metadata: authors: 0: author: Vagliasindi,Maria
count: World
docna: 0: docna: The Impact of COVID-19 on Mobility and Congestion
docty: Policy Research Working Paper
abstracts: cdata!: The COVID-19 pandemic has been posing
            unprecedented challenges for the transport sector. Urban
            travel has declined all over the world, but not uniformly
            for all modes; public transportation has taken the hardest
            blow. As a result, there has been a widespread reduction in
            public transport ridership: according to recent estimates,
            passenger numbers in cities around the world have been at
            the peak of the pandemic down 70 to 90 percent. This paper
            examines the short-run economic impact of the pandemic, as
            well as the generic and sector-specific restrictions that
            were adopted to control the outbreak of the pandemic, based
            on a rich database tracking on a daily basis public
            transport usage and traffic congestion. The analysis
            confirms the significant impact of sector-specific
            restrictions and broader lockdown measures in terms of
            reduction in urban mobility and congestion. The analysis
            finds that the spread of the disease itself had an economic
            impact distinct from that of the lockdown measures. There
            are also different results on the magnitude of impact of
            cross-sectoral vis-&#xe0;-vis sectoral restrictions on
            urban mobility and congestion. Whereas the magnitude of the
            spread of the disease is higher than the overall stringency
            of the lockdown, the impact of restrictions of public
            transit has been much greater than the spread of the disease
            and acts indirectly as a disincentive to move on the road.
            More effective safety measures, such as those related to the
            use of facial covering, are associated with higher use of
            public transport and an increase in the likelihood of low
            congestion. There is no evidence of intermodal competition
            between public transportation and road transport. In
            particular, the expansion of car registration has not led to
            a decrease in public transport mobility, but it is
            significantly associated with an increase in traffic
            congestion, particularly in mega-cities.
display_title: The Impact of COVID-19 on Mobility
            and Congestion
listing_relative_url: /research/2023/03/34018462/impact-covid-19-mobility-congestion
topicv3: Transport m1326661 135,Traffic Congestion m1326913 1817,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2023-03-09T00:00:00Z
dois: 10.1596/1813-9450-10351
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 28
author_keys: 0: author_key: 619680
model_extractions: Policy Research Working Paper 10351 The Impact of COVID-19 on Mobility and Congestion _Maria Vagliasindi_ Infrastructure Chief Economist Office March 2023
Policy Research Working Paper 10351

# **Abstract**

The COVID-19 pandemic has been posing unprecedented challenges for the transport sector. Urban travel has declined all over the world, but not uniformly for all modes; public transportation has taken the hardest blow. As a result, there has been a widespread reduction in public transport ridership: according to recent estimates, passenger numbers in cities around the world have been at the peak of the pandemic down 70 to 90 percent. This paper examines the short-run economic impact of the pandemic, as well as the generic and sector-specific restrictions that were adopted to control the outbreak of the pandemic, based on a rich database tracking on a daily basis public transport usage and traffic congestion. The analysis confirms the significant impact of sector-specific restrictions and broader lockdown measures in terms of reduction in urban mobility and congestion. The analysis finds that the spread of the disease itself had an economic impact distinct from that of the lockdown measures. There are also different results on the magnitude of impact of cross-sectoral vis-à-vis sectoral restrictions on urban mobility and congestion. Whereas the magnitude of the spread of the disease is higher than the overall stringency of the lockdown, the impact of restrictions of public transit has been much greater than the spread of the disease and acts indirectly as a disincentive to move on the road. More effective safety measures, such as those related to the use of facial covering, are associated with higher use of public transport and an increase in the likelihood of low congestion. There is no evidence of intermodal competition between public transportation and road transport. In particular, the expansion of car registration has not led to a decrease in public transport mobility, but it is significantly associated with an increase in traffic congestion, particularly in mega-cities.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at mvagliasindi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Impact of COVID-19 on Mobility and Congestion***

Maria Vagliasindi**

**JEL:** R40, R41, Q50, H10

**Keywords:** Transport sector, COVID 19, congestion, real time indicators

*Special thanks go to Xinyi Su provided invaluable support to the collection of data and information. Vivien Foster and Harris Selod provided very helpful suggestions.

** Maria Vagliasindi is (acting) Chief Economist in the Chief Economist Office of the Infrastructure Practice Group.
# **_1. Introduction_**

The COVID-19 pandemic significantly affected lifestyle and mobility worldwide, ranging from a dramatic decrease in travel to an unprecedented increase in teleworking. These impacts resulted from governmental measures, with some affecting whole sectors in the economy, and some affecting crucial infrastructure sectors, such as transport. After the outbreak of COVID-19, the local governments of many cities have suspended the operations of buses and subways to prevent people from being infected with COVID-19, reducing the gathering of crowds, and required new safety standards, including requirements to wear masks. These measures implied a major reduction in service capacity compared to the pre-COVID-19 era, by affecting ridership due to a dramatic decrease in travel demand levels as well as imposing regulations that have consequences for service capacity. Such restrictions have significantly affected people's mobility behavior, with individual choices to refrain from traveling in order to reduce exposure and risk of contamination.

