The Impact of COVID-19 Pandemic on Sustainable Development Goals

Gherghina, Ştefan Cristian (Ed.); Simionescu, Liliana Nicoleta (Ed.) Book The Impact of COVID-19 Pandemic on Sustainable Development Goals Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Gherghina, Ştefan Cristian (Ed.); Simionescu, Liliana Nicoleta (Ed.) (2024) : The Impact of COVID-19 Pandemic on Sustainable Development Goals, ISBN 978-3-7258-2068-9, MDPI - Multidisciplinary Digital Publishing Institute, Basel, https://doi.org/10.3390/books978-3-7258-2068-9 This Version is available at: https://hdl.handle.net/10419/321944 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ mdpi.com/journal/sustainability Special Issue Reprint The Impact of COVID-19 Pandemic on Sustainable Development Goals Edited by Ştefan Cristian Gherghina and Liliana Nicoleta Simionescu The Impact of COVID-19 Pandemic on Sustainable Development Goals The Impact of COVID-19 Pandemic on Sustainable Development Goals Editors S¸tefan Cristian Gherghina Liliana Nicoleta Simionescu Basel •Beijing •Wuhan •Barcelona •Belgrade •Novi Sad •Cluj •Manchester Editors S¸tefan Cristian Gherghina Bucharest University of Economic Studies Bucharest Romania Liliana Nicoleta Simionescu Bucharest University of Economic Studies Bucharest Romania Editorial Office MDPI AG Grosspeteranlage 5 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Sustainability (ISSN 2071-1050) (available at: https://www.mdpi.com/journal/sustainability/ special issues/sustainable development goals). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: Lastname, A.A.; Lastname, B.B. Article Title. Journal Name Year,Volume Number, Page Range. ISBN 978-3-7258-2067-2 (Hbk) ISBN 978-3-7258-2068-9 (PDF) doi.org/10.3390/books978-3-7258-2068-9 © 2024 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license. Contents About the Editors ..............................................vii S ,tefan Cristian Gherghina and Liliana Nicoleta Simionescu The Impact of COVID-19 Pandemic on Sustainable Development Goals Reprinted from: Sustainability 2024,16, 5406, doi:10.3390/su16135406 ................ 1 Taylor Hanna, Barry B. Hughes, Mohammod T. Irfan, David K. Bohl, Jos´e Sol´orzano, Babatunde Abidoye, et al. Sustainable Development Goal Attainment in the Wake of COVID-19: Simulating an Ambitious Policy Push Reprinted from: Sustainability 2024,16, 3309, doi:10.3390/su16083309 ................ 6 Bj¨orn Mestdagh, Olivier Sempiga and Luc Van Liedekerke The Impact of External Shocks on the Sustainable Development Goals (SDGs): Linking the COVID-19 Pandemic to SDG Implementation at the Local Government Level Reprinted from: Sustainability 2023,15, 6234, doi:10.3390/su15076234 ................ 23 Krystyna Brzozowska, Małgorzata Gorzałczy´nska-Koczkodaj, El˙ zbieta Ociepa-Kici´nska and Przemysław Pluskota The Impact of the COVID-19 Pandemic on Financial Condition and Mortality in Polish Regions Reprinted from: Sustainability 2023,15, 8993, doi:10.3390/su15118993 ................ 41 Cristi Spulbar, Lucian Claudiu Anghel, Ramona Birau, Simona Ioana Ermis ,, Laurent ,iu-Mihai Treap˘at and Adrian T. Mitroi Digitalization as a Factor in Reducing Poverty and Its Implications in the Context of the COVID-19 Pandemic Reprinted from: Sustainability 2022,14, 10667, doi:10.3390/su141710667 ............... 59 Beata Bieszk-Stolorz and Krzysztof Dmytr´ow Assessment of the Similarity of the Situation in the EU Labour Markets and Their Changes in the Face of the COVID-19 Pandemic Reprinted from: Sustainability 2022,14, 3646, doi:10.3390/su14063646 ................ 85 Beata Bieszk-Stolorz and Iwona Markowicz The Impact of the COVID-19 Pandemic on the Situation of the Unemployed in Poland. A Study Using Survival Analysis Methods Reprinted from: Sustainability 2022,14, 12677, doi:10.3390/su141912677 ...............105 Joseph Crawford Working from Home, Telework, and Psychological Wellbeing? A Systematic Review Reprinted from: Sustainability 2022,14, 11874, doi:10.3390/su141911874 ...............124 Indra Abeysekera, Emily Sunga, Avelino Gonzales and Raul David The Effect of Cognitive Load on Learning Memory of Online Learning Accounting Students in the Philippines Reprinted from: Sustainability 2024,16, 1686, doi:10.3390/su16041686 ................140 Rebeca Mart´ınez-Garc´ıa, Fernando J. Fraile-Fern´andez, Gabriel B´urdalo-Salcedo, Ana Mar´ıa Casta˜n´on-Garc´ıa, Mar´ıa Fern´andez-Raga and Covadonga Palencia Satisfaction Level of Engineering Students in Face-to-Face and Online Modalities under COVID-19—Case: School of Engineering of the University of Le´ on, Spain Reprinted from: Sustainability 2022,14, 6269, doi:10.3390/su14106269 ................161 v Elgiz Yılmaz Altunta¸s and Esin Cumhur Yal¸cın COVID-19 Pandemic Learning: The Uprising of Remote Detailing in Pharmaceutical Sector Using Sales Force Automation and Its Sustainable Impact on Continuing Medical Education Reprinted from: Sustainability 2023,15, 8955, doi:10.3390/su15118955 ................173 Eneko Tejada Garitano, Javier Portillo Berasaluce, Arantzazu L´opez de la Serna and Ander Arce Alonso Emotions of Educators Conducting Emergency Remote Teaching during COVID-19 Confinement Reprinted from: Sustainability 2024,16, 1456, doi:10.3390/su16041456 ................202 vi About the Editors S ,tefan Cristian Gherghina S ,tefan Cristian Gherghina, PhD Habil., is a Professor at the Department of Finance, Faculty of Finance and Banking, and a PhD supervisor at the Finance Doctoral School, Bucharest University of Economic Studies, Romania. His areas of interest focus on corporate finance and governance, quantitative finance, portfolio management, and sustainable development. He has authored and co-authored several books and articles published in top journals, and exhibited his research at many international conferences. He serves as a referee for various leading journals, while also being an Editorial Board Member of Sustainability,Economies, and the Journal of Risk and Financial Management, among other journals indexed by Clarivate Analytics, Web of Science, Social Sciences Citation Index (SSCI), Science Citation Index Expanded (SCIE), Emerging Sources Citation Index (ESCI), and other reputed international databases. Liliana Nicoleta Simionescu Liliana Nicoleta Simionescu, PhD. Habil., is a Professor at the Department of Finance, Faculty of Finance and Banking, and a PhD. supervisor at the Finance Doctoral School, Bucharest University of Economic Studies, Romania. Her fields of expertise are centered around public financial policies, sustainable public budgeting, public institutions finance, social protection and pensions, corporate tax, and applied econometrics in finance. She has authored several books, has had articles published in prestigious publications, and has presented her research at numerous international conferences. She serves as a referee for many prominent journals indexed by Clarivate Analytics, Web of Science, Social Sciences Citation Index (SSCI), Science Citation Index Expanded (SCIE), Emerging Sources Citation Index (ESCI), and other reputed international databases. vii Citation: Hanna, T.; Hughes, B.B.; Irfan, M.T.; Bohl, D.K.; Solórzano, J.; Abidoye, B.; Patterson, L.; Moyer, J.D. Sustainable Development Goal Attainment in the Wake of COVID-19: Simulating an Ambitious Policy Push. Sustainability 2024,16, 3309. https:// doi.org/10.3390/su16083309 Academic Editors: ¸Stefan Cristian Gherghina and Liliana Nicoleta Simionescu Received: 17 January 2024 Revised: 2 March 2024 Accepted: 1 April 2024 Published: 16 April 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Sustainable Development Goal Attainment in the Wake of COVID-19: Simulating an Ambitious Policy Push Taylor Hanna 1, Barry B. Hughes 1, Mohammod T. Irfan 1, David K. Bohl 1, JoséSolórzano 1, Babatunde Abidoye 2, Laurel Patterson 2and Jonathan D. Moyer 1,* 1Frederick S. Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, 2201 S. Gaylord St., Denver, CO 80208, USA; taylor[email protected] (T.H.); barry[email protected] (B.B.H.); [email protected] (M.T.I.); [email protected] (D.K.B.); [email protected] (J.S.) 2United Nations Development Programme, 1 United Nations Plaza, New York, NY 10017, USA; [email protected] (B.A.); laur[email protected] (L.P.) *Correspondence: [email protected] Abstract: Even before the COVID-19 pandemic, the world was not on course to meet key Sustainable Development Goals (SDGs) including SDG 1 (No Poverty) and SDG 2 (Zero Hunger). Some significant degree of additional effort was needed before the pandemic, and the challenge is now greater. Analyzing the prospects for meeting these goals requires attention to the combined effects of the pandemic and such additional impetus. This article assesses the impact of the COVID-19 pandemic on progress toward the SDGs and explores strategies to recover and accelerate development. Utilizing the International Futures (IFs) forecasting system and recognizing the near impossibility of meeting the goals by 2030, three scenarios are examined through to 2050: A pre-COVID-19 trajectory ( No COVID-19 ), the current path influenced by the pandemic (Current Path), and a transformative SDG-focused approach prioritizing key policy strategies to accelerate outcomes (SDG Push). The pandemic led to a rise in extreme poverty and hunger, with recovery projected to be slow. The SDG Push scenario effectively addresses this, surpassing the Current Path and achieving significant global improvements in poverty, malnutrition, and human development by 2050 even relative to the No COVID-19 path. The findings emphasize the need for integrated, transformative actions to propel sustainable development. Keywords: COVID-19; sustainable development goals; human development; poverty; hunger; forecasting human development 1. Introduction The Sustainable Development Goals (SDGs) serve as the global framework for driving progress toward accelerating human development. Even at their inception, the SDGs were understood as highly ambitious, and progress since 2015 has not been on pace to achieve the goals by the target date of 2030 [ 1 ]. In 2020, the outbreak of the COVID-19 pandemic led to not only more than 1.8 million deaths [ 2 ] but also shutdowns and mitigation measures worldwide, slowing economic growth. Inequality increased both across and within developing countries [ 3 , 4 ], and for the first time in decades, the global poverty rate increased, signaling a reversal of recent progress. While the economy has rebounded, growth has settled back to a positive but moderate pace. This along with the many other effects of COVID-19 is expected to have implications for progress toward SDG achievement across the SDG agenda [5–7]. Previous work explored how COVID-19 would impact the SDGs, with some studies broadly assessing the literature on developmental outcomes [ 5 , 8 ] or focusing on short-term quantitative outcomes across a wide range of indicators [ 9 ]. This research finds that the pandemic has indeed negatively affected many SDGs [ 8 , 10 ], setting overall progress back 8.2 percent in its first year [ 6 ]. Other research has focused on specific SDG indicators, Sustainability 2024,16, 3309. https://doi.org/10.3390/su16083309 https://www.mdpi.com/journal/sustainability 6 Sustainability 2024,16, 3309 including global poverty [ 3 , 11 – 16 ], food affordability [ 17 ], food insecurity [ 14 ], hunger [ 18 ], and maternal and child health and undernutrition [ 19 , 20 ]. This literature is also focused on effects in the first few years after COVID-19. Cooper et al. [ 21 ] project moderate and severe food insecurity through to 2030, though not in comparison to a baseline without the pandemic. Other work projects the outcomes from an integrated development push on SDG achievement but without considering the effect of COVID-19 [22]. Now, more than halfway through the SDG horizon and several years out of the initial COVID-19 outbreak, we fill gaps in the literature by reassessing progress and taking stock of the path we are currently on while assessing prospects for accelerating development. This paper advances the understanding of how the COVID-19 pandemic has affected progress toward achieving the first two SDGs (SDG 1: No Poverty; SDG 2: Zero Hunger) and improving the Human Development Index (HDI), which summarizes progress towards several of the SDGs. It then turns to explore how an ambitious push toward global development might make up for that setback. Previous work has proposed strategies for global responses that push beyond addressing the pandemic’s immediate effects [ 23 ], ranking response strategies in relation to the SDGs [ 24 ], and even mapped out potential future scenarios and how SDG progress may be positively or negatively affected [ 25 ]. There remains a gap in the literature related to quantitatively assessing responses to the generally accepted developmental setback caused by COVID-19 and how these responses might alter development in the long run. We fill this gap by exploring alternative multidimensional policy strategies that can improve long-term human wellbeing in spite of the pandemic. We find that even prior to the COVID-19 outbreak, the world was not on track to achieve the SDG agenda, reinforcing findings in the literature. We find that the effect of COVID-19 had an immediate adverse impact on progress toward the SDGs and that the shock will cast a long shadow, setting back progress for decades from where it would have otherwise been. In addition, we find that significant improvements can be made to the development trajectory that improve SDG attainment if a set of policy priorities are pursued. COVID-19 will not be the last shock to challenge global development, and it will be important to better understand how to overcome both it and challenges in the future. Therefore, one of our scenarios explores how a substantial and transformative agenda could accelerate progress toward the SDG targets by midcentury. This offers insight into how integrated action across policy areas can further sustainable development and set the world on a new path forward. We find that this scenario, while it does not result in achieving the Goals on time in all countries, is successful in quickly making up for the damage inflicted by COVID-19 and further in propelling progress for decades to come. This paper proceeds by first elaborating the methods used to drive the analysis with a particular emphasis on the structure of the modeling framework and scenario assumptions. Next, we present the results, highlighting how the COVID-19 pandemic is likely to change long-term development outcomes and what a successful set of policy strategies can do to further improve development outcomes beyond our Current Path of development. Finally, we discuss these findings and highlight methodological challenges as well as some implications of the policy strategies that are modeled. 2. Materials and Methods 2.1. International Futures This study uses the International Futures (IFs) forecasting system for forecasting and scenario analysis. IFs is an integrated assessment modeling platform with representation of 188 countries and capability to forecast out to 2100. It features numerous endogenized and interconnected sub-models with coverage of the following systems: agriculture [ 26 ], economics, education [ 27 ], energy, environment [ 28 ], demographics, governance [ 29 , 30 ], health [ 31 ], infrastructure [ 32 ], international politics [ 33 ], and technology. IFs is open-source and is free to use online or to download for offline use. The following sections describe key 7 Sustainability 2024,16, 3309 areas of the model for this work, but extensive model information and documentation is available for further detail [34]. IFs forecasts patterns of long-term economic growth using a recursive dynamic general equilibrium-seeking structure with a Cobb–Douglas production function and an endogenously dynamic Solow residual. Six capital sectors, labor by skill level, and the endogenously driven productivity term are shaped by forces within each of the 188 countries but also by international trade, official development assistance, foreign direct investment flows, and international migration with associated remittance patterns. A social accounting matrix structure accounts for flows across economic sectors and between households, firms, and governments and with the rest of the world. Representation of government finance within the social accounting matrix includes specification of revenues from domestic taxation streams and foreign assistance, while identified expenditures include transfer payments and direct spending in military, health, education, research and development (R&D), infrastructure, and residual categories. Other core features of the IFs model include partial-equilibrium agriculture and energy models physically elaborating those sectors of the total economy, an infrastructure model with access to information and communication technologies, electricity, water and sanitation, and paved roads [ 32 ], as well as well as an education model which simulates the grade-level flow of students through the education system and the pattern of educational attainment across adult life spans. Although the IFs system forecasts variables related to selected targets of all SDG goals, we focus here on three core outcome indicators in IFs: poverty, undernutrition, and the HDI, each described below. 2.1.1. Poverty Poverty rates in IFs are initialized using data from the World Bank [ 35 ], which come originally from household surveys and are driven by the model’s economic growth and inequality models [ 36 ]. The social accounting matrix structure of the economic model tracks financial flows to and from households resulting from labor earnings and transfers in interaction with firms and governments. Resultant disposable income is allocated to consumption or savings based on long-term country development patterns, demographic structure, and sectoral prices and interest rates. Poverty rates are estimated at per-capita household consumption levels assuming a log-normal distribution of household income, the shape of which is affected by changes to the Gini coefficient. For this analysis, we focus on extreme poverty, using the recently updated international poverty line of USD 2.15/day in 2017 US dollars at purchasing power parity. For countries lacking data values, the model estimates initial values using a cross-sectional function with GDP per capita. 2.1.2. Malnutrition Building on variables from and interactions across the demographic, general equilibrium economic, and partial-equilibrium agriculture models, IFs forecasts the prevalence of malnutrition as a function of available calories per capita, a coefficient of variation, and a minimum dietary energy requirement. The partial-equilibrium agriculture sub-model represents crop, meat, and fish production and trade and therefore calorie and protein availability [ 26 ], while the economic model generates consumption potential and calorie demand per capita as functions of GDP per capita and food prices. Demographics shape total country demand levels. As with the income that shapes poverty levels, access to calories is assumed to be distributed log-normally. The shape of the distribution is determined by the caloric coefficient of variation, which is driven by income growth, inequality, and social inequality, as represented by female labor participation and the youth dependency ratio. Data from the FAO are used to initialize the prevalence of malnutrition as well as calories per capita, the coefficient of variation, and the minimum daily energy requirement [26]. 8 Sustainability 2024,16, 3309 This analysis is focused on population-wide malnutrition to provide the broadest picture of progress toward eliminating hunger. It does not account for differing levels of hunger by gender or for young children, measures of which are also available in IFs. 2.1.3. Human Development Index The HDI has been designed and maintained by the United Nations Development Programme (UNDP) to measure general levels of human development in all countries across three basic dimensions—health, education, and living standards. The UNDP replaced an earlier and simpler version of the HDI with a more refined version in 2010 [ 37 ]. This index is a geometric mean of three normalized sub-indices, representing (1) life expectancy at birth, (2) an average of mean years of schooling completed at age 25 or older and the expected years of schooling upon entry to education, and (3) a logarithm of gross national income per capita at purchasing power parity (for which IFs substitutes gross domestic product at PPP). Data from the UNDP initialize the HDI values for each country in the 2019 base year of IFs [35]. Forecasts of the HDI in IFs are driven by several sub-models, especially the demographic, health, education, and economic models. The health model produces the life expectancy index. This model, drawing on data and approaches of the Global Burden of Disease project [ 38 ], represents 15 causes of ageand sex-specific mortality across communicable, non-communicable, and accident and injury categories, thereby providing the basis for the computation of life expectancy. The education model represents year-specific entry into and flow through primary, lower-secondary, upper-secondary, and tertiary education; the progression through these levels feeds the years of schooling attained by population cohorts at post-educational ages, and the demographic model carries age-specific education through the variable life spans of cohort members. The economic and demographic models determine GDP at PPP. 2.2. Scenarios We explore three scenarios aimed at evaluating where we are in terms of progress toward the SDGs and how we can collectively begin to narrow the gap between the road we are on and one that achieves the SDG agenda. These scenarios are modified from a set of four scenarios originally produced in 2020 and 2021 [ 39 ]. They have since been updated and modified to reflect recent data and the literature and run in an updated version of the IFs model. See Table 1 and following sections for a brief description of the three scenarios. Due to significant volatility in growth projections, Venezuela has been removed from the country set for this study. Table 1. Description of scenarios used in this analysis. Scenario Name Description No COVID-19 This scenario is a projection of the development path that the world was on prior to the COVID-19 outbreak. Current Path The Current Path reflects a baseline path of development in the future, including the effect of COVID-19. SDG Push This scenario simulates an integrated push toward SDG achievement through ambitious but achievable global interventions. 2.2.1. Current Path The Current Path can be thought of as the baseline development path, with the impacts of COVID-19 but without additional major shocks and without transformative policy change. Using the interconnected sub-models in IFs, this scenario reflects a dynamic unfolding of development patterns within and across countries as well as sectors. The Current Path uses exogenously imposed GDP growth rate data and projections from the latest version of the IMF’s World Economic Outlook through to 2025 [ 40 ]. The Current Path in IFs has been 9 Sustainability 2024,16, 3309 used widely in academic and policy-oriented work to describe the path that the world is currently on [28,30,33]. 2.2.2. No COVID-19 The No COVID-19 scenario serves as a counterfactual, simulating the path we would be on had there been no COVID-19 outbreak, and thus allows us to make a rough assessment of the pandemic impact on the goal path. It leans on the same logic that informs the Current Path scenario but uses data and projections made prior to the outbreak of COVID-19 . GDP growth rate projections from the IMF World Economic Report released in October 2019 [41] are imposed exogenously through to 2025. 2.2.3. SDG Push In the wake of the pandemic, UNDP [ 23 ] put forth guidance on how the world might not only recover from the pandemic but move beyond recovery to accelerate progress toward the SDGs, defining four key areas of response: governance (building a new social contract), social protection (uprooting inequalities), green economy (rebalancing nature, climate, economy), and digital disruption and innovation (for speed and scale). This SDG Push scenario is based on initial work by Hughes et al. [ 39 ], oriented around the key areas outlined by UNDP, and further builds on work by Moyer and Hedden [ 42 ]. Specific details about the individual scenario interventions and parameter changes within IFs are available in the Supplementary Information. In this scenario, the world pursues a set of policies that are designed to further sustainable development within planetary boundaries. Beginning with agricultural systems, sustainable development transformations include a shift away from meat-based diets towards plant-based diets, an increase in agricultural yields, and a reduction in loss (including losses in production, transmission, and consumption). In addition to the resulting increase in caloric availability, we also assume an increase in the equity of the distribution of calories, a simulation of cash transfer or food subsidy programs. Governments increasingly focus on programs that are core to human development, boosting spending on infrastructure, education, health (including a focus on family planning), and R&D while also increasing household transfers for welfare and pensions. Households benefit from expanded access to safe water, sanitation, information communication technology, and access to electricity as well as a reduction in traditional cookstoves. While government spending is important, governments in this scenario also improve the efficacy of this spending along with increasingly democratic institutions. The scenario also simulates a transformation in energy systems by implementing a progressive carbon tax (to USD 200 per ton for OECD countries and USD 50 for non-OECD countries), a progressive reduction in energy demand (greater and more rapid in OECD countries than in non-OECD countries), and improvements in energy efficiency. Future coal production is constrained while renewable energy development and investment is accelerated. Further environmental policies reduce overall water demand relative to the Current Path, reduce urban air pollutants, and increase forested land. The cumulative effect of these interventions is to make development less carbon intensive, more efficient, and less wasteful, while also pointing resources towards areas of investment that are crucial to multidimensional human wellbeing. 3. Results The following sections include the results of all three scenarios across three key outcome indicators: the population in extreme poverty, the undernourished population, and the Human Development Index. 3.1. Poverty The elimination of poverty is the first SDG (SDG 1) and highly connected to many of the others. Here, we focus on the international extreme poverty line of USD 2.15/day using 10 Sustainability 2024,16, 3309 2017 US dollars at purchasing power parity. For this analysis, a country or region is said to have eliminated extreme poverty if the portion of the population living below the extreme poverty line falls below 3 percent. Full global results are available in Table 2. Table 2. Results by scenario for SDG 1.1, using the percent of the population living on less than USD 2.15/day in 2017 US dollars. Source: IFs 8.10. 