2072-64142411-1406

10.17059/ekon.reg.2024-4-2

Assessment of Path Dependence Parameters in Technological Innovation across Russia’s Regions

Оценка параметров траектории развития технологических инноваций в промышленности российских регионов

https://orcid.org/0000-0002-1379-8053

Domnich

Yegor L.

Домнич

Егор Леонидович

chaosraven@yandex.ru

Economic Research Institute FEB RASИнститут экономических исследований ДВО РАН

2049931007

2024

Yegor L. Domnich

CC BY 4.0

2024

Егор Леонидович Домнич

CC BY 4.0

https://www.economyofregions.org/ojs/index.php/er/article/view/708

https://www.economyofregions.org/ojs/index.php/er/article/download/708/351

The dynamics of technological innovation in regional industries across Russia show significant spatial and temporal disparities. This variation underscores the importance of studying the parameters of innovation path dependence. This study evaluates the role of innovation path dependence and the current level of economic development in driving industrial innovation across various regions of Russia. Using a dynamic autoregressive model, this study examined the current innovation costs and outputs of industrial enterprises as functions of both innovation path dependence (measured by previous values) and the current economic development level (measured by gross regional product) across a panel of 70 Russian regions from 2000 to 2020, with detailed analyses for federal districts and periods 2000–2005, 2006–2010, 2011–2015, and 2016–2020. Results show that most Russian regions manifested a positive innovation path dependence only between 2011 and 2020. Conversely, from 2000 to 2005, some regions exhibited a negative path dependence, which hindered innovation growth. Throughout the entire 2000–2020 period, a region’s current economic development level was found to be a more influential factor in driving innovation than path dependence. The study concludes that the influence of innovation path dependence and regional economic development on innovation output was mainly a compromise—only one factor had a significant impact at a given time. This indicates that innovative enterprises across Russia are vulnerable. However, industries in the Urals and Siberia are an exception; in these regions, the factors at play consistently work together positively, making a substantial contribution to the success of innovation projects. These findings can provide insights into the spatial and temporal economic mechanisms driving innovative development in Russian regions.

Динамика технологических инноваций в промышленности регионов России характеризуется пространственно-временной неравномерностью, что делает актуальным изучение параметров траектории развития инноваций. Цель данного исследования — оценка сравнительной значимости траектории развития инноваций и текущего уровня экономического развития региона для инновационной динамики в промышленности регионов страны. С использованием динамической авторегрессионной функции выполнено моделирование текущих инновационных затрат и выпуска промышленных предприятий как функции от траектории развития инноваций (своих предыдущих значений) и текущего уровня экономического развития региона (измеряемого валовым региональным продуктом) на панели из 70 регионов России в 2000–2020 гг. с детализацией федеральных округов и периодов 2000–2005, 2006–2010, 2011–2015, 2016–2020 гг. Установлено, что положительная траектория развития инноваций, увеличивающая их текущий объем, в промышленности большинства регионов России сформировалась только в 2011–2020 гг. В то же время в 2000–2005 гг. в отдельных регионах наблюдалась устойчивая отрицательная траектория развития инноваций, создающая отрицательные экстерналии и негативно влияющая на прирост объема инноваций. В течение всего периода 2000–2020 гг. текущий уровень экономического развития региона был более важен для увеличения объема инноваций, чем траектория их развития. Влияние траектории развития инноваций и уровня экономического развития региона на текущий объем инноваций носит преимущественно компромиссный характер, то есть в конкретный момент времени воздействие чаще оказывал какой-то один из факторов. Это указывает на уязвимость инновационных проектов в рассматриваемый период. Выделяется промышленность Урала и Сибири, где рассматриваемые факторы систематически оказывали комплементарный (совместный) положительный эффект, что значительно увеличивает вероятность успешной реализации инновационных проектов. Полученные результаты представляют интерес в контексте пространственно-временной детализации экономических механизмов инновационного развития регионов России.

technological innovationspath dependenceindustry of Russiaregions of Russialevel of economic developmentdynamic modelling

технологические инновациитраектория развитияпромышленность Россиирегионы Россииуровень экономического развитиядинамическое моделирование

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