Post-exceptionalism and corporate interests in US agricultural policy

ABSTRACT

This paper examines post-exceptionalism in US food and agriculture policy. Using data on lobbying activity and campaign contributions, we find that corporations and organizations representing the banking industry, manufacturers of agricultural inputs, food processors, and the retail food sector allocate significant financial resources trying to influence food and agriculture policy. Although traditional peak associations of farmers and organizations representing the growers of specific commodities remain an important constituency in policy debates, agriculture is no longer a compartmentalized policy domain dominated by producer interests. Instead, food and agriculture resemble other domains of US policy in which corporations and trade associations leverage advantages in money and personnel to protect their bottom line.

KEYWORDS:

• Agriculture
• corporations
• interest groups
• lobbying
• United States

Acknowledgements

The authors would like to thank the anonymous referees and editors of this special issue for their advice on earlier versions this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Adam Sheingate is Professor of Political Science at Johns Hopkins University, [email protected].

Allysan Scatterday is a graduate student at the Johns Hopkins Bloomberg School of Public Health.

Bob Martin is Senior Lecturer at the Johns Hopkins Bloomberg School of Public Health.

Keeve Nachman, Assistant Professor of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health.

Notes

1. For more details on LDA reports, see http://www.senate.gov/legislative/resources/pdf/S1guidance.pdf.

2. For more details on CRP data, see http://www.opensecrets.org/lobby/.

3. Each observation included codes for the category, industry, and sector of each organization. The data is organized by client, meaning that for-hire lobbyists and in-house lobbyists are listed according to the client name.

4. We identified reports using specific bill numbers (H.R. 1947, H.R. 2642, H.R. 6083, S. 10, S. 954, and S. 3240) as well as text searches with the words ‘Farm Bill’ or bill titles such as ‘Agriculture, Reform, and Food.’ We also include reports mentioning federal nutrition programs.

5. We standardized each variable to have a mean of 0 and a standard deviation of 1. Our measure of resources is based on the first component (of four) which explained 0.40 of the variance in the original data. For more information on PCA and an application, see Vyas and Kumaranayake (2006).

6. A full listing along with replication materials is available in an online appendix. and can be accessed at https://figshare.com/projects/Post-Exceptionalism_and_Corporate_Interests_in_US_Agricultural_Policy/22387.

7. We coded each of the 2,118 observations in the dataset by assigning a topic based on the brief description of each report and then consolidating topics into sixteen themes. Observations that did not mention a specific topic were coded as ‘general/unknown’ and made up around 44% of our reports.

8. As before, we used the standardized total spent on lobbying, total number of reports, average lobbyists, and average ‘revolving door’ lobbyists to measure resources and used the first component of the PCA to rank organizations.

9. Every two years, one third of Senate seats is up for election. Consequently, the period in which Congress debated the most recent Farm Bill included Senators elected in 2008, 2010, or 2012.

10. These differences were significant at p < 0.01 in a two-tailed t-test comparison of means with unequal variances.

11. We constructed an affiliations matrix (368 × 535) that captures whether or not an entity active on the Farm Bill contributed to a given Member of Congress. We then transformed this data into an adjacency matrix in which each node represents the number of common ties between entities (e.g. contributions to the same Member of Congress). All network analyses were done using UCINET 6 software (Borgatti et al. 2002).