2019 2019 2030 2030 2050 2050 Scenario Global Value Countries Meeting Target Global Value Countries Meeting Target Global Value Countries Meeting Target No COVID-19 9 102 6.8 113 2.8 140 Current Path 9 102 7.5 107 3.1 139 SDG Push 9 102 6.6 117 1.5 159 Even prior to the outbreak of COVID-19, the world was not on track to meet SDG 1. Globally, an estimated 9 percent of the population (798 million people) lived in extreme poverty. Along the No COVID-19 trajectory, poverty was expected to decline gradually. In this scenario, 6.6 percent of the population (578 million) would still live in extreme poverty in 2030. By 2050, the world at a global level just meets the target, with 2.8 percent of the world (269 million). At a country level, 102 countries are estimated to have already met the SDG target in 2019. By 2050, they would be joined by an additional 37 countries achieving the goal. Along the Current Path affected by COVID-19, slowed economic growth resulted in an increase in poverty that could continue to affect progress toward SDG 1 for some time. In 2020 alone, we estimate an increase in the extreme poverty rate of 1 percentage point, reflecting nearly 80 million people pushed into extreme poverty by the pandemic in that year. This is a slightly greater effect than seen in previous work estimating the effect of COVID-19 on poverty using the IFs model (an estimated 73.9 million) [ 43 ] and the World Bank (estimating over 70 million) [ 44 ] and somewhat less than an estimates by Laborde et al. [ 14 ] in the most recent work by Mahler et al. [ 3 ], which finds a COVID-19induced increase in extreme poverty of 1.2 percentage points (90 million). As the economy rebounded somewhat, we forecast poverty reductions after the initial year, but these improvements will remain slow and behind the No COVID-19 counterfactual. By 2030, we project 7.5 percent of the population (635 million) in extreme poverty, still nearly 57 million more than the No COVID-19 scenario in the same year (Figure 1). By 2050, the world just misses reaching the target, with 3.1 percent of the population (298 million) still in poverty. In the SDG Push, poverty reduction accelerates as a result of interventions which boost growth and sustainable development. The poverty rate in the SDG Push scenario falls below that in the No COVID-19 world by 2029. By 2030, the extreme poverty rate reaches 6.6 percent (499 million people in extreme poverty, which is 81 million fewer than in the Current Path headcount). Global extreme poverty falls below 3 percent by 2042, and by 2050, it falls to 1.5 percent (104 million, or 137 million fewer than in the Current Path). Along the Current Path, the global poverty rate is projected to remain above 3 percent through the horizon chosen for this analysis. At a regional level, sub-Saharan Africa is home to the most people living in extreme poverty, with an estimated 404 million in 2019. But the effect of COVID-19 in the region was not as severe as in Central and Southern Asia, where 46 million people were pushed into extreme poverty due to COVID-19 in 2020, compared with just under 20 million in SSA (Figure 2). However, in the following years, the poverty difference in the CSA region is expected to fall, while in the SSA region it remains relatively steady, reflecting faster population growth in the sub-Saharan Africa region. 11 Sustainability 2024,16, 3309 Figure 1. Percent of world population living on less than USD 2.15/day across scenarios. Source: IFs 8.10. Figure 2. Difference between the number of people in poverty in the Current Path scenario and the No COVID-19 scenario, by region. Source: IFs 8.10. Figure 3 shows the rate of extreme poverty by region across all three scenarios in 2030 and 2050. The SDG Push begins to improve extreme poverty in regions where it is the most prevalent relative to the Current Path. In Europe and Northern America (ENA), Latin America and the Caribbean (LAC), and sub-Saharan Africa (SSA), the SDG Push makes up for the difference between the Current Path and No COVID-19 scenarios by 2030, while in others—Central and Southern Asia (CSA), Eastern and South-Eastern Asia (ESEA), 12 Sustainability 2024,16, 3309 Northern Africa and Western Asia (NAWA), and Oceania—the SDG Push still lags behind the No COVID-19 scenario (Figure 3a). (a) (b) Figure 3. ( a ) Percent of population living on less than USD 2.15/day across scenarios by region in 2030. Source: IFs 8.10. ( b ) Percent of population living on less than USD 2.15/day across scenarios by region in 2050. Source: IFs 8.10. By 2050, the SDG Push results in a significant decline in poverty rates across regions (Figure 3b). The SDG 1 goal of eliminating extreme poverty is achieved in all regions except SSA, where the poverty rate is still roughly half that projected along the Current Path. There are various mechanisms in the SDG Push scenario that improve poverty outcomes relative to the Current Path. Government transfer programs boost incomes directly, while a number of interventions also work to alleviate poverty indirectly through improvements to the economy and human development. Family planning programs reduce the future investment required to achieve similar outcomes in areas of education and health, driving up human wellbeing and promoting productivity gains. Government spending is reoriented towards education, health, infrastructure, and R&D sectors, leading to greater long-term gains in multidimensional development. More efficient use of agricultural and energy resources also unlocks economic gains that facilitate reductions in poverty. This combination of direct and indirect interventions leads to a virtuous cycle towards the eradication of poverty. 3.2. Malnutrition SDG 2 is to “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” and targets range from ensuring food access for vulnerable populations to measures addressing agricultural investments and trade. For this analysis, we focus more narrowly on population-wide undernutrition. Full global results are available in Table 3. Prior to the outbreak of COVID-19, we estimate that just under 8 percent of the global population (612 million people) suffered from malnutrition and that 67 countries had already met the SDG 2.1 goal of Zero Hunger. In a No COVID-19 world, we project that malnutrition would continue to fall but would not achieve the goal at a global level. By 2030, still more than 5 percent of the population (445 million) would suffer from malnutrition, with 95 countries meeting the target of 3 percent. At a global level, the target would be 13 Sustainability 2024,16, 3309 achieved by 2044, and by midcentury, the malnourished portion of the population would fall to 2.1 percent (203 million people). Table 3. Results by scenario for SDG 2.1, using the percent of the population suffering from malnutrition. Source: IFs 8.10. 2019 2019 2030 2030 2050 2050 Scenario Global Value Countries Meeting Target Global Value Countries Meeting Target Global Value Countries Meeting Target No COVID-19 7.9 67 5.3 95 2.1 134 Current Path 7.9 67 5.4 89 2.2 133 SDG Push 7.9 67 4.3 106 0.8 164 The COVID-19 outbreak in 2020 reduced economic growth globally and increased both poverty and hunger. We estimate that in 2020, the rate of malnutrition increased by nearly 0.5 percentage points or 37 million people relative to a No COVID-19 scenario (Figure 4). As the world recovered from that initial shock, hunger began to fall again but remained higher than it would have been otherwise. By 2030, 15 million more people are projected to be malnourished in the Current Path scenario compared to a No COVID-19 world. By 2050, 6.6 million more people are still projected to suffer from malnutrition as a result of the shadow of the pandemic. Figure 4. Percent of population malnourished, across the world, across scenarios. Source: IFs 8.10. However, in an SDG Push world, multiple interventions are made to address hunger through both food supply and accessibility. By 2030, global malnutrition is reduced by 1.1 percentage points compared to the Current Path, and by 2035 the global malnutrition rate falls below 3 percent, ten years before it is projected to in the Current Path. By 2050, the malnourished population falls to 0.8 percent (77 million) and 164 countries have met the SDG 2.1 target—31 more than are projected to do so in the Current Path. As in poverty, CSA is the region that experienced the largest increase in malnourishment due to COVID-19 (Figure 5). In 2020, the Current Path reflects an additional 22 million 14 Sustainability 2024,16, 3309 people in the region pushed into malnutrition compared with a No COVID-19 scenario (Figure 5), followed by SSA with just over 6 million. By 2050, the COVID-19 effect is not as large but still at nearly 5 million malnourished people, while the effect in SSA falls to meet that of many other regions. However, the effect also remains significant in NAWA, where over 2 million more people remain malnourished in 2050 in the Current Path. Figure 5. Difference between the number of people with malnutrition in the Current Path scenario and the No COVID-19 scenario, by region. Source: IFs 8.10. The SDG Push scenario simulates a gradual increase in equality of access to calories among other interventions. Even by 2030, the SDG Push results in a reduction in the rate of malnutrition below that in the No COVID-19 scenario in all regions (Figure 6a). (a) (b) Figure 6. 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[CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 22 Citation: Mestdagh, B.; Sempiga, O.; Van Liedekerke, L. The Impact of External Shocks on the Sustainable Development Goals (SDGs): Linking the COVID-19 Pandemic to SDG Implementation at the Local Government Level. Sustainability 2023,15, 6234. https://doi.org/ 10.3390/su15076234 Academic Editors: ¸Stefan Cristian Gherghina and Liliana Nicoleta Simionescu Received: 24 February 2023 Revised: 27 March 2023 Accepted: 2 April 2023 Published: 4 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article The Impact of External Shocks on the Sustainable Development Goals (SDGs): Linking the COVID-19 Pandemic to SDG Implementation at the Local Government Level Björn Mestdagh *, Olivier Sempiga and Luc Van Liedekerke Department of Management, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium *Correspondence: [email protected] Abstract: Using data from a survey we conducted in collaboration with the Association of Flemish Cities and Municipalities (VVSG), this article sought to examine the effects of the COVID-19 pandemic on the implementation of SDGs by Flemish local governments (cities and municipalities). Identifying such effects has usually been conducted on individuals and at a macro level and not at the organization and local government level. By using a counterfactual approach, we were able to disentangle various COVID-19 effects over time and learn how systems at the local level react to external shocks. The approach allowed us to single out the effects of the pandemic at the organizational level while looking into three distinct periods: before the pandemic, during the pandemic, and in its aftermath. Results showed that the COVID-19 pandemic slowed down the Flemish public sector’s implementation of SDGs at the local level. At the same time, COVID-19 allowed local public institutions to accelerate the implementation of a few SDGs (e.g., SDG1, SDG3) and to postpone a few SDG-related activities which would be resumed once the pandemic is ‘over’. COVID-19 is not only a challenge; it acts as a wake-up call and an opportunity to commit more towards the implementation of (certain) SDGs. Keywords: UN sustainable development goals (SDGs); COVID-19; SDG implementation; local governments 1. Introduction The COVID-19 pandemic has impacted social, economic, and environmental systems worldwide, slowing down and reversing the progress made in achieving the Sustainable Development Goals (SDGs) [ 1 ]. While some SDGs have been directly and hugely impacted, others have been indirectly affected by the global pandemic emergency [ 2 , 3 ]. At some point, the pandemic brought the advancement of Agenda 2030 to a standstill and, as a result, has put serious doubt on the achievement of SDGs [ 4 ]. Most of the existing research examines the effects of COVID-19 on SDGs at an individual level [ 5 ], while other studies focus on a macro level [ 6 ]. According to the Crisis in Context Theory (CCT), the way individuals react to external shocks is different from the reactions of the system, as both entities form different layers in a crisis model [ 7 ]. While it is important to analyze each layer involved in the crisis, there is little evidence on how the COVID-19 pandemic has stalled the implementation of the SDGs at the organizational level and how organizations reacted to the pandemic. We filled this gap in the literature by examining whether and to what extent COVID-19 has affected local governments’ SDG implementation. A growing body of research has already explored the relationship between COVID-19 and SDGs [ 8 – 10 ]. While some studies suggest that COVID-19 measures have brought a few positive impacts on the environment, for example, by reducing air pollutants and greenhouse gas emissions [ 11 , 12 ], other research gives a harsh judgment on the impact of COVID-19 on SDGs by claiming that COVID-19 has torn to shreds sustained economic growth and globalization, the two big assumptions on which SDGs’ success were built [ 8 ]. Specifically, following the shrinking of the global economy, different SDGs were negatively Sustainability 2023,15, 6234. https://doi.org/10.3390/su15076234 https://www.mdpi.com/journal/sustainability 23 Sustainability 2023,15, 6234 affected [ 3 , 5 ]. However, since most of these studies focused their main attention on how individuals were affected by COVID-19, we based our analysis on organizations. Building on earlier insights, we embraced a novel perspective in our research by investigating how the local public sector was hindered by COVID-19 in its efforts to implement SDGs. This approach allows us to gain a deeper understanding of how local public institutions may not only have been prevented from pursuing their efforts to achieve the Agenda 2030 but are also likely to have shifted their priorities to respond to the challenges presented to them by the pandemic, thereby accelerating on some SDGs. We examined how and to what extent public organizations (institutions) at the local level adjusted their strategies to respond to the crisis. During global shocks, organizations react differently, and hence it makes sense to investigate organizational behaviors when faced with crises. Furthermore, research has pointed out that COVID-19 delayed the achievement of some SDGs. For instance, it reversed approximately a decade in the world’s progress in reducing poverty [ 13 – 15 ]. It also slowed down the progress made in the area of health [ 5 ]. This further warrants our focus on the impact of COVID-19 on SDGs and how institutions reacted to the crisis by shifting priorities. A theory suggests that “Only a crisis—actual or perceived—produces real change. When that crisis occurs, the actions that are taken depend on the ideas that are lying around” [ 16 ] (p. 7). Faced with the pandemic, local governments were forced to respond within their capacity and were sometimes obliged to shift priorities away from the usual actions. Because of their limited exploration of the efforts of the public sector in achieving SDGs, earlier studies failed to appreciate how public institutions adapted their strategies and how SDG tools helped them to navigate through the pandemic without abandoning their efforts and engagement with SDG implementation. The consequences of COVID-19 on SDGs called organizations into action for prompt measures [ 3 ]. Building further on these insights, our study showed that some organizations ceased the ‘opportunity’ offered by the COVID-19 crisis to accelerate their implementation of a number of SDGs and were building on this momentum in the aftermath of the pandemic. Consequently, we aimed to gain a better understanding of the relationship between the two variables by analyzing how local governments’ implementations of SDGs were hindered or encouraged by COVID-19. To understand governmental institutions’ SDG implementation efforts and activities, we partly based our analysis on the SDG compass, which is a tool that different organizations utilize to apply SDGs at their levels. Meanwhile, there is little research on public institutions, especially at the local level vis-à-vis COVID-19 and SDGs, despite this being a topical issue in public administration. Relying on data from a survey we developed and conducted in July 2021 in collaboration with the VVSG (the Association of Flemish Cities and Municipalities), we investigated how Flemish public organizations’ SDG implementation activities and efforts were hampered by COVID-19. This provided us a better picture of how much the implementation of some SDGs was put on hold to focus on more urgent SDGs. We obtained insights on how local public institutions in Flanders (region that is part of the federal state of Belgium and has its own assigned powers, which were granted by the federal constitution. Flanders exercises these powers (e.g., cultural matters, welfare, education, economic matters, etc.) autonomously according to the principle of federal loyalty. Flanders has five provinces and 300 cities and municipalities with jurisdiction over a given territory. These local governments are autonomous on the one hand, and on the other hand, they are part of the state (coadministration). These administrations also have an open mandate, which means, among other things, that they can take their own initiative in many matters and levy their own taxes. For these reasons, mandataries are also directly elected at this level. Municipal powers are very broad and include everything related to the “municipal interest,” in other words, the collective needs of residents. In theory, a city/municipality in Flanders can do anything as long as it is not prohibited. Jurisdiction includes public works, social assistance, law enforcement, housing, education, etc.) may have shifted priorities to respond better to the crisis and may have come out of the crisis more equipped to pursue efforts to achieve the Agenda 2030. 24 Sustainability 2023,15, 6234 The remainder of this article is organized as follows. In Section 1, we discuss the literature regarding the impact of the COVID-19 pandemic on SDGs and elaborate on subsequent hypotheses. Building on the literature, we develop our argument on whether and to what degree COVID-19 impacts SDG implementation in the public sector at the local level. In Section 2, we show how data were collected and analyzed, while in Section 3, the results are discussed and analyzed. Finally, Section 4 concludes with a discussion and outlines possibilities for future research on the themes discussed in the paper. 2. Literature Review and Hypotheses Although SDGs are a recent phenomenon, there is a growing body of research developed on the impact of COVID-19 on SDGs [ 17 ]. The pandemic is likely to have threatened SDG achievement scheduled in 2030 [ 18 ]. The SDG implementation process is a key step because the Agenda 2030 framework may lead to different sustainability outcomes, depending on how it is implemented by the diverse set of competent agents [ 19 – 21 ]. Mazmanian and Sabatier [ 22 ] (p. 20) defined implementation as “the carrying out of a basic policy decision, usually incorporated in a statute but which can also take the form of important executive orders”. As a policy decision, SDGs identify the problem(s) to be addressed, stipulate the objective(s) to be pursued, and structure the implementation process. We regarded SDGs as a universal project to end poverty, protect the planet, and improve the lives and livelihoods of everyone everywhere [23]. Since the adoption of SDGs in 2015, private and public institutions at different levels have been encouraged to join hands in implementing SDGs. Evaluating countries’ trajectory, macro-level research has classified nations into five categories in light of their SDG implementation process: “decreasing” (country score is moving away from SDG achievement), “stagnating” (country score remains stagnant or is improving at a rate below 50% of what is needed for SDG achievement by 2030), “moderately increasing” (country score is increasing at a rate above 50% but below the rate needed for SDG achievement by 2030), “on track” (score is improving at the rate needed for SDG achievement by 2030), “maintaining goal achievement” (country score is on the level and remains at or above SDG achievement) [ 24 ]. We built on these insights to check the trajectory of the SDG implementation process among public organizations at the local level that form an important part of the national effort. We believe that local governments are the key implementers of policy decisions and produce the outcomes of those decisions in the governance process. It is at this level that all actions take place [ 25 ], and hence we find it important to study the local level. 3. Determining the Effects of the COVID-19 Pandemic on SDG Implementation 3.1. The Slowdown Effect: COVID-19 Slowed down Organizations’ SDG Implementation in General Since their adoption in 2015, many SDGs (e.g., economic growth, education for all, and poverty reduction) have experienced relative progress. The spread of COVID-19 seems to have changed the scenario. Based on the United Nations report [ 26 ], Fallah Shayan et al. [ 5 ] demonstrated how COVID-19 had dramatically disrupted the decreasing number of poor people. Due to the worldwide disruption of the economy and food supply chain, more people have suffered from malnutrition. Similarly, following school closures during lockdowns, so many students did not have basic equipment or access to attend online schools and may have fallen behind [ 5 ]. In a particular way, the COVID-19 pandemic is a major economic shock that has already increased economic insecurity, particularly for less educated people [ 27 , 28 ]. The economic insecurity was translated into job-related disruptions, including losing a job, a reduction in working hours, or a fall in income for millions of people [29]. While some studies investigate the pandemic’s social, economic, and environmental impacts separately or only focus on a few SDGs [ 30 – 34 ], other studies focus on all 17 goals, thereby giving a holistic picture [ 5 , 35 ]. In addition, most of these studies focus their studies 25 Sustainability 2023,15, 6234 on how COVID-19 has an effect on individuals. These studies maintain that the economic crisis that followed the pandemic is estimated to have flung 400 million people below the $1.90 poverty line [ 14 , 15 ], while the number of people who are likely to face acute food shortages highly increased during the pandemic [36]. Although there is a lot of research on how COVID-19 affects the individual in relation to SDGs, comprehensive studies on the pandemic’s impacts (both negative and positive) are still lacking in the context of impacts on organizations. Theories show that there is more than a surface layer of impact to every crisis. Besides individuals being affected, systems, subsystems, and stakeholders get affected [ 7 ]. Accordingly, our analysis concentrated on the organizational level rather than the individual level. Although we acknowledged that SDGs are generally meant to serve the well-being of individuals, organizations play a key role in SDG achievement through their investment in SDGs and in policy implementation that is specific to SDGs [ 37 ]. The success or the failure of SDGs, for that matter, is mainly dependent on the way different institutions manage to invest time, energy, and money in the SDG project bearing in mind the effects of external environmental factors (i.e., economic crisis, COVID-19). Moreover, the concrete realization of SDGs is impeded by how they are implemented by a diverse set of competent agents [ 23 ]. Our intention was not to address the reciprocal effects of crises on individuals and organizations but specifically on how local public organizations are affected. Organizations also play a key role in the realization of Agenda 2030 through various SDG implementation activities [ 38 ]. For example, the city of Antwerp has taken different initiatives to contribute to protecting the environment. One of the initiatives is called “Climate Streets” and has seen the city residents work hand in hand with local teams to green up the streets with more plants and natural features. This initiative that began in 2017 is about using permeable materials and rainwater recovery to cope with flooding and greenery to cope with heat stress during hot summers that have become more of a reality in recent years [ 39 ]. Literature shows that the public sector is specifically called upon to implement different policy instruments so as to ensure wide access to public services, adopt policies and strategies to achieve certain SDGs such as gender equality (SDG5) and job creation and entrepreneurship (SDG8), and invest resources in different instrumental areas, such as in research and innovation (SDG9) and multisector partnerships (SDG17) [ 40 ]. Hörisch [ 18 ] maintains that the pandemic has been found to severely threaten the achievement of the SDGs by shifting attention away from the many prior challenges of sustainable development. Prior research shows that the pandemic has had different degrees or types of impact on SDG implementation. It has negatively impacted most SDGs in the short term. Particularly, the targets of SDG1, 4, 5, 8, 9, 10, 11, and 13 have and will continue to have weakly to moderately negative impacts or what some scholars term restricting impacts. Although most of these impacts are likely to be short-term, these impacts add new challenges in achieving those SDGs by 2030 [ 34 ]. According to the organizational resilience theory [ 41 ] and organizational improvisation theory [ 42 ], organizations are naturally endowed with resilience to external shocks and can adapt to a fast-changing environment, but organizations adapt differently depending on various factors (i.e., the strength of employees, the strength of adaptive models already in place, substantial investment during normal times). COVID-19 appeared to be too strong for a number of organizations because they did not have mechanisms in place to adapt and to be resilient to external shocks and hence saw their implementation activities slow down. Simultaneously, COVID-19 has exposed the fragility of the 2030 Agenda, especially where organizations have a role to play. If COVID-19 has slowed the SDG project further, we expect that the pandemic will have some negative impacts on the SDGs at the organizational level by slowing down local government’s SDG implementation. Therefore, we hypothesized that: Hypothesis 1 (H1). Local governments’ implementation of the SDGs significantly slowed down due to the COVID-19 crisis. 26 Sustainability 2023,15, 6234 3.2. The Prioritization and Acceleration Effect: COVID-19 Led Organizations to Prioritize Some SDGs and to Accelerate Their Organizational Implementation Sunny et al. [ 34 ] contended that a few targets of SDG2, 3, 6, and 11 could have benefited from the positive impacts of the pandemic. These mini-impacts are called weakly promoting impacts. Others call these rare effect opportunities [ 8 ]. Even though the pandemic has had devastating impacts on some SDGs, surprisingly, other SDGs have benefitted from the crisis. For instance, COVID-19 provided hope in opportunities for facilitating the achievement of SDG13 (climate action). COVID-19 measures taken by governments in the fight against COVID-19 (i.e., lockdowns) have also brought a few positive impacts on the environment by, for example, reducing air pollutants and greenhouse gas emissions [11,12]. The pandemic has also opened a short-lived and narrow window of opportunities for sustainable transformation. The transformative opportunities consist of lessons learned for planning and actions, socio-economic recovery plans, the use of information and communication technologies and the digital economy, reverse migration and “brain gain,” and local governments’ exercising authorities [ 34 ]. Furthermore, although the pandemic will have restricting impacts on most SDGs in the short term, these restricting impacts may subside in the medium and long term and may even result in some promoting impacts. These promoting impacts are expected first of all because some countries would catch up with the ongoing progress in achieving the SDGs and utilize the generated transformative opportunities once the pandemic is under control [ 34 ]. Secondly, transformative opportunities are expected because SDGs are interconnected and interlinked. This means that “implementing the 2030 Agenda will bring about synergies—i.e., situations in which achievements on one goal contribute towards progress on other goals” [43] (p. 6). Organization theories and crisis theories show that institutions and humanity learn from the crisis and adopt more effective measures. The financial crisis of 2007–2008 has increased awareness about the repercussions that weak corporate governance and risk management practices can have on financial markets and the world’s economy. The challenges entailed in the climate change process and the depletion of natural resources (as well as air and water pollution and biodiversity loss) have increased demand for more responsible behavior and coordination at the global level from both public and private economic organizations [ 3 ]. As a consequence, many organizations have put efforts into socially responsible investment (SRI). Adopting SRI is one way to reduce the negative impact on society as a whole, thereby making changes and contributing to the ills that have been affecting human lives for many years and accelerating on SDG project [ 37 ]. For organizations, prioritization is recognized in academic and practitioner literature as a crucial initial step, as it enables focusing on a reduced set of priorities, thus making SDG implementation more effective and manageable [ 40 ]. The pandemic and its consequences resulted in an increased focus on healthcare systems, information and communication technologies (ICTs), and the digital economy [ 34 ]. At the local level, the increased focus is likely to be in line with the indicators for European cities to assess and monitor the UN SDGs. For instance, many cities in Flanders sensitized residents on the benefits of COVID-19 vaccination and provided their halls to facilitate vaccinations, and invested more money to help in the vaccination campaign. If a number of SDGs benefited from global shocks and crises and got prioritized, we expected that in their response to the pandemic and its consequences, the public sector would witness a certain degree of acceleration in its implementation in certain SDG areas. Therefore, our next hypothesis is: Hypothesis 2 (H2). Due to COVID-19, some SDGs were prioritized, and consequently, local governments’ implementation of these SDGs got accelerated. 