SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 3, 2023
Research article
DOI: https://doi.org/10.48554/SDEE.2023.3.2
Methodology of Financial Monitoring Based on Cluster Analysis for the
Implementation of National Projects in the Russian Regions
Nadezhda Yashina* , Oksana Kashina , Sergey Yashin , Natalia Pronchatova-Rubtsova
Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation
*
Corresponding author: [email protected]
Abstract
T
he need to take into account imbalances among regional indicators in the development of state
policy for financing national projects makes it necessary to develop a methodology that will enable
objective assessment of the effectiveness of socially significant projects in Russia. This paper
reports the development of a methodology for financial monitoring of national project implementations
in the constituent entities of the Russian Federation, taking into account the correlation of their target
indicators and using cluster analysis and methods in mathematical statistics. The proposed methodology
was tested on health and demography national project data obtained from the Federal Treasury of Russia,
the Federal State Statistics Service and the Accounts Chamber for 2020–2021. The analysis of public
funding for national projects based on centralization indices and target indicators for their implementation
enabled classifying the regions of Russia according to the levels of effectiveness and the financial risks
of implementing the projects. The results of the study correspond to the actual effectiveness of national
projects and can be used in the development of flexible state policy in financing national projects, taking
into account the level of the target indicators achieved.
Keywords: national project, target indicators, cluster analysis, financial monitoring
Citation: Yashina, N., Kashina, O., Yashin, S., Pronchatova-Rubtsova, N., 2023. Methodology of Financial
Monitoring Based on Cluster Analysis for the Implementation of National Projects in the Russian Regions.
Sustainable Development and Engineering Economics 3, 2. https://doi.org/10.48554/SDEE.2023.3.2
This work is licensed under a CC BY-NC 4.0
© Yashina, N., Kashina, O., Yashin S., Pronchatova-Rubtsova, N., 2023. Published by Peter the Great St.
Petersburg Polytechnic University
22 Enterprises and sustainable development of regions
SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 3, 2023
Научная статья
УДК 332.1
DOI: https://doi.org/10.48554/SDEE.2023.3.2
Методика Финансового Мониторинга Реализации Национальных Проектов в
Российских Регионах с Использованием Кластерного Анализа
Надежда Яшина* , Оксана Кашина , Сергей Яшин , Наталия Прончатова-Рубцова
Нижегородский государственный университет им. Н.И. Лобачевского, Нижний Новгород, Российская
Федерация
*
Автор, ответственный за переписку: [email protected]
Аннотация
Н
еобходимость учета процессов сбалансированности, диспропорций и поляризации
показателей регионов при разработке государственной политики финансирования
национальных проектов как залога успешного достижения стратегических целей и задач
государства обуславливает потребность развития методического инструментария, позволяющего
объективно оценить результативность социально-значимых проектов в российских регионах.
Статья посвящена разработке методики финансового мониторинга реализации национальных
проектов в субъектах Российской Федерации с учетом взаимосвязи их целевых показателей с
использованием кластерного анализа, а также методов математической статистики. Апробация
предложенной методики была проведена на основе данных Федерального казначейства России,
Федеральной службы государственной статистики и Счетной палаты за 2020–2021 гг. на
примере национальных проектов «Здравоохранение» и «Демография». Анализ государственных
ассигнований на национальные проекты в регионах России на основе индексов централизации
и установочных целевых индикаторов выполнения национальных проектов дает основание
классифицировать регионы России по уровням эффективности и финансовых рисков реализации
данных проектов. Результаты исследования полностью сопоставимы с фактическими
показателями исполнения национальных проектов и могут быть использованы при формирования
гибкой государственной политики финансирования национальных проектов с учетом уровня
достижения целевых показателей.
