Changing Lifestyles in Highly Urbanized Regions of Russia: Short- and Longer-Term Effects of COVID Restrictions

Abstract

The restrictions on business and social activity during the COVID-19 pandemic have led to significant changes in consumption patterns worldwide. Such changes are causing structural shifts in the markets of goods and services, thus affecting regional resilience. In this article, we aim to assess the changing structure of the consumption of goods and services in highly urbanized Russian regions under the impact of the COVID-19 pandemic and to analyze its effects on the lifestyle of the population. According to our results, some Russian regions demonstrate a return to previous consumption levels, while others exhibit the emergence of new dynamics. The conclusion is made that COVID restrictions have invoked a paradigm shift in consumer behavior toward investment in self-development, safety, and comfort. This observation should be taken into account when developing strategies for the recovery growth of regional economies.

1. Introduction

The COVID-19 pandemic has significantly changed the consumption patterns of goods and services worldwide. The introduced quarantine measures imposed severe restrictions on a large variety of business and social activities, at the same time as increasing the concern of citizens for their health. The unprecedented nature of this situation requires an analysis of the resulting economic transformations with a particular focus on highly urbanized regions, which proved most vulnerable to the challenges of the COVID-19 pandemic due to population density and urban infrastructure risks [1,2,3].

Researchers have observed changes related to the diet and physical activity of citizens [4,5], the formation of new consumer preferences [6], the development of remote forms of employment, the active use of modern digital technologies for making purchases and interacting with colleagues and family members [7], the emergence of new forms of leisure activities [8,9], etc. These studies have revealed, to one extent or another, a correlation between changing consumption volumes and structural economic shifts. In particular, the OECD Directorate for Trade and Agriculture conducted an analysis entitled Understanding Structural Effects of COVID-19 on the Global Economy, which defined those structural shifts as demand shocks, drawing attention to their different manifestations in different countries and regions [10].

Despite the above, few studies have addressed the question of whether the observed changes in the volume and structure of consumption were related only to the pandemic period and whether the societies returned to previous consumption levels upon the completion of the coronavirus crisis. In other words, the question remains whether the changes that have occurred are temporary or manifest an emerging long-term trend. This understanding is important for developing strategies to support regional economies. Such measures seem essential, since changes in the volume and structure of consumption associated with changes in the lifestyle of the population have caused structural shifts in the markets for goods and services, thus affecting regional resilience [11].

In this study, we aim to identify the transformations that have occurred in the volume and structure of the consumption of goods and services in highly urbanized Russian regions under the impact of COVID restrictions and to analyze their effects on the lifestyle of the population. In the sections of the article, we consecutively perform the following tasks. First, the major theoretical concepts are discussed and the current literature on the socioeconomic consequences of changing lifestyles following the coronavirus crisis is reviewed, with a special focus on the associated changes in the consumption of goods and services. Secondly, the methodology used for studying changes in the consumption patterns is detailed. Third, an analysis of changes in the volume and structure of consumption in highly urbanized Russian regions under the influence of the COVID-19 pandemic is carried out. Fourth, the obtained results are assessed to establish whether the observed transformations are of a short- or long-term nature and whether there is a connection with the changing lifestyle of the population. Fifth, the conclusions are formulated and the limitations of the study are outlined.

2. Conceptual Framework and Literature Review

2.1. Conceptual Framework

This study is based on the concept that lifestyle is a relatively stable form of the organization of people’s lives, determined by objective natural and sociocultural conditions. The lifestyle of a particular person integrates quantitative and qualitative aspects of their life and is reflected in the volume and types of goods and services consumed. This definition is based on the ideas of F. Kotler that lifestyle finds its expression in conscious consumption and is widely used in the modern scientific literature to explain consumption patterns in society [12,13,14,15].

Standard of living reflects the quantitative aspect of lifestyle and characterizes the economic well-being of citizens. This parameter is expressed in the volume of goods consumed. Conversely, quality of life characterizes the qualitative aspect of lifestyle and is measured by the satisfaction of various needs, including health, education, personal development, etc. At the same time, as noted by K. Sollis et al., a gap between the standard of living and the quality of life is possible provided that material well-being is not ensured by the ability to access goods and services that are valuable and ensure life satisfaction [16]. This gap was clearly manifested during the COVID-19 period, when, despite the availability of financial resources, the population had limited access to certain types of goods and services due to quarantine measures. This led to significant changes in the lifestyle of population, restricting their ability to participate in certain types of activities [17,18].

The interpretation of lifestyle through consumption volumes is rather controversial, since consumption is not always a defining characteristic of lifestyle. Thus, M. Cleveland et al. [19] argued that due to the abundance of many goods available to most people, the structure of consumption is largely formed under the influence of the media, reflecting not the actual lifestyle, but the desire to “try on” a certain social status. Nevertheless, most economic studies recognize the presence of a close relationship between consumption volumes and lifestyle, emphasizing that consumption is currently not only a way of satisfying basic needs, but also a way of expressing status and commitment to a certain social group.

The concept of lifestyle understood through the consumption patterns of certain goods and services allows us to focus on the economic behavior of citizens, while recognizing the complexity and diversity of factors that influence consumer choice, including (1) the beliefs and values that determine the choice of a particular model of behavior (cognitive processes), (2) the socioeconomic environment, including economic conditions and cultural traditions (environment).

According to previous research, changes in the existing consumption structure may reflect important aspects of lifestyle changes, e.g., an increase in alcohol consumption correlates with an increase in negative emotions and reflects a deterioration in the quality of life [20,21]. The types of food consumed can be an indicator of living standards, reflecting the quality of nutrition [22]. Valuable information about lifestyle can be obtained from the analysis of consumption volumes of certain goods and services that reflect the behavioral strategies of the population [23,24,25]. Initially, lifestyle was mainly considered in terms of the consumption of material objects at the level of everyday life; however, later, the sphere of leisure was also included in analysis [26,27,28] with a focus on the relationships between various types of activities and approaches to their implementation [29,30].

