# Impact of COVID-19 on the Russian Labor Market: Comparative Analysis of the Physical and Informational Spread of the Coronavirus

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

- Yandex DataLens—a service for data aggregation and analytics for identifying a number of COVID-19 cases per month in Russia;
- The official newsgroup of RBC (RosBusinessConsulting)—the largest non-state media holding and a leading company working in the field of mass media and information technologies providing the latest news, the main topics of the day in politics, economics, business, and life—in Vkontakte, one of the most popular Russian social media, for identifying a frequency of mentioning the topic “coronavirus”;
- Russian Federal State Statistics Service—the governmental statistics service that collects official statistical information on social, economic, demographic, environmental and other social processes taking place in the Russian Federation for measuring the dynamics of macroeconomic indicators;
- Yandex Wordstat—a service that helps to aggregate information on Yandex users’ requests daily, weekly, monthly, etc., for measuring the dynamics of the search queries on the Yandex search engine.

_{1}—the physical spread of coronavirus and X

_{2}—the informational spread of coronavirus in Russia from January to August 2020. The initial conceptual model hypothesizes an increase in the number of unemployed under the influence of the physical and informational forms of coronavirus spread, a decrease in average wages in the studied range of economic activities, an increase in supply in the labor market, and a decrease in employers’ demand for employees. It is also hypothesized that the physical and informational forms of coronavirus spread have a positive effect on the number of “remote work” searches on the Yandex search engine. Summarizing, using regression modeling, the logic of the behavior of the Russian society during the pandemic is indirectly investigated. The data obtained is mainly descriptive.

_{1}and X

_{2}, as well as Y

_{1}, Y

_{2}, ..., Y

_{6}is given by month from January to August 2020. The study allows us to draw conclusions about the extent to which the Russian economy, the labor market, and society were prepared for the changes caused by the coronavirus pandemic.

_{1}, Y

_{2}, ..., Y

_{6}, which characterize the Russian labor market from January to August 2020, the frequency of the appearance of news messages related to the topic of “coronavirus” in the RBC Vkontakte newsgroup was used as an indicator of the informational spread of coronavirus from January to August 2020. To conduct the study, an automated algorithm for collecting posts from the RBC Vkontakte group was built. Let us consider the stages of this algorithm (Rodionov et al. 2020):

- Formation of the primary data set. Within the framework of this stage, the news array is collected in accordance with the analyzed period. The source of primary information is the largest Russian social media Vkontakte, namely the newsgroup of RBC (RosBusinessConsulting), the largest non-state media holding and a leading company working in the field of mass media and information technologies providing the latest news, the main topics of the day in politics, economics, business, and life. Analysis of the news in this group makes it possible to understand the most relevant information messages during the spread of coronavirus in Russia. For automated parsing of information, Python 3 programming language was used. The result of this stage was eight tables containing the main news messages within the given months. To facilitate further analysis, tokens (meaningful units of text) were extracted from the news background by month. As part of the study, tokens that can give an idea of the information distribution of coronavirus in Russia were allocated.
- Tokenization of the primary data set. To determine the dynamics of the information distribution of coronavirus in Russia, the received array of primary data was tokenized. The result of this stage is a table containing the number of tokens related to the topic “coronavirus” by month. The presence of the following tokens in the primary data set was investigated: “кoрoнавирус” (coronavirus), “пандем” (pandem), “COVID”.

- The presence of structural breaks/structural outliers;
- Significance of Fisher’s F-test;
- P-level;
- Approximation error of the model;
- Multiple coefficient of determination R
^{2}; - Heteroscedasticity of residues;
- The presence of multicollinearity;
- Analysis of the relative coefficients of elasticity.

Potential multiple regression equations: | Potential paired regression equations: |

$\{\begin{array}{l}{\mathrm{Y}}_{1}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{2}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{3\text{-}1}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{3\text{-}2}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{3\text{-}2}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{3\text{-}3}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{3\text{-}4}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{3\text{-}5}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{3\text{-}6}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{4}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{5}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\\ {\mathrm{Y}}_{6\text{-}1}=\mathrm{a}+{\mathrm{b}}_{1}{\mathrm{X}}_{1}+{\mathrm{b}}_{2}{\mathrm{X}}_{2}\end{array}$ | $\{\begin{array}{l}{\mathrm{Y}}_{6\text{-}2}={{\mathrm{aY}}_{6\text{-}1}}^{\mathrm{b}}\\ {\mathrm{Y}}_{6\text{-}3}={{\mathrm{aY}}_{6\text{-}1}}^{\mathrm{b}}\\ {\mathrm{Y}}_{6\text{-}5}={{\mathrm{aY}}_{6\text{-}1}}^{\mathrm{b}}\\ {\mathrm{Y}}_{6\text{-}6}={{\mathrm{aY}}_{6\text{-}1}}^{\mathrm{b}}\end{array}$ |

_{1}, Y

_{2}… Y

_{6}are determined not only by X

_{1}and X

_{2}, but also by many other factors that are not included in the described model. Thus, every time the P-level of any Y exceeds the acceptable level of 10%, the multiple regression model is being optimized.