One of the sectors that has experienced a major impact is transportation, which is the focus of this paper. Urban travel has declined all over the world, but not uniformly for all modes; public transportation has taken the hardest blow, as shown by survey-based data (Molloy _et al.,_ 2020; Astroza _et al.,_ 2020, Tirachini and Cats, 2020). As a result, there has been a widespread reduction in public transport ridership: according to recent estimates, passenger numbers in cities around the world have been at the peak of the pandemic down 70% to 90% (Google, 2020). Much of the changes in travel demand has been attributed to individuals' adjustments in their daily travel activities such as replacing out-of-home activities with in-home activities. The impact of COVID-19 on public transport varies depending on the stage of the coronavirus spread. The loss of public transport demand can mostly be attributed to reduced travel demand resulting from a combination of public health restrictions, working from home, online schooling, shift to online shopping, transit service cuts, and loss of employment and fear of infection (HoneyRos ́es _et al.,_ 2020; De Vos, 2020).

The sharp reduction in public transportation demand due to the new physical distance behaviors and the fear of COVID-19 contagion challenges the future sustainability of mobility in cities. This is because in some cases the reduction in services has been accompanied by a reduced service supply and exacerbated by the perception of public transportation as riskier than private or personal means of transport because of the closer contact to other people that is possible, sometimes unavoidable, in public transportation vehicles and stations. Since the beginning of the pandemic, most public transport operators have quickly stepped up and taken concrete action to make transit systems COVID-safe for staff and passengers. But despite the best efforts of professionals across the sector, the COVID-19 crisis has dealt a massive blow to public transport. The economic and social effects of the COVID-19 outbreak in public transportation extend beyond service performance and health risks to sustainable mobility. However, little is known regarding individuals' immediate response in terms of adjusting their travel activities.

2
While these new trends in trip making behavior point towards a sustained reduction in public transport demand, they do not necessarily mean a reduction in transit modal share. New evidence seems to suggest that there has been a significant reduction in the modal share of transit in response to COVID-19 (Apple 2021; Orro _et al.,_ 2020). This shift can be attributed, at least in part, to negative passenger perceptions of the potential health risks of riding transit (Przybylowski _et al.,_ 2021). If these negative perceptions are not properly addressed during the transition stage, the reduction in the modal share of transit may be sustained for an extended period in the aftermath of the pandemic (Przybylowski _et al.,_ 2021; Tirachini and Cats, 2020). However, there is no analytical evidence of whether intermodal competition has taken place.

Aversion to crowded public transport is likely to push more journeys onto private road-based transport, with related issues for road congestion. Traffic congestion is a perpetual problem for the sustainability of transportation development. Traffic congestion causes delays, inconvenience, and economic losses to drivers, as well as air pollution. Identification and quantification of traffic congestion are crucial for decision-makers to initiate mitigation strategies to improve the overall transportation system’s sustainability. Road traffic is in fact considered as one of the main sources of air pollution in urban environments (Chowdhury _et al._ , 2021, Hamra _et al._ , 2015). In particular, among other pollutants, internal combustion engines emit nitrogen oxides like NO and NOx (mainly from diesel engines), which generate NO2 (Taquechel _et al._ , 2020) and particulate matter (Manisalidis _et al._ , 2020) such as PM10, which also originates through mechanical processes. Both primary and secondary pollutants are transported far from roads, thus worsening the air quality of whole urban areas. In addition, transport externalities soundly affect not only the environment, but also the society and economy of cities. For all these reasons, the United Nations included the creation of sustainable cities and communities as one of the 17 Sustainable Development Goals in agenda 2030, in which sustainable transport is one of the themes of several targets and goals.

Generally, studies have found that reductions in air pollution during the pandemic were most prominent for NO2, which is notably influenced by car traffic. While many other environmental indicators of air pollution (e.g., PM2.5, O3, CO) have been tied to adverse health outcomes (see Manisalidis _et al._ , 2020 for a review), a growing amount of evidence has specifically found adverse health effects from NO2 pollution. Rossi _et al._ (2020) indicated that vehicle flows significantly affect NO, NO2, and NOx concentrations, although no evidence of a relationship between traffic and PM10 was highlighted. According to this perspective, measures to limit traffic flows seem to be effective in improving air quality only in terms of reducing nitrogen oxide. Aletta _et al._ (2020) presented the results of a simulation approach using Floating Car Data (FCD) from vehicles equipped with an On-Board Unit GPS system. During the lockdown period, the traffic in Rome (Italy) was reduced by 64.6%, which had a positive impact on noise emissions reduction. On a broader scale, Chen et al. (2020) revealed that reductions in NO2 and CO were common but heterogeneous across the United States, after adjusting for temporal trends. Another USbased study by Berman & Ebisu (2020) found similar results at the county level; using two-sample aggregated 3
comparisons, they found that urban counties saw especially large reductions in NO2 after March 13, 2020, compared to historical data. At the global level, Venter et al. (2020) connected mobility data with environmental indicators to argue that a strong link exists between global vehicle transportation declines and the reduction in ambient NO2 exposure, though they did not assess how differences in lockdown policy measures differentially affected NO2.

