​​But cryptocurrencies are also exceptionally volatile over much shorter periods of time. ​Day-to-day price fluctuations of cryptocurrencies eclipse those of traditional currencies, stocks, and precious metals, and do so consistently across assets and time periods. This phenomenon is not entirely driven by the longer-term ups and downs reported in headlines. Bitcoin, Ethereum, and other cryptocurrencies frequently exhibit daily price drops during bull markets and increases during bear markets far in excess of traditional assets. The interactive chart below provides one way to visualize this day-to-day volatility—the daily percentage increase or decrease in price in U.S. dollars from the previous day. 

This interactive tool allows the reader to investigate the phenomenon of day-to-day volatility for different cryptocurrencies, traditional assets, and time periods. During the period 2018–2022, Bitcoin’s average daily change (​​measured as the absolute value of the percentage change from the previous day) was 2.87%, versus the Euro (0.34%), pound (0.43%), and yen (0.35%). Other major cryptocurrencies, such as Ethereum (3.76%), Ripple (4.04%), and Dogecoin (4.55%), exceed Bitcoin’s already-high fluctuations. 

The table below presents this statistic for each asset or index tracked by the data tool. 

Why is the day-to-day volatility of cryptocurrencies important? 

Despite much public discussion about cryptocurrencies as speculative investments or world-changing technology, their success ultimately hinges on widespread adoption as currencies—including as a medium of exchange. Day-to-day volatility creates exchange rate risk over short periods of time. This creates problems for a currency’s usefulness as a medium of exchange if one or both parties to the transaction need to quickly move their money into a different currency. Either the buyer or seller, or both, must take this exchange rate risk, increasing the transaction cost and, ultimately, the price. 

To date, the use of cryptocurrencies as a medium of exchange has taken off in only a small number of market niches, most notably dark net markets where mostly illicit goods are for sale. A 2018 article reported that Bitcoin’s high short-term volatility was adding to the cost and lowering the number of transactions on such platforms. 

There are likely multiple causes for the unusually high volatility of cryptocurrencies. While more widespread adoption may be part of the solution, other likely causes are structural and follow directly from the way cryptocurrencies are designed. Large banks and other financial firms hold huge reserves of traditional currencies, and stocks have market makers, both serving to smooth out short-term volatility and make exchange markets more liquid. Bitcoin, on the other hand, eschews large central intermediaries by design.   

Solutions lie in further entrepreneurial innovation, and that process is already well underway. Bitcoin’s ​​Lightning Network is designed to facilitate faster transactions at a larger scale. Stablecoins, pegged in value to fiat currencies like the dollar or other assets, eliminate high day-to-day volatility by design. They can be used to keep money in the crypto ecosystem—protected from short-term fluctuations and, in theory, easier and faster than traditional fiat currencies--to exchange with Bitcoin or Ethereum. However, their relative novelty opens the door for long-tail risk as well as fraud. 

These and other avenues carry some promise to address day-to-day volatility and make cryptocurrencies more viable for everyday use. But innovation must continue. The Lightning Network and Stablecoins both introduce the scope for large financial intermediaries and dependence on the fiat system that crypto pioneers sought precisely to avoid. Furthermore, the much larger number of people not yet sold on crypto may see these as further complications to already convoluted and risky alternatives to fiat. 

The crypto community must turn away from ​​voices such as Bitcoin maximalists that say the perfect solution is already in hand, and keep innovating and experimenting.  ​Regulators ​could do great harm by making rules that ossify this still-developing technology or cut off as-yet unrealized solutions that only a market process of discovery can deliver. 

We hope that the interactive tool provided here, which offers an intuitive way to visualize the phenomenon of day-to-day volatility in cryptocurrencies, will play a part in opening the conversation and potential for fresh ideas. 

Methodology 

We selected the top 10 cryptocurrencies by market capitalization from CoinMarketCap in addition to FTX’s FTT token. The top 10 cryptocurrencies include seven traditional cryptocurrencies and three stablecoins. We did not include the latter, which track the day-to-day volatility of fiat currencies by design, in the interactive chart, but do report their average daily changes in the summary table. Daily price and exchange rate data are sourced from Yahoo Finance via the R library quantmod. The only modification to the original source data occurred for the Ruble to Dollar data (RUBUSD=X). On Jan. 1, 2016, the original value appears to be off by a factor of 100, this value is divided by 100. Additionally, on June 13, 2022, and July 18, 2022, the adjusted close is outside of the bounds of the high and low—and inconsistent with historical data on the close price from The Wall Street Journal. These two values were replaced with the open price from the following day.

Daily percent change values are calculated from the percent change from the previous trading day’s adjusted close price. Our comparison of daily changes across different types of currencies and assets presents a challenge because different assets trade according to different schedules. Stocks trade on exchanges with daily opening and closing times and close on weekends and certain holidays. Traditional foreign exchange markets stay open around the clock, Monday through Friday, but close on weekends, and this is further complicated by time zones and different holidays globally. Cryptocurrencies trade continually.  

There is subjectivity inherent in addressing this issue. We chose to limit our analysis to the trading days of our traditional stock indices (S&P 500 & Russell 2000), which align with New York Stock Exchange trading days, and use reported adjusted close as the price. While this eliminates a small amount of data from the sample for cryptocurrencies, we conducted robustness checks and confirmed this does not drive our results about persistent differences in day-to-day percent changes. 

The post Examining day-to-day crypto volatility and why it’s important appeared first on Reason Foundation.

Government pension plans depend on annual investment results to help generate the funding needed to pay for the retirement benefits that have been promised to teachers, public safety, and other public workers. Since investment returns contribute to long-term public pension solvency trends, interested parties keep a close eye on the annual return results of these pension funds to see how they are performing compared to their own assumed rates of return. 

Reason Foundation’s list of public pension investment return results includes all major state pension plans that have reported their 2022 fiscal year results as of this writing.

The distribution of 2022 investment returns shows a significant range of results across all of the state pension plans reporting results at this time.

The Oklahoma Public Employees Retirement System reported a -14.5% return for its 2022 fiscal year, which is the lowest return rate among the public pension plans reporting results.

The New York State and Local Retirement System (NYSLRS) and the New York Police and Fire Retirement System (PFRS) reported 9.5% returns—the highest return rate in the nation for fiscal 2022, although their results are mostly attributed to plans’ 2022 fiscal year ending in March 2022, before the largest market losses in the 2022 calendar year.

Overall, the median investment return result for state pension systems in 2022 is -5.2%, which is far below the median long-term assumed rate of return for 2022 of 7% for the plans included in this list. With return results for the 2022 fiscal year so far below pension plans’ return assumptions, most state pension plans will see growth in their unfunded liabilities and a worsening of their reported funding levels.

With each public pension plan achieving different investment returns, the funding impact will also be different for each pension system.

Methodology

'Estimated Investment Gain/(Loss)' is calculated by taking the plan's FY 2020-21 Market Value of Assets and multiplying it by the difference between '2022 Return' and 'Assumed Rate of Return.' Estimated values are meant to approximate total amounts of investment loss that plans would fully & directly recognize this year due to FY 2021-22 return deviating from the assumption (i.e., not accounting for the smoothing mechanism). Investment returns shown are Net of Fees, if not stated otherwise. ‘Deviation from Assumed Rate of Return’ shows the difference between ‘2022 Return’ and ‘Assumed Rate of Return.' Positive returns are highlighted in light blue, and negative in orange. The distribution of the 2022 investment returns chart is based on the `normalized` probability density function, with all probabilities (i.e., all points on a line graph) summing up to 100%. 

