The Determinants of Targeted Transparency in the U.S. States: The Roles of Targeted Organizations and State Fiscal Environments

Abstract

Despite recent interest in transparency, not much scholarly attention has been paid to the determinants of government operation of targeted transparency policies, mandating targeted organizations including corporations or private entities, to disclose data to address market failures. This study explores how targeted organizations and state financial environments affect state operation of All-Payer Claims Databases (APCDs), a targeted transparency policy at the U.S. state level. The establishment and operation of APCDs is a recent state initiative to collect and utilize comparable health data on prices and quality measures from non-governmental actors as well as governmental actors, which can contribute to reducing information asymmetry between state health policymakers and providers and to constrain health care expenditures. We found that the operation of APCD was affected by insurance market competition and enrollment, and state financial environments, as well as by state political context and service demands. Especially, this study encourages future researchers to study how insurers as targeted organizations shape the state operation of the price transparency strategy.

Keywords:

• All payer claims databases
• insurance market competition
• medicaid burden
• state fiscal capacity
• targeted transparency

Acknowledgements

We thank anonymous reviewers for helpful comments on the manuscript. Also, we appreciate Dr. Frank Thompson for his valuable comments on the earlier version of the manuscript.

Notes

1 Our dataset ranges from 1996 to 2018 as Maryland has begun collecting claims data since 1998. Furthermore, Delaware and Washington have first collected data in 2017.

2 The generalized mixed effect logistic regression considers both a fixed-effect estimation and random-effect estimation, thereby preventing an omission of dummy variables (e.g. political context variables).

3 The HHI value shows “0” as greater competitiveness; otherwise “1” as less competitiveness. Thus, states with a higher level of market competitiveness tend to create APCDs.

4 Table 4 shows the results of the generalized mixed effect logit model which shows fixed effect estimations for interval variables and random effect estimation for discrete variables simultaneously (see Fagerland & Hosmer, 2012; Paul et al., 2013). Thus, the generalized mixed effect logit model does not need the Hausman test to choose a fixed effect estimation or random effect estimation.

Additional information

Notes on contributors

Shihyun Noh

Shihyun Noh is an assistant professor of public administration at the State University of New York College at Brockport, USA. His research interests include health policy and administration, intergovernmental implementation of federal programs, and state and local government administration.

Ji Hyung Park

Ji Hyung Park is an assistant professor in the Department of Public Administration at Soongsil University, Seoul, South Korea, His research interests are in public budgeting and finance, and urban management, focusing on citizen participation, forms of government, fiscal health, performance budgeting, revenue diversification, and city-county consolidation.