CC BY
5DOI: 10.29141/2658-5081-2023-24-4-4 EDN: UDXDWY JEL classification: J21, J23, J68, O11, O21
Maria Ye. Konovalova Samara State University of Economics, Samara, Russia Oksana V. Plyusnina Ukhta State Technical University, Ukhta, the Komi Republic,
Russia
Elena V. Fedotova Kaluga branch of the Russian State Agrarian University -
Moscow Timiryazev Agricultural Academy, Kaluga, Russia
Labour market flexibility as a factor in economic recovery after the COVID-19 pandemic
Abstract. The COVID-19 pandemic has largely undermined most economies in the world. To a significant extent the reasons behind this economic nosedive are rooted in the sphere of labour market. The imposed restriction measures made many workers stay at home, and part of them even lost their jobs. The speed of economic recovery in different countries varied depending on how quarantine restrictions were eased. One crucial factor in this process is labour market flexibility and its ability to adapt to changing economic conditions. The purpose of the paper is to explore the relationship between the labour market flexibility and the speed of GDP recovery when the pandemic was waning and the quarantine restrictions were being removed. Methodologically, the study relies on labour economics. The methods are multivariate and logistic regression analysis. The data for the study is sourced from the World Bank and Eurostat and comprises statistics for assessing labour market flexibility and GDP in various countries for 2020-2021. The findings indicate a reverse relationship between the speed of GDP growth recovery during the post-COVID period and the share of part-time employment. Yet taking into account that part-time employment is not only an indicator of the labour market flexibility, but also reflects a general slowdown in production and unemployment, we can attach low speed of recovery to a greater weakness of an economy. The obtained results are insufficient to reject the hypothesis that a more flexible labour market accelerates the recovery of a national economy. At the same time, the study demonstrates that the West European countries generally got over the consequences of the COVID-19 crisis faster than other EU countries. The research contributes to understanding the transformation processes in the labour market under the pandemic as well as provides support for labour market policy development and implementation.
Keywords: labour market; labour market flexibility; part-time employment; GDP; pandemic; the European Union.
For citation: Konovalova M. Ye., Plyusnina O. V., Fedotova E. V. (2023). Labour market flexibility as a factor in economic recovery after the COVID-19 pandemic. Journal of New Economy, vol. 24, no. 4, pp. 64-81. DOI: 10.29141/2658-5081-2023-24-4-4. EDN: UDXDWY.
Article info: received June 13, 2023; received in revised form July 20, 2023; accepted September 1, 2023
Introduction
The COVID-19 pandemic has led to severe negative consequences for the economies of both developed and developing countries. Due to the introduction of restrictive measures on free movement, many people were forced to stay at home and change their usual working patterns, adapt to new circumstances or reject them. As a result, there was a sharp decline in the labour market and a general fall in the production of goods and services [Karpunina et al., 2022; Gukasyan et al., 2022]. Thus, the causes of the pandemic-driven economic problems should be sought primarily in the labour sector.
A number of researchers (cf.: [Giri, Rana, 2020; Tadesse, Muluye, 2020; Sheremet, 2020; Levin et al., 2022]) note that developed economies were much more active in responding to the pandemic and introduced more stringent restrictions on the labour market and other aspects of life compared to developing economies. This may be due to their greater preparedness and resource capabilities to combat the pandemic. Owing to the economic stability and resources, developed countries appeared to be more effective in coping with the consequences of the pandemic and initiating the recovery process by adapting appropriate measures. Among such measures applied in the labour market are transferring some employees to remote work, changing type of employment, and guaranteeing social security for the working population for the periods when their enterprises stand idle [Gukasyan et al., 2022; Polujanova et al., 2023].
Labour market flexibility is determined by its ability to adapt to a changing environment and meet new requirements. A flexibility approach can be employed while hiring and firing workers, organising working hours and developing variable forms of employment [Kossek, Thompson, Lautsch, 2015; Sienkiewicz, 2016].
The purpose of the article is to examine how labour market flexibility affects the speed of GDP growth recovery during the periods of easing or lifting restrictions introduced following the outbreak of the COVID-19 pandemic. GDP growth recovery is the most important indicator of post-crisis economic revival.
Economic recovery also depends on what sectors dominate the country's economy. The services sector experienced the sharpest downturn [Lu et al., 2021]. The decline
was associated with the introduction of social distancing during the pandemic, which made it difficult or even impossible to provide some services. Restrictions on flights and free movement also took a huge toll on the tourism industry. The above restrictions persisted in some countries for a long time, which exerted a negative influence on the recovery of the sector. Thus, one objective of the study is to find out whether countries with a higher share of employment in the service sector faced greater difficulties in the process of economic recovery.
To broach the topic under study, it is of importance to reveal the peculiarities of the labour market in the Eastern Bloc countries in the EU [Hausermann, Kurer, Schwander, 2016; Shevchuk, Strebkov, Tyulyupo, 2021a]. One of the characteristics of these countries is that, with fairly high levels of human capital, the labour force in these nations is rather cheap. Well-educated and highly-skilled employees can substantially contribute to R&Ds and technological progress, as well as come up with new innovative ideas. All these research studies, ideas and innovative projects are often implemented in highly industrialised countries of Western Europe having in their possession the appropriate scientific and technological infrastructure.