Ключевые слова: национальный проект, целевые индикаторы, кластерный анализ, финансовый
мониторинг
Цитирование: Яшина, Н., Кашина, О., Яшин, С., Прончатова-Рубцова, Н., 2023. Методика Финансового
Мониторинга Реализации Национальных Проектов в Российских Регионах с Использованием Кластерно-
го Анализа. Sustainable Development and Engineering Economics 3, 2. https://doi.org/10.48554/SDEE.2023.3.2
Эта работа распространяется под лицензией CC BY-NC 4.0
© Яшина, Н., Кашина, О., Яшин, С., Прончатова-Рубцова, Н., 2023. Издатель: Санкт-Петербургский
политехнический университет Петра Великого
Предприятия и устойчивое развитие регионов 23
Methodology of Financial Monitoring Based on Cluster Analysis for the Implementation of National Projects in the Russian Regions
1. Introduction
Increased external challenges and threats have slowed the growth of Russia’s gross domestic prod-
uct as a basic source of financial resources, which is affecting standards of living and birth rates in the
country. Decree of the President of the Russian Federation No. 204, dated July 21, 2020, “On the Nation-
al Development Goals of the Russian Federation for the period up to 2030”,1 defined national targets for
the development of the country. The primary task of the state is to guarantee the well-being and health
of the citizens. President Putin V.V. noted that there is a “difficult situation” in Russia in the field of de-
mography and that it is necessary to ensure increases in both the birth rate and life expectancy.
To achieve the strategic goals and objectives of the state, tools are needed to assess the effective-
ness of national projects in the Russian regions (Fattakhov et al., 2019). The need for these tools is also
due to imbalances in the indicators of the regions, which should be taken into account in the development
of state policy for financing national projects. Among the top-priority national projects responsible for
economic growth and human capital development are those directed to health and demography. National
healthcare and demography projects are important strategic tasks in modern Russia, the implementation
of which will ensure development of the main components in the growth of human capital: longevity and
high-quality medical care for the population. The achievement of these objectives should be considered
taking into account their mutual correlation. Cluster analysis which has been tested in numerous studies
(Revnyakov, 2017; Pushkarev, 2018; Piskun and Khokhlov, 2019) can be conducted to solve these prob-
lems. In this regard, the current authors propose a methodology for financial monitoring of national proj-
ect implementations in Russian regions based on cluster analysis, which will make it possible to classify
the regions according to the level of potential threats to the implementation of national healthcare and
demography projects, monitor changes in achieving project targets, coordinate management activities at
all levels, and allocate financial resources in a timely manner.
2. Literature review
A characteristic feature of the Russian economy is the imbalance in the socio-economic develop-
ment of its regions due to their geographical location and the availability of natural and other resources
(Yashina et al., 2022(a); Yashina et al., 2022(b); Yudintsevand Troshkina, 2023). To assess local regional
disparities, multidimensional classifications, as well as methods of factor cluster and discriminant analy-
sis are widely used (Piskun and Khokhlov, 2019). The problem of regional disparities makes it necessary
to improve the system for monitoring national projects and government programmes in order to increase
the effectiveness of their implementation in the regions of the Russian Federation (Ezangina and Gro-
myshova, 2020). The need to improve the management of the socio-economic systems of regions has
been highlighted in numerous works (e.g. Bogovizetal, 2019; Romanovaetal, 2019; Chebyshev, 2021).
In addition to the divergence and convergence of the development of the regions and the country as a
whole, Ezangina and Gromyshova (2020) pointed out the lack of methodological support for the current
state strategic planning system, as well as the lack of transparent and accessible information to improve
this system, as key reasons for the imbalance in the level of regional socio-economic situations. These
issues were also discussed by Endovitsky et al. (2021) and Mishlanova (2022).
As mentioned earlier, an important national state task is to ensure sustainable positive indicators
in the fields of health and demography in the Russian regions, taking into account their uneven develop-
ment and risks (Averinetal, 2018; Ariste and Matteo, 2017; Kozlova et al., 2017). However, these indica-
tors should be considered taking into account the relationships between them (Gallardo-Albarrán, 2018;
Sharma, 2018; Mihalache, 2019). In particular, funding for healthcare, as one of the key instruments
of state policy, largely determines the quality of medical care provided (Shahetal., 2021; Soofi et al.,
2021). High-quality care contributes to a lower mortality rate in the country and a more favourable de-
mographic situation (Balkhi et al., 2021; Wirayuda and Chan, 2021). Ivankova et al. (2022) assessed the
relationship between funding for healthcare, mortality, and gross domestic product in OECD countries
1
Decree of the President of the Russian Federation No. 204 Dated July 21, 2020 “On the national development goals of the Russian Federation for the period until 2030”: official
internet portal of legal information. URL: http://publication.pravo.gov.ru/Document/View/0001202007210012
24 Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2
Yashina, N., Kashina, O., Yashin, S., Pronchatova-Rubtsova, N.