Liu _et al._ (2021) correct for temporal trends and accounted for lockdown timing in the Californian context, finding that sharp decreases in air pollutants occurred shortly after the lockdown policies were implemented. This illustrates the important influence of lockdown timing. Another study focusing on Seattle, WA, Cui (2020) find through sophisticated time series analyses that local traffic decreased considerably, while air quality improved during lockdown. He emphasizes the importance of adjusting these analyses to take into account meteorological conditions. Zhang _et al._ (2021) report that, compared with the base period from 2018 to 2019, the COVID-19 lockdown measures significantly reduced air pollutants in China. Winchester _et al._ (2021) find that when cities’ most stringent social distancing policies in the United States were implemented, a significant reduction in average daily congestion and decrease in average daily mobility and average daily NO2 compared to unrestricted days.

In this context, real-time information can play a most valuable role for policy makers in developing countries, particularly to address the questions highlighted in what follows. How has the massive blow in public transportation been affected by the evolution of the pandemic, sector-specific restrictions and the stringency of lockdowns? What were the relative impacts of the disease versus the sectoral restrictions and health policy remedy of lockdown? Has the use of private vehicles increased? What is the evidence that a silver lining story in terms of reduction in congestion can be drawn after the relaxation of lockdown measures?

The focus of these studies has been only on a selected number of countries, mostly the United States and developed countries with the exception of few emerging countries. Using a global sample of 186 cities in 39 countries, this paper assesses how mobility and congestion were affected by the pandemic and lockdown measures, including public transport restrictions.

This paper proceeds as follows. Section 2 describes the data that we will subsequently use to provide a real-time estimation of mobility and congestion. Section 3 provides estimates of the impact of the pandemic and lockdown evolution on mobility and congestion. Section 4 concludes with a discussion on policy implications.

4
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- _Pi,t_ is the indicator capturing the evolution of the pandemic through the daily number of infected cases and deaths per million due to COVID-19.

- _Ui,t_ are the monthly per capita registration of new cars in each country.

- _Wi,t_ is equal to one if date _t_ is a working date (that is, excluding national holidays or nonworking days such as weekends).

- _Ti,t_ represents the variables capturing for each city the minimum and average temperature as well as the level of precipitation.

- εi,t is an i.i.d. innovation term.

- β, ω, θ, θ, π, and γ are the estimated parameters.

This model can be used to test the following hypotheses.

**Hypothesis 1:** _Countries where the lockdown measures were effectively enforced were characterized by a greater decline in public transport mobility and urban congestion, with a differential impact of sectoral and cross sectoral measures._

This first hypothesis tests whether the reduction in mobility of public transport reflects the severity of the lockdown measures. In terms of equation (1), this translates into a coefficient value β<0. Since the stringency of the national lockdowns can be measured both using sectoral and cross sectoral indicators, as noted above, the econometric model can also be used to explore which of these has the most influence on the change in mobility and traffic congestion. Sector specific restrictions such as closures of public transport are expected to have a much higher impact on public transport mobility. More effective safety measures such as requirements to use face masks, on the other hand, are expected to be effective in terms of encouraging use of public transport.

**Hypothesis 2:** _Cities where loss of lives was experienced also display a greater decline in mobility and lower congestion._

The second hypothesis is that evolution of the pandemic, when countries start registering actual cases of death for COVID-19, also has a distinct economic impact over and above the lockdown measures themselves. In terms of equation (1), this would imply a coefficient value ω< 0.

10
**Table 1** **_Ex lanator Variables In luencin Mobilit and Low Con estion_** **_<u>p y f g y g</u>_**

|||**Expec**<br>|**ted Sign **|
|---|---|---|---|
|**Variables**|**Definition**|**Public**|**Low**|
|||**transit**|**congestion**|
||**PANDEMIC EVOLUTION**|||
|**Covid cases**|= number of (new) infected cases reported daily (per million)|**-**|**+**|
|**Covid deaths**|= number of (new) deaths from Covid-19 reported daily|**-**|**+**|
||**LOCKDOWN EVOLUTION**|||
||= averae value of the overnment resonse strinenc index based|||
|**Stringency index**|g g p gy<br>on the policy decisions taken, with the index ranging from 0 (less<br>stringent)to 1(most stringent).|**-**|**+**|
|**Public transit restrictions**|= 0 if no measures restricting the use of public transit<br>= 1 if the authorities recommend closing (or significantly reduced<br>volume/route/means of transport available)<br>= 2 if the authorities required closing (or prohibited most citizens<br>from using public transit)|**-**|**+**|
|**Facial covering**<br>**requirement**|= 0 if no policy requiring facial covering has been implemented<br>= 1 if facial covering has been recommended<br>= 2 if facial covering has been required in some specified<br>shared/public spaces outside the home with other people<br>present, or some situations when social distancing not possible<br>= 3 if facial covering has been required in all shared/public spaces<br>outside the home with other people present or all situations when<br>social distancing not possible<br>= 4 if facial covering has been required outside the home at all times<br>regardless of location orpresence of otherpeople|**+**|**-**|
|**Lockdown (April dummy)**|= 1 after the month of April|**-**|**+**|
||**TRENDS IN MOTORIZATION**|||
|**Car registration**|= new per capita new registrations of cars|**-**|**+**|
||**DAILY CONTROLS**|||
|**Working days**|= 1 if working day<br>= 0 otherwise (weekends or national holidays)|**+**|**-**|
|**Min temperature**|= minimum daily temperature|**+**|**-**|
|**Avg temperature**|= average daily temperature|**+/-**|**-**|
|**Precipitation**|= average daily temperature|**+**|**-**|
||**CITY CONTROLS**|||
|**City density**|= population per area (km2)|**-**|**-**|