The post The 2022 fiscal year investment results for state pension plans  appeared first on Reason Foundation.

The primary responsibilities of the legal system are to promote public safety and to provide for justice. Pressure to raise revenue, at best, undermines and, at worst, directly conflicts with those responsibilities. When incentives are misaligned, police departments and court systems become more concerned with taxation by citation than carrying out their core functions. Such conflicts of interest can undermine the legitimacy of the criminal justice system. 

Using data from the Census Bureau’s Annual Survey of State and Local Government Finances, Reason Foundation created this data visualization tool to shed some light on the amount of revenue generated through fines and fees in 2020, the most recent year for which data is available.

In 2020, local governments across the United States collected just under $9 billion in fines and fees. Local governments in three states—New York ($1.4 billion), California ($1.26 billion), and Texas ($1.17 billion)—collected well over a third of the $9 billion in fines and fees in 2020.

Local governments in New York, California, Texas, Illinois, Florida, Georgia, Ohio, New Jersey, Washington, and Pennsylvania** collected the most fines and fees in 2020. In all, 20 states saw their local governments bring in more than $100 million in fines and fees in 2020.

On a per capita basis, local governments in New York, Illinois, Texas, and Georgia collected more than $35 per resident in fines and fees in 2020. In contrast, local governments in New Hampshire, Connecticut, Maine, Nebraska, and Kentucky* collected less than $3 in fines and fees revenue per resident in 2020.

Local governments use fines and fees as a significant source of revenue

In 2019, local fines and fees revenue accounted for less than 2% of pre-pandemic state general revenue in all 50 states. The year 2017 is the most recent year for which comprehensive local revenue data are available. In 2017, 28,159 U.S. cities, townships, and counties reported a total of $4,975,608,000 in revenue from fines and fees after excluding jurisdictions without sufficient data (see data and methodology notes below). In most places, fines and fees accounted for less than 5% of general revenue.

However, a sizable minority of jurisdictions in the United States appear to be highly dependent on criminal justice and court-related fines and fees. At least 482 local governments derived 10% or more of their general revenue from fines and fees in 2017.

In fact, in 176 local U.S. jurisdictions, the money from fines and fees accounted for 20% or more of that government's total general revenue in 2017.**

Going further, there were 42 municipalities where fines, fees, and forfeits made up 50% or more of the general revenue.

Rural areas with relatively small populations tend to be the most dependent on fines and fees. Along major roadways, so-called speed-trap towns appear common. Due to data limitations, these numbers understate the scope and scale of the revenue generated by local fines and fees in many states.

U.S. cities, townships, and counties getting more than 5% of their revenue from fines and fees in 2017

For more information, please see Reason Foundation's recently released policy brief, "Fines and fees: Consequences and opportunities for reform."

Recommendations for reform

Fines and fees have turned many courts into revenue centers for state and local governments. While most governments do not derive a significant portion of their general revenues from fines and fees, some are almost entirely dependent on them. Nonetheless, fines and fees are not a reliable source of revenue.

Moreover, using fines and fees to directly fund courts, law enforcement agencies, or other government activities can result in undesirable conflicts of interest. In addition to these fiscal considerations, fines and fees have devastating consequences on low-income individuals, racial minority groups, and juveniles and their families.

The following policy recommendations address the primary fiscal considerations associated with fines and fees, ensure accountability, and promote fairness within the justice system.

Eliminate user fees and poverty penalties

User fees effectively transfer costs away from taxpayers and onto individual users of government services. While user fees are appropriate and desirable in many contexts, they do not make sense in the justice system. The rule of law benefits all members of society, and the users of courts—particularly defendants—are often those who are least likely to be able to pay for their operations. Funding law enforcement and court systems through user fees make access to justice regressive—costing the poor far more relative to their income or wealth than the middle class or wealthy. That is fundamentally unjust. Funding the legal system through user fees also reduces the opportunity for lawmakers to weigh funding priorities, rein in excess, and ensure that the system is funded appropriately.

Poverty penalties, including interest fees, late fees, payment plan fees, and collection fees are particularly regressive. These fees punish individuals for their financial status rather than their crimes, and this undermines the objective of fairness within the justice system. Moreover, such punitive financial penalties may hinder the ability of former offenders to reintegrate, contributing to high recidivism rates.

Fully fund courts from state budgets

Eliminating user fees in the justice system may require states to assume greater responsibility in funding their court systems. Allocating funds from general revenues would protect against potential conflicts of interest. The particular structure of court systems in each state may complicate this process, especially in states without unified court systems. This obstacle is worth overcoming to ensure that courts are adequately and equitably funded.

Develop standardized tools to determine the ability to pay and scale fines accordingly

Determination of a defendant's ability to pay fines should not be left solely to the subjective assessment of an individual judge. This can result in wildly different outcomes for otherwise comparable defendants. Establishing standard practices would ensure that individuals are treated equally under the law. Scaling fines according to an individual's ability to pay would also reduce the administrative costs associated with pursuing uncollectable debts.

Affidavits, bench cards, or ability-to-pay calculators may be used to standardize ability-to-pay determinations. Income-based fines, or day fines, could also be used to scale penalties according to an individual's financial status. There is room for experimentation among the states in this area. Still, state law should clarify the factors that are considered when determining an individual's ability to pay. Defendants should be made aware of these factors and what documentation they will be expected to provide.

Provide alternatives to monetary sanctions

Indigent defendants should be able to receive alternatives to monetary sanctions. Community service is one possible alternative to fines but it may prove overly burdensome for some. For example, community service may conflict with work schedules or family obligations. Such conflicts should be avoided, as maintaining employment and social ties are critical to reducing the risk of recidivism. Courts should be able to consider a range of alternatives, including waivers, job training, and drug or mental health treatment. Incarceration should never be considered an alternative to monetary sanctions, and fees should never be charged for alternatives to monetary sanctions.

Eliminate all fines and fees in juvenile cases

The objective of the juvenile justice system should be to rehabilitate young offenders and avoid future escalation in their criminality. Fines are a purely punitive measure and do not serve any rehabilitative function. They do, however, place undue financial burdens on youth and their families. Juveniles should be considered indigent by default, and their families should not be held responsible for any monetary sanctions they incur.

End driver's license suspensions for failure to pay

Driver's license suspensions are an inefficient and counterproductive penalty for failure to pay fines and fees. There are significant administrative costs associated with the enforcement of suspensions. Driver's license suspensions also inhibit the ability of individuals to secure and maintain employment necessary to fulfill their legal financial obligations.

Collect and publish data on court debt and collection practices

Data on fines and fees is needed for transparency, accountability, and fiscal responsibility. Without sufficient information, lawmakers, advocates, and other stakeholders are less able to understand the problems related to fines and fees and identify possible policy reforms.

Currently, most states do not adequately track the imposition and collection of fines and fees. When information is available, it is often dispersed among local governments, individual courts, and private collections firms. This practice not only undermines the ability of lawmakers to make informed policy and budgetary decisions but also contributes to the broader perception that the justice system is unfair and unaccountable. Enabling citizens to access information related to fines and fees would help restore the justice system's legitimacy and allow them to hold their lawmakers more accountable.