Despite lower levels of well-being compared to Western countries, Eastern nations of the EU are generally characterised by a more flexible labour market [Shevchuk, Strebkov, Tyulyupo, 2021b]. Hence, one can expect that these countries recovered more quickly from the pandemic crisis than the rest of the EU.
Theoretical approaches to studying labour market flexibility
Today's world has witnessed the active development of new forms of flexible work. Companies are increasingly choosing independent contractors and freelancers over permanent employees. The opportunity to perform job responsibilities remotely allows companies to hire qualified employees around the globe. At the same time, freelancers get additional opportunities to search for projects appropriate to their qualifications [Rani, Furrer, 2021].
An important role in the emergence of flexible forms of employment is attributed to information and communications technology which has radically transformed the world of work [Cherry, 2020]. A variety of digital platforms provide a space for worker-employer interaction. They are used both to search for permanent jobs and place short-term projects [Codagnone, Karatzogianni, Matthews, 2018; Vallas, Schor, 2020].
There are numerous terms and concepts utilised to characterise platforms for organising flexible forms of employment: "freelance online marketplaces", "online labour platforms" [Kassi, Lehdonvirta, 2018], "crowdwork platforms" [Howcroft, Berg-vall-Kareborn, 2019].
The key reasons for employees to choose such a flexible form of labour relations as freelancing are: a better work-life balance, flexible working hours, and freedom. It is noteworthy that financial issues for freelancers recede into the background1.
The factors that affect flexible forms of employment also come under academic scrutiny. One such factor is unemployment; growing unemployment rates lead to a surge in freelancing in certain countries and regions [Lobel, 2020]. Freelance as a creative mode of self-employment contributes noticeably to the economy of many countries [Baitenizov et al., 2019]. Braesemann, Lehdonvirta and Kassi [2022] found that urban and rural workers made disproportionate use of the online labour market.
The COVID-19 pandemic has led to serious consequences for the global labour market and induced extensive changes in economic activity.
Researchers look at various aspects of these impacts, including job losses, reduced consumer demand, worsening economic activity and rising unemployment [Blustein et al., 2020; Karpunina et al., 2022; Fraymovich et al., 2022].
They also analyse labour market support measures and economic stimulus taken by governments to mitigate the negative effects of a struggling labour market [Eichhorst, Marx, Rinne, 2020; Mentuh, Shevchuk, 2020].
There is an extensive amount of research investigating the essence and impact of labour market flexibility on economic dynamics. Some researchers concentrate on analysing the impact of flexible employment contracts and types of flexible working on employment growth and labour productivity [Craigwell, 2006; Sienkiewicz, 2016; Nazarova et al., 2022].
Some researchers confirm the positive influence of labour market flexibility on innovation and entrepreneurship [Martínez-Sánchez et al., 2019].
Others highlight the impact of working time flexibility on work-life balance [Kos-sek, Thompson, Lautsch, 2015; Attieh, 2022; Arutyunova et al., 2022].
A number of scientists emphasise the accelerated transformation of the labour market towards more flexible forms of employment during the COVID-19 pandemic in order to adapt to restrictions on free movement [Emmett et al., 2020; Gavin, Poorhosseinzadeh, Arrowsmith, 2022; Okunkova et al., 2023].
During the pandemic, a significant share of the workforce was transferred to a remote work setting, and many laid-off workers became independent online workers (freelancers) [Karpunina, Moiseev, Karpunin, 2022].
After the end of the pandemic, many employers chose to at least partially stick to the changes made to the work process, since the new work format seems more convenient and efficient to them [Marbun, 2023; Yang, Kim, Hong, 2023].
1 Freelance survey results, plus 30 companies hiring freelancers. (2018). https://www.flexjobs.com/blog/post/ freelance-survey-results-plus-companies-hiring-freelancers/.
Materials and methods
The first two hypotheses aim to identify the relationship between labour market flexibility and economic development processes.
H1: the higher the value of labour market flexibility indicators such as labour transitions by type of contract and share of part-time employment, the faster the GDP growth recovery after the COVID-19 pandemic.
The evolution of social and economic institutions is inevitably aligned with the development of the labour sphere, which, in turn, also positively influences various economic indicators. Thus, the economy at large and the labour sphere in particular develop in tandem.
H2: there is a positive relationship between the GDP per capita level and the values of labour market flexibility indicators, such as part-time employment, labour transitions by type of contract and employment agency workers.
The ratio of the number of the employed in industry and in the service sector is also viewed as a significant factor affecting the speed of economic recovery, since amid instability the population tends to increase spending on basic needs, including spending on food and housing. The tourism and entertainment sectors tend to experience significant customer attrition and suffer the greatest economic damage. Hence, these industries may face great difficulties in the process of recovery. On this basis, we can formulate H3:
H3: countries with a higher share of employment in the service sector will experience slower economic recovery compared to countries with a higher share of employment in industry or agriculture.
As noted earlier, the labour market in Eastern Europe is in general more flexible despite the fact that in terms of most economic indicators these territories are inferior to other EU nations. Thus, H4 is as follows:
H4: in the Eastern Bloc countries in the EU, the post-COVID economy is recovering at a faster rate than in the rest EU states.
To answer the question in H1 , multiple regression analysis is carried out, in which two indicators act as predictors: labour transitions by type of contract and the share of part-time workers in the total working population.