11
**Hypothesis 3:** _The expansion of car registration is expected to lead to a decrease in public transport mobility and an increase in traffic congestion, particularly in mega-cities._

This third hypothesis examines whether cities that have started experiencing an increase in the use of private cars as soon as restrictions were relaxed have increased intermodal competition away from public transport and have negatively affected congestion.

In addition, the following specification is also estimated.

where the additional variables are: • _Dt_ is a dummy for the month where all countries in the sample reached the peak of the pandemic.

This specification allows the testing of a fourth hypothesis.

**Hypothesis 4:** _The pandemic and the associated lockdown measures are associated with a structural break in economic activity across all countries that goes beyond individual countries’ specific lockdown measures._

This fourth hypothesis goes more in depth to determine whether the pandemic and associated lockdown measures caused a structural break in economic activity after a certain threshold for all cities in the sample. To test such a hypothesis, a dummy capturing the peak of the impact is introduced and interacted with the right-hand side variables. This then provides an automatic test of whether the parameters differ before and after this structural break. In equation (2), this would signify a coefficient value of π> 0.

The full set of explanatory variables is presented in Table 1.

# **_3.2 Empirical results_**

Table 2 provides the results of the fixed effect regressions, estimating for the basic specification (1), which can be used to test the stated hypotheses. Hypotheses 1 and 2 are validated as the coefficients ω, which can be interpreted as estimates of the effect of the spread of the disease, and the coefficient β, which estimates whether the effect of the lockdown measures are both negative and significant at the 1 percent confidence level. It is also worth noting that in the case of overall lockdown measures β is in absolute terms lower than ω, implying a distinct and higher impact associated to the spread of the disease across all specifications confirming the strong effect of the perception of risks felt by users of public transit.

12
**Table 2**

**Regression Results of Public Transport Mobility with overall restriction (country fe)**

|**(1)**|**(2)**|**(3)**|
|---|---|---|
|<br>**PANDEMIC & LOCKDOWN EVOLUTI**<br>1035***|<br>**ON**<br>0945***|<br>0945***|
|**Covid deaths (per Million)**<br>-.<br>(0.023)|-.<br>(0.022)|-.<br>(0.022)|
|**Stringency Index**<br>-0.691***<br>(0.004)|-0.661***<br>(0.004)|-0.6621***<br>(0.004)|
|**CONTROLS**|10772***|10772***|
|**Working days**|.<br>(0.259)|.<br>(0.259)|
|**Min temperature**|0.008<br>(0.019)|0.008<br>(0.017)|
|**Avg temperature**|-0.065***<br>(0.017)|0.208***<br>(0.016)|
|**Precipitation**|-1.635***<br>(0.071)|-1.635***<br>(0.071)|
|**City density**||-0.150<br>(0.012)|
|**COUNTRY FE**<br>||<br>|
|**Country FE**<br>Yes|Yes|Yes|
|**Monthly dummy**<br>Yes|Yes|Yes|
|**Constant**<br>-3.534<br>(7.417)|-13.999<br>(7.482)|-26.528***<br>(7.509)|
|**N**<br>59,777|59,777|59,777|
|**N cities**<br>164|164|164|
|**Wald χ2**<br>151703.72***|163759.75***|163762.71***|
|**Within R**<sup>**2**</sup><br>0.7176|0.7329|0.7331|
|**Between R**<sup>**2**</sup><br>0.6690|0.6636|0.6671|
|**Overall R**<sup>**2**</sup><br>0.7085|0.7197|0.7204|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent 13
**Table 3**

