Decline in Mobility: Public Transport in Poland in the Time of the COVID-19 Pandemic

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Article Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic

Michał Wielechowski 1 , Katarzyna Czech 2,* and Łukasz Grz˛eda 1

1 Department of Economics and Economic Policy, Institute of Economics and Finance, Warsaw University of Life Sciences-SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland; [email protected] (M.W.); [email protected] (Ł.G.) 2 Department of Econometrics and Statistics, Institute of Economics and Finance, Warsaw University of Life Sciences-SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland * Correspondence: [email protected]

Received: 13 August 2020; Accepted: 25 September 2020; Published: 29 September 2020

Abstract: The aim of the paper is to assess changes in mobility in public transport in Poland, as a consequence of the development of the COVID-19 pandemic. We analyse the problem from the country and regional (voivodeships) perspective. The data come from Google COVID19 Community Mobility Reports, the Ministry of Health of Poland, and the Oxford COVID-19 Government Response Tracker. The research covers the period between 2 March and 19 July 2020. The obtained results show that there is negative but insignificant relationship between human mobility changes in public transport and the number of new confirmed COVID-19 cases in Poland. The strength and statistical significance of the correlation varies substantially across voivodeships. As far as the relationship between changes in mobility in public transport and the stringency of Polish government’s anti-COVID-19 policy is concerned, the results confirm a strong, negative and significant correlation between analysed variables at the national and regional level. Moreover, based on one factor variance analysis (ANOVA) and the Tukey’s honest significance test (Tukey’s HSD test) we indicate that there are significant differences observed regarding the changes in mobility in public transport depending on the level of stringency of anti-COVID-19 regulation policy both in Poland and all voivodeships. The results might indicate that the forced lockdown to contain the development of the COVID-19 pandemic has effectively contributed to social distancing in public transport in Poland and that government restrictions, rather than a local epidemic status, induce a greater decrease in mobility.

Keywords: COVID-19; coronavirus; pandemic; human mobility; public transport; government policy

JEL Classiﬁcation: I18; O18; R40; L90

1. Introduction COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a seventh coronavirus that can spread between humans (Andersen et al. 2020). The World Health Organization (WHO) classified the COVID-19 epidemic as a global pandemic on 11 March 2020 (Maier and Brockmann 2020). The novel coronavirus pandemic has hit the global economy and societies on an unprecedented scale since the Great Depression (1929–1933), and is epidemically compared to the Spanish flu of the 1918 (Barro et al. 2020; Laing 2020). The scientists refer to the COVID-19 pandemic as the giant black swan phenomenon (Goodell 2020; Mazzoleni et al. 2020; Wind et al. 2020). The spread of COVID-19 has resulted in a reduction in economic activity and led to significant threats to the financial stability of many countries in the world (Boot et al. 2020). The COVID-19

Economies 2020, 8, 78; doi:10.3390/economies8040078 www.mdpi.com/journal/economies Economies 2020, 8, 78 2 of 24 crisis has shown both policymakers and the general public that natural disasters can create huge, global, direct, and adverse economic impacts (Goodell 2020). Nicola et al.(2020) show the strong, negative consequences of the pandemic in all sectors of the economy including primary, secondary and service sectors. The coronavirus pandemic has proven to be a formidable challenge for many European Union countries, including Poland (Czech et al. 2020; Goniewicz et al. 2020). According to Cochrane(2020), there is a strong need to create and implement a retail economy rescue plan that would prevent bankruptcies and insolvencies where possible. The challenge facing the government is to choose an effective economic policy that meets the condition of social sensitivity. Jones et al.(2020) emphasize that governments’ responses to the COVID-19 pandemic show a search for a balance between health and economic goals. According to Eichenbaum et al.(2020) the implementation of a restrictive policy preventing the spread of the coronavirus pandemic increases the depth of the recession and fiscal instability, but enables the achievement of an extremely important and fundamental goal, i.e., saving human lives. On the other hand, some researchers do not agree with the opinion that such strict restrictions and limitations should be introduced (Jelnov 2020). The spread of the COVID-19 pandemic has caused the implementation of unprecedented measures restricting travel, movement and activity participation in many countries across the world (De Vos 2020). A variety of stringency policies, including stay-at-home policy, school, public institutions and workplace closures, cancellation of mass events and public gatherings, and restrictions on public transport have affected approximately 90% of the world’s population, contributing to a general reduction in mobility on an unprecedented scale (Gössling et al. 2020). Tian et al.(2020) indicate that suspending public transport, closing entertainment venues, and ban on public gatherings are associated with reductions in case incidence. Jinjarak et al.(2020) show that countries with more stringent government policies have reported lower COVID-19 mortality growth rates. In their view, the relationship between the stringency of government policy and the mortality growth is negative and more pronounced in countries with older population, higher rates of urbanization, greater democratic freedom, and larger international travel flows. Yuksel et al.(2020) show that anti-COVID-19 policy adherence is not stable and fluctuates depending on the amount of information on COVID-19 that is available in a particular region and the level of risk aversion. In their opinion, the degree of social distancing depends on the government policy, particularly the social campaign affecting perception of the coronavirus pandemic by people in different regions. Bonaccorsi et al.(2020), Pepe et al.(2020), Pullano et al.(2020) and Queiroz et al. (2020) indicate significant relationship between human mobility and government restriction tightening to tackle the COVID-19 pandemic. Warren and Skillman(2020) find that the COVID-19 pandemic has resulted in large reduction in mobility both in the US and globally. They indicate that half of the individuals in the entire state of New York spent most of their day within less than 100 feet from their initial position. Glaeser et al.(2020) show a visible decrease in urban mobility in USA accompanied by substantial heterogeneity both across space and time. Badr et al.(2020) reveal the mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA. Coven and Gupta(2020) and Almagro and Orane-Hutchinson(2020) find disparities in mobility responses to the COVID-19 pandemic rooted in income. Oztig and Askin(2020) show that countries with higher population density are found to be more likely to have more COVID-19 cases than other countries. Moreover, it should be emphasized that the strength of analysed relations also depends on the mobility reduction, which differs across nations and regions due to their initial mobility structure (Galeazzi et al. 2020). Schlosser et al.(2020) find that in the aftermath of COVID-19 pandemic long-distance travel has been strongly reduced, having an effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions. Cartenì et al.(2020) show that mobility habits are correlated with the number of COVID-19 infections. Similarly, Yilmazkuday(2020), based on daily Google mobility data covering 130 countries, claims that government stay-at-home policy is effective showing that lower mobility is associated with lower COVID-19 cases and deaths. Economies 2020, 8, 78 3 of 24