The dependent variable is the GDP growth rate for EU countries from 2020 to 2021. This is due to the fact that the world economy suffered the greatest damage during the COVID-19 pandemic in 2020 [Ogaboh Agba, Ocheni, Agba, 2020]. The GDP growth rate by 2021 demonstrates the speed of these countries' economic recovery.
Multiple regression with two predictors is expressed as formula (1):
7 = p0 + № + p2X2 + s , (1)
where Y is a dependent variable (the outcome we try to forecast); Xi and X2 are two independent variables used to predict the value of Y; Po, Pi and P2 are regression coefficients for each predictor; e is random error or irregularity not explained by the model.
To test H2, the EU countries are categorised into two conditionally proportionate groups (14 and 13 countries) by GDP per capita1. The first group (with a high level of GDP per capita) includes the following countries: Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Ireland, Italy, Luxemburg, Malta, the Netherlands, Spain, and Sweden. The rest of the EU countries are included in the second group. To that end, the countries of the European Union are divided into two categories: economically developed and developing in accordance with the definition by the World Bank2. To do so, we perform logistic regression analysis of the dependence of the developed country status on labour market flexibility indicators. In the analysis, we identified the following three indicators as predictors: labour transitions by type of contract; part-time employment as a percentage of the total employment; and temporary employment agency workers. We used the following formula (2):
log(odds) = Po + PiXi + P2X2 + P3X3 , (2)
where log(odds) is log-odds ratio for the dependent variable; X2, X2 and X3 are three independent variables used to predict log-odds values; Po, Pi, P2 and P3 are regression coefficients for each predictor. Logistic regression uses a logistic function to convert a linear combination of predictors into a probability.
To test H3 implying that countries with a higher employment share in the service sector see slower recovery from the pandemic, we divide the EU countries into conditionally proportionate groups (14 and 13 countries). The first group covers the economies with a higher employment share in the service sector, and the second one - the rest of the EU states. Next, we conduct analysis of variance between the groups by the GDP growth rate in 2020-2021. The analysis implies finding the F-statistic, which is calculated by the ratio of the between-group variance to the within-group variance.
To test H4, we conduct a similar analysis: the first group comprises the Eastern Bloc countries in the EU, such as the Czech Republic, Estonia, Slovakia, Slovenia, Bulgaria, Croatia, Hungary, Latvia, Lithuania, Poland and Romania; and the second group includes the other EU members.
The article was based on statistics from the World Bank and the EU statistical service (Eurostat).
1 World Bank. GDP per capita. https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?most_recent_value_ desc=true.
2 World Bank. Global Economic Prospects. https://openknowledge.worldbank.org/server/api/core/bitstreams/ fe01a687.
Research results
Labour market flexibility plays a significant part in the speed of post-crisis economic recovery. With high market flexibility, there are several reasons for the recovery process to accelerate:
1) employers can better adapt their business models and change production volumes owing to the ability to regulate employee numbers;
2) labour resources are more efficiently distributed between industries, which is especially important amid crisis and instability. For example, during such periods, the demand for labour in the manufacturing sector is much higher than in the tourism and entertainment sectors;
3) unemployment periods get shorter as employers can bring in job opportunities in response to increased demand for goods and services. This creates favourable conditions for employment of those who have lost their job or want to change it;
4) there is an increase in the investment attractiveness of the economy, since a flexible market reduces risks for enterprises to go bust due to employers capable of regulating personnel costs effectively.
To measure labour market flexibility, the study uses three indicators provided by the statistical service Eurostat1:
Labour transitions by type of contract. It is an indicator reflecting the frequency of changing the type of contract between employees and employers, including full-time, part-time and temporary employment contracts.
Part-time employment as a percentage of the total employment. A high share of part-time workers indicates the ability to adapt to changing market needs.
Temporary employment agency workers. This indicator takes into account the number of temporary workers hired through employment agencies. The more there are of them, the faster the market reacts to changes in demand for labour resources.
These three indicators will allow making a more complete and holistic assessment of the labour market flexibility level in various countries and its impact on the speed of economic recovery in the post-pandemic period.
For the purpose of the study, data on the EU countries was analysed. Along with more detailed and accurate labour statistics available, there are other reasons for choosing the EU states for analysis, such as the following.
Different levels of economic development. The EU includes both countries with a high level of development, such as Germany, France, Belgium, Denmark, and countries
1 Source: Eurostat. Labour transitions by type of contract. https://ec.europa.eu/eurostat/databrowser/product/
page/ILC_LVHL32_custom_6188421; Eurostat. Part-time employment as percentage of the total employment
for young people by sex, age and country of birth. https://ec.europa.eu/eurostat/databrowser/product/page/YTH_
EMPL_060_custom_6188696; Eurostat. Temporary employment agency workers. https://ec.europa.eu/eurostat/
databrowser/product/page/LFSA_QOE_4A6R2_custom_6188933.
with a lower level of development located mainly in the South-East of Europe (Greece, Bulgaria, Romania, Croatia).
Diversity of economic models. The EU countries implement different economic models, such as socially oriented, market and combined [Hay, 2004]. This allows exploring the impact of labour market flexibility in different contexts and comparing the results.
