We're here today because we're partners -- partners in an important mission; that is to say, providing the nation with the best statistics and analysis we can, so that policymakers in government, universities and in the private sector can make the most informed decisions on our nation's science and engineering enterprise. This is particularly difficult these days for a couple of reasons.
First, all of us are under increasingly tight budgets, and we know well that worse is yet to come--at least we in government know that. We have to do more with less, and this signals the need for more partnerships and better cooperation in working toward all our common goals. Ironically, as our budgets get tight, our data become ever more necessary as our customers--namely, the science policymakers--need to deal with tougher and tougher tradeoffs. Our budgets get smaller, and the needs for our data become greater. I know we have had increasing requests for data, not just from the Director's office here at NSF but from all of the science policymakers in Washington and some from overseas.
The second difficulty we have as producers of data is the changing structure of the science enterprise, in particular the changing labor market for scientists and engineers. Measuring S&E labor markets is like trying to measure a man for a suit of clothes as he's running down the street. You have to run right alongside of him, you have to run just as fast, and you have to be twice as agile.
So we're going to review our data together today, and with a particular purpose in mind. This set of data we produce has many, many uses, but today we're going to focus on how much light our data can shed on a very important issue for science and engineering, namely, the state of the labor market for doctoral scientists and engineers.
There are a couple of interesting reports on this subject. One of the best was written in 1957, and here I refer to "The Demand and Supply of Scientific Personnel," written in 1957 by David Blank and George Stigler, who later won the Nobel Prize in economics. This book reminds us of several things: one, these employment issues go in cycles, and they never go away; and, two, you simply can't understand these issues unless you make use of federal data together with data from the associations.
This book, which I recommend to you if you can find it still in print, is absolutely superb in the way it blends together data from every possible source. For us data people, it's a good lesson in the value of cooperation and partnerships, which is the theme of today's meeting.
The other recent report, of course, is the COSEPUP report that a few of us have received, but haven't had much time to digest. It says in one of its findings that the data produced by NSF need to be more extensive and timelier, and yet when you look at the book, I don't believe you'll ever be able to find an example of a book that makes more use of all of the data we already have. It's a tremendous work of putting together data in a good way to reach conclusions.
Another thing this report says is what is needed to cure some of the problems that ail science and engineering labor markets is more information about what's happening, so that graduate students can make more informed career choices. So it's another call for better data -- and one that I think we need to heed and do something about.
So to start with today, a number of you have been good enough to volunteer to make brief presentations of the data you produce and what it says about the employment picture. As the day progresses, we'll give you an overview of our data, some of the new things we're doing here at NSF; and then, perhaps the most important part of the meeting, is towards the end when we discuss ways to cooperate in filling any of the gaps that we have uncovered earlier in the day.