The Regional Comprehensive Economic Partnership (RCEP) is one of the most important mega-regional trade agreements signed to date. Yet, it failed to include an Investor-State Dispute Settlement (ISDS) mechanism in its investment chapter. What explains this omission? To unpack this, we examine international negotiations as a two-step process. In the first stage, we theorize that initial preferences towards ISDS are based on countries’ orientation toward foreign direct investment (FDI), experience with ISDS, and past treaty practice. Second, we theorize that during protracted negotiations, adverse regime developments and domestic politics can have a profound impact on treaty design. To test our framework, we examine the RCEP negotiations. Our analysis shows that mounting cases as well as the eroding norm of ISDS in other treaties lowered support for ISDS as the negotiations progressed. Then, a change of government in Malaysia shifted that country’s position dramatically, which tipped the balance against ISDS in the final round of negotiations. Our findings have important implications for the international investment regime. They highlight the factors that determine countries’ initial preferences while also demonstrating the importance of developments during the negotiations, which can lead to the abandonment of the institutional status quo.

The potential for mutual influence or “spillover” between economic and security cooperation is a long-standing area of interest for policymakers and scholars alike. This paper examines how network dynamics affect spillover. We focus on two prominent types of formal bilateral cooperation—defense cooperation agreements (DCAs) and bilateral investment treaties (BITs)—both of which have proliferated dramatically in the post-Cold War international system. We argue that existing theoretical and empirical approaches to economic-security spillover focus too strictly on influences at the bilateral level. As with other forms of international cooperation, BITs and DCAs comprise larger international networks. Governments develop portfolios of BITs or DCAs with distinct structural goals in mind, and they implement specific strategies in pursuing those goals. With BITs, governments follow a network-hierarchy strategy that allows them to influence treaty design and protect their firms. In DCAs, governments instead favor a network-community strategy focused on pooling collective security goods among groups of like-minded collaborators. When these network strategies complement one another, they promote cooperative economic-security spillover. When they conflict, however, they inhibit spillover, such that cooperation in economic or security issues discourages cooperation in the opposing issue area.

Our aim with this special issue on the future of artificial intelligence (AI) politics, policy, and business is to give space to considering how the balalnce between risk and reward from AI technologies is and perhaps should be pursued by the public and private sectors. Ultimately, private firms and regulators will need to work collaboratively, given the complex networks of actors involved in AI development and deployment and the potential for the technology to alter existing policy regimes. We begin the introduction of this special issue of Business & Politics with a discussion of the growth in AI technology use and discussions of appropriate governance, followed by a consideration of how AI-related politics, policy, and business intersect. We then summarize the contributions of the authors in this issue and conclude with thoughts about how political science, public administration, and public policy scholars have much to offer, as well as much to study, the establishment of effective AI governance.

When and how does stakeholder credibility matter in shaping public opinion? We explore this question in a real-world setting: in order to fight its citizens’ financial exclusion—a key barrier to development in Indian Country—American Indian Nation “A” negotiated the first entry of the first bank to its reservation. The bank is owned by American Indian Nation “B.” To the Federal Reserve, the bank branch is a potential proof-of-concept for the capacity of tribe-to-tribe investment to improve capital access in underserved Native communities. The bank’s success ultimately depends on whether Nation A’s citizens use its services; in the months before its opening, all three stakeholders independently attempted to influence public opinion toward the bank. We collaborated to conduct a first-of-its-kind survey of Nation A’s tribal members, finding high baseline buy-in especially given the bank’s nationality, but weak and even counterproductive treatment effects of pro-banking cues provided by Nation A and the Federal Reserve. Our results make clear the practical benefits of theory-building around stakeholder credibility, and the crucial role of individual attitudes in the political economy of development.