**Regression Results of Public Transport Mobility with sectoral restriction (country fe)**

|**(2.a)**<br>**PANDEMIC & LOCKDOWN EVOLUTI**<br>1480***|**ON**<br>1304***|**(2.b)**<br>1260***|
|---|---|---|
|**Covid death (per Million)**<br>-.<br>(0.025)|-.<br>(0.025)|-.<br>(0.026)|
|**Public transport closure**<br>-12.669***<br>(0.155)|-11.974***<br>(0.136)|-11.996***<br>(0.136)|
|**Public transport facial covering**<br>1.170***<br>(0.080)|1.239***<br>(0.059)|1.219***<br>(0.077)|
|**CONTROLS**|12408***|12439***|
|**Working days**|.<br>(0.287)|.<br>(0.287)|
|**Min temp**|0.092***<br>(0.019)|0.093***<br>(0.019)|
|**Avg temp**|0.229**<br>(0.018)|0.232***<br>(0.018)|
|**Precipitation**|-1.378***<br>(0.079)|-1.363***<br>(0.071)|
|**City density**||-0.016<br>(0012)|
|**TRENDS IN MODAL COMPETITIO**|**N**|.<br>0.544***|
|**Car registration**||<br>(0.012)|
|**COUNTRY FE**<br>|||
|**Country FE**<br>Yes|Yes|Yes|
|**Monthly dummy**<br>Yes|Yes|Yes|
|**Constant**<br>-4.010<br>(7.454)|-21.899<br>(7.513)|-21.515***<br>(7.348)|
|**N**<br>59,777|59,777|59,777|
|**N cities**<br>186|186|186|
|**Wald χ2**<br>110836.80***|122569.48***|122635.90***|
|**Within R**<sup>**2**</sup><br>0.6498|0.6724|0.6725|
|**Between R**<sup>**2**</sup><br>0.6677|0.6573|0.6610|
|**Overall R**<sup>**2**</sup><br>0.6536|0.6697|0.6706|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

14
When we turn to introduce sector specific restrictions in Table 3, results are significantly different. The impact of restrictions on public transit has clearly the highest impact, much higher than the spread of the disease. More effective safety measures, such as those related to the use of facial covering are positively and significantly associated to the use of public transport, though their magnitude is small.

Hypothesis 3 is not validated across all specifications, implying that intermodal competition has not taken place. All the coefficients θ capturing the extent of intermodal competition coming from the increase in new car registration impacted positively rather than negatively public transit mobility. The use of car registration in Table 2 could introduce an endogeneity issue, as the specification does not control for public transport closure which could simultaneously affect public transport mobility and the purchase of cars as measured by car registrations. Accordingly, in Table 2 such a variable has been dropped, and has been added only in Table 3, where the introduction of public transport closure as an explanatory variable provides a way to address the endogeneity issue.

Among the other controls that are included in Tables 2 and 3, it is worth noting the expected positive sign of higher mobility during working days, and as the average temperature is decreasing or precipitation are lower. Robustness checks have also been undertaken, including additional control variables, such as city density. The idea is to control for the more intense supply of public transport as well as greater congestion ceteris paribus in denser cities.

Tables 4 and 5 replicate the same regressions using as independent variable capturing the change in congestion compared to the pre-pandemic period. As the variable is a dummy, equal to 1 if it identifies a day in 2020 with the congestion level that is at least 50% lower than the congestion level on the corresponding day in the previous year (0 otherwise), we perform probit regressions.

Hypotheses 1 and 2 are validated as the coefficients ω, which can be interpreted as estimates of the effect of the spread of the disease, and the coefficient β, which estimates whether the effect of the lockdown measures are both positive and significant at the 1 percent confidence level. It is also worth noting that β and ω display similar size, implying a distinct and higher impact associated to the spread of the disease across all specifications.

When we turn to introduce sector specific restrictions, as expected, the impact of restrictions on public transit has clearly the highest impact, much higher than the spread of the disease, and acts indirectly as a disincentive also to move on the road. More effective safety measures, such as those related to the use of facial covering are associated to higher use of public transport and an increase in the likelihood of low congestion.

Hypothesis 3 is also confirmed across all specifications. All the coefficients θ capturing the increase in new car registration negatively impacted the probability of low traffic congestion. Among the other controls that are included in Tables 1 and 2, it is worth noting the expected negative sign of lower congestion during working days, and as the negative impact on congestion as the average temperature is increasing or precipitation are intensified.

15
Robustness checks have also been undertaken, including additional control variables. It is also worth noting that city density is negatively associated to lower level of congestion, confirming that denser cities are more congested.

**Table 4**

**Regression Results of Low Congestion with overall restriction (country fe)**

|**(1)**|**(2)**|**(3)**|
|---|---|---|
|<br>**PANDEMIC & LOCKDOWN EVOLUT**<br>***|<br>**ION**<br>***|<br>***|
|**Covid death (per Million)**<br>0.053<br>(0.003)|0.048<br>(0.003)|0.090<br>(0.0036)|
|**Stringency Index**<br>0.040***<br>(0.0004)|0.040***<br>(0.0005)|0.077***<br>(0.001)|
|**CONTROLS**|||
||1617***|3018***|
|**Working days**|-.<br>(0.030)|-.<br>(0.060)|
|**Min temp**|0.0007<br>(0.003)|0.002<br>(0.005)|
|**Avg temp**|-0.012***<br>(0.002)|-0.022***<br>(0.005)|
|**Precip**|-0.048***<br>(0.010)|-0.098***<br>(0.020)|
|**City density**||-0.003**<br>(0.001)|
|**COUNTRY FE**|||
|**Country FE**<br>Yes|Yes|Yes|
|**Time dummy**<br>Yes|Yes|Yes|
|**Constant**<br>-1.541***<br>(0.385)|-0.859**<br>(0.427)|-3.472***<br>(0.877)|
|**N**<br>68076|68076|68076|
|**N cities**<br>186|186|186|
|**Wald χ2**<br>16227.92***|16319.18***|12899.60***|
|**Log likelihood**<br>-19162.402|-17563.367|-17343.466|
|**LR (chi2)**<br>1996.67***|2349.993***|2361.97***|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

