Welfare Costs of Travel Reductions within the U.S. due to COVID-19

Follow @yilmazkuday

 

Welfare Costs of Travel Reductions within the U.S. due to COVID-19

One sentence summary: The cumulative welfare costs of reduced travel with respect to January 20th, 2020 is about 11% as of April 19th, 2020 within the U.S., with a range between 7% and 16% across counties.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Regional Science, Policy and Practice.

 

The corresponding working paper is available here.

 
Abstract
Using daily county-level travel data within the U.S., this paper investigates the welfare costs of travel reductions due to COVID-19 for the period between January 20th and September 5th, 2020. Welfare of individuals (related to their travel) is measured by their inter-county and intra-county travel, where travel costs are measured by the corresponding distance measures. Important transport policy implications follow regarding how policy makers can act to mitigate welfare costs of travel reductions without worsening the COVID-19 spread.

 

Non-technical Summary

After the World Health Organization declared the coronavirus disease 2019 (COVID-19) as a pandemic on March 11th, 2020 and the U.S. federal government declared National Emergency on March 13th, 2020 due to COVID-19, individuals in the U.S. started traveling less due to health concerns, lockdowns or stay-at-home orders. Although these travel reductions are useful to fight against COVID-19, they also result in welfare losses for individuals who get utility out of traveling for leisure, social or recreational purposes.

Using daily county-level travel data from the U.S., this paper investigates the welfare costs of reduced travel during the COVID-19 pandemic. For motivational purposes, a simple model is introduced to measure the welfare of individuals depending on their travel behavior. Travel costs are measured by the distance across (or within) U.S. counties. The implications of the model are estimated by using daily data on inter-county and intra-county travel between January 20th and September 5th, 2020. 

 

The corresponding results show that the negative effects of distance on travel have rapidly increased during the first half of April 2020, after which a gradual recovery has been experienced until June 2020 across U.S. counties.

 

These distance effects are further connected to the welfare of individuals by using the implications of the model. In technical terms, this is achieved by connecting the time-varying effects of distance on travel across (or within) the U.S. counties to the welfare of individuals by taking the total derivative of their utility measured by their travel.

The corresponding results suggest that the cumulative welfare costs of reduced travel with respect to January 20th, 2020 has reached its highest value of about 11% on April 19th, 2020 for the U.S., with a range between 7% and 16% across U.S. counties.

 

When the heterogeneity across U.S. counties on April 19th, 2020 is further investigated, it is shown that initial travel patterns of counties (during the month of January) is correlated with the cumulative welfare costs of reduced travel, suggesting that more-traveling counties in the pre-COVID-19 era have experienced higher welfare costs.

 

When we investigate the political reasons behind the highest cumulative reduction in welfare specifically on April 19th, 2020, we observe that it is the day when the highest portion of U.S. counties have experienced stay-at-home orders. 

 

 

As the estimated welfare losses in this paper (due to traveling less for leisure, social or recreational purposes) are large and significant, there are several implications for policy makers regarding how they can act to mitigate these welfare losses without worsening the COVID-19 spread. Possible policy recommendations include learning from historical experiences and transport policy actions during earlier pandemics, preparing legal and regulatory frameworks as well as supporting guidelines and contingency plans for traveling, providing safety for the health and economic conditions of the transport personnel, sharing information not only with the public but also among different layers of government, adjusting operating times or the travel mode, or hiring contract tracers to detect exposed travelers quickly. Considering these policy recommendations would not only mitigate the spread of COVID-19 but also let individuals travel with fewer concerns, which is essential to reduce the severity of the welfare costs of travel reductions estimated in this paper.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Regional Science, Policy and Practice.

 

The corresponding working paper is available here.

COVID-19 and Unequal Social Distancing across Demographic Groups

Follow @yilmazkuday

 

COVID-19 and Unequal Social Distancing across Demographic Groups

One sentence summary: Blacks and Hispanics, as well as lower-income and lower-educated people, were able to experience relatively less social distancing amid COVID-19.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Regional Science, Policy and Practice.

The corresponding working paper is available here.
 
Abstract
This paper analyzes whether social distancing experienced by alternative demographic groups within the U.S. has been different amid COVID-19. The formal investigation is achieved by using daily state-level mobility data from the U.S. covering information on the demographic categories of income, education and race/ethnicity. The results show that social distancing have been experienced more by higher-income, higher-educated or Asian people after the declaration of National Emergency on March 13th, 2020. Since alternative demographic groups were subject to alternative employment opportunities during this period (e.g., due to being able to work from home), redistributive effects of COVID-19 are implied that require demographic-group specific policies.

