Nonlinear Effects of Mobility on COVID-19 in the U.S.: Targeted Lockdowns Based on Income and Poverty

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Nonlinear Effects of Mobility on COVID-19 in the U.S.: Targeted Lockdowns Based on Income and Poverty

One sentence summary: The positive effects of mobility on COVID-19 increase with certain demographic or socioeconomic characteristics.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Economic Studies.

The working paper version is available here.

 
Abstract

This paper investigates nonlinearities in the relationship between mobility and COVID-19 cases or deaths. The formal analysis is achieved by using county-level daily data from the U.S., where a difference-in-difference design is employed. Nonlinearities in the relationship between mobility and COVID-19 cases or deaths are investigated by regressing weekly percentage changes in COVID-19 cases or deaths on mobility measures, where county fixed effects and daily fixed effects are controlled for. The main innovation is achieved by distinguishing between the coefficients in front of mobility measures across U.S. counties based on their demographic or socioeconomic characteristics. The results suggest that the positive effects of mobility on COVID-19 cases or deaths increase with population, per capita income, or commuting time as well as with having certain occupations, working in certain industries, attending certain schools, or having certain educational attainments. Important policy implications follow regarding where mobility restrictions would work better to fight against COVID-19 through targeted lockdowns.

 

 

Non-technical Summary

The relationship between the spread of COVID-19 and social interactions through mobility is well established. Accordingly, several governments have employed lockdowns to slow down the spread of COVID-19. However, this relationship by itself does not suggest anything related to targeted lockdowns that can be useful when policy makers face trade-offs between health-related concerns and economic slowdown as certain group of people or certain communities can be more vulnerable to the spread of COVID-19.

This paper investigates how the relationship between mobility and the COVID-19 spread changes with demographic or socioeconomic characteristics. The formal investigation is achieved by using daily county-level data from the U.S., where a difference-in-difference approach is employed. The nonlinear relationship between mobility and COVID-19 cases or deaths is investigated by regressing weekly percentage changes in COVID-19 cases or deaths on mobility measures, where county fixed effects and daily fixed effects are controlled for; accordingly, county-specific factors that are constant over time and day-specific factors that are common across U.S. counties are already controlled for. The main innovation is achieved by distinguishing between the coefficients in front of mobility measures across U.S. counties based on their demographic or socioeconomic characteristics that we utilize as threshold variables.
 

Several demographic or socioeconomic characteristics of U.S. counties are considered for investigating the nonlinear relationship between mobility and the COVID-19 spread. These include 45 different variables based on the categories of population characteristics, economic variables, occupations, employment in industries, school attendance, educational attainment, and race. The motivation behind including these potential threshold variables comes from the existing literature, where several studies have shown how the spread of COVID-19 is related to these demographic or socioeconomic characteristics. 

 

The results of the nonlinear investigation suggest that the positive effects of mobility on COVID-19 cases or deaths increase with population, per capita income, or commuting time as well as with having certain occupations, working in certain industries, attending certain schools, or having certain educational attainments. Since mobility restrictions to fight against COVID-19 would work better in counties where the positive effects of mobility on COVID-19 cases or deaths are bigger, it is implied that policy makers can consider targeted lockdowns based on the threshold variables identified in this paper.

 

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Economic Studies.

The working paper version is available here.

 

Welfare Costs of COVID-19: Evidence from U.S. Counties

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Welfare Costs of COVID-19: Evidence from U.S. Counties

One sentence summary: The average (across days) welfare reduction during COVID-19 is about 11% for the average U.S. county and up to about 46% across counties.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Regional Science.

The working paper version is available here.

 
Abstract

Using daily U.S. county-level data on consumption, employment, mobility and the coronavirus disease 2019 (COVID-19) cases, this paper investigates the welfare costs of COVID-19. The investigation is achieved by using implications of a model, where there is a trade-off between consumption and COVID-19 cases that are both determined by the optimal mobility decision of individuals. The empirical results show evidence for about 11% of an average (across days) reduction of welfare during the sample period between February and December, 2020 for the average county. There is also evidence for heterogeneous welfare costs across U.S. counties and days, where certain counties have experienced welfare reductions up to 46% on average across days and up to 97% in late March, 2020 that are further connected to the socioeconomic characteristics of the U.S. counties.