However, Askitas et al.(2020), investigating mobility pattern across 135 countries, find that restrictions on internal movement, public transport closures and international travel controls do not lead to a significant decrease in new COVID-19 infections. The article addresses the impact of the COVID-19 pandemic on the population-related mobility in public transport. The number and nature of interpersonal contacts between infected and healthy individuals affect the transmission of infectious diseases. Confined and crowded environments, including transport hubs, that are visited by people daily can become hot-spots for spreading infectious diseases (Goscé and Johansson 2018; Browne et al. 2016; Morawska et al. 2020). Public transportation vehicles, particularly buses, trams, trains and metros, which are used daily by millions of people, might contribute to the spread of infectious diseases among their users (Musselwhite et al. 2020; Troko et al. 2011). However, only a paucity of evidence describes SARS and MERS coronaviruses transmission events linked to transportation systems or hubs (Browne et al. 2016). Nevertheless, it is proved that social distancing measures are important in order to contain an emerging disease (Eubank et al. 2004). Wang(2014) based on daily data from Taipei City in Taiwan found a 50% decrease in underground ridership during the peak of the 2003 SARS pandemic. Lau(2003), based on Hong Kong, showed that the proportion of people avoiding congested places, including public transport hubs, s increased significantly only in the initial phase of the SARS epidemic. The control measures based on limiting or constraining human mobility are being considered as important in containing the spread of infectious diseases (Bajardi et al. 2011). The impact of travel restrictions due to an epidemic remained difficult to measure in the past due to mobile phone data limitations. Peak et al.(2018), based on routinely collected mobile phone data during the Ebola epidemic in Sierra Leone revealed a dramatic reduction in human mobility only during a 3-day lockdown, while mobility quickly returned to normal patterns after the restrictions were lifted. There are two main approaches to studying the issue of public transport from the perspective of the COVID-19 pandemic. On the one hand, scientists investigate the impact of the transport sector on the spread of COVID-19. Jinjarak et al.(2020) find that higher initial mobility negatively impacts the epidemic status, particularly the mortality rate. Majority of studies investigate how the transport sector and the changes in transport mobility affect the COVID-19 pandemic spread in China, the country were the novel coronavirus appeared (Chinazzi et al. 2020; Kraemer et al. 2020). Zheng et al.(2020) indicate that COVID-19 cases imported via transportation are a key factor in the pandemic spread. Linka et al.(2020) suggest that an unconstrained mobility would have significantly accelerated the spread of the COVID-19 pandemic, particularly in Central Europe, Spain and France. According to Muller et al.(2020) and Musselwhite et al.(2020) public transport plays a significant role in the spread of infectious diseases, thus attempts to control the spread of infections via public transportation reduces the infection speed. On the other hand, researchers examine how the COVID-19 pandemic, the accompanying combination of government restrictions and social fears of contracting and spreading COVID-19 when using mass transport modes reduce passenger transport demand and worsen the overall situation in transport. Aloi et al.(2020) investigate the e ffects of the pandemic lockdown on urban mobility. They show that an overall mobility in the northern city of Santander, Spain have fallen by 76% and the number of public transport users have dropped by up to 93%. The medical researchers forecast that subsequent waves of the pandemic may result in new waves of social distancing in the near future (Wu et al. 2020). De Vos(2020) claims that social distancing which reduces direct physical interactions between individuals in order to stop the spread of the virus, has become the new norm in the society. The economic and social effects of the COVID-19 pandemic on public transport does not concern only service performance, financial viability, health risks, social equity, and sustainable mobility but goes much further (Tirachini and Cats 2020). Our contribution is that by applying Pearson’s and Spearman’s rank correlation coefficients, factor variance analysis (ANOVA) and the Tukey’s honest significance test (Tukey’s HSD test) we can investigate the COVID-19 pandemic-driven changes in mobility in public transport in Poland from Economies 2020, 8, 78 4 of 24

Economies 2019, 7, x FOR PEER REVIEW 4 of 24 the country and regional (voivodeships) perspective. To our knowledge, there have been no similar from the country and regional (voivodeships) perspective. To our knowledge, there have been no studies conducted yet. similar studies conducted yet. The outline of the paper is as follows: the next section presents the aim of the study, The outline of the paper is as follows: the next section presents the aim of the study, research research hypotheses and a description of the material and research methods used. The subsequent hypotheses and a description of the material and research methods used. The subsequent section sets section sets out the empirical findings and discussion, with the final section offering our conclusions. out the empirical findings and discussion, with the final section offering our conclusions. 2. Methodology 2. Methodology The aim of the paper is to assess the changes in mobility in public transport in Poland as a consequenceThe aim ofof thethe developmentpaper is to assess of the the COVID-19 changes in pandemic. mobility Thein public expected transport key factors in Poland affecting as a consequencechanges in mobility of the indevelopment public transport of the during COVID-19 the COVID-19 pandemic. pandemic The expected are the epidemickey factors status affecting in the changescountry andin mobility the stringency in public of governmenttransport during restriction the COVID-19 policy to tackle pandemic the coronavirus. are the epidemic It is in linestatus with in Warrenthe country and and Skillman the stringency(2020) who of indicate government that arestrictio large decreasen policy in to mobility tackle the on coronavirus. a global scale It has is in been line withassociated Warren with and the Skillman outbreak (2020) of the who COVID-19 indicate threatthat a andlarge specific decrease government in mobility directives. on a global In scale order has to beenachieve associated the main with aim ofthe the outbreak study, we of buildthe COVID-19 three research threat hypotheses. and specific government directives. In order to achieve the main aim of the study, we build three research hypotheses. Hypothesis 1. The relationship between the changes in mobility in public transport and new laboratory-confirmed Hypothesiscases of COVID-19 1. The in relationship Poland is negative between and the significant. changes in mobility in public transport and new laboratory- confirmed cases of COVID-19 in Poland is negative and significant. Hypothesis 2. The relationship between the changes in mobility in public transport and the stringency of Polish Hypothesisgovernment’s 2. anti-COVID-19 The relationship policy between is negative the chan andges significant. in mobility in public transport and the stringency of Polish government’s anti-COVID-19 policy is negative and significant. Hypothesis 3. There is significant difference in changes in mobility in public transport regarding “low”, “medium”Hypothesis and 3. “high” There stringencyis significant of Polish difference government’s in changes anti-COVID-19 in mobility in policy.public transport regarding “low”, “medium” and “high” stringency of Polish government’s anti-COVID-19 policy. Due to the diverse epidemic status and expected different mobility behaviour in particular provincesDue to of the Poland, diverse we epidemic verify the status hypotheses and expected from the different country mobility as well behaviour as all 16 voivodeshipsin particular (provinces)provinces of perspective, Poland, we i.e.,verify Greater the hypotheses Poland, Holy from Cross, the country Kuyavian-Pomeranian, as well as all 16 Lesser voivodeships Poland, (provinces)Lower Silesian, perspective, Lublin, i.e., Lubusz, Greater Ł óPoland,d´z, Masovian, Holy Cross, Opole, Kuyavian-Pomeranian, Podlaskie, Pomeranian, Lesser Silesian,Poland, LowerSubcarpathian, Silesian, Warmian-Masurian, Lublin, Lubusz, Ł andódź, West Masovian, Pomeranian. Opole, Figure Podlaskie,1 presents Pomeranian, the administrative Silesian, Subcarpathian,division of Poland Warmian-Masurian, into voivodeships. and West Pomeranian. Figure 1 presents the administrative division of Poland into voivodeships.

Figure 1. The administrative division of Poland into voivodeships. DS—Lower Silesian; KP— Figure 1. The administrative division of Poland into voivodeships. DS—Lower Silesian; KP— Kuyavian-Pomeranian; LU—Lublin; LB—Lubusz; LD—Łód´z;MA—Lesser Poland; MZ—Masovian; Kuyavian-Pomeranian; LU—Lublin; LB—Lubusz; LD—Łódź; MA—Lesser Poland; MZ—Masovian; OP—Opole; PK—Subcarpathian; PD—Podlaskie; PM—Pomeranian; SL—Silesian; SK—Holy Cross; OP—Opole; PK—Subcarpathian; PD—Podlaskie; PM—Pomeranian; SL—Silesian; SK—Holy Cross; WN—Warmian-Masurian; WP—Greater Poland; ZP—West Pomeranian. Source: own elaboration. WN—Warmian-Masurian; WP—Greater Poland; ZP—West Pomeranian. Source: own elaboration.