## 3. Results

_{1}and X

_{2}increased the values of Y

_{1}, i.e., the physical and informational spread of the coronavirus increased the number of officially registered unemployed aged 15 and over. When the physical spread of the coronavirus X

_{1}changed by 1%, the number of officially registered unemployed age 15 and over increased from 0.56 to 0.82%; when the informational spread changed by 1%, the indicator increased from 0.51 to 1.77%.

_{1}and X

_{2}increased the values of Y

_{2}, i.e., the physical and informational spread of the coronavirus increased the total number of unemployed aged 15 and over. When the physical spread changed by 1%, the total number of unemployed aged 15 and over increased from 0.21 to 0.22%; when information spread of the coronavirus changed by 1%, the total number of unemployed aged 15 and over increased from 0.06 to 0.11%.

_{1}and X

_{2}had a multidirectional effect on Y

_{3-1}, i.e., the physical spread of the coronavirus decreased the average monthly nominal gross wages of employees in wholesale and retail trade, repair of motor vehicles, and motorcycles; while the informational spread of the coronavirus increased the indicator. When the physical spread changed by 1%, the average monthly nominal gross wages decreased from −0.9 to −0.15%; when the information spread of the coronavirus changed by 1%, the indicator increased from 0.05% to 0.07%.

_{1}and X

_{2}decreased the values of Y

_{3-2}, i.e., the physical and informational spread of the coronavirus decreased the average monthly nominal gross wages of employees in hotels and public catering businesses. When the physical spread changed by 1%, the average monthly nominal gross wages of employees in hotels and public catering businesses decreased from −0.09 to −0.22%; when the information spread of the coronavirus changed by 1%, the indicator decreased from −0.01 to −0.08%.

_{1}and X

_{2}increased the values of Y

_{3-3}, i.e., the physical and informational spread of the coronavirus increased the average monthly nominal gross wages of employees in information and communication activities. When the physical spread of coronavirus X

_{1}changed by 1%, the average monthly nominal gross wages of employees in information and communication activities increased from 0.02 to 0.07%; when the information spread of the coronavirus changed by 1%, the indicator increased from 0.08 to 0.12%.

_{2}increased the values of Y

_{3-4}, i.e., when the information spread of the coronavirus changed by 1%, the average monthly nominal gross wages of employees in financial and insurance activities increased from −0.003 to 0.089%.

_{1}and X

_{2}decreased the values of Y

_{3-5}, i.e., the physical and informational spread of the coronavirus decreased the average monthly nominal gross wages of employees in health and social service activities. When the physical spread of coronavirus X

_{1}changed by 1%, the average monthly nominal gross wages of employees in health and social service activities decreased from −0.06 to −0.19%; when the information spread, X

_{2}changed by 1%, the indicator decreased from −0.03 to −0.38%.

_{1}decreased the values of Y

_{3-6}, i.e., when the physical spread of the coronavirus changed by 1%, the average monthly nominal gross wages of employees in culture, sports, leisure, and entertainment activities decreased from −0.02 to −0.13%.

_{1}and X

_{2}increased the values of Y

_{4}, i.e., the physical and informational spread of the coronavirus increased the employers’ demand for employees, declared to the bodies of the employment service. When the physical spread of coronavirus X

_{1}changed by 1%, the employers’ need for employees, declared to the employment service bodies, increased from 0.04 to 0.3%; when the informational spread changed by 1%, the indicator increased from 0.1 to 0.65%.

_{1}increased the values of Y

_{5}, i.e., the physical spread of the coronavirus increased the workload of the unemployed population per 100 announced vacancies. When the physical spread of the coronavirus changed by 1%, the indicator increased from 0.38 to 0.62%.

_{1}and X

_{2}had a multidirectional effect on Y

_{6-1}, i.e., the physical spread of the coronavirus increased the number of search queries on the Yandex search engine “удаленная рабoта” (remote work), while the informational spread of coronavirus decreased the indicator. When the physical spread of the coronavirus changed by 1%, the number of search queries increased from 0.23 to 0.25%; when the information spread of the coronavirus changed by 1%, the indicator decreased from −3.21 to −3.39%.

_{6-1}increased the values of Y

_{6-2}, i.e., search queries on the Yandex search engine “удаленная рабoта” (remote work) increased the number of search queries “пoсoбие пo безрабoтице” (unemployment benefit). When search queries on the Yandex search engine “удаленная рабoта” (remote work) changed by 1%, search queries “пoсoбие пo безрабoтице” (unemployment benefit) increased from 0.35 to 0.47%.

_{6-1}increased the values of Y

_{6-3}, i.e., search queries on the Yandex search engine “удаленная рабoта” (remote work) increased the number of search queries “закрыть бизнес” (close a business). When search queries on the Yandex search engine “удаленная рабoта” (remote work) changed by 1%, the search queries “закрыть бизнес” (close a business) increased from 61 to 79%.