The quarantine measures during the COVID-19 pandemic, which limited access to certain goods and services, the ability to travel, business and social activity, forced people to change their lifestyles. This, as a number of researchers note, was especially evident in highly urbanized regions, whose residents tend to lead a more active life compared with those in rural areas [31,32,33].

2.2. Literature Review

At the beginning of the pandemic, the research on lifestyle changes among populations focused on assessing the sociopsychological health consequences of the pandemic, primarily in children and adolescents, for whom social isolation posed the greatest risk. However, as the virus spread and quarantine measures became more stringent, the economic consequences of lifestyle changes began to be more evident. These consequences were analyzed based on the volume of the consumption of material and non-material goods.

Research studies into the economic consequences of the changes in consumer behavior patterns of citizens associated with the lifestyle adjustments under the impact of the coronavirus crisis can be broadly distinguished into the following directions.

Consequences associated with differences in perceived pandemic threats by citizens. Some researchers argue that the fear of being infected and the fear of negative consequences of the disease have raised the demand for products aimed at strengthening the immune system and safety [34]. At the same time, there is no consensus among researchers as to whether the emergence of those threats encouraged a healthier lifestyle or, conversely, led to the development of bad habits.

The study by Sultana et al. [35] noted a decrease in alcohol and tobacco consumption as a result of citizens’ increased attention to their health. Other researchers, on the contrary, emphasized that constant stress and increased depressive mood have led to increased alcohol consumption, especially among young men [20,21,36]. Scapaticci et al. [37] stated that higher levels of obesity and nutritional deficiencies were recorded during the pandemic, since many households were forced to save by purchasing cheaper and unhealthier products while leading a sedentary lifestyle.

A change in attitudes towards unnecessary expensive purchases is another consequence of citizens’ perception of pandemic-related risks. Concerning this question, there are also conflicting points of view. Thus, Siwek [38] noted that citizens have begun to postpone or refrain from making some purchases due to the financial uncertainty caused by the pandemic. To a greater extent, this trend was observed in low-income countries and regions, where COVID-19 resulted in the loss of income sources, a reduction in working hours, and increased debt burden [39,40]. However, other studies showed that after several months of the pandemic, people, having recovered from the initial shock, began to actively make expensive purchases, creating comfort at home, as well as buying higher quality, more durable, and environmentally friendly goods [41].

Consequences associated with the introduction of quarantine measures. The conducted analysis of scientific publications showed that the quarantine-related changes in lifestyle and consumer behavior can be described as “changes in availability”. In this regard, researchers consider two main aspects. Firstly, those changes caused by the restrictions on the movement of citizens. Secondly, those caused by the restriction or termination of the activity of individual enterprises and organizations. Thus, many citizens began to cook at home, since cafes and restaurants were closed [42]. Tourist trips abroad were sharply replaced by domestic travel [43,44]. Citizens started to commute to work in a different mode [45]. Online shopping with cashless transactions, using courier delivery, became more popular [46].

Spending most of the working and free time at home, people turned their attention to living conditions. Thus, during the pandemic, citizens tended to reassess their housing needs, focusing on the comfort and functionality of their living spaces, purchasing appropriate household items and carrying out repairs [47,48,49]. At the same time, Roy [50] stated that the introduction of remote forms of employment and the prevalence of passive leisure predetermined the increased demand for various electronic devices and gadgets.

Consequences of changing consumer demand reflected in structural changes in regional economies [51,52,53,54]. When assessing structural economic changes, researchers unanimously come to the conclusion that the changes in demand were particularly severe in the retail and consumer service sectors [55]. The transport sector, hotel businesses, and tourism were mentioned as the economic sectors that suffered the most during the coronavirus crisis [56,57,58]. However, research showed that numerous industrial enterprises also suffered significant losses as a result of reduced demand for their products [59,60].

At the same time, some industries felt the positive impact of the pandemic. This primarily concerned the healthcare and pharmaceutical sectors, which experienced an increase in demand for medical goods and services, and also received government support [61,62]. The gaming industry reported a growth in financial indicators, due to the increased interest in games as a leisure activity during the quarantine period [63]. The same applied to IT companies that supported remote work [64].

Researchers have begun to actively use the term “resilience” to characterize the ability of regional economies to adapt to the challenges of the coronavirus crisis [65,66,67]. The question of which factors ensure “resilience” has generated considerable academic debates. For example, a number of studies emphasize the role of the consumer factor. Thus, Kim et al. [52] noted that, due to different levels of the intensity of interpersonal interaction inherent in different industries, the COVID-19 pandemic caused structural economic changes that had a significant impact on regional resilience. The study by Zhang et al. [68] showed that the product space plays a key role in shaping regional resilience. This knowledge is essential for developing effective measures of adaptation to emerging challenges.

Since the end of the coronavirus crisis and the transition of national/regional economies to recovery growth, researchers are increasingly focusing on whether the socioeconomic consequences of the COVID-19 pandemic have a short-term or long-term character [69,70,71]. In this respect, one controversial and methodologically important issue consists of how to differentiate between short-term and long-term effects. Some studies [3,72,73] noted that during the coronavirus crisis, time seemed to “compress and accelerate”, thus suggesting the inapplicability of the classical division of short-term vs. long-term changes accepted in the theories of cyclical changes. One distinct feature of the coronavirus crisis was its significantly faster deployment and greater unpredictability of consequences, in contrast to crises caused by economic factors [67,74,75,76].

As a result, a number of researchers have stated that the short- or long-term nature of the socioeconomic consequences of the COVID-19 pandemic can be assessed based on the rate at which the system returns to its initial (pre-shock) state [65,66]. In cases where upon completion of the shock impact, the main socioeconomic indicators deviate from their initial values insignificantly, the effect is assumed to be short-term. In cases where upon completion of the shock impact, the indicators do not return to their initial values and the trend of their change persists, this may indicate a structural transformation of the entire system and the presence of long-term consequences [15,77,78]. However, we believe that it would be more correct to talk about longer-term effects, rather than long-term ones in their strict sense. We thereby resolve the emerging logical contradiction between the traditional definition of short-term and long-term periods, while still describing the specifics of the temporal factor during the pandemic period.