3.3. The Postponement Effect: During COVID-19, Organizations Postponed Some SDG Implementation-Related Activities Following the devastating impacts of the COVID-19 pandemic on SDGs and the apparent impossibility of reaching the Agenda 2030, various researchers, as well as practitioners, 27 Sustainability 2023,15, 6234 have called on the UN to rethink the world’s sustainable development strategy. For instance, following the slowdown of progress on the SDGs due to COVID-19, Naidoo and Fisher [ 8 ] argued that the world needs to define priorities better and probably focus on a few broad strategic goals rather than all 17 SDGs. A Nature editorial went further to proclaim that it is time to revise SDGs in order to make the goals more achievable [ 44 ]. Those who called for revision were aware that it is not just the COVID-19 crisis that made SDGs beyond our reach. The SDG project was all along slow and impossible to achieve. Reports on the first phase of the SDG agenda (2015–2020) showed unequivocally that progress toward achieving the SDGs had been slow in all parts of the world prior to the COVID-19 crisis [ 24 , 26 , 45 ]. However, this did not remove the fact that the advent of COVID-19 worsened the situation. Since the COVID-19 pandemic affected the planet, the United Nations raised the stakes for SDGs by viewing it as vital for COVID-19 recovery, which leads to greener, more inclusive economies and stronger, more resilient societies [ 45 ]. There is a strong conviction that achieving the SDGs would bring about a safer, more stable world with fewer natural and manmade hazards, thus lowering the likelihood of future crises occurring [ 10 ]. Backsliding on the progress already made on the SDGs not only imperils prospects for eradicating basic deprivations but also reduces resilience to other shocks in the future, especially for those least able to cope. Maintaining the progress already made must continue to be a priority during the crisis response and beyond—supporting those at immediate risk of poverty, hunger, or disease while facilitating their safe return to work and education and their access to healthcare [46]. According to the second step of the SDG compass [ 47 ], organizations are encouraged to determine their priorities, relying on an assessment of their positive-and-negative, currentand-potential impacts on SDGs across their value chains. Due to the impact that COVID-19 had on the organization, the latter may have had to postpone some activities. It is expected that while institutions put more focus on maintaining some SDG activities, they willingly or unwillingly make a choice to postpone other activities, hoping to resume them once the crisis is behind them. We, therefore, hypothesized that: Hypothesis 3 (H3). Due to the COVID-19 crisis, local governments postponed certain SDG implementation activities. 4. Materials and Methods 4.1. Sample and Procedures The population for this study consisted of all 300 Flemish cities and municipalities. All of them were given the opportunity to participate voluntarily in an online Qualtrics survey, drafted in Dutch and held in July 2021, to fully grasp the potential COVID-19 pandemic effects. Through the Association of Flemish Cities and Municipalities (VVSG), respondents were mailed the link to this self-administered questionnaire with an accompanying cover letter, and this mail was directed primarily to sustainability or environmental staff. The surveys were thus completed by a civil servant on behalf of each municipality/city. One of the cover letter’s key messages was that for one city/municipality, only one response was demanded, and this was verified using some control variables (type of city/municipality, province, number of citizens). To counteract possible common method bias, participants were further informed that their responses would remain anonymous and confidential [ 48 ]. In total, 220 participants completed the survey, but 90 were excluded from further analysis since it concerned partial (missing values) and some double participation. The final sample of complete responses thus comprised 130 unique cities and municipalities, resulting in a response rate of 43.3%. We also noted that cities and municipalities of all categories and all regions are included in the 130 unique ones, resulting in a representative sample. The data retrieved from the survey were subsequently used to describe the status of the variable SDG implementation of Flemish cities and municipalities and to attempt to determine the relationship between the COVID-19 pandemic and the SDG implementation 28 Sustainability 2023,15, 6234 of cities/municipalities based on the hypotheses drawn up above. In order to do so, some of these data were statistically tested using SPSS 28.0. 4.2. Survey Design This study was intended to provide some empirical evidence on the relationship between the COVID-19 pandemic and SDG implementation of individual organizations, more specifically, cities and municipalities. To investigate, the survey sections of interest for this article were: (1) an assessment of the status of the organizations’ SDG implementation (currently [the time the survey was completed], counterfactually [given the non-existence of COVID-19], and future), (2) an overview of SDG implementation activities (currently [the time the survey was completed], counterfactually [given the non-existence of COVID-19], and future), (3) an assessment of the direct impact of COVID-19 on the organizations’ SDG implementation (currently [the time the survey was completed], and in the future), and (4) an assessment of the direct impact of COVID-19 on the organization’s implementation of the individual SDGs. Participants were thus asked both directly and indirectly about the relationship between the variables COVID-19 and SDG implementation. To measure the variables, the following survey questions were used: “What do you consider to be the impact of the COVID-19 crisis on the SDG Implementation of your city/municipality so far(1)/within a year(2)?” (answers: no, slowdown, acceleration), and “In your assessment, how far along is your city/municipality in implementing the SDGs currently(1)/had there been no COVID-19(2)/within a year(3)?” (answers: no SDG implementation, early stage, somewhat advanced, advanced, far advanced, complete SDG implementation). The survey acted thus also as a way to help cities and municipalities evaluate their past and present achievement of the SDGs in light of COVID-19 and to project themselves into the future. To gain insights into SDG implementation activities of cities and municipalities, this paper made use of a model consisting of several steps, which appeared in both academic literature and practitioner guides for all types of organizations. This five-step model is also known as the SDG compass [ 47 ]. The SDG compass is a tool that was created to help institutions to implement SDGs in different states of their programs and strategies. The SDG compass guide is addressed to all governmental and non-governmental development actors who are looking for practical guidance on how to further mold their organization and programs to Agenda 2030 and the underlying principles [ 49 ]. Although there are many tools, we found the SDG compass to be easier to understand and apply by all local governments. The five steps are: understanding the SDGs, defining SDG priorities, setting SDG goals, integrating the SDGs, and reporting and communicating on the SDGs. These five steps of SDG implementation are well-known to practitioners and hence very recognizable to the participants of this survey. Unfortunately, we did not know exactly how many of the surveyed cities and municipalities actually used or even knew the SDG compass. However, we were confident that they were all familiar with the five-step model, especially since the Flemish government’s SDG manual for governmental organizations is inspired by the SDG compass and thus relies on the same five-step model [ 50 ]. Hence, to measure the variable, the following survey questions were used: “In your assessment, to what extent is your city/municipality implementing the SDGs? Please indicate the activities that your city/municipality is doing currently(1)/had there been no COVID19(2)/within a year(3)?” (answers: no, understanding the SDGs, defining priorities, setting goals, integrating, reporting and communicating). Since the SDG compass guide aims to provide practical and operational support to organizations in their efforts to design, implement, monitor, and evaluate their interventions in a way that respects and contributes to Agenda 2030 [ 49 ], we used it to evaluate how local governments were able to implement SDGs during COVID-19 and analyze how the very implementation could have been hindered. A few other studies used SDG tools to evaluate the organization’s engagement with SDGs or to provide new insights. Grainger-Brown and Malekpour [ 51 ] used different SDG tools (SDG compass, global reporting initiative, and the ‘SDG industry matrix’) in their review research of different strategic SDG tools. Muff, 29 Sustainability 2023,15, 6234 Kapalka, and Dyllick [ 52 ], in turn, enriched the SDG compass by introducing process knowhow and content expertise in order to facilitate its application in the strategic processes of businesses. Such tools and frameworks are a way of aligning global goals to “micro” strategies [3]. 5. Results 5.1. The Slowdown Effect The respondents’ assessment of the status of the organizations’ SDG implementation (Figure 1) clearly indicated that at the moment of questioning, more than 90% of the Flemish cities and municipalities (90% of the sample) were already actively engaged in SDG implementation, with more than half being at an early stage of SDG implementation. Looking at the counterfactual numbers (given the non-existence of the COVID-19 crisis), the results cautiously showed that if COVID-19 had not existed, Flemish cities and municipalities would have been further ahead with their SDG implementation. In this hypothetical situation, more than half would already be at a somewhat advanced stage of SDG implementation or further. Looking at the future situation (within 1 year), the results showed a commitment of Flemish cities and municipalities to keep on engaging in SDG implementation. Most organizations indicated they would be at an advanced or even far advanced stage of SDG implementation one year later. To conclude, Figure 1 cautiously shows that COVID-19 has had a slowdown effect on the SDG implementation of Flemish cities and municipalities. Figure 1. SDG Implementation of Flemish cities/municipalities. Table 1 shows the basic statistics of the SDG implementation of Flemish cities and municipalities in all three situations. The basic statistics also tentatively indicated that had it not been for COVID-19, Flemish cities and municipalities would have been further advanced in their SDG implementation (mean 2.65) than the current situation (mean 2.41). The numbers also showed the previously mentioned commitment of the organizations to keep on engaging in SDG implementation in the future (mean 3.06). The paired-sample t-test results showed that if COVID-19 had not been there, SDG implementation of Flemish cities and municipalities would have been 0.246 higher than the current situation. It was found that, since the significance value for change in SDG implementation is less than 0.05, the average hypothetical rise of 0.246 was not due to chance variation and could be attributed to the COVID-19 crisis. This indicated again that COVID-19 slowed down SDG implementation in Flemish cities and municipalities. Hence, H1 is supported, and there is a statistically significant difference between SDG implementation in the COVID-19 situation and in the hypothetical counterfactual situation (with no COVID-19). 30 Sustainability 2023,15, 6234 Table 1. Paired-Sample Statistics ‘SDG Implementation of Flemish cities/municipalities’. Mean Standard Deviation Standard Error Mean SDG Implementation Currently (A) 2.41 0.851 0.075 SDG Implementation if COVID-19 had not been there (B) 2.65 0.895 0.079 SDG Implementation within 1 year (C) 3.06 1.002 0.088 Paired-sample correlations Correlation Significance. A & B 0.828 <0.001 Paired-samples test Paired differences Mean difference t Significance. (2-tailed) A&B −0.246 −5.457 <0.001 n = 130 Supplementary, the participants were also asked directly about the impact of COVID19 on the SDG implementation of their organizations. Figure 2 shows that almost 50% of them (64 in total) indicated that in the current situation, COVID-19 caused a slowdown in the SDG implementation of their organizations. The main reasons given for this are: changing priorities, lack of manpower, and less interest. This reinforced the acceptance of H1: local governments’ implementation of SDGs significantly slowed down due to the COVID-19 crisis. It is worth noting that in addition, a few cities and municipalities (five in total) also indicated that the COVID-19 crisis had had just the opposite effect on the SDG implementation and that they have shifted up a gear (seemed to have accelerated). Although this concerned an absolute minority (less than 4%), we might note that for these cities and municipalities, COVID-19 could also have created an opportunity for them to accelerate the implementation of SDGs. Figure 2. Direct impact of COVID-19 on SDG Implementation of cities/municipalities. Our study revealed that almost 50% of cities and municipalities indicated that COVID19 slowed down organizations’ SDG implementation and that this slowdown was significant. Our findings are in line with earlier studies. Shula et al. [ 4 ] concluded that the 31 Sustainability 2023,15, 6234 Data Availability Statement: Data are unavailable due to participants’ organizations’ privacy restrictions. 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From global goals and planetary boundaries to public governance—A framework for prioritizing organizational sustainability activities. Sustainability 2018,10, 2741. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 40 Citation: Brzozowska, K.; Gorzałczy´nska-Koczkodaj, M.; Ociepa-Kici´nska, E.; Pluskota, P. The Impact of the COVID-19 Pandemic on Financial Condition and Mortality in Polish Regions. Sustainability 2023, 15, 8993. https://doi.org/10.3390/ su15118993 Academic Editors: ¸Stefan Cristian Gherghina and Liliana Nicoleta Simionescu Received: 12 March 2023 Revised: 19 May 2023 Accepted: 24 May 2023 Published: 2 June 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article The Impact of the COVID-19 Pandemic on Financial Condition and Mortality in Polish Regions Krystyna Brzozowska , Małgorzata Gorzałczy´nska-Koczkodaj, El˙ zbieta Ociepa-Kici´nska and Przemysław Pluskota * Institute of Spatial Management and Socio-Economic Geography, Faculty of Economics, Finance and Management, University of Szczecin, Mickiewicza 64, 71-101 Szczecin, Poland; [email protected] (K.B.); [email protected] (M.G.-K.); [email protected] (E.O.-K.) *Correspondence: przemyslaw[email protected] Abstract: The study aimed to assess the impact of the COVID-19 pandemic on the financial condition and mortality in Polish voivodeships. To achieve this objective, the relationship between the number of deaths before and during the pandemic and the financial condition of the provinces in Poland was studied. The study covered the years 2017–2020, for which a one-way ANOVA was used to verify whether there was a relationship between the level of a province’s financial condition and the number of deaths. The results of the study are surprising and show that before the COVID-19 pandemic, there was a higher number of deaths in provinces that were better off financially, but the relationship was not statistically significant. In contrast, during the pandemic, a statistically significant strong negative correlation between these values was proven, which, in practice, shows that regions with better financial conditions had a higher number of deaths during COVID-19. Keywords: self-government finances; financial condition of local administrative units; number of deaths; COVID-19 1. Introduction The pandemic brought about by the SARS-CoV-2 virus changed the way the world functioned, the economy, and citizens’ lives. It also affected the condition of public finances, including self-government finances. The research completed by the key financial institutions has shown that the COVID-19 pandemic had an impact on reducing the income of local administrative units—LAUs (municipalities, poviats, and voivodships), mainly as a result of decreasing the tax proceeds, and also on increasing the expenses, which finally resulted in decreased possibilities of contracting debt. Taking a look from a financial perspective makes it possible to comprehensively evaluate LAU functioning and its development capabilities [ 1 ]. Finance management in LAU should foster rational expenditure of public financial resources and make correct decisions regarding the management of monetary funds [ 2 ]. An important issue in LAU evaluation is its financial condition understood as the self-government’s ability to balance the recurring expenses with recurring sources of income while fulfilling the statutory tasks stipulated by the legal regulations. Other definitions of financial conditions take into account the possibility of financing the services on a continuous basis, the complexity of healthy finance, the ability to pay liabilities, and keeping the current level of services while maintaining the resistance to changes taking place over time [ 3 ]. The terms ‘financial condition’ and ‘financial situation’ are used interchangeably and defined in different ways [ 1 , 4 – 10 ]. In view of the research studies presented in this paper, it was decided that the most pertinent definition of financial condition will be the entity’s ability to timely meet its financial liabilities and the ability to sustain the services provided to the public [11]. Sustainability 2023,15, 8993. https://doi.org/10.3390/su15118993 https://www.mdpi.com/journal/sustainability 41 Sustainability 2023,15, 8993 Researchers all over the world have taken up numerous studies regarding the impact of COVID-19 on the activity of business entities. The research studies covered the influence of COVID-19 on the labor market and its inequalities [ 12 ], also taking into consideration people with disabilities [ 13 ], the school education process in Germany [ 14 ], and also on employment in LAUs in the USA, in particular, the situation of decreased income and increased expenses [ 15 ]. Other research studies also addressed the relationship between the COVID-19 pandemic and the mortality rate [ 16 ], the economic development rate [ 17 ], and the population size, which has an impact on burdening the healthcare system [ 18 ]. An analysis of the average life expectancy has shown an increased mortality risk during the pandemic, globally as well as in Poland [ 19 ], and more lost years of life expectancy than before the pandemic [ 20 ]. Additionally, the analysis covered the impact of COVID-19 nationally [ 21 ], in local sectors [ 22 ], and the federal budgets in the USA [ 23 ] on the subject of small and medium enterprises [ 24 , 25 ], their creditworthiness, and the system of guarantees and suretyships for SMEs [ 26 ]. Rama Iyer and Simkins [ 27 ] analyzed 81 articles regarding COVID-19 and the economy, according to citation counts in Google Scholar. They divided the selected articles into five thematic areas: investments and assets valuation, macroeconomics and banking, resources, business finance, and others. The studies regarded the capital market, labor market, education market, and condition of enterprises and economies on the macro scale. Despite such extensive research available in the literature, the authors of this paper identified a shortage of studies regarding the financial condition of local administrative units (LAUs), such as cities and municipalities, second-tier administrative units (poviats), or self-governments of provinces (voivodeships). This is particularly important because, in a COVID-19 pandemic, regional economies were vulnerable to measures taken by central governments and instruments used in mitigating the pandemic’s effects. At the same time, measures taken on a national scale had an impact on regions and their economic situation, however, with diverse effects [ 28 ]. In this context, the financial situation of LAUs in that period should be considered in two aspects. On one side, from the point of view of the need to increase expenses to counteract the COVID-19 pandemic and to eliminate its effects, and parallel to that in terms of decreased current proceeds (i.a. from lease and rental fees, due to releasing entrepreneurs from the duty to pay such charges as a result of temporary suspension of their activity). The study aimed to assess the impact of the COVID-19 pandemic on the financial condition and mortality in Polish regions. To achieve the goal, the relationship between the number of deaths and the financial condition of voivodeships in Poland in the period before the COVID-19 pandemic and during the course of it. This research study was based on the statistical and financial data found in budget implementation reports submitted by LAUs in Poland for the years 2017–2020 (latest available data). For each region, financial condition indicators (in relative terms) were computed, which made it possible to draw conclusions regarding the impact of COVID-19 on their financial standing, and its influence on the mortality rate. The research scope covered 16 self-governing voivodeships in Poland [ 29 , 30 ]. Voivodeships in Poland are interchangeably referred to as regions (NUTS2), In the same breadth and they constitute one of the three levels of LAUs, along with municipalities and poviats. The paper consists of several sections put in a logical sequence. Section one includes theoretical aspects connected with the discussed issues, prepared on the basis of the literature review. Additionally, it contains the analyses of the concept and factors of LAU’s financial condition as well as metrics of its evaluation. The next section discusses the scale and effects of the COVID-19 pandemic and its impact on public finances and mortality rates in the regions. Section three presents the research study concept along with a description of the applied research methods. Section four shows the research results, and the last one contains conclusions. 42 Sustainability 2023,15, 8993 2. The Concept of the Financial Condition of Regions Each self-governing voivodeship functions and operates on the basis of the same regulatory framework. This includes not only the systemic and administrative norms but first and foremost the aspects of the functional affiliation, in accordance with which the relevant legal acts (Act on municipality self-government, poviat self-government, and voivodeship government) contain a catalog of statutory tasks, which imposes on each voivodeship the duty to carry out the same tasks (where one of the major tasks is fostering the development of a given voivodeship and creation of mechanisms and instruments to stimulate its development). In order that the enumerated tasks may be implemented, the legislator (i.a. in the Act on public finance or in the Constitution) indicated concrete sources of income, which are identical for every voivodeship. This means that in the legal and financial aspects the situation of all voivodeships at the starting point is the same. Financial condition is in most cases treated as a synonym for the terms financial situation or financial standing. Dylewski et al. [ 7 ] pointed out that the concepts of financial condition and financial situation are almost identical and they added that the financial situation of a LAU is the state of finances that makes it possible to finance the implementation of statutory tasks, meeting the quantitative and qualitative requirements at a given time and in the future, which is specified by the time frame of the measurement, whereas, the financial condition is a result of decisions taken by local administrative units. Kopy´scia´nski and Rólczy´nski [ 6 ] supplemented this view saying that on the one hand the financial situation goal of an entity’s activity, and on the other hand an outcome of decisions taken earlier. Zawora [ 1 ], in turn, underlined that the financial condition is not only a derivative of implemented public tasks and projects, but it itself constitutes a source of pro-effective activities. According to Natrini and Ritonga [ 11 ], the financial condition describes a LAU’s ability to meet its financial liabilities, and based on the evaluation the self-government is able to specify how to meet the public needs, and how to make use of the resources in a more productive manner. A LAU in a good financial condition usually maintains an appropriate level of services despite a decrease in the fiscal income, it identifies long-term economic or demographic changes and adapts to them, and prepares resources to meet future needs. However, when under fiscal stress, a LAU usually faces problems with balancing the budget, experiences decreased levels of services, encounters difficulties with adapting to the socio-economic conditions, and has limited resources for financing future needs [ 31 ]. Proper assessment of a LAU’s financial condition is not an easy process due to the complexity of the phenomenon, and in order to make the assessment objective financial ratios are most often used in practice [10]. According to Ritonga et al. [ 5 ], only a few researchers attempted to explain the factors influencing the financial condition, quoting papers that enumerate the main determinants of the financial condition of LAUs in various countries. The most frequently indicated determinants include: population size/density, environmental conditions, the state of the local economic base, governmental policies and financial practices (tax levels) having an impact on local resources, labor costs, costs of capital and other production resources. It is possible to state that in general terms the factors that are the most frequently identified are broadly defined socio-economic ones. Due to the complexity of factors influencing the financial condition of self-government, there are not any easy or immediate ways to understand the diversification of the financial situations of self-government. Taking into account the factors mentioned above, it is possible to distinguish six metrics that illustrate the financial condition of a LAU [4]: •ability to meet short-term liabilities (short-term solvency); •ability to meet operating liabilities (budget liquidity); •ability to meet long-term liabilities (long-term solvency); •ability to overcome unexpected events in the future (financial flexibility); •ability to effectively execute property rights (financial independence); • ability to provide services for the community (solvency in terms of services provision). 