_{6-1}decreased the values of Y

_{6-4}, i.e., search queries on the Yandex search engine “удаленная рабoта” (remote work) decreased the number of search queries “oткрыть бизнес” (open a business). When search queries on the Yandex search engine “удаленная рабoта” (remote work) changed by 1%, the search queries “oткрыть бизнес” (open a business) decreased from −0.68 to −1.9%.

_{6-1}increased the values of Y

_{6-6}, i.e., search queries on the Yandex search engine “удаленная рабoта” (remote work) increased the number of search queries “oнлайн-курсы” (online courses). When search queries on the Yandex search engine “удаленная рабoта” (remote work) changed by 1%, the search queries “oнлайн-курсы” (online courses) increased from 0.7 to 1.2%.

- The spread of the new coronavirus infection has indeed contributed to an increase in the unemployment rate in the Russian labor market (see equations Y
_{1}and Y_{2}). - The impact of the physical and informational spread of coronavirus on the dynamics of the average monthly nominal gross wages of employees by type of economic activity can be described as follows:
- -
- for the section “Wholesale and retail trade; repair of motor vehicles and motorcycles” wages increased in the period January to February 2020, which may be primarily due to the fear that arose in the Internet environment in anticipation of the spread of a new infection, then fell from March to May 2020, after which it stabilized. Interestingly, the physical spread of coronavirus negatively affected the average wage level, while the informational spread did it in a positive way (see equation Y
_{3-1}). - -
- for the section “Hotels and public catering business”, the average level of wages demonstrated a decrease from January to April 2020. Starting from May, the analyzed values gradually levelled out (see equation Y
_{3-2}). - -
- for the section “Information and communication activities”, an increase in the average level of wages happened in the period from January to April 2020, since the physical and informational spread of coronavirus contributed to the intensification of activities related to the creation of content of various forms; in April to May the impact of coronavirus on the analyzer started to decline (see equation Y
_{3-3}). - -
- for the section “Financial and insurance activities”, the model demonstrated an increase in average wages from January to March 2020. Further, from March to August, there was a negative dynamic in average wages. It is worth mentioning that in the process of optimization by the P-level criterion, the X
_{1}indicator (physical spread of coronavirus) was completely excluded from this model, see equation Y_{3-4}. - -
- the section “Health and social service activities” was characterized by an unstable change in the average level of wages from January to April 2020, however, since April, the average level of wages increased rapidly and reached its peak values in June 2020 (see equation Y
_{3-5}). - -
- the section “Culture, sports, leisure, and entertainment activities” was characterized by negative impact of coronavirus spread on average wages, and in this case, we are talking only about the physical spread of coronavirus, since according to the P-level criterion, indicator X
_{2}(informational spread of coronavirus) was completely excluded from the model, see equation Y_{3-6}.

- There was a gradual decrease in the demand of employers for employees from January to April 2020. Further, the physical and informational spread of coronavirus, after reaching its peak values in April–May, begins to invariably increase the demand of employers for employees; and it is noteworthy that the informational spread of coronavirus had the greater impact on Y
_{4}(see equation Y_{4}). - With regard to the impact of coronavirus spread on the workload of unemployed population per 100 announced vacancies from January to August 2020, the analyzed indicator increased sharply since March 2020. Such dynamics speak about the increase in the number of citizens wishing to start working, and an increase in competition for every vacancy. In this model, X
_{2}(informational spread of coronavirus) was completely excluded according to the P-level criterion, that is, the described dependence was a consequence of the physical spread of coronavirus only (see equation Y_{5}). - The influence of the spread of coronavirus on the dynamics of search query “remote work” on the Yandex search engine from January to August 2020 was also proved. The increase in the number of search queries from February to April may be due to the physical spread of coronavirus in Russia and the lack of sufficient information about the new disease—people were afraid of its potential impact on the labor market, therefore, they were looking for ways to make money in the new reality. In general, it was found that the physical spread of coronavirus increased the number of related search queries on the Yandex search engine from January to August 2020, while informational spread of coronavirus, on the contrary, reduced the number of these search queries, and it was the latter that had the greatest impact on Y
_{6-1}, see equation Y_{6-1}. - As for the dynamics of related search queries on the Yandex search engine, the following dependencies were identified:
- -
- analysis of the dynamics of the search query “unemployment benefit” demonstrated the presence of a structural outlier in March 2020, which is quite natural: it was in March that the first cases of COVID-19 infection were registered in Russia, and there was a massive closure of enterprises due to the introduction of severe restrictive measures, people literally remained without means of livelihood, therefore, they showed a particular interest in possible support from the state in the form of unemployment benefits. Overall, the change in the number of searches for “remote work” contributed to an increase in the number of searches for “unemployment benefit” in March 2020; starting from April, the number of searches for “unemployment benefit” gradually began to decline (see equation Y
_{6-2}). - -
- analysis of the dynamics of the search query “close a business” on the Yandex search engine also demonstrated the presence of a structural outlier in March 2020: as mentioned earlier, in early March, the first cases of COVID-19 infection were registered in Russia, there was a consistent introduction of severe restrictive measures, and already at the end of the month, many enterprises were closed due to unprofitability and the impossibility of further functioning, thus, people began to especially actively search for relevant information on the Internet. The structural outlier was also observed in August 2020; this point can primarily be explained by the seasonal nature of some types of business (see equation Y
_{6-3}). - -
- analysis of the dynamics of the search query “open a business” on the Yandex search engine shows the presence of a structural outlier in June 2020, which can also be explained by seasonal specifics, the beginning of the tourist season, as well as the gradual removal of some restrictions in Russia. However, the model shows a negative trend in the “open a business” search query over the analyzed period. Y
_{6-1}has a negative impact on the dynamics of Y_{6-4}, meaning that the dynamics of the search query “remote work” invariably reduce the number of search queries “open a business” (see equation Y_{6-4}). - -
- analysis of the dynamics of the search query “self-employment” showed no connection with the dynamics of the search query “remote work”; this is the only model that has not been confirmed.
- -
- analysis of the dynamics of the search query “online courses” shows the presence of structural outliers in March and June 2020. The structural outlier in March may be associated with the tense situation in connection with the spread of COVID-19, the anxiety of citizens was accompanied by a decrease in interest in online courses and an increase in interest in other types of information (in particular, the search for information on measures of support from the state during the pandemic). Structural outliers in June may be related to the onset of the holiday season (see equation Y
_{6-6}).