16
**Table 5**

**Regression Results of Low Congestion with sectoral restriction (country fe)**

|**PANDEMIC & LOC**|**(2.a)**<br>**KDOWN EVOLUTI**<br>0093***|**ON**<br>0080***|**(2.b)**<br>0064***|
|---|---|---|---|
|**Covid death (per Million)**|.<br>(0.002)|.<br>(0.003)|.<br>(0.003)|
|**Public transport closure**|0.595***<br>(0.013)|0.629***<br>(0.015)|0.598***<br>(0.015)|
|**Public transport facial covering**|0.110***<br>(0.005)|0.107***<br>(0.009)|0.105***<br>(0.005)|
|**CON**|**TROLS**|***|**|
|**Working days**||-1.670<br>(0.030)|-1.666<br>(0.030)|
|**Min temp**||-0.003<br>(0.002)|-0.002<br>(0.002)|
|**Avg temp**||-0.016***<br>(0.002)|-0.017***<br>(0.002)|
|**Precipitation**||-0.061***<br>(0.009)|-0.064***<br>(0.071)|
|**City density**|||-0.001*<br>00007|
|<br>**TRENDS IN M**<br>|**OTORIZATION**||(.)<br>-0.226***|
|**Car registration**|||(0.015)|
|**COUN**|**TRY FE**<br>|||
|**Country FE**|Yes|Yes|Yes|
|**Monthly dummy**|Yes|Yes|Yes|
|**Constant**|-1.377<br>(0.295)|-0.862**<br>(0.431)|-0.610<br>(0.431)|
|**N**|68076|68076|68076|
|**N cities**|186|186|186|
|**Wald χ2**|9068.76***|15739.15***|15825.39***|
|**Log likelihood**|-26248.126|-18961.454|-18839.815|
|**LR (chi2)**|1506.92***|2276.12***|2227.78***|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

17
To further test Hypothesis 3 we run separate regressions depending on the size of the cities. We classify mega-cities as those cities where population is above 8 million people, intermediate when it is between 800,000 and 8 million, and small when lower than 800,000 people. As we know, some organizational and management problems are common to most cities in the world, whatever their size. The most obvious similarity is that all are too dependent on road transport. The burden of rising levels of car ownership is growing throughout the countries. The commensurate increase in congestion on already crowded streets is threatening to incapacitate the bus systems that many middle- to low-income commuters still rely on.

Increase in motorization through new car registration has a differential impact depending on city size, as shown in Tables 6 and 7. The most significant effect of higher car registration level is displayed in mega-cities, who experience both an increase in public transit mobility and an increase in traffic congestion. The increase in traffic congestion in mega-cities not only aggravates commuters but also isolates them with time-consuming, unreliable, and expensive commutes.

Another interesting result concerns the differential impact of city density within different types of cities. The increase in city density is worsening the decline in public transport significantly only in mega-cities. The different coefficients across groups of cities based on density could capture behavioral aspects (e.g. fear to ride public transport may be greater due to an increased risk of contamination in denser urban contexts). Also, the likelihood of low congestion in mega-cities as well as in intermediate cities is lower as city density increases. Surprisingly, the evolution of the pandemic in terms of Covid deaths is showing a higher coefficient in smaller cities.

18
**Table 6**

**Regression Results of Public Transport Mobility depending on size of cities (country fe)**

|**(Megacities)**|**(Small cities)**|**(Intermediate)**|
|---|---|---|
|**PANDEMIC & LOCKDOWN EVOLU**<br>***|**TION**<br>***|***|
|**Covid death (per Million)**<br>-0.474<br>(0.079)|-1.241<br>(0.036)|-1.273<br>(0.044)|
|**Public transport closure**<br>-13.166***<br>(0.369)|-16.012***<br>(0.246)|-8.216***<br>(0.179)|
|**Public transport facial covering**<br>-0.498***<br>(0.209)|1.206***<br>(0.132)|1.501***<br>(0.104)|
|**CONTROLS**|||
|**Working days**<br>11.365***<br>(0.666)|12.932***<br>(0.527)|12.042***<br>(0.189)|
|**Min temperature**<br>-0.459***<br>(0.055)|0.361***<br>(0.031)|0.012<br>(0.026)|
|**Avg temperature**<br>0.488***<br>(0.056)|0.013<br>(0.029)|0.322***<br>(0.025)|
|**Precipitation**<br>-1.132***<br>(0.158)|-1.462***<br>(0.141)|-1.245***<br>(0.109)|
|**City density**<br>-0.008***<br>(0001)|-0.234<br>(0424)|-0.330<br>(0380)|
|.<br>**TRENDS IN MODAL COMPETITI**<br>***|.<br>**ON**<br>***|.<br>***|
|**Car registration**<br>1.521<br>(0.399)|0.720<br>(0.165)|0.410<br>(0.189)|
|**COUNTRY FE**<br>|||
|**Country FE**<br>Yes|Yes|Yes|
|**Monthly dummy**<br>Yes|Yes|Yes|
|**Constant**<br>-58.712***<br>(1.986)|-32.630**<br>(7.918)|-72.216***<br>(5.410)|
|**N**<br>6588|27,992|25,197|
|**N cities**<br>18|77|69|
|**Within R**<sup>**2**</sup><br>0.7495|0.6704|0.6828|
|**Between R**<sup>**2**</sup><br>0.9327|0.4666|0.8778|
|**Overall R**<sup>**2**</sup><br>0.7844|0.6434|0.7315|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent 19
**Table 7**

