What is the total unfunded liability of the US government?
One of the main political issues of 2009 was the health care reform plan that Congress is still working on. Due to the vociferous debate over the plan, American citizens have probably become much more informed about the amount of debt owed by the United States government. Much of that debt is in the hands of countries like China and that fact has also caught the public’s attention.
But there is another type of debt that is not talked about as often. I am referring to what is called unfunded liabilities. In essence, the United States government has made promises to pay money today and in the future to its citizens. We are talking about Social Security and Medicare.
The government raises funds for these expenses from various taxes and then uses the money to fund the program. These programs are considered unfunded liabilities because, projecting into the future, tax revenues will not be able to finance projected expenditures. The numbers are truly staggering. The unfunded Social Security liability is projected to be $ 17.5 trillion.
In reality, Medicare’s unfunded liability is expected to be much higher. Medicare actually has Parts A, B and D, Part A funds hospital care. Part B funds doctor visits and Part D funds prescription drugs. The unfunded liability of part A is estimated at $ 36 trillion, part B at $ 37 trillion, and part D at $ 15 trillion.
The total amount of the unfunded liability is just over $ 100 trillion, or roughly $ 33,000 for every man, woman, and child in the country. And since the Federal Reserve estimates that the private net worth of all Americans combined is just over $ 50 trillion, you can see the problem.
The reason many are concerned is that the only two ways to rectify the situation are to dramatically increase taxes or cut promised benefits. Since most analysts feel that it is politically very difficult to cut promised benefits, most anticipate major tax increases in the future. There are some analysts who are much more optimistic about the problem, arguing that there are so many assumptions built into these analyzes that they could be significantly inaccurate.