To determine the changes in mobility in public transport we use Google data from COVID-19 Community Mobility Reports on transit stations category developed by Google. Google has developed the reports in order to assess how peoples’ movements have changed throughout the pandemic. The reports visualize changes in the number of visits and time spent in certain place categories, such as such retail and recreation, supermarkets and pharmacies, parks, public transport, workplaces and residential, in relation to the baseline period prior to the outbreak of the pandemic. Due to different routines on weekdays and weekends, baseline periods represent a normal value for each day of the week, given as median value over the five-week period from 3rd January to 6th February 2020 (Google 2020; Ritchie 2020). Google highlights the usage of anonymized data provided by applications such as Google Maps. COVID-19 Community Mobility Reports identify six categories of public mobility places (Google 2020): retail and recreation (restaurants, cafes, shopping centres, theme parks, museums, libraries, and movie theatres), grocery and pharmacy (grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies), parks (national parks, public beaches, marinas, dog parks, plazas, and public gardens), transit stations (public transport hubs, including subway, bus and train stations), workplaces, and residential (places of residence). The data on daily new laboratory-confirmed cases of COVID-19 in Poland and 16 voivodeships was collected from the official Twitter account of the Ministry of Health of the Republic of Poland. To measure the stringency of the government response to the outbreak of the COVID-19 pandemic we use data on the Stringency index from the Oxford COVID-19 Government Response Tracker developed by the Blavatnik School of Government (Hale et al. 2020). The index provides a systematic measure of tracking the severity level of government responses to the COVID-19 pandemic in time across more than 150 countries. The Stringency index consists of eight individual government response measures, including: school closing, workplace closing, public events cancellations, restrictions on gathering size, public transport closing, stay-at-home requirements, restrictions on internal movements, restrictions on international travel and public information campaign. The index ranges from 0 to 100 (Hale et al. 2020). The research covers the period between 2 March and 19 July 2020, i.e., the time since the first laboratory-confirmed COVID-19 case in Poland up to the latest available data (as of 24 July 2020). In order to avoid mismatching different people’s mobility routines on weekdays and weekends we convert daily data into weekly averages. In we apply Pearson’s and Spearman’s rank correlation coefficients and corresponding significance tests to measure the relationship between: changes in mobility in public transport and new laboratory-confirmed cases of COVID-19, and mobility changes in public transport and the Stringency index level changes. Measuring correlation between two or more variables requires knowledge of their joint distribution. As the joint distribution is normal, the Pearson’s correlation coefficient is applied. Otherwise, the relationship between variables is calculated based on Spearman’s rank correlation coefficient. The joint distribution of analysed variables’ normality is determined with Shapiro Wilk test. The Shapiro-Wilk test is the most powerful normality test that can be also applied for small sample size and for all levels of skewness and kurtosis of distributions (Yap and Sim 2011). Additionally, we supply our study with one factor variance analysis (ANOVA) and the Tukey’s honest significance test (Tukey’s HSD test) to check whether the stringency of Polish government’s anti-COVID-19 policy has significant impact on changes in mobility in public transport both from country and regional perspective. The Stringency index is assumed to be the factor. Based on the stringency index daily data we build qualitative variable which has three levels. The first level-“low”, represents low level of Polish government stringency policy, i.e., the stringency index lower than 40. The “medium” level is assigned to all days when the stringency index is in the range between 41 and 70. The “high” level is attributed to all daily periods with severe anti-COVID regulations, i.e., the stringency index higher than 71. Economies 2020, 8, 78 6 of 24

3. Research Results and Discussion The rapid increase in laboratory-conﬁrmed COVID-19 cases across the world result in growing international concerns. In line with WHO recommendations, the Polish government began implementing restrictions and limitations aimed at limiting the spread of COVID-19. Table1 presents an order of events related to the spread of the COVID-19 epidemic in the world and in Poland, as well as Polish government anti-COVID-19 restrictions related directly or indirectly to transportation.

Table 1. The coronavirus pandemic development and the Polish government anti-COVID-19 policy regulations related to transportation—timeline.

Description Date Global perspective Unpublished Chinese government report on first cases of a novel coronavirus infections. 17 November 2019 The first confirmed case of a novel coronavirus infection in Wuhan, China. 8 December 2019 The first confirmed cases of a novel coronavirus infection in Europe, Italy. 23 January 2020 WHO declares the novel coronavirus outbreak a Public Health Emergency of International Concern. 30 January 2020 The International Committee on Taxonomy of Viruses (ICTV) names a novel coronavirus: SARS-CoV-2. 11 February 2020 WHO announces an official name for the disease caused by the coronavirus: COVID-19. WHO announces that COVID-19 can be officially described as a pandemic. 11 March 2020 Poland The first laboratory-confirmed case of COVID-19, Lubusz voivodeship. 4 March 2020 Border controls and ban on mass events. 10 March 2020 The first reported COVID-19 death, Greater Poland. 12 March 2020 school closing, restrictions on internal movement 12 March 2020 The government announces an epidemic threat on the territory of Poland 14 March 2020 Restriction on functioning of selected institutions and workplaces, remote working obligation 14 March 2020 Suspension of international passenger air and rail connections, restrictions on cross-border movements 15 March 2020 The government announces epidemic in Poland. 20 March 2020 Restrictions on movement except commuting to work, essential matters of everyday life and 24 March 2020 volunteering in the fight against COVID-19. Maximal number of passengers on public transport (buses, trams, metro) equal half the number of seats. 24 March 2020 Restrictions on gathering, stay-at-home policy implementation 31 March 2020 Mandatory nose and mouth covering in public places 16 April 2020 The first stage of lifting restrictions related to COVID-19 (possibility of movement for recreational 20 April 2020 purposes, easing restrictions in trade and services). The second stage of lifting restrictions related to COVID-19 (opening of shopping centres, hypermarkets 4 May 2020 and hotels). The third stage of lifting restrictions related to COVID-19 (partial abolition of public transport 18 May 2020 restrictions to increase the number of passengers to 30% of all seats and standing places). The fourth stage (first part) of lifting restrictions related to COVID-19 (lifting the limits of people in 30 May 2020 shops, post offices, restaurants, and places of religious worship). Partial abolition of public transport restrictions to increase the number of passengers to 100% of seats or 1 June 2020 50% of all seats and standing places, resumption of internal passenger flights. The fourth stage (second part) of lifting restrictions related to COVID-19 (full opening of hotels, 6 June 2020 swimming pools, fitness clubs, and places of culture, enabling gatherings up to 150 persons). Lifting restrictions on traffic at borders 13 June 2020 Resumption of international passenger flights. 16 June 2020 Source: own elaboration based on data of the Chancellery of the Prime Minister of Poland(2020) and the Service of the Republic of Poland(2020).

Compared to other European Union (EU) Member States, Poland is one of the last countries to experience the outbreak of the COVID-19. By 19 July 2020 more than one per mile of Poles have experienced the coronavirus. At the same time the number of deaths related to COVID-19 has EconomiesEconomies 20192020,, 78,, x 78 FOR PEER REVIEW 77 of of 24 24 Economies 2019, 7, x FOR PEER REVIEW 7 of 24 experienced the coronavirus. At the same time the number of deaths related to COVID-19 has reached experienced the coronavirus. At the same time the number of deaths related to COVID-19 has reached 1623,reached resulting 1623, resulting in a 4% incoronavirus a 4% coronavirus mortality mortality rate. Epidemic rate. Epidemic status status in Poland in Poland is diverse is diverse across across 16 1623, resulting in a 4% coronavirus mortality rate. Epidemic status in Poland is diverse across 16 voivodeships,16 voivodeships, as Figure as Figure 2 depicts.2 depicts. voivodeships, as Figure 2 depicts.

FigureFigure 2. 2. Laboratory-confirmedLaboratory-conﬁrmed COVID-19 COVID-19 cases cases in in Poland Poland by by voivodeship, voivodeship, by by 19 19 July July 2020. 2020. Source: Source: Figure 2. Laboratory-confirmed COVID-19 cases in Poland by voivodeship, by 19 July 2020. Source: ownown elaboration elaboration based based on on Ministry Ministry of ofHealth Health of ofthe the Republic Ministry of Poland of Health data of (2020). the Republic of Poland own elaboration based on Ministry of Health of the Republic of Poland data (2020). data (2020). The COVID-19 crisis has hit all forms of transport, including both public and private sectors, The COVID-19 crisis has hit all forms of transport, including both public and private sectors, nationallyThe COVID-19 and internationally. crisis has Global hit all formsroad transp of transport,ort, by the including end of March both public 2020, has and decreased private sectors, more nationally and internationally. Global road transport, by the end of March 2020, has decreased more thannationally 50% compared and internationally. to the same Global period road in 2019. transport, The flight by the activity end of Marchwas almost 2020, 75% has decreasedlower by mid- more than 50% compared to the same period in 2019. The flight activity was almost 75% lower by mid- Aprilthan 50%2020, compared compared to theto the same same period period in 2019. in 2019 The ﬂight(IEA activity2020). A was combination almost 75% of lower the bygovernment mid-April April 2020, compared to the same period in 2019 (IEA 2020). A combination of the government restrictions2020, compared and tosocial the samefears periodof contracting in 2019 (IEAand 2020spreading). A combination COVID-19 ofwhen the government using mass restrictions transport restrictions and social fears of contracting and spreading COVID-19 when using mass transport modesand social has fearscaused of contractingthe reduction and in spreading passenger COVID-19 transport when demand. using massFigure transport 3 presents modes the has monthly caused modes has caused the reduction in passenger transport demand. Figure 3 presents the monthly numbersthe reduction of passengers in passenger carried transport by public demand. transport Figure in Poland3 presents from the January monthly to numbersJune 2020. of We passengers observe numbers of passengers carried by public transport in Poland from January to June 2020. We observe acarried substantial by public decrease transport in passenger in Poland transport from January in Po toland June since 2020. the We COVID-19 observe a pandemic substantial outbreak decrease in in a substantial decrease in passenger transport in Poland since the COVID-19 pandemic outbreak in Marchpassenger 2020. transport By the end in Poland of April since the thetotal COVID-19 number of pandemic passenger outbreak in public in transport March 2020. had Bydropped the end by of March 2020. By the end of April the total number of passenger in public transport had dropped by 77%April in the comparison total number to ofJanuary passenger 2020. in Due public to transport the government had dropped restrictions by 77% implementation, in comparison to Januarythe air 77% in comparison to January 2020. Due to the government restrictions implementation, the air passenger2020. Due transport to the government had become restrictions least used. implementation, As a consequence the airof passengersubsequent transport stages of had restriction become passenger transport had become least used. As a consequence of subsequent stages of restriction easingleast used. in June As a2020, consequence nearly 21 of million subsequent passengers stages ofin restrictionPoland were easing carried in June by 2020,public nearly transport, 21 million 38.6 easing in June 2020, nearly 21 million passengers in Poland were carried by public transport, 38.6 percentpassengers more in than Poland in May were (Figure carried 3). by public transport, 38.6 percent more than in May (Figure3). percent more than in May (Figure 3).