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Share of the average annual numbers employed in the branches of the Russian economy (highlighting the role of the service sector).

Period | January | February | March | April | May | June | July | August | |
---|---|---|---|---|---|---|---|---|---|

Indicator | |||||||||

The number of COVID-19 cases per month in Russia, pax. | 0 | 0 | 2337 | 104,161 | 299,345 | 242,006 | 192,132 | 155,338 | |

Frequency of mentioning the topic “coronavirus” in the RBC (RosBusinessConsulting) news group in Vkontakte social network, pcs | 81 | 189 | 702 | 639 | 629 | 402 | 332 | 226 | |

The number of officially registered unemployed age 15 and over, ths. pax. | 839 | 869 | 855 | 1834 | 2543 | 3152 | 3637 | 3953 | |

Total number of unemployed age 15 and over, ths. pax. | 3482 | 3425 | 3485 | 4286 | 4513 | 4606 | 4731 | 4808 | |

Average monthly nominal gross wages of employees by type of economic activity: | |||||||||

- -
- wholesale and retail trade; repair of motor vehicles and motorcycles, rub.;
| 40,685 | 40,940 | 46,359 | 42,335 | 39,136 | 42,302 | 41,258 | 39,928 | |

- -
- hotels and public catering business, rub.;
| 30,047 | 28,986 | 27,964 | 23,243 | 23,409 | 24,897 | 25,483 | 25,252 | |

- -
- information and communication activities, rub.;
| 76,215 | 78,949 | 87,942 | 92,422 | 81,754 | 82,354 | 83,716 | 80,209 | |

- -
- financial and insurance activities, rub.;
| 87,471 | 110,167 | 122,066 | 117,345 | 105,078 | 98,060 | 107,286 | 97,207 | |

- -
- health and social service activities, rub.;
| 44,565 | 43,246 | 44,957 | 42,355 | 53,147 | 53,740 | 52,501 | 49,567 | |

- -
- culture, sports, leisure, and entertainment activities, rub.
| 46,737 | 46,225 | 50,512 | 42,702 | 45,308 | 45,823 | 42,753 | 43,997 | |

Dynamics of the employers’ demand for employees, declared to the bodies of the employment service, pax. | 1,464,000 | 1,496,000 | 1,493,000 | 1,346,000 | 1,385,000 | 1,518,000 | 1,639,000 | 1,692,000 | |

Workload of unemployed population per 100 announced vacancies, pax *. | 57.3 | 58.1 | 57.3 | 136.2 | 183.7 | 207.6 | 221.9 | 233.6 | |

Dynamics of the search queries on the Yandex search engine “удаленная рабoта” (remote work). | 312,209 | 286,713 | 546,777 | 774,219 | 444,288 | 351,269 | 321,044 | 304,241 |

Search Query | “удаленная рабoта” (Remote Work) | “пoсoбие пo безрабoтице” (Unemployment Benefit) | “закрыть бизнес” (Close a Business) | “oткрыть бизнес” (Open a Business) | “самoзанятoсть” (Self-Employment) | “oнлайн-курсы” (Online Courses) | |
---|---|---|---|---|---|---|---|