 

 

  

Non-technical Summary

The coronavirus disease 2019 (COVID-19) has resulted in not only numerous casualties but also unprecedented reductions in economic activity. Since both COVID-19 cases and economic activity are positively related to mobility, individuals have faced trade-offs regarding the optimal amount of mobility that they should have. It is implied that investigating the welfare changes due to COVID-19 requires taking into account the mobility of individuals.

Based on this background, this paper investigates the welfare costs of COVID-19 by considering the interaction between COVID-19 cases, economic activity and mobility of individuals. A multi-region model is introduced to motivate the empirical investigation, where individuals optimally decide on their mobility that further determines their current consumption and future COVID-19 cases. The parameters and unknown variables of the model are estimated by using daily U.S. county-level data on consumption, employment, mobility and COVID-19 cases.

The estimation results confirm that economic activity (measured by either consumption or employment) increases with mobility of individuals. The estimation results also confirm the positive relationship between mobility and COVID-19 cases. These results are robust to the consideration of county-specific factors that are constant over time and time-varying nationwide factors that are common across counties.
 

The implications of the model are further used to investigate welfare costs of COVID-19 and its components based on economic activity and COVID-19 cases. The corresponding model implications suggest evidence for about 11% of an average (across days) reduction of welfare during the sample period between February and December, 2020 for the average U.S. county. 

 

When welfare costs are decomposed into those due to each model component, it is shown that COVID-19 cases contribute the most to welfare reductions in early months of COVID-19, whereas they have similar contributions with consumption/employment starting from about May 2020. Mobility contributes negatively to welfare in a steady way during the sample period, whereas other factors have been more effective in early months of COVID-19. In terms of the contribution of each welfare component as an average across days, increases in COVID-19 cases reduce welfare by about 6.7% for the average county (up to 14.2% across counties), whereas consumption reductions contribute to welfare costs by about 3.7% for the average county (up to 43.2% across counties). The contribution of mobility (with respect to other factors) is much more on average (across days) during the sample period.

 

 

 

The empirical results of this paper also provide evidence for heterogeneous welfare costs across U.S. counties and days, where certain counties have experienced welfare reductions up to 46% on average across days and up to 97% in late March, 2020. These results are robust to the consideration of alternative data sets as well as alternative parameter values considered in the model.

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Regional Science.

The working paper version is available here.

  

COVID-19 and Housing Prices: Evidence from U.S. County-Level Data

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COVID-19 and Housing Prices: Evidence from U.S. County-Level Data

One sentence summary: The effects of COVID-19 cases on housing prices are negative and significant after controlling for other factors.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Review of Regional Research.

The working paper version is available here.

 
Abstract

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on housing prices at the U.S. county level. The effects of COVID-19 cases on housing prices are formally investigated by using a two-way fixed effects panel regression, where county-specific factors, time-specific factors, and mobility measures of individuals are controlled for. The benchmark results show evidence for negative and significant effects of COVID-19 cases on housing prices, robust to the consideration of several permutation tests, where the negative effects are more evident in counties with higher poverty rates. Exclusion tests further suggest that U.S. counties in the state of California or the month of May 2020 are more responsible for the empirical results, although the results based on other counties and months are still in line with the benchmark results.

  

Non-technical Summary

The coronavirus disease 2019 (COVID-19) has resulted in not only a health crisis through its direct effects but also an economic one through its indirect effects. These indirect effects are reflected in housing prices within the U.S. in an unequal way across counties, where housing prices have increased by about $1,408 on average across counties on a monthly basis (between February 2020 and August, 2021), with a range between $1,979 of a reduction and $14,963 of an increase. Within this context, what is the contribution of COVID-19 cases on this heterogeneity representing unequal changes in housing prices across U.S. counties? The answer to this equation depends on several channels that affect the housing market at the local (U.S. county) level.