Non-technical Summary

The coronavirus disease 2019 (COVID-19) has been declared as a pandemic by the World Health Organization on March 11th, 2020, whereas the U.S. has declared National Emergency about it on March 13th, 2020. Accordingly, several governments around the world have implemented stay-at-home orders as COVID-19 spreads mainly through person-to-person contact. Although some of these orders were based on demographic characteristics such as age groups due to the way that COVID-19 affects people of alternative ages, in practice, knowledge and attitudes have been different across other demographic characteristics such as income, education, race, ethnicity, gender, occupation, population, and place of current residence. Since economic activity is highly related to mobility, these developments imply potential redistributive effects of COVID-19 across demographic groups that require the attention of policy makers.

Based on this motivation, this paper analyzes how alternative demographic groups have experienced social distancing with the U.S. amid COVID-19. Daily state-level mobility data for social interactions covering the period between January 21th, 2020 and June 26th, 2020 are utilized for alternative demographic categories of income, education and race/ethnicity. The descriptive statistics for the median U.S. state suggest that social distancing have been experienced more by higher-income, higher-educated or Asian people after the declaration of National Emergency on March 13th, 2020. This observation is mostly due to these groups having relatively higher levels of social interaction (with respect to other groups) before the declaration of National Emergency, because all groups have experienced similar levels of social interaction after the declaration.

Since the descriptive statistics for the median U.S. state do not control for any state-specific development such as state-level policies or the health system of the state that may change over time, a formal investigation is also achieved by using a panel regression analysis. The objective of this regression is to capture how different demographic groups have achieved social distancing after controlling for factors that are state-time specific (e.g., state-level policies on certain days) or group-state specific (e.g., higher-income individuals in certain states socially interacting differently from other higher-income individuals in other states).

The results of the formal investigation support the descriptive statistics by showing that social distancing has been experienced more by higher-income, higher-educated or Asian people compared to other demographic groups after the declaration of National Emergency. In particular, the social distancing experienced by the highest-income group after the declaration of National Emergency has been about 31% and 32% more than the first and the second income quartiles, respectively, and 25% more than the third income quartile. The social distancing experienced by the highest-education group after the declaration of National Emergency has been about 53% more than the first education quartile, 46% more than the second education quartile, and 34% more than the third education quartile. The social distancing experienced by the Asian race after the declaration of National Emergency has been about 20% more than blacks and Hispanics, and 18% more than whites.

Important policy implications follow, especially when it is considered that higher-educated, higher-income or Asian people were able to work at home and maintain employment during COVID-19 due to their occupations, whereas lower-educated workers, blacks or Hispanics were not able to work at home due to their occupations and thus became unemployed. In particular, although higher-income, higher-educated or Asian people have experienced higher social distancing after the declaration of National Emergency, since social interaction levels are similar across demographic groups after the declaration, redistributive effects of COVID-19 are implied due to different demographic groups being or not being able to work at home. Accordingly, demographic-group specific policies are required to reduce not only the overall economic impact of COVID-19 but also the corresponding inequality across demographic groups.


The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Regional Science, Policy and Practice.

The corresponding working paper is available here.

Stay-at-Home Works to Fight Against COVID-19: International Evidence from Google Mobility Data

Follow @yilmazkuday

 

Stay-at-Home Works to Fight Against COVID-19: International Evidence from Google Mobility Data

One sentence summary: Stay-at-home works to fight against COVID-19.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Human Behavior in the Social Environment.

 

The working paper version is available here.

 
Abstract
Daily Google mobility data covering 130 countries over the period between February 15th, 2020 and May 2nd, 2020 suggest that less mobility is associated with lower COVID-19 cases and deaths. This observation is formally tested by using a difference-in-difference design, where country-fixed effects, time-fixed effects as well as the country-specific timing of the 100th COVID-19 case are controlled for. The results suggest that 1% of a weekly increase in being at residential places leads into about 70 less weekly COVID-19 cases and about 7 less weekly COVID-19 deaths, whereas 1% of a weekly decrease in visits to transit stations leads into about 33 less weekly COVID-19 cases and about 4 less weekly COVID-19 deaths, on average across countries. Similarly, 1% of a weekly reduction in visits to retail & recreation results in about 25 less weekly COVID-19 cases and about 3 less weekly COVID-19 deaths, or 1% of a weekly reduction in visits to workplaces results in about 18 less weekly COVID-19 cases and about 2 less weekly COVID-19 deaths.

