In his final “Chairman’s Corner” blog post as head of the White House’s Recovery Act Transparency and Accountability Board, Earl Devaney highlights the need for orderly publication of data about government spending.

There is bi-partisan legislation now in the Congress—it’s called the Digital Accountability and Transparency Act, or DATA Act—that could accomplish this mission. But the reform bill faces an uphill battle, primarily because some in the bureaucracy prefer the status quo—a hodgepodge of data collection and display sites that, frankly, makes no sense at all unless you believe your government should confuse you.

The DATA Act would establish an independent board within the executive branch to track federal spending, and it would require federal agencies and recipients of federal funds to comply with reporting requirements set up by the board.

The board would “designate common data elements, such as codes, identifiers, and fields, for information required to be reported by recipients or agencies” (section 102 of the reported version, adding a new §3611 to title 31 of the U.S. code). The bill’s author, Rep. Darrell Issa (R-CA), spoke at our September Capitol Hill briefing, rolling out our legislative data model.

On Wednesday, another Cato Capitol Hill briefing highlighted the results of our work the last few months to model federal budgeting, appropriating, and spending. Should the DATA Act become law, the model we’ve been working on can illuminate the work of the proposed board. Use of our model will help ensure that the structure of government spending data supports public oversight use cases.

I don’t know that there needs to be a board—certainly not a permanent one. The bill authorizes more money than I think is required for the board, and the Congressional Budget Office’s cost estimate for implementing the requirements of the DATA Act seems wildly high. But the dynamics set in motion by making government spending more transparent may well reduce government spending by well more than even these high estimated costs.