43 Sustainability 2023,15, 8993 The issue of assessing a LAU’s financial condition is extremely important from the point of view of the informative value for decision-making and information purposes, as well as in view of the ability or inability to contract debt. Such an assessment provides information that facilitates decision-making with regard to implementing new tasks, and at the same time, it enables evaluation of the activities completed so far by the self-government within a specific scope [ 32 ]. The Polish literature on the subject presents the results of LAU’s financial condition evaluation obtained via various methods, including those based on empirical metrics or synthetic indicators. Researchers most often rely on, for example, the indicators proposed by the Ministry of Finance. In view of the differentiation between the concepts of financial condition and financial situation, they supplement the indicators proposed by the Ministry with additional metrics [ 3 , 10 , 33 – 36 ]. The most frequently applied indicators to assess LAU’s financial condition include the growth rate of income and expenses of LAUs in connection with the evaluation of the budget balances and with the operating result and the result of assets-related activities. The prevailing opinion is that any given unit’s condition is best reflected by the level of income and expenses per capita [ 33 ]. In self-governmental practice, indicators of this kind are applied in order to make comparisons in terms of time and space in relation to other LAUs (particularly the neighboring ones) which in some aspects may be competitors e.g., when potential investors choose a location for investment or when residents want to start their business activity. Among the concepts, the one that deserves attention is the approach taken by the Regional Accounting Chamber which in its analysis of threats to LAU’s financial management applies 9 criteria selected on the basis of indicators related to debt and financial results (total liabilities to income ratio, accumulated debt to income ratio, presence of payables due, individual debt repayment ratio, current expenses and debt to current income ratio, share of operating surplus/deficit in total income, lack of funds to cover an operating deficit, funds to be carried forward to next year’s budget based on LAU’s budget implementation balance sheet for the previous year, budget result to income ratio) [ 34 ]. Based on the outcome of LAU’s credibility evaluation carried out by means of discriminant analysis methods, Adamczyk & Dawidowicz [ 3 ] pointed out that indicators of the greatest importance are those which in their structure comprise categories such as total income value, level of own income, operating surplus value, debt level, and debt service cost. Additionally, their research has shown that more diverse sets of indicators should be applied when analyzing the financial condition of bigger LAUs, e.g., cities with poviat (second-tier of local government administration in Poland) rights: or voivodeships (regions). 3. The Impact of the COVID-19 Pandemic on the Economy The first cases of infections with the SARC-CoV-2 virus were identified in the city of Wuhan (province of Hubei, China) in December 2019. In March 2020 the World Health Organisation (WHO) announced “a global pandemic” [ 37 ]. Initially, the virus spread in China and Europe, but in the second quarter of 2020, it was present all over the world [ 24 , 38 ]. The COVID-19 pandemic quickly sprawled out, resulting in human tragedies and economic losses, affecting both developed and developing countries. By the end of 2020, more than 79 million SARC-CoV-2 infections were detected, resulting in more than 1.7 million deaths all over the world [ 39 ]. In Poland, the first case of a SARC-CoV-2 infection was identified on 4 March 2020 in Lubuskie voivodeship [ 40 ]. From the beginning of the pandemic till the end of 2020, over 1.25 million SARC-CoV-2 infections were detected in Poland, and there were more than 27 thousand deaths caused by COVID-19 [39]. The SARS-CoV-2 pandemic changed the way the world was functioning, affecting the economy and the citizens’ lives, it also left its mark on the condition of public finances [ 21 ]. No country escaped the negative consequences [ 41 , 42 ]. The financial outcomes of the pandemic include on the one hand decreased public revenues (mainly from taxes), which was caused i.a. by restricted business activity, lower income, and decreased activity of households [ 43 , 44 ]. On the other hand, there was a rise in COVID-related expenses to compensate for the losses experienced by enterprises [ 45 ]. Most surveyees (63%) of 44 Sustainability 2023,15, 8993 the OECD-European Committee of the Regions expected that the socio-economic crisis caused by the COVID-19 pandemic would have a significantly negative effect on local self-governments [ 46 ]. The findings of the initial studies and analyses were pessimistic with regard to the condition of local administrative units of each level [ 47 , 48 ]. In general, however, in many countries, the shock was partially cushioned by measures taken by central governments in the area of financial transfers [ 35 ]. In 2020 most countries introduced measures to support local and regional finances, to partially mitigate the effects of the economic shock. Cities and regions faced new challenges connected with the condition of local and regional economies, caused by the unpredictability of income levels and their reallocation in response to unpredicted events. Many self-governments had to cope with ‘the scissors effect’, i.e., increased levels of self-government expenses accompanied by decreased levels of income [46,49,50]. The research done by the World Bank has shown that the COVID-19 pandemic led to a decrease in the income of local administrative units, mainly as a result of decreased proceeds from taxes, increased expenses caused by extraordinary items, decreased creditworthiness and reduced ability to contract debt [ 51 , 52 ]. The research on the impact of COVID-19 on regional finances in Europe, Asia, and Africa have shown an average decrease in income of 10% and an average increase in expenses of 5%. The main reason for that decrease was a reduction in proceeds from taxes and charges, lease or sale of assets, and smaller transfers from central governments. This entailed the need to borrow money in order to cope with crisis situations, and suspending or abandoning of key investments [ 49 ]. Authorities all over the world took steps to prevent the virus from spreading and to mitigate its effects, the result of which was a total or partial closure of the whole economy sectors [ 53 ], which in turn reduced the activity of business entities [ 43 , 44 ] triggering longterm effects [54], in particular for tourism and aviation industry [55]. They ranged widely from travel restrictions to national and regional lockdowns, keeping social distance, and other measures fostering the formation of unconventional geopolitical and socio-spatial movements [ 28 ]. In response to the virus propagation, authorities imposed restrictions on transport, economic and industrial activity in many countries. The scope and scale of the COVID-19 impact were unprecedented and heterogeneous, with major implications for crisis management and political reactions [ 56 ], on the one hand being an object of scientific research, and on the other generating effects which will be experienced for many years. COVID-19 had an impact on everyday life, causing far-reaching consequences in the area of healthcare and economy, and also in the social dimension [ 57 , 58 ]. According to the analysts, the effects of those measures were dramatic, and business activity slumped on a global scale [ 38 ]. According to P. Brinca et al. [ 53 ], the pandemic was unique in terms of its nature and size, the uncertainty of its duration, and demand and supply shocks as well as various unforeseeable effects. Economists compare the pandemic time with the Great Depression of the 1930s and the Great Recession of 2008. Even though the financial crisis of 2008 and the COVID-19 pandemic were different in terms of scope and time of impact [ 28 ], both of them influenced economies in many countries [ 59 ]. Still, according to economists, the COVID-19 pandemic has had the greatest impact on the economy since the Great Depression, at least in the short run. The preventive measures taken will influence the duration of the recession and the recovery time needed by the economy to return to the state from before the pandemic [ 23 ]. Nevertheless, the actual negative impact of COVID-19 turned out to be smaller than initially estimated. This was mainly due to the financial support received from the central government to strengthen the financial condition of local administrative units, maintaining fairly stable proceeds from taxes (mainly from the property tax) coming to the local budgets, and also savings in expenses, resulting from limiting or abandoning the local investments [ 50 ]. The financial situation of local administrative units in Poland was not found to be dramatically deteriorated, however, this may not justify the optimism of the central government in that regard [ 60 ]. Compared to central governments, self-governments have less effective instruments to respond to economic shocks, even though their proceeds are 45 Sustainability 2023,15, 8993 less sensitive to deterioration of the economic situation than those of central governments. The effectiveness of the tools being at the disposal of local or regional self-governments is smaller, the tools also have a moderate impact on the short-term situation of the budget and on the economic situation in the region [ 35 , 47 ]. The impact of COVID-19 on regional and local finances is not unambiguous due to the possibility of the continuation of the pandemic and its effects [ 61 ]. Undoubtedly, the negative impacts did not spread evenly. Due to the territorial aspect of the COVID-19 crisis [ 52 ], regions were not affected in the same way and its mediumand long-term effects are diverse [ 35 , 62 ]. The differentiating factors for this impact include e.g., the sensitivity of a region to the operation of global value chains [ 63 ], the share of vulnerable sectors (tourism, accommodation, catering) in the local economy [64], kinds of income and budget expenses in the particular types of LAUs. The development of the pandemic also affected the number of deaths and mortality rates, which reached levels not seen for a long time. This also drew the attention of scientists, who began to study the problem [58,65]. Even though over the last several decades the number of deaths all over the world decreased, the last years showed a slight rise in the number, and in 2020 the rise was considerable. Additionally in Poland, the year 2020 saw a significantly higher mortality rate compared to the previous years, which was directly and indirectly caused by the COVID-19 pandemic (Figure 1). 7 8 9 10 11 12 13 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Figure 1. Mortality rate per 1000 population in Poland over the years 1960–2020. Source: own study based on Data from World Bank [62]. 4. Materials and Methods The object of the research was local government units at the voivodeship level. In Poland are 16 LAUs, all of which were covered by the research (total sample). Based on the review of the literature regarding the financial condition of self-governments, legal regulations concerning their operation, and the range of suggested study indicators, and also based on the conclusions derived from the literature review and indicators used by the Central Statistical Office in Poland, a set of variables was selected to describe an LAU’s financial condition. To maintain the logic of the research process and the comparability of data, the input data used in the calculations come from the Local Data Bank of the Central Statistical Office, Regional Accounting Chambers, and from financial data derived from the budget reports of all the voivodeships for the years 2017–2020. The research period was divided into two parts, before and during the pandemic [ 66 , 67 ], enabling on the one side, to analyze of data from the years preceding the pandemic, which provides a baseline, 46 Sustainability 2023,15, 8993 and on the other side, the cyclical nature of the publication of data by the Polish Central Statistical Office (many months after the end of the year) makes 2020, the last complete data, covering the pandemic period. To ensure comparability of the analyzed data, they were expressed in relative terms (the source data which were expressed on the ‘per capita’ basis were left unchanged, whereas the remaining data were recalculated per 10,000 population, which was determined by the volume and clarity of received results). The study covered all the regional self-governments in Poland, i.e., 16 voivodeships (Table 1). Financial data of the analyzed LAUs used in the evaluation of the financial condition were presented in Appendix A (Table A1). Table 1. Features describing the financial situation of Polish regions. Feature LTB/STB property income per capita in PLN LTB property expenses per capita in PLN LTB operating surplus as % of total income per 10,000 population LTB ratio of financing capital expenditure with operating surplus LTB planned debt amount in PLN per capita (for a given year) STB investment volume indicator per 10,000 population in PLN LTB annual debt to total income percentage ratio STB Source: own work. Due to the short data presentation period connected with the COVID-19 pandemic by voivodeship (since 23 November 2020), the number of deaths in the particular voivodeships was assumed to specify the impact of the COVID-19 pandemic on the financial condition of the regions in Poland. For that purpose, the mortality rate per 10,000 population was applied. A study of the relationship between mortality rates and socio-economic status was conducted by Aykaç and Etiler [ 65 ]. On the other hand, Yavuz and Etiler [ 58 ] looked for a relationship between urban health indicators and the third wave of COVID-19 on a regional basis. Their results indicate COVID-19 cases are higher in more developed cities with higher manufacturing sector activity. The calculation of the financial condition was based on the financial data from all voivodeships and the synthetic indicator method which required standardization of the data. The study of the relationships required the previous specifications of the socioeconomic development level and the financial standing of the whole sample. The evaluation of the analyzed phenomenon applied the synthetic indicator method [ 68 ], the application of which involved several stages [ 69 ]. In the first stage, variables describing the phenomenon were selected [ 70 – 72 ] and used in constructing a matrix. The next stage consisted in converting the metrics in order to normalize them to basic features [ 73 ]. It was assumed that when a high value of the diagnostic variable for a given phenomenon is associated with beneficial growth, the feature is considered Larger-the-better (LTB), whereas, in a situation where a low value of the variable is beneficial for the phenomenon in question, it is deemed smaller-the-better (STB) [ 74 , 75 ]. The following formulas were applied in the calculations [69,71]: for LTB factors (S)Zij =xij −min(xij) maxxij−minxij for STB factors (D)Zij =maxxij−xij maxxij−min(xij where: Zij—diagnostic variable, falling within the range from 0 to 1; xij —the feature value for a given region; min xij—the lowest value of the feature among the examined regions; max xij—the highest value of the feature among the examined regions. 47 Sustainability 2023,15, 8993 Table A1. Cont. Voivodeship Year Property Income Per Capita (PLN) Total Expenses Per Capita (PLN) Operating Surplus as % of Total Income Per 10,000 Population (%) Ratio of Financing Capital Expenditure with Operating Surplus (%) Planned Amount of Debt Per Capita (for a Given Year) (%) Investment Volume Indicator Per 10,000 Population (PLN) Ratio of Annual Debt to Total Income (%) ´ Sl ˛askie 22.15 268.47 0.0353 0.7206 150.59 612,495 55.06 ´ Swi˛etokrzyskie 113.11 386.00 0.1043 0.3745 132.64 1,430,997 32.38 Warmi´nskomazurskie 44.93 363.88 0.0500 0.3279 209.80 779,372 58.86 Wielkopolskie 28.21 336.23 0.0531 0.6101 95.58 989,017 29.33 Zachodniopomorskie 131.81 472.55 0.0646 0.2828 138.55 1,841,225 29.33 Dolno´sl ˛askie 2018 80.92 396.63 0.0545 0.6025 210.21 1,134,539 48.61 Kujawskopomorskie 50.38 392.36 0.0681 0.4679 131.44 1,154,862 34.47 Lubelskie 77.51 437.30 0.0621 0.3284 335.83 1,638,904 82.97 Lubuskie 73.22 445.81 0.1100 0.3260 173.63 1,445,360 41.18 Łódzkie 43.86 324.46 0.0743 0.6496 125.31 948,820 37.40 Małopolskie 87.81 386.66 0.0469 0.4711 122.21 1,361,976 30.37 Mazowieckie 26.12 489.37 0.0568 1.2562 185.81 1,340,993 33.79 Opolskie 174.74 504.68 0.1160 0.2828 119.31 2,130,931 22.66 Podkarpackie 176.97 537.23 0.0917 0.4202 92.08 2,612,734 16.36 Podlaskie 249.23 671.03 0.0885 0.1622 107.43 3,858,776 18.00 Pomorskie 113.08 449.74 0.0632 0.3640 93.97 1,808,170 20.98 ´ Sl ˛askie 50.18 318.73 0.0474 0.7743 132.94 967,667 38.30 ´ Swi˛etokrzyskie 195.50 564.67 0.1181 0.2499 121.01 3,102,732 22.94 Warmi´nskomazurskie 64.50 428.76 0.0900 0.3529 254.22 1,455,065 63.70 Wielkopolskie 27.93 363.97 0.0506 0.6231 115.79 1,003,941 32.70 Zachodniopomorskie 198.39 559.71 0.0531 0.1962 172.37 2,547,042 31.15 Dolno´sl ˛askie 2019 58.71 384.33 0.0614 0.6678 182.80 1,085,665 44.92 Kujawskopomorskie 45.81 431.88 0.1112 0.6907 130.31 1,444,978 30.09 Lubelskie 190.03 576.78 0.0707 0.2977 329.60 2,872,741 58.33 Lubuskie 108.97 517.12 0.1071 0.2662 210.40 1,967,707 43.68 Łódzkie 72.16 387.98 0.0641 0.4970 251.78 1,261,237 63.47 Małopolskie 63.02 390.55 0.0565 0.6752 108.99 1,184,400 26.26 Mazowieckie 40.87 597.31 0.0504 0.8752 148.38 1,912,433 24.15 Opolskie 165.29 519.24 0.1483 0.3971 100.60 2,056,248 17.95 Podkarpackie 151.17 496.82 0.0959 0.5179 99.83 2,146,005 18.33 Podlaskie 368.60 835.10 0.0869 0.1444 183.75 5,321,012 24.55 Pomorskie 155.75 563.64 0.0773 0.3706 114.64 2,683,275 20.82 ´ Sl ˛askie 38.76 328.39 0.0522 0.9840 107.31 881,677 29.34 ´ Swi˛etokrzyskie 179.66 551.18 0.1579 0.3966 108.20 2,787,006 19.23 Warmi´nskomazurskie 91.90 473.45 0.0878 0.3930 254.60 1,506,189 53.72 Wielkopolskie 91.59 450.83 0.0548 0.4598 126.19 1,846,122 28.50 Zachodniopomorskie 83.45 463.86 0.0998 0.5052 146.67 1,574,015 31.24 54 Sustainability 2023,15, 8993 Table A1. Cont. Voivodeship Year Property Income Per Capita (PLN) Total Expenses Per Capita (PLN) Operating Surplus as % of Total Income Per 10,000 Population (%) Ratio of Financing Capital Expenditure with Operating Surplus (%) Planned Amount of Debt Per Capita (for a Given Year) (%) Investment Volume Indicator Per 10,000 Population (PLN) Ratio of Annual Debt to Total Income (%) Dolno´sl ˛askie 2020 69.65 381.86 0.0930 1.2270 141.81 1,044,769 29.84 Kujawskopomorskie 67.38 510.33 0.1223 0.7856 129.51 1,747,413 23.97 Lubelskie 95.78 474.97 0.0461 0.3877 326.64 1,244,255 66.04 Lubuskie 150.71 613.78 0.1585 0.3832 227.17 2,548,829 37.41 Łódzkie 84.01 419.42 0.0851 0.6019 230.67 1,541,501 52.18 Małopolskie 151.79 591.41 0.0360 0.3422 142.89 2,150,655 23.75 Mazowieckie 29.37 614.11 0.0422 1.0638 174.94 1,398,053 26.82 Opolskie 125.31 554.43 0.1959 0.7580 81.09 1,632,610 12.67 Podkarpackie 173.44 565.19 0.0884 0.4552 127.19 2,492,443 21.10 Podlaskie 269.82 728.75 0.0961 0.2196 244.34 3,679,158 34.34 Pomorskie 72.66 455.41 0.0790 0.6100 104.53 1,428,247 22.13 ´ Sl ˛askie 93.96 426.40 0.0347 0.3696 101.16 1,745,141 24.65 ´ Swi˛etokrzyskie 110.18 478.39 0.2279 0.8256 95.66 1,903,624 17.22 Warmi´nskomazurskie 198.59 588.08 0.1168 0.3681 242.27 2,773,166 39.62 Wielkopolskie 108.33 469.58 0.0739 0.6963 116.78 1,926,712 22.48 Zachodniopomorskie 140.38 566.76 0.1179 0.5512 173.38 2,212,195 28.52 Source: own study based on resolutions on adopting budgets and long-term financial forecasts for voivodeships, resolutions on amending budgets and long-term financial forecasts for voivodeships for the years 2017–2020, and budget implementation reports. 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Digitalization as a Factor in Reducing Poverty and Its Implications in the Context of the COVID-19 Pandemic. Sustainability 2022,14, 10667. https://doi.org/ 10.3390/su141710667 Academic Editor: Donato Morea Received: 17 June 2022 Accepted: 23 August 2022 Published: 26 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Digitalization as a Factor in Reducing Poverty and Its Implications in the Context of the COVID-19 Pandemic Cristi Spulbar 1, Lucian Claudiu Anghel 2, Ramona Birau 3,*, Simona Ioana Ermis ,2, Laurent ,iu-Mihai Treapăt2and Adrian T. Mitroi 4 1Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania 2Faculty of Management, National University of Political Studies and Public Administration, 30a Expozit ,iei Blvd., Sector 1, 012244 Bucharest, Romania 3Doctoral School of Economic Sciences, University of Craiova, 200585 Craiova, Romania 4The Faculty of Finance and Banking, Bucharest University of Economic Studies, 5-7 Mihail Moxa Street, District 1, 010961 Bucharest, Romania *Correspondence: [email protected] Abstract: In the present economic context, one of the most important topics of discussion is that regarding sustainable development. According to the agenda developed by the United Nations, one of the most important objectives for the present decade is represented by the list of the Sustainable Development Goals. The Sustainable Development Goals can be divided into five pillars: people, planet, prosperity, partnership and peace. One of the first stipulated goals of the UN agenda is the eradication of poverty and famine. We consider that a significant influence on the eradication of poverty is represented by the development of technology. In this paper, the authors aim to establish a connection between the rate of technological development and the poverty headcount rate. To measure the digital development of the analyzed countries, we decided to compose an index of digital development by taking into account indicators made available by the International Telecommunication Union and the poverty headcount ratio, as was calculated by the World Bank database. This empirical study is of interest for the implications that it has in shaping governmental policies regarding easing the access to digital technology. The method used to quantify the influence of digital development on poverty was the panel data GMM vector autoregressive model for a dataset composed of 35 countries for the period between 2005 and 2018. The results indicate that an increase in digital development will lead to a reduction in the poverty headcount rate. These results imply that by increasing access to technology, countries could help reduce their level of poverty. In this paper, we will also analyze the way in which adopting digital development leads to better economic performance when faced with the COVID-19 pandemic. The results of the present study are of great interest to the scientific community and the public due to the implications of digital development in the field of economics and the combined effect of this phenomenon and the COVID-19 pandemic. We thus conclude that by encouraging digital development and through adopting new technologies, the government can lead to the eradication of poverty. This seems counterintuitive due to the fact that investment in shelter and primary goods can be seen as one of the primary ways of developing the economy. We conclude that better and more consistent results regarding the reduction of poverty can be obtained by increasing the digital development of a country. Keywords: poverty; panel data; digitalization index; economic development; COVID-19 pandemic; digital development; Sustainable Development Goals (SDGs) 1. Introduction One of the most discussed problems of the present is sustainability. The problem of sustainable development was defined for the first time in its present form in the Brundtland Report [ 1 ] published in October 1987, where the concept gained additional focus regarding Sustainability 2022,14, 10667. https://doi.org/10.3390/su141710667 https://www.mdpi.com/journal/sustainability 59 Sustainability 2022,14, 10667 the building of a socially inclusive and environmentally sustainable form of economic development (at first, the concept had a bigger and greater focus on the environment, such as in the definition offered by the International Union for the Conservation of Nature [ 2 ] in 1980). Taking into account the importance of the concept, the present paper proposes a model for analyzing the influence of digital development on the poverty headcount ratio as calculated by the World Bank. We considered the poverty headcount ratio to be a determinant factor of sustainable development due to its priority on the agenda of the United Nations in the 2030 Sustainable Development Goals [ 3 ]. In the following sections, we will review the articles that we considered to be of the highest importance in the development of the present paper, and we will develop a digitalization index and quantify its influence by using the methodology of the vector autoregressive model for the panel data. The scope of this paper is to answer the following research question: RQ: How does the increase in technological development influence the rate of poverty at a national level, and how does digital development relate to the response to the COVID-19 pandemic? The present article was developed as a result of studying the literature, with the scope of assessing the way in which sustainable development and poverty relate to digital development and presenting the main topics of research in the field of the Sustainable Development Goals and their implementation. From this extensive study, the authors discovered a research gap represented by the way in which the relation between digital development, expressed as an indicator measured by precise metrics, has an influence on the poverty headcount rate. We decided to analyze the way in which the real impact of technology adoption can be quantified. In order to measure this, the authors decided to implement a vector autoregressive model with panel data, with this being, in our opinion, an original contribution to this field of study (i.e., the application of quantitative methods with calculable results). Another interesting approach that we developed in the present paper is the building of a digital development index. This allowed us to compare the level of development of the analyzed countries and also gain a better understanding of their evolution in the given time frame. The article also allows for a worldwide view of digital development and technology adoption by ranking 175 countries for the year 2019. In order to present the results of the research, the authors decided to first present a brief discussion regarding the main topics of research in the field of the Sustainable Development Goals and their implementation. In this article, we also investigate the relation between digital development and resilience to external shocks. One of the more significant shocks of the last decades was the COVID-19 pandemic, and in this paper, we will try to show that the more developed countries from a digital standpoint were less impacted by the pandemic. This literature review is continued by a quantitative research study that consists of using a panel data vector autoregressive model to better understand the relation between the rate of poverty and the digital development of a country. At the end of the paper, we present a set of discussions regarding the relation of the findings to the relevant scientific literature and the research limitations, along with further study directions, and a section of conclusions in which the authors present the theoretical implications of the present study, the possibility of the model to be used by international organizations for promoting the application of technology affordability programs and the perspectives of this field of knowledge in light of the present article. This study takes into consideration only the correlation between the digital development and poverty, with this being due to the fact that the authors wanted to have an isolated view of how digital development and poverty are related. The authors recognize the fact that poverty is a very complex subject with many underlying connections including but not limited to societal institutions, education, democracy, rule of law and other factors. Even if the present study offers a small insight into the way in which poverty is related to digitalization, due to the mentioned limitations, we consider it to be of interest because of its global scale and for generalizing the relation between poverty and digital development. The originality of the current paper 60 Sustainability 2022,14, 10667 is represented by the fact that we present a worldwide view of the digitalization process and the relation between digital development and the reduction of the poverty headcount as a significant influence on this phenomenon. Additionally, the present paper also aims to make sense of the patterns regarding digital development at a worldwide level by presenting the results of a vector autoregressive model with dynamic panel data for the period between 2005 and 2018. These developments and the overview of the scientific literature lead to the conclusion that an increase in digital development at a country level will lead to a decrease in the poverty headcount ratio, thus making an impact in the context of the Sustainable Development Goals promoted by the United Nations. 2. Literature Review In the following section, the authors develop, in a brief manner, the three main points of interest of the scientific literature that were considered when developing our paper in order to study the relation between digital development and the poverty headcount rate. This review presents in an organized structure the methodologies used and the main influences that the authors had in developing this paper. In accordance with this, the authors decided to present the main papers in each of the three main identified ideas relating to our research question. 