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, US citizens have probably become much more informed about the amount of debt the US government has. Much of that debt is held by countries like China and that fact has also caught the attention of the public.

But, there is another type of debt that is not talked about as often. I am referring to what are called unfunded liabilities. In essence, the US 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 finance the program. These programs are considered unfunded liabilities because, projected into the future, the tax revenues will not be able to finance the projected expenses. The numbers are actually quite staggering. Social Security’s unfunded liability is projected to be $17.5 trillion.

In reality, Medicare’s unfunded liability is projected to be much larger. Medicare actually has parts A, B, and D, with Part A funding hospital care. Part B funds doctor visits and Part D funds prescription drugs. The unfunded liability for part A is estimated at $36 billion, part B at $37 billion, and part D at $15 billion.

The total amount of unfunded liability amounts to just over $100 billion, or about $33,000 for every man, woman, and child in the country. And since the Federal Reserve estimates the private net worth of all Americans combined to be 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 sharply raise taxes or cut promised benefits. Since most analysts feel it is politically too difficult to cut promised benefits, most forecast significant tax increases in the future. There are some analysts who are much more optimistic about the issue, arguing that there are so many assumptions built into these analyzes that they could be significantly inaccurate.

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