At a minimum, states should collect and publish information related to fines and fees, including:

• The monetary amount of fines and fees levied annually;
• The revenue generated by fines and fees;
• How fines and fees revenue is allocated;
• And the costs associated with the collection of the fines and fees.

*Correction: A previous version of this post misstated Kentucky's fines and fees data as $31 per capita rather than $2.50 per capita.

**Correction: A previous version of this data incorrectly displayed data for Jamestown, South Carolina, where Jamestown, Pennsylvania, is located. Jamestown, Pennsylvania, should not have appeared on the map, "U.S. cities, townships, and counties getting more than 5% of their revenue from fines and fees in 2017," above, which shows municipalities that get more than 5% of their general revenue from fines and fees. As reported by the Census Bureau, Jamestown, Pennsylvania, collected approximately $2,000 in fines and forfeits revenue in 2017, which accounted for less than 5% of the town's general revenue that year.

The correct data for Jamestown, South Carolina, is as follows:
Fines and Forfeits: $105,000
Percent of revenue: 64.4%
General Revenue: $163,000
Per Capita: $1,313

This error was made in the GPS settings of the "U.S. cities, townships, and counties getting more than 5% of their revenue from fines and fees in 2017" above, but the error was not made in any state-level calculations.

Notes on data and methodology

In general, there is a severe lack of data regarding the revenue generated from fines and fees. This lack of data can make it difficult for policymakers to assess the scale of the problem and the potential impacts of reform. In the absence of data, perceived reliance on fines and fees revenues to fund court systems and other government activities can present a significant obstacle to reform.

The Annual Survey of State and Local Finances includes a line item for "Fines and Forfeits." According to the Census Bureau's classification manual, Fines and Forfeits includes revenue from:

• Penalties imposed for violations of the law
• Civil penalties (e.g., for violating court orders)
• Court fees, if levied upon conviction of a crime or violation
• Court-ordered restitution to crime victims where the government actually collects the monies
• Forfeits of deposits held for performance guarantees or against loss or damage (such as forfeited bail and collateral)

The Census Bureau's survey of state and local government finances has been administered annually since 1957. A census is conducted every five years (years ending in '2' and '7'). In the intervening years, a sample of state and local governments is used to collect data. A new sample is selected every five years (years ending in '4' and '9'). Even in census years, many values are imputed rather than being collected directly.

In our analysis of individual local governments, we excluded any city, county, or township whose "fines and forfeits" value was imputed. We also excluded any jurisdiction that reported zero general revenues or for whom more than 50% of line items were imputed. As a result of those data filters, approximately 8,330 cities, townships, and counties were excluded from our analysis.

The post Local governments collected $9 billion in fines and fees in 2020 appeared first on Reason Foundation.

At the end of the second quarter of 2022, the $30.6 trillion debt of the United States federal government was 1.2 times larger than the annual economic output of the country. The U.S. is now reaching federal debt levels, as a share of gross domestic product (GDP), that we have not seen since the end of World War II.

Federal spending is increasingly untethered from fiscal realities. From 1965 to 2022, the federal government ran an annual budget deficit in 52 of the 57 years.

The annual federal budget deficits during and following the Great Recession of 2007-2009 were dwarfed by the recent federal deficits of 2020 and 2021, however, when annual budget deficits were $3.1 and $2.8 trillion respectively. The COVID-19 pandemic and accompanying lockdowns and policies sparked the largest spending bills in American history, including the $2.2 trillion CARES Act signed by then-President Donald Trump in March 2020. A year later, in March 2021, President Joe Biden signed the $1.9 trillion American Rescue Plan Act.

After accounting for inflation, the national debt jumped by almost $5 trillion in less than two years—rising from $25.9 trillion in the first quarter of 2020 to $30.6 trillion at the end of the second quarter of 2022. To get a sense of the magnitude of the growth of the debt, the current debt of more than $30 trillion translates to each American individual owing $91,814 based on the U.S. Bureau of Economic Analysis (BEA) estimate of 333 million Americans. This is an increase in the national debt of nearly $14,000 per person just since the first quarter of 2020.

While the increase in the national debt during the pandemic has been particularly shocking, it is consistent with a decades-long, bipartisan trend of deficit spending where government expenditures consistently exceed government receipt of money. When tax revenue is insufficient to cover government spending, the government must issue U.S. Treasury bonds, shorter-term obligations like bills and notes, or other debt instruments. 

Federal Debt Holders

The federal debt is often classified into two buckets: intragovernmental holdings and debt held by the public.

Intragovernmental Debt

Intragovernmental holdings are government debt held by government agencies. As of September 8, 2022, intragovernmental holdings totaled $6.6 trillion, which is 21.4% of the total outstanding public debt. The largest share of this intragovernmental debt is held by the Social Security Trust Fund (46%).

Debt Held by the Public

Debt held by the public can be broken down into debt held by the U.S. public, foreign entities, or the U.S. Federal Reserve. The U.S. public is a broad category that encompasses domestic non-federal investors. It includes state and local governments, private pension funds and insurance companies, banks, and other investors. Foreign entities include the governments and central banks of other countries and private international investors. 

In recent years, even relative to the first two groups of debt holders, the U.S. Federal Reserve has greatly increased its holding of government debt. The Federal Reserve buys the debt with newly created reserves, but these purchases raise the risk of inflation by monetizing the debt. Since new reserves can increase the nation’s supply of money, they can lead to higher prices as more dollars chase the same volume of goods and services. The Federal Reserve asserts, “Federal Reserve purchases of Treasury securities from the public are not a means of financing the federal deficit.” But Federal Reserve asset purchases are traditionally a means of circulating newly printed bills. While new tools like interest on monetary reserves can mitigate the impact of such expansion, the dramatic increase of Federal Reserve debt purchases (which include mortgage debt and corporate bonds as well as Treasurys) is a serious concern.

Given the persistence of federal deficit spending, if demand for U.S. debt does not keep pace with debt accumulation, the risk of debt monetization via Federal Reserve purchases rises further.

Foreign Holders of U.S. Debt

Demand for U.S. debt has increased because the dollar is the de facto reserve currency of the world. The Bretton Woods system, which pegged other currencies to the U.S. dollar which was redeemable for gold, effectively ended after President Richard Nixon suspended dollar-to-gold convertibility. Since that point, the nations belonging to the Organization of the Petroleum Exporting Countries (OPEC) have principally denominated oil sales in U.S. dollars, therefore boosting demand for America’s debt.

The United States heavily relies on foreign buyers for debt financing, which can potentially be a liability if or when international conflicts arise. Russia held $139 billion in U.S. debt in 2013. After the Russian annexation of Crimea in 2014, the U.S. responded with aggressive sanctions and threats to remove Russia from the Society for Worldwide Interbank Financial Telecommunication (SWIFT) system. In response, Russia’s central bank began divesting from U.S. Treasurys. Today the West is once again sanctioning Russia after its invasion of Ukraine.

Historically, many countries have relied on the safety and stability of U.S. Treasurys. Western sanctions on Russia are a reminder that this “risk-free” asset is not risk-free for those failing to align with American foreign policy. The mid-March reporting that Saudi Arabia may begin pricing Chinese oil sales in yuan is an indication that U.S. financial dominance is not completely unchallengeable. Yuan-denominated oil sales could further erode Chinese demand for our debt, which has declined in recent years.