Differences in labour laws. The EU countries exercise different rules and regulations regarding labour relations and worker protection. For instance, in some of them, e.g., France, Germany, and Belgium, labour laws are stricter in terms of dismissal and working hours flexibility, while in others regulations are more flexible [Deakin, Malmberg, Sarkar, 2014].
Development level of social programmes. The EU countries have various social programmes, such as social security systems, unemployment benefits and support for workers who have lost their jobs. The difference in these programmes may affect the ability of workers to adapt to changes in the labour market and the overall process of economic recovery [Gladkov, 2018].
Digitalisation and innovation. More developed EU countries can continue to promote and apply new digital technologies and innovations during an economic downturn [Kukina et al., 2022]. This has a positive effect on transferring workers in some professions to remote work, as well as on training new specialists through distance learning technologies.
Table 1 presents data for 27 EU countries concerning labour market flexibility, GDP in 2020 and 2021, and an indication of whether the countries are economically developed.
Table 1. Labour market flexibility and GDP in the EU member states
EU country GDP per capita, US dollars GDP in 2020, million US dollars GDP in 2021, million US dollars GDP growth rate, % Labour transitions by type of contract, % Part-time employment, % Employment agency workers, %
Austria 53,638 435,225 480,368 10.4 1.0 23.4 2.1
Belgium 51,268 525,212 594,104 13.1 0.4 23.9 1.9
Bulgaria 12,222 70,240 84,056 19.7 0.5 3.2 0.2
Croatia 17,748 57,472 68,955 20.0 1.2 4.6 0.9
Cyprus 31,552 25,008 28,408 13.6 0.4 13.3 2.0
Czech 26,823 245,975 281,778 14.6 0.4 7.2 1.2
Denmark 68,008 355,222 398,303 12.1 1.7 45.8 0.6
Estonia 27,944 31,370 37,191 18.6 1.5 17.9 0.5
Finland 53,490 271,892 297,302 9.3 0.9 28.5 1.6
France 43,659 2,639,009 2,957,880 12.1 0.7 17.0 2.4
Germany 51,204 3,889,669 4,259,935 9.5 0.2 24.1 5.5
Table 5 (concluded)
EU country GDP per capita, US dollars GDP in 2020, million US dollars GDP in 2021, million US dollars GDP growth rate, % Labour transitions by type of contract, % Part-time employment, % Employment agency workers, %
Greece 20,193 188,926 214,874 13.7 1.3 18.4 0.3
Hungary 18,772 157,182 181,848 15.7 0.6 5.2 0.3
Ireland 100,172 425,852 504,183 18.4 1.1 27.7 2.5
Italy 35,770 1,896,755 2,107,703 11.1 1.8 23.8 0.9
Latvia 21,080 34,602 39,854 15.2 2.2 12.9 1.9
Lithuania 23,713 56,847 66,445 16.9 0.6 8.9 2.1
Luxembourg 133,590 73,993 85,506 15.6 0.6 15.1 1.2
Malta 34,218 14,933 17,364 16.3 0.6 11.4 7.6
Netherlands 57,708 909,793 1,012,847 11.3 1.4 64.0 3.0
Poland 18,000 599,449 679,445 13.3 1.1 7.3 0.5
Portugal 24,598 229,032 253,663 10.8 2.0 12.0 1.4
Romania 14,927 251,362 284,088 13.0 1.6 7.6 0.4
Slovakia 21,782 106,697 116,527 9.2 0.4 11.2 4.5
Slovenia 29,291 53,707 61,749 15.0 1.8 15.6 3.5
Spain 30,104 1,276,963 1,427,381 11.8 0.7 25.0 3.5
Sweden 61,143 547,054 635,664 16.2 1.9 34.5 1.1
Source: Own compilation based on World Bank. Global Economic Prospects. https://openknowledge. worldbank.org/server/api/core/bitstreams/fe01a687; Eurostat. Labour transitions by type of contract.
https://ec.europa.eu/eurostat/databrowser/product/page/ILC_LVHL32_custom_6188421; Eurostat.
Part-time employment as percentage of the total employment for young people by sex, age and country
of birth. https://ec.europa.eu/eurostat/databrowser/product/page/YTH_EMPL_060_custom_6188696;
Eurostat. Temporary employment agency workers. https://ec.europa.eu/eurostat/databrowser/product/ page/LFSA_QOE_4A6R2_custom_6188933.
Based on data from Table 1, multiple regression analysis was carried out; its results are presented in Table 2.
Table 2. Coefficients of the multiple regression model of the dependence of the economic recovery speed in the EU on labour market flexibility
Predictors Coefficient Standard error z-value p-value
(Constant) 14.90376 1.32597 11.240 4.78e-11
Labour transitions by type of contract 0.79565 1.03361 0.770 0.4489
Part-time employment -0.09550 0.04523 -2.111 0.0453
Note: The multiple regression model was estimated using the RStudio environment.
The results of the analysis allow us to draw the following conclusions.
Constant (14.90376) is positive and significant (p < 0.001), which indicates that it has a strong effect on the speed of economic recovery, regardless of the other predictors. This may be indicative of other factors affecting this indicator that are not considered in the model.
Labour transitions by type of contract (0.79565) is not statistically significant (p = 0,4489), which means that this variable exerts no significant impact on the speed of economic recovery. This may illustrate that the change in type of contract is not the main factor contributing to economic recovery.
