ECPR

Install the app

Install this application on your home screen for quick and easy access when you’re on the go.

Just tap Share then “Add to Home Screen”

ECPR

Install the app

Install this application on your home screen for quick and easy access when you’re on the go.

Just tap Share then “Add to Home Screen”

Advocacy Coalitions in E-Health Policy: a Comparative Case Study on the Introduction of E-Health Records in Austria and Germany

Comparative Politics
Governance
Institutions
Social Policy
Welfare State
Coalition
Policy Change
Simon Bogumil
Helmut-Schmidt-University/University of the Armed Forces Hamburg
Simon Bogumil
Helmut-Schmidt-University/University of the Armed Forces Hamburg
Tanja Klenk
Helmut-Schmidt-University/University of the Armed Forces Hamburg

Abstract

In the Advocacy Coalition Framework (ACF), beliefs and values are identified as the causal driver of political behavior (Weible, Sabatier et al. 2009). Consequently, contextual factors such as the institutional environment of policy subsystems have traditionally not been the central focus of most ACF applications. This has somewhat changed in recent years with the introduction of the concept of ‘long-term coalition opportunity structures’ into the framework (Sabatier and Weible 2007, Jenkins-Smith et al. 2014) where three contextual variables influencing coalition composition and strategies are identified: the degree of consensus needed for major policy change, the degree of openness of political systems as well as overlapping societal cleavages (ibid.). In health policy research, where health systems are often grouped into different regime types, institutions have long been a major focus of interest. In the upcoming domain of e-health politics, National Health Insurance (NHS) countries are identified as being more successful in digitalizing their health care systems than countries with a Social Health Insurance (SHI) system (Thiel et al. 2018). A main reason for this is seen in the governance structures of SHI systems where the role of the state in health policy-making is substantially curtailed and some powerful interest groups such as physician associations enjoy considerable power and resources (Behm and Klenk 2019). Despite these obstacles, some SHI countries perform significantly better in the digitalization of their healthcare systems than others, while the reasons for this are largely unknown. In our paper, we try to explain this puzzle by comparing the introduction of personal electronic records (e-health records) in two SHI countries: Austria who is a medium-level performer in health system digitalization – 9th place out of 17 in recent benchmarking (Thiel et al. 2018) – and Germany who is clear laggard (16th out of 17). By using the ACF, we analyze the composition of advocacy coalitions against the background of each country’s political opportunity structures. Even though both countries belong to the same regime type and exhibit corporatist systems of interest intermediation, there are pertinent differences in governance structures below regime type (e.g. dominance of the hospital sector in Austria or competition between insurance funds in Germany) that may affect the consensus needed for major policy change and, thus, explain differences in coalition composition and strategies. Methodologically, we rely on a combination of content analysis and typification (Kuckartz 2016). In a first step, we code and paraphrase the most important policy positions as expressed in written statements for legislative hearings to reconstruct individual actor’s policy core beliefs as defined by Sabatier and Jenkins-Smith (1999). In a second step, we use the resultant case summaries of each participating actor to group actors with similar policy beliefs into the same coalition. Analytically, we confirm the assumption of Larsen et al. (2006) that corporatist welfare states have advocacy coalitions with solid cores and fuzzy edges; empirically, we show that long-term opportunity structures in the two countries under consideration are indeed different and account for differences in e-health digitalization.