Non-Observed Economy in Russian Agricultural Sector: Spatial Aspect

Relevance. The task of objective and accurate assessment of agricultural production by various economic entities is considered relevant and important for the implementation of various public goals, for example, for monitoring the development of the industry, organizing effective state policy. In this regard, scientists strive to develop and offer for use the most accurate methods for accounting for the volumes of agricultural products produced in Russia and its regions. Despite the fact that most economists recognize the existence of the non-observed economy in the agricultural sector, scientists have not yet proposed adequate approaches to determining its scale. Methods. The work applies statistical analysis, method of recalculation, cartographic method, and comparative analysis. The study implies a level of the non-observed economy in agriculture as a share of shadow operations of legal entities, output of peasant (farmer) enterprises and individual entrepreneurs and household farms in relation to agricultural output. Results. The paper reveals the drift of most Russian regions towards groups with lower levels of the non-observed economy in agriculture. At the national level, its scale is also decreasing — from 46.5% in 2017 to 40.8% in 2022. These trends are primarily due to a reduction in the share of household farms in the structure of producers. The highest level of non-observed economy prevailed in the subjects of the North Caucasus Federal District, as well as in certain regions of the Siberian and Far Eastern Federal Districts, characterized by high unemployment and low wages. There, subsidiary household plots compensate for the lack of funds for local residents by providing them with food. In 2022, the share of the non-observed economy in relation to agricultural output exceeding 80% is noted in the republics of Tyva, Dagestan, Kalmykia, Altai, in Astrakhan Region, Trans-Baikal and Khabarovsk Territories, and Jewish Autonomous Region. Values in the range of 60–80% are typical for the republics of Karachay-Cherkessia, KabardinoBalkaria, North Ossetia — Alania, Buryatia, Khakassia, Chechnya, Kamchatka Krai, Saratov and Orenburg regions. The lowest level of non-observed economy in agriculture is mainly in the subjects of the Central, Northwestern, Volga Federal Districts. These are Belgorod, Kursk, Tula, Tambov, Oryol, Ryazan, Lipetsk, Pskov, Leningrad, Penza, Kirov regions, the Republic of Mordovia, etc.

Authors

Institutions

Publication Details

Journal
Agrarian science
Published
2025-02-18
DOI
https://doi.org/10.32634/0869-8155-2025-391-02-168-176
Primary Topic
Taxation and Compliance Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Non-Observed Economy in Russian Agricultural Sector: Spatial Aspect

I. V. Filimonov, S. K. Seitov, С.В. Киселев, В.А. Самсонов
Agrarian science
Taxation and Compliance Studies
article

Non-Observed Economy in Russian Agricultural Sector: Spatial Aspect

I. V. Filimonov, S. K. Seitov, С.В. Киселев, В.А. Самсонов
article en

Abstract

Relevance. The task of objective and accurate assessment of agricultural production by various economic entities is considered relevant and important for the implementation of various public goals, for example, for monitoring the development of the industry, organizing effective state policy. In this regard, scientists strive to develop and offer for use the most accurate methods for accounting for the volumes of agricultural products produced in Russia and its regions. Despite the fact that most economists recognize the existence of the non-observed economy in the agricultural sector, scientists have not yet proposed adequate approaches to determining its scale. Methods. The work applies statistical analysis, method of recalculation, cartographic method, and comparative analysis. The study implies a level of the non-observed economy in agriculture as a share of shadow operations of legal entities, output of peasant (farmer) enterprises and individual entrepreneurs and household farms in relation to agricultural output. Results. The paper reveals the drift of most Russian regions towards groups with lower levels of the non-observed economy in agriculture. At the national level, its scale is also decreasing — from 46.5% in 2017 to 40.8% in 2022. These trends are primarily due to a reduction in the share of household farms in the structure of producers. The highest level of non-observed economy prevailed in the subjects of the North Caucasus Federal District, as well as in certain regions of the Siberian and Far Eastern Federal Districts, characterized by high unemployment and low wages. There, subsidiary household plots compensate for the lack of funds for local residents by providing them with food. In 2022, the share of the non-observed economy in relation to agricultural output exceeding 80% is noted in the republics of Tyva, Dagestan, Kalmykia, Altai, in Astrakhan Region, Trans-Baikal and Khabarovsk Territories, and Jewish Autonomous Region. Values in the range of 60–80% are typical for the republics of Karachay-Cherkessia, KabardinoBalkaria, North Ossetia — Alania, Buryatia, Khakassia, Chechnya, Kamchatka Krai, Saratov and Orenburg regions. The lowest level of non-observed economy in agriculture is mainly in the subjects of the Central, Northwestern, Volga Federal Districts. These are Belgorod, Kursk, Tula, Tambov, Oryol, Ryazan, Lipetsk, Pskov, Leningrad, Penza, Kirov regions, the Republic of Mordovia, etc.

Agrarian science(2)
Lomonosov Moscow State University (RU), Moscow State University (TJ)
Openalex Percentile: Top 96%
Taxation and Compliance Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.