smh.com.au
Economic Modeling's Stagnation Hinders Accurate Prediction and Productivity Understanding
Ross Gittins, economics editor, criticizes the stagnation of economic modeling, pointing to its failure to accurately predict economic trends and its reliance on simplified neoclassical models, hindering progress in understanding key factors like productivity and resulting in unreliable forecasts.
- What are the primary consequences of the current economic modeling approach's inability to accurately predict economic trends, and what specific examples illustrate this failure?
- The article critiques the stagnation of economic modeling, highlighting its failure to accurately predict economic trends like wage growth and its overreliance on simplified neoclassical models. This has resulted in unreliable forecasts, such as those in federal budgets, and a lack of progress in understanding crucial economic factors like productivity.
- How does the emphasis on mathematical modeling in economics hinder the incorporation of findings from fields like behavioral economics, and what are the implications of this for economic theory?
- The core issue is the prioritization of mathematical rigor over factual accuracy and adaptability within economic modeling. This adherence to established neoclassical models, despite their known limitations and inaccurate predictions, hinders the field's ability to evolve and provide relevant insights. This contrasts sharply with scientific practices where models constantly adapt to new data.
- What fundamental changes in approach are needed to improve the accuracy and relevance of economic modeling, and how can this promote better policy-making regarding productivity and other key economic indicators?
- The article suggests that the mathematical focus in economics has diverted attention from addressing the oversimplifications and dubious assumptions within prevailing models. This has led to a lack of progress in understanding complex economic phenomena like productivity, despite its critical importance to living standards. A shift towards a more iterative and data-driven approach is needed to improve prediction accuracy and provide useful policy guidance.
Cognitive Concepts
Framing Bias
The article frames economists as resistant to change and clinging to outdated models, employing words like "religion" and "revealed truth" to describe their approach. The narrative structure emphasizes the shortcomings of the neoclassical model and the lack of progress in economics, shaping the reader's perception of the field. The headline and introduction already suggest a critical stance towards conventional economic thought. For instance, the use of the word "religion" to describe economics, is loaded language intended to negatively characterize the field.
Language Bias
The author uses loaded language such as "secular religion", "revealed truth", and "heretical advice" to describe the field of economics, which presents a negative and biased tone. Terms like "boffins" and "lefty lightweight sociologists" also carry negative connotations. More neutral alternatives would include describing economics as a "dominant paradigm", "established model", or "conventional approach". Suggesting alternatives to the loaded language creates a more objective tone and reduces the level of bias.
Bias by Omission
The article omits discussion of potential alternative economic models or perspectives beyond the neoclassical model, limiting the reader's understanding of the range of economic thought. It focuses heavily on the limitations of the neoclassical model without exploring alternatives in detail. This omission might lead readers to believe that the neoclassical model is the only or dominant paradigm, neglecting other economic schools of thought which could offer additional insights into productivity or other economic issues.
False Dichotomy
The article presents a false dichotomy between the pursuit of scientific rigor through mathematical modeling and the pursuit of truth in economics. It suggests that prioritizing mathematical models prevents economists from focusing on addressing oversimplifications and dubious assumptions. This framing ignores the potential for mathematical modeling to contribute to a more accurate understanding of economic phenomena, if used appropriately.
Sustainable Development Goals
The article criticizes the lack of evolution in economic models, which perpetuates existing inequalities by failing to address crucial issues like productivity improvement and the unequal bargaining power in labor markets. This stagnation hinders the development of effective policies to address income disparities and promote inclusive growth, thus negatively impacting SDG 10 (Reduced Inequalities).