85161

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Доклад

Название: 

Using Big Data in Labor Productivity Analysis

ISBN/ISSN: 

979-8-3195-4898-6

DOI: 

10.1109/SCM71573.2026.11625898

Наименование конференции: 

  • 29th IEEE International Conference on Soft Computing and Measurements (SCM-2026, St Petersburg)

Наименование источника: 

  • Proceeding of the 29th IEEE International Conference on Soft Computing and Measurements (SCM-2026, St Petersburg)

Город: 

  • Санкт-Петербург

Издательство: 

  • IEEE

Год издания: 

2026

Страницы: 

https://ieeexplore.ieee.org/document/11625898
Аннотация
This paper presents a GDP growth model that takes into account resource constraints on investment and labor. Within the model, the problem of distributing development activities is reduced to a fractional knapsack problem, the optimal solution to which is achieved by ranking activities based on the efficiency of using a scarce resource. Under labor resource constraints, labor productivity serves as the efficiency criterion. Labor productivity calculations were conducted for 993,000 organizations across all industries. To process the initial data, large-scale data analysis methods were used, based on public financial statements and information on the number of employees from the Federal Tax Service of Russia. A statistically significant power-law dependence of labor productivity on the size of the organization was established. The proposed approach allows for the substantiation of development priorities for industries and regions based on objective data on labor productivity and can also be used to adjust development programs taking into account human resource constraints.

Библиографическая ссылка: 

Дранко О.И., Трушин К.Ю. Using Big Data in Labor Productivity Analysis / Proceeding of the 29th IEEE International Conference on Soft Computing and Measurements (SCM-2026, St Petersburg). СПб.: IEEE, 2026. С. https://ieeexplore.ieee.org/document/11625898.