This distinction allows us to capture the specifics of consumer reactions to unexpected shocks and determine whether they manifest just a temporary adjustment to new circumstances that will disappear upon the stabilization of the situation, or whether they represent longer-term changes in preferences that are likely to persist in the future.

Despite the significant number of studies examining the socioeconomic consequences of changes in consumption patterns caused by changes in the lifestyle of populations under the impact of the COVID-19 pandemic, researchers have mainly focused on the state of specific economic sectors and their ability to withstand and adapt to shocks. However, it remains unclear whether these changes in consumption were just a temporal phenomenon or have formed a persisting trend following the stabilization of the situation. This question is the focus of our present study.

3. Methodology

Highly urbanized Russian regions with a share of urban population above the Russian average (74.9%) [79] were selected as the research subject. It was highly urbanized regions that turned out to be most susceptible to pandemic shocks [80,81,82].

Official data from the Federal State Statistics Service of Russia (Rosstat) for the period from 2019 to 2022 were used. This period was selected since the World Health Organization officially announced the beginning of the pandemic on 11 March 2020, and the end of the COVID-19 pandemic on 5 May 2023 [83,84]. Within the period under review, three main stages in the development of the situation were identified:

• 2019, reflecting the established lifestyle of the population before the pandemic;
• 2020 and 2021—the period of the COVID-19 pandemic, when restrictive measures were applicable;
• 2022, characterized by the lifting of all COVID restrictions and the beginning of the recovery growth of the Russian economy.

To ensure the comparability of indicators expressed in monetary terms, they were adjusted to 2019 prices (taking the consumer price index into account).

In the first stage of the study, a general description of highly urbanized regions of Russia was given and the consumption structure that developed therein in the pre-pandemic period was characterized. To that end, the method of descriptive statistics was used. To elucidate the existing similarities and differences in the regional consumption structure, a hierarchical cluster analysis was carried out. This analysis was based on the square of the Euclidean distance and the Ward method, which were selected for being suitable for creating closely located small clusters.

In the second stage, the percentage change in the volume and structure of consumption in the regions under consideration was analyzed in comparison with the base year of 2019. The following indicators were used to characterize the consumption of goods and services:

• The amount of consumer spending per capita for consumption purposes: food and beverages; alcohol; clothing and footwear; housing and communal services; household goods and appliances; healthcare; transport, communications; telecommunications; recreation and culture; education; and hotels (rubles).
• The share of consumer spending per capita for consumption purposes in the total amount of spending: food and beverages; alcohol; clothing and footwear; housing and communal services; household goods and appliances; healthcare; transport, communications; telecommunications; recreation and culture; education; and hotels (%).

When interpreting the results obtained, similar studies [85,86,87] were used as a reference. In these works, a change of 25% or more was interpreted as significant; from 10 to 24% as average; and below 10% as insignificant.

A hierarchical cluster analysis was also carried out to identify the direction and nature of trends in the changing consumption structure in the regions.

We proceeded from the idea that consumer behavior is influenced by (a) economic factors (income level, inflation, and availability of credits), which determine the purchasing power of consumers; (b) non-economic factors (social, cultural, psychological, and personal), which determine consumer preferences. In order to establish which factors determined the change in the consumption structure, a correlation and regression analysis of panel data for the three years under consideration was conducted followed by a study of the relationship between the indicators. The latter included the volume and share of consumer spending per capita by consumption purpose and the indicators of the consumer price index, average per capita income, and the average annual key rate of the Central Bank of the Russian Federation. The relationship was assessed using the Chaddock scale, according to which a correlation coefficient of up to 0.5 renders the relationship insignificant. Conversely, under a coefficient value of 0.5 or higher, the relationship is considered significant. A regression analysis was conducted only for the indicators with a significant relationship. The p-value was calculated to test the statistical significance of the correlation.

In the third stage, the results obtained were assessed to establish whether the observed changes in the volumes and structure of consumption were of a short-term nature or could be considered as an emerging and persisting trend associated with changes in the lifestyle of the population at the regional level. In the research studies into the socioeconomic consequences of the COVID-19 pandemic, the short- or longer-term nature of effects is assessed based on the ability of the system to return to its original state [77,78]. In cases where after the completion of a shock impact, the indicators of the system deviate slightly from the pre-shock values (up to 10%), the effect is considered to be of a short-term nature. Conversely, when the indicators do not return to their previous value and the trend of their change continues, this may indicate the change in the system’s entire structure and the presence of a long-term effect.

In this study, in view of the above, a return to the previous volumes and structure of consumption upon the completion of COVID restrictions were considered short-term changes (deviation in the volumes and structure of consumption in 2021 compared with 2019 by no more than 10%). A deviation in consumption volumes in 2021 and 2022 compared with 2019 by more than 25% against the background of a continuous increase in the magnitude of deviations in the period under review was considered a manifestation of a longer-term effect.

4. Results

4.1. Characteristics of Highly Urbanized Regions of Russia Prior to the Pandemic

In Russia, there are 31 regions that could be defined as highly urbanized. Geographically, these regions are located throughout the country and have different historical reasons for their high level of urbanization. One reason is related to the period of industrial construction and the development of natural resources, which led to the formation of a dense urban network as a result of the migration of population from other areas in search of better living conditions and employment opportunities. This group includes regions of the East European Plain, the Volga Region, the Urals, and Western Siberia. The second reason is related to the unsuitability of natural conditions for agriculture. As a result, cities with non-agricultural land use were formed. Such regions include the areas of the European North, the Far North, and the Far East.

