The Impact of Population Credit Behaviour on Employment in the Informal Sector in Russian Regions

Employment in the informal sector is a significant issue for national economies, with approximately one-fifth of Russia’s economically active population working in this sector. At the same time, household debt levels are rising in many regions, which could drive additional work, including in the informal sector. This study explores whether credit behaviour influences changes in informal sector employment across Russian regions. Using panel data from 2005 to 2021 for 83 Russian regions, the analysis draws on data from Rosstat and EMISS. The initial panel consisted of 1,411 observations. The estimation was conducted using the system GMM method. The dependent variable represents the share of people employed in the informal sector out of the total employed population aged 15-72 in each region. Key explanatory variables include total population debt and debt related to housing and mortgage loans. Control variables account for the region’s development level, economic structure, age-specific unemployment rates, family dynamics, and education institutions. The econometric analysis reveals a statistically significant negative impact of mortgage and housing debt on informal sector employment, while no effect was found for total debt. Additionally, higher levels of education and industrial production correlate with a reduction in informal sector employment. Conversely, an increase in GRP, a larger agricultural sector, higher unemployment among the elderly, and rising divorce rates contribute to higher informal sector employment. These findings can inform the development and implementation of state strategies and national projects.

Authors

Publication Details

Journal
Economy of Regions
Published
2025-01-01
DOI
https://doi.org/10.17059/ekon.reg.2025-1-10
Primary Topic
Socioeconomic and Demographic Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Impact of Population Credit Behaviour on Employment in the Informal Sector in Russian Regions

С. В. Дорошенко, O. V. Sanaeva
Economy of Regions
Socioeconomic and Demographic Analysis
article

The Impact of Population Credit Behaviour on Employment in the Informal Sector in Russian Regions

С. В. Дорошенко, O. V. Sanaeva
article en

Abstract

Employment in the informal sector is a significant issue for national economies, with approximately one-fifth of Russia’s economically active population working in this sector. At the same time, household debt levels are rising in many regions, which could drive additional work, including in the informal sector. This study explores whether credit behaviour influences changes in informal sector employment across Russian regions. Using panel data from 2005 to 2021 for 83 Russian regions, the analysis draws on data from Rosstat and EMISS. The initial panel consisted of 1,411 observations. The estimation was conducted using the system GMM method. The dependent variable represents the share of people employed in the informal sector out of the total employed population aged 15-72 in each region. Key explanatory variables include total population debt and debt related to housing and mortgage loans. Control variables account for the region’s development level, economic structure, age-specific unemployment rates, family dynamics, and education institutions. The econometric analysis reveals a statistically significant negative impact of mortgage and housing debt on informal sector employment, while no effect was found for total debt. Additionally, higher levels of education and industrial production correlate with a reduction in informal sector employment. Conversely, an increase in GRP, a larger agricultural sector, higher unemployment among the elderly, and rising divorce rates contribute to higher informal sector employment. These findings can inform the development and implementation of state strategies and national projects.

Economy of RegionsVol. 21(1)
Openalex Percentile: Top 91%
Socioeconomic and Demographic Analysis
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.