Anti-Crisis Industrial Policy of the Constituent Entities of the Russian Federation during the COVID-19 Pandemic

Koshkin Andrey Vyacheslavovich ORCID: 0000-0001-9942-7722 Postgraduate student, Department of Economic Theory and Economic Policy, St. Petersburg State University 199034, Russia, Saint Petersburg, nab. University, 7-9 a.koshkin@spbu.ru Shcheglov Maksim Yur'evich ORCID: 0000-0003-1388-520X Master’s Student, Department of Economic Theory and Economic Policy, St. Petersburg State University 199034, Russia, Saint Petersburg, nab. University, 7-9 st069668@student.spbu.ru Other publications by this author     Bit-Zaya George Aleksandrovich ORCID: 0009-0007-9126-5846 Student, Department of Political Institutions and Applied Political Research, St. Petersburg State University 199034, Russia, Saint Petersburg, nab. University, 7-9 st094247@student.spbu.ru Kalinichenko Anastasia Sergeevna ORCID: 0009-0006-4789-9924 Student, Department of Political Administration, St. Petersburg State University 199034, Russia, Saint Petersburg, nab. University, 7-9 st089468@student.spbu.ru

Mikhaleva Diana Maksimovna ORCID: 0009-0000-4027-6579 Student, Department of Constitutional Law, St. Petersburg State University 199034, Russia, Saint Petersburg, nab. University, 7-9 st107486@student.spbu.ru

introduction

The COVID-19 pandemic was one of the first, but certainly not the last "black swan" for the global economy. All the two years it took to write the works of this cycle, there has been a qualitative and quantitative increase in crisis factors. In these conditions, the study of the anti-crisis paradigm acquires both unconditional relevance and undeniable applied importance.

During the beginning of the pandemic, there was a sharp increase in measures that can be classified as anti-crisis industrial policy measures (in the resource-industry tradition of interpreting this term) both in the countries of the world ^[1] and regionally within Russia ^[2]. The very topic of industrial policy is very controversial in economic theory. It is safe to say that there are more than 15 different interpretations of the concept of "industrial policy", which contradict each other to one degree or another ^[3]. In a broad sense, most of these interpretations can be reduced to a dichotomy of institutional and resource-industry approaches to the interpretation of industrial policy.

The regional authorities in Russia faced a curious situation during the outbreak of the pandemic. On the one hand, they were faced with the need to make some managerial decisions to maintain their regional economies, and on the other hand, they faced uncharacteristic independence and a fundamentally different level of responsibility for the results of their activities ^[4]. At the same time, this stimulated the search for management solutions for anti-crisis response in regional governments and specialized commissions of regional legislative assemblies. In the context of the continuing resource constraints of regional budgets, the analysis of the range of developed measures to support regional economies is also important ^[5].

The COVID-19 pandemic period is localized in the dates from February 2020 to February 2022. We can say for sure that the main array of government support measures related to the outbreak of the pandemic was concentrated in the spring of 2020, but in order to identify more detailed follow-up support measures, the 2020 study period was extended to February inclusive. February 2022 is the month of the outbreak of hostilities, which overshadowed all the crisis significance of the pandemic. After February 2022, it is impossible to say whether this or that decision was aimed at alleviating the crisis factors of the pandemic or the consequences of military operations and related acts of economic discrimination of the Russian Federation within the global economy.

The very theory of the existence of anti-crisis industrial policy as a single transformational form of industrial policy for all other types has a fundamental scientific novelty. It seems that the analysis of significant empirical material within the framework of the theory of a particular type of anti-crisis industrial policy has a certain scientific value in terms of contribution to the verification of this theory.

METHODOLOGY AND METHODS

The methodological basis of this work is a wide range of works on industrial policy, including its theoretical conceptualization. Thus, this study is based on the theoretical models and concepts of industrial policy by F.F. Rybakov ^[6], D. Rodrik ^{[7; 8; 9; 10]} and a representative of the St. Petersburg School of Industrial Policy V.Y. Pashkus ^[11]. With regard to the essence of anti-crisis management and state anti-crisis regulation, the work is based on N.F. Chebotarev's monograph "State anti-crisis regulation and challenges of a new reality" ^[12], as well as on the research of Lishenko and Mechikova ^[13].