50000 50000 40000 40000 30000 30000 20000 20000 10000 10000 Passengers in Passengers thousands 0 Passengers in Passengers thousands 0 Total Railway Road Air Total Railway Road Air January February March April May June January February March April May June Figure 3. Passenger transport in Poland from January to June 2020 by means of transport, in 1000 persons. FigureSource: 3. own Passenger elaboration transport based in on Poland Statista from(2020 January). to June 2020 by means of transport, in 1000 Figure 3. Passenger transport in Poland from January to June 2020 by means of transport, in 1000 persons. Source: own elaboration based on Statista (2020). persons. Source: own elaboration based on Statista (2020). Economies 2020, 8, 78 8 of 24 Economies 2019, 7, x FOR PEER REVIEW 8 of 24

TheThe mobility mobility is is likely likely to to be be affected aﬀected by the epid epidemicemic status status in in the country and and the the stringency stringency of of governmentgovernment restriction policypolicy toto tackletackle thethe coronavirus. coronavirus. Figure Figure4 depicts4 depicts the the average average weekly weekly changes changes in inmobility mobility in publicin public transport transport and theand number the number of average of average weekly COVID-19weekly COVID-19 new cases. new Google cases. COVID-19 Google COVID-19Community Community Mobility data Mobility shows thatdata peoples’ shows that movements peoples’ have movements changed have dramatically changed throughout dramatically the throughoutpandemic. Accordingthe pandemic. to Troko According et al.(2011 to )Troko every et means al. (2011) of transport every means can be of treated transport as a placecan be where treated it ashas a place become where diﬃ cultit has to become avoid interpersonal difficult to avoid contact. interpersonal Therefore, contact. people shouldTherefore, avoid people public should transport avoid in publicorder nottransport to be infected. in order not to be infected.

20 500 10 0 400 -10 300 -20 -30 200 -40 -50 100 percentage points

Mobility changes in Mobility -60 New COVID-19 cases New COVID-19 -70 0 2/03 16/03 30/03 13/04 27/04 11/05 25/05 8/06 22/06 6/07 20/07 Mobility changes in public transport (LHS) COVID-19 confirmed cases (RHS) Figure 4. Mobility changes in public transport and new laboratory-conﬁrmed cases of COVID-19 in Figure 4. Mobility changes in public transport and new laboratory-confirmed cases of COVID-19 in Poland from 2 March to 19 July 2020. Source: own elaboration based on data from Google COVID-19 Poland from 2 March to 19 July 2020. Source: own elaboration based on data from Google COVID-19 Community Mobility Reports (Google 2020) and Ministry of Health of the Ministry of Health of the Community Mobility Reports (Google 2020) and Ministry of Health of the Republic of Poland (2020). Republic of Poland(2020).

FigureFigure 44 indicatesindicates that that the the increase increase in in the the number number of of new new COVID-19 COVID-19 cases cases was was associated associated with with the thesharp sharp decline decline in the in mobility the mobility of people of people in public in transport,public transport, particularly particularly till mid-April till mid-April 2020. However, 2020. However,since the beginning since the beginning of May, a largeof May, increase a large in increase human mobilityin human was mobility observed, was despiteobserved, the despite constantly the constantlyhigh number high of number new laboratory-confirmed of new laboratory-confirm cases ofed COVID-19. cases of COVID-19. It may suggest It may that suggest the strength that the of strengththe relationship of the betweenrelationship mobility between in public mobility transport in public and the transport epidemic and status the has epidemic decreased status with has the decreaseddevelopment with of the the development COVID-19 pandemic of the COVID-19 in Poland. pandemic Table2 shows in Poland. estimated Table correlation 2 shows coe estimatedfficients correlation coefficients between changes in mobility in public transport and the number of new between changes in mobility in public transport and the number of new confirmed cases across the confirmed cases across the country and in each of the 16 voivodeships. The normality of joint country and in each of the 16 voivodeships. The normality of joint distribution of analysed variables is distribution of analysed variables is tested with the Shapiro Wilk test. When estimated p-values are tested with the Shapiro Wilk test. When estimated p-values are lower than 0.05 level of significance, lower than 0.05 level of significance, the null hypothesis of normality is rejected. When the joint the null hypothesis of normality is rejected. When the joint distribution is normal, the Pearson’s distribution is normal, the Pearson’s correlation coefficient is applied. Otherwise, we use the correlation coefficient is applied. Otherwise, we use the Spearman’s rank correlation coefficient. Spearman’s rank correlation coefficient. The results presented in Table2 indicate the existence of negative relationship between mobility The results presented in Table 2 indicate the existence of negative relationship between mobility changes in public transport and new laboratory-confirmed cases of COVID-19, both in Poland changes in public transport and new laboratory-confirmed cases of COVID-19, both in Poland and in and in the vast majority of voivodeships. However, the results are significant, at 0.05 significance the vast majority of voivodeships. However, the results are significant, at 0.05 significance level, only level, only for Kuyavian-Pomeranian, Lower Silesian, Masovian, Pomeranian and West Pomeranian for Kuyavian-Pomeranian, Lower Silesian, Masovian, Pomeranian and West Pomeranian voivodeships, for which the research results are consistent with the first hypothesis presented in the voivodeships, for which the research results are consistent with the first hypothesis presented in the paper. The estimated Spearman’s rank correlation coefficient for Poland equals 0.29, while at the paper. The estimated Spearman’s rank correlation coefficient for Poland equals −0.29, while at the regional level the correlation coefficients varies substantially from 0.10 to 0.69. The strongest negative regional level the correlation coefficients varies substantially from 0.10− to −0.69. The strongest relationship is observed for Lower Silesian, Masovian and West Pomeranian. The obtained results negative relationship is observed for Lower Silesian, Masovian and West Pomeranian. The obtained indicate regional differences in the strength and significance of the relationship between changes in results indicate regional differences in the strength and significance of the relationship between mobility in public transport and new confirmed cases. Voivodeships that have a significant, strong and changes in mobility in public transport and new confirmed cases. Voivodeships that have a negative correlation are those in which increasing number of new COVID-19 cases coincide with significant, strong and negative correlation are those in which increasing number of new COVID-19 stricter government restrictions, when human mobility in Poland decreased the most. What may cases coincide with stricter government restrictions, when human mobility in Poland decreased the be surprising the relationship is insignificant but positive for Silesian, the province most affected by most. What may be surprising the relationship is insignificant but positive for Silesian, the province the coronavirus pandemic. It may be due to the fact that in this voivodeship a large number of new most affected by the coronavirus pandemic. It may be due to the fact that in this voivodeship a large COVID-19 cases occurred at the time of easing the restrictions. number of new COVID-19 cases occurred at the time of easing the restrictions. Economies 2020, 8, 78 9 of 24

Table 2. The relationship between mobility changes in public transport and new laboratory-conﬁrmed cases of COVID-19 in Poland.

