This dissertation examines the nonmarket strategies that firms use to respond to and to create change in the social and political environment. In the first chapter, I document the extent of corporate political connections across Europe and analyze the institutional contingencies of their impact. I find that a political connection increases the annual value of public procurement contracts awarded to firms, but that this positive impact is moderated by accountability institutions that enable different bodies of government to check one another’s power. When accountability institutions are weaker, firms become more likely to hold political connections, and the competition between rival firms to form political connections intensifies, suggesting that nonmarket competition in the political arena becomes increasingly consequential as the constraints of political patronage are weakened. In the second chapter, I analyze the grantmaking behavior of private foundations in the United States and find evidence consistent with philanthropy being used as a tax-advantaged method to influence politics through grantmaking to politically active nonprofits. In the third chapter, co-authored with Brian Ganson and Witold J. Henisz, we develop theories to explain how a firm’s relational strategies might impact the structure of relationships, and hence conflict risk, between groups in conflict-affected areas. Firms can affect broader societal outcomes when their actions, such as the unequal distribution of benefits to majority groups over minority groups, change the tenor of conflict and cooperation between identity groups delineated by ethnic, class, geographic, cultural, or other divides. As such, firms become implicated in shaping how they and their stakeholders reach mutually acceptable settlements in response to collective challenges. In the fourth chapter, co-authored with Anne S. Jamison, we propose a set of a methods to map the structure of relationships between firms and stakeholders in conflict-affected areas. We utilize geo- and time-tagged data from media reports and apply natural language processing techniques to construct a network of relationships between political figures, civil society actors, and firms in Rwanda.