Today, China and Japan account for nearly one-third of all foreign holdings of U.S. debt. Given America’s friction with China and the population decline experienced in Japan, it is not a certainty that these two countries will indefinitely continue to sweep up large volumes of additional U.S. debt.  

Ultimately, the United States government must understand that we do not have an unlimited capacity for financing our deficit spending. This will become even more difficult as we pay out the rapidly growing liabilities for programs like Social Security and Medicare.

Other Long-Term Federal Financial Obligations

Organizations incur long-term financial obligations in forms other than bonds and the U.S. federal government is no exception. Some common types of financial obligations include pension and retiree health care costs for veterans, civilian federal employees, and the general public (through Social Security and Medicare benefit commitments). Looking at the federal government's balance sheet as of 2021, public holdings of U.S. Treasury securities make up less than one-quarter of total federal liabilities. Unfunded entitlements, like Medicare and Social Security, account for the most at 59% of obligations.

Overall federal obligations have now surpassed $300,000 per American. While substantial in their own right, the debt obligations of state and local governments across the country are dwarfed by the various categories of federal debt.

Conclusion

Unfortunately, the United States does not seem positioned for economic expansion like it was the last time the debt-to-gross domestic product (GDP) ratio was this high during the post-World War II era. Following WWII, debt was reined in by brief periods of inflation and several decades of exceptional economic growth.

In the first two quarters of 2022, the U.S. economy experienced negative growth. With weak or negative economic growth expected, and no significant restriction on federal spending in sight, the debt-to-GDP ratio will continue to rise.

Jeffrey Rogers Hummel, Professor Emeritus in the Economics Department at San Jose State University, was consulted on the “Federal Reserve Assets as Percentage of Publicly Held Debt” chart.

Data & Methodology

• Federal Spending Versus Receipts: 
  • Overall data is from the U.S. Department of Commerce’s Bureau of Economic Analysis (BEA) National Income and Product Accounts data, specifically Table 3.2 (Government Current Receipts and Expenditures, which is reported quarterly). 
    • Spending data is from “Current Expenditures” on lines 24 and 44. 
    • Revenue data is from “Current Receipts” on lines 1 and 41. 
    • The inflation-adjusted data series are adjusted according to Q2 2022 dollars using the U.S. GDP implicit price deflator (FRED: GDPDEF) sourced from BEA.
    • The percent of GDP data series are calculated using quarterly Gross Domestic Product data (FRED: GDP) sourced from BEA.

• National Debt: Data are sourced from the U.S. Department of the Treasury—titled “Federal Debt: Total Public Debt” (FRED: GFDEBTN).
  • The debt data are inflation-adjusted to Q2 2022 dollars with the U.S. GDP implicit price deflator (FRED: GDPDEF) sourced from the U.S. Department of Commerce’s Bureau of Economic Analysis (BEA).
  • Per capita figures are derived from the above data and divided by U.S. population (FRED: B230RC0Q173SBEA) data sourced from BEA.
  • The percent of GDP data series are calculated using quarterly Gross Domestic Product data (FRED: GDP) sourced from BEA.

• Who Holds the Federal Debt: Data is sourced from the U.S. Department of Treasury.
  • Federal Reserve Banks: Public debt securities held by Federal Reserve banks. Data are sourced from Treasury (FRED: FDHBFRBN). The data are reported and presented at the quarterly level.
  • Foreign Entities: Federal debt held by foreign investors. Data are sourced from Treasury (FRED: FDHBFIN).
  • U.S. Public: Calculated by subtracting debt held by “Federal Reserve Banks” (FRED: FDHBFRBN) and “Foreign Entities” (FRED: FDHBFIN) from a FRED data stream, from Treasury, called “Federal Debt Held by the Public” source from Treasury (FRED: FYGFDPUN).
  • Agencies & Trusts: Federal debt held by agencies and trusts. Data are sourced from Treasury (FRED: FDHBATN).
  • All data series above are inflation-adjusted to Q2 2022 dollars with the U.S. GDP implicit price deflator (FRED: GDPDEF).
  • The percent of GDP data series are calculated using quarterly Gross Domestic Product data (FRED: GDP) sourced from BEA.

• Federal Reserve Assets
  • Total Federal Reserve Assets: The Federal Reserve has a balance sheet that contains both assets and liabilities. Notes in circulation, bank reserves, and other liabilities. On the asset side of the ledger, the Federal Reserve has securities that include things like: U.S. Treasurys, mortgage-backed securities, loans to banks or other institutions, and liquidity swaps with central banks from other countries. Total Federal Reserve assets as a percentage of publicly held debt are calculated by dividing reserve bank credit (FRED: RSBKCRNS) by total public debt outstanding (Treasury: tot_pub_debt_out_amt).
  • Total Treasury Deposits at the Federal Reserve: When the U.S. Treasury issues public debt and deposits the proceeds at the Federal Reserve this is considered a treasury deposit. This figure as a percentage of publicly held debt is calculated by taking reserve bank credit (FRED: RSBKCRNS) and backing out three other data streams (FRED: WTREGEN; FRED: WLRRAL; and FRED: WORAL) and dividing by total public debt outstanding When the Fed borrows from the private sector, it does through what is referred to as reverse repurchase agreements. That amount is calculated by backing out of reserve bank credit in two data streams (FRED: WREPODEL and FRED: WREPOFOR). The overall total of these two forms of Fed borrowing as a percentage of publicly held debt is the sum of the five data streams divided by total public debt outstanding.
    
• Biggest Foreign Holders: Data is retrieved from the US Treasury Department’s Major Foreign Holders of U.S. Treasury Securities historic tables, which due to aggregations below certain thresholds may result in missing data for certain years for some countries. Data are adjusted to 2021 dollars with the US GDP implicit price deflator (FRED: A191RD3A086NBEA).

• Beyond Publicly Held Debt
  • Federal: Data for these series are taken from the Financial Reports of the United States Government for the years 2000 through present published by the U.S. Treasury Department. 
    • The data is inflation-adjusted to 2021 dollars with the US GDP implicit price deflator (FRED: A191RD3A086NBEA). 
    • Per capita figures are derived from the above data and divided by U.S. population (FRED: B230RC0A052NBEA) data sourced from BEA. 
    • The percent of GDP data series are calculated using annual Gross Domestic Product data (FRED: GDPA) sourced from BEA.
  • State & Local: The bonded debt data come from the U.S. Census Bureau’s Survey of state and Local Government Finance (2020 and 2021 levels are extrapolated). 
    • Pension Debt: Data are sourced from the Board of Governors of the Federal Reserve System (FRED: BOGZ1FL223073045Q). Excluded here are state and local retiree healthcare liabilities for which time series data are not available. Reason Foundation has estimated that these liabilities totaled $1.2 billion in 2019. 
    • The data is inflation-adjusted to 2021 dollars with the US GDP implicit price deflator (FRED: A191RD3A086NBEA). 
    • Per capita figures are derived from the above data and divided by U.S. population (FRED: B230RC0A052NBEA) data sourced from BEA.
    • The percent of GDP data series are calculated using annual Gross Domestic Product data (FRED: GDPA) sourced from BEA.

The post Debtor Nation appeared first on Reason Foundation.

