -41997.70 46.56200
## 3 2016 Alaska 13951.25 35646.61 -21695.37 39.13765
## 4 2016 Arizona 46847.28 106611.67 -59764.39 43.94198
## 5 2016 Arkansas 26449.64 52981.67 -26532.03 49.92224
## 6 2016 California 785191.08 1535600.73 -750409.65 51.13250us_pensions <- state_pensions %>%
select(region, assets, liabilities) %>%
filter(region == "United States")## Warning: package 'bindrcpp' was built under R version 3.4.4head(us_pensions)## region assets liabilities
## 1 United States 3819272 8035858
## 2 United States 3679019 7750743
## 3 United States 3729542 7450159
## 4 United States 3553819 7087203
## 5 United States 3168133 6169118
## 6 United States 2850608 5982460# Difference between Assets and Liabilities, U.S.
# TODO
# gap_plot <- Net cash flows to public defined benefit plans peaked in 1997 and became negative in 2009. Thus, this metric indicates that public defined benefit plans have been in decline for almost two decades - and the trend is only accelerating. A significant inflection point in the data occurs in 2009, when net cash flow to public DB plans turns negative.
Any system which pays out more than it receives is unsustainable. Since 2009, state and local government pensions have failed to raise sufficient revenue to cover mounting obligations. Essentially, public pensions in the last decade function as a transfer program funded by debt, rather than by contributions or investment returns.