Period | |||||||

30 December 2019–05 January 2020 | 38,867 | 73,440 | 468 | 19,622 | 14,482 | 9837 | |

6 January 2020–12 January 2020 | 66,895 | 117,298 | 656 | 30,606 | 22,516 | 19,873 | |

13 January 2020–19 January 2020 | 80,054 | 150,339 | 938 | 28,627 | 29,761 | 35,569 | |

20 January 2020–26 January 2020 | 75,069 | 125,755 | 968 | 28,278 | 33,872 | 33,377 | |

27 January 2020–02 February 2020 | 72,118 | 151,968 | 917 | 26,506 | 28,676 | 29,931 | |

3 February 2020–9 February 2020 | 72,560 | 160,086 | 937 | 27,339 | 26,413 | 27,288 | |

10 February 2020–16 February 2020 | 69,379 | 143,053 | 898 | 26,783 | 25,119 | 25,251 | |

17 February 2020–23 February 2020 | 64,352 | 122,176 | 872 | 25,452 | 23,030 | 25,038 | |

24 February 2020–01 March 2020 | 73,513 | 210,073 | 910 | 24,484 | 23,166 | 22,322 | |

2 March 2020–8 March 2020 | 58,522 | 231,315 | 832 | 21,760 | 22,086 | 21,553 | |

9 March 2020–15 March 2020 | 71,987 | 735,978 | 991 | 24,092 | 22,568 | 22,134 | |

16 March 2020–22 March 2020 | 164,389 | 736,750 | 2088 | 20,977 | 21,630 | 28,199 | |

23 March 2020–29 March 2020 | 178,123 | 513,297 | 4695 | 18,288 | 19,110 | 164,692 | |

30 March 2020–05 April 2020 | 194,204 | 470,800 | 2597 | 15,739 | 21,262 | 404,015 | |

6 April 2020–12 April 2020 | 160,449 | 369,894 | 1707 | 18,987 | 23,496 | 475,446 | |

13 April 2020–19 April 2020 | 144,252 | 323,867 | 1433 | 18,855 | 26,173 | 431,281 | |

20 April 2020–26 April 2020 | 134,205 | 375,101 | 1411 | 19,569 | 26,444 | 408,051 | |

27 April 2020–03 May 2020 | 106,832 | 274,514 | 1177 | 18,599 | 23,225 | 289,428 | |

4 May 2020–10 May 2020 | 93,454 | 224,172 | 1025 | 18,140 | 18,653 | 210,853 | |

11 May 2020–17 May 2020 | 107,140 | 211,569 | 1235 | 21,283 | 33,575 | 263,145 | |

18 May 2020–24 May 2020 | 106,719 | 226,780 | 1191 | 23,786 | 27,829 | 223,895 | |

25 May 2020–31 May 2020 | 104,191 | 195,686 | 1276 | 55,583 | 28,257 | 306,554 | |

1 June 2020–7 June 2020 | 93,973 | 217,116 | 1366 | 121,517 | 28,856 | 307,206 | |

8 June 2020–14 June 2020 | 72,591 | 179,959 | 1054 | 158,718 | 21,976 | 283,556 | |

15 June 2020–21 June 2020 | 86,380 | 162,998 | 1088 | 140,123 | 24,046 | 257,111 | |

22 June 2020–28 June 2020 | 72,365 | 147,916 | 996 | 110,502 | 42,783 | 235,343 | |

29 June 2020–05 July 2020 | 71,299 | 198,422 | 847 | 88,384 | 43,036 | 203,203 | |

6 July 2020–12 July 2020 | 74,055 | 178,117 | 751 | 72,759 | 31,454 | 186,269 | |

13 July 2020–19 July 2020 | 73,250 | 148,497 | 838 | 64,612 | 28,420 | 155,406 | |

20 July 2020–26 July 2020 | 74,063 | 155,396 | 786 | 56,712 | 27,910 | 143,893 | |

27 July 2020–02 August 2020 | 68,803 | 208,083 | 848 | 49,895 | 27,751 | 154,048 | |

3 August 2020–9 August 2020 | 67,822 | 207,415 | 954 | 47,452 | 27,125 | 150,110 | |

10 August 2020–16 August 2020 | 70,007 | 166,346 | 2167 | 42,665 | 26,362 | 143,222 | |

17 August 2020–23 August 2020 | 70,939 | 169,481 | 1520 | 40,188 | 26,149 | 134,322 | |

24 August 2020–30 August 2020 | 70,015 | 199,135 | 1263 | 39,370 | 28,111 | 134,357 |

No. | Indicator | Designation | Measure | Тype of Indicator | Source |
---|---|---|---|---|---|

1. | The number of COVID-19 cases per month in Russia. | X_{1} | pax | exogenous | Yandex DataLens Public (n.d.) |

2. | The frequency of mentioning the topic “coronavirus” in the RBC (RosBusinessConsulting) news group in Vkontakte social network. | X_{2} | pcs | exogenous | Official News Group (n.d.) |

3. | The number of officially registered unemployed age 15 and over. | Y_{1} | ths. pax | endogenous | Russian Federal State Statistics Service (n.d.) |

4. | Total number of unemployed age 15 and over. | Y_{2} | ths. pax | endogenous | Russian Federal State Statistics Service (n.d.) |

5. | Average monthly nominal gross wages of employees by type of economic activity: | Y_{3} | rub. | endogenous | Russian Federal State Statistics Service (n.d.) |