**Regression Results of Low Congestion depending on size of cities (country fe)**

|**(Megacities)**<br>**PANDEMIC & LOCKDOWN EVOLU**<br>***|**(Small cities)**<br>**TION**<br>***|**(Intermediate)**<br>***|
|---|---|---|
|**Covid death (per Million)**<br>0.060<br>(0.018)|0.150<br>(0.009)|0.072<br>(0.011)|
|**Public transport closure**<br>1.056***<br>(0.061)|1.041***<br>(0.056)|1.167***<br>(0.042)|
|**Public transport facial covering**<br>0.453***<br>(0.041)|-0.112***<br>(0.034)|0.205***<br>(0.025)|
|**CONTROLS**|||
|**Working days**<br>-2.671***<br>(0.123)|-3.683***<br>(0.111)|-2.983***<br>(0.082)|
|**Min temp**<br>0.019*<br>(0.012)|-0.016**<br>(0.007)|0.003<br>(0.007)|
|**Avg temp**<br>-0.051***<br>(0.011)|-0.010<br>(0.007)|-0.017***<br>(0.004)|
|**Precip**<br>-0.020<br>(0.031)|-0.224***<br>(0.039)|-0.041***<br>(0.006)|
|**City density**<br>-0.002**<br>(0001)|-0.010<br>(0023)|-0.050<br>(0055)|
|.<br>**TRENDS IN MOTORIZATION**<br>***|.<br> <br>***|.<br>***|
|**Car registration**<br>-1.155<br>(0.139)|-0.168<br>(0.037)|-0.518<br>(0.005)|
|**COUNTRY FE**<br>|||
|**Country FE**<br>Yes|Yes|Yes|
|**Time dummy**<br>Yes|Yes|Yes|
|**Constant**<br>2.557***<br>(0.731)|2.064***<br>(0.427)|1.327***<br>(0.491)|
|**N**<br>9882|28182|30012|
|**N cities**<br>27|77|82|
|**Wald χ2**<br>2022.59***|5586.96***|5158.68***|
|**Log likelihood**<br>-3046.4335|-7540.9512|-7626.5672|
|**LR (chi2)**<br>228.28***|861.46***|674.77***|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

20
To start to test hypothesis 4 and identify signs of structural break in the data before and after a threshold date, we first report below the values of monthly dummies in Table 8 for the regressions of Tables 3 and 5. As we can see, the sign associated with the dummy variable capturing the month of April is the highest in absolute terms and is negative (positive) and statistically significant at the 1 percent confidence level in the case of mobility (low congestion).

**Table 8 Values of monthly dummies**

|Feb-20|6.727***|0.151***|
|---|---|---|
|Mar-20|-2.272***|1.393***|
|Apr-20|-10.948***|1.74***|
|May-20|-5.896***|1.167***|
|Jun-20|-0.113***|0.448***|
|Jul-20|3.548|-0.005|
|Aug-20|6.390***|-0.221***|
|Sep-20|7.233***|-0.244***|
|Oct-20|10.453***|-0.327***|
|Nov-20|8.588***|-0.218***|
|Dec-20|11.762***|-0.712***|

To go further in identifying the structural break, Table 9 interacts all variables with a dummy for the break and interacts it with the right-hand side variables. The first and third columns report the coefficients associated to the right-hand side variables as in specification (2M) and (2C) respectively, whereas the second and fourth reports the coefficients associated to the interacted variable of the “break” dummy and the respective right-hand side variables.

As all variables and their respective interacted terms are significant and in almost all cases at the 1 percent confidence level, this means that the parameters differ significantly before and after the break, validating Hypothesis 2 and confirming the presence of a structural break. In terms of interpreting the sign of the interacted terms, the impact of the pandemic evolution is somewhat smoothened after the structural break. On the other hand, the increase in the stringency of the lockdown measure is instead strengthened, confirming that mobility is significantly affected by restrictions. The fact that the stringency of restrictions had greater impacts after the peak of the pandemic may be due to an increased compliance due to peer effects or learning about the seriousness of the disease. New car registration after the structural break is also reducing the days with low congestion after the peak of the pandemic.