Highly urbanized Russian regions have both single-industry and multi-industry types of economies. A single-industry economy is typical of regions producing commodity materials (Khanty-Mansi Autonomous Okrug-Yugra, Yamalo-Nenets Autonomous Okrug, Komi Republic, Tyumen Region, etc.), as well as old industrial regions (Ivanovo Region, Magadan Region, Murmansk Region, Sakhalin Region, etc.), the formation and development of which took place during the period of accelerated industrialization, when the main type of territorial placement of production was the specialization and concentration of production within one industry [88]. As a rule, a multi-industry economy is a characteristic of the regions that include cities with a population of over a million, forming its diversified core (Novosibirsk Region, Moscow Region, Sverdlovsk Region, Republic of Tatarstan, Krasnoyarsk Krai, Chelyabinsk Region, Samara Region, Omsk Region, Perm Krai, Volgograd Region, and Nizhny Novgorod Region).

Highly urbanized Russian regions exhibit significant differences in demographic characteristics, income levels, environmental factors, and climatic factors, as well as in the development of transport and social infrastructure. These differences are reflected in the behavior patterns [89]. For example, natural and climatic factors have a significant impact on the volume of energy consumption and also determine the structure of food consumption. Regions that are attractive to tourists are characterized by higher consumer spending on transportation, hotels, recreation, and culture. Regional demographic differences and differences in household income also contribute to differences in the consumption of certain goods and services.

Some characteristics of highly urbanized Russian regions that can explain the differences in the established patterns of the consumption of goods and services are presented in

Table 1. Socioeconomic characteristics of highly urbanized Russian regions in 2019 [79].

Economic Region	Administrative Region	Proportion of Population over Working Age, %	Average per Capita Monetary Income of the Population, RUB	Features of the Sectoral Structure of the Economy
Central Russia	Vladimir Region	28.8	25,358	Multi-industry process manufacturing
	Ivanovo Region	28.3	25,794	Textile industry
	Moscow Region	24.0	47,201	Multi-industry process manufacturing
	Tver Region	28.8	27,211	Multi-industry process manufacturing
	Yaroslavl Region	27.9	28,658	Multi-industry process manufacturing
The European North and the Far North	Republic of Karelia	26.8	30,854	Multi-industry process manufacturing
	Komi Republic	22.5	35,356	Single-industry economy, fuel, and energy complex
	Arkhangelsk Region	26.5	81,041	Multi-industry process manufacturing
	Kaliningrad Region	24.8	28,905	Multi-industry process manufacturing
	Murmansk Region	21.8	44,237	Mining and metallurgical production
	Khanty-Mansi AO–Yugra	15.9	53,208	Single-industry economy, fuel, and energy complex
	Yamalo-Nenets AO	11.8	83,088	Single-industry economy, fuel, and energy complex
	Magadan Region	21.1	65,357	Single-industry economy, fuel, and energy complex
The Volga Region	Volgograd Region	27.0	24,158	Multi-industry process manufacturing
	Republic of Tatarstan	24.4	35,707	Multi-industry process manufacturing
	Perm Krai	24.3	30,588	Multi-industry process manufacturing
	Kirov Region	28.9	23,604	Multi-industry process manufacturing
	Nizhny Novgorod Region	27.4	33,817	Multi-industry process manufacturing
	Samara Region	26.6	29,421	Multi-industry process manufacturing
	Saratov Region	27.3	22,757	Multi-industry process manufacturing
	Ulyanovsk Region	28.6	23,698	Multi-industry process manufacturing
The Urals and Western Siberia	Sverdlovsk Region	25.2	39,094	Multi-industry process manufacturing
	Chelyabinsk Region	25.3	25,425	Multi-industry process manufacturing
	Krasnoyarsk Krai	22.6	31,739	Multi-industry process manufacturing
	Irkutsk Region	22.2	26,306	Multi-industry process manufacturing
	Kemerovo Region	25.2	24,886	Multi-industry process manufacturing
	Novosibirsk Region	24.4	30,535	Multi-industry process manufacturing
The Far East	Kamchatka Krai	20.1	52,674	Fishing industry and hunting
	Primorsky Krai	24.1	36,883	Fish and food industry
	Khabarovsk Krai	22.4	41,459	Multi-industry process manufacturing
	Sakhalin Region	22.7	59,015	Extraction and export of energy raw materials

.

Descriptive statistics of the consumption structure in these regions in 2019 are presented in

Table 2. Descriptive statistics of consumption structure in highly urbanized regions of Russia in 2019.

Index	Food	Alcohol	Clothes and Footwear	Household Appliances	Healthcare	Transport	IT and Communications	Recreation and Culture	Hotels	Education
Average value	30.8	3.2	7.7	5.4	4.0	16.1	3.2	7.2	2.8	0.8
Mode	31.8	2.7	7.3	6.7	4.0	22.3	3.4	6.3	3.5	0.7
Median	30.3	3.1	7.8	5.4	3.9	15.2	3.2	6.6	2.9	0.7
Dispersion	21.1	0.7	1.1	1.2	0.5	25.5	0.1	3.7	1.5	0.2
Standard deviation	4.6	0.8	1.1	1.1	0.7	5.0	0.3	1.9	1.2	0.4
Minimum value	24.3	1.5	5.4	3.7	2.5	8.5	2.7	4.4	0.8	0.3
Maximum value	40.7	5.8	10.1	8.1	5.6	30.0	3.7	12.4	5.7	2.0
Coefficient of variation	0.15	0.26	0.14	0.20	0.17	0.31	0.10	0.27	0.45	1.7
Asymmetry	0.47	0.74	–0.16	0.32	0.34	0.74	–0.09	0.86	0.41	0.48

.

The data provided shows the predominance of food products in the consumption structure of all regions, although their share varies between 24.3% and 40.7%. The smallest share in the consumption structure is occupied by communication services and telecommunications, expenses on alcohol and tobacco products, as well as hotels. At the same time, the largest spread in the share of expenses in the consumption structure was observed for education: from 2% in the Sakhalin Region to 0.3% in the Yaroslavl Region. The asymmetry value ranges from –1 to 1. It indicates a normal distribution of the data, which allows the application of parametric analysis.