2.1. Sustainable Development Goals and Their Implementation at a Worldwide Scale One of the most cited papers that analyzed the concept of the Sustainable Development Goals is the one written by Griggs et al. [ 4 ] with the title “Policy: Sustainable development goals for people and planet”. The conclusions of this paper indicate that global stability depends on integration of the goals, such as combating poverty and securing human wellbeing in the plans of the United Nations. Another interesting article is the one written by French and Koze [ 5 ]. This article analyzes the ways in which statistics regarding poverty are calculated and their accuracy for the indicators that measure the level of poverty. This paper estimates that in 2013, approximately 385 million children were living on less than USD 1.90 per day. These data are, however, stated as being an approximation due to the fact that 63% of countries do not publish data regarding child poverty, with this being in the context of the UN Sustainable Development Goals agenda for 2030, in which the eradication of poverty is the first priority. An interesting overview of the subject is described in “A Systematic Study of Sustainable Development Goal (SDG) Interactions” by Pradhan [ 6 ]. We decided to analyze the Sustainable Development Goals set by the UN Agenda for 2030 for potential synergies between them. It is stated that the first goal of the agenda (the eradication of poverty) has a synergistic relation with most of the other goals, and the twelfth goal (responsible consumption and production) is described by the authors as being the most likely to suffer trade-offs. This is due to the implications of reducing the use of coal and oil use in industry which, if carried out in an unprepared economy, will lead to unemployment and poverty. The article concludes that in order for the goals to become obtainable by the 227 analyzed economies, they must be adopted in a non-obstructive way, and the current strategies of implementation should take into account the level of development of the analyzed countries. Another interesting paper is the one written by Filho et al. [ 7 ], which presents a series of three case studies to show how the Sustainable Development Goals are an opportunity to advance equal opportunities and foster the economic development of countries by promoting sustainable development. The topic of sustainable development has been linked in the literature with the resilience of the economy. One such paper is the one written by Folke et al. [ 8 ]. In this article, the authors describe two fundamental errors in the design of environmental policies: the implicit assumption that the ecosystem’s responses to the influence generated by humans are defined by linearity and predictability and that the environment and human society can be treated separately when designing a policy. The authors used the concept of resilience, defined as the capacity to change, learn 61 Sustainability 2022,14, 10667 and develop, to analyze the best strategies to increase the economy’s capacity and adapt in the present climate. Another interesting view on the subject is presented in the article written by Hickel [ 9 ]. According to the author, there in is an inherent contradiction in the two sides of the sustainable development concept, as stated in the Sustainable Development Goals in of the United Nations between the goal of yearly global economic growth of 3% and the protection of the environment (as stated in goals 6, 12, 13, 14, and 15). The paper states that the by accepting the global economic growth rate at 3%, it is almost impossible to achieve any reductions in the aggregate global resource use. In our opinion, this offers an interesting view, due to the alternative of downscaling resource use in order to reach the target of climate change rate reduction in high-income nations by introducing quantified objectives for resource use. In the scientific literature, there are views [ 10 – 12 ] that state that the evolution of sustainable development is difficult to quantify, and its influence on macroeconomic indicators is challenging to analyze. In this paper, we aim to present the means of measuring the impact of digital development on an essential part of sustainable development: the reduction of the poverty headcount (this being part of the first two Sustainable Development Goals (reducing poverty and eradicating famine) stated by the United Nations). In other articles [ 13 , 14 ], we can see that the relevant scientific literature considers using indicators for assessing the evolution of the Sustainable Development Goals agenda. These attempts deal with studying the progress for a short period of time and for various regions. Due to the fact that, in the present paper, we seek to analyze the progress toward the reduction of poverty which has been manifesting in the last two decades, we decided to use the poverty headcount rate as a proxy for sustainable development, and we aimed to determine its correlations and relations with a technology development index. In addition to the mentioned scientific papers, a major contribution in the advancement of the measurement of poverty is the 2030 Agenda itself [ 3 ]. This represents a holistic approach to the problems that the United Nations consider to be fundamental to solve until 2030. In the case of the first goal, which is the eradication of poverty in all its forms, the agenda offers several targets: eradicate extreme poverty, reduce poverty by at least 50%, implement nationally appropriate social protection systems, equal rights to ownership, basic services, technology and economic resources, build resilience to environmental economic and social disasters, the mobilization of resources to end poverty and the establishment of poverty eradication frameworks at all levels. As stated, we are interested in the eradication of extreme poverty. The indicator that the UN considers to be the most important is the proportion of the population living below the international poverty line, aggregated by sex, age, employment status and geographical location. The UN considers the poverty line to be USD 1.90 per day, and in this paper, we used the USD 5.50 poverty line indicator due to its availability for more countries and because we considered that for developed countries, the USD 5.50 per day threshold was closer to the national poverty line (which is linked to an indicator of the second target—reduce poverty by at least 50%—with the indicator of the proportion of the population living below the national poverty line). For example, the USA poverty line was USD 35 per day in 2020 [ 15 ], and India’s was USD 12 per day in urban areas and USD 7.50 in rural areas in 2005 [16]. By analyzing the literature regarding the Sustainable Development Goals and their relation to the economic development of a country, we can state that they represent more than a list of goals. They represent a development program for bettering the future of the world and a blueprint for sustainable development. With that being said, in the present paper, we attempt to analyze the relation between digital development and the poverty rate. This is due to the attempt to obtain a focused view on the relation between these two variables in order to observe if encouraging digital development (e.g., by subsidizing the acquisition of computers) could lead to advancement in the Sustainable Development Goals. In addition, the adoption of technology could also lead to an increase in equity due to more access to information and opportunities. 62 Sustainability 2022,14, 10667 2.2. Measuring the Impact of Digital Development on Poverty In the scientific literature, there has been a number of articles that focus on analyzing the effect of digital development on the poverty level. One such paper is the one written by Kwilinski et al. [ 17 ], where the digital economy and society index were used to evaluate the digitalization of the countries of the European Union and were analyzed along with the AROPE indicator (people at risk of poverty and social exclusion). As the main research methods, the paper implements a correlation analysis and uses the Monte Carlo method to take into consideration the probability that a change in the value of the AROPE indicator will happen in 2021. The conclusions state clearly that the countries with a higher digitalization level have a lower percentage of people in poverty and lower social exclusion risk. Other articles [ 18 , 19 ] argue that in the case of the African continent, mobile phone development has led to a significant increase in informal financial development, even though its effects are less noticeable at the macroeconomic level, and that the use of mobile phones with internet access in 44 African countries in the period between 2000 and 2016 has led to an increase in financial inclusion. Other literature review-based studies [ 20 ] claim that there are few papers that can present a causal inference between ICT development and poverty. The interaction between the internet and mobile phone access or other technologies and poverty is a topic of focus for many papers [ 21 – 25 ], which have applied a multitude of methodologies in order to analyze this relation for different countries. Additionally, in the scientific literature, there has been a trend toward analyzing the impacts of technology as a means of inclusion and access to information on poverty in either South Asia or Sub-Saharan Africa [ 26 ] or in Latin America [ 27 ]. The studies conclude that in the case of South Asia and Sub-Saharan Africa, the adoption of new technologies is an important factor in sustaining the reduction of poverty in developing countries. In the case of Latin America, the study proposes a heterodox type of growth strategy in order to counter the perceived inequality generated by the acceleration of wealth creation. In studying individual countries, from several studies that we considered to be of interest [ 28 – 31 ], due to their implications for the present article, we found that the majority of the results indicate that the impact of internet adoption was mainly a positive one, as it reduced the rates of poverty. However, a problem still remains regarding the affordability of computers and internet access. Furthermore, in several articles [ 32 , 33 ], there has been a focus on the relation between the internet and technology and the knowledge economy. This relation is significant because the growth in the percentage of internet users can increase the transition to the knowledge economy, and this favors the reduction of the poverty rate. 2.3. Using an Index to Measure Digital Development The use of an index to measure digital development has been widely described in the scientific literature, and several articles [ 34 – 37 ] have proposed and used indices for measuring digital development, such as the one written by Archibugi and Coco [ 34 ], which had a focus on the developing countries and calculated a proprietary index—ArCo— based on three main components: the creation of technology, the available technological infrastructure, and the level of development of human skills. In the present paper, we considered that a better focus for our index was the personal adoption of technological development. As such, the authors used indicators that were related to the adoption of technology by ordinary citizens. Approaches regarding the measurement of the impact of the personal adoption of technology have been published [ 38 – 41 ], with the novelty of our approach being the effort to quantify the level of digital personal adoption at the country level by using a digital development index built with data made available by the International Telecommunication Union. Another important field of study in the scientific literature is the analysis of the differences in digital development between different regions of a country or between countries [ 42 – 52 ]. These studies analyze the concept of the digital divide. The digital divide 63 Sustainability 2022,14, 10667 Table 2. This table presents the rankings of the countries according to the index. Rank Country 1 Hong Kong, China 2 United Arab Emirates 3 Japan 4 Korea (Rep. of) 5 Germany 6 Singapore 7 Switzerland 8 United Kingdom 9 France 10 Netherlands 128 Central African Rep. 129 Ethiopia 130 Liberia 131 South Sudan In Table 2, we decided to implement a restriction regarding the size of the population for the analyzed countries. This was performed to eliminate the bias of the index toward small countries (e.g., Luxembourg, Seychelles, etc.). The results for the full data sample are presented in Appendix A (Table A1). We decided to present a version of the table without the small countries because we considered that the results were significant for bigger and larger countries, due to the index being composed of indicators that were presented for 100 inhabitants. In Figure 3, the authors present the evolution of four countries from the database that were considered to be of interest: Hong Kong, the United States of America, Germany, and South Africa. We can see that all the countries have evolved over time, but the digital index saw significant growth in the case of Hong Kong in the last 5 years. On the y-axis, we present the value of the digital development index, and on the x-axis, we present the years for which the index was calculated. Figure 3. Evolution of the index for the selected countries. By analyzing Figure 3, we can see that in the case of South Africa, the digital development index saw a significant increase in the analyzed time period. The authors also observed the fact that the analyzed countries experienced growth in digital development, but the difference between Hong Kong and South Africa remained relatively constant for 70 Sustainability 2022,14, 10667 the analyzed time period. Moreover, the US, Germany, and South Africa were closer in the terms of digital development in 2019 compared with their positions in 2000. 4.2. The Relation between the Poverty Headcount Ratio and Digitalization We decided to study the relation between the digital development index and the headcount poverty rate (as measured by the poverty headcount ratio at USD 5.50 a day (2011 PPP) (percentage of population) [ 64 ]) by applying a two-step dynamic panel vector autoregressive estimation with two lags. In the model, we used the first difference of the indicators by implementing a natural logarithm difference between the current value and the last value registered by the variable. The data used for the model were for the time period between 2005 and 2018 for the following countries: Armenia, Austria, Belgium, Belarus, Costa Rica, the Czech Republic, Denmark, the Dominican Republic, Ecuador, Spain, Estonia, Finland, France, Georgia, Greece, Honduras, Hungary, Indonesia, Kazakhstan, Lithuania, Latvia, Moldovia, the Netherlands, Norway, Panama, Peru, Poland, Portugal, Paraguay, the Russian Federation, Slovenia, Sweden, Turkey, Ukraine, and the United States of America. We selected these 35 countries due to the available data regarding the poverty headcount ratio. In this way, only these countries had all the data available for the analyzed time period. This explains the way in which the model was constructed. One useful indication was that we had countries from almost all of the continents (except Oceania), and this allowed us to still present a global overview for the selected data. The results of the estimation of the model are presented in Figure 4. We can state that the poverty rate was influenced by the digital development index for both lags. For the first lag of the digital development index, the value of the coefficient was − 1.7551 and was significant for a threshold of 95% for the poverty rate headcount. Moreover, the second lag of the digital development index was significant in the equation for the poverty rate, having a value of 1.3426. Consequently, we can state that the values of the poverty rate headcount were influenced by the values of the digital development index, with the value of the index in the previous year exerting a significant influence on reducing the poverty rate headcount, and the coefficient for the second lag indicated a positive influence on the poverty headcount rate. This could be due to the fact that the time interval was short and may have presented contradictory phases of the evolution of society. Figure 4. Vector autoregression model results for the analyzed data. 71 Sustainability 2022,14, 10667 In Figure 4, ID__1 represents the evolution of the digital development index, and the Pov_rate represents the evolution of the poverty rate calculated as the logarithmic difference of the values of the poverty headcount ratio at USD 5.50 a day (2011 PPP) (percentage of population) [ 86 ], as calculated by the World Bank. In Figure 5, we can see the results of the impulse response function for the vector autoregressive model. Figure 5. Impulse response function of the poverty headcount rate for the digitalization index. By analyzing the results in Figure 5, we can see that there was a certain significant impact from the poverty rate headcount on the digital development index. This is important because it implies that the poverty rate can be decreased by the digital development index. Moreover, the shock had an effect on the last periods analyzed. Even though the impulse response function seemed to depict a small but significant effect, the results of the VAR model estimation as stated presented a significant coefficient of − 1.7551 for the variable of the first lag of digital development in the equation that approximated the value of the poverty headcount ratio. 4.3. The Performance of the Digital Developed Economies during the COVID-19 Pandemic In the following section, we present an analysis regarding the way in which the gross domestic products for the most digitally developed countries have evolved in the time of the COVID-19 pandemic. For this, we take the most developed countries according to our digital development index and compare them to the other countries in the sample. In Figure 6, the authors depict the evolution of the economic growth values for four countries that were considered to be in the category of the most-developed countries from the studied sample (Switzerland, Germany, Japan and South Korea) and compared them to the average of the countries that are members of the OECD, European Union and the euro area. By analyzing the figure, we can observe that the digitally developed countries had more stable economic growth in the pandemic period when compared with the average of the European Union, eurozone or the OECD. This fact indicates that a higher level of digital development led to a more equilibrated response to the pandemic’s shock. The complete data of the figure are depicted in Table 3, with the data being available from the OECD database [93]. 72 Sustainability 2022,14, 10667 Ͳϭϯ Ͳϴ Ͳϯ Ϯ ϳ ϭϮ ϮϬϮϬͲYϭ ϮϬϮϬͲYϮ ϮϬϮϬͲYϯ ϮϬϮϬͲYϰ ϮϬϮϭͲYϭ ϮϬϮϭͲYϮ ϮϬϮϭͲYϯ ϮϬϮϭͲYϰ , h :WE <KZ ϭϵ K hϮϳ Figure 6. Evolution of the economic growth for the studied countries. Table 3. Economic growth of the analyzed countries as percentages. Date CHE DEU JPN KOR EA19 OECD EU27 2020-Q1 −1.59 −1.76 0.49 −1.26 −3.53 −1.70 −3.09 2020-Q2 −6.14 −10.00 −7.90 −3.05 −11.67 −10.45 −11.27 2020-Q3 6.30 9.04 5.28 2.35 12.82 9.49 11.91 2020-Q4 0.04 0.74 1.76 1.21 −0.40 1.02 −0.20 2021-Q1 −0.24 −1.68 −0.40 1.72 −0.12 0.75 0.07 2021-Q2 1.97 2.17 0.64 0.83 2.16 1.72 2.11 2021-Q3 1.87 1.67 −0.80 0.21 2.32 1.12 2.18 2021-Q4 0.16 −0.35 0.98 1.34 0.25 1.21 0.45 In Table 3, the authors present the results of presenting the economic growth from the first quarter of 2020 to the fourth quarter of 2021. The analyzed countries were Switzerland, Germany, Japan and Korea. In the table, we also present the values reported for the euro area, the OECD and the European Union. The countries that were the most digitally developed, such as Japan (ranked third in 2019) and South Korea (ranked fourth in 2019) had a growth rate higher than the eurozone, OECD and the European Union. In the following part, we present the discussions regarding the findings of this article and the conclusions. By analyzing these results, we can conclude that the countries that had better digital development had a better evolution during the COVID-19 pandemic and also had a less powerful impact than in the case of the average of the OECD, the euro area and the European Union countries. The number of countries in the sample was relatively small compared with the number of countries used in the index (35 vs. 175) due to the lack of data regarding the poverty headcount ratio for the period between 2005 and 2018. 5. Discussion In the present article, we described how to compute an index for digital development that takes into account the personal adoption of technology. This was achieved by taking into account data made available by the International Telecommunication Union in order to obtain a better understanding of the level of development of each country and offers the possibility of comparing the digital development of the countries. Some researchers [ 94 ] revealed that the transportation and accommodation sectors have been significantly affected by COVID-19-related lockdowns, but on the contrary, other sectors of the sharing economy such as freelance work, streaming services and online deliveries have reached increasing levels of development and profit. At the quantitative level, the most likely effect of the COVID-19 pandemic on the global economy has been 73 Sustainability 2022,14, 10667 quantified to be between USD 5.8 trillion and 8.8 trillion, equivalent to 6.4–9.7% of the global gross domestic product (GDP) as approximated by the Asian Development Bank (ADB) on May 2020 [ 95 ]. According to other researchers [ 96 ], the COVID-19 pandemic raised unprecedented challenges while earnestly affecting all businesses worldwide. The results of the vector autoregressive model led to the conclusion that there is a significant statistical influence from the digital development index that we constructed in this paper and the poverty headcount rate as described by the World Bank indicator (poverty headcount ratio at USD 5.50 a day (2011 PPP) (percentage of population)) [ 86 ]. This led to the idea that by increasing the access to technology, governments could contribute to the reduction of poverty. This reduction in poverty could lead to the advancement of the Sustainable Development Goals and progress in implementing the 2030 Agenda of the UN [ 3 ]. The presented findings are similar to the ones obtained in other papers [ 26 – 31 ], with the exception that the data sample used in this article contained countries from all over the world, and we provided a comparison of the influence of digital development on the reduction of poverty that is easier to understand at a global level. In this context, we can state that the digital development of a country leads to a reduction in the poverty headcount. These results agree with those of the majority of the papers in the scientific literature. An interesting development contribution is the way in which digital development is measured in the present article. The authors decided to use an index that measured the personal adoption of technology due to the effect that technology at an individual level has on the reduction in poverty. The usefulness of the present research is the fact that it proves that the adoption of technology at the personal level leads to a reduction in poverty. This could be used as the basis for shaping policies regarding the eradication of poverty at the international level. By increasing the access to technology among the population, citizens could access job opportunities that they otherwise would not have seen, or they could have access to information at an unprecedented scale. These results could be seen as a continuation of the work conducted in several papers [ 43 , 46 , 50 – 52 ] regarding the way in which technology increases the opportunity to participate in the economy for all the citizens. In this case, the authors recommend an increase in the interest that governments have in the adoption of technology at the personal level by creating programs which encourage the use of and access to technology and also make the acquisition of IT devices easier (e.g., this could be achieved with vouchers or discounts for an individual’s first computer). In addition, we note that the results of this paper seem to generalize certain findings for specific continents [ 18 , 19 , 26 , 27 ], such as the beneficial effect of digital development on the reduction of poverty in Africa and for the 35 countries analyzed in the model (which are mostly in Europe and the North and South American continents). In this way, our study demonstrates a clear relation between digital development (concentrated on the personal adoption of technology due to the composition of the index: the percentage of individuals using the internet, the mobile cellular subscriptions for 100 people and the fixed telephone subscriptions for 100 people) and the reduction of poverty. This confirms the findings of several studies [ 43 , 46 , 50 – 52 , 67 – 70 ], which stated that the adoption of the internet leads to the reduction of poverty. The mechanism of this influence is, as stated by Dawood [ 52 ], for the case of the rural communities of northern Malaysia, and it works by encouraging a connection between individuals and action at the grass roots level. This implies that technology changes society by giving power to the people to communicate and create groups in an easier and more interest-based way. For example, a group that promotes the creation of parking lots in a certain area of the city could promote the idea on the internet and, by doing this, make it more visible for the city council. In this way, we consider that the adoption of the internet will lead to the reduction of poverty by increasing the freedom of the population and access to information, which promotes a better understanding of the way government functions. Additionally, the index presented in this paper could be used on its own for assessing the digital development of the world’s countries. In this way, the index is similar to the one developed by Archibugi and Coco [ 34 ] in their paper “A New Indicator of Technological 74 Sustainability 2022,14, 10667 Capabilities for Developed and Developing Countries (Arco)”, which ranked Sweden as the most digitally developed country in 2000. In our index, the most digitally developed country in 2019 was Hong Kong, which in [ 34 ] was number 21. This growth seems to be confirmed by the results presented in Figure 3. In this way, the index developed in our article, along with the digital divide indices developed in other papers [ 42 – 52 ], can offer a way to compare digital development and the progress of countries in the adoption of technology. Possible future researchers could, by using the methodology described in this paper, compare results and see the way in which countries evolved from a digital development standpoint. The present paper describes an original and interesting research study regarding the influence of the personal adoption of digital technology on the poverty rate. The authors also appreciate that the results of the present study could be of interest to governmental institutions and to international organizations such as the United Nations and the OECD due to its implications for the planning and achievement of the Sustainable Development Goals, as stated in the 2030 Agenda [ 3 ]. In this way, the present study could inspire similar approaches and lead to advancement of the rate of the eradication of poverty by making technology available to all citizens. Regarding the relation between digital development and the COVID-19 pandemic, we can state that the economies of the more digitally developed countries have been more stable in the face of the COVID-19 pandemic. These observations are in line with the relevant scientific literature [ 59 – 75 ], meaning that digital development helps develop a stronger economy (that responds to external shocks, such as the COVID-19 pandemic, better). This observation leads to the idea that by developing the digital capacity of a country, the authors can improve its response in the face of the COVID-19 pandemic. The presented results are similar to those in the scientific literature, and they present a correlated and significant view of digital development as a key factor in reducing the poverty headcount ratio. Our results are similar to the ones presented in several cited studies [ 43 , 46 , 50 – 52 , 67 – 70 ], and by generalizing aspects that were observed at continental level by several articles [ 18 , 19 , 26 , 27 ], these results should be of interest to the scientific and academic communities as well as researchers in the economic area. 6. Conclusions Starting from the research hypothesis stated in the introduction, (“How does the increase in technological development influence the rate of poverty at the national level, and how does digital development relate to the response to the COVID-19 pandemic?”) we can say that this