Variable Shapiro-Wilk Test Statistic p-Value Correlation Coefficient p-Value POLAND 0.87 0.01 0.29 S 0.21 − Greater Poland 0.97 0.76 0.36 P 0.15 − Holy Cross 0.98 0.91 0.11 P 0.64 Economies 2019, 7, x FOR PEER REVIEW − 9 of 24 Kuyavian-Pomeranian 0.75 <0.001 0.50 S 0.02 − LesserTable Poland 2. The relationship between 0.77 mobility changes<0.001 in public transport 0.14and Snew laboratory- 0.55 − confirmed cases of COVID-19 in Poland. Lower Silesian 0.97 0.84 0.76 P <0.001 − LublinVariable Shapiro-Wilk 0.89 Test Statistic 0.03p-Value Correlation0.24 CoefficientS p-Value0.30 POLAND 0.87 0.01 −0.29 S 0.21 S Lubusz 0.59 <0.001 0.10P 0.66 Greater Poland 0.97 0.76 −0.36 0.15 Łód´zHoly Cross 0.91 0.98 0.05 0.91 −0.100.11 P 0.640.67 Kuyavian-Pomeranian 0.75 <0.001 −0.50 S 0.02 Masovian 0.95 0.34 0.58 P 0.01 Lesser Poland 0.77 <0.001 −0.14 S 0.55 OpoleLower Silesian 0.98 0.97 0.94 0.84 −0.760.39 P P <0.0010.09 Lublin 0.89 0.03 −0.24 S 0.30 Podlaskie 0.97 0.85 0.22 P 0.34 Lubusz 0.59 <0.001 −0.10 S 0.66 PomeranianŁódź 0.80 0.91 <0.0010.05 0.100.45 P S 0.670.04 − Masovian 0.95 0.34 −0.58 P 0.01 Silesian 0.92 0.08 0.10 P 0.67 Opole 0.98 0.94 −0.39 P 0.09 S SubcarpathianPodlaskie 0.810.97 0.010.85 −0.120.22 P 0.340.61 S Warmian-MasurianPomeranian 0.960.80 0.58<0.001 −0.450.30 P 0.040.20 Silesian 0.92 0.08 −0.10 P 0.67 P West Pomeranian 0,97 0.72 0.69S <0.001 Subcarpathian 0.81 0.01 −0.12 0.61 Note:Warmian-MasurianP Pearson’s correlation coefficient, S Spearman’s0.96 rank correlation0.58 coefficient. Source:−0.30 P own calculations0.20 using R basedWest on dataPomeranian from Google COVID-19 Community 0,97 Mobility Reports 0.72 (Google 2020)− and0.69 MinistryP of Health<0.001 of the Ministry of Health of the Republic of Poland(2020). Note: P Pearson’s correlation coefficient, S Spearman’s rank correlation coefficient. Source: own calculations using R based on data from Google COVID-19 Community Mobility Reports (Google Figure2020)5 shows and Ministry that withof Health the of introduction the Republic of of Poland subsequent (2020). government restrictions, a sharp decline in the mobilityFigure of5 shows people that in with public the transportintroduction was of observedsubsequent until government mid-April restrictions, 2020. It a might sharp indicatedecline that the forcedin the mobility lockdown of people has proven in public to transport be effective was in observed reducing until interpersonal mid-April 2020. contacts It might in indicate order to that halt the pandemicthe forced development. lockdown has Starting proven from to be theeffective second in re halfducing of April interpersonal 2020, an contacts increase in inorder mobility to halt inthe public transportpandemic is visible, development. which canStarting be explained from the second by the half fact of that April the 2020, Polish an increase society in has mobility learned in public to function moretransport effectively is visible, despite which the can social be explained distancing byregulations. the fact that the Strong Polish easing society of has government learned to function regulations resultedmore in effectively a decrease despite in the the Stringency social distancing index levelregulations. in Poland Strong from easing the beginningof government of June regulations 2020. In the sameresulted period in further a decrease increase in the in Stringency mobility index in public level transportin Poland from is visible. the beginning of June 2020. In the same period further increase in mobility in public transport is visible.

20 100 10 0 80 -10 -20 60 -30 40 -40 -50 20 Stringency Index percentage points -60 Mobility changes in changes Mobility -70 0 2/03 16/03 30/03 13/04 27/04 11/05 25/05 8/06 22/06 6/07 20/07

Mobility changes in public transport (LHS) Stringency index (RHS)

Figure 5. Changes in mobility in public transport and the Stringency index level changes in Poland fromFigure 2 March 5. Changes to 19 Julyin mobility 2020. in Source: public transport own elaboration and the Stringency based on index data level from changes Google in Poland COVID-19 Communityfrom 2 March Mobility to 19 Reports July 2020. (Google Source: 2020 own) and elab theoration Oxford based COVID-19 on data Government from Google Response COVID-19 Tracker Community Mobility Reports (Google 2020) and the Oxford COVID-19 Government Response (Hale et al. 2020). Tracker (Hale et al. 2020).

Table 3 presents the results of Shapiro Wilk test and estimated correlation coefficients between changes in mobility in public transport and stringency of government restriction policy to tackle the coronavirus. The study reveals strong, negative and significant relationship both for Poland and all 16 voivodeships. It is consistent with the second hypothesis. It implies that the higher is the severity Economies 2020, 8, 78 10 of 24

Table3 presents the results of Shapiro Wilk test and estimated correlation coe fficients between changes in mobility in public transport and stringency of government restriction policy to tackle the coronavirus. The study reveals strong, negative and significant relationship both for Poland and all 16 voivodeships. It is consistent with the second hypothesis. It implies that the higher is the severity level of government responses to the COVID-19 pandemic, the lower is the mobility in public transport. The estimated Spearman’s rank correlation coefficient for Poland equals 0.84. Correlation coefficients − range from 0.72 to 0.86 across voivodeships. − − Table 3. The relationship between changes in mobility in public transport and the Stringency index level changes in Poland.

Variable Shapiro-Wilk Test Statistic p-Value Correlation Coefficient p-Value POLAND 0.89 0.02 0.84 S <0.001 − Greater Poland 0.91 0.06 0.83 P <0.001 − Holy Cross 0.88 0.02 0.84 S <0.001 − Kuyavian-Pomeranian 0.92 0.08 0.74 P <0.001 − Lesser Poland 0.91 0.08 0.85 P <0.001 − Lower Silesian 0.90 0.04 0.86 S <0.001 − Lublin 0.89 0.03 0.85 S <0.001 − Lubusz 0.91 0.07 0.78 P <0.001 − Łód´z 0.89 0.03 0.84 S <0.001 − Masovian 0.92 0.11 0.85 P <0.001 − Opole 0.92 0.10 0.79 P <0.001 − Podlaskie 0.87 0.01 0.83 S <0.001 − Pomeranian 0.90 0.04 0.82 S <0.001 − Silesian 0.87 0.01 0.82 S <0.001 − Subcarpathian 0.87 0.01 0.84 S <0.001 − Warmian-Masurian 0.91 0.07 0.72 P <0.001 − West Pomeranian 0.92 0.10 0.75 P <0.001 − Note: P Pearson’s correlation coefficient, S Spearman’s rank correlation coefficient. Source: own calculations using R based on data from Google COVID-19 Community Mobility Reports (Google 2020) and the Oxford COVID-19 Government Response Tracker (Hale et al. 2020).

The obtained results presented in Tables2 and3 suggest that the relationship between the changes in mobility in public transport and the stringency of anti-COVID-19 government restriction policy is stronger than the relationship between the mobility and new laboratory-confirmed cases of COVID-19. It suggests that government restrictions and ongoing pandemic information campaigns have a greater impact on human mobility rather than information about new confirmed cases. It is in line with the results of Engle et al.(2020) who show that government stay-at-home restrictions induce a greater mobility decrease than a rise of local infection rate. As the above presented results show, that government stringency policy is a key factor affecting mobility changes during the COVID-19 pandemic. We apply one factor variance analysis (ANOVA) and the Tukey’s honest significance test (Tukey’s HSD test) in order to analyse the patterns of mobility in public transport both from country and regional perspective. The results of the ANOVA test are presented in Table4. Stringency of Polish government’s anti-COVID-19 policy is assumed to be the factor while the changes in mobility in public transport are considered as a dependent variable. Economies 2020, 8, 78 11 of 24

Table 4. One factor analysis of variance (ANOVA) between the Stringency index levels and changes in mobility in public transport.