Companies often donate to support public service delivery in US cities. Although this can help alleviate budgetary struggles for those governments, it is unclear what effect it may have on the individual residents receiving the services. In this paper, we argue that people who receive services funded in part by corporate donations are less likely to hold their local governments accountable if the services are of poor quality, because they no longer conceive of themselves as being the sole set of interests the government is catering to. We test our theory using a survey experiment with a realistic fictional government email and find evidence that, when compared with people receiving strictly taxpayer-funded services, people who are told services are provided in part by companies are less likely to take the quality of services into account when they vote.

Why do voters shun some business tycoons yet elect others into power? As structural conditions facilitate the entry of super-wealthy actors into politics, the differential electoral support across business elites suggests a puzzle. We conceptualize four mechanisms behind the popular support for “tycoon candidates”: competence signaling, framing, fame, and clientelism. To test their relative efficacy, we leverage an experiment embedded in a nationally representative survey in South Africa, an important developing democracy where certain tycoons are successfully running for office. We find that, across distinct electoral appeals by tycoon candidates, clientelism is particularly effective, especially for mobilizing support from the less affluent voters. Racial framing significantly decreases support among white voters. Meanwhile, tycoons’ competence signaling or fame do not help them at the ballot box. By identifying the micro-level underpinnings of voter support across tycoon candidates, our study contributes to the literatures on business and politics, voting behavior, and clientelism.

There is a consensus in the finance literature that stock markets generally perform well ahead of holidays. However, I argue that this relationship does not hold in the Chinese context, given that public holidays are associated with increased collective action and repression. I propose two possible mechanisms: (1) Chinese investors take cues from the political environment and will thus act more conservatively in the market prior to public holidays or (2) the government increases intervention to stabilize the stock market during these periods. I test this relationship using daily stock exchange data from Shanghai and Shenzhen. In addition, I corroborate the theoretical mechanism by testing whether there is similar conservatism before focal points on the dissident calendar. This research note contributes to our understanding of the Chinese investment market and raises general questions about the representativeness of the finance literature. In addition, this research speaks to the costs of authoritarianism and preserving social stability in these contexts.

The myriad applications of artificial intelligence (AI) by the private and public sectors have exploded in the public consciousness in the postpandemic period. However, researchers and businesses have been working on AI technology applications for decades, and in many ways, governments are rushing to catch up. This article presents an argument that the future of AI policy in the United States will be driven in large part by current and future state-level policy experiments. This argument is presented by drawing on scholarship surrounding federalism, regulatory fragmentation, and the effects of fragmentation on business and social equity. The article then presents the case of autonomous vehicle policy in the states to illustrate the degree of current fragmentation and considers the effects of layering new AI policies on top of existing rules surrounding privacy, licensing, and more. Following this consideration of existing research and its application of AI policy, the article presents a research agenda for leveraging state differences to study the effects of AI policy and develop a cohesive framework for governing AI.

This paper explores the potential causal relationship between political orientation and education investment by using panel data from 21 OECD countries from 1970 to 2020 and utilizing estimators that address endogeneity (i.e. 2SLS, System GMM, and Lewbel 2SLS). In particular, using communist influence as a physical instrument for political orientation, we find a positive impact of the right political orientation on education investment, and the impact of the left orientation is negative. The positive impact from the right orientation is also stronger than the negative impact from the left. Moreover, these core results are robust to alternative measures of political orientation and education investment, alternative estimators that address endogeneity, and the moderation effect of innovation.

Public decision-makers incorporate algorithm decision aids, often developed by private businesses, into the policy process, in part, as a method for justifying difficult decisions. Ethicists have worried that over-trust in algorithm advice and concerns about punishment if departing from an algorithm’s recommendation will result in over-reliance and harm democratic accountability. We test these concerns in a set of two pre-registered survey experiments in the judicial context conducted on three representative U.S. samples. The results show no support for the hypothesized blame dynamics, regardless of whether the judge agrees or disagrees with the algorithm. Algorithms, moreover, do not have a significant impact relative to other sources of advice. Respondents who are generally more trusting of elites assign greater blame to the decision-maker when they disagree with the algorithm, and they assign more blame when they think the decision-maker is abdicating their responsibility by agreeing with an algorithm.

