Economics seminars

Joel Mokyr, Philippe Aghion and Peter Howitt examine why technological innovation can sustain economic growth despite the disruption it creates. Mokyr considers the historical development of useful knowledge, the connection between understanding and invention, and the institutional and political conditions needed for continuing progress. Aghion develops the economics of creative destruction, connecting innovation incentives with entry, incumbent firms and competition. He discusses how theoretical models and empirical evidence inform policies intended to support productivity growth. Howitt explains the development of the creative-destruction growth model and explores implications for competition, patent policy, international trade and technological change. The lectures also consider artificial intelligence and employment, distinguishing the historical resilience of growth from uncertainty about new forms of automation.

Partha Dasgupta considers how economic progress should be measured during an ecological crisis. Drawing on his work on natural capital, he examines an approach to economics that assigns value to nature alongside other assets. The discussion asks how changing the way economies value the natural world could change the way they protect it. Dasgupta connects economic measurement with environmental sustainability and the challenge of recognising nature’s contribution to human well-being.

Susan Athey examines how machine learning and digital interventions can help explain and improve workers’ transitions between jobs. One project uses Swedish administrative data to identify groups whose earnings and employment are less resilient after layoffs, revealing substantial differences among workers within the same firms and labour markets. A second line of work uses transformer models and large language models to represent careers and analyse gender wage gaps, identifying settings in which large unexplained differences persist. Two further projects develop and evaluate digital interventions intended to help disadvantaged workers enter expanding occupations in information technology and data science. The lecture connects new methods for measuring labour-market disadvantage with evidence about practical interventions.

Victor Chernozhukov develops sharp bounds on omitted-variable bias for a broad class of causal quantities. The framework covers averages of potential outcomes, average treatment effects, average derivatives, and policy effects generated by shifts in covariate distributions within general nonparametric causal models. Using the Riesz–Fréchet representation of the target quantity, the analysis expresses the bias bound through the additional variation that unobserved variables introduce into the outcome and the relevant Riesz representer. Debiased machine learning then provides flexible statistical inference for the components of these bounds that can be learned from observed data. The approach connects sensitivity analysis for unmeasured confounding with modern causal estimation.

David Autor examines why employment persists as machines take over tasks, and where new kinds of work come from. He distinguishes technologies that replace workers’ expertise from those that complement it, tracing occupational change, labour-market polarisation and the emergence of new work. The lecture turns to artificial intelligence and the possibility of using it to expand the range of people who can perform valuable expert tasks. Examples include writing, healthcare and elder care, alongside the risks of automation that leaves people poorly equipped to handle failures. Autor considers the roles of education, institutional choices and investment in directing technological change toward better work. His account treats the future distribution of jobs and earnings as something shaped by decisions rather than an inevitable consequence of technical capability.

When making decision under risk, people often exhibit behaviours that classical economic theories cannot explain. Newer models that attempt to account for these ‘irrational’ behaviours often lack neuroscience bases and require the introduction of subjective and problem-specific constructs. Here, we present a decision-making model inspired by the prediction error signals and introspective neuronal replay reported in the brain. In the model, decisions are chosen based on ‘anticipated surprise’, defined by a nonlinear average of the differences between individual outcomes and a reference point. The reference point is determined by the expected value of the possible outcomes, which can dynamically change during the mental simulation of decision-making problems involving sequential stages. Our model elucidates the contribution of each stage to the appeal of available options in a decision-making problem. This allows us to explain several economic paradoxes and gambling behaviours. Our work could help bridge the gap between decision-making theories in economics and neurosciences.

This research panel explores how large and unconventional datasets can produce high-frequency inflation forecasts. Its starting point is renewed concern about inflation following substantial monetary and fiscal stimulus. Alberto Cavallo presents research on real-time inflation measurement. Yves Lemperiere, Adam Rej, José Scheinkman and Michael Woodford join the discussion of alternative data and its economic and financial applications. The panel connects timely measurement with the problem of assessing inflation while conventional economic statistics arrive with a delay.