The hierarchical classification of regions based on consumption structure indicators identified three main groups of regions (

Table 3. Groups of regions by consumption structure in 2019.

No.	Region	Features of the Consumption Structure
1	Khabarovsk Krai, Republic of Tatarstan, Murmansk Region, Sverdlovsk Region, Primorsky Krai, Nizhny Novgorod Region, Sakhalin Region, Kemerovo Region, Komi Republic, Krasnoyarsk Krai, Kirov Region, Magadan Region, Chelyabinsk Region, Yaroslavl Region	Average values of the share of expenses in all areas.
2	Yamalo-Nenets AO, Irkutsk Region, Samara Region, Republic of Karelia, Kamchatka Krai, Khanty-Mansi AO–Yugra, Perm Krai, Arkhangelsk Region, Moscow Region	The highest share of transportation costs, as well as expenses on recreation and culture, with the lowest share of expenses on food, clothing and footwear, as well as household appliances.
3	Saratov Region, Novosibirsk Region, Kaliningrad Region, Ivanovo Region, Ulyanovsk Region, Volgograd Region, Tver Region, Vladimir Region	The highest share of expenses is for food, clothing and footwear, and household appliances. The lowest share of expenses is for transportation, recreation and culture, hotels.

).

The formation of clusters generally corresponds to the established ideas that consumer decision making models are based on the relationship between the volume of the consumption of goods and the amount of consumer income [90].

The first, and the most numerous, cluster includes regions with the average values of the share of expenses in all areas of consumption for the sample. In terms of demographic characteristics and income level, the regions are also at an average level. These regions are represented by both single-industry and multi-industry economies and are geographically located in all economic zones of Russia.

The second cluster is represented by regions with a predominantly commodity focus, where the workers are involved in shift work. As a result, these regions are characterized by the lowest rates of the share of the disabled population and relatively higher rates of per capita income. The Moscow and Samara Regions, distinguished by a high level of innovative activity, are also included in this group. The regions of this group are characterized by a higher share of expenses on intangible goods, as well as on goods that are not essential.

The third cluster is formed mainly by regions of the East European Plain and also includes the Saratov Region (Volga Region) with relatively low household incomes and a fairly high proportion of the disabled population. The Novosibirsk Region also falls into this group, despite higher household and working-age population incomes. These regions are characterized by a shift in spending toward the consumption of food and material goods, while the share of spending on non-material goods has the lowest values among the regions under consideration.

4.2. Analysis of Changes in the Volume and Structure of the Consumption of Goods and Services During the Pandemic and After Its Completion

The conducted analysis of changes in consumption volumes in the studied regions showed that in the first year of the pandemic, almost all regions recorded a decrease in almost all household expenses, especially on recreation, culture, and hotels. A decrease in expenses on food, clothing, and footwear, as well as, oddly enough, on healthcare services, is noticeable. Expenses on communication services, as expected, began to grow. In addition, in many regions, expenses on household items and appliances increased. At the same time, in the Yamalo-Nenets Autonomous Okrug, expenses not only did not decrease, but even increased.

As the pandemic progressed, the situation began to improve somewhat in 2021, and many regions began to see a gradual increase in spending on material goods. Spending on recreation and culture, as well as on transport, continued to decline in most regions, and more significantly than in 2020. At the same time, spending on healthcare and education increased. Spending on communication services grew steadily.

As the situation stabilized in 2022, spending in most regions not only returned to pre-pandemic levels, but also increased slightly, in particular on food and alcohol, clothing and footwear, and household items. The most significant increase in spending was on education. The exception is spending on transport, as well as on recreation and culture and hotels, which in most regions have not returned to pre-pandemic levels. Healthcare spending, after the growth noted in 2021, decreased in 2022 in most regions, amounting to a value lower than the pre-pandemic level. Only the Yamalo-Nenets Autonomous Okrug, Yaroslavl and Arkhangelsk Regions, and Khabarovsk Krai demonstrated significant growth in healthcare spending. It should be noted that the Yamalo-Nenets Autonomous Okrug and Arkhangelsk Region as a whole are characterized by a significant growth in spending in many areas.

These changes in consumption volumes were also reflected in changes in the consumption structure. The descriptive statistics of the consumption structure, developed in the regions in 2022, are presented in

Table 4. Descriptive statistics of consumption structure in highly urbanized regions of Russia in 2022.

Index	Food	Alcohol	Clothes and Footwear	Household Appliances	Healthcare	Transport	IT and Communications	Recreation and Culture	Hotels	Education
Average value	32.8	3.2	7.9	5.9	4.3	12.9	4.5	5.2	2.6	1.5
Mode	32.0	2.4	8.9	6.4	4.5	11.6	4.8	4.9	3.4	1.3
Median	32.5	3.0	7.7	6.0	4.4	11.9	4.4	4.9	2.4	1.4
Dispersion	12.3	0.8	1.7	1.2	0.7	8.5	0.4	2.3	1.6	0.2
Standard deviation	3.5	0.9	1.3	1.1	0.8	2.9	0.6	1.5	1.3	0.4
Minimum value	26.9	1.9	5.9	3.7	2.4	6.8	3.8	2.7	0.7	1.0
Maximum value	42.5	5.1	10.4	8.5	5.9	20.1	6.7	9.4	5.8	2.5
Coefficient of variation	0.11	0.28	0.17	0.19	0.20	0.23	0.13	0.29	0.49	0.26
Asymmetry	0.62	0.51	0.33	0.33	–0.11	0.52	1.71	0.90	0.69	0.54

.

The decrease in the level of data dispersion, which reflects the “smoothing” of differences in the consumption structure among the regions, is an obvious fact. Moreover, this smoothing turned out to be most pronounced in relation to food, transportation costs, information technology and communications, and education. At the same time, the spread of expenses on recreation and culture increased.

The most noticeable change in the consumption structure in the regions was the significant increase in expenses on education and IT and communications. The average share of food consumption has increased slightly, while the average expenses on transport, as well as on recreation and culture, have decreased.

