Unequal Unemployment Effects of COVID-19 and Monetary Policy across U.S. States

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Unequal Unemployment Effects of COVID-19 and Monetary Policy across U.S. States

One sentence summary: There is evidence for unequal unemployment effects of COVID-19 and the corresponding national monetary policy across U.S. states. 

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Behavioral Economics for Policy.

The corresponding working paper is available here.

 
Abstract
This paper shows that daily Google trends can be used as an alternative to conventional U.S. data (with alternative frequencies) on unemployment, interest rates, inflation and coronavirus disease 2019 (COVID-19). This information is used to investigate the effects of COVID-19 and the corresponding monetary policy on the U.S. unemployment, both nationally and across U.S. states, by using a structural vector autoregression model. Historical decomposition analyses show that the U.S. unemployment is mostly explained by COVID-19, whereas the contribution of monetary policy is almost none. An investigation based on the U.S. states further suggests that COVID-19 and the corresponding monetary policy conducted based on nationwide economic developments have resulted in unequal changes in state-level unemployment rates, suggesting evidence for distributive effects of national monetary policy.

 

Non-technical Summary
The weekly unemployment claims were about 281,000 in the week ending March 14th, 2020 according to the U.S. Department of Labor, reaching its highest level since September 2nd, 2017. In the corresponding news release, the U.S. Department of Labor announced the following statement:

"During the week ending March 14, the increase in initial claims are clearly attributable to impacts from the COVID-19 virus. A number of states specifically cited COVID-19 related layoffs, while many states reported increased layoffs in service related industries broadly and in the accommodation and food services industries specifically, as well as in the transportation and warehousing industry, whether COVID-19 was identified directly or not."

where the Coronavirus Disease 2019 (COVID-19) was shown to be responsible. Even after five months, weekly unemployment claims were about 1,106,000 in the week ending August 15th, 2020 when the U.S. Department of Labor further announced the following statement:

"The COVID-19 virus continues to impact the number of initial claims and insured unemployment."

where the continuous severity of COVID-19 effects on the U.S. unemployment can still be observed.

This paper investigates the dynamic relationship between COVID-19 and the U.S. unemployment by considering the effects of U.S. monetary policy, both nationally and across U.S. states. Since this investigation requires data on unemployment, interest rates, inflation and COVID-19, which are only available in alternative (e.g., daily, weekly, monthly) frequencies, this paper uses Google search queries capturing the desired variables on a daily basis. The sample covers the daily period between January 1st, 2020 and August 24th, 2020.

Before moving to the formal investigation, it is first shown that daily Google trends can be used as an alternative to conventional U.S. data (with alternative frequencies) on unemployment, interest rates, inflation and developments related to COVID-19.

 

 

The nationwide formal analysis for the U.S. is achieved by employing a four-variable structural vector autoregression (SVAR) model, where daily data on COVID-19, unemployment, interest rates, and inflation are used. The results show that COVID-19 has increased unemployment both in the long-run and the short-run, while monetary authorities have reacted to COVID-19 by reducing the interest rate, which has helped reducing the unemployment rate in a minor way. 

 

Historical decomposition analyses further show that the U.S. unemployment is mostly explained by COVID-19, whereas the contribution of monetary policy is almost none.

The implications for the U.S. state-level unemployment are further investigated by including a fifth variable in SVAR model, which is daily unemployment obtained for 50 states and the District of Columbia. The results based on individual state-level analyses suggest evidence for unequal unemployment effects of COVID-19; e.g., COVID-19 has negatively affected unemployment in the state of Washington by about four times of that in New Hampshire. 

 

The results also suggest evidence for unequal unemployment effects of national monetary policy across U.S. states. In particular, accommodative (national) monetary policy has helped reducing unemployment only in certain states, whereas unemployment in certain others have not benefited at all from it.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Behavioral Economics for Policy.

The corresponding working paper is available here.

COVID-19 Effects on the S&P 500 Index

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COVID-19 Effects on the S&P 500 Index

One sentence summary: Having 1% of an increase in cumulative daily COVID-19 cases in the U.S. results in about 0.01% of a cumulative reduction in the S&P 500 Index after one day and about 0.03% of a reduction after one week.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Applied Economics Letters.

 

The working paper version is available here.

 
Abstract
This paper investigates the effects of the coronavirus disease 2019 (COVID-19) cases in the U.S. on the S&P 500 Index using daily data covering the period between January 21st, 2020 and August 10th, 2021. The investigation is achieved by using a structural vector autoregression model, where a measure of the global economic activity and the spread between 10-year treasury constant maturity and the federal funds rate are also included. The empirical results suggest that having 1% of an increase in cumulative daily COVID-19 cases in the U.S. results in about 0.01% of a cumulative reduction in the S&P 500 Index after one day and about 0.03% of a reduction after one week. Historical decomposition of the S&P 500 Index further suggests that the negative effects of COVID-19 cases in the U.S. on the S&P 500 Index have been mostly observed during March 2020.

 

Non-technical Summary
The coronavirus pandemic 2019 (COVID-19) has killed 618,363 people in the U.S. as of August 10th, 2021, with corresponding COVID-19 cases of 36,152,620. This has created a significant turmoil not only in the global economic activity but also in financial markets around the world. This turmoil can best be observed by the Standard & Poor's (S&P) 500 Index, which is the benchmark financial and economic indicator in the U.S. and fell from about 3,386.15 on February 19th, 2020 to about 2,237.40 on March 23rd, 2020, corresponding to about 41% of a fall, although it achieved a great recovery with record braking values such as 4,436.75 on August 10th, 2021.

