What (or Who?) Determines U.S. Health Care Policy? Using SNA to Discover the "Influencers" and the "Blockers"

Social Network Analysis Blogpost

Yes, I plan to take the second half.

What (or Who?) Determines U.S. Health Care Policy?

Following the 2010 U.S. Supreme Court decision in Citizens United (Citizens v. Federal Election Commission), the political impact of special interest groups began to enter into the traditional American public discourse. The Citizens United case has mobilized certain voters and inspired them to call for campaign finance reform. On the 2020 campaign trail, Democratic nominees in the election will often gripe about how the 2010 case changed "money in politics" by permitting unlimited campaign donations from corporations and certain political instruments (e.g., independent expenditures) as a method of free speech. 

Yet, money in politics has been a structural facet of and bottleneck in the American political system since well before 2010, and corporations are far from the only actors. This leads us to wonder “who else?” and “how do they exert their influence?”

Before answering those questions, however, it is necessary to determine a way to measure influence. I propose using legislative mechanisms to measure influence in two ways: (1) the revolving door of players jumping “in and out of Congress” (both at the staff and higher-levels) and (2) how these actors use their voting power (e.g., do they vote YAY or NAY on relevant legislation).

Using a combination of existing academic literature on campaign finance and social network analysis,[1] publicly available data (e.g., campaign data from the Federal Election Commission and legislative analysis from congress.gov) , empirical social network analysis (SNA), and possibly personnel databases from identified “key players” (e.g., those with significant influence over the legislative process), I will create a network to show who is involved in the decision-making process and how their influence is translated on and off Capitol Hill.

Because this is an enormous undertaking, my research will focus on the votes taken on select pieces of legislation that affect the underpinnings of the U.S. health care system, such as The Patient Protection and Affordable Care Act (2010) and the American Health Care Act (2017).[2] To winnow the field of 535 legislators, I will focus on the 100 members of the Senate, looking at Senators with attributes indicating longevity and relevance in the health care space. To this end, I plan to create 2 attribute files: (1) a Senate file that that includes, among other things, length of term, any previous relevant employment, committee assignments, highest dollar donations from industry players and (2) a Legislative decisions file that includes the voting decisions of the Senators both on the floor and in committee, any amendments filed, etc. to determine which Senate offices (e.g., Senators and their staffs) display the most relevant networks.

Methodology for Analysis

Research Questions:

·      In addition to the aforementioned, “who else” and “how”, who are the key players on and off the Hill? (e.g., Who is being the most “influenced” and, outside of the expected players, who is doing the “influencing?” Is staff influential? Are there just a few key players dominating the game?) Do legislative measures indicate influence?

Goals and Expectations

·      I hope to use SNA to identify what or who the “blockers” are to being able to provide a legislative fix to the U.S. health care system. Being able to pinpoint this change will supplement my goal in trying to identify what changes need to be made to the health care system, as I will understand the impediments and implications to such a system. 

Use of SNA

·      I will use centrality measures such as degree, betweenness, and eigen-vector (e.g. the Girvan-Newman method) to determine the level, number, influence, and connections of key players “on the Hill” and “off the Hill.”

·      I will use the aforementioned Senate attribute file to display the network, utilizing clique analysis to identity the various clusters and connections across the spectrum and how or whether that impacts influence and policymaking.

Limitations to Address

·      Availability of Personnel Data: In prior positions, I had access to extensive personnel databases that lent insights and perhaps contributed to this undertaking. Now that I no longer have access to those files, I may need to find additional, reliable personnel data.

·      Time: As previously acknowledged, this project is no easy feat, and will be difficult to accomplish in the three-months’ time. As I get a clearer picture of the “money in politics” landscape, I will determine whether to narrow the scope and analysis of the project.

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[1] Certain academics have used social network analysis to talk about power in politics (e.g., William Dolmhoff’s “Who Rules America” theory) and special interest groups (e.g., Steve Martin’s Social Capital at the Capitol) but, based on my cursory research, both the exploration of the revolving door at the staff-level and the focus on health care administrative policy will be unique to this project.

[2] Further research will allow me to accurately identify the three to five bills that would allow me to best utilize the available data.