This paper investigates this heterogeneity representing unequal changes in housing prices across U.S. counties due to COVID-19 cases. The formal investigation is achieved by using a two-way fixed effects panel regression, where county-specific and time-specific factors are controlled for. Several mobility measures of individuals are also considered as control variables as they not only represent the overall economic activity at the U.S. county level over time but also the developments in the housing sector at the U.S. county level over time due to staying at home as a housing-demand shifter.

The benchmark empirical results show evidence for negative and significant effects of COVID-19 cases on housing prices, and they have been confirmed by several robustness checks based on permutation tests, exclusion tests, or interactions with other variables. 

 

When the channels of causality are further investigated, poverty is shown to be an important factor. Specifically, the U.S. counties with higher rates of poverty have experienced more reductions in housing prices due to COVID-19, whereas those with lower rates of poverty have experience almost no changes (or sometimes increases) in housing prices. Therefore, there is evidence for unequal effects of COVID-19 on housing prices across U.S. counties due to poverty differences.

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Review of Regional Research.

The working paper version is available here.

 

 

 

Drivers of Global Trade: A Product-Level Investigation

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Drivers of Global Trade: A Product-Level Investigation

One sentence summary: Supply-side factors, capturing production and exporting costs in source countries, are responsible for about 85% of changes in global trade between 1995 and 2018.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Economic Journal.

Working paper version is available here.

Abstract
This paper investigates the drivers of global trade at the six-digit product level by using the implications of a model that are consistent with a large class of trade models. The drivers of global trade at the product level are identified first by estimating the product-level bilateral trade implications of the model and second by aggregating the fitted estimation results across bilateral countries using Taylor series. The empirical results suggest that supply-side effects (capturing production or exporting costs in source countries) contribute to changes in global trade more than six times the demand-side effects (capturing economic activity or preferences in destination countries) and more than ten times the effects of bilateral trade costs (capturing bilateral protectionism measures). Several product-level implications follow.

  

Non-technical Summary

Global merchandise trade has increased by more than 6 trillion U.S. dollars between 1995-2018. This increase is mostly accounted for by products such as machinery/electrical (with a contribution of about 26%), mineral products (with a contribution of about 18%), chemicals and allied industries (with a contribution of about 11%), and transportation (with a contribution of about 11%). Across broad economic categories, trade of intermediate inputs account for about 66% of this increase, whereas trade of capital goods and intermediate inputs account for about 18% and 16%, respectively. Although these statistics provide useful information on products or categories that drive the global trade, policy making requires knowledge on the economic forces that are responsible for the contribution of these products or categories.

This paper investigates the economic drivers of global trade by using six-digit product level data covering the years 1995-2018. These economic drivers are identified by using the implications of a large class of trade models, where bilateral trade between any two countries depend on source prices, bilateral trade iceberg costs, and a measure of economic activity at the destination country. Based on this motivation, a simple trade model is introduced of which implications are used for decomposing the changes in global trade into those due to supply-side factors (capturing source prices and thus production or exporting costs in source countries), demand-side factors (capturing economic activity or preferences in destination countries), and bilateral trade costs (capturing bilateral protectionism measures).

The knowledge of the decomposition of changes in global trade is important especially countries focusing on export-led growth, because if supply-side factors are effective in explaining changes in global trade, source countries may want to invest more into their production technologies, infrastructure, financial depth, operational costs of exporting, costs related to entering foreign markets, or modifying their products for individual foreign markets. In contrast, if demand-side factors are effective, source countries may want to invest in removing information barriers (e.g., through advertising their products) to affect preferences of destination countries. Finally, if bilateral trade costs are effective, source countries may want to get involved in negotiations to reduce trade barriers (e.g., through free trade agreements).

