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on Heterodox Microeconomics |
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Issue of 2021‒11‒22
five papers chosen by Carlo D’Ippoliti Università degli Studi di Roma “La Sapienza” |
| By: | Sun, Ran; Nolan, James; Kulshreshtha, Suren |
| Keywords: | Environmental Economics and Policy |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae21:315340&r= |
| By: | Darden, Michael E. (Tulane University); Dowdy, David (Johns Hopkins University); Gardner, Lauren (Johns Hopkins University); Hamilton, Barton H. (Washington University, St. Louis); Kopecky, Karen A. (Federal Reserve Bank of Atlanta); Marx, Melissa (Johns Hopkins University); Papageorge, Nicholas W. (Johns Hopkins University); Polsky, Daniel (Johns Hopkins University); Powers, Kimberly (North Carolina State University); Stuart, Elizabeth (Johns Hopkins University); Zahn, Matthew V. (Johns Hopkins University) |
| Abstract: | Facing unprecedented uncertainty and drastic trade-offs between public health and other forms of human well-being, policy makers during the Covid-19 pandemic have sought the guidance of epidemiologists and economists. Unfortunately, while both groups of scientists use many of the same basic mathematical tools, the models they develop to inform policy tend to rely on different sets of assumptions and, thus, often lead to different policy conclusions. This divergence in policy recommendations can lead to uncertainty and confusion, opening the door to disinformation, distrust of institutions, and politicization of scientific facts. Unfortunately, to date, there have not been widespread efforts to build bridges and find consensus or even to clarify sources of differences across these fields, members of whom often continue to work within their traditional academic silos. In response to this "crisis of communication," we convened a group of scholars from epidemiology, economics, and related fields (e.g., statistics, engineering, and health policy) to discuss approaches to modeling economy-wide pandemics. We summarize these conversations by providing a consensus view of disciplinary differences (including critiques) and working through a specific policy example. Thereafter, we chart a path forward for more effective synergy between disciplines, which we hope will lead to better policies as the current pandemic evolves and future pandemics emerge. |
| Keywords: | economics, epidemiology, public health, COVID-19, behavior modeling, health outcomes, health-wealth tradeoffs |
| JEL: | C8 H0 I1 J |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp14838&r= |
| By: | Tom Broekel; Rune Dahl Fitjar; Silje Haus-Reve |
| Abstract: | Contemporary research highlights the importance of relatedness, diversity, and complexity for regional economic development. However, few empirical studies simultaneously test the relevance of all these dimensions or examine how their importance varies across distinct spatial contexts. The literature also concentrates on explaining regional diversification, whereas we know less about how they affect economic and employment growth. In addition, most studies have examined industrial relatedness at the expense of the at least similarly crucial occupational dimension when studying knowledge-based regional development. The chapter discusses these issues and presents a study on how occupational diversity, complexity and relatedness shape employment growth in Norway to illustrate how an occupational perspective on regional industries can add to the understanding of evolutionary economic development. |
| Keywords: | relatedness, diversity, complexity, occupation, region, Norway |
| JEL: | R11 O31 O33 J24 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2135&r= |
| By: | Fabri, Charlotte; Moretti, Michele; Passel, Steven Van |
| Keywords: | Environmental Economics and Policy, Research Methods/ Statistical Methods |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae21:314966&r= |
| By: | Andrea Coletta; Matteo Prata; Michele Conti; Emanuele Mercanti; Novella Bartolini; Aymeric Moulin; Svitlana Vyetrenko; Tucker Balch |
| Abstract: | Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents' actions. In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. It allows to set up pools of traders with diverse strategies to mimic the financial market trader population, and test the performance of new experimental strategies. Since individual agent-level historical data is typically proprietary and not available for public use, it is difficult to calibrate multiple market agents to obtain the realism required for testing trading strategies. To addresses this challenge we propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based "world" agent can generate meaningful orders in response to an experimental agent. We integrate our synthetic market generator into ABIDES, an open source simulator of financial markets. By means of extensive simulations we show that our proposal outperforms previous work in terms of stylized facts reflecting market responsiveness and realism. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.13287&r= |