The conducted clustering of regions according to the indicators of the consumption structure established in 2022 also allows us to identify three groups of regions with the same characteristics (

Table 5. Groups of regions by consumption structure in 2022.

No.	Region	Features of the Consumption Structure
1	Komi Republic, Krasnoyarsk Krai, Magadan Region, Republic of Tatarstan, Sverdlovsk Region, Nizhny Novgorod Region, Chelyabinsk Region, Yaroslavl Region, Yamalo-Nenets Autonomous Okrug, Samara Region, Republic of Karelia, Kamchatka Krai, Perm Krai, Moscow Region, Kaliningrad Region, Ivanovo Region	Average values of the share of expenses in all areas.
2	Khabarovsk Krai, Murmansk Region, Primorsky Krai, Sakhalin Region, Kemerovo Region, Kirov Region, Novosibirsk Region, Khanty-Mansi AO–Yugra, Arkhangelsk Region, Irkutsk Region	The highest share of transportation costs, as well as expenses on recreation and culture, with the lowest share of expenses on food, clothing and footwear, as well as household appliances.
3	Saratov Region, Ulyanovsk Region, Volgograd Region, Tver Region, Vladimir Region	The highest share of expenses is for food, clothing and footwear, and household appliances. The lowest share of expenses is for transportation, recreation and culture, and hotels.

); however, the assignment of regions to groups has changed.

It can be seen from the data in Table 5 that almost one half of the highly urbanized regions (16 out of 31) have seen a return to the previous (pre-pandemic) consumption structure (deviation up to 10%); they retained their positions in the clusters. At the same time, in the other half of the regions (15 out of 31), the consumption structure has changed. As a rule, the most significant changes in the consumption structure occurred in the regions of Siberia and the Far East.

Thus, a significant part of the regions of the Far East and Siberia is characterized by a significant decrease in the share of the consumption of material goods, as well as the share of expenses on recreation and culture and transportation costs in comparison with the pre-pandemic period. A noticeable fact is the decrease in the number of regions in the third cluster, characterized by a significant predominance of expenses on the consumption of material goods and food products over expenses on intangible goods.

The change in the volume and structure of consumption in the regions can be explained by various factors. One of these reasons may be a higher level of inflation compared with a lower level of economic growth in the regions. To test the hypothesis about the presence of this connection, a correlation analysis was conducted, the results of which are presented in

Table 6. Correlation analysis results.

Indicator	Food	Alcohol	Clothes and Footwear	Housing and Communal Expenses	Household Appliances	Healthcare	Transport	IT and Communications	Recreation and Culture	Education	Hotels
For consumption volumes
Consumer Price Index	0.062	−0.063	0.026	−0.083	−0.020	0.114	−0.263	0.378	−0.299	0.375	0.0489
Average Per Capita Monetary Income of the Population	0.706	0.593	0.704	0.740	0.648	0.457	0.625	0.649	0.497	0.462	0.205
The Central Bank’s interest rate	0.148	0.040	0.218	0.009	0.100	0.102	−0.154	0.274	−0.043	0.362	0.178
For the consumption structure
Consumer Price Index	0.188	−0.042	0.169	−0.072	0.034	0.202	−0.318	0.654	−0.390	0.514	0.064
Average Per Capita Monetary Income of the Population	−0.456	−0.015	−0.048	0.039	0.017	−0.399	0.266	0.064	0.158	0.115	−0.045
The Central Bank’s interest rate	0.067	−0.034	0.273	−0.171	0.053	0.027	−0.277	0.335	−0.109	0.405	0.191

(significant dependence is highlighted in bold).

The calculation of the p-value confirmed the statistical significance of the correlation (

Table 7. Calculation of the p-value parameter.

	Food	Alcohol	Clothes and Footwear	Housing and Communal Expenses	Household Appliances	Healthcare	Transport	IT and Communications	Recreation and Culture	Education	Hotels
For consumption volumes
Consumer Price Index	3 × 10−148	1.05 × 10−180	3.51 × 10−180	1.04 × 10−172	1.23 × 10−179	2.04 × 10−183	1.15 × 10−142	1.49 × 10−190	8.26 × 10−168	1.16 × 10−187	3.8 × 10−181
Average Per Capita Monetary Income of the Population	1.6 × 10−52	1.43 × 10−52	1.45 × 10−52	1.46 × 10−52	1.44 × 10−52	1.44 × 10−52	1.46 × 10−52	1.43 × 10−52	1.44 × 10−52	1.42 × 10−52	1.43 × 10−52
Key rate of Central Bank	1.4 × 10−91	1.13 × 10−39	0.18128	6.33 × 10−43	1.78 × 10−12	4.89 × 10−29	7.60 × 10−37	1.36 × 10−27	1.00 × 10−08	3.67 × 10−55	1.27 × 10−39
For consumption structure
Consumer Price Index	1.23 × 10−87	3.97 × 10−57	9.25 × 10−68	8.90 × 10−71	1.54 × 10−61	6.19 × 10−70	1.88 × 10−47	3.31 × 10−57	1.36 × 10−42	1.01 × 10−22	2.22 × 10−28
Average Per Capita Monetary Income of the Population	2.71 × 10−43	1.75 × 10−51	2.37 × 10−50	1.25 × 10−49	6.34 × 10−51	4.67 × 10−51	1.44 × 10−49	2.95 × 10−51	3.22 × 10−51	5.95 × 10−52	1.82 × 10−51
Key rate of Central Bank	1.38 × 10−91	9.25 × 10−62	8.27 × 10−70	2.95 × 10−71	4.04 × 10−65	2.63 × 10−73	7.05 × 10−48	1.27 × 10−62	1.82 × 10−44	1.85 × 10−36	2.89 × 10−34

).