Modern research on industrial policy is mainly based on two approaches: resource-based and institutional ^[14]. Among the authors who adhere to the resource-industry approach are: M. Okuno-Fujiwara, K. Suzumura ^[15], L. Otis, J. Graham ^[16], F.F. Rybakov ^[17], V.Y. Pashkus, A.B. Asadulaev and J.A. Aliaskarova ^[18]. The institutional approach to the interpretation of the concept of "industrial policy" is followed by J. Forman-Peck, J. Frederico J. ^[19], D. Rodrik, A.I. Tatarkin and O.A. Romanova ^[20], N.D. Kondratiev ^[21].

An important part of the terminological apparatus of this work is the concept of "anti-crisis industrial policy". In this study, the authors will understand anti-crisis industrial policy as "a set of public policy measures aimed at compensating for the crisis effects in relation to the framework conditions for business entities in industry, as well as similar measures to minimize the negative effect of the expected crisis in the future."

This definition is formulated in the tradition of the St. Petersburg School of Industrial Policy and in the paradigm of the resource-industry approach to the interpretation of the concept of "industrial policy". The resource-industry approach generally corresponds to the most common understanding of the term "industrial policy" in Western economics and intersects with the terms "sectoral" or "sectoral" policy, which involves government actions aimed at supporting or developing specific sectors and sectors of the economy. In addition, the interpretations contained in presidential decrees, federal laws and government programs also reflect the position of the resource-industry approach rather than an alternative institutional one. The May 2018 decrees of the President of the Russian Federation (On national goals and strategic objectives for the development of the Russian Federation for the period up to 2024: Decree of the President of the Russian Federation), FZ-N488 "On Industrial Policy of the Russian Federation" and the Concept of Industrial Policy of St. Petersburg until 2025 can be cited as vivid examples of this.

During the study of various anti-crisis regulation measures in the countries of the world ^{[1; 22]}, it was noted that, regardless of the type of industrial policy in force in a country, its range of anti-crisis industrial policy measures is similar to the same ranges of measures in other countries. In the course of this study, the experience of 9 countries in introducing 47 specialized solutions (measures) was analyzed. According to the results of a study of anti-crisis industrial policy in 9 countries and 32 regions of the Russian Federation during the COVID-19 pandemic, the most popular measures of anti-crisis industrial policy can be named: tax incentives, sectoral social benefits and benefits for municipal and state leases for small and medium-sized enterprises. Probably, the popularity of these specific measures was influenced by the nature of the crisis under study.

An analysis of the anti-crisis industrial policy of the Russian regions in a sample of 32 regions and 91 specialized solutions showed that the anti-crisis nature of industrial policy has changed the very range of decisions ^[23]. However, if in the case of countries it was possible to talk about a narrowing of the spectrum due to the heterogeneity of types of industrial policy, then in the framework of studying the anti-crisis industrial policy of the subjects of the Russian Federation, on the contrary, an expansion of the range of industrial policy measures was observed.

The variety of measures possible for state intervention at the level of a regional government or regional legislature must be reduced to a finite list. Within the framework of this study, the authors propose to identify the following measures of the economic policy of the region:

• • • benefits for renting regional and municipal property;
    • tax benefits;
    • financial and consulting support for exporters;
    • grants to social entrepreneurs;
    • direct financial support for business;
    • lump-sum payments to certain categories of citizens;
    • long-term payments to social workers;
    • industry subsidies;
    • deferred payments for housing and communal services;
    • increased purchases from SMEs.

It is important to note that the inclusion in the definition of measures aimed at anticipating negative crisis effects is a debatable issue. Thus, such anti-crisis activities of the state are not tied to the transition to an anti-crisis industrial policy regime and can be carried out long before the crisis within the framework of the type of industrial policy adopted in the country. As the experience of studying anti-crisis monetary policy shows, the division of anti-crisis measures into proactive and reactive ones has research potential. In turn, the analysis of the typologization of various types of industrial policy confirms the applicability of the dichotomy of proactivity to the concept of industrial policy.