The interactive map below shows the funded ratios for state-managed public pension systems from 2001 to 2022. A funded ratio is calculated by dividing the value of a pension plan’s assets by the projected amount needed to cover the retirement benefits already promised to workers. The funded ratio values for 2022 are projections based on a -6% investment return. 

Year-to-year changes in investment returns and funded ratios tend to grab attention, but longer-range trends give a better perspective of the overall health of public pension systems.

In 2001, only one state, West Virginia, had an aggregated funded ratio of less than 60%. By the end of 2021, four states—Illinois, Kentucky, New Jersey, and Connecticut—had aggregate funded ratios below 60%.

If investment returns are -6% or worse in the 2022 fiscal year, Reason Foundation’s analysis shows South Carolina would be the fifth state with a funded ratio below 60%. 

Over the same period, 2001 to 2021, the number of states with state-managed pensions with funded ratios above 90% fell from 33 to 20. If all plans return a -6% investment return assumption for 2022, Reason Foundation projects the number of states that have funded levels above 90% would shrink from 20 to six.  The six states with funded levels that would still be above 90% after -6% returns for 2022: Delaware, Nebraska, New York, South Dakota, Washington, and Wisconsin. 

Importantly, the -6% investment return assumption for the 2022 fiscal year used in this map may be too optimistic for some public pension plans. The S&P 500 lost 12% of its value over the 2022 fiscal year from July 1, 2021, to June 30, 2022. Vanguard’s VBIAX, which mimics a typical 60/40 stock-bond portfolio, was down 15% for the fiscal 2022 year ending in June 30, 2022. Thus, given the condition of financial markets this year, the public pension plans with fiscal years that ended in June 2022 are likely to report negative returns for the 2022 fiscal year.  

Another useful long-term trend to look at are the unfunded liabilities of state-run pension plans. Whereas a pension system’s funded ratio takes the ratio of assets to liabilities, unfunded liabilities are the actual difference between the pension plan’s assets and liabilities. Unfunded liabilities can be conceptualized as the pension benefits already promised to workers that are not currently funded by the plan. Again, the values for the 2022 unfunded liabilities map are a projection using an investment return of -6%. 

The five states with the largest unfunded liabilities are California, Illinois, New Jersey, Pennsylvania, and Texas. In fiscal year 2021, the unfunded liabilities of those states totaled $434 billion and would jump to $620 billion in 2022 with a -6% return.  

For more information on the unfunded liabilities and funded ratios of state-run pensions, please visit Reason’s 2022 Public Pension Forecaster.

Notes

ⁱ The state-funded ratios in this map were generated by aggregating (for state-managed plans) the market value of plan assets and actuarially accrued liabilities. Prior to 2002, Montana and North Carolina reported data every two years, therefore for 2001 figures from 2002 are used. Figures for Washington state do not include Plan 1, an older plan that is not as well funded.

ⁱⁱ The discount rate applied to plan liabilities will impact the funded ratio of a plan. Therefore, the map above can be best thought of as a snapshot of state-funded ratios based on plan assumptions by year. Overly optimistic assumptions about a pension plan’s investment returns will result in artificially high-funded states. Conversely, pulling assumptions downward, while prudent, will result in a worse-looking funded ratio over the short term.

ⁱⁱⁱ In addition to projections for fiscal 2022, some public pension plans in 29 states have yet to report their complete fiscal 2021 figures and therefore include a projection estimate for 2021 as well. Thus, 2021 projections were used for at least one plan in the following states: Alabama, Alaska, Arkansas, California, Colorado, Georgia, Hawaii, Illinois, Kansas, Louisiana, Massachusetts, Michigan, Missouri, Nebraska, New Hampshire, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Texas, Tennessee, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.

The post Projecting the funded ratios of state-managed pension plans appeared first on Reason Foundation.

Based on a -6% return for fiscal 2022, the aggregate unfunded liability of state-run public pension plans will be $1.3 trillion, up from $783 billion in 2021, the Pension Integrity Project finds. With a -6% return in 2022, the aggregate funded ratio for these state pension plans would fall from 85% funded in 2021 to 75% funded in 2022. 

The 2022 Public Pension Forecaster below allows you to preview changes in public pension system funding measurements for major state-run pension plans. It allows you to select any potential 2022 investment return rate to see how the returns would impact the unfunded liabilities and funded status of these state pension plans on a market value of assets basis.

The nation’s largest public pension system, the California Public Employees’ Retirement System (CalPERS), provides a good example of how much one bad year of investment returns can significantly impact unfunded liabilities, public employees, and taxpayers.

If CalPERS’ investment returns come in at -6% for 2022, the system’s unfunded liabilities will increase from $101 billion in 2021 to $159 billion in 2022, a debt that would equal $4,057 for every Californian. Its funded ratio will drop from 82.5% in 2021 to 73.6% in 2022, meaning state employers will have less than three-quarters of the assets needed to pay for pensions already promised to workers. 

Similarly, the Teacher Retirement System of Texas (TRS) reported $26 billion in unfunded liabilities in 2021. If TRS posts annual returns of -6% for the fiscal year 2022, its unfunded liabilities will jump to $40 billion, and its funded ratio will drop to 83.4%. The unfunded liability per capita is estimated to be $1,338. 

The table below displays the estimated unfunded liabilities and the funded ratios for each state if their public pension systems report -6% or -12% returns for 2022. 

Estimated Changes to State Pension Unfunded Liabilties, Funded Ratios

The first three quarters of the 2022 fiscal year clocked in at 0%, 3.2%, and -3.4% for public pensions, according to Milliman. The S&P 500 is down more than 20% since January, suggesting that the fourth quarter results will be more bad news for pension investments.

Considering the average pension plan bases its ability to fund promised benefits on averaging 7% annual investment returns over the long term, plan managers are preparing for significant growth in unfunded liabilities, and a major step back in funding from 2021. 

The significant levels of volatility and funding challenges pension plans are experiencing right now support the Pension Integrity Project’s position last year that most state and local government pensions are still in need of reform, despite the strong investment returns and funding improvements in 2021. Unfortunately, many observers mistook a single good year of returns—granted a historic one—as a sign of stabilization in what was a bumpy couple of decades for public pension funding. On the contrary, this year’s returns, as well as the growing signs of a possible recession, lend credence to the belief that public pension systems should lower their return expectations and view investment markets as less predictable and more volatile. 

State pension plans, in aggregate, have struggled to reduce unfunded liabilities to below $1 trillion ever since the Great Recession, seeing this number climb to nearly $1.4 trillion in 2020. Great results from 2021 seemed to finally break this barrier, with the year’s historically positive investment returns reducing state pension debt to about $783 billion. Now, state-run pension plans will again see unfunded liabilities jump back over $1 trillion, assuming final 2022 results end up at or below 0%. 

It is important not to read too much into one year of investment results when it comes to long-term investing. But during this time of economic volatility, policymakers and stakeholders should recognize that many of the problems that kept public pension systems significantly underfunded for multiple decades still exist. And many pension plans are nearly as vulnerable to financial shocks as they were in the past.

Going forward, state and local leaders should continue to seek out ways to address and minimize these risks, making their public retirement systems more resilient to an uncertain future. 

Webinar on using the 2022 Public Pension Forecaster:

The post Unfunded public pension liabilities are forecast to rise to $1.3 trillion in 2022 appeared first on Reason Foundation.