According to Table 6, the volume of the consumption of goods and services in the period under study depended, as expected, directly on the level of income, while the latter had no significant connection with consumer prices and the level of availability of loans. The hypothesis about the presence of a connection between the consumption structure and the factors of economic development of the national economy has not been confirmed as well. For most indicators, the changes in the structure of consumption are not associated with inflationary factors or changes in income and lending opportunities determined by the key rate of the Central Bank. A moderately pronounced connection is observed only for the indicators of the consumer price index and the share of costs for IT, communication, and education. At the same time, the regression analysis carried out for these relationships showed that the change in the consumer price index by 49% explains the change in the share of costs for IT and communication and by 29% for the change in the share of costs for education (

Table 8. Regression analysis.

Education
Regression statistics
Multiple R		0.544
R square		0.296
Normalized R square		0.279
Standard error		0.475
Dispersion analysis
	df	SS	MS	F	F significance
Regression	3	11.408	3.803	16.8	3.41 × 10−09
Discrepancy	120	27.101	0.226
Total	123	38.510
	Coefficients	Standard error	t-statistics	p-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Consumer Price Index	0.081	0.017	4.620	9.72 × 10−06	0.05	0.12	0.056	0.11
Average Per Capita Monetary Income of the Population	5.07 × 10−06	2.78 × 10−06	1.825	0.07	−4.3 × 10−07	1.06 × 10−05	−4.3 × 10−07	1.06 × 10−05
The Central Bank’s interest rate	0.006	0.029	0.223	0.82	−0.05	0.06	−0.05	0.06
IT and communications
Regression statistics
Multiple R		0.707
R square		0.494
Normalized R square		0.481
Standard error		0.647
Observations		124
Dispersion analysis
	df	SS	MS	F	F significance
Regression	3	49.003	16.33	39.02	1.13 × 10−17
Discrepancy	120	50.227	0.42
Total	123	99.230
	Coefficients	Standard error	t-statistics	p-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Consumer Price Index	0.23	0.024	9.49	2.78 × 10−16	0.179	0.273	0.179	0.273
Average Per Capita Monetary Income of the Population	7.91 × 10−06	3.78 × 10−06	2.09	0.038577	4.22 × 10−07	1.54 × 10−05	4.22 × 10−07	1.54 × 10−05
Key rate of Central Bank	−0.13	0.04	−3.37	0.001011	−0.213	−0.055	−0.213	−0.055

).

All of this confirms our assumption that the observed changes in consumer behavior were mainly due to changes in consumer preferences of the population associated with the conditions of the coronavirus crisis, limiting the possibilities of business and social activity.

5. Discussion

The COVID-19 pandemic has led to significant shifts in the volume and structure of public consumption as a result of changes in people’s lifestyles. At the same time, although the coronavirus crisis changed the income level and impeded the rates of economic growth in the setting of higher inflation, these factors did not significantly affect consumer preferences. Despite the presence of individual speculative outbreaks (purchase/sale of currency, apartments) caused by the first reaction to the pandemic shocks in 2020, as well as the shocks associated with the aggravation of the global geopolitical situation in 2022, they generally did not affect the consumption structure in Russian regions. However, significant changes in consumer behavior were precisely due to lifestyle adjustments during the coronavirus crisis, associated with the introduction of quarantine measures and the existing threat to life and health.

It should be noted that it is the qualitative aspects of lifestyle that have changed to a greater extent, since the coronavirus crisis primarily affected people’s value priorities and attitudes toward material and non-material things, rather than the level of material well-being. The indicators of the total income of the population, adjusted for inflation, showed a growth in most cases, not a decline, while the amount of spending on individual goods and services underwent significant changes. These findings are fully consistent with the idea that during the COVID-19 pandemic, people’s lives became neither better nor worse, but of a different quality [91,92,93].

Our findings confirm the data obtained by other authors that the pandemic has led to a noticeable increase in online shopping worldwide [94,95,96], which is reflected in an increase in spending on IT and communications. Citizens have significantly reduced their use of public transport [97]. In general, our study confirms the conclusions of other researchers that the changes in the volumes of the consumption of individual goods and services lead to the redistribution of the consumption structure, rather than to a fundamental change. In this setting, the share of costs associated with satisfying basic needs increases [98].

At the same time, the idea that the transformation of consumer needs as a result of the pandemic reflects a large-scale shift toward increased attention to one’s health [99,100], has not found practical confirmation in Russian regions. On the contrary, we found that in most highly urbanized Russian regions, healthcare costs did not change significantly during the pandemic and, after the end of the pandemic, even decreased in most regions compared with the pre-pandemic period. Concerning expenditure on alcohol and tobacco, no decrease was observed. It is also difficult to agree with the assertion of some researchers that during the coronavirus crisis, citizens mainly purchased essential goods, considering other goods and services less important [101], taking into account the significant increase in the share of spending on education, as well as on household items, including expensive household appliances.

Overall, it can be argued that the reduction in spending on food, clothing, and footwear with a simultaneous increase in spending on healthcare and household items turned out to be short-term effects, which disappeared as soon as the restrictions were lifted. Therefore, a return to the previous consumption patterns was detected.

The differences in the changes in consumer preferences in the regions, reflected in the changes in their positions in clusters formed by the consumption structure, confirm the conclusions of a number of researchers that the behavioral attitudes of citizens are determined not only by previously established consumption models, but can also be associated with a different level of the impact of the coronavirus crisis and different responses by regional governments, including the strength of quarantine measures [102].

At the same time, our research has revealed new dynamics, previously not mentioned by other researchers. Thus, despite the existing differences in the volumes and structure of the consumption of goods and services in individual regions, the regions under analysis have shown the emergence of new trends, which may have a longer-term nature. These include the following trends.

First, the recovery growth of regional economies, complicated by the uncertainty related to variations in the key and inflation rates and the emergence of new geopolitical shocks, has affected consumer preferences in terms of the consumption / saving choice. In the overall consumption structure, the share of individual expenses of an investment nature, e.g., on education, is steadily increasing. It can be assumed that these investments are not related exclusively to extending their list of qualifications, but rather to boosting their labor potential with the purpose of raising the standard and quality of life. This conclusion also indicates that societies are gradually developing an understanding that investments in human capital are the responsibility of not only the state or business, but also of every individual. Therefore, the population as a productive force is becoming increasingly ready to make such investment choices.

