Research

I introduce an indefinitely repeated prisoner’s dilemma in which the continuation probability depends on how many players cooperate. Theoretically, the payoff from mutual cooperation and the continuation probability it induces act as substitutes in sustaining cooperative equilibria. I test this in a laboratory experiment with a 2×2 between-subjects design that independently varies these two parameters. Three results emerge. First, an increase in the continuation probability associated with joint cooperation promotes cooperation only when it makes cooperation sustainable in equilibrium. Second, early in the experiment, payoff incentives raise cooperation more than theoretically equivalent continuation incentives; this asymmetry disappears with experience. Third, estimated strategies display substitutability between the two forces once treatments are matched on strategic uncertainty.

A common cause of market failures is asymmetric information. For this reason, the reliance on market incentives and signals requires that quality of goods and services is properly observable and verifiable. This requirement is hard to meet in the case of credence goods, including most social services. In such environment, nonprofit providers can offer additional quality assurance compared to for-profit entities. When quality becomes better observable and verifiable, and hence could earn a market premium, market incentives are closer aligned with social welfare, and the quality gap expected between nonprofit and for-profit provision is likely to narrow. We explore this conjecture theoretically and empirically, using in the empirical part the case of US nursing homes during the COVID-19 pandemic. The pandemic supplied new tangible and publicly observable nursing home performance measures such as infection and death rates among residents. These measures could serve as care quality indicators, revealing aspects and attributes of the nursing home care that remained hidden before the pandemic. The data reveal significant initial gaps between for-profit and nonprofit nursing homes in COVID-19 infection rates. However, in the ensuing catching-up process triggered by increased transparency, these gaps steadily declined, eventually leading to statistical parity between two types of ownership. We explore the role of local market structure in the adjustment of nursing home industry to the pandemic; retroactively evaluate the reliability of the official ranking system in predicting nursing homes’ performance; and look for evidence of sustainable learning-by-doing effect of the pandemic.

This article is the first attempt to systematically study local public finance in contemporary Russia. We document that local governments do not have sufficient own-source revenues, are increasingly more dependent on intergovernmental fiscal aid, lack access to market borrowing, and suffer from structural flaws in the design of intergovernmental fiscal relations. Additionally, we present the results of the modified Brown’s 10-point test to compare local fiscal health across the Russian regions. Finally, we assess the strength of local fiscal incentives in 2012–2021 and demonstrate that local governments in Russia lack capacity to foster local economic growth through the tax code.

Most of what we know about how AI changes economic decisions concerns the person who uses it. However, in many interactions what also matters is what our beliefs are of our opponent using AI assistance. In this paper we ask how people play against a counterpart who has access to AI, and whether the answer depends on the expertise of the opponent. In a hybrid experiment, online participants from four age cohorts play the 11-20 money request game against lab participants who are novices or experts in strategic reasoning, with or without an AI assistant. We truthfully communicate opponent information to the participants. We find that opponent description has no effect on people’s requests on average, but people think considerably longer when the opponent is an expert with AI. We conclude that they treat AI as something that amplifies expertise rather than replaces it in this context. Baby Boomers and Millennials hold opposite views of AI effect, which is what hides the overall effect leading to the flat averages.

This paper studies how non-exponential discounting affects cooperation in repeated strategic interactions. While the experimental literature on infinitely repeated games typically assumes exponential discounting, evidence on intertemporal choice suggests that individuals may place disproportionate weight on immediate outcomes relative to future consequences. I examine how incorporating present-biased preferences changes incentives for cooperation in the infinitely repeated Prisoner’s Dilemma and whether behavior differs from predictions based on standard exponential discounting. I develop theoretical predictions and an experimental design that allows cooperation to be compared across different intertemporal incentive structures. The project aims to better understand the role of time preferences in sustaining cooperation and, more broadly, how the structure of intertemporal incentives interacts with strategic uncertainty in repeated games.