for the period 1994–2016. The study was conducted by the authors taking into account types of health-
care systems. The working-age population was the object of the study. The authors found that countries
with high healthcare funding had lower mortality rates and higher gross domestic products compared
to countries with an insurance-based healthcare system (Bismarck system). In this regard, it is obvious
that the risks of not meeting the targets of national projects in the fields of health and demography are
mutually reinforcing.
The authors of a number of publications have applied cluster analysis as a tool for assessing the
effectiveness of various regional strategies, including in the field of innovation (Khayrullina, 2014;
Revnyakov, 2017; Pushkarev, 2018). Cluster analysis allows us to identify objects in numerous classi-
fication features using many variables. Piskun and Khokhlov (2019) confirmed the hypothesis that any
region can be described by a set of interrelated variables that reflect its socio-economic situation over
the analysed time interval. Despite a large number of scientific publications devoted to various aspects of
regional development, insufficient attention has been paid to financial monitoring of the national projects
implemented in the Russian regions that would take into account the relationships between their indica-
tors based on cluster analysis. The issue of expanding the set of criteria for evaluating the effectiveness
of national projects needs further development and justification.
3. Materials and methods
Our methodology for financial monitoring of the implementation of national projects in the Rus-
sian regions using cluster analysis of government subsidies for national projects and criteria for their
effectiveness contains several stages.
The first stage includes the development of a database of the target indicators of national projects
based on information from the Ministry of Finance of the Russian Federation and the Federal State
Statistics Service. The methodology for assessing the effectiveness of public financing for the imple-
mentation of national projects is based on the analysis of two systems of indicators: indicators of public
funding and indicators for setting target indicators for national projects. The methodology was tested on
health and demography national projects.
The system of public funding itself includes two indicators: budget execution in the context of
the analysed national projects: % (FDH 1); and budget execution in the context of the analysed national
projects per inhabitant, in rubles (FDH 2).
The system of target indicators of the analysed national projects includes the values presented in
Table 1.
Table 1. Target indicators for the implementation of health and demography national projects
Health national project Symbol Demography national project Symbol
Mortality of the working-age population, per 100,000 people of ICH 1 Life expectancy of citizens at the age of 55, ICD 1
the population of the corresponding age years
Mortality from diseases of the circulatory system, per 100,000 ICH 2 Healthy life expectancy, years ICD 2
population
Mortality from neoplasms, including malignant ones, per ICH 3 Mortality of the population older than work- ICD 3
100,000 population ing age per 100,000 people of the popula-
tion of the corresponding age
Infant mortality, the number of children who die before the age ICH 4 Total fertility rate, number of children per ICD 4
of 1 year, per 1000 live births woman
Number (share) of citizens leading a healthy ICD 5
lifestyle, %
Employment rate of women with pre- ICD 6
school-aged children
Further, in relation to the system of indicators, the criteria for the centralization of public funding
and target indicators for the implementation of the analysed national projects in the Russian regions were
Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2 25
Methodology of Financial Monitoring Based on Cluster Analysis for the Implementation of National Projects in the Russian Regions
determined:
1. Level of centralization ( LCij ), representing the share of public funding and the concentration of
the set targets of national projects by region (1);
2. Index of centralization ( ICij ), defined as the sum of the squared levels of centralization for
each region of Russia (2) by analogy with the Herfindahl–Hirschman index. However, the centralization
index has a different interpretation and is adapted to a specific task, which is to determine the degree of
concentration of public financial resources and to achieve the specified target indicators of national proj-
ects in a given territory. The centralization index is calculated for each indicator included in the system,
that is, the indices are determined for each indicator in the system of public funding and target indicators
for the implementation of national projects (formulas 1, 2):
Pj
LCij = , (1)
∑ j Pj
N
where Pj is the value of the i-th indicator in the system of indicators of budget appropriations or
the system of target indicators of the national project implementation in the j-th region.
