Introduction

This project explores the political influence of two of the most powerful sectors in the United States economy, Pharmaceuticals and Big Tech. I chose pharma and big tech because of their economic dominance, political influence, and central roles in innovation and infrastructure. I researched how ties to former government officials, lobbying behavior, and economic variables influenced the behavior of legislative outcomes. Previous studies have shown that corporations and their affiliated groups can significantly impact elections through political spending, like super PACs (e.g., Baumgartner et al., 2009; Drutman, 2015). I took a multivariate approach to investigating what factors can play a role in how companies get their way. Some characteristics like lobbying expenditures, firm size, and sector identity increase a company’s likelihood of policy “wins.” My analysis focuses on how often companies get their way, and how tactics like revolving door dynamics are dynamic with resource-based structural power.

Model

My model looked at one primary outcome variable, which is whether or not a data point “won” on a given policy. A firm/association (data point) may have “won” in 2 given scenarios. First, the firm must support a policy, and then that policy is passed or enacted by the POTUS. The second way a firm could have won is if they opposed a policy, and it was rejected or died in Congress. The firms were chosen from the 2017 Maplight data sets and picked according to the frequency of their appearance in the data set. The multivariate logistic regression approach accounts for the combined effects of multiple factors influencing policy success.

The multivariate models help address potential confounding by including a range of organizational attributes in the same equation. This means that instead of looking at each variable in isolation, the model simultaneously considers factors. The factors looked at were lobby spending from 2014-2017, whether or not it is an association, the location of the headquarters, distance from D.C., political alignment, spending ratio (2016), founding year, and the number of patents. The models produced include, but are not limited to, a coefficient plot, a linear regression plot, and a linear regression table. (Data also includes data points from finance and energy sectors of the US economy, which is in part because data was collected in whole for two separate projects)

Data

This project’s data set is a dynamic combination of mainly MapLight data with hand-collected data from OpenSecrets.org. The population of interest is major U.S. corporations active in federal lobbying between 2014 and 2017. From there, data was aggregated regarding the multivariate approach and then organized and analyzed in Stata. The population of interest is major U.S. corporations active in federal lobbying between 2014 and 2017, and political alignment and spending ratios were determined from the 2016 presidential election year. The final sample includes over 100 organizations, primarily in the Pharma and Tech sectors, selected due to their frequent appearance in the lobbying records. I focused on variables with theoretical links to policy influence: market capitalization and employee size as proxies for firm structural power, lobbying expenditures as a direct measure of political investment and discursive power, and association membership and HQ proximity to D.C. as indicators of institutional access.

Results

Figure 1: Coefficient Plot Figure 2: Linear Regression Scatter Plot

This analysis examines which factors predict whether a company’s lobbying position aligns with legislative outcomes. A coefficient plot compares two models: one with only sector indicators, and another adding firm-level traits like market cap, employees, lobbying spend, and D.C. proximity (Figure 1). Pharma firms consistently show higher odds of success, even after controls, while Tech firms show weaker effects. A scatterplot of supportive lobbying versus win rates (Figure 2) reveals a weak relationship, suggesting that simply backing bills does not increase success. Supporting more bills also does not predict higher passage rates, highlighting the limited effectiveness of proactive lobbying. Overall, sector identity and structural power, not support behavior, are stronger predictors of policy influence, and results remain statistically robust across models.

Discussion

The objective of this project was to investigate whether companies in the Pharmaceutical and Technology sectors are more successful at influencing U.S. federal policy—and to explore what organizational characteristics drive that success. Using lobbying data from MapLight, tracking expenditure from OpenSecrets, and revolving door data, I modeled the likelihood of a firm “winning” on policy based on factors like sector affiliation, firm size, lobbying spend, proximity to D.C., and political access.

The analysis supports my theory: Pharma firms are significantly more likely to achieve favorable policy outcomes, even after controlling for structural and financial advantages. While Tech firms also performed relatively well, their influence appears more variable. Surprisingly, simply supporting legislation did not predict greater success, suggesting that more subtle or defensive lobbying tactics may be at play. It could be the case that tech firms have pre-established agenda setting power and their needs are already taken care of. These results seem reasonable given Pharma’s entrenched regulatory ties and historical lobbying dominance. The policy implication is clear: influence is not just about money, it’s about networks, structural power, and sector identity.

References

[1] D. Sica, Measuring Corporate Influence in Washington Through Lobbying and Revolving Door Networks, Undergraduate Research Poster, University of Massachusetts Amherst, 2025.

[2] K. Young, Professor, Department of Economics, University of Massachusetts Amherst. Expertise in political economy and corporate lobbying dynamics, personal communication, 2024-2025.

[3] F. R. Baumgartner, J. M. Berry, M. Hojnacki, B. L. Leech, and D. C. Kimball, Lobbying and Policy Change: Who Wins, Who Loses, and Why. Chicago, IL: University of Chicago Press, 2009. L. Drutman, The Business of America is Lobbying: How Corporations Became Politicized and Politics Became More Corporate. New York, NY: Oxford University Press, 2015.