research study presents a clear and significant influence between the digital development of a country and the poverty headcount ratio, as calculated by the World Bank [ 64 ]. The present paper shows a correlation between the adoption of technology at the personal level (due to the way in which the digital development index is calculated: considering the percentage of individuals using the internet, the mobile cellular subscriptions per 100 people and the fixed telephone subscriptions per 100 people) and the reduction in the poverty headcount ratio. In addition, this study’s contributions to the general field of knowledge regarding the analysis of digital development, as well as its importance in the reduction of the poverty headcount ratio, are significant and interesting. First, this study establishes a connection between the development of the digital capacity of a country, as measured by using the digital development index, and economic development. The index is calculated as the sum of the following indicators: the percentage of individuals using the internet, the mobile cellular subscriptions per 100 people and the fixed telephone subscriptions per 100 people, as published by the International Telecommunication Union [ 76 ]. This connection is similar to the one described in several papers [ 34 – 37 ] that have proposed and used indices for measuring digital development, such as the one written by Archibugi and Coco [ 34 ]. These interesting results are doubled by the interesting connection between the digital development of a country and the reduction of the poverty headcount ratio. This result is of great interest due to the interesting effects that digitalization has for increasing the wealth of nations. In this case, such an 75 Sustainability 2022,14, 10667 observation, though stated in several papers [ 18 , 19 ], only applied for limited datasets that were related to single continents. For example, Asongu [ 18 ] and Evans [ 19 ] presented a hypothesis that explains, in the case of the African continent, the way in which mobile phone development led to a significant increase in informal financial development, even though its effects were less noticeable at the macroeconomic level, and that the use of mobile phones with internet access in 44 African countries in the period between 2000 and 2016 led to an increase in financial inclusion, although the literature review-based studies [ 20 ] make claims that there are few papers that can present a causal inference between ICT development and poverty. The fact is that the interaction between the internet, mobile phone access or other technologies and poverty, which was also the focus of many papers [ 21 – 25 ], was demonstrated in this research paper for the 35 countries that were analyzed in the data sample. Additionally, in the scientific literature, there has been a trend toward analyzing the impacts of technology as a means of inclusion and access to information on poverty, either in South Asia, Sub-Saharan Africa [ 26 ] or in Latin America [ 27 ]. This also makes our study of interest regarding the way in which digital development has led to worldwide developments instead of regionally based implications. This concentration of the study on proving and presenting results at the global level is, in our opinion, its biggest strength and sets it apart as an interesting and dynamic approach of a much-studied and debated economic and social phenomenon [97–99]. The results of this study suggest that by making technology more affordable and available for a population, the Sustainable Development Goal of eradicating poverty could be accomplished in a faster and more efficient way. One of the challenges that we met in the development of the present study was the lack of data regarding the poverty rate of the analyzed countries, with this being due to the fact that we determined the application of the model to only be for 35 countries and for the period between 2005 and 2018, as these were the only available data on the subject at the national level. This paper presents a quantifiable method of analyzing the impact of digital technology adoption at the personal level on the poverty headcount ratio. This approach is an original one due to the way in which we can observe the impact of the possible increase in the adoption of technology. Of additional interest is the composition of the digital development index. The index allowed us to compare the evolution of the countries in the analyzed period of time. The index is easy to build and can offer a benchmark for developing a comparative analysis between different countries and observing the way in which policies have shaped the evolution of digital development in each country. Another contribution of the index could be its use in the better understanding of the problems of developing countries, as we observed that the countries of the African continent were the ones that presented the greatest gap in digital development. This is of interest due to the fact that sustainable development should promote growth at the global level. In this context, the calculation of this index in the future could provide researchers with a perspective of the progress made by developing countries, and it could also serve as an indicator of appropriate regional policy and a sustainable approach at the international level. On the other hand, a notable research limitation is that the data analyzed in this paper deal with country-based indicators. This approach leads to the exclusion of the idea that within a country, there might be several levels of technological adoption, depending on the regions of the country or whether the population lives in rural or urban environments. Moreover, in the development of the study, the authors noticed a lack of data regarding the poverty headcount ratio for most of the world’s countries. This fact led us to use only 35 countries in the final study in order to develop the vector autoregressive model. Another limitation to take into consideration is represented by the bias of the index toward small countries. This is explained by the way in which the index takes into account indicators that are expressed as percentages. Although such an approach could favor small countries, using nominal values of the indicators (e.g., millions of internet users) could lead to confusing results, and the index is more understandable as a total score of all the percentage-based 76 Sustainability 2022,14, 10667 indicators. Another limitation is represented by the fact that poverty itself is a complex phenomenon, and in this paper, the authors attempted to analyze only the relation between digital development and poverty without taking into consideration other determining factors of poverty, such as governmentand society-related factors, in order to analyze the relation at a fundamental and singular level. However, this approach has the advantage and the limitation of offering a clear view of only a small piece of the relations of poverty and its determining factors. Another interesting effect of digital development on the economy is raising its resilience in front of external shocks. One such shock is the COVID-19 pandemic, with which the world has been confronted in the last two years. By analyzing the evolution of the most digitally developed economies, during the pandemic, we can conclude that the ones that were the most developed according to the digital development index (Japan and South Korea) performed better than the European Union, euro zone and OECD averages. This is of interest because it shows that by increasing the access to technology, the government not only reduces the poverty headcount ratio, but it also makes the economy more resilient to outside shocks. This leads to the idea that digital development has many positive impacts that should be further researched. The present study also has interesting managerial implications for presenting a global perspective of the digital development of the world’s countries. For a company that wants to become active in a digitally developed country, it could use the results of this study as a guideline to analyzing the development of each country. In addition, the relation between the digital development of a country and reduction of the poverty headcount is useful for quantifying the way in which a certain country could evolve in the future from a digital standpoint. Further research studies should focus on investigation of the relation between technological adoption and other Sustainable Development Goals. This could be useful because it holds significance in demonstrating that technology is an important driving force in the creation of a more sustainable economy. Another interesting research direction could be the impact of the technology price level on the poverty headcount rate, so this could be of interest due to the connections between the affordability of technology and its adoption. Thus, it can be concluded that the present research paper presents an established and clear correlation between digital development and the poverty headcount ratio. This is not only a new approach to the field of study but also presents significant relevance to the reader. In this way, this article is of great interest to all of the scientific community and the political decision factors due to the implications of the research performed. In this way, by encouraging digital development of the population (by adopting new technologies), the government can lead to reduction of the poverty headcount ratio. Even though investment in shelter and primary goods can be seen as the way to go for a developing nation, we conclude that better and more consistent results regarding the reduction of poverty can be obtained by increasing the digital development of the country. Author Contributions: All authors contributed equally to this research. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. 77 Sustainability 2022,14, 10667 Appendix A Figure A1. Generalized impulse response function. Table A1. List of countries based on rank in 2019. Country Rank in 2019 Country Rank in 2019 Hong Kong, China 1 San Marino 89 United Arab Emirates 2 Ghana 90 Malta 3 Paraguay 91 Japan 4 Cabo Verde 92 Korea (Rep. of) 5 Côte d’Ivoire 93 Seychelles 6 Albania 94 Montenegro 7 Faroe Islands 95 Luxembourg 8 Algeria 96 Germany 9 Indonesia 97 78 Sustainability 2022,14, 10667 Table A1. Cont. Country Rank in 2019 Country Rank in 2019 Singapore 10 Sri Lanka 98 Switzerland 11 Dominican Rep. 99 Cyprus 12 Curacao 100 United Kingdom 13 British Virgin Islands 101 France 14 Guatemala 102 Netherlands 15 Palestine 103 Iceland 16 Suriname 104 Estonia 17 Maldives 105 Lithuania 18 Egypt 106 Taiwan, Province of China 19 Australia 107 United States 20 Chile 108 Israel 21 Argentina 109 Kuwait 22 Bolivia (Plurinational State of) 110 Russian Federation 23 Gambia 111 Belarus 24 Bahamas 112 Denmark 25 Namibia 113 Spain 26 Senegal 114 Uruguay 27 Greenland 115 Costa Rica 28 Mali 116 Sweden 29 Kyrgyzstan 117 Portugal 30 Syrian Arab Republic 118 Austria 31 Cuba 119 Thailand 32 Moldova 120 Greece 33 India 121 Mauritius 34 Kenya 122 Italy 35 Jamaica 123 Slovenia 36 Nigeria 124 Iran (Islamic Republic of) 37 Saint Vincent and the Grenadines 125 Canada 38 Guinea 126 Qatar 39 Lesotho 127 Belgium 40 Cameroon 128 Brunei Darussalam 41 Bangladesh 129 Slovakia 42 Burkina Faso 130 Serbia 43 Zimbabwe 131 Ireland 44 Benin 132 Malaysia 45 Zambia 133 Finland 46 Ecuador 134 Norway 47 Iraq 135 Monaco 48 Sao Tome and Principe 136 Czech Republic 49 Guinea-Bissau 137 Saudi Arabia 50 Timor-Leste 138 Oman 51 Djibouti 139 Hungary 52 Mauritania 140 Kazakhstan 53 Tanzania 141 Poland 54 Sierra Leone 142 Croatia 55 Sudan 143 Bahrain 56 Rwanda 144 Georgia 57 Bhutan 145 South Africa 58 Togo 146 Puerto Rico 59 Pakistan 147 Romania 60 Nicaragua 148 El Salvador 61 Haiti 149 Latvia 62 Venezuela 150 Botswana 63 Vanuatu 151 China 64 Honduras 152 Vietnam 65 Macao, China 153 Panama 66 Jordan 154 Gibraltar 67 Angola 155 79 Sustainability 2022,14, 3646 agricultural commodity prices were much greater than they used to be in recent years [ 8 ]. Unconventional policy decisions introduced by national governments may have been more dangerous than the pandemic itself [ 9 ]. Western societies, including the European ones, appeared more vulnerable in the face of a new pandemic compared to Eastern ones [ 10 ]. Achieving sustainable development, with respect to the environment and economics has become quite a challenging task in recent years. This has been influenced by the recent economic crisis and the effects of climate change. In the context of the COVID-19 pandemic outbreak, sustainability can be considered not only from an economic or environmental point of view, but also from a public health perspective [ 11 ]. The pandemic crisis has demonstrated the existence of a complex set of inequalities that have emerged within the global system at different levels: the global economy and finance, healthcare, education, the judiciary, governance, the non-governmental sphere, public affairs, business and entrepreneurship, political rights and civil liberties, family life, etc. The downturn in industrial production and introduced restrictions hampering economic activity have particularly affected the labour market. EU Member States have significant differences in the level of human development. A distinction has been made between dynamically developing regions and regions that differ significantly from this level [ 12 – 17 ]. In such situation, it is extremely important to constantly supervise changes in the level of social development of the EU countries and to determine the rank that a given country occupies in relation to the other ones. The Sustainable Development Goals Report 2021 indicates that the COVID-19 crisis disturbed economic activities in the whole world and caused the strongest recession since the Great Depression [ 18 ]. Around 255 million full-time jobs were lost in 2020—about four times more than during the global financial crisis of 2007–2009. The pandemic has caused great risk to the workers in informal employment because they do not have protection against illness or lockdowns. The crisis affected young workers and women particularly strongly. The aim of the study is the assessment of the similarity of the situation in the EU labour markets and their changes using selected indicators in the period before and during the COVID-19 pandemic. Therefore, two research questions arise: Q1–Does the COVID-19 pandemic influence similarities of the labour markets in the EU countries? Q2–Does the COVID-19 pandemic influence similarities of changes in the labour markets in the EU countries? Until now, many scientific articles have appeared on the economic impact of the COVID-19 pandemic. These are mostly studies on individual countries or small groups of them. However, to our knowledge, there have been no published studies comparing time series for all EU countries before and during the pandemic. There is therefore a research gap in this area, which we are trying to fill with our study. We refer our analysis to the benchmark countries, i.e., the ones that most closely meet the Sustainable Development Goals related to the labour market. We assess the similarity of the situation by using the TOPSIS method, and the similarity of changes by using the Dynamic Time Warping method. We obtain homogeneous clusters of countries due to time series similarity using hierarchical clustering. The manuscript is organised as follows: Section 2 presents the literature review. In Section 3, we present the materials and research methods. Section 4 presents the results of empirical analysis. In Section 5, we present the discussion of obtained results. The manuscript ends with conclusions. 2. Literature Review Achieving sustainable development worldwide requires a fair and balanced social and economic environment [ 19 ]. Barska et al. [ 20 ] assess the human development of EU countries in the background of sustainable development from 2014 to 2018. On the basis of their results, we can draw a conclusion that many countries experience positive trends 86 Sustainability 2022,14, 3646 that bring them closer to the successful implementation of the sustainable development paradigm. However, they are also observing unfavourable trends. In almost half of the EU countries, the percentage of poverty-stricken working people and the risk of poverty for older people (65+) increased between 2014 and 2018. The analysis also reveals that there are large discrepancies between the countries studied, relating to different areas of human development. They are particularly clear for labour market indicators. Sweden, Denmark, and The Netherlands are among the highest-ranking countries for several thematic areas. At the opposite end of the spectrum are Romania, Bulgaria, Greece, and Italy. In many countries, there are significant gaps between the thematic areas. They perform very well in some areas and very poorly in others. Every EU country has a room for improvement in at least one of the analysed areas, but there are also countries (e.g., Romania, Bulgaria, and Greece) that need to change and improve in all examined areas. Sustainable economic development needs a well-balanced workforce of young and older people [ 21 ]. As the balance is moving towards older people, the productivity tends to suffer. Furthermore, the older people demand more from health services. The results show that there is a significant difference between the developed and developing EU countries. It suggests the need for specific policies and strategies for the labour market integration of older people. It also implies higher public health expenditures, which has consequences for EU labour market performance. Crises of all kinds have a negative impact on labour market equilibrium. The recovery period poses significant risk for sustainable development goals. It is therefore important for communities in pandemic-affected countries to prepare for a return to sustainable growth. Kapecki [ 22 ] analyses the impact of environmental, financial, and humanitarian crises on sustainable development. He pays particular attention to the financial crisis of 2007 and the crisis caused by the outbreak of the COVID-19 pandemic. Since the onset of the pandemic, various studies have been carried out on its impact on the economies of particular countries and the world economy. As the pandemic continues to develop, the results of early studies are also changing. Studies on the negative impact of the pandemic on global GDP have started to appear in the global literature [ 23 – 27 ] and on financial performance on global stock exchanges [28–32] . In line with previous literature [33,34] , the COVID-19 pandemic can trigger financial panics and lead governments to adjust their economic policies, as in other crisis periods [35]. Su et al. [ 36 ] (2022) analyse the relation of COVID-19 to corporate sustainability from the point of view of both internal organization and external social environment. They attempt to analyse and find implications for companies and society to better cope with crises and achieve sustainable development in the post-pandemic era. They conclude that if enterprises aim at maintaining sustainable development in the post-COVID-19 era with coexistence of challenges and opportunities, they must have full integration of internal and external resources. They should also rely on digital transformation to achieve survival, development, and upgrade. The pandemic causes many negative emotions amongst employees such as loneliness, anxiety, fear, worry, or collapse. This, in turn, affects job performance and employee satisfaction, then poses dangers to sustainable human resource management. In analyses related to the impact of COVID-19 on the economic and financial situation, a trend of research on the impact of COVID-19 on the labour market has emerged. In times of crisis, the labour market becomes one of the first to experience severe turbulence. Employability is one of the parameters that has changed significantly due to the existence of the global health crisis. People employed in flexible forms of employment, which are hardly subject to any legal protection, are the first to lose their jobs [ 37 , 38 ]. Informal workers, youth, and women [ 39 ], as well as small traders, the self-employed, migrant workers, and daily wage earners [ 40 ] were the first to experience employment problems. Nivakoski and Mascherini [ 41 ] note that the COVID-19 may have had a different impact on gender equality than previous recessions. Emerging evidence suggests that women’s paid work has declined in many countries due to both labour demand and labour supply factors. 87 Sustainability 2022,14, 3646 Demand for labour declined because women’s work is often associated with close contact with other people, for example hospitality, travel, personal care, and cleaning. These are the industries for which activities were significantly reduced at the start of the pandemic. Botha et al. [ 42 ] showed the significant negative relationship between labour market shocks triggered by the COVID-19 crisis and financial wellbeing. They show these labour market shocks are disproportionately felt by people at the lower end of the financial wellbeing distribution. In 2020, the tourism industry was particularly affected, including that in European countries. Changes in the number of arrivals and overnight stays were related to the degree of restrictions imposed [43]. Svabova et al. [ 44 ] analyses changes in unemployment in the Slovak Republic due to the impact of the anti-spreading regulations adopted by the government. The authors showed that the reduction in economic activity of firms operating in Slovakia resulted in a decrease in consumer demand, which put pressure on employers to reduce costs through lay-offs. This resulted in an increase in unemployment. The restrictions adopted to prevent the spread of COVID-19 had a negative impact on the Slovak labour market. Many analyses highlight the major change brought about in the EU-27 by ‘teleworking’ and the instability of traditional jobs in the new context based on digitisation [ 45 – 48 ]. This situation was exacerbated during the pandemic period. The empirical findings point to a situation of deep economic crisis generated by the economic downturn and high unemployment rates in the EU-27. Galik et al. [ 49 ] assess labour market flexibility using the TOPSIS method and multicriteria decision analysis (MCDA) methods. The processes of sustainable industrial relations are considered in the context of shaping labour market flexibility in 15 European Union countries between 2009 and 2018. Their results indicate that the TOPSIS method is a suitable approach for measuring labour market flexibility on the international scale. Moreover, with regard to labour force phenomena, this method provides an opportunity to examine the impact of individual factors related to social and employment policies in the context of sustainable development and socio-economic growth. The lack of precise tools for forecasting the development of national and transnational labour markets, especially in the COVID-19 era, highlights the importance of such a method for planners and policy makers. The empirical study by Gavriluta et al. [ 50 ] presents the situation of employability in the EU-27 under the conditions of the COVID-19 pandemic. The crisis caused by the pandemic highlights existing differences in the labour market in different regions of Europe. These differences have often increased under the influence of regulations introduced by national governments. The socio-economic category most affected by the economic impact of the COVID-19 pandemic is young people with primary or secondary education. In their conclusions, the authors stress that such phenomena as an increase in education levels and a reduction in gender inequalities and material and social deprivation should be correlated with economic freedom and increased opportunities for entrepreneurship. Such measures are beneficial in the context of sustainable development in the EU. An interesting study is conducted by Guo et al. [ 51 ] on the economic impact on COVID19 vaccination rates in the USA. They find that there is positive correlation between both the county-level per capita income and county-level unemployment rates and county-level COVID-19 vaccination rates across the U.S. However, these associations are divergent with respect to race/ethnicity. 3. Materials and Methods 3.1. Materials We use the quarterly Eurostat data for all 27 EU member states, available online at: https://ec.europa.eu/eurostat/web/main/data/database (accessed on 7 February 2022). The data cover the basic indicators of labour market: • unemployment rate; • activity rate; • employment rate. 88 Sustainability 2022,14, 3646 We consider every indicator for the total population, for young people (aged 15–24 years), and for people aged 55 years or more. The only exception is the unemployment rate, which excludes the data for people aged 55 years or more. The reason for this is the lack of data for Malta and Luxembourg. The data cover the period starting at the 1st quarter of 2018 and ending at the 3rd quarter of 2021. We divide the period into two sub-periods. The first (1st quarter 2018–4th quarter 2019) is the pre-pandemic period, and the second (1st quarter 2020–3rd quarter 2021) is the pandemic period. We consider the countries that closely meet the Sustainable Development Goal (SDG) related to the labour market as the benchmark ones. The SDG that includes the indicators related to the labour market is the SDG8 (Promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all). SDG8 is one of the 17 Sustainable Development Goals (SDGs) that were established by the United Nations General Assembly in 2015 [ 52 ]. Progress towards the goals is measured, monitored, and evaluated through 17 indicators. SDG8 has a total of twelve targets. These are: sustainable economic growth (8.1); diversify, innovate, and upgrade for economic productivity (8.2); promote policies to support job creation and growing enterprises (8.3); improve resource efficiency in consumption and production (8.4); full employment and decent work with equal pay (8.5); promote youth employment, education, and training (8.6); end modern slavery, trafficking, and child labour (8.7); protect labour rights and promote safe working environments (8.8); promote beneficial and sustainable tourism (8.9); universal access to banking, insurance, and financial services (8.10); increase aid for trade support (8.a); and develop a global youth employment strategy (8.b). SGD8 is the aspiration that the economic sector of each country should provide its citizens with the necessary needs for a good life, regardless of their origin, race, or culture. As we wish to assess the SDG indicators for every analysed quarter, we select the following, as only they were available in the form of quarterly data: • young people neither in employment nor in education or training (NEET); • employment rate; • long-term unemployment rate. 3.2. Methods We perform the analysis in the following steps: 1. By means of the TOPSIS method, we create the ranking of the countries with respect to fulfilment of the sustainable development goals regarding the labour market. The best countries in the whole period create the benchmark. 2. For every quarter, by means of the TOPSIS method, we assess the situation of the EU countries in their labour markets, using unemployment, activity, and employment rates for total population, for young people, and for people aged 55 years or more. 3. With respect to the values of the TOPSIS measure, we select the groups of countries with very good, rather good, rather poor, and very poor situation in their labour markets. 4. We analyse similarities of time series of the situation in the labour markets (assessed by the TOPSIS method) between the countries by means of the Dynamic Time Warping (DTW) method in the pre-pandemic and pandemic periods. 5. The similarities between the time series are assessed by means of the DTW distance. 6. The DTW distance is then used in hierarchical clustering to distinguish the homogeneous clusters of countries with respect to similarity of changes of the situation in their labour markets in both pre-pandemic and pandemic periods. 