2
P
M
M
IC ∑IC ∑ N , (2)
2 j
= =
ij ij
∑j j
=i 1 =i 1 P
where ICij is the level of centralization of the i-th indicator in the j-th region.
The centralization index ( ICij ) ranges from 0 to 1 (formula 3); the greater the value of this indica-
tor, the higher the concentration of budget allocations and the level of achievement of target indicators
for the implementation of national projects in a particular region.
0 < ICij ≤ 1. (3)
The third stage of the development of our methodology for monitoring national projects involves
ranking for each index of centralization of public finance; the higher the rank, the lower the level of effec-
tiveness of indicators for each analysed national project. The ranking is carried out by the centralization
indices of financing, both in the context of national projects, %, and per one inhabitant (in rubles), etc.
The final rank of the public funding is determined on the basis of the total rank. The final total rank
serves as a criterion for determining the levels (9 levels) of potential risks of the national project imple-
mentation in the system of indicators that characterize public funding. The value of the final total rank
(FDH) decreases with the level of financial risks of the national project implementation and vice versa.
At the fourth stage, the ranking is carried out for each centralization index in the system of the
target indicators set for the implementation of the national project, in particular, for health national proj-
ects – ICH 1, ICH 2, ICH 3, ICH 4; and for demography national projects – ICD 1, ICD 2, ICD 3, ICD
4, ICD 5, ICD 6.
A lower index of centralization for ICH 1, ICH 2, ICH 3, or ICH 4 (health national projects) or ICD
3 (demography national projects) indicates a lower rank for the target indicator. For the other indicators
ICD 1, ICD 2, ICD 4, ICD 5, and ICD 6 (demography national projects), on the contrary, the centraliza-
tion index decreases as the rank for the target indicator increases.
The final rank for all the target indicators for national project implementations is determined on the
basis of the total rank (FTR), which serves as a criterion for determining the effectiveness of the imple-
mentation of a national project; the lower the value of the final total rank (FTR), the fewer the threats to
26 Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2
Yashina, N., Kashina, O., Yashin, S., Pronchatova-Rubtsova, N.
the implementation and vice versa.
The final values in the system of public funding and target indicators for the implementation of a
national project are the criteria for clustering regions according to the level of effectiveness and financial
risks of the national project (Figure 1).
3 FTR 1 cluster 4 cluster 6 cluster
Target indicators
2 FTR 7 cluster 2 cluster 5 cluster
1 FTR 9 cluster 8 cluster 3 cluster
1 FDH 2 FDH 3 FDH
Level of funding
Figure 1. Effectiveness matrix for national project implementations in the Russian regions based on a
comparison of the level of public funding and achievement in the specified target values of the projects
The fifth stage consists in interpreting the obtained monitoring results based on the clustering of
regions by public funding level and target indicators for the implementation of national projects (Table
2). For region clustering, a non-overlapping algorithm was used, according to which each region was
to be included in only one cluster. The key requirement for clustering optimization was to minimize the
standard error of partitioning. The cluster centre was defined using the centralization indices, which were
discussed above.
Table 2. Characteristics of clusters of national project implementations in the Russian regions
Cluster name Correlation between level of Correlation of level of effectiveness and potential
funding and target indicators financial risks of health and demography national
project implementations
1 cluster 1 FDH – 3 FTR low effectiveness / low risk
2 cluster 2 FDH – 2 FTR balanced level of effectiveness and risks
3 cluster 3 FDH – 1 FTR high effectiveness / high risk
4 cluster 2 FDH – 3 FTR low effectiveness / moderate risk
5 cluster 3 FDH – 2 FTR moderate effectiveness / high risk
6 cluster 3 FDH – 3 FTR extremely low effectiveness / highest risk
7 cluster 1 FDH – 2 FTR medium effectiveness / low risk
8 cluster 2 FDH – 1 FTR high effectiveness / medium level of risk
9 cluster 1 FDH – 1 FTR highest effectiveness / low risk
Region clustering will allow us to identify and study in detail possible local factors that contribute
to problems in public funding and the implementation of national projects in the health and demography
fields. In addition, the results will contribute to the development of a national strategy and of tactics
adapted to a specific region in order to achieve the target values of national projects.
Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2 27
Methodology of Financial Monitoring Based on Cluster Analysis for the Implementation of National Projects in the Russian Regions
4. Results
The methodology was tested on the database of the Federal Treasury of Russia, the Federal State
Statistics Service of the Russian Federation, and the Accounts Chamber for 2020–2021. The analysis of
the implementation of healthcare and demography national projects based on the centralization indices
of public funding and target indicators enables us to classify the regions of Russia according to poten-
tial threats to the implementation of these projects. Potential threats to national projects are the risks of
failure to achieve the expected socio-economic effects and financial risks caused by the impacts of both
external and internal economic factors. The results of clustering Russian regions in accordance with the
proposed methodology for financial monitoring of national projects are presented in Table 3.
Table 3. Clusters of Russian regions according to level of effectiveness and risk in implementing na-
tional projects related to demography and healthcare
Subject of the Russian Federa- National Project Fund- Class of specified target Cluster
tion ing Class (FDH) indicators (FTR)
Magadan region 1 FDH 3 FTR cluster 1
Altai Republic 1 FDH 3 FTR cluster 1
Ryazan Oblast 1 FDH 3 FTR cluster 1
Chukotka Autonomous Okrug 1 FDH 3 FTR cluster 1
Kaluga region 2 FDH 2 FTR cluster 2
Republic of Buryatia 2 FDH 2 FTR cluster 2
Khanty-Mansi Autonomous
Okrug 2 FDH 2 FTR cluster 2
Sevastopol 3 FDH 1 FTR cluster 3
Kabardino-Balkar Republic 3 FDH 1 FTR cluster 3
Republic of Ingushetia 3 FDH 1 FTR cluster 3
Republic of Tatarstan (Tatarstan) 3 FDH 1 FTR cluster 3
Tyumen region 3 FDH 1 FTR cluster 3
Chechen Republic 3 FDH 1 FTR cluster 3
Chuvash Republic-Chuvashia 3 FDH 1 FTR cluster 3
Amur region 2 FDH 3 FTR cluster 4
Arhangelsk region 2 FDH 3 FTR cluster 4
Vologda region 2 FDH 3 FTR cluster 4
Voronezh region 2 FDH 3 FTR cluster 4
Jewish Autonomous Region 2 FDH 3 FTR cluster 4
Novosibirsk region 2 FDH 3 FTR cluster 4
Primorsky Krai 2 FDH 3 FTR cluster 4
Republic of Kalmykia 2 FDH 3 FTR cluster 4
Republic of Karelia 2 FDH 3 FTR cluster 4
Komi Republic 2 FDH 3 FTR cluster 4
Republic of Khakassia 2 FDH 3 FTR cluster 4
Tambov region 2 FDH 3 FTR cluster 4
Tver region 2 FDH 3 FTR cluster 4
Tomsk region 2 FDH 3 FTR cluster 4
Tula region 2 FDH 3 FTR cluster 4
28 Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2
Yashina, N., Kashina, O., Yashin, S., Pronchatova-Rubtsova, N.