Part-time employment (-0.09550) has a negative coefficient, which shows that an increase in the share of part-time employment is associated with a decrease in the speed of economic recovery. This variable is statistically significant (p = 0.0453), which confirms its marked influence and may indicate the need to view full-time employment as a factor contributing to faster economic recovery.
Graphically, the relationship between the part-time employment and GDP growth is shown in Figure below. It demonstrates the obvious interdependence between the indicators, which is, however, non-linear. This proves that there might be a variety of other factors influencing economic growth that need to be taken into account.
Regression relationship between part-time employment and GDP growth
Coefficients of the logistic regression model of the dependence of GDP per capita in the EU on labour market flexibility are presented in Table 3.
Table 3. Coefficients of the logistic regression model of the dependence of GDP per capita
in the EU on labour market flexibility
Predictors Coefficient Standard error z-value p-value
(Constant) -0.211 0.198 1.066 0.2973
Labour transitions by type of contract -0.273 0.134 -2.038 0.0532
Part-time employment 0.3274 0.006 4.778 8.1e-05
Employment agency workers 0.027 0.044 1.093 0.2857
Note: The multiple regression model was estimated using the RStudio environment.
Let us interpret the results of logistic regression (Table 3). The coefficient value for part-time employment (0.3274) indicates the change in the log-odds of the dependent variable for a one-unit change in that predictor. The value is positive, which shows a positive relationship between part-time employment and the likelihood that a country is developed. The p-value is less than 0.05 (8.1e-05), which suggests that the relationship is indeed present.
The other two predictors - labour transitions by type of contract and employment agency workers - did not exhibit statistically significant results as their p-values were more than 0.05, although the value of the former was 0.0532, nearing this level. Thus, based on the sample presented, there is no sufficient evidence to confirm that these predictors have a significant effect on the dependent variable.
Next, compare the speed of economic recovery in countries with high and low share of the employed in the service sector. In Introduction, we have outlined a number of the reasons why a high share of employment in the service sector results in a slower post-pandemic economic recovery. Discuss them in more detail.
First, most services, e.g., tourism, hospitality, catering and entertainment, require people to be in direct contact. Amid the COVID-19 pandemic, many restrictions, such as social distancing, were introduced, which greatly impacted the ability to provide services. Restrictions on public assembly, public spaces and business closures significantly reduced demand for services and made it difficult to restore them.
Second, there is a restrictive effect on international mobility: many services such as tourism and hospitality depend on international travel and tourism demand. During the pandemic, international flights were restricted, borders closed, and quarantine measures introduced. This had a major impact on people's ability to travel and caused a significant decline in demand for services in this industry.
Third, the service sector is highly dependent on consumer spending and consumer confidence. During times of economic uncertainty such as a pandemic, consumers tend to reduce their spending on non-critical services to focus on basic needs. This
also impedes the recovery of the service sector. The resumption of the full functioning of industrial and agricultural enterprises is gaining in importance; accordingly, the return of employees of these enterprises to work is happening at a rapid pace. It is of top priority to resume the full functioning of industrial and agricultural enterprises; hence, there is a massive return of workers to the office.
Compare GDP growth in 2020-2021 for two groups of the EU countries. The first group comprises states with a higher share of employment in the service sector, these are Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Ireland, Luxemburg, Malta, the Netherlands, Spain, Sweden, and Greece. The rest 13 EU member states are included in the second group. The average growth rate in the first group is 13.1 %, and in the second group - 14.6 %. We apply analysis of variance to find out whether these differences are statistically significant. The analysis results are given in Table 4.
Table 4. Analysis of variance of GDP growth in the EU countries: The impact of the share of the employed in the service sector
Sources of variance Number of degrees of freedom Sum of squares Standard deviation F-statistics p-value
Group 1 20.73 20.734 2.216 0.149
Residuals 25 233.87 9.355 - -
Based on the analysis results, the p-value is 0.149, which is not enough to confirm the hypothesis.
Here are several factors indicating the high flexibility of the labour market in Eastern European countries.
Most of these countries have low barriers to entrepreneurship and simplified business registration procedures. This can encourage the emergence of more small and medium-sized enterprises and self-employed workers and create more opportunities for flexible employment.
Eastern European countries have a significant number of online workers providing services remotely through online platforms [Sienkiewicz, 2016]. This format of work can attract both local specialists and foreign employers.
In most Eastern European countries, highly competitive labour markets encourage employers to offer more flexible working conditions in order to attract and retain talented workers [Galgoczi, 2017].
Thus, the average GDP growth rate for these countries was 15.56 %, while for the rest of the EU countries it was 12.83 %. This is consistent with our hypothesis; however, to confirm it, we also apply analysis of variance (Table 5).
Table 5. Analysis of variance of GDP growth in the Eastern Bloc countries and the rest EU member states
Sources of variance Number of degrees of freedom Sum of squares Standard deviation F-statistics p-value
Group 1 48.67 48.67 5.908 0.0226
Residuals 25 205.94 8.24 - -
The p-value is 0.0226, which is less than 0.05 threshold. Hence, we have confirmed the hypothesis that the Eastern Bloc countries of the EU exhibit a faster post-pandemic GDP recovery rate compared to the rest of the EU member states. This is mainly due to the specificities of the labour market in these countries, which is highly flexible and adaptable to changing economic conditions.
