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Hunter L. Clark, Jeffrey B. Dawson, and Maxim Pinkovskiy
China’s economy was the first to be hit by the COVID-19 outbreak, the first to be locked down, and the first to begin an economic recovery. We examine the impact of the COVID-19 crisis on China’s GDP growth using a set of alternative growth indicators. Our analysis finds that China’s official GDP growth figures over the first three quarters of this year have been broadly in line with alternative indicators and that growth presently is staging a strong rebound and providing a boost to the global economy. However, this rebound faces potential headwinds in the forms of high levels of debt, declining return to capital accumulation, and a shrinking working-age population in China.
William Chen, Marco Del Negro, Keshav Dogra, Shlok Goyal, and Alissa Johnson
This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since June 2020.
As usual, we wish to remind our readers that the DSGE model forecast is not an official New York Fed forecast, but only an input to the Research staff’s overall forecasting process. For more information about the model and variables discussed here, see our DSGE model Q & A. Note that interactive charts are now available for DSGE model forecasts.
In response to the pandemic, the New York Fed’s DSGE model has been modified because the economic disruptions caused by COVID-19 are likely different from standard business cycles. The model now includes additional shocks designed to reflect phenomena like lockdowns and social distancing (the model description on the GitHub page describes these changes in some detail). To incorporate the substantial uncertainty surrounding future economic activity, we construct three possible scenarios, described below, that differ in the projected severity of the pandemic and its effects on economic behavior. Our final forecast combines these individual scenarios by weighting them according to our a priori views on how likely each scenario is. The weights on the three scenarios are 80, 10, and 10 percent, respectively. We partly inform these views using the most recent (August) Survey of Professional Forecasters (SPF) probabilistic survey for year-over-year 2020 GDP growth.
Rajashri Chakrabarti, Sebastian Heise, Davide Melcangi, Maxim Pinkovskiy, and Giorgio Topa
In our previous post, we looked at the effects that the reopening of state economies across the United States has had on consumer spending. We found a significant effect of reopening, especially regarding spending in restaurants and bars as well as in the healthcare sector. In this companion post, we focus specifically on small businesses, using two different sources of high-frequency data, and we employ a methodology similar to that of our previous post to study the effects of reopening on small business activity along various dimensions. Our results indicate that, much like for consumer spending, reopenings had positive and significant effects in the short term on small business revenues, the number of active merchants, and the number of employees working in small businesses. It is important to stress that we are not expressing any views in this post on the normative question of whether, when, or how states should loosen or tighten restrictions aimed at controlling the COVID-19 pandemic.
Rajashri Chakrabarti, Sebastian Heise, Davide Melcangi, Maxim Pinkovskiy, and Giorgio Topa
Editor’s note: We have clarified the description of data used for the analysis since this post was first published (September 23, 5:27 p.m.)
The spread of COVID-19 in the United States has had a profound impact on economic activity. Beginning in March, most states imposed severe restrictions on households and businesses to slow the spread of the virus. This was followed by a gradual loosening of restrictions (“reopening”) starting in April. As the virus has re-emerged, a number of states have taken steps to reverse the reopening of their economies. For example, Texas and Florida closed bars again in June, and Arizona additionally paused operations of gyms and movie theatres. Taken together, these measures raise the question of how closures and reopenings affect consumer spending. In this post, we investigate how much consumer spending increased after the reopenings. It is important to stress that we are not expressing any views on the normative question of whether, when, or how states should loosen or tighten restrictions aimed at controlling the COVID-19 pandemic.
William Chen, Marco Del Negro, Michele Lenza, Giorgio Primiceri, and Andrea Tambalotti
U.S. inflation used to rise during economic booms, as businesses charged higher prices to cope with increases in wages and other costs. When the economy cooled and joblessness rose, inflation declined. This pattern changed around 1990. Since then, U.S. inflation has been remarkably stable, even though economic activity and unemployment have continued to fluctuate. For example, during the Great Recession unemployment reached 10 percent, but inflation barely dipped below 1 percent. More recently, even with unemployment as low as 3.5 percent, inflation remained stuck under 2 percent. What explains the emergence of this disconnect between inflation and unemployment? This is the question we address in “What’s Up with the Phillips Curve?,” published recently in Brookings Papers on Economic Activity.
At the end of March, we launched the Weekly Economic Index (WEI) as a tool to monitor changes in real activity during the pandemic. The rapid deterioration in economic conditions made it important to assess developments as soon as possible, rather than waiting for monthly and quarterly data to be released. In this post, we describe how the WEI has measured the effects of COVID-19. So far in 2020, the WEI has synthesized daily and weekly data to measure GDP growth remarkably well. We document this performance, and we offer some guidance on evaluating the WEI’s forecasting abilities based on 2020 data and interpreting WEI updates and revisions.
William Chen, Marco Del Negro, Ethan Matlin, and Reca Sarfati
Editor’s note: The release of the March 2020 DSGE forecast was postponed as New York Fed economists shifted their focus to the COVID-19 pandemic. In conjunction with the release of the June 2020 forecast, we’ve decided to post the March 2020 forecast for the record as well.
Ozge Akinci, William Chen, Marco Del Negro, Ethan Matlin, and Reca Sarfati
Editor’s note: The release of the March 2020 DSGE forecast was postponed as New York Fed economists shifted their focus to the COVID-19 pandemic. With the June 2020 forecast now out, we’ve decided to post this forecast for the record as well.
This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since December 2019. As usual, we wish to remind our readers that the DSGE model forecast is not an official New York Fed forecast, but only an input to the Research staff’s overall forecasting process. For more information about the model and variables discussed here, see our DSGE model Q & A.
Mary Amiti, Sang Hoon Kong, and David E. Weinstein
Starting in early 2018, the U.S. government imposed tariffs on over $300 billion of U.S. imports from China, increasing the average tariff rate from 2.7 percent to 17.5 percent. Much of the escalation in tariffs occurred in the second and third quarters of 2019. In response, the Chinese government retaliated, increasing the average tariff applied on U.S. exports from 5.7 percent to 20.4 percent. Our new study finds that the trade war reduced U.S. investment growth by 0.3 percentage points by the end of 2019, and is expected to shave another 1.6 percentage points off of investment growth by the end of 2020. In this post, we review our study of the trade war’s effect on U.S. investment.
Ozge Akinci, Gianluca Benigno, and Albert Queralto
The COVID-19 outbreak has triggered unusually fast outﬂows of dollar funding from emerging market economies (EMEs). These outflows are known as “sudden stop” episodes, and they are typically followed by economic contractions. In this post, we assess the macroeconomic eﬀects of the COVID-induced sudden stop of capital flows to EMEs, using our open-economy DSGE model. Unlike existing frameworks, such as the Federal Reserve Board’s SIGMA model, our model features both domestic and international ﬁnancial constraints, making it well-suited to capture the eﬀects of an outﬂow of dollar funding. The model predicts output losses in EMEs due in part to the adverse eﬀect of local currency depreciation on private-sector balance sheets with dollar debts. The ﬁnancial stresses in EMEs, in turn, spill back to the U.S. economy, through both trade and ﬁnancial channels. The model-predicted output losses are persistent (consistent with previous sudden stop episodes), with financial effects being a significant drag on the recovery. We stress that we are only tracing out the effects of one particular channel (the stop of capital flows and its associated effect on funding costs) and not the totality of COVID-related effects.
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