The Polls: State‐Level Presidential Approval: Results from the Job Approval Project

In this article, I report on a new data set that recently has become publicly available, the Official State Job Approval Ratings Project. With support from the National Science Foundation, Thad Beyle, Richard Niemi, and Lee Sigelman collected state-level job approval data for the president, governors, and U.S. senators. (1) In this article, I will focus on the presidential job approval data from 1981 through 1999. (2) Across this time period, more than one thousand state-level job approval readings exist. Such a large n allows us to test ideas and hypotheses that other limited n designs cannot afford and follows the advice of Brace and Jewett (1995) that the states can be a useful site to test theories and ideas developed at the national level. In the first part of this article, I discuss some of the properties of these state-level presidential job approval ratings. In the second half of the article, I use these data to test two competing theories of the impact of economic perceptions on presidential approval, the retrospection model versus the prospection model. Considerable debate exists over which standard the public uses in assessing presidential job approval (compare Erikson, MacKuen, and Stimson 1992, 2000, 2002, with Clarke and Stewart 1994 and Norpoth 1996). The large N of the job approval ratings data set provides some analytic leverage that is not possible with national-level data. This exercise provides one example of how these new data can be put to use. Properties of the State Job Approval Data and Cautions in Using the Data There are several issues relating to this new data set that merit discussion. The data come from many survey firms, use many different question wording formats, and contain extensive missing data gaps, and the state publics are not necessarily randomly surveyed either across time or across states. Variety of Polling Firms and Question Formats The job approval data come from a variety of data sources and polling organizations. Thus, rather than have a data set based on only one standard question, we have an amalgamation that uses a large variety of questions. This introduces numerous issues when trying to compare the level of presidential approval in one state at one time point with presidential approval in another state at another time. First, the Beyle-Niemi-Sigelman team collected data from all sources available, which varied in quality. For instance, sometimes only positive evaluations of the president were recorded by the survey organizations, made public by them, or made available to the data collectors (see Beyle, Niemi, and Sigelman 2001). Thus, one is advised not to use the percentage positive (as is done by most who employ the national Gallup time series) or the difference in the positive and negative percentage that approve of the president. Rather, the formula Percentage Positive/(Percentage Positive + Percentage Negative) is recommended (Beyle, Niemi, and Sigelman 2001; Cohen and Powell 2001). Second, the Beyle-Niemi-Sigelman codebook does not include question wording, which was often unavailable. Beyle-Niemi-Sigelman do categorize by type of question, but here they generally only distinguish subject matter, whether the question is a global assessment of the president, like the Gallup-styled approval question, or a policy-specific question. However, we are supplied with information on the nature and number of response categories. Twenty-one different response category schemes were used, although four types predominate, composing 93 percent of the total. These are (1) excellent, good/fair, poor (48.5 percent of the total); (2) approve, disapprove (30.0 percent); (3) excellent, good/only fair, poor (8.1 percent); and (4) excellent, good/fair, poor, very poor (6.1 percent). One cannot determine from these response categories how many different question wording formats belong in each response category set. …