Non-technical Summary
Coronavirus disease 2019 (COVID-19) has been declared as a pandemic on March 11th, 2020 by the World Health Organization. Due to the high number of COVID-19 cases and deaths, several countries reacted to this pandemic by issuing stay-at-home orders, because COVID-19 spreads mainly through person-to-person contact. Nevertheless, as shown in the figure below, countries have alternative changes in their mobility over time based on Google mobility data.

In particular, across countries, as of May 2nd, 2020, the reduction in visits to retail & recreation ranges between 21% and 95%, that of grocery & pharmacy ranges between 8% and 98%, that of parks ranges between 12% and 95%, that of transit stations ranges between 27% and 100%, and that of workplaces ranges between 14% and 92%, whereas the increase in being at residential places ranges between 8% and 55%, all with respect to the baseline determined by Google.

This paper investigates the relationship between country-specific changes in mobility and the corresponding COVID-19 cases/deaths. This is achieved by using daily data on COVID-19 cases and deaths as well as Google mobility data covering 130 countries around the world for the period between February 15th, 2020 and May 2nd, 2020. Descriptive statistics suggest that both COVID-19 cases and deaths are lower in countries with less mobility.

The formal investigation is achieved by using a difference-in-difference design, where weekly changes in COVID-19 cases or deaths are regressed on weekly changes in mobility. After controlling for county-fixed effects, time-fixed effects, and country-specific timing of the 100th COVID-19 case, the results suggest that 1% of a weekly increase in being at residential places leads into about 70 less weekly COVID-19 cases and about 7 less weekly COVID-19 deaths, whereas 1% of a weekly decrease in visits to transit stations leads into about 33 less weekly COVID-19 cases and about 4 less weekly COVID-19 deaths, on average across countries.

Similarly, 1% of a weekly reduction in visits to retail & recreation results in about 25 less weekly COVID-19 cases and about 3 less weekly COVID-19 deaths, or 1% of a weekly reduction in visits to workplaces results in about 18 less weekly COVID-19 cases and about 2 less weekly COVID-19 deaths. Finally, 1% of a weekly reduction in visits to grocery & pharmacy or parks results in about 1 less weekly COVID-19 death, although the effects on COVID-19 cases are statistically insignificant.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Human Behavior in the Social Environment.

 

The working paper version is available here.

COVID-19 Spread and Inter-County Travel: Daily Evidence from the U.S.

Follow @yilmazkuday

 

COVID-19 Spread and Inter-County Travel: Daily Evidence from the U.S.

One sentence summary: Lower inter-county travel is associated with lower COVID-19 cases and deaths.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Transportation Research Interdisciplinary Perspectives.

The corresponding working paper is available here.

 
 
Abstract
Daily data at the U.S. county level suggest that coronavirus disease 2019 (COVID-19) cases and deaths are lower in counties where a higher share of people have stayed in the same county (or travelled less to other counties). This observation is tested formally by using a difference-in-difference design controlling for county-fixed effects and time-fixed effects, where weekly changes in COVID-19 cases or deaths are regressed on weekly changes in the share of people who have stayed in the same county during the previous 14 days. A counterfactual analysis based on the formal estimation results suggests that staying in the same county has the potential of reducing total weekly COVID-19 cases and deaths in the U.S. as much as by 139,503 and by 23,445, respectively.

 

 

Non-technical Summary
As of September 2nd, 2020, the number of people who have lost their lives in the U.S. due to the coronavirus disease 2019 (COVID-19) has reached 181,129, whereas the number of cases has reached 5,909,266. Since COVID-19 spreads mainly through person-to-person contact, different layers of government in the U.S. reacted to this development by implementing travel restrictions, both internationally and domestically, which is similar to other countries or other time periods. However, these restrictions do not cover the U.S. in a nationwide way, since the federal government has left such policy decisions to local governments.
 

Based on this background, this paper investigates whether inter-county travel within the U.S. has any implications for COVID-19 cases or deaths. This is achieved by using U.S. daily data at the county level covering the period between January 21th, 2020 and September 2nd, 2020. Inter-county travel is measured by using data from smartphone devices. Descriptive statistics suggest that both COVID-19 cases and deaths are lower in counties where a higher share of people have stayed in the same county (or a fewer share of people have travelled across counties) during the previous 14 days.

