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Regional Economics: Theory and Practice
 

Tangible and intangible incentives of key executives of management bodies of regions and districts: Modeling and assessment

Vol. 21, Iss. 8, AUGUST 2023

Received: 23 July 2020

Received in revised form: 6 August 2020

Accepted: 20 August 2020

Available online: 15 August 2023

Subject Heading: ECONOMIC-MATHEMATICAL MODELING

JEL Classification: C63, E17, O21, O36

Pages: 1547–1574

https://doi.org/10.24891/fc.26.10.2170

Sergei N. YASHIN National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
jashinsn@yandex.ru

https://orcid.org/0000-0002-7182-2808

Egor V. KOSHELEV National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
ekoshelev@yandex.ru

https://orcid.org/0000-0001-5290-7913

Sergei A. BORISOV National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
ser211188@yandex.ru

https://orcid.org/0000-0002-6829-0230

Subject. This article explores the relationship between financial and non-financial motivations of top executives of State administration agencies in the context of aligning the interests of board-level managers and the general public.
Objectives. The article aims to create a model for assessing the financial and non-financial incentives of top managers of the administration bodies of regions and districts to develop a reasonable reward and recognition scheme.
Methods. For the study, we used a multi-objective genetic algorithm.
Results. The article presents a developed model for assessing the financial and non-financial incentives of top managers of the administration bodies of regions and districts. As well, it presents certain results of an analysis of board-level managers' incentives use through applying the model.
Relevance. The results obtained can be useful to government agencies to develop a reasonable system of financial and non-financial incentives of the agencies' top leadership.

Keywords: motivation, senior management, multi-objective genetic algorithm

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