A Horizon Actuarial Services report released in Aug. 2021 surveyed firms like JPMorgan, BlackRock, and BNY Mellon to find that long-term investment returns are expected to decrease across the main asset classes that public pension plans invest in. The Horizon report states:

“Over the last five years, expected returns have declined for all but a few asset classes. The steepest declines have been for fixed income investments such as US corporate bonds and Treasuries, where return expectations have fallen more than 100 basis points since 2019. These declines were driven by recent monetary and fiscal policy interventions, and may have significant implications for multiemployer pension plans.”

Some public pension plans have made the prudent decision to reduce their investment return assumptions in light of this cautionary market outlook. Notably, the New York Common Retirement Fund lowered its assumed rate of return 90 basis points, which takes the assumed rate of return from 6.8% to 5.9%. This 5.9% expected return fits between Horizon’s 10- and 20-Year forecasts of 5.38% and 6.25%.

To get a better sense of the investment outlook for state pension plans, we created a tool that runs a simulation of the investment performance of a hypothetical public pension portfolio over 30 years. It displays the growth of $1 in assets; a distribution of the compound annual growth rate for those 30 years; and the simulation estimated probability of hitting several return assumptions.

The tool utilizes assumptions on asset returns, volatilities, and correlations pulled from BlackRock, BNY Mellon, Horizon Actuarial Services, JPMorgan, and Research Affiliates to simulate portfolio returns. Specifically, the model uses a Monte Carlo simulation with 10,000 simulations of the portfolio over 30 years. Users can select which capital market assumptions they want to run the model with. Additionally, users can select between the national average pension asset allocation, a 60/40 stock-bond portfolio, or a custom portfolio. Step-by-step directions on how to use the tool can be found below.

It is important to note that while this tool uses the latest assumptions from reputable financial advisors, they are still mere speculations on market performance. Additionally, while this approach for portfolio simulation is commonly used in the financial industry, it does not account for the time-variant nature of asset correlations. In times of financial stress, for example, assets can be more tightly correlated than they are in normal market conditions—aggravating portfolio losses. This was true during the Great Recession from December 2007 to June 2009 and could be true in a future crisis as well.

How to Use the Tool

1. To run the portfolio simulation with the preloaded settings, simply hit the “Run Simulation” button in the top right.
2. To modify the inputs, select the “Control Panel” button in the top left.
3. The user can modify both the asset returns, volatilities, and correlations by firm (both Horizon’s 10-year and 20-year assumptions are included) via the “Capital Market Assumptions” dropdown.
4. Asset allocation can be switched between the national average for state pension plans or a 60/40 stock-bond split. Additionally, asset allocation can be customized via the “Custom” button. Note that the portfolio must equal 100% to run the simulation.
5. Once selections are completed, click “Run Simulation” to see the results.

Outputs

1. Assets: Displays the 25^th percentile to 75^th percentile (middle 50 percent of the data) of asset growth for $1 for the 10,000 simulations over 30 years.
2. Distribution: Displays the distribution of compound annual growth rates for the 10,000 simulations over 30 years. Opposed to the arithmetic return, the compound annual growth rate (or geometric return) better represents the long-run growth of an asset. Also displayed are two lines: (1) the median simulation return and (2) where 7% return sits in the distribution which is often used as the assumed rate of return for public pension plans.
3. Return Probability: Displays the probability of the portfolio reaching various assumed rates of return given the results of the 10,000 simulations.

The post Modeling how public pension investments may perform over the next 30 years appeared first on Reason Foundation.

This analysis reveals that most state pension plans saw significant drops in funding in the early and late 2000s, followed by a minor rally during the largest bull market run of our nation’s history. In the last five years, the average has remained in the lower 70 percent range.

Fortunately, a strong year of returns in FY 2021 will result in a significant bounce to the funding status of nearly all plans. Still, twenty years ago, state pension plans were nearly 100 percent funded in aggregate, and most plans are still working to overcome funding shortfalls that arose over a decade ago during the Great Recession. This year’s funded ratio increase does not change the trends plans have experienced for the last two decades and such history should inform plan assumptions going forward.

The graphic below shows each state’s public pension funded ratio (total reported actuarial assets compared to liabilities for all plans aggregated by state) from 2001 to 2020.

Methodology: Displayed funded ratios are the quotient of the actuarial value of assets (AVA) and actuarial accrued liabilities (AAL) of state-managed plans. The discount rate is a weighted average of state-managed plans’ discount rates with AAL as the weight.  Source: Data primarily come from Public Plans Data and is supplemented by Pension Integrity Project analysis of annual financial reports.

One of the largest reasons funded ratios have declined over time is investment returns coming in below plan expectations and insufficient annual contributions from states. For years, most pension plans have held too high assumed rates of investment returns that have led to underfunding. Despite excellent returns this year, a number of plans have taken prudent steps to lower their assumed rate of return in 2021.

A pension plan’s discount rate is also highly influential on a plan’s funded ratio and if the rate is too high it will hide the true extent of the state’s public pension liabilities. As some public pension plans have prudently adjusted their discount rates down to realistic levels, they have also revealed the true cost of fully funding their public pension systems. This affects funded ratios by revealing a higher pension liability. The second chart of this tool shows the change in each state’s discount rate between 2001 and 2020.

The post State pension plan funded ratios in 2020 appeared first on Reason Foundation.

We recommend viewing this interactive chart on a desktop for the best user experience. If you are having trouble viewing the chart and interactive options on your device, please find a mobile-friendly version here. 

The post Investment return results for state pension plans appeared first on Reason Foundation.

A single year, or even several years, of above-average investment returns would be welcome news for public pension plans. But, a year or two of great returns will not resuscitate the public pension plans at risk of financial insolvency. Public pension plans with growing debt are in need of structural reforms that protect taxpayers, employees and retirees. A key component of these reforms is setting realistic assumptions about future investment returns.

For the last 20 years, state and local pension plans’ assumed rates of return have been far too optimistic. The distributions of average (geometric mean) assumed investment returns and actual returns from 2001 to 2020 demonstrate this. The figure below shows the distribution of the average assumed investment return rate versus actual investment returns for 200 of the largest state and local pension plans in the United States. The median assumed rate of return over the last 20 years was 7.7 percent per year, the median actual rate of investment return for these public pension plans was 5.7 percent.

This two percent difference helps to explain the nearly 30 percent drop in the average pension plan funded ratio over the same period. In recent years, many pension plans lowered their assumed rates of return. As visualized below, the distribution of state and local pension plan assumed rates of return (blue) are moving closer to the distribution for the 20-year average (orange). 

Although progress has been made there is still a significant gap between the assumptions for investment returns and actual returns from the last two decades. For public pension plans, falling short of investment return expectation, even by a small percentage, can add millions to billions in pension debt over time.

In short, state and local governments are betting that the next few decades will not be like the last two. Policymakers should continue to lower overly optimistic assumed rates of return. By doing so, they can reduce the risk of future growth in unfunded pension liabilities, protecting taxpayers and government workers.

The post Public Pension Plans Need to Put a Year of Good Investment Returns In Perspective appeared first on Reason Foundation.

Funded ratios are used to display the dollars a pension plan has saved compared to the amount the plan will need to fulfill pension promises already made to public workers and retirees.