I present a theoretical framework that links different configurations of organized violence to global patterns in foreign direct investment (FDI). Insurgents, states, and rogue government agents all use violence for political purposes (i.e., incapacitating rivals), but they vary in how they use violence for economic purposes (i.e., generating income). Applying Olson’s (1993) concepts of “roving” and “stationary” banditry, I hypothesize that violence perpetrated by rebels and rogue agents indeed depresses a host country’s commercial appeal, but that violence perpetrated willfully by the state doesn’t. This claim is tested against data on FDI “entry” by several thousand multinational corporations between 1994 and 2018.

Existing research on lobbying has predominantly focused on its material returns, such as equity returns, stock prices, and government contracts while overlooking its informational impact. This paper addresses this gap by investigating to what extent and under what conditions policymakers assimilate information delivered through corporate lobbying. Drawing on an informational perspective, it proposes that the informational effect of lobbying is moderated by the information asymmetry between policymakers and firms. Focusing on the U.S. ride-hailing industry, this study utilizes a unique dataset on U.S. state legislatures’ adoption of the model policy lobbied by ride-hailing companies. The results reveal that the informational impact of corporate lobbying is highly contingent upon the presence of information asymmetry between policymakers and firms, which can be attributed to policymakers’ resources for independent information gathering, information deliberation through public hearings or media discussions, and countervailing lobbying efforts.

Defense contractors play an essential role in US security operations. Among other responsibilities, contractors manufacture arms, manage logistics, analyze intelligence, and carry out cybersecurity operations. Historically, defense contractors were either publicly traded corporations or privately owned companies. The past two decades, however, have seen a major shift in the ownership structure of the US defense industry. Private equity firms—once niche actors in the US national security marketplace—have carried out over 1,500 deals involving defense contractors since 2000. This study employs a mixed-methods research design to assess the effects of private equity investment on the financial stability of the US defense industry. Using data on over 8,000 defense contractors, the inquiry finds that contractors with private equity backing experience bankruptcy at higher rates than contractors with no prior private equity investment. In addition, the study evaluates private equity activity in the military satellite services market—a field of growing importance in the strategically important domain of outer space. The inquiry’s findings have notable international security implications. Given the US’ role as a lynchpin in numerous alliances around the world, higher rates of bankruptcy among defense contractors have the potential to affect the security of the United States and its allies.

Political instability is commonly thought to discourage investment and reduce economic growth. We challenge this consensus by showing that instability does not systematically depress investment. Using an event study approach, we examine daily returns of national financial indices in every country that experienced an irregular regime change subject to data availability. Returns following resignations are large and positive (+4 percent), while those following assassinations are negative and smaller in magnitude (−2 percent). The impact of coups tends to be negative (−2 percent), but we show that a pro-business coup results in large positive returns (+10 percent). We also find evidence that authoritarian or anti-business regime changes are more likely to lead to capital flight than democratic or pro-business changes. The immediate impact of political instability on investment is therefore dependent on the type of regime change and its expected impact on future growth.

Political scientists know surprisingly little about the political behavior of inventors, or those who produce new technologies. I therefore merged US patent and campaign contribution (DIME) data to reveal the donation behavior of 30,603 American inventors from 1980 through 2014. Analysis of the data produces three major findings. First, the Democratic Party has made significant inroads among American inventors, but these gains increasingly come from only a few regions and flow to a relatively small number of candidates. Second, deeper geographic trends explain most of the change in aggregate donation patterns. Third, inventors do not strategically donate to candidates outside their own district and, since 2006, inventors increasingly contribute to relatively centrist employer PACs with weak ties to the Democratic Party. These findings suggest that the interaction between market-oriented policy and American electoral institutions may inhibit the formation of broad cross-regional coalitions to support the knowledge economy.

