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Ballotpedia:Who Runs the States, Part Three, Methodology

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Who Runs the States

Main Report Pages
Main PagePart 1Partisanship InfographicPart 2Part 3

Partisanship Results Report (Part 1)
Executive SummaryState Partisanship AnalysisPartisan Control of GovernorshipsPartisan Control of State LegislaturesPartisan Control of State SenatesPartisan Control of State HousesState Government TrifectasOverall Partisan Control: Bright, Medium and Soft StatesChanges of Partisan Domination over 22 yearsYear-to-Year Changes in State Partisan ControlTrifectas and Presidential Election PatternsConclusionMethodologyAppendix AAppendix B

State Quality of Life Index (SQLI) Report (Part 2)
Executive SummaryState Quality of Life Index (SQLI)About the IndexOverall RankingsDramatic Changes from 1st Half to 2nd HalfIndividual IndicatorsMethodologyAppendices

Partisanship and (SQLI) Overlay Report (Part 3)
IntroductionComparing Partisanship and the State Quality of Life Index (SQLI) RankingsDescription of the dataTrends and correlationsMethodologyKey Values for Fifty-State RegressionsAppendices
Praise or blame is extended to political parties for the economic, educational, health and other quality of life outcomes that result from the policies those parties enact into law. To better understand which political party enjoys power in each of the states, Ballotpedia has analyzed state government control from 1992-2013 using the concept of a "partisan trifecta." A partisan trifecta is defined as when a state's governorship and legislative chambers are controlled by the same political party.

The two major political parties claim that their policies will lead to better outcomes. What does the data show?

At Ballotpedia, we explored these issues in a three-part study, Who Runs the States.

This page contains the section of Part Three pertaining to the Methodology.

Methodology

Partisanship/Quality of Life Regression Methodology

In our analysis of trends in rankings, we used several different regression models to explore tendencies and potential relationships. In our examination of the aggregate state rankings, we used an ordinary least squares bivariate regression to find correlations between the state performance rankings and the degree to which the state was Republican or Democratic according to our scoring method. The partisanship score functioned as the independent variable, and the aggregate state life quality rankings served as the dependent variable. We did not control specifically for region or population.

Another test of the aggregate rankings used a probit regression, which is designed to show relationships between dependent variables with a dichotomous outcome (0 or 1) and independent variables that may be any real number. All state governments in each year were then coded as one (1) for having a trifecta government (Democratic or Republican) or zero (0) for not having a trifecta government. These values were then regressed with the state’s performance ranking for that year using the probit regression. This probit regression was also performed with a two-year lag on the dependent variable, which correlates a performance ranking in a given year with a trifecta ranking two years prior. This was done to test the difference between having a trifecta or a mixed government on quality of life.

Using this same dichotomous variable for trifecta or non-trifecta governments, we used a panel data series regression, which allows us to identify separate time periods and changes over time, to find trends in the performance of trifecta and non-trifecta governments. With this regression, we were able to incorporate the fixed effects associated with each state and control for more lurking or unaccounted for variables than our earlier analyses. However, the available data in rankings form, relative to other states or types of government, severely restricts the analytical power of this type of regression. For our independent variables, we used both the same-year trifecta ranking and the two-year lagged trifecta ranking for the independent variables in two separate regressions, with performance ranking relative to other states in that year as the dependent variable.

Finally, we used the panel data series regression with fixed effects for the comparison between the overall rankings of the states and the partisan coding of the states in each year. These metrics provide slightly stronger analysis of the data than the previous panel regression, though they do not measure the specific influence of trifectas. In these regressions, the dependent variable was the state performance ranking for each year, and the independent variables were the state governments coded one through nine (1 through 9) as outlined in the “Overall Partisanship” section, both in the same year and lagged two years in separate regressions. Again, measuring coding limits the analytical effectiveness of these regressions, but still provides some sense as to the broader trends in the data.

See also

External links