3.2.1. The TOPSIS Method The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is the technique created for the need of the multi-criteria decision making. It is, however, also widely used in the multivariate statistical analysis. It was created by Hwang and Yoon [ 53 ] and is based on the weighed distance of each object (in our case country) from the so-called 89 Sustainability 2022,14, 3646 pattern (i.e., the best values of variables in the dataset) and from the anti-pattern (i.e., the worst values of variables in the dataset). A starting point of the TOPSIS method is the observation matrix X: X=⎡ ⎢ ⎢ ⎢ ⎣ x11 x12 ··· x1m x21 x22 ··· x2m . . .. . ..... . . xn1xn2··· xnm ⎤ ⎥ ⎥ ⎥ ⎦(1) where xij is the value of j -th variable in i -th object ( i= 1, ... , n , j= 1, ... , m ), m is the number of variables, and nis the number of objects. As all variables in our dataset are measured on the ratio scale, we can normalise them by means one of the quotient inversions (such normalisation method preserves the scale strength): zij =xij ∑n i=1x2 ij (2) where zij is the normalised value of j-th variable in i-th object (i=1,...,n,j=1,...,m). The next step of the TOPSIS method is determination of weights. The problem of determining the variables’ weights is not an easy task. There is also no single method of weights determination recognised as best. We can assign weights of variables statistically (on the basis of variables’ dispersion, mutual correlations, or entropy measures) or by using the expert methods. The first method would cause weights in every analysed period to be different, and the second would indicate a high degree of subjectivism. If there is no clear indication that some variables are more important than the others, we should assume equal weights. We multiply the normalised values of variables by their weights, thus creating the weighed, normalised observation matrix: tij =wjzij,i=1, . . . , n,j=1, . . . , m(3) where wj=1 mare the variables’ weights (j=1, ···,m). In the next step of the TOPSIS method we calculate the pattern ( Ab ) and the antipattern (Aw): Ab=max itij|j∈J+,min itij|j∈J−|i=1, . . . , n=tb1,...,tbj,...,tbm(4) Aw=min itij|j∈J+,max itij|j∈J−|i=1, . . . , n=tw1,...,twj,...,twm(5) where J+ indicates stimulants (variables for which the highest values are the most desirable) and J− indicated destimulants (variables for which the lowest values are the most desirable). Next, we calculate the weighed distances of each object from the pattern ( d+ i0 ) and anti-pattern (d− i0) by means of the Euclidean metric: d+ i0=   m ∑ j=1tij −tbj2,i=1, . . . , n(6) d− i0=   m ∑ j=1tij −twj2,i=1, . . . , n(7) 90 Sustainability 2022,14, 3646 Finally, we calculate the composite measure qi: qi=d− i0 d− i0+d+ i0 ,i=1, . . . , n(8) The composite measure qi has the following properties: qi∈[ 0,1 ] , maxi{qi} —the best object, and mini{qi}—the worst object. 3.2.2. The Dynamic Time Warping Method The Dynamic Time Warping (DTW) method was invented by Bellman and Kalaba [ 54 ]. Originally, it was used for speech recognition problems [ 55 – 57 ]. Other fields of its application include music information retrieval [ 58 ], gesture recognition [ 59 ], or in bioinformatics [ 60 ]. It is now more and more often used in research on time series describing economic and social phenomena. Landmesser [ 61 ] uses it to find similarities in time series describing the dynamics of the number of cases and deaths of COVID-19 in the provinces of Poland. Dmytrów and Bieszk-Stolorz [ 62 ] look for correlations and links between unemployment rates and unemployment duration in the Visegrad countries. Dmytrów et al. [ 63 ] assess the links between the number of COVID-19 cases and the energy commodity sector. Denkowska and Wanat [ 64 ] use the DTW algorithm to group insurance institutions by the similarity of their contribution to systemic risk, as expressed by DeltaCoVaR. Stübinger [ 65 ] uses the DTW method to find optimal causal path algorithm for the minute-by-minute data of the S&P 500 constituents from 1998 to 2015. By using the DTW algorithm we can measure similarity between two time series. The DTW method looks for an optimal alignment between them by using the dynamic programming. The alignment is described by a given scoring function. Let X=(x1 , x2 , ... , xN) and Y=(y1 , y2 , ... , yM) be two time series. In order to be able to compare them, both time series must be normalised. The most frequently used method is z -normalisation. The need for normalisation of time series is often highlighted in classification or clustering methods with the DTW and other distance measures [66,67]. Next, we define the local cost measure for two elements of X and Y by means of the equation: cxi,yj=xi−yj,i=1,...,N,j=1,...,M(9) We calculate this measure for every pair of elements of X and Y , thus obtaining the local cost matrix LCM ∈RN×M . The optimal alignment between time series X and Y is the one having minimal overall cost. The point-to-point match between the time series Xand Yis represented by the time warping path. It is a sequence p=(p1 , ... , pL) , where pl=(nl , ml)∈{ 1, ... , N}× { 1, ... , M} for l∈{ 1, ... , L}(L∈{max (N,M),...,N+M−1}) . The sequence satisfies three conditions: boundary, monotonicity, and step size conditions [ 68 ]. The boundary condition ensures that the first and the last element of p are p1=( 1,1 ) and pL=(N , M) , respectively. It means that the first (last) index from the first sequence must be matched with the first (last) index from the second one. The monotonicity condition ensures that the path always moves up, right, or up and right of the current position, i.e., pl+1−pl∈ {(1,0),(0, 1),(1,1)} for l= 1, ... , L− 1. The step size condition ensures that every index from the time series X must be matched with one or more indices from the time series Y (and vice versa). The optimal match is the one that satisfies all the above-mentioned conditions and that has the minimal total cost. The total cost cp(X,Y)of a warping path pis defined as: cp(X,Y)= L ∑ l=1 c(xnl,yml)= L ∑ l=1 |xnl −yml|(10) 91 Sustainability 2022,14, 3646 The optimal match between Xand Yis then: DTW(X,Y)=cp∗(X,Y)=mincp(X,Y)|p∈P(11) where Pis the set of all possible warping paths. By means of the DTW algorithm, we find the path that minimises the alignment between X and Y . It iteratively steps through the local cost matrix and aggregates the cost. We find the optimal path p∗ by using a dynamic programming algorithm. The obtained value DTW(X,Y)is the measure of distance between the time series Xand Y. We use obtained by the Equation (11) distances between all time series to create the dissimilarity matrix. In the next step, we use this matrix in agglomerative hierarchical clustering of time series [ 69 ]. Its main advantage is the visualisation capabilities. In our research we use the Ward’s method to minimise the variance within the clusters. We check the robustness of the clustering algorithm and set the number of clusters by means of the silhouette index. Clustering methods are used in many economic and social issues. Rozmus [ 70 ] uses different measures of stability to group EU member states by their level of sustainability. Zalewska [ 71 ] uses cluster analysis to identify and compare determinants influencing the opinion of students and lecturers on the evaluation of the possibility and effectiveness of introducing the CQI system in Polish higher education. Sikora-Alicka [ 72 ] performs a comparative analysis of Polish teaching hospitals. The aim of her study is to confirm the thesis that teaching hospitals, despite significant organisational and functional differences, due to the specificity of their activities, do not differ significantly in the structure of generated costs. Małkowska et al. [ 73 ] use the TOPSIS method and cluster analysis to measure and assess the impact of digital transformation on the EU countries. Roman et al. [43] use cluster analysis to group European countries in terms of changes in tourism due to the outbreak of the pandemic. 4. Results 4.1. Sustainable Development Goals Related to Labour Market In the first step of the analysis, we create the ranking of countries with respect to fulfilment of the sustainable development goals (SDG) regarding the labour market. We consider the following variables: young people neither in employment nor in education or training (NEET) (SDG 8 1) , employment rate (SDG 8 2) , and long-term unemployment rate (SDG 8 3) . Variables SDG 8 1 , SDG 8 2 , and SDG 8 3 are related to the implementation of SDG8 targets 8.5 and 8.6 and relate directly to the labour market. The first and the third variables are the destimulants, while the second is the stimulant. We set the pattern and anti-pattern values for the whole period (Table 1). Table 1. Pattern and anti-pattern values of the SDG-related variables. Source: own calculations on the basis of the Eurostat data. Specification (SDG81)(SDG82)(SDG83) pattern 5.0% 71.5% 12.5% anti-pattern 24.4% 43.3% 73.1% The best (pattern) value of the percentage of NEETs (5.0%) is in the first quarter of 2020 in the Netherlands and the worst (anti-pattern) (24.4%) in the second quarter of 2020 in Italy. The best value of the employment rate (71.5%) is in the third quarter of 2021 in the Netherlands and the worst (43.3%) in the second quarter of 2020 in Greece. The pattern value of the long-term unemployment rate (12.5%) is observed in the third quarter of 2019 in Sweden and the anti-pattern (73.1%) in the third quarter of 2018 in Slovakia. For the the percentage of NEETs and the long-term unemployment rate, the relative differences between the best and the worst values are very large. The best value of the former is just above 1 5of the worst and for the latter this ratio equals about 1 6. 92 Sustainability 2022,14, 3646 We then apply the Equations (1)–(8) to calculate the TOPSIS measure for every quarter of the analysed period. Having calculated the TOPSIS measures, for every quarter we calculate the median ( Me ), the first ( Q1 ) and the last ( Q3 ) quartile, and divide the countries into four groups (Table 2). Table 2. Groups with respect to fulfilment of the SDGs. Source: own elaboration. Group Value of the TOPSIS Measure A—very high fulfilment (Q3,1] B—rather high fulfilment (Me,Q3] C—rather low fulfilment (Q1,Me] D—very low fulfilment [0, Q1] The countries, for which the TOPSIS measure calculated for labour market variables that are related to SDGs falls in the first interval— (Q3 ,1 ] in the largest number of quarters of the analysed period, create the benchmark. In the whole analysed period (first quarter 2018–third quarter 2021) we can distinguish four countries that fulfil the sustainable development goals related to the labour market to the highest degree—Denmark, The Netherlands, Finland, and Sweden. Therefore, we treat these countries as the benchmark in further analysis. It is also worth noting that, on the other side, there are countries with very low fulfilment of the SDGs—Bulgaria, Greece, Spain, Italy, Romania, and Slovakia. These countries have the lowest degree of fulfilment of SDGs related to the labour market in the whole analysed period. We present the results of grouping countries with respect to the fulfilment of the SDGs related to the labour market in Table 3. Table 3. Groups of countries with respect to fulfilment of the SDGs. Source: own calculations on the basis of the Eurostat data. Country 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 Belgium C C C C C C C C D DCDCCC Bulgaria D D D D D D D D D D D D D D D Czechia A A B B B B B B B A ABABA Denmark A A A A A A A A A A A A A A A Germany B B B B C C C B B C C B B B B Estonia A B B A B A A A A A A ABAB Ireland B C C C B C C B B C C C A A A Greece D D D D D D D D D D D D D D D Spain D D D D D D D D D D D D D D D France B B B B B B B B B B B B C C B Croatia D D D D D C D D C C C CDCD Italy D D D D D D D D D D D D D D D Cyprus C C C C C D C C C C D C C D C Latvia C C C C C C C B C B B B C B B Lithuania B A A B B B B C C C C B C C C Luxembourg A A A A A A A A A B A A B B C Hungary C C B B B C B B B B B C C C B Malta C B C C A A A C A A B A A C A Netherlands A A A A A A A A A A A A A A A Austria B B A A A B B A B B B B B A B Poland C C C C C B B C C B C C B B C Portugal C C C C C C C C C C C C C C C Romania D D D D D D D D D D D D D D D Slovenia B B B B C B C C C C B C B B C Slovakia D D D D D D D D D D D D D D D Finland A A A A A A A A A A A A A A A Sweden A A A A A A A A A A A A A A A 4.2. Assessment of the Situation of EU Countries in Their Labour Markets We use the following variables for assessment of the situation in the labour markets of the EU countries: x1—total unemployment rate (in %); x2—unemployment rate for people aged 15–24 years (in %); x3—total activity rate (in %); 93 Sustainability 2022,14, 3646 x4—activity rate for people aged 15–24 years (in %); x5—activity rate for people aged 55 years or more (in %); x6—total employment rate (in %); x7—employment rate for people aged 15–24 years (in %); x8—employment rate for people aged 55 years or more (in %). Many authors also identify other indicators of labour market conditions, including job finding and separation rates, job vacancy rate, long-term unemployment rate, hours of work, wages and compensation costs, labour productivity, and employment in the informal economy [ 74 , 75 ]. However, including them in our analysis is impossible for two main reasons. First, they are not always quarterly, but annual data. Second, they are not available for all EU countries. As the time series used must be complete due to the methods used, we have decided to limit ourselves to only the selected variables. In order to initially assess the general situation in the labour market in the EU, we present some basic descriptive statistics (arithmetic mean, standard deviation, coefficient of variation, median, skewness, minimum, and maximum) for total unemployment, activity, and employment rates in Tables A1–A3 in Appendix A. The average and median values of analysed variables had been improving during the pre-pandemic period. When the state of the pandemic was declared (11 March 2020), the indicators had begun to deteriorate and reached their worst values in the third quarter of 2020. The general situation then started to improve. All analysed indicators reached the best values in the whole analysed period in the third quarter of 2021. We may have been observing a revival from the recession caused by the COVID-19 pandemic. However, further data on the situation in the EU labour market are rather unclear due to the ongoing war in Ukraine. The unemployment rate has much higher volatility than the activity and employment rates. It means that there is much higher difference between the best (Czechia, Germany, and The Netherlands) and the worst (Greece, Spain, or Italy) countries. Additionally, in case of Greece and Italy, we can observe high, outlying values of the unemployment rate. This causes a high, positive skewness of the distribution of this indicator. In case of the activity and employment rates, the distributions have moderate, negative skewness. The pattern and anti-pattern values for all variables in the whole period are presented in Table 4. Table 4. Pattern and anti-pattern values. Source: own calculations on the basis of the Eurostat data. Specification x1x2x3x4x5x6x7x8 pattern 2.0% 5.1% 74.4% 79.9% 83.6% 71.5% 73.5% 78.7% anti-pattern 20.7% 44.0% 53.3% 19.4% 39.0% 43.3% 11.8% 37.6% The best values of the unemployment rates (total and for young people) are in Czechia (in the whole year 2019 and in the first quarter of 2020 for the former and in the fourth quarter of 2019 for the latter). The worst values of the unemployment rates are in Greece (in the first quarter of 2018 for both rates). The pattern values of activity and employment rates for total population and for young people are in the Netherlands (all of them in the third quarter of 2021), while the pattern values of these indicators for people aged 55 years or more are in Sweden (for activity rate in the fourth quarter of 2020 and for employment rate in the fourth quarter of 2018). The anti-pattern value of total activity rate is in Italy (in the second quarter of 2020), activity rate for young people in Bulgaria (in the third quarter of 2021) and activity rate for people aged 55 years or more in Romania (in the second quarter of 2018). The worst values of total employment rate and employment rate for young people are in Greece (both in the second quarter of 2020), employment rate for young people in and employment rate for people aged 55 years or more in Romania (in the second quarter of 2018). Interestingly, we cannot say that the pandemic period has brought a worsening of the labour market indicators—in 6 out of 8 cases, the best values have been achieved 94 Sustainability 2022,14, 3646 during the pandemic period. In half of cases, the worst values of indicators have happened in the pre-pandemic period. In addition, as in the case of indicators related to the sustainable development goals, the differences are sometimes very high. Such a situation is the case of total unemployment rate (the worst value is over 10 times higher than the best one) and unemployment rate for young people (the worst value is almost 9 times higher than the best one). We now repeat the TOPSIS method for the variables describing the situation in the labour market. After applying Equations (1)–(8) and calculating the TOPSIS measure, we obtain the assessment of the situation in the labour market. We then calculate median and quartiles for every quarter in the analysed period and create the groups of countries. The intervals are the same as in Table 2. However, in case of the assessment of the situation, the groups are as follows: A—very good situation, B—rather good situation, C—rather poor situation, and D—very poor situation. We present the results in Table 5. Table 5. Groups of countries with respect to the situation in their labour markets. Source: own calculations on the basis of the Eurostat data. Country 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 Belgium C D C C C C C C C C C C C C C Bulgaria C C D C C C C C C D C C C C C Czechia B B B B B B B B B A A B B A B Denmark A A A A A A A A A A A A A A A Germany A A A A A A A A A A A A A A A Estonia A A B A A B A A A B B B A B B Ireland B BABAAAAABBABBA Greece D D D D D D D D D D D D D D D Spain D D D D D D D D D D D D D D D France D D D D D D D D D C C C C C C Croatia D D D D D D D D D D D D D D D Italy D D D D D D D D D D D D D D D Cyprus D C C D D C C C C C C C C C B Latvia C C B C C C C B C B B B C C C Lithuania C B B B B B B C C C C C B C C Luxembourg C C C C C C D C D D D D C B C Hungary B B B B B C B B B B B B B B B Malta A A A A A A A A A A A A A A A Netherlands A A A A A A A A A A A A A A A Austria A A A A A A A A A A A A A A A Poland B C C C C B B B B A B B B B B Portugal C C C C CDCDCCDDDDD Romania D D D D D D D D D D D D D D D Slovenia B B C B B B C C B C C C C C B Slovakia C C C C C C C C C C C C D D D Finland B B B B B B B B B B A A A A A Sweden A A A A B A B B B B B B B B C Denmark, Germany, Malta, The Netherlands, and Austria are amongst the countries with the best situation in their labour markets in the whole analysed period (in both prepandemic and pandemic periods). On the other hand, Greece, Spain, Croatia, Italy, and Romania are the countries with the worst situation in the whole analysed period. When we compare the results of analysis of the situation in the labour markets to fulfilment of SDGs, it turns out that the best situation does not always correspond with the highest fulfilment of the SDGs. Denmark and The Netherlands are the countries with the best outlook with respect to both their labour markets and fulfilment of SDGs (in the whole analysed period). Finland and Sweden are always among the best with respect to fulfilment of the SDGs, but not in the case of their general situation in their labour markets. Interestingly, their situation changes in different directions. Finland, since the beginning until the end of 2nd quarter 2020, is in group “B” and in group “A” afterwards, while Sweden is in group “A” until the 2nd quarter 2019, in group “B” afterwards and until the end of the 2nd quarter 2021. 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Res. 2021,16, 2843–2857. [CrossRef] 104 Citation: Bieszk-Stolorz, B.; Markowicz, I. The Impact of the COVID-19 Pandemic on the Situation of the Unemployed in Poland. A Study Using Survival Analysis Methods. Sustainability 2022,14, 12677. https://doi.org/10.3390/ su141912677 Academic Editors: ¸Stefan Cristian Gherghina and Liliana Nicoleta Simionescu Received: 30 August 2022 Accepted: 2 October 2022 Published: 5 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article The Impact of the COVID-19 Pandemic on the Situation of the Unemployed in Poland. A Study Using Survival Analysis Methods Beata Bieszk-Stolorz * and Iwona Markowicz Institute of Economics and Finance, University of Szczecin, 71-101 Szczecin, Poland *Correspondence: [email protected] Abstract: Many studies point to the impact of the COVID-19 pandemic on the socio-economic situation of countries and, consequently, on the achievement of sustainable development goals. Although termed a health crisis, the pandemic has also had an impact on the labour market. The imposed restrictions caused companies to close or reduce their operations. Employees switched to remote work, but also often lost their jobs temporarily or permanently. However, the impact of the pandemic on the labour market is not so obvious. This is indicated by our research and that of other researchers. In this paper, we used individual data on the unemployed registered at the labour office in Szczecin (Poland) and were thus able to apply survival analysis methods. These methods allowed us to assess changes in the duration of unemployment and the intensity of taking up work for individual cohorts (unemployed people deregistered in a given quarter). The results indicate, on the one hand, the problems in the labour market during the pandemic and, on the other hand, the adapted reaction of the unemployed to the situation and the acceleration of the decision to accept an offered job. Keywords: registered unemployment; COVID-19 pandemic; survival analysis 1. Introduction Even before the pandemic emerged, the global economy had slowed down. According to The Sustainable Development Goals Report 2021 [ 1 ], the health crisis caused by COVID19 disrupted economic activity worldwide and caused the worst recession since the Great Depression. COVID-19 created a heightened adverse impact on human life, the economy, the environment, and the energy and transport sectors compared to the pre-COVID-19 scenario [ 2 ]. In 2020, 255 million full-time jobs were lost. This is about four times the number lost during the global financial crisis in 2009. The pandemic has placed informally employed workers, young workers, workers with disabilities, and women at a particular risk. There is a high probability of job loss for temporary agency workers, marginal parttime workers, on-call workers, and independent contractors in sectors that are heavily affected [ 3 ]. The authors of the Sustainable Development Goals Report 2022 [ 4 ] found that in 2021 the global economy started to rebound and effected some improvement in terms of unemployment. However, this recovery varied considerably across regions, countries, sectors, and labour market groups. In lowand lower middle-income countries, small firms were particularly disadvantaged. The conflict in Ukraine is expected to seriously slow global growth in 2022. In the face of all these events, the process of achieving the Sustainable Development Goals is threatened. The COVID-19 pandemic increased unemployment as manufacturing and other industries closed due to mandated social isolation, resulting in a descent into poverty [ 5 ]. Adverse changes have affected many spheres of human life. Environmental pollution increased due to an increase in organic and inorganic waste [ 6 ]. With the development of the COVID-19 pandemic, very severe economic degrowth and imbalances Sustainability 2022,14, 12677. https://doi.org/10.3390/su141912677 https://www.mdpi.com/journal/sustainability 105 Sustainability 2022,14, 12677 began [ 7 – 10 ]. Among other things, the achievement of SDG Goal 8—to promote sustained, inclusive, and sustainable economic growth; full and productive employment; and decent work for all—was threatened. Alibegovic et al. [ 11 ] ranked this goal among the most threatened by the pandemic. High unemployment rates in the EU suggested labour market imbalances and signalled an economic recession. A study by Gavrilu t , ăet al. [ 12 ] suggests that the labour market situation in Poland during the pandemic was not bad. The unemployment rate increased between 2020 and 2021 but was much lower than in many EU-27 countries (below average). Poland’s employment rate was only slightly lower than the average set for the EU-27. Poland’s relatively good position is also confirmed by a study by Lee et al. [ 13 ]. In the ranking of economic resilience for 52 countries worldwide, Poland achieved an average score. However, as Lee et al. [ 14 ] point out, the labour market indicators in the initial phase of the pandemic should be viewed with caution. Unemployment does not indicate the actual scale of disruption for workers, as many people keep their jobs but are not working (therefore, they are considered employed), have lost their jobs but do not search for jobs (therefore, they are considered inactive), or are working shorter hours (therefore, they are considered employed again). In the longer term, the impact of the pandemic on labour markets in the EU will become evident once protective labour market policies are abolished [ 15 ]. Currently, the labour market situation in Poland is good considering the situation in EU countries (Figure 1). In terms of the harmonised unemployment rate, Poland ranks third among the countries with the lowest rates.         &]HFKLD *HUPDQ\ 3RODQG 0DOWD +XQJDU\ 1HWKHUODQGV 6ORYHQLD %XOJDULD $XVWULD /X[HPERXUJ 'HQPDUN ,UHODQG (VWRQLD %HOJLXP 5RPDQLD &\SUXV 3RUWXJDO /LWKXDQLD (8 &URDWLD 6ORYDNLD )LQODQG /DWYLD )UDQFH 6ZHGHQ ,WDO\ *UHHFH 6SDLQ  Figure 1. Harmonised unemployment rate in EU countries in the 1st quarter of 2022. Source: own elaboration on the basis of data from Statistics Poland—registered unemployment quarter 2022 (https://stat.gov.pl/en, accessed on 1 June 2022). The unemployment rate in Poland has been steadily decreasing since the beginning of its EU accession. In May 2004, it was 19.4% and in October 2019 it was only 5.0%. This trend was unfortunately bucked during the pandemic period. At the end of 2020, the unemployment rate was 6.3% and at the end of 2021 it was 5.4%. The year 2020 was unique due to the global COVID-19 pandemic. This also severely influenced the labour market, which, similar to a lens, concentrates the effects of the major changes taking place in both the economy and social life. In Poland, a state of epidemic was announced in March 2020. Both its course and the measures taken to prevent the spread of the virus disrupted the typical market game of supply and demand for labour [16]. In this emergency situation, many companies closed down or reduced their activities. Of course, this translated into job losses, increased unemployment, and a decrease in the population’s income. As we have already noted, this situation was temporary. In many countries, legislation, online learning, and work opportunities have been put in place to reduce the negative effects of the pandemic. Nevertheless, it has been pointed out that 2020 106 Sustainability 2022,14, 12677 was of a specific significance to the labour market situation. In Poland, the situation before the pandemic was systematically improving (the working population was increasing, and the unemployed population was decreasing). The positive trends in the labour market, as indicated by the increasing employment rate, the stabilisation of the activity rate, and the decreasing unemployment rate, were disrupted after the announcement of the epidemic in March 2020. As Statistics Poland points out, the largest changes in the economic activity of the population—resulting from the circumstances accompanying the fight against the COVID19 pandemic—took place in the second quarter of 2020. The introduction of remote work and the reduction in the average weekly working time were important aspects. It should be noted that in the years of an economic boom, the second and the third quarter of a year compared to the fourth and the first quarter are usually characterised by decreasing unemployment, whereas this seasonal periodicity was not maintained in 2020. We put forward the following research hypotheses: Hypothesis 1 (H1): An increase in unemployment as a result of the health crisis. Hypothesis 2 (H2): A longer job search time after the onset of the pandemic. Hypothesis 3 (H3): A reduced intensity of taking up work after the onset of the pandemic. These hypotheses were based on the belief that the pandemic, as a crisis phenomenon, would have a negative impact on the labour market. The closure of companies or the reduction in their activities clearly indicates a reduced demand for labour during a difficult period. The aim of the research is to assess the impact of the COVID-19 pandemic on the situation of the unemployed in Szczecin. The study uses individual data on the duration of unemployment for people registered with the Poviat Labour Office in the period from Q1 2019 to Q1 2022. Such data enable the use of survival analysis methods to indicate whether the duration of a job search by the unemployed changed during the period of the pandemic’s emergence. Thanks to the possibility of using censored data, it was possible to include people who were deregistered for work and those who were deregistered for other reasons. It should be added that Szczecin is a large voivodeship city. It is located in the northwestern part of Poland. It has around 400,000 inhabitants. As in other cities, it is not only a place of work for the inhabitants of Szczecin, but also for the inhabitants of the region. Its location on the Bay of Pomerania makes Szczecin a port city with access to the Baltic Sea. Szczecin used to be a shipbuilding city. Today, employment in the shipyards is marginal. However, Szczecin still has a port that forms a single entity with the port of ´ Swinouj´scie. It is dominated by the growing e-commerce sector, the logistics sector, the information technology sector, and modern business services. Special economic zones and large warehouses (Zalando, Amazon, etc.) are being established in the vicinity of Szczecin. This creates jobs for the capital of the region. Thus, the directions of the development of the labour market in Szczecin are the same as in other cities and the research results obtained may be helpful in creating labour market policies in crisis periods in other cities, Poland, and other countries. The manuscript is organised as follows: Section 2 presents the literature review. In Section 3, we present the data and research methods. Section 4 presents the results of the empirical analysis. In Section 5, we present the discussion of the obtained results. The manuscript ends with our conclusions. 