Context

The Correlates of State Policy Project aims to compile, disseminate, and encourage the use of data relevant to U.S. state policy research, tracking policy differences across and change over time in the 50 states. We have gathered more than nine-hundred variables from various sources and assembled them into one large, useful dataset. We hope this Project will become a “one-stop shop” for academics, policy analysts, students, and researchers looking for variables germane to the study of state policies and politics.

Content

The Correlates of State Policy Project includes more than nine-hundred variables, with observations across the U.S. 50 states and time (1900 – 2016). These variables represent policy outputs or political, social, or economic factors that may influence policy differences across the states. The codebook includes the variable name, a short description of the variable, the variable time frame, a longer description of the variable, and the variable source(s) and notes.

Take a look at the codebook PDF to get more information about each column

Acknowledgements

This aggregated data set is only possible because many scholars and students have spent tireless hours creating, collecting, cleaning, and making data publicly available. Thus if you use the dataset, please cite the original data sources.

Jordan, Marty P. and Matt Grossmann. 2016. The Correlates of State Policy Project v.1.10. East Lansing, MI: Institute for Public Policy and Social Research (IPPSR).

This dataset was originally downloaded from

http://ippsr.msu.edu/public-policy/correlates-state-policy

These data include CongressData, a panel dataset describing US congressional districts, and the Correlates of State Policy Project data (CSPP), which describe US state years. They each contain a variety of variables describing politics, economics, and social issues.

The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. Overall 30 researchers conduct and promote research on the causes, consequences and nature of Good Governance and the Quality of Government - that is, trustworthy, reliable, impartial, uncorrupted and competent government institutions. The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained. A second objective is to study the effects of Quality of Government on a number of policy areas, such as health, the environment, social policy, and poverty. The dataset was created as part of a research project titled “Quality of Government and the Conditions for Sustainable Social Policy”. The aim of the dataset is to promote cross-national comparative research on social policy output and its correlates, with a special focus on the connection between social policy and Quality of Government (QoG). The data comes in three versions: one cross-sectional dataset, and two cross-sectional time-series datasets for a selection of countries. The two combined datasets are called “long” (year 1946-2009) and “wide” (year 1970-2005). The data contains six types of variables, each provided under its own heading in the codebook: Social policy variables, Tax system variables, Social Conditions, Public opinion data, Political indicators, Quality of government variables. QoG Social Policy Dataset can be downloaded from the Data Archive of the QoG Institute at http://qog.pol.gu.se/data/datadownloads/data-archive Its variables are now included in QoG Standard. Purpose: The primary aim of QoG is to conduct and promote research on corruption. One aim of the QoG Institute is to make publicly available cross-national comparative data on QoG and its correlates. The aim of the QoG Social Policy Dataset is to promote cross-national comparative research on social policy output and its correlates, with a special focus on the connection between social policy and Quality of Government (QoG). The dataset combining cross-sectional data and time-series data for a selection of 40 countries. The dataset is specifically tailored for the analysis of public opinion data over time, instead uses country as its unit of observation, and one variable for every 5th year from 1970-2005 (or, one per module of each public opinion data source). Samanni, Marcus. Jan Teorell, Staffan Kumlin, Stefan Dahlberg, Bo Rothstein, Sören Holmberg & Richard Svensson. 2012. The QoG Social Policy Dataset, version 4Apr12. University of Gothenburg:The Quality of Government Institute. http://www.qog.pol.gu.se

This dataset merged public health data from National Immunization Surveys Data from 2006 to 2016 and matching state policy data from Correlates of State Policy Project (CSPP), the U.S. Department of Agriculture/Economic Research Services (USDA/ERS) Supplemental Nutrition Assistance Program (SNAP) Policy Index, the U.S. Bureau of Labor Statistics (BLS), Centers for Medicare & Medicaid Services (CMS), and the Census Bureau. The integrated dataset compiles of variables in breastfeeding outcome, child’s and mother’s socio-demographic characteristics, and state-level policy measures, including SNAP participation rates, SNAP policy indices, unemployment rates, and Children’s Health Insurance Program (CHIP) enrolment rates. This multidisciplinary dataset included information of a total of 219,904 children with 103 variables.

Pearson correlations between different public health-related variables with the percentage of voters in the county that voted for Donald Trump or Hilary Clinton, and the Republican margin shift (from 2012 to 2016).

Because the American states operate under balanced budget requirements, increases in spending in one area typically entail equal and opposite budget cuts in other programs. The literature analyzing the correlates of government spending by policy area has mostly ignored these tradeoffs inherent to policy-making, failing to address one of the most politically interesting and important dimensions of fiscal policy. Borrowing from the statistical literature on compositional data, we present more appropriate and efficient methods that explicitly incorporate the budget constraint into models of spending by budget category. We apply these methods to eight categories of spending from the American states over the years 1984--2009 to reveal winners and losers in the scramble for government spending. Our findings show that partisan governments finance their distinct priorities by raiding spending items that the opposition prefers, while different political institutions, economic conditions, and state demographics impose different tradeoffs across the budget.

Data set on suicide, state SNAP participation, state EITC, and correlates. From multiple sources.

Correlations between public and private sector wages and GDP.