Regarding the methodology, the decomposition of changes in global trade is achieved first by estimating the product-level bilateral trade implications of a trade model and second by aggregating the fitted estimation results across bilateral countries using Taylor series to obtain global product-level measures. This methodology results in identifying the contribution of supply-side factors, demand-side factors and bilateral trade costs to changes in product-level global trade between 1995 and 2018. The corresponding results suggest that supply-side effects have contributed to changes in global trade by about 85%, followed by demand-side effects with a contribution of about 13% and by bilateral trade costs with a contribution of about 8%. The corresponding contribution of residuals by only about -6% capturing unexplained part of the data by the model implications or approximation due to using Taylor series further supports the investigation.

 

Across products, supply-side effects explain cumulative changes in product-level global trade between 47% (for Textiles) and 97% (for Chemicals & Allied Industries). In comparison, demand-side effects explain cumulative changes in product-level  global trade between 3% (for Stone/Glass) and 45% (for Animal & Animal Products). Finally, bilateral trade costs contribute to product-level global trade between 3% (for Chemicals & Allied Industries or Wood & Wood Products) and 24% (for Textiles). Across broad economic categories, supply-side factors contribute to global trade between 62% (for consumption goods) and 89% (for intermediate goods), demand-side factors contribute to global trade between 8% (for intermediate goods) and 29% (for consumption goods), and bilateral trade costs contribute to global trade between 7% (for intermediate goods) and 13% (for consumption goods).

 

Since supply-side factors are shown to be the main drivers of global trade, it is implied that rather than purely focusing on reducing bilateral trade costs through trade negotiations, one additional way for source countries to increase their exports is to reduce their production costs, say, by investing more into technology, infrastructure, or financial depth, while another way is to reduce their export-related costs such as operational costs of exporting, costs related to entering foreign markets or modifying their products for individual foreign markets. 

 

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Economic Journal.

 

Working paper version is available here.

Inflation Convergence over Time: Sector-Level Evidence within Europe

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Inflation Convergence over Time: Sector-Level Evidence within Europe

One sentence summary: Average half-life of inflation differentials across European countries has decreased from about 15 months to about 8 months within the last two decades.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Finance.

Working paper version is available here.

 
Abstract

This paper investigates inflation convergence among European countries by using sector-level data for the period between 1997:M1 and 2019:M12. Panel unit root tests at the country-sector level are conducted by using moving windows, which is useful to analyze changes in inflation convergence and the corresponding speed of convergence over time. The results suggest evidence for inflation convergence for the majority of sectors within Europe, although disruptions have been experienced by certain countries, especially during the 2008 financial crisis. Regarding the speed of inflation convergence, the average half-life across European countries has decreased from about 15 months to about 8 months during the sample period. Important sector-level implications follow for European Union (EU) candidate countries and non-euro EU member countries regarding the Maastricht Treaty.

Non-technical Summary

Inflation convergence is one of the important criteria in the Maastricht Treaty to ensure price stability and integration within the European Union (EU). This criterion not only requires member countries to have a high degree of price stability but also calls for a price performance that is sustainable for the adoption and continuous circulation of euro. Accordingly, when candidate countries are considered for EU membership or the Euro Area (EA), part of the evaluation is achieved through inflation convergence. Moreover, even when a country is an EU member or within EA, its performance of price stability is evaluated over time for sustainability. It is implied that an investigation of inflation convergence within Europe over time is essential for the price stability and continuous integration of EU.

This paper achieves such a time-varying investigation for inflation convergence among European countries. The formal analysis is conducted by using four-digit sector-level inflation data from 34 countries covering the monthly period between 1997:M1-2019:M12, where five year (i.e., sixty months) moving windows are considered to have a time-varying investigation. Panel unit root tests are used to investigate the convergence of inflation rates at the country-sector level. In particular, country-sector specific panel estimations are achieved by comparing sector-level inflation rates of each country with those of other countries within Europe; i.e., the cross-sectional dimension of the panel unit root tests consist of countries at the sector level.