Whereas this result provides strong evidence of the presence of a structural break in the short term, we are not yet in a position to answer whether this structural break will persist in the long-run.

21
**Table 9**

**Regression Results of structural break in  public transport and congestion**

|**(Transit)var**<br>**(Transit)int**|**(Lowcong)var**<br>**(Lowcong)int**|
|---|---|
|**PANDEMIC & LOCKDOWN EVOLUTION**<br> <br>|<br>|
|**Covid death (per Million)**<br>-2.289***<br>(0.066)<br>2.148***<br>(0.069)|0.530***<br>(0.048)<br>-0.549***<br>(0.048)|
|**Stringency Index**<br>-0.711***<br><br>-0.047***<br>|0.039***<br><br>0.006***<br>|
|(0.005)<br>(0.007)|(0.001)<br>(0.001)|
|**COUNTRY LEVEL CONTROLS**<br> <br>|<br>|
|**Working days**<br>10.848***<br>(0.604)<br>2.880***<br>(0.675)|2.404***<br>(0.059)<br>0.984***<br>(0.066)|
|**Min temperature**<br>0.429***<br>(0.034)<br>-0.290***<br>(0.036)|-0.022***<br>(0.006)<br>0.014**<br> (0.006)|
|**Average temperature**<br>-0.225***<br>(0.033)<br>0.352***<br>(0.034)|0.021***<br>(0.005)<br> -0.025**<br>(0.005)|
|**Precipitation**<br>-0.546***<br>(0.156)<br>-1.064***<br>(0.177)|-0.099***<br>(0.029)<br>0.047<br>(0.003)|
|**Cit densit**<br>0.0003<br>-0.021***|-0.001<br>-0.0002|
|**y y**<br>(0.012)<br>(0.002)|(0.001)<br>(0.0004)|
|**TRENDS IN MOTORIZATION**<br>***<br>|***<br>|
|<br>-5.412<br>-0.014|-0.381<br> -0.375***|
|**New car registration**<br><br>||
|<br>(0.121)<br>(0.104)|(0.021)<br> (0.020)|
|**COUNTRY FE**<br> <br>|<br>|
|**Ct d thl FE**<br>Yes<br>Yes|Yes<br>Yes|
|**ounry an mony**||
|**Constant**<br>-8.984<br>(7.069)|0.0176<br>(0.0178)|
|**N**<br>59777|68076|
|**N cities**<br>164|186|
|**Wald χ2**<br>135765.34***|14048.20***|
|**Within R**<sup>**2**</sup><br>0.6946|na|
|**Between R**<sup>**2**</sup><br>0.6669|na|
|**Overall R**<sup>**2**</sup><br>0.6895|na|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

22
# **_4. Conclusions_**

Empirical estimates of the size of the economic shocks triggered by the pandemic and lockdown evolution have been scarce since official economic indicators are only made available with a significant lag. In this paper, we provide an illustration by tracking the evolution of high-frequency variables, related to the trends in public transit data as well as traffic congestion.

The availability of such high-frequency variables allows us to investigate the economic impact of lockdowns, which have been the main public health policy implemented by governments around the world to contain the pandemic. It has been argued that these measures, which are useful in “flattening the curve” of health costs, may come at high economic costs, but also that a silver lining of reduction in congestion is only a temporary result, reverted by the increase in motorization, The analysis in this paper also confirms the significant cost of such measures in terms of reduction in urban mobility in terms of public transport and traffic congestion. Interestingly, it shows that beyond the impact of the lockdowns, also the spread of the disease itself has an economic impact distinct from that of lockdown measures, as the incidence of death per population increases the drop in activity associated with the spread of the disease, and is statistically highly significant, both in terms of reduction in urban mobility by public transport and a reduction in traffic congestion.

We also find different results on the magnitude of impact of cross sectoral vis-à-vis sectoral restrictions on urban mobility and congestion. Whereas the magnitude of the spread of the disease is higher than the overall stringency of the lockdown, we turn to introduce sector specific restrictions and, as expected, results are significantly different. The impact of restrictions on public transit is much higher than the spread of the disease and acts indirectly as a disincentive also to move on the road. More effective safety measures, such as those related to the use of facial covering are associated with higher use of public transport and an increase in the likelihood of low congestion.

We find no evidence of intermodal competition between public transportation and road transport. In particular, the expansion of car registration has not led to a decrease in public transport mobility. Increasing car registration has been, however, significantly associated with an increase in traffic congestion, particularly in mega-cities.

Since the beginning of the pandemic, most public transport operators have quickly stepped up and taken concrete action to make transit systems COVID-safe for staff and passengers. But despite the best efforts of professionals across the sector, as testified by the positive impact of enhanced safety measures, the COVID-19 crisis has dealt a massive blow to public transport, which has not recovered to the pre-Covid levels. On the other hand, congestion has been ramping up, also fueled by the expansion in new car registration.

23