St. Petersburg 3 FDH 2 FTR cluster 5
Krasnodar region 3 FDH 2 FTR cluster 5
Moscow region 3 FDH 2 FTR cluster 5
Murmansk region 3 FDH 2 FTR cluster 5
Penza region 3 FDH 2 FTR cluster 5
Perm region 3 FDH 2 FTR cluster 5
Republic of Adygea (Adygea) 3 FDH 2 FTR cluster 5
Republic of Dagestan 3 FDH 2 FTR cluster 5
Republic of Crimea 3 FDH 2 FTR cluster 5
Mari El Republic 3 FDH 2 FTR cluster 5
Rostov region 3 FDH 2 FTR cluster 5
Udmurt republic 3 FDH 2 FTR cluster 5
Altai region 3 FDH 3 FTR cluster 6
Astrakhan region 3 FDH 3 FTR cluster 6
Belgorod region 3 FDH 3 FTR cluster 6
Bryansk region 3 FDH 3 FTR cluster 6
Vladimir region 3 FDH 3 FTR cluster 6
Volgograd region 3 FDH 3 FTR cluster 6
Transbaikal region 3 FDH 3 FTR cluster 6
Ivanovo region 3 FDH 3 FTR cluster 6
Irkutsk region 3 FDH 3 FTR cluster 6
Karachay-Cherkess Republic 3 FDH 3 FTR cluster 6
Kemerovo region 3 FDH 3 FTR cluster 6
Kirov region 3 FDH 3 FTR cluster 6
Kostroma region 3 FDH 3 FTR cluster 6
Krasnoyarsk region 3 FDH 3 FTR cluster 6
Kurgan region 3 FDH 3 FTR cluster 6
Kursk region 3 FDH 3 FTR cluster 6
Leningrad region 3 FDH 3 FTR cluster 6
Lipetsk region 3 FDH 3 FTR cluster 6
Nizhny Novgorod region 3 FDH 3 FTR cluster 6
Novgorod region 3 FDH 3 FTR cluster 6
Omsk region 3 FDH 3 FTR cluster 6
Orenburg region 3 FDH 3 FTR cluster 6
Oryol region 3 FDH 3 FTR cluster 6
Pskov region 3 FDH 3 FTR cluster 6
Republic of Bashkortostan 3FDH 3 FTR cluster 6
Samara region 3 FDH 3 FTR cluster 6
Saratov region 3 FDH 3 FTR cluster 6
Sverdlovsk region 3 FDH 3 FTR cluster 6
Smolensk region 3 FDH 3 FTR cluster 6
Stavropol region 3 FDH 3 FTR cluster 6
Ulyanovsk region 3 FDH 3 FTR cluster 6
Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2 29
Methodology of Financial Monitoring Based on Cluster Analysis for the Implementation of National Projects in the Russian Regions
Khabarovsk region 3 FDH 3 FTR cluster 6
Chelyabinsk region 3 FDH 3 FTR cluster 6
Yaroslavl region 3 FDH 3 FTR cluster 6
Kamchatka Krai 1 FDH 2 FTR cluster 7
Nenets Autonomous Okrug 1 FDH 2 FTR cluster 7
Republic of Mordovia 1 FDH 2 FTR cluster 7
Republic of Sakha (Yakutia) 1 FDH 2 FTR cluster 7
Sakhalin region 1 FDH 2 FTR cluster 7
Kaliningrad region 2 FDH 1 FTR cluster 8
Republic of North Ossetia-Alania 2 FDH 1 FTR cluster 8
Moscow 1 FDH 1 FTR cluster 9
Tyva Republic 1 FDH 1 FTR cluster 9
Yamalo-Nenets Autonomous
Okrug 1 FDH 1 FTR cluster 9
A detailed analysis of the obtained data confirmed a close correlation between the results of re-
gional clustering based on the proposed method of financial monitoring and information on the achieve-
ment of the target indicators of the national projects under study – healthcare and demography. For ex-
ample, the Nizhny Novgorod region fell into the 6th cluster, which is characterized by an extremely low
level of effectiveness and the highest level of financial risk in the implementation of national projects
in the fields. Information from the Electronic Budget system2 and the Chamber of Control Accounts of
the Nizhny Novgorod region3 was used as a database for the established indicators of national project
implementation. According to official data on total public funding of all projects, 3.4% of funds were
allocated for the implementation of the healthcare national project and 20.2% of funds were allocated
for the demography project. According to information published by the Nizhny the Chamber of Control
Accounts of the Nizhny Novgorod region, the percentage of deviations from the target values for the
demography project was 27.3% and for the healthcare project 39.0%. According to the Federal State
Statistics Service, the Nizhny Novgorod region ranked 60th in terms of birth rate and 65th in terms of
mortality rate among the regions of the Russian Federation in 2021, while decreases in birth rate and life
expectancy and increases in mortality rate and morbidity were recorded. In accordance with the method-
ology for calculating the Federal State Statistics Service, the highest rank (place) is assigned to regions
with the most critical values of indicators (the higher the rank, the worse the socio-economic indicators).
