Yaroslavl, Yaroslavl, Russian Federation
Saint Petersburg, St. Petersburg, Russian Federation
JEL O32 Management of Technological Innovation and R&D
JEL O38 Government Policy
JEL R11 Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
JEL C21 Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions
JEL C25 Discrete Regression and Qualitative Choice Models • Discrete Regressors • Proportions • Probabilities
Drawing on 2024 data from the Russian Federal State Statistics Service (Rosstat), this study examines the regional determinants of organizational innovation activity across 85 constituent entities of the Russian Federation. The dependent variable is defined as the share of innovation-active organizations. The principal explanatory factors include internal expenditure on research and development, per capita fixed capital investment, per capita gross regional product (GRP), the number of students enrolled in secondary vocational education programs, and a dummy variable capturing resource-based economic specialization. Three complementary model specifications are employed to test the hypotheses: a linear model with HC3 robust standard errors, a fractional logit model, and a median quantile regression. The findings indicate that the scale of the research and development base exhibits the most robust positive association with innovation activity. Aggregate fixed capital investment, once GRP and sectoral specialization are controlled for, displays a conditional negative correlation, reflecting structural characteristics of the Russian economy wherein a substantial share of capital investment is concentrated in extractive industries and infrastructure projects. Resource-oriented regions are, on average, characterized by lower levels of innovation activity at comparable resource endowments. The number of secondary vocational education students does not exhibit an independent statistically significant effect. The model accounts for approximately 42 percent of the cross-regional variation. The paper discusses the limitations arising from the cross-sectional design, weak instrumental identification, and spatial dependence, and outlines directions for future research employing panel and spatial econometric models.
nnovation activity; organizations; Russian regions; research and development; innovation potential; fractional logit model; robust regression; resource-based specialization; Rosstat.
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