In the article, the proactivity or reactivity of certain measures is also important. To what extent does the regional government act as a anticipatory actor in a given situation, or are its actions dictated by a completely unavoidable need for their adoption? The government may limit itself solely to responding to the current problems of the industrial sector, or it may try to anticipate the development of the situation and solve the problem even before the first symptoms of the problem itself appear. Within the framework of the topic considered in this paper, most measures will obviously be aimed at eliminating the negative consequences of the COVID-19 pandemic, although the emergency mobilization of production capacities for the creation of a vaccine can indeed be defined as a measure aimed at directly eliminating the cause of the crisis.

THE RESULTS OF THE STUDY

To analyze the measures of regional anti-crisis policy, the entire general population of the regions of the Russian Federation was analyzed (85 regions for the period from 02/01/2019 to 02/01/2022). The total number of analyzed and typologized measures of anti-crisis industrial policy of the subjects of the Federation amounted to 1,196. A summary table of the frequency of use of types of anti-crisis industrial policy by federal districts of the Russian Federation was compiled (Table 1).

Table 1. Frequency of anti-crisis industrial policy measures by federal districts of the Russian Federation.

Source: compiled by the authors based on an analysis of the websites of regional administrations.

It is worth noting the significant uneven distribution of the types of measures involved by the regions of the federal districts. This allows us to make an assumption about the influence of the factor of the federal district in the formation of the anti-crisis industrial policy of the region. This may be a consequence of a similar geographical location (especially relevant for the regions of the Far Eastern Federal District) or certain managerial traditions within the framework of supervising presidential plenipotentiaries in federal districts. The allocation of the "Increase in purchases from SMEs" type did not justify itself at all.

To assess the intensity of the anti-crisis industrial policy of the subjects of the Russian Federation, empirical material collected at the previous stage of the study was used. Thus, the number of anti-crisis industrial policy measures and the scale of anti-crisis industrial policy were taken into account. The scale was determined based on the volume of expenditures of the consolidated budget of the subject of the Russian Federation by sections (mobilization preparation of the economy, National Economy and Social Policy), divided into GRP for 2020 and 2021 at current prices, as well as the share of these expenditures in the consolidated budget of the subject of the Russian Federation for 2020 and 2021.. When calculating the fractions, rounding percentages to hundredths was used. So, 4 auxiliary indicators turned out: the share of expenses from GRP 2020, the share of expenses from GRP 2021, the share of these expenses in the consolidated budget of the subject of the Russian Federation for 2020 and 2021. Each of the auxiliary indicators was translated into a rating scale from 0 to 10. The final scale of measures is the sum of the scores of four indicators, transformed according to an auxiliary 4–point scale (insignificant (1) - small (2) – medium (3) – significant (4)).

For the final assessment of measures, the function of multiplying the indicator "number of measures" by "assessment of the scale of measures" was used, followed by conversion to a scale from 1 to 30, where 1 is the least intensive policy, and 30 is the most intensive. A table with the number of anti-crisis industrial policy measures and the final assessment on this scale are presented in the table below (Table 2).

Table 2. Measures of anti-crisis industrial policy of the regions and their assessment.

Source: compiled by the authors on the basis of data from the Federal Treasury of the Russian Federation: https://roskazna.gov.ru/ispolnenie-byudzhetov/konsolidirovannye-byudzhety-subektov/974 /

To assess the dynamics of socio-economic indicators, it was decided to use data from the Federal State Statistics Service (Rosstat). For the analysis and subsequent scaling of the regions, data were collected on the dynamics of GRP per capita, on the dynamics of the unemployment rate, as well as on the dynamics of the consumer price index in the regions. For each indicator, a rank scale was used, followed by conversion to a scoring scale in the range from 0 to 10, where a value of 0 marks the regions most affected from the point of view of the analyzed indicators, and a value of 10 marks the least affected regions. When ranking regions by indicators, if their indicators were identical (rounded to hundredths), the regions occupied an identical place in the ranking.