Thus, the negative trends in the fields of healthcare and demography confirm the low effectiveness of
national project implementations in the Nizhny Novgorod region, justifying its place in the 6th cluster.
5. Discussion
The results of the study confirm the applicability of cluster analysis to assessing the effectiveness
of national projects, based on the correspondence of public funding volume with national project tar-
get value achievement, which has been discussed in a number of research works (Khayrullina, 2014;
Revnyakov, 2017; Pushkarev, 2018). However, it was proved that the amount of public funding for
national projects is not a determining factor in the success of their implementation, which was also not-
ed in the work of Ezangina and Gromyshova (2020). For example, among the regions with the largest
amount of funding, only three (Moscow, Tyva Republic, Yamalo-Nenets Autonomous Okrug) fell into
the 9th cluster, which is characterized by the highest level of effectiveness and low financial risk. At the
same time, the Republic of North Ossetia-Alania is characterized by a high level of effectiveness in the
implementation of national projects, with a moderate financial risk despite the relatively low volume of
public funding.
It is obvious that the financial monitoring of national projects should be carried out taking into
2
Unified portal of the budget system of the Russian Federation “Electronic budget”. https://budget.gov.ru/Регионы
3
Chamber of Control and Accounts: official website. https://ksp.r52.ru/
30 Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2
Yashina, N., Kashina, O., Yashin, S., Pronchatova-Rubtsova, N.
account the relationships and interdependence of the results achieved (Balkhi et al., 2021; Wirayuda and
Chan, 2021; Ivankova et al., 2022); therefore, the proposed methodology can be improved by expanding
the set of national project indicators and developing models based on them.
6. Conclusion
The study confirmed the importance of improving financial monitoring as an element of state con-
trol over the implementation of national projects in the Russian regions.
The hypothesis was proved that the risks of not achieving the targets of national projects in the
fields of health and demography reinforce each other. The problems in achieving target indicators for
healthcare and demographic national projects implementation in the Russian regions are caused by the
following factors:
- lack of one-time support for the births of fourth, fifth, and subsequent children;
- lack of in vitro fertilization cycles for families with infertility;
- low employment level for women with children of preschool age;
- lack of access to preschool education for children aged 1.5 to 3 years;
- insufficient coverage of citizens older than working age with preventive examinations, including
clinical examinations;
- lack of geriatric centres and geriatric departments;
- high mortality rate of women aged 16–54 and men aged 16–59 years;
- insufficiency of public funding to meet national goals in the fields of health and demography in
regions with insufficient own financial resources; and
- shortage of personnel to meet national goals in the fields of health and demography.
The correlation of the results of the study with the actual implementation of national projects
confirms the effectiveness of the proposed methodology for their financial monitoring based on cluster
analysis. The data obtained in the course of monitoring can be used by state authorities to develop a flex-
ible strategy for national project funding in the Russian regions, taking into account the level of target
indicator achievement.
Acknowledgement
The study was carried out within the framework of the realization of the Strategic Academic Lead-
ership Programme “Priority 2030”, project Н-426-99_2022–2023 “Socio-economic models and technol-
ogies for creative human capital development in the innovative society”
References
Ariste, R., Di Matteo, L., 2017. Value for money: an evaluation of health funding in Canada. Int. J. Health Econ. Manag. 17, 289–310.
https://doi.org/10.1007/s10754-016-9204-6
Averin, G., Zviagintseva, A., Konstantinov, I., Shvetsova, A., 2018. Method and criteria for assessing the sustainable development. J. Soc.