The result presented in Table4 implies the significant relationship between the stringency of Polish government’s anti-COVID-19 policy and changes in mobility in public transport both in Poland and all voivodeships at the 1% level of significance. The results indicate that there is a significant difference in mean changes in mobility between at least two levels of stringency factor. In order to analyse the pattern of difference between means we test all pairwise comparisons among means applying Tukey’s HSD test. The test results are presented in Table A1 in AppendixA. Tukey’s HSD test results show that there is a significant difference between the changes in mobility in public transport at both “high” and “medium” stringency levels in comparison to “low” level, and at “high” in comparison to “medium” level at 1% significance level in Poland and all analysed voivodeships. Figure6 depicts the mean changes in mobility in public transport in Poland depending on three stringency factor levels, i.e., “low”, “medium” and “high”. Figure6 indicates the existence of significant di fferences in the average changes in mobility in public transport in Poland at all three levels of stringency factor, which has been confirmed by Tukey’s HSD test (Table A1). From the country’s perspective we observe a decrease of average mobility in public transport in entire research period. During the soft Polish government’s anti-COVID-19 policy periods, the average decrease in mobility in public transport is rather minor. As the anti-COVID-19 policy becomes more stringent, the mean level of population mobility in public transport substantially declines. Figure A3a,b in the appendix show the mean mobility changes in public transport in all 16 voivodeships at three stringency factor levels. Figure A3 indicates not only the significant differences in mean changes in mobility in public transport at three levels of stringency factor but also the difference in the magnitude of mobility changes among Polish voivodeships. Figure7 displaying changes in mobility in public transport dependent on the level of the stringency of anti-COVID-19 policy implies regionally differentiated people’s appearance in public transportation system and hubs. Economies 2019, 7, x FOR PEER REVIEW 11 of 24

Figure 6 depicts the mean changes in mobility in public transport in Poland depending on three stringency factor levels, i.e., “low”, “medium” and “high”.

Table 4. One factor analysis of variance (ANOVA) between the Stringency index levels and changes in mobility in public transport.

Region F Statistics p-Value POLAND 84.72 <0.001 Greater Poland 84.18 <0.001 Holy Cross 65.19 <0.001 Kuyavian-Pomeranian 62.87 <0.001 Lesser Poland 105.2 <0.001 Lower Silesian 92.78 <0.001 Lublin 69.47 <0.001 Lubusz 72.01 <0.001 Łódź 64.88 <0.001 Masovian 92.88 <0.001 Opole 70.08 <0.001 Podlaskie 61.07 <0.001 Pomeranian 95.07 <0.001 Silesian 59.26 <0.001 Subcarpathian 76.20 <0.001 Warmian-Masurian 60.56 <0.001 West Pomeranian 68.81 <0.001 Source: own calculations using R based on data from Google COVID-19 Community Mobility Reports (Google 2020) and the Oxford COVID-19 Government Response Tracker (Hale et al. 2020). Figure 6 indicates the existence of significant differences in the average changes in mobility in public transport in Poland at all three levels of stringency factor, which has been confirmed by Tukey’s HSD test (Table A1). From the country’s perspective we observe a decrease of average mobility in public transport in entire research period. During the soft Polish government’s anti- COVID-19 policy periods, the average decrease in mobility in public transport is rather minor. As the anti-COVID-19 policy becomes more stringent, the mean level of population mobility in public transport substantially declines. Figure A3a,b in the appendix show the mean mobility changes in public transport in all 16 voivodeships at three stringency factor levels. Figure A3 indicates not only the significant differences in mean changes in mobility in public transport at three levels of stringency factor but also the difference in the magnitude of mobility changes among Polish voivodeships. Economies 2020Figure, 8, 787 displaying changes in mobility in public transport dependent on the level of the stringency12 of 24 Economiesof anti-COVID-19 2019, 7, x FOR PEER policy REVIEW implies regionally differentiated people’s appearance in 12public of 24 transportation system and hubs. Figure 6. The mean changes in mobility in public transport at three levels of the stringency of Polish government’s anti-COVID-19 policy in Poland from 2 March to 19 July 2020. Source: own calculations using R based on data from Google COVID-19 Community Mobility Reports (Google 2020) and the Oxford COVID-19 Government Response Tracker (Hale et al. 2020).

Figure 7 shows that in the “low” level of stringency policy the largest regional diversity is observed. Surprisingly, only half of the voivodeships experience a decline in average mobility in public transport. Masovian (MZ) represents the highest drop in mobility (−15%) while in Kuyavian- Pomeranian (KP) a reverse trend is visible (+16%). As far as “medium” and “high” stringency levels are concerned, all sixteen voivodeships experience a substantial decrease in mobility in public transport. However, the magnitude of changes in mobility varies across Polish regions. It is worth adding that during the most severe anti-COVID-19 policy period, in as many as seven voivodeships, the mobility decreased by more than half compared to the baseline period. Moreover, the highest Figuremobility 6. The decrease, mean changesregardless in the mobility stringency in public level, is transport observed at in three Masovian levels (MZ), of the Lower stringency Silesian of (DS) Polish government’sand Lesser Poland anti-COVID-19 (MA), while policy Kuyavian-Pomeranian in Poland from 2 March (KP), toWest 19 JulyPomeranian 2020. Source: (ZP) and own Warmian- calculations usingMasurian R based (WN) on data are fromcharacterised Google COVID-19 by the lowest Community decline Mobility in people’s Reports appearance (Google 2020in public) and the Oxfordtransportation COVID-19 system Government and hubs. Response Tracker (Hale et al. 2020).

-5

-25

-45

-65 SL LB SK ZP DS KP PD LD PM PK WP OP LU WN MZ MA

medium low high

Figure 7. The mean changes in mobility in public transport at three levels of the stringency of Polish government’sFigure 7. anti-COVID-19The mean changes policy in mobility in 16 in voivodeshipspublic transport fromat three 2 Marchlevels of tothe 19 stringency July 2020. of Polish DS—Lower government’s anti-COVID-19 policy in 16 voivodeships from 2 March to 19 July 2020. DS—Lower Silesian; KP—Kuyavian-Pomeranian; LU—Lublin; LB—Lubusz; LD—Łód´z;MA—Lesser Poland; Silesian; KP—Kuyavian-Pomeranian; LU—Lublin; LB—Lubusz; LD—Łódź; MA—Lesser Poland; MZ—Masovian;MZ—Masovian; OP—Opole; OP—Opole; PK—Subcarpathian; PK—Subcarpathian; PD—Podlaskie; PD—Podlaskie; PM—Pomeranian; PM—Pomeranian; SL—Silesian; SL—Silesian; SK—HolySK—Holy Cross; Cross; WN—Warmian-Masurian; WN—Warmian-Masurian; WP—Greater WP—Greater Poland; Poland; ZP—WestZP—West Pomeranian. Source: Source: own calculationsown calculations using R based using on R data based from on Googledata from COVID-19 Google COVID-19 Community Community Mobility Mobility Reports Reports (Google 2020) and the(Google Oxford 2020) COVID-19 and the Oxford Government COVID-19 Response Governme Trackernt Response (Hale Tracker et al. (Hale2020 ).et al. 2020).