2. Literature review The dynamics of the COVID-19 pandemic and its impact on the socio-economic situation have become the subject of many scientific studies [ 17 – 25 ]. The results of these studies have been published in a number of journals [ 26 ]. These studies include the impact 107 Sustainability 2022,14, 12677 of COVID-19 on the efficiency of healthcare systems [ 27 ], on energy markets [ 28 , 29 ], on labour markets [ 30 – 32 ], on capital markets [ 33 – 35 ], and on selected industries [ 36 , 37 ]. For the most part, the results of the studies indicate a negative impact of the pandemic on the socio-economic situation. The emergence of COVID-19 caused an economic shock in many countries on an unprecedented scale. This manifested itself as a decline in the value of the GDP. For example, during the first wave of the pandemic, the decline in the value of the GDP in the UK was 21%—the largest in over 60 years [ 38 ]. The decline in the value of the GDP in the USA during the same period was 31.7% [ 39 ]. However, some researchers also highlight the positive impact of COVID-19 on sustainable development. This refers to the reduction in greenhouse gas emissions [ 40 ]. The level of greenhouse gas emissions could have decreased by about 10%. According to Cortes and Forsythe [ 41 ], the pandemic has exacerbated pre-existing inequalities in the Canadian labour market. The employment losses were widespread. They more intensely affected the lower-paying occupations and industries. People from disadvantaged groups were in a particularly difficult situation in the labour market, such as Hispanics, younger workers, those with lower levels of education, and women. Blustein et al. [ 42 ] also report that the pandemic reveals and exacerbates existing inequalities in the labour market. They think that the COVID-19 pandemic grants the opportunity to define and describe how precarious work creates physical, behavioural, relational, economic, psychological, and emotional vulnerabilities that worsen outcomes from crises. The authors also postulate the application of rigorous quantitative methods to develop a new understanding of the nature of unemployment during this period and to develop and assess interventions. Antipova [ 43 ] studied economic impacts in the context of social disadvantage. This work specifically considers economic conditions in regions with pre-existing inequalities and examines labour market outcomes in already socially vulnerable areas. More marginalised regions may have broader economic damages related to the pandemic. The outcomes of the study in [ 44 ] highlight that the pandemic increases the unemployment rate robustly mostly in European economies. The results show that Germany, Spain, and the UK have experienced a positive and significant change in unemployment due to COVID-19. France and Italy are experiencing a better employment situation with respect to the COVID-19 pandemic. That is one of the rare negative effects of the virus on the European labour market. Botha et al. [ 45 ] analysed the impact of COVID-19 on the labour market in Australia. They showed that the introduction of a wage subsidy (JobKeeper) and increased welfare benefit payments were unable to eliminate the uncertainty felt by individuals about their future financial situation. Mamgain [ 46 ] analysed the labour market in India during the pandemic. He pointed out that those at risk of losing their jobs during this period were mainly migrant workers, self-employed, small traders, daily wage labourers, youth, and women, with the latter two being the worst affected, as they mostly work in the grey zone of the Indian economy. The agricultural sector absorbed surplus labour. The author points out that in addition to measures aimed at improving the current labour market situation, the skilling/reskilling of the labour force to work in post-COVID-19-changed situations is important. According to Edwards et al. [ 47 ], the U.S. labour market continued to recover in 2021 from the recession caused by the coronavirus pandemic. Both the number of people who were unemployed and the unemployment rate decreased over the year, although both measures were still above their pre-pandemic levels. At the start of the pandemic in the USA, the number of unemployed in each category separated by the duration of unemployment increased. In 2021, the number of short-term unemployed persons declined. That number began to decrease as people either returned to work, stopped looking, or moved into the longer duration categories. This is evidenced by the number of long-term unemployed and their share of total unemployment in 2021. Both these indicators remained well above the levels seen before the pandemic. Gherghina et al. [48] showed that e-commerce has largely saved jobs, reduced spending, and provided employment. Of course, the pandemic is one of many factors affecting the labour market in Poland. Geopolitical factors, which can be external and internal, have a large impact. Among the 108 Sustainability 2022,14, 12677 external factors, the labour market in Poland is influenced by its EU membership [ 49 , 50 ]. The internal factors may include the efficiency of the use of funds earmarked for counteracting unemployment [ 51 ]. Dmytrów and Bieszk-Stolorz [ 52 ] analysed the relationship between the unemployment rate and the median duration of unemployment in the Visegrad countries between 2001 and 2017. The research indicated the existence of a lag in the response of the duration of unemployment to changes in the unemployment rate. The authors showed that in the analysed years, Poland had the shortest lag (1 year) and the highest correlation between these indicators. The implication is that an increase in the duration of unemployment can occur even one year after an increase in the unemployment rate. Thus, the effects of the pandemic may still be felt after the fluctuations caused by the pandemic, i.e., in 2022 or even 2023. However, the study of this relationship will be hampered by the rapid changes in the Polish labour market caused by the outbreak of the war in Ukraine. During the pandemic, a remote form of working became widespread. In Poland, these changes were not that favourable. In 2018, before the pandemic, the share of remote work was 4.6%, lower than the EU average of 5.2%. In 2021, the share in Poland increased to 8.9%, while the EU average was 12.3% [ 32 ]. However, the percentage has varied from quarter to quarter. The research conducted by Statistics Poland showed that the percentage of people working remotely in Poland was 11% in Q1 of 2020, 2.8% in Q3 of 2020, and 14.2% in Q1 of 2021 [53]. Pavolováet al. [ 7 ] analysed the impact of COVID-19 on the economic development of selected EU countries in terms of selected macroeconomic indicators. The selected countries were the Visegrad group countries. They found that during the pandemic years (2020–2021), Poland showed the best economic development and Slovakia showed the worst. Abrhám and Vošta [ 8 ] assessed the selected economic indicators in the Member States of the EU in the period from 2010–2020. They showed that in 2020, the unemployment rate for the EU27 countries increased by 5.9%, while in 2021 it decreased by 2.8%. In some countries, including Poland, it was the opposite. The unemployment rate in Poland decreased by 3% in 2020 and increased by 6.2% in 2021. In order to discuss unemployment in Poland during the pandemic, it is necessary to mention the determinants of this phenomenon. At the turn of the 20th and 21st centuries, unemployment in Poland was characterised by fluctuations. At the end of the 20th century, there was a large increase in unemployment as a result of the economic slowdown and the transformation of the economy (16.4% in 1993). After Poland’s accession to the EU (2004), unemployment fell steadily from 19.0% as a result of economic recovery. By the onset of the pandemic, unemployment was decreasing (to 5.2% in 2019). It is emphasised that this decline is the result of an increase in demand for labour and that state policy should be directed towards the creation of new jobs. In Poland, the situation on the labour market until the pandemic was very favourable. The number of unemployed was decreasing. The unemployment rate in Poland in 2019 was at the lowest level in the EU, just after Czechia. During the pandemic, the registered unemployment rate rose from 5.2 per cent to a maximum of 6.6 per cent in February 2021. During a health crisis, a policy of increasing labour demand is difficult or even impossible. Existing jobs are shrinking, no new jobs are being created, and no new companies are being established. This situation applies to all countries. The labour market has undoubtedly suffered during the pandemic both in Poland and elsewhere. Research shows that employment in U.S. fell by 21% in April 2022 compared to February and an increase was seen in June [ 54 ]. Research also confirms that the pandemic is the cause of increased unemployment in Slovakia [ 55 ]. Since March 2020, the registered unemployment rate has increased (from 6.2% to 8.4% in July) and there was a large influx of jobseekers in April. 109 Sustainability 2022,14, 12677 3. Materials and Methods 3.1. Data Our research is based on the individual data from the Poviat Labour Office (Polish abbreviation PUP) in Szczecin (Poland). The study, therefore, focused on registered unemployment. It should be noted that only Poviat Labour Offices in Poland are the source of such data. The Central Statistical Office in Poland and Eurostat only have aggregated data, which are not useful for studies using survival analysis models. For this reason, the data used in the study are unique. The data extracted included 31,961 people deregistered from the labour office in the period 01.01.2019–31.03.2022. The data included the date of registration and the date of deregistration, as well as the reason for deregistration. The reasons for deregistration can vary, and there are dozens of them. Since we analysed the event of taking up a job, we divided all the unemployed into two groups: those who took up a job and those who were deregistered for other reasons (e.g., resignation from the agency of the office, going abroad, or retirement). Based on the date of registration and deregistration, we determined the duration of registered unemployment—the random variable T. The end event of the observation is taking up a job. Deregistration for other reasons was taken as a censored observation. The analysis was conducted for the 13 quarters comprising the research period. The size of each subgroup is shown in Table 1. Table 1. Persons deregistered from the PUP in Szczecin in the period Q1 2019–Q1 2022. Quarter Complete Observations Censored Observations Total I 2019 1490 1853 3343 II 2019 1429 1826 3255 III 2019 1402 1788 3190 IV 2019 1499 1589 3088 I 2020 1426 1337 2763 II 2020 1000 309 1309 III 2020 1529 332 1861 IV 2020 1518 449 1967 I 2021 1359 632 1991 II 2021 1467 727 2194 III 2021 1417 1041 2458 IV 2021 1424 913 2337 I 2022 1188 1017 2205 Total 18,148 13,813 31,961 The observation was terminated in the first quarter of 2022. There were two reasons for this: 1. Reduction in the number of cases and deaths due to COVID-19. Accordingly, the Polish government lifted sanitary regime restrictions in 2022. 2. The labour market in Poland in the next quarter, i.e., Q2 2022, was undoubtedly heavily influenced by the start of war in Ukraine. Poland has taken in several million refugees, who have had a major influence on the labour market, also in Szczecin. 3.2. Methodology In the study of the duration of unemployment, we used the survival analysis methods. These methods enabled the use of censored data. The basis of the survival analysis is a random variable Tdescribing the duration (survival time) of an individual in a particular state. The observation of an individual continues until an event occurs that ends the observation. If the event does not occur within the specified time interval, such an observation is assumed to be censored. The inclusion of censored observations in subsequent analyses is one of the many advantages of survival analysis. Originally, these methods were used in demography, medicine, and reliability theory. In the case of the duration of a person’s life or the operating time of a device, certain regularities have been observed that make 110 Sustainability 2022,14, 12677 parametric methods possible. In the case of the duration of socio-economic phenomena, the distribution of the duration of the phenomenon is mostly unknown, so non-parametric or semiparametric methods are used. Methods of the survival analysis are used in the real estate market [ 56 , 57 ], in the capital market [ 29 , 34 ] in the study of the duration of firms [ 58 , 59 ], in the study of duration of trade relationships [ 60 ], and in the labour market [ 61 , 62 ]. The cumulative distribution function of random variable T(F(t)) describes the probability of an event occurring no later than time t. The basic function in survival analysis is the survival function S(t), which describes the probability that an event will not occur by time t.Itis described by the following formula [63]: S(t)=P(t>T)=1−F(t)(1) where T—duration; F(t)—cumulative distribution function of random variable T. Since for most socio-economic phenomena the distribution of duration is not known, studies often use the non-parametric Kaplan–Meier estimator [64]: ˆ S(ti)= i ∏ j=11−dj njfor i=1, 2, . . . , k, (2) where ti—the point in time when at least one event occurs, t1<t2<···<tk,t0=0; di—number of events in time ti; ni—number of units observed in time ti,ni=ni−1−di−1−zi−1; zi—number of censored observations in time ti. Quartiles of the random variable with the cumulative distribution function F(t) are determined from the relation: F(t)= 0.25, F(t)= 0.50 and F(t)= 0.75. Duration quartiles are moments of time for which the survival function S(t) takes the following values: S(t)= 0.25, S(t)= 0.50, and S(t)= 0.75. Not all quartiles of duration can exist. This is because of the existence of censored observations. During the observational period, not all individuals belonging to the cohort experience the event. These ones still remain in the cohort. Two survival curves can be compared. Appropriate tests can be used for this purpose. They allow us to analyse the significance of differences between two survival curves. There are many of them, and we do not have a consistent set of criteria to decide which test has the greatest power and should be used in the analysis. Some of them are more sensitive to the course of the survival curve in its initial part and others in its final part. The sample size, probability density of the survival function, and censorship mechanism determine the power of these tests [ 65 ]. We used two tests in the study: the log-rank test and Gehan’s generalised Wilcoxon test. The log-rank test (also known as the Mantel log-rank test, the Cox Mantel log-rank test, and the Mantel–Haenszel test) is the most commonly used test for comparing survival distributions. It can be applied to data with progressive censoring and gives equal weight to early and late failures. It assumes that survival curves for the two groups are parallel. In practice, this is often not the case. Then, the generalised Wilcoxon Gehan test (also known as the Breslow’s test and Gehan’s test) can be applied. It is applicable to data where there is progressive censoring. When the survival functions are not parallel and when there are few censored data, the Wilcoxon Gehan test has greater power than the log-rank test. It has low power when the degree of censoring is high. It gives more weight to early failures [ 66 ]. If we are comparing survival curves in the initial run, we should use this test. The survival analysis examines the intensity of occurrence of an event in the moment t under the condition of survival until time t. This intensity is described by the hazard 111 Sustainability 2022,14, 12677 threat of a worsening situation in the labour market, likely alongside fewer opportunities to work in the grey economy, influenced the acceleration of decisions among the unemployed. Cortes and Forsythe [ 41 ] also indicate that the influence of the COVID-19 pandemic on the labour market is heterogeneous in the United States. This heterogeneity applies to various occupations and industries as well as demographic subgroups. The authors concluded that the pandemic has had the effect of exacerbating pre-existing inequalities. Lofton et al. [ 69 ] highlight the particularly difficult situation of mothers in the labour market during the pandemic (due to school closures). According to Groshen [ 70 ], the impact of the pandemic on the labour market in the U.S. was significant. The initial shock was very abrupt and deep by all historical standards. The author used two indicators, the national unemployment rate and the change in payroll jobs, in the study. The COVID-19 pandemic has had a particularly strong impact on the tourism sector. It has affected the countries of the Mediterranean area to the highest degree. In addition to the high number of infections and deaths, there have been significant economic losses in the region [ 71 ]. The impact of the COVID-19 pandemic on the labour market has encouraged many scientists to examine and quantify its consequences in this area. The literature mainly presents macroeconomic analyses. There is a lack of studies aimed at examining the impact on the labour market in the individual municipalities. In addition, Kotera and Schmittman [ 72 ] used macro and micro data to study the labour market. They concluded that the pandemic in Japan had a large negative impact on employment, labour force participation, earnings, and labour market mobility. Our research is also part of this trend, and it concerns the labour market of smaller areas of the country (regarding the specifics of business and demographic characteristics). It is the peculiarities of both the local and national labour markets that are important in the processes of response and adaptation in a pandemic situation [ 73 , 74 ]. Zieli´nski [ 75 ] analysed the impact of the pandemic on the labour market in the Visegrad Group (V4) countries, including the Polish labour market. He compared the years 2018–2019 to 2020–2021, and the results of his study coincide with our observations. He showed that the pandemic affected labour market imbalances relatively moderately. Importantly, it stopped the trend of decreasing unemployment rates observed in all V4 countries in 2018–2019. The highest unemployment rate in Poland in Q1 2021 corresponded to the lowest number of hours worked per week (usual weekly hours of work). Poland experienced a return to pre-pandemic unemployment rates in Q4 2021. The speed at which jobs are taken up during a pandemic depends on the type of job. Research indicates that the greatest negative impact of a pandemic is on the catering industry and sales and customer service jobs. In contrast, jobs in warehousing and transport may increase as a result of the increase in e-commerce and the delivery of goods to customers [ 76 ]. The research shows that in Australia and Canada, the increase in the number of vacancies being posted online mentioning ‘work from home’ arrangements was especially strong [77]. Losing a job or not being able to obtain one are particularly acute situations during a crisis. The pandemic fuels unemployment, whose source is usually exogenous to the individual and can affect mental health [ 78 ]. The fear of a difficult situation may mobilise individuals to intensify their job search and to accept any kind of job. Different findings from ours are presented by Hensvik et al. [79] . According to these authors, the COVID-19 pandemic particularly affected labour markets. There was a sharp increase in unemployment and a decline in job vacancies. Depending on how the intensity of job search changes after the shock, the supply side of the labour market may exacerbate or mitigate the effects of the shock on the demand for labour. The authors analysed how jobseekers in Sweden adjusted the intensity and direction of their search at the onset of the crisis. They found that job search intensity fell by 40% in March and April 2020 and returned to its previous level in July 2020. They explain the drop in search intensity by a decline in the number of vacancies and by fears of illness on the part of both employers and potential employees (the Swedish government’s preventive measures were extremely mild). According to Sheldon [ 80 ], unique to the current crisis in Switzerland is the sharp upsurge in both 118 Sustainability 2022,14, 12677 the incidence and duration of unemployment. These two variables have never increased so quickly in such a short period. Hensvik et al. [ 79 ], similarly to Bernstein et al. [81] , indicate a change of direction with respect to job searches. Both studies point to the phenomenon of ‘a flight to safety in labour market’ occurring during a health crisis. Small firms or self-employment are less frequently chosen as places/forms of work. We also find that the increase in the intensity of the unemployed taking up work during the pandemic is a result of changes in the direction of the search for employment. Many companies are closing down or scaling back their operations, new entrepreneurial initiatives are not being created, and there is a lack of casual or informal work. This contributes to a greater propensity to accept jobs in the office. 6. Conclusions Our study confirmed the findings of other researchers who found that the impact of the pandemic on the labour market had not been unequivocal and varied according to the economic situation of the country studied. This is also highlighted by the authors of The Sustainable Development Goals Report 2022. Developed economies are experiencing a more robust recovery. Of particular concern is the confluence of crises, dominated by COVID-19, climate change, and conflicts. Research conducted by Statistics Poland [ 82 ] shows a decline in labour demand in 2020 and a large increase in 2021. This shows that the initial uncertainty associated with the outbreak of the pandemic was contained fairly quickly. However, we must emphasise here that the Polish labour market in 2022 and beyond will be influenced by current political and economic events. How this will affect the Szczecin labour market will only become apparent in a few months’ time. This will be a stimulus for us to conduct further research. The limitation of our study was the use of data from only one large city, Szczecin. However, due to the state policy, regional analyses are more important than an overall analysis (concerning Poland as a whole). We must point out that the applied survival analysis methods require the use of individual data. Public statistics only provide aggregated data. Therefore, the added value of our study is the acquisition of such data and their use in the study of registered unemployment. On the other hand, another advantage of the methods we have chosen is the possibility of using the censored data. The obtained empirical results may prove valuable for scientists interested in the influence of the pandemic on the labour market. These analyses are also important for political decision-makers involved in the efforts of mitigating the negative effects of the COVID-19 pandemic within national and regional economic systems. The study presented here could also be useful in the effort to refine the theoretical approach to the economic crisis caused by the spread of the virus. Our research showed that the registered unemployed took up work relatively quickly, but there are always people on the registers who often move into long-term unemployment. Political decision-makers can reduce the negative impact of COVID-19 on the unemployed by promoting more training and active labour market policies to facilitate their return to work at a decent job, which would be particularly beneficial for those who have been unemployed for a long-term. The study presented here could also be useful in the effort to refine the theoretical approach to the economic crisis caused by the spread of the virus. After a significant increase at the start of the pandemic, unemployment is falling in many OECD countries [ 83 ]. However, unemployment is projected to be higher in most of them than before the crisis. However, in the context of this pandemic and the accompanying labour market policies, unemployment alone provides only a partial picture. In the early phases of the crisis, a large number of people withdrew from the labour market due to constraints on their job search and the increased burden of their household responsibilities. At the same time, many people who maintained employment experienced a reduction in working hours (job retention programmes). It is warned that in the future, many of the workers most affected by the pandemic may find it difficult to return to their previous jobs due to a lack of appropriate skills (e.g., due to new technologies in 119 Sustainability 2022,14, 12677 production). Support in the form of upskilling and retraining is needed to ensure that the recovery is socially inclusive. An interesting solution is the introduction of a temporary increase in unemployment benefits in Sweden [ 79 ]. This is certainly a large help for the people in difficult situations, but it is a solution for wealthy countries. The full impact of the crisis on the labour market is not yet behind us [ 83 ]. There is, therefore, a need for continued labour market research. Medical sources point to the possibility of further waves of pandemics, although we expect these to be much smaller in scope due to the prevalence of vaccination and treatment experience. We will also observe changes that are driven by the experience of entrepreneurs, such as the increased prominence of e-commerce at the expense of traditional trade. Author Contributions: Conceptualization, B.B.-S. and I.M.; methodology, B.B.-S. and I.M.; software, B.B.-S. and I.M.; validation, B.B.-S. and I.M.; formal analysis, B.B.-S. and I.M.; investigation, B.B.-S. and I.M.; resources, B.B.-S. and I.M.; data curation, B.B.-S. and I.M.; writing—original draft preparation, B.B.-S. and I.M.; writing—review and editing, B.B.-S. and I.M.; visualization, B.B.-S. and I.M.; supervision, B.B.-S. and I.M.; project administration, B.B.-S. and I.M.; funding acquisition, B.B.-S. and I.M. All authors have read and agreed to the published version of the manuscript. Funding: The project is financed within the framework of the program of the Minister of Science and Higher Education under the name “Regional Excellence Initiative” in the years 2019–2022; project number 001/RID/2018/19; the amount of financing PLN 10,684,000. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data come from: https://stat.gov.pl/en (accessed on 7 July 2022) and Poviat Labour Office in Szczecin. Conflicts of Interest: The authors declare no conflict of interest. References 1. The Sustainable Development Goals Report 2021. Available online: https://unstats.un.org/sdgs/report/2021/ (accessed on 30 May 2022). 2. Nundy, S.; Ghosh, A.; Mesloub, A.; Albaqawy, G.A.; Alnaim, M.M. Impact of COVID-19 pandemic on socio-economic, energyenvironment and transport sector globally and sustainable development goal (SDG). J. Clean. 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