5.1 | wholesale and retail trade; repair of motor vehicles and motorcycles; | Y_{3-1} | |||

5.2 | hotels and public catering business; | Y_{3-2} | |||

5.3 | information and communication activities; | Y_{3-3} | |||

5.4 | financial and insurance activities; | Y_{3-4} | |||

5.5 | health and social service activities; | Y_{3-5} | |||

5.6 | culture, sports, leisure, and entertainment activities. | Y_{3-6} | |||

6. | Dynamics of the employers’ demand for employees, declared to the bodies of the employment service. | Y_{4} | pax | endogenous | Russian Federal State Statistics Service (n.d.) |

7. | The workload of the unemployed population per 100 announced vacancies. | Y_{5} | pax | endogenous | Russian Federal State Statistics Service (n.d.) |

8. | Dynamics of the search queries on the Yandex search engine. | pcs | Yandex Wordstat (n.d.) | ||

8.1 | “удаленная рабoта” (remote work). | Y_{6-1} | endogenous and exogenous | ||

8.2 | “пoсoбие пo безрабoтице” (unemployment benefit). | Y_{6-2} | endogenous | ||

8.3 | “закрыть бизнес” (close a business). | Y_{6-3} | endogenous | ||

8.4 | “oткрыть бизнес” (open a business). | Y_{6-4} | endogenous | ||

8.5 | “самoзанятoсть” (self-employment). | Y_{6-5} | endogenous | ||

8.6 | “oнлайн-курсы” (online courses). | Y_{6-6} | endogenous |

**Table 4.**System of multiple and paired regression equations to describe the validated conceptual model.

Multiple Regression | ||||||||
---|---|---|---|---|---|---|---|---|

Equation | The Presence of Structural Breaks/Structural Outliers | Significance of Fisher’s F-Test | P-Level (for F-Test) | Approximation Error of the Model | Multiple Coefficient of Determination R^{2} | The Statistical Significance for Each Coefficient | The Presence of Multicollinearity | Analysis of the Relative Coefficients of Elasticity |

$\begin{array}{l}{\mathrm{Y}}_{1}\\ =-246.8+0.02\\ \ast {\mathrm{X}}_{1}+19.01\ast {\mathrm{X}}_{2}\\ -1.5\ast {10}^{-13}\ast {\mathrm{X}}_{1}^{3}\\ -0.08\ast {\mathrm{X}}_{2}^{2}\\ +0.00008\ast {\mathrm{X}}_{2}^{3}\end{array}$ | No structural breaks, structural outliers are extremely insignificant | 0.007 | <0.1 | 5.8% | 99.7% | ${\mathrm{X}}_{1}$ *** ${\mathrm{X}}_{2}$ ** ${\mathrm{X}}_{1}^{3}$ *** ${\mathrm{X}}_{2}^{2}$ ** ${\mathrm{X}}_{2}^{3}$ ** | No multicollinearity between X_{1} and X_{2} is observed | When X_{1} changes by 1%, Y_{1} increases from 0.56% to 0.82%.When X _{2} changes by 1%, Y_{1} increases from 0.51% to 1.77%. |

$\begin{array}{l}{\mathrm{Y}}_{2}\\ =3345.74+0.01\\ \ast {\mathrm{X}}_{1}+2.8\ast {\mathrm{X}}_{2}-3.2\\ \ast {10}^{-8}\ast {\mathrm{X}}_{1}^{2}\\ -0.016{\mathrm{X}}_{2}^{2}\\ +0.00002\ast {\mathrm{X}}_{2}^{3}\end{array}$ | Absence of structural breaks and structural outliers | 0.0002 | <0.1 | 0.2% | 99.9% | ${\mathrm{X}}_{1}$ *** ${\mathrm{X}}_{2}$ *** ${\mathrm{X}}_{1}^{2}$ *** ${\mathrm{X}}_{2}^{2}$ *** ${\mathrm{X}}_{2}^{3}$ *** | No multicollinearity between X_{1} and X_{2} is observed | When X_{1} changes by 1%, Y_{2} increases from 0.21% to 0.22%.When X _{2} changes by 1%, Y_{2} increases from 0.06% to 0.11%. |

$\begin{array}{l}{\mathrm{Y}}_{3\text{-}1}\\ =40551.36-0.1\\ \ast {\mathrm{X}}_{1}+0.000001\\ \ast {\mathrm{X}}_{1}^{2}-2.75\ast {10}^{-12}\\ \ast {\mathrm{X}}_{1}^{3}+0.01\ast {\mathrm{X}}_{2}^{2}\end{array}$ | No structural breaks, structural outliers are extremely insignificant | 0.003 | <0.1 | 1% | 98.9% | ${\mathrm{X}}_{1}$ *** ${\mathrm{X}}_{1}^{2}$ *** ${\mathrm{X}}_{1}^{3}$ *** ${\mathrm{X}}_{2}^{2}$ *** | No multicollinearity between X_{1} and X_{2} is observed | When X_{1} changes by 1%, Y_{3-1} decreases from −0.9% to −0.15%.When X _{2} changes by 1%, Y_{3-1} increases from 0.05% to 0.07%. |