Second, the development of online consumption is an obvious trend. This is evidenced by the increasing volume of online purchases, the emergence and development of various e-commerce systems, and the decreasing level of the consumption of goods that cannot be consumed online (reduction in spending on recreation, transportation, hotels). The decline in an active lifestyle during the pandemic with the prevalence of various online forms of leisure (online cinemas, online museums, online travel, online games, etc.) and the use of remote work services tends to persist. This can also have important consequences for the economy, associated, on the one hand, with the expansion of growth opportunities (audience expansion, business scaling, new types of goods and services, etc.), and, on the other, with the emergence of new risks, such as increased unemployment in certain areas of activity, fraud associated with the use of data, etc.

Our assumption that these trends are not short-term and may have a longer-term character is confirmed by data on the purposes of using the internet by the population. Thus, in 2020, the share of Russian citizens using the internet for distance learning was 9.6%, for the purchase of goods and services—35.7%. In 2023, the values of these indicators were already 17.4% and 57.1%, respectively. Moreover, a new direction of using the internet has appeared—cultural purposes (14.2%) [103,104]. The share of expenses on educational needs grew from 0.8% in 2019 to 1.6 in 2023 [65].

The growing interest in online education due to the COVID-19 pandemic has been confirmed by the CEDEFOP Center, which conducted an analysis of queries on the Google Trends platform related to online learning [105]. The EU Council’s plan for digitalization in Europe noted a sharp increase in demand for online learning during the coronavirus crisis [106]. The “virtualization” of lifestyle as a result of the COVID-19 pandemic can be evidenced by data from the International Telecommunication Union, according to which the number of internet users increased by 800 million people in 2021 [107]. The report by the World Bank Group “Digital Progress and Trends in 2023” notes that the COVID-19 pandemic has influenced the transition from traditional formats of the consumption of goods and services to online consumption, as well as to the use of cashless payments [108].

It can therefore be stated that COVID restrictions and the respective changes in the lifestyle of the population may have longer-term effects that have manifested in the changing paradigm of consumer behavior toward investing in self-development, safety, and comfort, as well as online consumption.

6. Conclusions

We established that the inability to maintain a flexible and dynamic lifestyle as a result of COVID-19 restrictions resulted in both the emergence of alternative forms of need satisfaction among the population and shifts in consumer preferences. In contrast to the theoretical concepts of the sociology of consumption, we pay attention not to the motivation of consumer behavior, but rather to the economic consequences of changes in the volume and structure of consumption. At the same time, examining changes in the lifestyle of the population during the coronavirus crisis through the prism of changes in the volume and structure of consumption allowed us to focus on the economic behavior of citizens, while recognizing the complexity and diversity of factors that influence consumer choice.

The results of our study should be considered in light of some limitations, which are to be eliminated by future research efforts based on new data on the structure of consumption in geographically diverse regions.

1. Our main conclusions have been derived based on descriptive trends, which will require additional statistical verification involving a larger amount of data. In this regard, a number of important questions remain, such as the following. Will the trends of the virtualization of consumption, as well as increased attention to a healthy lifestyle and educational development, continue to strengthen? Which industries and which social groups of the population will benefit most from the observed changes in consumer preferences? These questions should be addressed using quantitative methods.

2. In addition, our research sample includes Russian regions and official Russian statistics. In order to confirm (or refute) our findings, data on other regions and countries should be compared, based on rigorous peer review evaluation. The main focus should be on resolving the following questions. Are the above trends characteristic of all countries and regions in the post-pandemic period, or do they have some geographical, socioeconomic, and cultural specifics? To what extent have the manifestations of these trends been influenced by the severity of the coronavirus crisis and the restrictive measures introduced by the governments? Will these trends influence future governmental decisions, e.g., in the area of digitalization?

3. It should be noted that the lifestyle of populations in highly urbanized regions has certain differences from that in rural regions. In our study, given the high proportion of the urban population in the regions under consideration, we assumed that these changes primarily affected urban centers. However, this assumption requires additional verification to reveal which changes in consumer preferences are common (or specific) to residents of urban and rural areas. Has the structure of the consumption of the population in rural areas changed? Which of the changes in consumption were short-term, and which persisted after the end of the pandemic? Did the difference in the lifestyle of the urban and rural populations affect the structure of their consumption during the pandemic and post-pandemic period?

4. The standard and quality of life are affected by numerous factors, not only by changes in the lifestyle of the population and their consumption patterns. In particular, Oviedo et al. [109] noted the role of social well-being in shaping consumer behavior. However, changes in consumption patterns may still reflect changes in lifestyle, leading to the formation of persisting trends in consumption behaviors, expressed in increased investment in human capital, as well as in the virtualization of consumption. Considering this connection, is it possible to quantify changes in the lifestyle of the population associated with the occurrence of external shocks? Which of these changes are associated with changes in the quality of life and which are related to changes in the standard of living? How can governments protect the population when the quality and standard of living deteriorate significantly in the face of shocks?

5. In this study, we define the changes in consumer preferences that persist after the shock event and are likely to retain their influence in the future as “longer-term” effects. We believe that this term is more accurate (vs. “long-term”) in reflecting the temporal specifics of the unfolding of pandemic shocks and the probabilistic aspect of the described effects. In turn, this requires further elaboration of the ontological aspects of regional resilience problems.

Although the coronavirus pandemic and its consequences are no longer on the current research agenda, it seems that the problem of studying the impact of shocks of various natures on people’s lifestyles is not losing its relevance. It is impossible to exclude the recurrence of sudden external events associated with various diseases of a pandemic nature, natural disasters, etc. Answers to the above questions may elucidate and promptly prevent the impact of such shocks on the standard and quality of life of people and their consumer preferences, thereby forecasting changes in the socioeconomic landscape.