Since descriptive statistics cannot control for any county-specific characteristics or time-specific changes that are common across counties, a formal investigation is achieved by using a difference-in-difference design, where county-fixed effects and time-fixed effects are controlled for. The estimation results suggest that if a person lives in a county where the average person has travelled less compared to the previous week, it is better for this person to stay in her county to reduce the possibility of catching COVID-19 as her county has lower COVID-19 cases or deaths due to other people in that county travelling less. 

 

The estimation results are further used to answer the following hypothetical question based on a counterfactual analysis: What would happen to the number of COVID-19 cases and deaths in each county if all people would stay in the same county? 

 

 

The results suggest that staying in the same county has the potential of reducing total weekly COVID-19 cases and deaths in the U.S. as much as by 139,503 and by 23,445, respectively. At the county level, staying in the same county has the potential of reducing COVID-19 cases between 2 and 209 across counties, and it has the potential of reducing county-specific COVID-19 deaths up to 35. It is implied that staying in the same county (i.e., travelling less across counties) would help fighting against COVID-19. 

 

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

COVID-19 and Daily Oil Price Pass-Through

Follow @yilmazkuday

 

COVID-19 and Daily Oil Price Pass-Through

One sentence summary: Following an increase in the number of U.S. COVID-19 cases, there is evidence for complete pass-through (incomplete pass-through) of crude oil prices into the U.S. gasoline spot (retail) prices.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Energy Research Letters.

 

The working paper version is here.

 
Abstract
This paper investigates the (crude) oil price pass-through into gasoline spot and gasoline retail prices in the U.S. due to the effects of coronavirus disease 2019 (COVID-19). The investigation is achieved by using daily data in a structural vector autoregression framework. The oil price pass-through is measured as the cumulative impulse response of gasoline spot or gasoline retail prices divided by the cumulative impulse response of oil prices, both following a percentage change in total number of the U.S. COVID-19 cases. The results suggest evidence for complete pass-through of oil prices into gasoline spot prices, whereas the corresponding pass-through into gasoline retail prices is about 29 percent in the long run.

 

Non-technical Summary
Total number of coronavirus disease 2019 (COVID-19) cases in the U.S. has been recorded as more than 30 million as of April 2021 according to the Centers for Disease Control and Prevention. This number is reflected as a substantial drop in the economic activity in the U.S. as individuals have voluntarily started experiencing social distancing to fight against COVID-19 and several layers of government in the U.S. have further implemented stay-at-home orders starting from March 2020. This reduction in economic activity has also resulted in higher unemployment rates and thus lower overall expenditure of individuals. Accordingly, the demand for both crude oil and gasoline has been reduced dramatically, whereas supply shocks due to the OPEC disagreement starting from March 2020 have further contributed to the turmoil of crude oil prices around the globe.

Based on this period of strong volatility due to the COVID-19 crisis, this paper investigates the pass-through of crude oil prices into the U.S. gasoline spot and gasoline retail prices. This is achieved by using the implications of a structural vector autoregression (SVAR) model, where weekly percentage changes of daily endogenous variables are used for the crude oil prices, gasoline spot prices, and gasoline retail prices. Weekly percentage changes in daily total number of COVID-19 cases in the U.S. enter as an exogenous variable in this framework. The pass-through of crude oil prices into gasoline prices is measured by the cumulative impulse response of gasoline spot or gasoline retail prices divided by the cumulative response of crude oil prices, both following a percentage change in the U.S. COVID-19 cases.

The empirical results based on the crude oil price data of "Brent Spot Price FOB (Dollars per Barrel)" provide evidence for complete pass-through of crude oil prices into gasoline spot prices. In particular, 1% of a weekly increase in daily crude oil prices results in about 1.1% of a weekly increase in daily gasoline spot prices in the U.S. after one week, 1% after one month, and again 1% after two months.

The results also suggest that the pass-through of oil prices into gasoline retail prices in the U.S. is incomplete, both in the short run and the long run. Specifically, 1% of a weekly increase in daily crude oil prices results in about 0.15% of a weekly increase in daily gasoline retail prices after one week, 0.29% after one month, and again 0.29% after two months. The empirical results are highly similar when the crude oil price data of "Cushing, OK WTI Spot Price FOB (Dollars per Barrel)" are used.

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Energy Research Letters.

 

The working paper version is here.