Of the indicators of labour market flexibility under study, only the share of parttime employment has a significant impact on economic processes. The reason for this is that employment agency workers make up only a small share of the workforce. The share of labour transitions by type of contract during the pandemic was also weak and concentrated in a narrow range between 0.2 % and 2.2 % across the EU countries.
Conclusion
In the article, we have established an inverse relationship between the share of parttime employment and the economic growth rate expressed as GDP growth in the period of 2020-2021. This contradicts H1, which suggests that the more flexible the labour market, the faster the economic recovery from the COVID-19 pandemic. However, the presence of the described relationship is insufficient to refute H1 , since a high share of part-time employment can be caused not only by targeted measures to increase labour market adaptability, but also by the lack of demand for additional labour at enterprises during a period of falling consumer demand. To clarify this aspect, additional research needs to be carried out that considers the indicators of labour market flexibility for other years or time periods, since amid instability these indicators may undergo profound changes due to rising unemployment.
H2, which assumes that developed countries have a more flexible labour market compared to developing ones, was confirmed in the study. We proved that developed nations possess high-quality social security and worker protection systems. On the one hand, it provides additional protection for workers and allows them to safely seek new employment options that suit their individual needs and preferences. On the other hand, it allows employers to respond more quickly to changes in labour demand, which triggers an anti-crisis mechanism during periods of economic instability.
When substantiating H3, we compared the speed of economic recovery in countries with a high and low share of the employed in the service sector. In the former case, countries displayed a slower speed of post-pandemic recovery; however, this phenomenon has not received statistically significant evidence. To obtain these statistical data, a similar research should be undertaken that would cover a larger number of countries in order to achieve more accurate and statistically significant results.
The pace of recovery from the COVID-19 crisis in Eastern European countries was found to be faster compared to the rest of the EU member states, which is primarily due to the peculiarities of the labour market in these countries. Thus, studying their labour market in terms of flexibility and other parameters may be useful for generalising their experience and spreading it to other states. This will enhance adaptability to crisis events.
The results obtained can be utilised as a methodological framework for the implementation of public policy aimed at shaping a more flexible labour market that provide opportunities for rapid economic recovery after pandemics and other crisis phenomena.
It is worth noting that the findings obtained are relevant to pandemic-caused crises in the first place, rather than to traditional downturns driven by cyclical economic development. The reason is that amid pandemic many restrictive measures on free movement and public assembly are introduced. This can severely damage the labour market, whereas in other crises, a decline in the labour market is more a consequence of ongoing events in the economy rather than their cause. However, due to the increasing threat of the emergence and spread of new deadly viruses throughout the world, the study is of high relevance and significance.
References
Arutyunova A. E., Khanseviarov R. I., Okunkova E. A., Alieva P. R., Makareva E. A. (2022). Youth, work and skills: A map of the transformation of the workforce and employment. Studies in Big Data, vol. 110, pp. 149-160.
Attieh H. (2022). The effect of new normal leadership and workplace flexibility on employee burnout and work-life balance during COVID-19 era. Thesis of the dissertation. Lebanese American University. https://doi.org/10.26756/th.2022.397.
Baitenizov D. T., Dubina I. N., Campbell D. F., Carayannis E. G., Azatbek T. A. (2019). Freelance as a creative mode of self-employment in a new economy (a literature review). Journal of the Knowledge Economy, vol. 10, no. 1, pp. 1-17. https://doi.org/10.1007/s13132-018-0574-5.
Blustein D. L., Duffy R., Ferreira J. A., Cohen-Scali V., Cinamon R. G., Allan B. A. (2020). Unemployment in the time of COVID-19: A research agenda. Journal of Vocational Behavior, vol. 119, 103436. https://doi.org/10.1016/j.jvb.2020.103436.
Braesemann F., Lehdonvirta V., Kassi O. (2022). ICTs and the urban-rural divide: Can online labour platforms bridge the gap? Information, Communication & Society, vol. 25, no. 1, pp. 34-54. https://doi.org/10.1080/1369118X.2020.1761857.
Cherry M. A. (2020). Back to the future: A continuity of dialogue on work and technology at the ILO. International Labour Review, vol. 159, no. 1, pp. 1-23. https://doi.org/10.1111/ ilr.12156.
Codagnone C., Karatzogianni A., Matthews J. (2018). Platform economics: Rhetoric and reality in the "sharing economy". Emerald Group Publishing. 205 p. https://doi. org/10.1108/9781787438095.
Craigwell R. (2006). Foreign direct investment and employment in the English and Dutch-speaking Caribbean. ILO Subregional Office for the Caribbean. 57 p.
Deakin S., Malmberg J., Sarkar P. (2014). How do labour laws affect unemployment and the labour share of national income? The experience of six OECD countries, 1970-2010. International Labour Review, vol. 153, no. 1, pp. 1-27. https://doi.org/10.1111/j.1564-913X.2014.00195.x.
Eichhorst W., Marx P., Rinne U. (2020). Manoeuvring through the crisis: Labour market and social policies during the COVID-19 pandemic. Intereconomics, vol. 55, no. 6, pp. 375-380.