Having a sector-level investigation is essential to avoid any aggregation bias. This type of an investigation is also useful to obtain sector-specific policy implications, especially for EU candidate countries and non-euro EU member countries, as such an investigation can reveal the sectors that are responsible for non-convergence (if any). Moreover, different from country-level analyses where evidence for only convergence versus non-convergence can be obtained, having a sector-level investigation results in obtaining information on the total expenditure share of sectors for which there is evidence for inflation convergence.

 

When there is evidence for convergence (if any) for a particular sector in a particular country, the corresponding speed of convergence is further investigated; this is convenient to observe how the speed of convergence has changed over time at the country-sector level. The corresponding results show that inflation convergence is achieved for all sectors in several countries for most of the sample period, although the total expenditure share of sectors experiencing convergence is as low as about 75% across countries.

 

Once estimations are achieved at the country-sector level, the corresponding results are further aggregated across sectors (of each country) to have country-specific results for inflation convergence. These country-specific results suggest that there is evidence for stability over time for most countries except for certain time periods that mostly coincide with the 2008 financial crisis. In particular, countries such as Bulgaria, Estonia, France, Ireland, Iceland, Lithuania, Latvia and United Kingdom have experienced disruptions in their sector-level inflation convergence processes during the 2008 financial crisis, whereas countries such as Switzerland, Hungary, Italy, Poland, Slovakia and especially Turkey have experienced disruptions in their sector-level inflation convergence processes starting from around 2015. Regarding the speed of convergence, the average half life across countries has decreased from about 15 months between 1997:M1-2001:M12 to about 8 months between 2015:M1-2019:M12.

 

Sector-level half-life estimates for the median country suggest that before the official circulation of the euro (i.e., between 1997:M1 and 2001:M12), half-life estimates have an average (across sectors) of about 17 months, with a range between 10 months (for "food and non-alcoholic beverages") and 26 months (for "restaurants and accommodation services"). By the latest period of 2015:M1-2019:M12, the average half-life estimate (across sectors) have reduced to about 11 months, with a range between 5 months (for "clothing and footwear") and 35 months (for "restaurants and accommodation services"). 

 

 

It is implied that especially "restaurants and accommodation services" is responsible for not having any further reductions in half-life estimates over time; as indicated in earlier studies, this can be fixed by having more labor mobility across countries, product diversification and trade openness.

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Finance.

Working paper version is available here.

 

Fighting Against COVID-19 Requires Wearing a Face Mask by Not Some but All

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Fighting Against COVID-19 Requires Wearing a Face Mask by Not Some but All

One sentence summary: Causal effects of social interaction on COVID-19 are statistically eliminated when more than 85% of people "always" wear a face mask.

The corresponding academic paper by Hakan Yilmazkuday is available as a working paper here.

 
Abstract

This paper investigates the effects of wearing a face mask on fighting against coronavirus disease 2019 (COVID-19). The formal analysis is achieved by using a difference-in-difference design, where U.S. county-level data on changes in COVID-19 cases or deaths are regressed on lagged changes in social interaction of people measured by Google mobility. The main contribution is achieved by distinguishing between the effects of social interaction on COVID-19 in mask-wearing versus non-mask-wearing counties determined by Dynata surveys. After controlling for county-specific and time-specific factors, the results show that social interaction causally increases both COVID-19 cases and deaths across U.S. counties. Wearing a face mask starts working to fight against COVID-19 only if more than 75% of people in a county "always" wear a face mask, while the effects of social interaction on COVID-19 are statistically eliminated when more than 85% of people in a county "always" wear a face mask.

  

Non-technical Summary

Social interaction between people is accepted as one of the key determinants for the spread of viruses leading to infections, including coronavirus disease 2019 (COVID-19). Despite its leading effects on COVID-19, social interaction is still necessary to prevent the corresponding societal and economic costs. Accordingly, wearing a face mask in public has been suggested by several studies to be able to continue having social interactions during the COVID-19 era as mask wearing reduces the transmissibility per contact by reducing transmission of infected droplets in both laboratory and clinical contexts.