The interactive tool below shows each state’s public pension funded ratio and how it compares to the aggregated national funded ratio (total assets compared to liabilities for all plans run by each state) from 2001 to 2019. For example, California’s funded ratio is currently 71.6 percent, which means the state has roughly 71 cents saved for every dollar of pension benefits it owes. 

This analysis reveals that most state pension plans saw significant drops in funding in the early and late 2000s, followed by a minor rally during the largest bull market run-up in our nation’s history. In the last five years, the average seems to have leveled at around 72 percent. Initial analysis of 2020 data shows that state aggregated funded ratios remain roughly unchanged from 2019 at 72 percent. 

There are many reasons funded ratios have declined and stagnated. The largest of these are investment returns coming in below plan expectations and insufficient annual contributions.

Another factor that should be considered when comparing pension funding between states is how they have adjusted their discount rates, which is how plans calculate the present value of promised benefits. A discount rate is highly influential on a plan’s funded ratio and if the rate is too high—meaning it reflects an unrealistic expectation on long-term investment returns—it will hide the true extent of the state’s public pension liabilities.

As some public pension plans have prudently adjusted their discount rates down to realistic levels, they have also revealed the true cost of fully funding their public pension systems. This affects funded ratios by revealing a higher pension liability. The second chart of this tool shows the change in each state’s discount rate between 2001 and 2019. 

The post How State Pension Funding Ratios Have Declined Over Time appeared first on Reason Foundation.

The Florida Retirement System (FRS) was fully funded as recently as 2008 but since that time the plan has failed to rebound from steep losses during the 2007-2009 financial crisis. Instead of digging back to full funding during the longest bull run in stock market history, the pension plan’s debt increased by $18.9 billion after 2009 (on an actuarial basis).

The interactive chart below shows how the pension plan’s debt has grown since 2001 and displays how the funded ratio for FRS declined by 7 percent over that same period.

Financial forecasting shows that if FRS meets its investment return assumptions for the next 18 years, it would improve its funding to 90 percent by 2038. Unfortunately, looking at both historical investment performance and future economic and stock market forecasts, this rate of investment return is unlikely to be met.

Over the last 20 years, the Florida Retirement System’s compound annual growth rate was only 5.62 percent, which is well short of the 7 percent it hopes to earn in the future. Future return analysis based on FRS’s target asset allocation and 10-to-15-year market forecasts from four large financial institutions show that FRS has less than a 50-50 chance of meeting or exceeding its 7 percent return target.

The interactive chart below shows a realistic funding scenario for FRS given the unlikelihood the plan will meet its investment targets in the long term. When modeling a 6 percent investment return and two recession over the next 30 years, we can see that FRS would be below 70 percent funded by 2050.

The employer contribution chart below shows how much employer costs would rise if FRS were to meet this fate. Under this scenario, taxpayer costs would nearly triple.

These analyses make it clear that Florida’s public pension plan has not recovered from the 2007-2009 market challenges of over a decade ago, and it doesn’t look to be equipped to effectively handle any future economic and market challenges either.

To prevent unfunded liabilities from spinning out of control, FRS needs structural reform. The needed reforms include implementing more realistic actuarial assumptions, using clear-eyed risk assessments to gauge future performance, and creating shorter timelines to pay off future unfunded liabilities when they are created.

The post Analysis of Florida’s Pension Investment Performance and Future Outlook Reveal Need for Reform appeared first on Reason Foundation.

In cooperation with the Texas Public Policy Foundation, Reason Foundation has created the Texas Student Transfer Dashboard to shine a light on open enrollment trends in the state. The interactive tool below maps the 2018-2019 student transfer data for every public school district in Texas. It also provides charts that show these trends by school district ratings and school district student demographics.

There are two types of open enrollment: Intradistrict refers to transfers within a child’s home school district, while interdistrict refers to transfers outside of a child’s home district. The subject of this research is exclusively on interdistrict open enrollment.

Our analysis finds that three percent of Texas students transferred to a traditional public school outside of their assigned school district in the 2018-2019 school year.

Importantly, these students tended to transfer to higher-performing school districts as measured by state accountability grades. In 2018-2019, roughly 45,000 Texas students transferred to a higher-performing school district at least a letter grade above their residentially assigned district.

We also find that students tend to leave lower-income school districts at greater rates than higher-income school districts, although it’s not possible to determine the demographics of these students due to lack of state and school district reporting policies.

Additionally, we observed that Texas school districts accept transfer students at varying rates, with some school districts enrolling thousands of students from outside of their boundaries and others enrolling none at all. Because the state does not report school district transfer policies it is difficult to know how widespread transfer opportunities are and whether additional seats are available for students.

It is clear though that there are substantial policy barriers to ensuring that all of Texas’ students have access to robust open enrollment opportunities. These include inadequate protections for families, opaque transfer processes, as well as limited support for transportation outside of a student’s residentially-assigned school district.

There is also a real need for increased transparency and reporting on open enrollment at the state level. While Texas’ open enrollment data is vastly better than most states, it still falls well short of providing stakeholders with the information they need to make sound policy decisions. Because of this, it is impossible to determine the demographics of transfer students, their reasons for transferring, or even the schools they are leaving from and going to. Steps should be taken to ensure these and other data—including school districts’ transfer policies—are collected and reported regularly.

The interactive tool below allows users to see which school districts in Texas have the highest rates of student transfers. The tool also shows how student demographics and district performance impact transfer trends. The full dashboard is also available here.

The post Analysis of Texas School District Open Enrollment Data appeared first on Reason Foundation.

In 2001, fixed-income investments made up 31.5 percent of US public pension plan investments. Less than 20 years later, in 2019, these investments made up 23.2 percent of plan assets. The decline in interest rates was even larger over that time period. At the start of 2001, the interest rate on a 10-year Treasury note was above 5 percent. At the end of 2019, before the COVID-19 pandemic, it fell below 2 percent.

Furthermore, in 2001, 91 percent of all public pension plan assets were invested in public equities, fixed income, or cash. The latest data shows that these investments now make up only 72 percent of the public pension system investment pie. This decline again shows the steady movement away from fixed-income assets in the last two decades.

The data visualization below shows the relationship between declining interest rates and public pension asset allocations. As corporate and treasury bond rates have fallen, public pension plans have invested their funds differently.

The visualizations display how private equity investments have grown in popularity. Private equity allocations are now the third-largest asset class for public pension plans, growing from 3.62 of nationwide plan portfolios in 2001 to 9.15 percent in 2019.

Portfolio managers should be free to pursue whatever investment philosophies they believe are in the best long-run interests of their plan members. However, policymakers, pension plan members, and taxpayers should be aware of these trends and the risks that come with them. Pension systems and lawmakers need to address the growing risk of volatility in ways that maintain a plan’s resiliency to unpredictable market factors. Also, plan stakeholders should be wary of a situation where the tail wags the dog—with pension systems swapping safety for risk and volatility as they chase outdated and overly optimistic investment return assumptions.

The post The Relationship Between Public Pension Investments and Declining Bond Yields appeared first on Reason Foundation.

The post Infographic: Texas Charter Funding Gap appeared first on Reason Foundation.

In 2019, public charter schools received about $10,824 per pupil compared to $11,637 for public school districts, a funding disadvantage of $813 per pupil. Over the last five years, the funding gap has grown by almost 36 percent- from $596 to $813.