Emmett J., Schrah G., Schrimper M., Wood A. (2020). COVID-19 and the employee experience: How leaders can seize the moment. McKinsey&Company. https://www.mckinsey. com/capabilities/people-and-organizational-performance/our-insights/covid-19-and-the-employee-experience-how-leaders-can-seize-the-moment#/.
Fraymovich D. Y., Konovalova M. E., Roshchektaeva U. Y., Karpunina E. K., Avagyan G. L. (2022). Designing mechanisms for ensuring the economic security of regions: Countering the challenges of instability. Popkova E. G., Polukhin A. A., Ragulina J. V. (eds.) Towards an increased security: Green innovations, intellectual property protection and information security. Lecture Notes in Networks and Systems, vol. 372. Springer, Cham. https://doi. org/10.1007/978-3-030-93155-1_63.
Galgoczi B. (2017). Why Central and Eastern Europe needs a pay rise (ETUI Research Paper - Working paper 2017.01). ETUI. 30 p. http://dx.doi.org/10.2139/ssrn.2965393.
Gavin M., Poorhosseinzadeh M., Arrowsmith J. (2022). The transformation of work and employment relations: COVID-19 and beyond. Labour and Industry, vol. 32, no. 1, pp. 1-9. https://doi.org/10.1080/10301763.2022.2038353.
Giri A. K., Rana D. R. (2020). Charting the challenges behind the testing of COVID-19 in developing countries: Nepal as a case study. Biosafety and Health, vol. 2, no. 2, pp. 53-56. https://doi.org/10.1016/j~.bsheal.2020.05.002.
Gladkov N. I. (2018). Strategic management in conditions of uncertainty and high turbulence. Ekonomika i biznes: teoriya i praktika = Economics and Business: Theory and Practice, no. 6, pp. 47-49. (In Russ.)
Gukasyan Z. O., Tavbulatova Z. K., Aksenova Z. A., Gasanova N. M., Karpunina E. K. (2022). Strategies for adapting companies to the turbulence caused by the COVID-19 pandemic. Advances in Science, Technology & Innovation (pp. 639-645). Springer, Cham.
Hausermann S., Kurer T., Schwander H. (2016). Sharing the risk? Households, labor market vulnerability, and social policy preferences in Western Europe. The Journal of Politics, vol. 78, no. 4, pp. 1045-1060. https://doi.org/10.1086/686972.
Hay C. (2004). Common trajectories, variable paces, divergent outcomes? Models of European capitalism under conditions of complex economic interdependence. Review of International Political Economy, vol. 11, no. 2, pp. 231-262.
Howcroft D., Bergvall-Káreborn B. (2019). A typology of crowdwork platforms. Work, Employment and Society, vol. 33, no. 1, pp. 21-38. https://doi.org/10.1177/0950017018760136.
Karpunina E. K., Moiseev S. S., Karpunin K. D. (2022). Transformation of the labor market during the pandemic: New economic security risks. Drukerovskiy vestnik = Drucker's Bulletin, no. 1 (45), pp. 156-165. (In Russ.)
Karpunina E. K., Moskovtceva L. V., Zabelina O. V., Zubareva N. N., Tsykora A. V. (2022). Socio-economic impact of the COVID-19 pandemic on OECD countries. In: Popkova E. G., Andronova I. V. (eds.) Current problems of the world economy and international trade (Research in economic anthropology (vol. 42, pp. 103-114.). Emerald Publishing Limited, Bingley. https://doi.org/10.1108/S0190-128120220000042011.
Kassi O., Lehdonvirta V. (2018). Online labour index: Measuring the online gig economy for policy and research. Technological Forecasting and Social Change, vol. 137, pp. 241-248. https://doi.org/10.1016Aj.techfore.2018.07.056.
Kossek E. E., Thompson R. J., Lautsch B. A. (2015). Balanced workplace flexibility: Avoiding the traps. California Management Review, vol. 57, no. 4, pp. 5-25. https://doi.org/10.1525/ cmr.2015.57.4.5.
Kukina E. E., Fomenko N. M., Alekhina O. F., Smirnova E. V., Pecherskaya O. A. (2022). Long-term effects of COVID-19: How the pandemic highlighted the global digital divide. Ostrovskaya V. N., Bogoviz A. V. (eds.) Big data in the GovTech system. Studies in big data (vol. 110, pp. 137-148). Springer, Cham. https://doi.org/10.1007/978-3-031-04903-3_17.
Levin A. T., Owusu-Boaitey N., Pugh S., Fosdick B. K., Zwi A. B., Malani A., ... Meye-rowitz-Katz G. (2022). Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications. BMJ Global Health, vol. 7, no. 5, e008477. https://doi.org/10.1136/bmjgh-2022-008477.
Lobel O. (2020). We are all gig workers now: Online platforms, freelancers & the battles over employment status & rights during the Covid-19 pandemic. San Diego Law Review, vol. 57, no. 4. https://dx.doi.org/10.2139/ssrn.3725090.
Lu L., Peng J., Wu J., Lu Y. (2021). Perceived impact of the Covid-19 crisis on SMEs in different industry sectors: Evidence from Sichuan, China. International Journal of Disaster Risk Reduction, vol. 55, 102085. https://doi.org/10.1016/jj.ijdrr.2021.102085.