Classic economists proposed that economic choices rely on the computation and comparison of subjective values. This hypothesis continues to inform economic theory and experimental research, but behavioral measures are ultimately not sufficient to prove the proposal. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods. Moreover, neuronal activity in this area suggests the formation of a decision. However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking choices to value signals in the brain remains correlational. In my talk, I will present recent results showing that neuronal activity in OFC are causal to economic choices.

Framing effects in individual decision-making have puzzled economists for decades because they are hard, if at all, to explain with rational choice theories. Why should mere changes in the description of a choice problem affect decision-making? Here, we examine the hypothesis that changes in framing cause changes in the allocation of attention to the different options – measured via eye-tracking – and give rise to changes in decision-making. We document that the framing of a sure alternative as a gain – as opposed to a loss – in a risk-taking task increases the attentional advantage of the sure option and induces a higher choice frequency of that option – a finding that is predicted by the attentional drift-diffusion model (aDDM). The model also correctly predicts other key findings such as that the increased attentional advantage of the sure option in the gain frame should also lead quicker decisions in this frame. In addition, the data reveal that increasing risk aversion at higher stake sizes may also be driven by attentional processes because the sure option receives significantly more attention – regardless of frame – at higher stakes. We also corroborate the causal impact of framing-induced changes of attention on choice with an additional experiment that manipulates attention exogenously. Finally, to study the precise mechanisms underlying the framing effect we structurally estimate an aDDM that allows for frame and option-dependent parameters. The estimation results indicate that – in addition to the direct effects of framing-induced changes in attention on choice – the gain frame also causes (i) an increase in the attentional discount of the gamble and (ii) an increased concavity of utility. Our findings suggest that the traditional explanation of framing effects in risky choice in terms of a more concave value function in the gain domain is seriously incomplete and that attentional mechanisms as hypothesized in the aDDM play a key role.

Richard Blundell examines inequalities in income, living standards, wealth, health, family circumstances, opportunities and political influence. He considers how weak living-standard growth after the financial crisis sharpened these divisions, and how the COVID-19 pandemic intensified some existing inequalities while exposing new ones. The lecture asks how to balance taxes and welfare benefits with minimum wages, human-capital investment and competition policy. It argues that the tax and benefit system alone cannot resolve low wages and earnings inequality. Blundell outlines the IFS–Deaton Review of inequality in the twenty-first century and discusses the pandemic’s implications for policy responses.

Mariana Mazzucato, William Janeway and Sidney Winter discuss the role of government in innovation, from basic research through commercialisation. Mazzucato challenges the view that the public sector should simply withdraw and allow private enterprise to innovate, arguing that public institutions have often taken risks that businesses were unwilling to bear. The panel examines public investment across the innovation process and the consequences of shrinking the budgets of agencies involved in technological development. It asks whether governments should actively help create markets as well as address market failures, and how public and private institutions can contribute to economic growth.

Ariel Pakes introduces the foundations of demand estimation for differentiated products. Starting from individual preferences and choices, he explains how consumer heterogeneity can be aggregated into market demand and used to study prices, substitution and welfare. The lecture compares modelling products directly with representing them through characteristics, including observed and unobserved attributes. It examines how different assumptions about tastes generate substitution patterns, using examples involving cars and other differentiated goods. Pakes discusses logit models, vertical and horizontal differentiation, and the implications of demand assumptions for markups, competition and policy analysis.

Ernst Fehr uses experimental evidence to examine the psychological consequences of authority in economic interactions. Although power pervades political, social and economic life, its motivational origins and effects are not fully understood. The lecture investigates why people want to exercise authority, how possessing it can strengthen motivation, and how being deprived of it can undermine motivation. These findings connect the allocation of decision-making power with incentives, cooperation and behaviour inside organisations.