To help illustrate these trends and their importance to students, Reason Foundation’s education policy team created an interactive dashboard that uses statewide and metro school district data to allow users to examine important revenue and student demographic trends.

The new, interactive data dashboard is available here: Texas Charter School Funding Analysis. And you can also find a mobile-friendly version of the dashboard here: Texas Charter School Funding Analysis (mobile).

It is important to account for student demographics when evaluating how education dollars are allocated to Texas schools. Thus, the dashboard looks at important trends in student demographics, such as special education needs, low-income status, and limited English proficiency, which all have varying degrees of added educational costs.

The data show that statewide, public charter schools serve greater proportions of economically-disadvantaged and limited English proficiency students but a smaller proportion of special education students than public school districts.

Despite public charter students now accounting for about 5.8 percent of all Texas public school students in average daily attendance, they receive only 5.4 percent of all statewide education revenue.

You can find more information on Texas charter school funding, including revenue comparisons that control for cost factors such as location and student demographics here: Fiscal Explainer – Texas Charter School Funding Analysis 

You can also find an infographic on Texas charter school funding here: Infographic – The Texas Charter Funding Gap

The post Examining Student Funding in Texas Charter Schools and Traditional Public Schools appeared first on Reason Foundation.

Funded ratios are a simple and useful metric that can help to assess the financial health of a pension plan. Calculated by dividing the projected value of a pension plan’s assets by the cost of its promised pension benefits, funded ratios can reveal if a pension system is on track to be able to pay for the retirement benefits that have been promised to workers.

Over time, changes in a pension plan’s funded ratio, also referred to as a pension’s funded status, can show the rate at which the plan’s debt is growing.

In 2001, West Virginia was the only state where public pension plans had an aggregate funded ratio of less than 60 percent. However, 18 years later, in 2019, nine states faced aggregate funded ratios below 60 percent.

In that same time period, the number of states with funded ratios below 70 percent (but above 60 percent) grew from three to 14. Together, these numbers show that, as of 2019, 23 states had less than 70 percent of the assets on hand that they need to be able to pay for promised future retirement benefits.

Perhaps even more alarming is the fact that over the last two decades, the number of states with fully-funded pensions fell from 20 to just one. As of 2019, South Dakota was the only state without any public pension debt.

The interactive map below shows the change in each state’s aggregated pension plan funded ratio from 2001 to 2019. Because many states administer multiple public pension plans we combined the pension liabilities and actuarial value of assets of all the pension plans in a state to calculate their aggregate funded ratio for the data visualization.

We recommend viewing this interactive chart on a desktop for the best user experience.

Previous analysis has shown that the average state-level funded ratio, using the market value of assets, dropped from 97.7 percent in 2001 to roughly 73.6 percent in 2019. This decline of 24.1 percentage points is cause for concern for workers, taxpayers, and lawmakers.

As public pension debt grows, so does the cost of saving for retirement benefits. Public pension underfunding not only puts taxpayers on the hook for growing pension debt but could jeopardize the retirement security of teachers, public safety officials, and other state employees. Left unaddressed, pension debt will also continue to pull resources from other public priorities like road repairs and K-12 education in most states.

The post Public Pension Plans’ Funded Ratios Have Been Declining for Years appeared first on Reason Foundation.

To provide better access to Arizona’s K-12 school finance data and present stakeholders with important education funding trends, Reason Foundation’s education policy team has created this interactive data portal.

Interactive Data Visualization Tool: Arizona School Finance Dashboard 

This tool allows users to select student demographic variables like student poverty rates, ethnicity, special education counts, and English language learners as a percentage of district population to see how these factors compare to per-pupil revenue distributions.

The data and charts displayed in the portal ultimately show how varying degrees of property wealth can drive education funding inequities across the state. These funding disparities often result in disadvantaged students not getting their fair share of the state’s education resources.

For example, in looking at the school districts in Arizona’s largest county, Maricopa, the tool shows that Isaac Elementary School District, where 38 percent of students are in poverty, receives $7,502 per student. In stark contrast, Queen Creek Unified School District, where just five percent of students are in poverty, gets $13,812 per student— 184 percent more than Isaac Elementary School District.

We recommend viewing this interactive chart on a desktop for the best user experience. 

Please note that the interactive tool will automatically sleep after a certain idle time and can be restarted by simply refreshing the page.

The post Arizona School Finance Dashboard appeared first on Reason Foundation.

Reason Foundation’s series of policy briefs offer a Student-Centered Funding Roadmap for Policymakers, including:

• A Student-Centered Funding Roadmap for Policymakers
• Streamline: How to Allocate Education Dollars Strategically
• Equalize: Funding Policies That Put All Kids on a Level Playing Field
• Empower: How to Put Families and School Leaders in the Driver’s Seat
• Inform: Give Stakeholders the Information They Need to Make Sound Education Funding Decisions

The post Infographic: How Student-Centered Funding Works and How to Get There appeared first on Reason Foundation.

Bad assumptions, missed payments and underperforming investments have created over $40 billion worth of debt—unfunded liabilities—that cost students and school districts more and more money each year.

Use the tool below to select any K-12 public school district in Michigan that contributes to the Michigan Public School Employees Retirement System (MPSERS) and see how unfunded liabilities are taking millions from that school district’s K-12 classrooms on a yearly basis.

Using data for the 2017-18 fiscal year, you will see that each student in the state has a staggering amount of pension debt tied to them and that normal pension system costs (what school districts contribute each year to pay for current employees’ future retirement benefits) are dwarfed by high debt payments for yesterday’s workforce.

Find the full interactive dashboard here or simply use the tool below.

To see the data for your school district use the expandable sidebar on the left side of the tool below.

We recommend viewing this interactive dashboard on a desktop for the best user experience. Please note that the tool will automatically sleep after a certain idle time and can be restarted by simply refreshing the page.

Reason Foundation’s Pension Integrity Project finds that in 2018, Detroit Public Schools spent $2,202 per student on MPSERS debt, which equals nearly 27 percent of the district’s per-pupil foundation grant from the state. Detroit Public Schools’ total retirement costs ate up $111,047,484 of the over $700 million budget in 2018.

In stark contrast, the normal cost—the cost of actual retirement benefits earned that year—was only $252 per Detroit Public Schools student. This means that if the state would have made full pension contributions and maintained realistic investment return assumptions for the last two decades, there would be almost $1,800 in additional funding for each of Detroit Public School students during that year. Instead, more and more money is being taken out of classrooms each year to pay for over $40 billion in MPSERS debt.

Lansing Public Schools had similar contribution rates, spending $2,428 per pupil and $25,403,305 in total MPSERS costs in 2018. These contributions ate up 32 percent of the school district’s per pupil grant and over 15 percent of the school district’s total budget. If MPSERS was not in debt, Lansing Public Schools would only have needed to contribute $274 per student to fund retirement benefits.

If they are left unaddressed, historical problems and a lack of accountability within the retirement system will continue to threaten promised pension benefits and jeopardize school funding.

During the current coronavirus pandemic, decreased state revenues and increasingly volatile stock markets add to these concerns. It is crucial that going forward, even during times of fiscal crisis, that Michigan makes its full MPSERS payments and maintains responsible funding policies.

The post Examining How Much Money That Pension Debt Takes Away From Michigan’s Classrooms Each Year appeared first on Reason Foundation.