The Policy Consequences of Motivated Information Processing Among the Partisan Elite (WP-13-02)
Sarah Anderson and Laurel Harbridge
Policymakers are bombarded with information, enough that they cannot process it all. Combining the theories of disproportionate information processing and motivated reasoning, which the authors call motivated information processing, Anderson and Harbridge argue that policymaking by elected officials reflects partisan biases in the treatment of information that have previously been observed among citizens. With surprising frequency, motivated information processing would cause Democrats to make large cuts to the budget and Republicans to make large increases as necessary accuracy corrections after pursuing their directional goals. The effects of motivated information processing ought to be larger on issues more closely aligned with the parties and further from an election. The researchers test these observable implications on budgetary data at the subaccount level, finding evidence that Democrats engage in motivated information processing and that the effects of it are felt more on social spending and in off-election years.
Sarah Anderson, Assistant Professor of Environmental Politics, Bren School of Enivronmental Science and Management, University of California, Santa Barbara
Laurel Harbridge, Assistant Professor of Political Science, and Faculty Fellow, Institute for Policy Research, Northwestern University