As the development and use of artificial intelligence (AI) continues to grow, policymakers are increasingly grappling with the question of how to regulate this technology. The most far-reaching international initiative is the European Union (EU) AI Act, which aims to establish the first comprehensive, binding framework for regulating AI. In this article, we offer the first systematic analysis of non-state actor preferences toward international regulation of AI, focusing on the case of the EU AI Act. Theoretically, we develop an argument about the regulatory preferences of business actors and other non-state actors under varying conditions of AI sector competitiveness. Empirically, we test these expectations using data from public consultations on European AI regulation. Our findings are threefold. First, all types of non-state actors express concerns about AI and support regulation in some form. Second, there are nonetheless significant differences across actor types, with business actors being less concerned about the downsides of AI and more in favor of lax regulation than other non-state actors. Third, these differences are more pronounced in countries with stronger commercial AI sectors. Our findings shed new light on non-state actor preferences toward AI regulation and point to challenges for policymakers balancing competing interests in society.

There is substantial cross-national variation in the level of regulatory clarity surrounding cryptocurrencies. What explains these differences? And, more broadly, what drives the divergent historical development of market regulation in different jurisdictions? To answer these questions, we present a new conceptual framework centered on the concept of market legibility. This term, inspired by the sociological literature, refers to the extent to which markets are made legible to the state through standardization. We contend that state supply of, and market demand for, legibility drives the primary political-economic dynamics of market regulation. Specifically, these factors combine to produce ideal type states of legibility that correspond to both distinct stages of market development and the relative level of regulatory clarity in any one jurisdiction. This framework is utilized to conduct a comparative historical analysis of cryptocurrency regulation in the EU, US, UK, and Japan. By performing these tasks, this article corrects the common assumption that states are constantly striving to impose their authority on unwilling markets. It demonstrates instead that state and private actor preferences to make markets legible vary, conditioning, in turn, the political economy of regulatory governance.

This paper documents significant partisan divides across a range of corporate cultural values. Using panel data of 2,424 S&P 1500 firms spanning the period from 2001 to 2018, we find that firms whose top management teams lean toward the Democratic Party exhibit higher cultural values of integrity, teamwork, innovation, respect, and quality, in comparison with firms with executives leaning toward the Republican Party. In addition, we find that the partisan gap diminishes when firms have less entrenched management or locate in states with stronger judicial accountability. Our findings lend support to earlier research suggesting that leader characteristics are associated with corporate culture.

What strategies work best for enforcing sanctions? Sanctions enforcement agencies like the US Office of Foreign Assets Control (OFAC) face resource limitations and political constraints in punishing domestic firms for violating sanctions. Beyond monetary fines, sanctions enforcement actions also serve a “naming and shaming” function that tarnishes violators’ reputations. Larger, higher-profile companies tend have much more at stake in terms of their reputations than smaller or less well-known firms. At the same time, punishing higher-profile companies for sanctions violations is likely to generate more publicity about the risks and potential consequences of not complying with sanctions. We theorize that OFAC should impose larger fines on high-profile companies to draw attention to those cases, make the enforcement actions more memorable, and enhance the reputational costs that they inflict. We conduct a statistical analysis of OFAC enforcement actions from 2010 to 2021 and find support for our theory.

The rapid emergence of artificial intelligence (AI) technology and its application by businesses has created a potential need for governmental regulation. While the federal government of the United States has largely sidestepped the issue of crafting law dictating limitations and expectations regarding the use of AI technology, US state legislatures have begun to take the lead in this area. Nonetheless, we know very little about how state legislatures have approached the design, pursuit, and adoption of AI policy and whether traditional political fault lines have manifested themselves in the AI issue area. Here, we gather data on the state-level adoption of AI policy, as well as roll call voting on AI bills (classified on the basis of consumer protection versus economic development), by state legislatures and analyze the political economy of AI legislation. We find that rising unemployment and inflation are negatively associated with a state’s AI policymaking. With respect to individual legislator support, we find that liberal lawmakers and Democrats are more likely to support bills establishing consumer protection requirements on AI usage. The results suggest that economic concerns loom large with AI and that traditional political fault lines may be establishing themselves in this area.