Figure7Our shows research that results in the do “low” not correspond level of stringency with Badr et policy al. (2020), the largestwho reveal regional the strong diversity correlation is observed. Surprisingly,between only mobility half ofpatterns the voivodeships and decreasing experience number of newCOVID-19 a decline in average cases. There mobility are certainly in public other transport. drivers/factors explaining voivodeship-specific relationship between changes in mobility in public Masovian (MZ) represents the highest drop in mobility ( 15%) while in Kuyavian-Pomeranian (KP) transport and epidemic status. It may be population density− (Oztig and Askin 2020), initial mobility a reversestructure trend (Galeazzi is visible et ( +al.16%). 2020), As personal far as “medium”income (Coven and and “high” Gupta stringency2020; Almagro levels and areOrane- concerned, all sixteenHutchinson voivodeships 2020) or experience diverse perception a substantial of the decrease coronavirus in mobility pandemic in publicas a consequence transport. However,of the magnitudegovernmental of changes information in mobilitycampaign (Yuksel varies et across al. 2020). Polish However, regions. they were It is not worth our subject adding matter. that during the mostThe severe results are anti-COVID-19 in line with Pullano policy et al. period, (2020) who, in as based many on asFrance, seven indicate voivodeships, that the pandemic the mobility lockdown was very effective in decreasing people mobility. Pepe et al. (2020) observe a substantial decreased by more than half compared to the baseline period. Moreover, the highest mobility decrease, human mobility reduction in Italy following the national lockdown. Similar results are also obtained regardlessa by the Queiroz stringency et al. (2020) level, and is observedBonaccorsi inet Masovianal. (2020) who, (MZ), based Lower on region Silesianal analysis, (DS) andobserve Lesser the Poland (MA), whilereduction Kuyavian-Pomeranian in the circulation of people (KP), in Westmost neighbourhoods Pomeranian (ZP) after and government Warmian-Masurian social distancing (WN) are characterisedpolicies byimplementation. the lowest decline Moreover, in our people’s research appearance results correspond in public to the transportation studies which focus system on the and hubs. Ourimpact research of previous results epidemics, do not correspond particularly with SARS Badr and etEbola, al.(2020 on ),population who reveal changes the strongin mobility correlation between(Wang mobility 2014; patternsLau 2003; and Peak decreasing et al. 2018). number However, of the newCOVID-19 question remains cases. whether There theare restrictions certainly other aimed at limiting human mobility have a significant effect on the reduction in new COVID-19 cases drivers/factors explaining voivodeship-specific relationship between changes in mobility in public transport and epidemic status. It may be population density (Oztig and Askin 2020), initial mobility structure (Galeazzi et al. 2020), personal income (Coven and Gupta 2020; Almagro and Orane-Hutchinson 2020) or diverse perception of the coronavirus pandemic as a consequence of governmental information campaign (Yuksel et al. 2020). However, they were not our subject matter. The results are in line with Pullano et al. (2020) who, based on France, indicate that the pandemic lockdown was very effective in decreasing people Economies 2020, 8, 78 13 of 24 mobility. Pepe et al.(2020) observe a substantial human mobility reduction in Italy following the national lockdown. Similar results are also obtained a by Queiroz et al.(2020) and Bonaccorsi et al.(2020) who, based on regional analysis, observe the reduction in the circulation of people in most neighbourhoods after government social distancing policies implementation. Moreover, our research results correspond to the studies which focus on the impact of previous epidemics, particularly SARS and Ebola, on population changes in mobility (Wang 2014; Lau 2003; Peak et al. 2018). However, the question remains whether the restrictions aimed at limiting human mobility have a significant effect on the reduction in new COVID-19 cases (Schlosser et al. 2020; Yilmazkuday 2020; Askitas et al. 2020). Our study reveals the regional diversity in the magnitude of mobility changes in public transport depending on the level of severity of the government anti-COVID-19 policy. Explaining the drivers of above mentioned specific, regional relationship raises some challenges for the future research. The literature provides some factors that affect human mobility patterns, including population density (Oztig and Askin 2020), initial mobility structure (Galeazzi et al. 2020), personal income (Coven and Gupta 2020; Almagro and Orane-Hutchinson 2020) or diverse perception of the coronavirus pandemic as a consequence of governmental information campaign (Yuksel et al. 2020).

4. Conclusions Poland is one of the last European Union countries to have experienced the outbreak of COVID-19 pandemic. The epidemic status in Poland is relatively mild. The Polish government restrictions severity and stringency are comparable to those of the other countries affected by the coronavirus. A combination of government lockdown and social fears of contracting and spreading COVID-19 has caused a substantial reduction in passengers’ public transport demand in Poland. From the country as well as voivodeship perspective, the study confirms the statistically significant negative relationship between changes in mobility in public transport and the stringency level of Polish government anti-COVID-19 policy. It might indicate that the forced lockdown to contain the development of the COVID-19 pandemic has effectively contributed to social distancing in public transport in Poland. Moreover, based on ANOVA and Tukey’s HSD test we indicate that there are significant differences when it comes to the changes in mobility in public transport depending on the level of stringency of anti-COVID-19 regulation policy both in Poland and all analysed voivodeships. Furthermore, a substantial regional differentiation is visible regardless the stringency policy level. The highest mobility decrease is observed in Masovian, Lower Silesian and Lesser Poland, while Kuyavian-Pomeranian, West Pomeranian and Warmian-Masurian are characterised by the lowest decline in people’s appearance in public transport. However, the significant negative relationship between changes in mobility in public transport and the epidemic status in Poland is not observed. Due to the diverse numbers of laboratory-confirmed COVID-19 cases and expected different mobility behaviour in individual regions of Poland, the assessed relationships vary substantially across voivodeships. The results are significant only for Kuyavian-Pomeranian, Lower Silesian, Masovian, Pomeranian and West Pomeranian voivodeships. What can be considered surprising, the relationship is insignificant but positive for Silesian, the province most affected by the COVID-19 pandemic. Explaining the drivers of voivodeship-specific relationship between changes in mobility in public transport and the level of severity of the government anti-COVID-19 policy, in particular population density, initial mobility structure, personal income and people’s novel coronavirus pandemic perception pose a challenge for the future research.

Author Contributions: Conceptualization: M.W., K.C.; methodology: M.W., K.C.; validation M.W., K.C.; formal analysis K.C., M.W., Ł.G.; writing—original draft preparation: M.W., K.C., Ł.G.; writing—review and editing: M.W., K.C., Ł.G.; supervision: M.W., K.C.; project administration: K.C., M.W. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conﬂicts of Interest: The authors declare no conﬂict of interest. Economies 2020, 8, 78 14 of 24 Economies 2019, 7, x FOR PEER REVIEW 14 of 24

Appendix A Appendix A

Holly Cross Greater Poland Kuyavian-Pomeranian Lesser Poland

16 20 70 20 30 50 70 10 14 10 10 0 60 25 60 12 0 0 -10 50 50 -10 -10 20 0 -20 10 -20 40 -20 40 -30 8 15 -30 30 -30 30 -40 6 -40 -40 10 -50 -50 20 20 4 -50 -50 -60 5 2 -60 10 -60 10 -70 0 -70 0 -70 0 -100 0 -80 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07

Lower Silesian Lublin Lubusz Łódź

80 20 18 20 18 10 70 10 10 10 70 16 16 0 60 0 0 0 60 14 14 -10 -10 -10 -10 50 12 12 50 -20 -20 -20 -20 10 10 40 40 -30 -30 -30 -30 8 8 30 30 -40 -40 -40 -40 6 6 -50 -50 20 20 -50 -50 -60 4 -60 4 10 10 -70 2 -70 2 -60 -60 0 -80 0 -80 0 -70 0 -70 2/03 6/04 11/05 15/06 20/07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07

Masovian Opole Podlaskie Pomeranian

120 10 20 30 20 20 25 20 0 18 20 18 10 100 -10 16 10 16 0 20 0 14 0 14 -10 80 -20 12 -10 12 -20 15 -20 -30 60 10 -20 10 -30 -40 8 -30 8 -40 10 -40 -50 40 6 -40 6 -50 -60 4 -50 4 -60 5 -60 20 -70 2 -60 2 -70 0 -80 0 -70 0 -80 0 -80 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07

Silesian Subcarpathian Warmian-Masurian West Pomeranian

Figure A1. Cont. Economies 2020, 8, 78 15 of 24 Economies 2019, 7, x FOR PEER REVIEW 15 of 24

300 10 30 20 7 20 14 20 0 10 10 10 250 25 6 12 0 -10 0 0 -10 5 10 200 20 -10 -10 -20 -20 4 -20 8 -20 150 -30 15 -30 3 -30 6 -30 -40 -40 100 10 -40 -40 -50 2 4 -50 -60 -50 -50 50 5 1 2 -60 -70 -60 -60 0 -70 0 -80 0 -70 0 -70 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07

Figure A1. Changes in mobility in public transport and new COVID-19 laboratory-conﬁrmed cases in Poland by voivodeship from 2 March to 19 July 2020. Source: Figure A1. Changes in mobility in public transport and new COVID-19 laboratory-confirmed cases in Poland by voivodeship from 2 March to 19 July 2020. Source: own own elaboration based on data from Google COVID-19 Community Mobility Reports (Google 2020) and Ministry of Health of the Ministry of Health of the Republic of elaboration based on data from Google COVID-19 Community Mobility Reports (Google 2020) and Ministry of Health of the Republic of Poland (2020). Poland(2020).