The "GRP per capita" column represents the ratio of the difference between GRP per capita for 2021 and GRP per capita for 2020 to GRP per capita for 2021 as a percentage. The unemployment column reflects the dynamics of the number of unemployed aged 15 and over as a percentage of the unemployment rate in 2020 compared to 2021. The "Inflation rate" column reflects the difference between the consumer price index in the region for 2021 and the same index for 2020. This column is intended to show the dynamics of inflation rates in the regions. The calculations were carried out by the authors independently on the basis of the data presented in the statistical collection "Regions of Russia. Socio-economic indicators. 2022."

The total assessment of the impact on the dynamics of socio-economic indicators of the regions during the COVID-19 crisis is a combination of estimates of all three indicators. Thus, the assessment of the dynamics of socio-economic indicators is a scale with a rating score of up to 30 points. A table with data on the above scale is presented below (Table 3).

Table 3. Assessment of the dynamics of socio-economic indicators of the regions during the COVID-19 crisis.

Source: compiled by the authors on the basis of the Collection ROSSTAT "Regions of Russia. Socio-economic indicators. 2022."

Let's imagine the indicator of the scale of assessment of the intensity of anti-crisis industrial policy with the value along the abscissa axis, and the indicator of the scale of the impact of the crisis on socio-economic indicators with the value along the ordinate axis. As a result, a graph was built, diagrams of the dispersion of regions according to the specified variables. (Figure 1.)

Figure 1. Dispersion diagram of the assessment of the intensity of anti-crisis industrial policy and the impact of the crisis on the socio-economic indicators of the region.

Source: compiled by the authors on the basis of Table 2 and Table 3.

For greater clarity, we will introduce a separate designation for the regions of each federal district. In this case, the graph will be transformed as follows (Figure 2.).

Figure 2. The dispersion diagram of the assessment of the intensity of anti-crisis industrial policy and the impact of the crisis on the socio-economic indicators of the federal districts of the Russian Federation.

Source: compiled by the authors on the basis of Table 2 and Table 3.

The most obvious option for interpreting the data obtained may be a correlation analysis – a search for the interdependence of the intensity of the anti-crisis industrial policy pursued by the regions and the crisis dynamics of socio-economic indicators. The correlation was searched using the following formula, where and are the average values of the arrays:

The value of the correlation coefficient for this data series was 0.165, which does not allow us to talk about the presence of a significant correlation. Thus, we cannot confirm the existence of significant links between the indicators of the intensity of industrial anti-crisis industrial policy and the dynamics of socio-economic indicators of the regions of the Russian Federation.

Based on the results obtained, the authors decided to classify and cluster regions by assessing the impact of the crisis on the socio-economic development of the region and assessing the intensity of anti-crisis policy measures.

Clustering was carried out using the Ward method and the square of the Euclidean distance. Before clustering, the authors standardized the data of indicators for assessing the impact of the crisis on the socio-economic development of the region and assessing the intensity of anti-crisis policy measures.

Cluster analysis showed four main clusters (Figure 3 and Table 4):

(1) regions that practically did not implement an anti-crisis industrial policy and suffered during the crisis;

(2) regions with significant economic stability and rather than resorting to an active anti-crisis policy;

(3) regions, the values of indicators of the dynamics of socio-economic indicators of which are on average and below the level, but on average more intensive anti-crisis policy;

(4) regions that have pursued an active anti-crisis industrial policy and have achieved good socio-economic indicators.

Figure 3. Clustering of regions to assess the intensity of anti-crisis industrial policy and the impact of the crisis on the socio-economic indicators of the regions.

Source: compiled by the authors on the basis of Table 2 and Table 3.

Table 4. Affiliation of regions to clusters.

Source: compiled by the authors on the basis of Table 2 and Table 3.

There was no strict relationship between the region's affiliation to the federal district and its cluster. Nevertheless, we can note that almost all regions of the Volga Federal District are in cluster 2, and the Ural and Southern Federal Districts are on the border of clusters 2 and 1. Cluster 3 is mainly represented by the regions of the Far Eastern and Siberian and federal districts. And the small cluster 4 consists of the Ryazan Region (Central Federal District), as well as the Kabardino-Balkarian and Karachay-Cherkess Republics (North Caucasus Federal District). The relatively high performance of the last two regions is a consequence of active budget expenditures and relative resistance to economic crises due to their sectoral industrial specialization.

