The July 2020 Wall Street Journal Economic Forecast Survey was published on July 9, 2020. The headline is “WSJ Survey: Strong U.S. Recovery Depends on Effective Covid-19 Response.”
I found numerous items to be notable – although I don’t necessarily agree with them – both within the article and in the “Economist Q&A” section.
In the latest survey, 70% of economists said they expect the recovery to resemble a “swoosh” shape similar to the Nike logo, with a large drop followed by a gradual recovery. That was broadly unchanged from the two previous monthly surveys and a contrast to the predictions of Trump administration officials, who have predicted a swift, V-shaped recovery.
As seen in the “Recession Probability” section, the average response as to whether the economy will be in a recession within the next 12 months was 54.41%. The individual estimates, of those who responded, ranged from 0% to 100%. For reference, the average response in June’s survey was 73.54%.
As stated in the article, the survey’s 60 respondents were academic, financial and business economists. Not every economist answered every question. The survey was conducted July 2 – July 7, 2020.
The current average forecasts among economists polled include the following:
full-year 2020: -5.64%
full-year 2021: 4.70%
full-year 2022: 3.22%
December 2020: 9.07%
December 2021: 6.75%
December 2022: 5.61%
December 2023: 4.93%
10-Year Treasury Yield:
December 2020: .86%
December 2021: 1.25%
December 2022: 1.68%
Please Note – The above is excerpted from the EconomicGreenfield.com (published by RevSD, LLC) post of July 10, 2020, titled “The July 2020 Wall Street Journal Economic Forecast Survey”
RevSD, LLC offers the above commentary for informational purposes only, and does not necessarily agree with the views expressed by these outside parties.
RevSD, LLC is a management consulting firm and strategic advisory that focuses on the analysis of current and future weak(ening) economic conditions, and offers businesses and other entities advice, strategies, and actionable methods on how to optimally adapt to such challenging, complex conditions.