This paper attempts to understand the reasons behind the volatility in the S&P 500 Index during COVID-19 by using daily data between January 21st, 2020 (when the first COVID-19 case was reported in the U.S.) and August 10th, 2021 (the latest day available when this paper was written). As this volatility in the S&P 500 Index may be due to COVID-19 or any other factor (e.g., the economic activity or interest rates), a formal analysis is required to identify the causal effects of COVID-19 on the S&P 500 Index. Such an investigation is achieved in this paper by using a structural vector autoregression (SVAR) model, where the S&P 500 Index is used together with a measure of the global economic activity and the spread between 10-year treasury constant maturity and the federal funds rate in the U.S. Since COVID-19 is an exogenous shock, percentage changes in cumulative daily COVID-19 cases in the U.S. are included as an exogenous variable in this framework.

Following several early or recent studies in the literature, the global economic activity is measured by the Baltic Exchange Dry Index (BDI). This is a daily published index by the Baltic Exchange in London, and it reflects the shipping costs (due to using vessels of various sizes covering multiple maritime routes) regarding the transportation of raw commodities (e.g., grain, coal, iron ore, copper). Since these shipping costs are determined by the supply and demand forces in the global market, they are robust to any speculative manipulation or any government intervention by construction. The spread between 10-year treasury constant maturity and the federal funds rate in the U.S. not only reflects the term premium (between long-run and short-run interest rates) but also the future expectations in the U.S. economy.

The empirical results suggest that having 1% of an increase in cumulative daily COVID-19 cases in the U.S. results in about 0.01% of a cumulative reduction in the S&P 500 Index after one day and about 0.03% of a reduction after one week.

 

Historical decomposition of the S&P 500 Index further suggests that the negative effects of COVID-19 cases in the U.S. on the S&P 500 Index have been mostly observed during March 2020. 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Applied Economics Letters.

 

The working paper version is available here.

Coronavirus Disease 2019 and the Global Economy

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Coronavirus Disease 2019 and the Global Economy

One sentence summary: Increases in COVID-19 cases are consistent with negative demand shocks in the global economic activity and negative supply shocks in the global transportation of commodities.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Transport Policy.

 

The working paper version is available here.

 
Abstract
Using daily data on the coronavirus disease 2019 (COVID-19) cases from China and the rest of the world, this paper investigates the corresponding effects on the global economic activity. The empirical results based on a structural vector autoregression model using crude oil prices (COP) and the Baltic Exchange Dry Index (BDI) are consistent with increases in COVID-19 cases acting as negative demand shocks in the global economic activity (reflected as reductions in COP) and negative supply shocks in the global transportation of commodities (reflected as increases in BDI). The historical decomposition results further suggest that the effects of COVID-19 cases on COP and BDI have been mostly observed in the early COVID-19 period.

Non-technical Summary 

This paper investigates the effects of COVID-19 on the global economy. This is achieved by considering the corresponding effects on crude oil prices (COP) and the Baltic Exchange Dry Index (BDI). Specifically, COP can capture demand changes (among others) in the global economic activity, whereas BDI is daily published by the Baltic Exchange in London, and it reflects the shipping costs (due to using vessels of various sizes covering multiple maritime routes) regarding the transportation of raw commodities (e.g., grain, coal, iron ore, copper). Since these shipping costs are determined by the supply and demand forces in the global market, they are robust to any speculative manipulation or any government intervention by construction.

The formal investigation is achieved by using a structural vector autoregression (SVAR) model, where the endogenous variables are selected as weekly percentage changes in daily COP and daily BDI. Since COVID-19 is an exogenous shock (i.e., it is not determined by either COP or BDI), percentage changes in daily COVID-19 cases in China and the rest of the world (ROW) are included as exogenous variables in this framework. Using daily data covering the period between January 28th, 2020 and November 15th, 2021, cumulative impulse responses of COP and BDI are estimated to identify the effects of COVID-19 on the global economy.

The corresponding results suggest that 1% of a weekly increase in daily COVID-19 cases in China results in about 0.02% of a cumulative increase in BDI after one week and about 0.03% of a cumulative increase after three months. Similarly, 1% of a weekly increase in daily COVID-19 cases in ROW results in about 0.05% of a cumulative increase in BDI after one week, although the effects become insignificant in longer horizons. These results are consistent with increases in COVID-19 cases acting as negative supply shocks in the transportation of commodities.

 

The results also suggest that 1% of a weekly increase in daily COVID-19 cases in China results in about 0.02% of a cumulative reduction in COP, whereas 1% of a weekly increase in daily COVID-19 cases in ROW results in about 0.13% of a cumulative reduction in COP after one week and 0.15% after one month. These results are consistent with a lower global demand (reflected as reductions in COP) following increases in COVID-19 cases. The historical decomposition results suggest that the effects of COVID-19 cases on BDI and COP have mostly been observed in the early COVID-19 period.

 

The relationship between BDI and COP further suggests that positive BDI shocks result in cumulative reductions in COP in the long run, whereas positive COP shocks result in cumulative increases in BDI both in the short run and the long run that are consistent with a complete pass-through of COP into BDI. It is implied that unexpected BDI increases in the SVAR model mostly capture negative supply shocks in the global transportation of commodities, because an increase in BDI results in a reduction of the demand for crude oil, which is a cost factor in the transportation sector. This result is supported by the Joint Open Letter to United Nations agencies from the global maritime transport industry on March 19th, 2020, where the global maritime transport industry has requested certain exemptions for international seafarers as the national regulations disrupt the supply of transportation.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Transport Policy.

 

The working paper version is available here.