Before proceeding to the conclusion, we will highlight the leading regions and outsider regions in terms of anti-crisis industrial policy measures for each cluster (Table 5).

Table 5. Leading regions and outsider regions in terms of the effectiveness of anti-crisis industrial policy.

Source: compiled by the authors on the basis of Table 2, Table 3 and Table 4.

conclusion

A comparative analysis of the regional anti-crisis industrial policy of the subjects of the Russian Federation was presented within the framework of five factors divided into two scales. The first scale measured the intensity of anti-crisis industrial policy and used the number of anti-crisis industrial policy measures, as well as the scale of anti-crisis policy measures. In controversial cases, attention was drawn to the variety of anti-crisis industrial policy measures in the region. The second scale measured the impact of the crisis on the socio-economic indicators of the regions and used for this the dynamics of GRP per capita, the dynamics of the number of unemployed aged 15 and older, as well as the dynamics of inflation growth rates. Based on the results of such scaling, each region received two scores on a 30-point scale, which formed a series of data for correlation and cluster analysis.

No correlation was detected during the correlation analysis. This result is particularly interesting. It can mean one of two things: either mistakes were made in the calculations of the scales, or the regional anti-crisis industrial policy of Russia in the analyzed period of time is not related to the volume of financing. This opens up a wide scope for further research, since such a result requires additional verification. In order to consider the bold conclusion voiced fully proven, it seems reasonable to significantly expand the number of analyzed socio-economic indicators in future studies. Also, a possible option for improving the methodology for assessing the intensity of the anti-crisis industrial policy of the regions of the Russian Federation is the inclusion of a parameter for the effectiveness of the management of regional governments (the quality of political institutions and the quality of management).

For a more visual cluster analysis, a regional dispersion diagram was constructed with the parameters of the scales for assessing the intensity of anti-crisis industrial policy and the result of scaling the impact of the crisis on the socio-economic indicators of the region. Four clusters were identified:

(1) regions that practically did not implement an anti-crisis industrial policy and suffered during the crisis;

(2) regions with significant economic stability and rather than resorting to an active anti-crisis policy;

(3) regions, the values of indicators of the dynamics of socio-economic indicators of which are on average and below the level, but on average more intensive anti-crisis policy;

(4) regions that have pursued an active anti-crisis industrial policy and have achieved good socio-economic indicators.

There was no obvious connection between the region's affiliation to the federal district and the cluster. However, it was noted that a significant part of the regions of the Volga Federal District are in cluster 2, and the Ural and Southern Federal Districts are on the border of clusters 2 and 1. Cluster 3 was mainly represented by the regions of the Far Eastern and Siberian and federal districts

Based on the clustering of regions, the leading regions and outsider regions were identified for each cluster, based on an assessment of the dynamics of the impact of the crisis on the socio-economic indicators of the regions.

A summary table of the frequency of use of various types of anti-crisis industrial policy measures was also compiled. It seems appropriate to recall that the classification itself is author's and needs some kind of refinement. Thus, it was noted that there is an excessive differentiation of the types of measures by frequency of use. This indicates the need to improve the classification of anti-crisis industrial policy measures. Some types need to be detailed, and some need to be considered for integration with other types. It also seems appropriate to note that differentiation by individual types may not be caused by classification problems, but may be a consequence of a similar geographical location (especially relevant for the regions of the Far Eastern Federal District) or certain managerial traditions within the framework of the presidential plenipotentiaries in charge of the federal districts.

Thus, we can conclude with the prospects for further research, which will undoubtedly be continued. It is necessary to expand the number of analyzed socio-economic indicators and include data for 2022 in the analysis, analyze the possibility of vulnerability sustainability factors in leading and outsider regions, improve the very classification of anti-crisis industrial policy measures and re-conduct correlation and cluster analysis. In this case, based on the results obtained, it will be possible to speak with a certain degree of confidence about the effectiveness of a separate regional anti-crisis industrial policy, as well as about the effectiveness of individual anti-crisis industrial policy measures in the regional plane.