Marion Dumas

The institution of citizen suits is a decentralized form of public participation that allows citizens to influence the implementation of public laws in courts. How does this institution influence policymaking? This article proposes a model of citizen suits. It then analyzes how this institution influences legislative decisions. The legislature bargains to choose the budget, distributive spending, and spending on an ideologically contested public good (e.g., health care or environmental protection). I find that citizen suits enable courts to forge a compromise between opponents and proponents of the public good by responding to the diverse claims of citizens. Anticipating the mobilization of citizens in courts, legislators in turn craft more socially efficient bills, with less distributive spending, which better represent the distribution of preferences for the public good compared to when citizens have no role in the implementation of legislation.

To build better theories of cities, companies, and other social institutions such as universities, requires that we understand the tradeoffs and complementarities that exist between their core functions, and that we understand bounds to their growth. Scaling theory has been a powerful tool for addressing such questions in diverse physical, biological and urban systems, revealing systematic quantitative regularities between size and function. Here we apply scaling theory to the social sciences, taking a synoptic view of an entire class of institutions. The United States higher education system serves as an ideal case study, since it includes over 5,800 institutions with shared broad objectives, but ranges in strategy from vocational training to the production of novel research, contains public, nonprofit and for-profit models, and spans sizes from 10 to roughly 100,000 enrolled students. We show that, like organisms, ecosystems and cities, universities and colleges scale in a surprisingly systematic fashion following simple power-law behavior. Comparing seven commonly accepted sectors of higher education organizations, we find distinct regimes of scaling between a school’s total enrollment and its expenditures, revenues, graduation rates and economic added value. Our results quantify how each sector leverages specific economies of scale to address distinct priorities. Taken together, the scaling of features within a sector along with the shifts in scaling across sectors implies that there are generic mechanisms and constraints shared by all sectors, which lead to tradeoffs between their different societal functions and roles. We highlight the strong complementarity between public and private research universities, and community and state colleges, that all display superlinear returns to scale. In contrast to the scaling of biological systems, our results highlight that much of the observed scaling behavior is modulated by the particular strategies of organizations rather than an immutable set of constraints.

AbstractThere is growing interest in using text as data in social science research, particularly in economics. The availability of large amounts of digitized text material such as social media posts, newspapers, firms? annual reports, and patents, combined with new computer techniques, makes it increasingly possible for researchers to use this type of information. The aim of this article is to discuss the potential of these techniques for the field of environmental economics and policy.

Transitioning away from dirty and towards clean technologies is critical to reduce carbon emissions, but the race between clean and dirty technologies is taking place against the backdrop of improvements in general-purpose technologies (GPT) such as information and communication technologies (ICT) and artificial intelligence (AI). We show how, in theory, a GPT can affect the direction of technological change and, in particular, the competition between clean and dirty technologies. Second, we use patent data to show that clean technologies absorb more spillovers from AI and ICT than dirty technologies and that energy patenting firms with higher AI knowledge stocks are more likely to absorb AI spillovers for their energy inventions. We conclude that ICT and AI have the potential to accelerate clean energy innovation.

Previous studies have modeled green technological change as innovations in the process of production (e.g., abatement technologies or energy sources). But greening the economy also requires changing products. The automotive industry, for example, needs to massively deploy alternative-fuel vehicles. Product manufacturing occurs within supply-chain networks, and developing new products typically requires complementary investments by suppliers. We study the incentives for green product innovation in industrial networks and how policies can affect them. We follow the industrial organization theory of product differentiation, and model green product innovations as upgrades in product quality where inputs from suppliers are essential for upgrading quality. We show that suppliers can be innovation bottlenecks and render policy instruments less effective. We provide an explicit mechanism for the role of institutions that help actors coordinate on the long-term direction of innovation. We discuss how our results help organize several findings from case studies in the automotive industry.