 

Despite the consistency in the recommendation that especially symptomatic individuals should use face masks, discrepancies have been observed in the general public and community settings regarding face-mask wearing. Accordingly, this paper investigates the effects of wearing a face mask on the causal relationship between social interaction and COVID-19 cases or deaths. This initially requires confirming the causal relationship between social interaction and COVID-19 cases or deaths. This confirmation is achieved by using daily data from U.S. counties on COVID-19 cases or deaths as well as social interaction measures based on Google mobility for the period between February 15th, 2020 and August 30th, 2020. The formal analysis is achieved by using a difference-in-difference design, where U.S. county-level data on changes in COVID-19 cases or deaths are regressed on lagged changes in social interaction of people after controlling for county-specific and time-specific factors. The results of this initial investigation confirm that higher social interaction leads to higher COVID-19 cases and deaths across U.S. counties.

 

After confirming the causal relationship between social interaction and COVID-19 cases (or deaths), we continue with a secondary investigation regarding the effects of wearing a face mask on this relationship. In order to do so, we categorize U.S. counties as mask-wearing counties versus non-mask-wearing counties by using Mask-Wearing Survey Data collected by Dynata at the request of New York Times from 250,000 survey respondents. This categorization of U.S. counties results in splitting the effects of social interaction on COVID-19 cases/deaths into those in mask-wearing counties versus non-mask-wearing counties.

 

 

The results of this secondary investigation reveal that wearing a face mask starts working to fight against COVID-19 cases or deaths only if more than 75% of people in a county "always" wear a face mask, while the effects of social interaction on COVID-19 are statistically eliminated when more than 85% of people in a county "always" wear a face mask. Therefore, it is possible to continue having social interactions without any statistically significant effects on COVID-19 cases if a community-wide wearing of face masks can be achieved. This result is important as it provides insights about how societal and economic costs due to COVID-19 can be prevented by wearing a face mask by not some but all.

The corresponding academic paper by Hakan Yilmazkuday is available as a working paper here.

Changes in Consumption in the Early COVID-19 Era: Zip-Code Level Evidence from the U.S.

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Changes in Consumption in the Early COVID-19 Era: Zip-Code Level Evidence from the U.S.

One sentence summary: Spending on goods and services that can (cannot) be consumed at home has increased (decreased) amid COVID-19.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Risk and Financial Management.

 

The working paper version is available here.

 
Abstract
Using monthly zip-code level data on credit card transactions covering 16 U.S. cities, this paper investigates changes in consumption at local commercial places during the early coronavirus disease 2019 (COVID-19) era. Since using aggregate-level data can suppress valuable information on consumption patterns coming from zip codes, the main contribution is achieved by estimating common factors across zip codes that are controlled for factors that are zip-code and time specific as well as those that are zip-code and sector specific. The estimation results based on common factors across zip codes show that relative consumption of products and services that can be consumed at home (e.g., grocery, pharmacy, home maintenance) has increased up to 56% amid COVID-19 compared to the previous year, whereas relative consumption of products and services that cannot be consumed at home (e.g., fuel, transportation, personal care services, restaurant) has decreased up to 51%. Similarly, after controlling for the corresponding factors, online shopping has increased up to 21%, while its expenditure share has increased by up to 16% compared to the pre-COVID-19 period.

Non-technical Summary
Consumption within the U.S. is reduced significantly due to the coronavirus disease 2019 (COVID-19). This reduction has been through both the direct impact of COVID-19 due to lockdowns or social distancing and its indirect impact through financial market shocks and their effects on the real economy. The nationwide consumption fall in the U.S. is also evident widely across sectors (except for grocery) and especially for products purchased through offline (rather than online) shopping.

However, such a nationwide observation can easily suppress valuable information on consumption patterns coming from more disaggregated areas as their effects may cancel each other out during the aggregation process. For example, when zip codes are considered, spending on a particular sector may increase in one zip code, whereas it may decrease in another, resulting in no significant impact at the aggregate level. Therefore, using data from more disaggregated areas is important to understand the changes in consumption patterns amid COVID-19.

