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on History and Philosophy of Economics |
| By: | Woodcock, Ramsi |
| Abstract: | Progressives who respond to conservative law and economic arguments by rejecting neoclassical economic theory are making a mistake because neoclassical economics is the only ideology that honors the modern view—associated with the Death of God narrative in Western culture—that there are no longer universal standards of value. To make a case for redistribution of wealth that appeals to the modern view regarding value—one that progressives themselves hold—progressives must engage with economics. Fortunately, the concept of the gains from trade in neoclassical economics (also known as “surplus” or “economic rent”) allows progressives to make a strong case for redistribution of wealth because the gains from trade do not need to be appropriated by any particular market participant in order for efficiency to prevail in markets. This approach is called “inframarginalism” to contrast it with the conservative view that the valuations of the marginal buyer and seller pin down price in competitive markets and therefore leave progressives no room for redistribution. |
| Date: | 2024–08–09 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:xghn8_v1 |
| By: | Paul Dolan; Richard Layard; Gillian Tett; Helen Ward |
| Abstract: | The late Nobel laureate Daniel Kahneman had a long association with CEP. At an LSE event, Richard Layard, Gillian Tett and Paul Dolan discussed how his work influenced psychology, economics and society - and their experience of researching and debating with him. |
| Keywords: | behavioural economics, kahneman |
| Date: | 2025–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepcnp:697 |
| By: | Advani, Arun (University of Warwick & IFS); Ash, Elliott (ETH Zurich); Boltachka, Anton (ETH Zurich); Cai, David (LSE); Rasul, Imran (UCL & IFS) |
| Abstract: | Issues of racial justice and economic inequalities between racial and ethnic groups have risen to the top of public debate. Economists ability to contribute to these debates is based on the body of race-related research. We study the volume and content of race-related research in economics. We base our analysis on a corpus of 225 000 economics publications from 1960 to 2020 to which we apply an algorithmic approach to classify race-related work. We present three new facts. First, since 1960 less than 2% of economics publications have been race-related. There is an uptick in such work in the mid 1990s. Among the top-5 journals this is driven by the American Economic Review, Quarterly Journal of Economics and the Journal of Political Economy. Econometrica and the Review of Economic Studies have each cumulatively published fewer than 15 race-related articles since 1960. Second, on content, while over 50% of race-related publications in the 1970s focused on Black individuals, by the 2010s this had fallen to 20%. There has been a steady decline in the share of race-related research on discrimination since the 1980s, with a rise in the share of studies on identity. Finally, we apply our algorithm to NBER and CEPR working papers posted over the last four decades, to study an earlier stage of the research process. We document a balkanization of race-related research into a few fields, and its continued absence from many others – a result that holds even within the subset of research examining issues of inequality or diversity. We discuss implications of our findings for economists’ ability to contribute to debates on race and ethnicity in the economy. JEL Codes: A11 ; B41. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:warwec:1548 |
| By: | Barbara Tversky |
| Abstract: | Following an LSE event discussing the legacy of Daniel Kahneman (5 March 1934 to 27 March 2024), Barbara Tversky remembers him and his work. |
| Date: | 2025–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepcnp:698 |
| By: | Klebel, Thomas; Traag, Vincent |
| Abstract: | Sound causal inference is crucial for advancing the study of science. Incorrectly interpreting predictive effects as causal might be ineffective or even detrimental to policy recommendations. Many publications in science studies lack appropriate methods to substantiate their causal claims. We here provide an introduction to structural causal models. Such models, usually represented in a graphical form, allow researchers to make their causal assumptions transparent and provide a foundation for causal inference. We illustrate how to use structural causal models to conduct causal inference using regression models based on simulated data of a hypothetical structural causal model of Open Science. The graphical representation of structural causal models allows researchers to clearly communicate their assumptions and findings, thereby fostering further discussion. We hope our introduction helps more researchers in science studies to consider causality explicitly. |
| Date: | 2024–02–09 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:4bw9e_v1 |