We measure media bias by estimating ideological scores for several major media outlets. To compute this, we count the times that a particular media outlet cites various think tanks and policy groups, and then compare this with the times that members of Congress cite the same groups. Our results show a strong liberal bias: all of the news outlets we examine, except Fox News' Special Report and the Washington Times, received scores to the left of the average member of Congress. Consistent with claims made by conservative critics, CBS Evening News and the New York Times received scores far to the left of center. The most centrist media outlets were PBS News Hour, CNN's Newsnight, and ABC's Good Morning America; among print outlets, USA Today was closest to the center. All of our findings refer strictly to news content; that is, we exclude editorials, letters, and the like. Copyright (c) 2005 Massachusetts Institute of Technology.

Researchers often seek to understand the effects of state policies or institutions on individual behavior or other outcomes in sub-state-level observational units (e.g., election results in state legislative districts). However, standard estimation methods applied to such models do not properly account for the clustering of observations within states and may lead researchers to overstate the statistical significance of state-level factors. We discuss the theory behind two approaches to dealing with clustering-clustered standard errors and multilevel modeling. We then demonstrate the relevance of this topic by replicating a recent study of the effects of state post-registration laws on voter turnout (Wolfinger, Highton, and Mullin 2005). While we view clustered standard errors as a more straightforward, feasible approach, especially when working with large datasets or many cross-level interactions, our purpose in this Practical Researcher piece is to draw attention to the issue of clustering in state and local politics research.

Abstract:Several recent studies have reported a robust association between income inequality and aggregate health outcomes across countries and across U.S. states. However, most of these studies examine only a single cross-section of data and employ few (or even no) control variables. We examine the relation between income inequality and aggregate health outcomes across thirty countries over a four decade span and across 48 U.S. states over five decades. We find little support for claims that there exists a robust association between income inequality and aggregate health outcomes across either countries or states.

There is a vast empirical literature on the allocation of corporate PAC contributions in Congressional elections and the in ue n c e that these contributions have on the po l i c y -ma k i n g process. The attention gi v e n to PAC contributions is far in excess of their actual importance. Corporate PAC contributions account for about 10% of Congressional campaign spending and major corporations allocate far more money to lobbying or philanthropy than their af liated PACs make in contributions.