Based on this motivation, this paper investigates sector-level as well as online versus offline consumption patterns within the U.S. by using monthly zip-code level data (covering 16 U.S. cities) on credit card transactions for local commercial purchases. The main strategy is to identify common factors across zip codes representing sector-level or online versus offline consumption patterns at the U.S. national level that do not suffer from an aggregation problem. This is achieved by estimating sector-time fixed effects or shopping channel-time fixed effects in the monthly zip-code level data, where factors that are zip-code and time specific as well as those that are zip-code and sector specific are controlled for.

The results based on the sector-level data show that consumption of products and services that can be consumed at home (e.g., grocery, pharmacy, home maintenance) have increased by up to 56% during the lockdown period starting from March 2020, whereas consumption of products and services that cannot be consumed at home (e.g., fuel, transportation, personal care services, restaurant) have decreased by up to 51%.

 

This result is analogous to the one that has been used to explain the reduction in economic activity, unemployment or social distancing experience by workers' ability of working from home. The difference in this paper is that it is consuming at home that can be connected to the sectoral heterogeneity in consumption changes amid COVID-19.

The results based on online versus offline shopping show that online shopping has increased by up to 21%, while its expenditure share has increased by up to 16% compared to the pre-COVID-19 period.


The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Risk and Financial Management.

 

The working paper version is available here.


 

COVID-19 and Exchange Rates: Spillover Effects of U.S. Monetary Policy

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COVID-19 and Exchange Rates: Spillover Effects of U.S. Monetary Policy

One sentence summary: The spillover effects of U.S. monetary policy have been effective only for certain countries that can be explained by the disease outbreak channel.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Atlantic Economic Journal.

The working paper version is available here.

 
Abstract
This paper investigates the spillover effects of U.S. monetary policy on exchange rates of 11 emerging markets and 12 advanced economies during the pre-COVID-19 versus COVID-19 periods. The investigation is achieved by a structural vector autoregression model, where year-on-year changes in weekly measures of economic activity, exchange rates and policy rates are used. The empirical results suggest evidence for the spillover effects of U.S. monetary policy for several countries during the pre-COVID-19 period, whereas they have been effective only for certain countries during the COVID-19 period that can be explained by the disease outbreak channel. Important policy implications follow.

 

Non-technical Summary

The Coronavirus Disease 2019 (COVID-19) has reduced economic activity in an unprecedented way. This reduction has resulted in extraordinary unemployment levels around the world. Accordingly, several central banks, including the U.S. Federal Reserve System, have reacted to the economic developments due to COVID-19 by reducing their policy rates.

This paper investigates the spillover effects of U.S. monetary policy on exchange rates during the pre-COVID-19 versus COVID-19 periods. The main objective is to investigate whether these spillover effects have been effective during the COVID-19 period. Country-specific analyses are conducted for 11 emerging markets and 12 advanced economies, where monetary policies of these countries are also controlled for. The formal investigation is by a structural vector autoregression (SVAR) model, where year-on-year growth rates of weekly measures of economic activity, exchange rates, and policy rates are used during the pre-COVID-19 versus COVID-19 periods.

The spillover effects of U.S. monetary policy are investigated by accepting the U.S. economy as an exogenous block to be used in the SVAR estimation of each country. We focus on the cumulative impulse response of exchange rates (constructed as appreciation of currencies) to a negative shock on the (shadow) federal funds rate. We also investigate the contribution of (shadow) federal funds rate to the exchange rate volatility of domestic currencies based on the forecast error variance decomposition.
 

The empirical results suggest that there is evidence for the spillover effects of U.S. monetary policy for almost all countries during the during the pre-COVID-19 period, whereas they have been effective for only certain countries during the COVID-19 period. When we further investigate the reasons behind the heterogeneity across countries, we show that only the exchange rates of countries that were successful in fighting against COVID-19 were subject to the spillover effects of U.S. monetary policy during the COVID-19 period

 

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Atlantic Economic Journal.

The working paper version is available here.