Description

We use social network analysis to better understand historic data on the administration of local governments. Despite advances in e-government applications, the public sector lags behind in analytics because information is locked in legacy data formats. Can e-government researchers bridge the gap between legacy data and analytics? We argue that computational analytic methods can explain patterns that have gone unquestioned in previous research on government. We consider how state government authority can be explained using a network perspective. We investigate methodological challenges in building a weighted network to confirm existing measures for calculating the power of the state governor. This project reports on the initial step in a broader study to cover all 50 states across multiple years and agencies. We explain where the power shifted across states and time. Computational analysis of existing government data matches findings from previous studies as well as adding additional explanatory power.

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Understanding Shifting Dynamics of Power in State Governments through Social Networks

We use social network analysis to better understand historic data on the administration of local governments. Despite advances in e-government applications, the public sector lags behind in analytics because information is locked in legacy data formats. Can e-government researchers bridge the gap between legacy data and analytics? We argue that computational analytic methods can explain patterns that have gone unquestioned in previous research on government. We consider how state government authority can be explained using a network perspective. We investigate methodological challenges in building a weighted network to confirm existing measures for calculating the power of the state governor. This project reports on the initial step in a broader study to cover all 50 states across multiple years and agencies. We explain where the power shifted across states and time. Computational analysis of existing government data matches findings from previous studies as well as adding additional explanatory power.