Economies 2020, 8, 78 16 of 24 Economies 2019, 7, x FOR PEER REVIEW 16 of 24

Holly Cross Greater Poland Kuyavian-Pomeranian Lesser Poland

90 20 90 20 90 40 90 10 80 10 80 10 80 80 0 20 70 0 70 0 70 70 -10 60 -10 60 -10 60 0 60 -20 50 -20 50 -20 50 50 -30 -20 40 -30 40 -30 40 40 -40 30 -40 30 -40 30 -40 30 -50 20 -50 20 -50 20 20 -60 -60 10 -60 10 -60 10 10 -70 0 -70 0 -70 0 -80 0 -80 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07

Lower Silesian Lublin Lubusz Łódź

90 20 90 20 90 10 90 10 80 10 80 10 80 0 80 0 0 0 70 70 70 -10 70 -10 -10 -10 60 60 60 60 -20 -20 -20 -20 50 50 50 50 -30 -30 -30 -30 40 40 40 40 -40 -40 -40 -40 30 30 30 30 -50 -50 -50 -50 20 -60 20 -60 20 20 10 -70 10 -70 10 -60 10 -60 0 -80 0 -80 0 -70 0 -70 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07

Masovian Opole Podlaskie Pomeranian

90 10 90 30 90 20 90 20 80 0 80 20 80 10 80 10 70 -10 70 10 70 0 70 0 0 -10 60 -20 60 60 -10 60 -10 -20 50 -30 50 50 -20 50 -20 -30 40 -40 40 40 -30 40 -30 -40 30 -50 30 -40 30 -40 30 -50 20 -60 20 -50 20 -50 20 -60 10 -70 10 -60 10 -60 10 -70 0 -80 0 -70 0 -70 0 -80 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07

Silesian Subcarpathian Warmian-Masurian West Pomeranian

Figure A2. Cont. Economies 2020, 8, 78 17 of 24 Economies 2019, 7, x FOR PEER REVIEW 17 of 24

90 10 90 20 90 20 90 20 80 0 80 10 80 10 80 10 0 70 -10 70 70 0 70 0 -10 60 60 60 -10 60 -10 -20 -20 50 50 50 -20 50 -20 -30 -30 40 40 40 -30 40 -30 -40 -40 30 30 30 -40 30 -40 -50 -50 20 20 -60 20 -50 20 -50 10 -60 10 -70 10 -60 10 -60 0 -70 0 -80 0 -70 0 -70 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07 2-03 6-04 11-05 15-06 20-07

Figure A2. Changes in mobility in public transport and the Stringency index level changes in Poland by voivodeship from 2 March to 19 July 2020. Source: Figure A2. Changes in mobility in public transport and the Stringency index level changes in Poland by voivodeship from 2 March to 19 July 2020. Source: own elaboration own elaboration based on data from Google COVID-19 Community Mobility Reports (Google 2020) and the Oxford COVID-19 Government Response Tracker based on data from Google COVID-19 Community Mobility Reports (Google 2020) and the Oxford COVID-19 Government Response Tracker (Hale et al. 2020). (Hale et al. 2020).

Economies 2020, 8, 78 18 of 24

Table A1. Tukey’s honest signiﬁcance test (Tukey’s HSD test).

Average Value of Changes in Tukey’s HSD Test Region Stringency Policy Level Mobility in Public Transport Diﬀerence p-Value low 5.24 low-medium <0.001 − POLAND medium 34.32 low-high <0.001 − high 52.02 medium-high <0.001 − low 5.90 low-medium <0.001 − Greater Poland medium 34.63 low-high <0.001 − high 52.45 medium-high <0.001 − low 6.00 low-medium <0.001 Holy Cross medium 24.53 low-high <0.001 − high 41.95 medium-high <0.001 − low 16.48 low-medium <0.001 Kuyavian-Pomeranian medium 19.02 low-high <0.001 − high 49.25 medium-high <0.001 − low 8.43 low-medium <0.001 − Lesser Poland medium 42.29 low-high <0.001 − high 60.97 medium-high <0.001 − low 10.10 low-medium <0.001 − Lower Silesian medium 40.20 low-high <0.001 − high 57.30 medium-high <0.001 − low 1.19 low-medium <0.001 − Lublin medium 32.92 low-high <0.001 − high 50.13 medium-high <0.001 − low 1.71 low-medium <0.001 Lubusz medium 23.37 low-high <0.001 − high 44.78 medium-high <0.001 − low 2.67 low-medium <0.001 − Łód´z medium 29.20 low-high <0.001 − high 45.05 medium-high <0.001 − low 15.05 low-medium <0.001 − Masovian medium 43.92 low-high <0.001 − high 59.78 medium-high <0.001 − low 8.67 low-medium <0.001 Opole medium 26.71 low-high <0.001 − high 48.68 medium-high <0.001 − low 0.81 low-medium <0.001 Podlaskie medium 24.17 low-high <0.001 − high 42.28 medium-high <0.001 − low 7.00 low-medium <0.001 Pomeranian medium 27.31 low-high <0.001 − high 53.07 medium-high <0.001 − low 0.57 low-medium <0.001 − Silesian medium 27.73 low-high <0.001 − high 43.47 medium-high <0.001 − low 1.14 low-medium <0.001 − Subcarpathian medium 33.93 low-high <0.001 − high 51.35 medium-high <0.001 − low 6.95 low-medium <0.001 Warmian-Masurian medium 21.88 low-high <0.001 − high 42.63 medium-high <0.001 − low 6.52 low-medium <0.001 West Pomeranian medium 19.17 low-high <0.001 − high 42.43 medium-high <0.001 − Source: own calculations using R based on data from Google COVID-19 Community Mobility Reports (Google 2020) and the Oxford COVID-19 Government Response Tracker (Hale et al. 2020). Economies 2019, 7, x FOR PEER REVIEW 19 of 24

Warmian- medium −21.88 low-high <0.001 Masurian high −42.63 medium-high <0.001 low 6.52 low-medium <0.001 West Pomeranian medium −19.17 low-high <0.001 high −42.43 medium-high <0.001

EconomiesSource:2020, 8own, 78 calculations using R based on data from Google COVID-19 Community Mobility Reports19 of 24 (Google 2020) and the Oxford COVID-19 Government Response Tracker (Hale et al. 2020).

(a)

Figure A3. Cont. EconomiesEconomies2020 2019, 8, ,7 78, x FOR PEER REVIEW 2020 of of 24 24

(b)

FigureFigure A3. A3. ((aa)) TheThe meanmean changeschanges in mobility in in public public tran transportsport at at three three levels levels of of the the stringency stringency of ofPolish Polish government’s government’s anti-COVID anti-COVID-19-19 policy policy in in Polish Polish voivodeships voivodeships from from 2 March toto 1919 JulyJuly 2020.2020. Source:Source: own own calculations calculations using using R R based based on on data data from from Google Google COVID-19 COVID-19 Community Community Mobility Mobility Reports Reports (Google(Google 2020 2020)) and and the the Oxford Oxford COVID-19 COVID-19 Government Government Response Response Tracker Tracker (Hale (Hale et et al. al. 2020 2020).). ( b(b)) The The meanmean changes changes in in mobility mobility in in public public transport transport at at three three levels levels of of the the stringency stringency of of Polish Polish government’s government’s anti-COVID-19anti-COVID-19 policy policy in in Polish Polish voivodeships voivodeships from from 2 2 March March to to 19 19 July July 2020. 2020. Source: Source: own own calculations calculations usingusing R R based based on on data data from from Google Google COVID-19 COVID-19 Community Community Mobility Mobility Reports Reports ( Google(Google 2020 2020)) and and the the OxfordOxford COVID-19 COVID-19 Government Government Response Response Tracker Tracker (Hale (Hale et et al. al. 2020 2020).).

Economies 2020, 8, 78 21 of 24

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