The economic importance of science-intensive manufacturing can be assessed by the share of its output in the gross regional product of each constituent entity. The list contains 96 rows, where the first is the total for the Russian Federation, and the others include federal districts and individual regions. Data for 2016–2018 are calculated according to the OKVED 2 classifier and are presented in percent
Chart of the share of output from high-tech and science-intensive industries in GRP by Russian regions, 2016–2018
Kaluga Region
Tula Region
Ryazan Region
Smolensk Region
Russian Federation from the sum of constituent entities of the Russian Federation
Rosstat data for 2016–2018. Rows: 96, period: 3. Measurement unit — OKVED 2. Breakdown by positions: Russian Federation as the sum of constituent entities of the Russian Federation; Central Federal District; Belgorod Region; Bryansk Region; Vladimir Region and others
Kaluga Region: the indicator increased from 34.3 to 37.8 for 2016–2018 — by 10.2%. Of the 24 largest rows, 15 increased, 9 decreased
The rows in the list allow for comparison of the high-tech sector's contribution to the economies of different regions and entire federal districts. It shows how specific regions' indicators relate to the national level or the district average. Differences in values reflect varying degrees of technological sophistication and economic specialization in each constituent entity
What is visible in these numbers
Five largest values: Kaluga Region (37.8), Yaroslavl Region (29.1), Northwestern Federal District (25.3), Tula Region (25.1), Vladimir Region (24.9). The gap between the largest and smallest is 10.2 times. The top five account for 27.7% of the total for all positions
Observations are annual, with 3 observation periods in the dataset. There are 75 values in the table out of 75 possible, which is 100% — almost no empty cells remain
These series cannot be stitched together Over the common period
2016 values diverge — Rosstat recalculated the indicator using a revised methodology. They cannot be combined into a single chart: a break would appear at the junction, which did not actually occur
Indicator for 2016
here
in the adjacent series
divergence
Центральный федеральный округ
22,0
20,5
−1,5
Белгородская область
11,1
10,4
−0,7
Брянская область
19,4
17,5
−1,9
Владимирская область
24,3
22,3
−2,0
Воронежская область
18,7
18,5
−0,2
Ивановская область
21,3
18,8
−2,5
Калужская область
34,3
31,2
−3,1
Костромская область
17,3
16,1
−1,2
Курская область
18,2
17,5
−0,7
Липецкая область
11,7
11,3
−0,4
Московская область
23,3
20,5
−2,8
Орловская область
17,2
16,3
−0,9
More in the section Statistical yearbook "Sustainable Development Goals in the Russian Federation»
Statistics are compiled from the Sustainable Development Goals Yearbook in the Russian Federation. The information is relevant for annual monitoring of regional economic structures. The data may be useful to economists, regional authorities, and researchers studying the level of innovative development in territories
How it is calculated
Such data are needed when a figure in a presentation will have to be defended with a reference to an official source
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