$\begin{array}{l}{\mathrm{Y}}_{3\text{-}2}\\ =30026.7-0.09\\ \ast {\mathrm{X}}_{1}-2.9\ast {\mathrm{X}}_{2}\\ +6.43\ast {10}^{-7}\ast {\mathrm{X}}_{1}^{2}\\ -1.24\ast {10}^{-12}\ast {\mathrm{X}}_{1}^{3}\end{array}$ | Absence of structural breaks and structural outliers | 0.005 | <0.1 | 2% | 98.4% | ${\mathrm{X}}_{1}$ ** ${\mathrm{X}}_{2}$ * ${\mathrm{X}}_{1}^{2}$ ** ${\mathrm{X}}_{1}^{3}$ ** | No multicollinearity between X_{1} and X_{2} is observed | When X_{1} changes by 1%, Y_{3-2} decreases from −0.09% to −0.22%.When X _{2} changes by 1%, Y_{3-2} decreases from −0.01% to −0.08%. |

$\begin{array}{l}{\mathrm{Y}}_{3\text{-}3}\\ =74295.5+0.06\\ \ast {\mathrm{X}}_{1}+20.9\ast {\mathrm{X}}_{2}\\ -2.67\ast {10}^{-7}\ast {\mathrm{X}}_{1}^{2}\end{array}$ | Absence of structural breaks and structural outliers | 0.003 | <0.1 | 2% | 95.9% | ${\mathrm{X}}_{1}$ ** ${\mathrm{X}}_{2}$ *** ${\mathrm{X}}_{1}^{2}$ *** | No multicollinearity between X_{1} and X_{2} is observed | When X_{1} changes by 1%, Y_{3-3} increases from 0.02% to 0.07%.When X _{2} changes by 1%, Y_{3-3} increases from 0.08% to 0.12%. |

$\begin{array}{l}{\mathrm{Y}}_{3\text{-}4}\\ =96651.58+0.043\ast {\mathrm{X}}_{2}^{2}\end{array}$ | Insignificant structural breaks. Structural outlier in February. | 0.03 | <0.1 | 8% | 54.7% | ${\mathrm{X}}_{2}^{2}$ ** | - | When X_{2} changes by 1%, Y_{3-4} changes from −0.003% to −0.089% |

$\begin{array}{l}{\mathrm{Y}}_{3-5}\\ =45471.8-0.14\\ \ast {\mathrm{X}}_{1}+1.9\ast {10}^{-6}\ast {\mathrm{X}}_{1}^{2}\\ -4.33\ast {10}^{-12}\ast {\mathrm{X}}_{1}^{3}\\ -0.09\ast {\mathrm{X}}_{2}^{2}\\ +0.00013{\mathrm{X}}_{2}^{3}\end{array}$ | Absence of structural breaks and structural outliers | 0.01 | <0.1 | 1% | 99.4% | ${\mathrm{X}}_{1}$ ** ${\mathrm{X}}_{1}^{2}$ ** ${\mathrm{X}}_{1}^{3}$ ** ${\mathrm{X}}_{2}^{2}$ * ${\mathrm{X}}_{2}^{3}$ * | No multicollinearity between X_{1} and X_{2} is observed | When X_{1} changes by 1%, Y_{3-5} decreases from −0.06% to −0.19%. When X_{2} changes by 1%, Y_{3-5} decreases from −0.03% to −0.38%. |

$\begin{array}{l}{\mathrm{Y}}_{3\text{-}6}\\ =47716.8-0.05\\ \ast {\mathrm{X}}_{1}+1.58\\ \ast {10}^{-6}\ast {\mathrm{X}}_{1}^{2}\end{array}$ | Insignificant structural breaks. Structural outlier in March. | 0.09 | <0.1 | 4% | 61.5% | ${\mathrm{X}}_{1}$ ** ${\mathrm{X}}_{1}^{2}$ * | - | When X_{1} changes by 1%, Y_{3-6} decreases from −0.02% to −0.13% |

$\begin{array}{l}{\mathrm{Y}}_{4}\\ =1214415.4\\ +4263.5\ast {\mathrm{X}}_{2}+1.87\\ \ast {10}^{-5}\ast {\mathrm{X}}_{1}^{2}-5.45\\ \ast {10}^{-11}\ast {\mathrm{X}}_{1}^{3}-17.7\\ \ast {\mathrm{X}}_{2}^{2}+0.017\ast {\mathrm{X}}_{2}^{3}\end{array}$ | No structural breaks, structural outliers are extremely insignificant | 0.07 | <0.1 | 2% | 97% | ${\mathrm{X}}_{2}$ * ${\mathrm{X}}_{1}^{2}$ * ${\mathrm{X}}_{1}^{3}$ * ${\mathrm{X}}_{2}^{2}$ ** ${\mathrm{X}}_{2}^{3}$ ** | No multicollinearity between X_{1} and X_{2} is observed | When X_{1} changes by 1%, Y_{4} increases from 0.04% to 0.3%.When X _{2} changes by 1%, Y_{4} increases from 0.1% to 0.65%. |