Marbun I. A. N. A. (2023). The embracing workplace flexibility: Will virtual meeting technology remain successful during post Covid-19 pandemic? Jurnal Studi Komunikasi, vol. 7, no. 1, pp. 249-268. https://doi.org/10.25139/jsk.v7i1.5033.
Martínez-Sánchez Á., Vela-Jimenez M. J., Abella-Garces S., Gorgemans S. (2019). Flexibility and innovation: Moderator effects of cooperation and dynamism. Personnel Review, vol. 48, no. 6, pp. 1548-1564. https://doi.org/10.1108/PR-12-2017-0397.
Mentuh N., Shevchuk O. (2020). Tax benefits as an element of supporting economic entities in the COVID-19 fighting period: Comparative and legal aspects. Analizy i Studia CASP, vol. 9, no. 1, pp. 21-31.
Nazarova I. G., Galieva G. F., Sazanova E. V., Chernenko E. M., Karpunina E. K. (2022). Labor market and employment problems: analysis of long-term dynamics and prospects of development in Russian regions. In: Popkova E. G. Imitation market modeling in digital economy: Game theoretic approaches. Lecture Notes in Networks and Systems (vol. 368, pp. 711-722). Springer. https://doi.org/10.1007/978-3-030-93244-2_77.
Ogaboh Agba A. M., Ocheni S. I., Agba M. S. (2020). COVID-19 and the world of work dynamics: A critical review. Journal of Educational and Social Research, vol. 10, no. 5, 119. https://doi.org/10.36941/jesr-2020-0093.
Okunkova E. A., Kosorukova I. V., Lazareva T. G, Korolyuk E. V., Bogomolova A. V. (2023). Global gig economy: Prospects and key growth threats for developing countries. International Journal of Work Innovation, vol. 3, no. 4, pp. 403-417. https://doi.org/10. 1504/IJWI.2023.128862.
Polujanova N. V., Yashin S. N., Sazanova E. V., Ponomarev S. V., Konovalova M. E., Galieva G. F. (2023). Assessment of changes in the online labour market in the context of the COVID-19 pandemic: Comparison of the dynamics of rural and urban participation. International Journal of Work Innovation, vol. 3, no. 4, pp. 367-381. https://doi.org/10.1504/ IJWI.2023.128859.
Rani U., Furrer M. (2021). Digital labour platforms and new forms of flexible work in developing countries: Algorithmic management of work and workers. Competition & Change, vol. 25, no. 2, pp. 212-236. https://doi.org/10.1177/1024529420905187.
Sheremet A. N. (2020). Pandemic of inequality. Socio-economic aspects and consequences of COVID-19. Meditsina. Sotsiologiya. Filosofiya. Prikladnye issledovaniya = Medicine. Sociology. Philosophy. Applied research, no. 4, pp. 136-141. https://doi.org/10.24411/2686-9365-2020-00004. (In Russ.)
Shevchuk A., Strebkov D., Tyulyupo A. (2021a). Always on across time zones: Invisible schedules in the online gig economy. New Technology, Work and Employment, vol. 36, no. 1, pp. 94-113. https://doi.org/10.1111/ntwe.12191.
Shevchuk A., Strebkov D., Tyulyupo A. (2021b). The geography of the digital freelance economy in Russia and beyond. In: Will-Zocholl M., Roth-Ebner C. (eds.) Topologies of digital work. Dynamics of virtual work (pp. 19-50). Palgrave Macmillan, Cham. https://doi. org/10.1007/978-3-030-80327-8_2.
Sienkiewicz L. (2016). Employment flexibility in Poland: Manifestation of flexicurity policies or labour market imbalances? Romanian Journal of Labour and Social Studies (RJLSS), vol. 1, no.1, pp. 98-118.
Tadesse S., Muluye W. (2020). The impact of COVID-19 pandemic on education system in developing countries: A review. Open Journal of Social Sciences, vol. 8, no. 10, pp. 159-170. https://doi.org/10.4236/jss.2020.810011.
Vallas S., Schor J. B. (2020). What do platforms do? Understanding the gig economy. Annual Review of Sociology, vol. 46, pp. 273-294. https://doi.org/10.1146/annurev-soc-121919-054857.
Yang E., Kim Y., Hong S. (2023). Does working from home work? Experience of working from home and the value of hybrid workplace post-COVID-19. Journal of Corporate Real Estate, vol. 25, no. 1, pp. 50-76. https://doi.org/10.1108/JCRE-04-2021-0015.
Information about the authors Maria Ye. Konovalova, Dr. Sc. (Econ.), Prof. of Economic Theory Dept. Samara State University of Economics, Samara, Russia. E-mail: mkonova.l@mail.ru Oksana V. Plyusnina, Cand. Sc. (Econ.), Associate Prof., Associate Prof. of Economics and Management Dept. Ukhta State Technical University, Ukhta, the Komi Republic, Russia. E-mail: oxana.p07@mail.ru
Elena V. Fedotova, Cand. Sc. (Econ.), Associate Prof., Associate Prof. of Information Technologies, Accounting and Economic Security Dept. Kaluga branch of the Russian State Agrarian University - Moscow Timiryazev Agricultural Academy, Kaluga, Russia. E-mail: elenaf1972@yandex.ru
© Konovalova M. Ye., Plyusnina O. V., Fedotova E. V., 2023