$\begin{array}{l}{\mathrm{Y}}_{5}\\ =54.5+0.0012\ast {\mathrm{X}}_{1}\\ -8.22\ast {10}^{-15}\ast {\mathrm{X}}_{1}^{2}\end{array}$ | Absence of structural breaks and structural outliers | 0.0004 | <0.1 | 14% | 95.4% | ${\mathrm{X}}_{1}$ *** ${\mathrm{X}}_{1}^{2}$ *** | - | When X_{1} changes by 1%, Y_{5} increases from 0.38% to 0.62% |

$\begin{array}{l}{\mathrm{Y}}_{6\text{-}1}\\ =1347493.78+1.71\\ \ast {\mathrm{X}}_{1}-10367.4\ast {\mathrm{X}}_{2}\\ -8.57\ast {10}^{-6}\ast {\mathrm{X}}_{1}^{2}\\ +0.71\ast {\mathrm{X}}_{1}^{3}+29.6\\ \ast {\mathrm{X}}_{2}^{2}-0.7\ast {\mathrm{X}}_{2}^{3}\end{array}$ | Absence of structural breaks and structural outliers | 0.002 | <0.1 | 0.15% | 99.9% | ${\mathrm{X}}_{1}$ *** ${\mathrm{X}}_{2}$ *** ${\mathrm{X}}_{1}^{2}$ *** ${\mathrm{X}}_{1}^{3}$ *** ${\mathrm{X}}_{2}^{2}$ *** ${\mathrm{X}}_{2}^{3}$ *** | No multicollinearity between X_{1} and X_{2} is observed | When X_{1} changes by 1%, Y_{6-1} increases from 0.23% to 0.25%.When X _{2} changes by 1%, Y_{6-1} decreases from −3.21% to −3.39%. |

Paired regression | ||||||||

$\begin{array}{l}{\mathrm{Y}}_{6\text{-}2}\\ =1.045\ast {{\mathrm{Y}}_{6\text{-}1}}^{1.075}\end{array}$ | Insignificant structural breaks. Structural outlier in March. | <0.05 | <0.05 | 2.7% | 57% | ${\mathrm{Y}}_{6\text{-}1}$ *** | - | When Y_{6-1} changes by 1%, Y_{6-2} increases from 0.35% to 0.47% |

$\begin{array}{l}{\mathrm{Y}}_{6\text{-}3}\\ =0.0215\ast {\mathrm{Y}}_{6\text{-}1}{}^{0.9572}\end{array}$ | Insignificant structural breaks. Structural outlier in the end of March. | <0.05 | <0.05 | 3.6% | 63.6% | ${\mathrm{Y}}_{6\text{-}1}$ *** | - | When Y_{6-1} changes by 1%, Y_{6-3} increases from 61% to 79% |

$\begin{array}{l}{\mathrm{Y}}_{6\text{-}4}\\ =5\ast {10}^{7}\ast {\mathrm{Y}}_{6\text{-}1}{}^{-0.631}\end{array}$ | Insignificant structural breaks. Structural outlier in June. | <0.05 | <0.05 | 5.9% | 11.6% | ${\mathrm{Y}}_{6\text{-}1}$ ** | - | When Y_{6-1} changes by 1%, Y_{6-4} decreases from −0.68% to −1.9% |

No connection between Y_{6-5} и Y_{6-1} (P-level > 0.05) | ||||||||

$\begin{array}{l}{\mathrm{Y}}_{6\text{-}6}\\ =0.0001\ast {\mathrm{Y}}_{6\text{-}1}{}^{1.8131}\end{array}$ | Insignificant structural breaks. Structural outliers in March and June. | <0.05 | <0.05 | 8.24% | 30.9% | ${\mathrm{Y}}_{6\text{-}1}$ *** | - | When Y_{6-1} changes by 1%, Y_{6-6} increases from 0.7% to 1.2% |

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**MDPI and ACS Style**

Rodionov, D.; Ivanova, A.; Konnikova, O.; Konnikov, E.
Impact of COVID-19 on the Russian Labor Market: Comparative Analysis of the Physical and Informational Spread of the Coronavirus. *Economies* **2022**, *10*, 136.
https://doi.org/10.3390/economies10060136

**AMA Style**

Rodionov D, Ivanova A, Konnikova O, Konnikov E.
Impact of COVID-19 on the Russian Labor Market: Comparative Analysis of the Physical and Informational Spread of the Coronavirus. *Economies*. 2022; 10(6):136.
https://doi.org/10.3390/economies10060136

**Chicago/Turabian Style**

Rodionov, Dmitriy, Anastasia Ivanova, Olga Konnikova, and Evgenii Konnikov.
2022. "Impact of COVID-19 on the Russian Labor Market: Comparative Analysis of the Physical and Informational Spread of the Coronavirus" *Economies* 10, no. 6: 136.
https://doi.org/10.3390/economies10060136