Sci. Res. 1, 106–112. https://doi.org/doi.org/10.32861/jssr.spi1.106.112
Balkhi, B., Alshayban, D., Alotaibi, N., 2021. Impact of healthcare funding on healthcare outcomes in the Middle East and North Af-
rica (MENA) region: a cross-country comparison, 1995–2015. Front. Public Health 8, 624–962. https://doi.org/10.3389/
fpubh.2020.624962
Bogoviz, A., Elykomov, V., Osipov, V., Kelina, K., Kripakova, L., 2019. Barriers and perspectives of formation of the e-healthcare system
in modern Russia. Studies in Computational Intell. 826, 917–923. https://doi.org/10.1007/978-3-030-13397-9_94
Chebyshev, I., 2021. Monitoring of cash execution of the state budget during the implementation of national projects and the choice of
economic development directions. Vestnik Universiteta 5, 134–140. https://doi.org/10.26425/1816-4277-2021-5-134-140
Endovitsky, D., Endovitskaya, E., Golovin, S., Churikov, A., 2021. Monitoring the implementation of the national healthcare project. Lec-
ture Notes in Networks and Systems 205, 871–878. https://doi.org/10.1007/978-3-030-73097-0_97
Ezangina, I., Gromyshova, O., 2020. Directions for improving the monitoring system of state programs of socio-economic development of
Russia. Finance: Theory and Practice 24(5), 112–127. https://doi.org/10.26794/2587-5671-2020-24-5-112-127
Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2 31
Methodology of Financial Monitoring Based on Cluster Analysis for the Implementation of National Projects in the Russian Regions
Fattakhov, R., Nizamutdinov, M., Oreshnikov, V., 2019. Assessment of the sustainability of the socio-economic development of the regions
in Russia. World of New Economy 13(2), 97–110. https://doi.org/10.26794/2220-6469-2019-13-2-97-110
Gallardo-Albarrán, D., 2018. Health and economic development since 1900. Econ. Hum. Biol. 31, 228–237. https://doi.org/10.1016/j.
ehb.2018.08.009
Ivankova, V., Gavurova, B., Khouri, S., 2022. Understanding the relationships between health funding, treatable mortality and economic
productivity in OECD countries. Front. in Public Health 10, 1–14. https://doi.org/10.3389/fpubh.2022.1036058
Khayrullina, M., 2014. Innovative territorial clusters as instruments of Russian regions development in global economy. Procedia Econom-
ics and Finance 16, 88–94. https://doi.org/10.1016/S2212-5671(14)00778-3
Kozlova, O., Nifantova, R., Makarova, M., 2017. Methods of the assessment of economic losses caused by the mortality of the population
employed in regional economy. Econ. Reg. 13, 511–523. https://doi.org/10.17059/2017-2-16
Mihalache, I., 2019. Health state of human capital in the economic theory. Postmodern Open. 10, 182–192. https://doi.org/10.18662/po/102
Mishlanova, M., 2022. Development trends of the Russian system of national projects. Transport. Res. Procedia 63, 1575–1581. https://
doi.org/10.1016/j.trpro.2022.06.170
Piskun, E., Khokhlov, V., 2019. Economic development of the Russian Federation’s regions: factor-cluster analysis. Economy of Region.
5(2), 363–376. https://doi.org/10.17059/2019-2-5
Pushkarev, A., 2018. Cluster analysis of regional innovation activity in Russia in 2010–2015. R-ECOMONY 4(1), 10–17. https://doi.
org/10.15826/recon.2018.4.1.002
Revnyakov, G., 2017. Analysis of the regional financial cluster strategies implementation. Russian J. Ind. Econ. 10(1), 82–88. https://doi.
org/10.17073/2072-1633-2017-1-082-088
Romanova, T., Andreeva, O., Sukhoveeva, A., Kaptsova, V., 2019. Targeting the principal implementation in the system of social support.
Int. J. Econ. Bus. Admin. 7(2), 52–62. https://doi.org/10.35808/ijeba/370
Shah, M., Rehman, A., Zeeshan, M., Afridi, F., 2021. Public health funding and health outcomes in Pakistan: evidence from quantile autore-
gressive distributed lag model. Risk. Manag. Healthc. Policy 14, 3893–3909. https://doi.org/10.2147/RMHP.S316844
Sharma, R., 2018. Health and economic growth: evidence from dynamic panel data of 143 years. PLoS ONE 13(10), 1–20. https://doi.
org/10.1371/journal.pone.0204940
Soofi, M., Matin, B., Karyani, A., Rezaei, S., Soltani, S., 2021. Health-care determinants of mortality and recovered cases from COVID-19:
do heath systems respond COVID-19 similarly? J. Educ. Health Promot. 10, 34485557. https://doi.org/10.4103/jehp.jehp_1509_20
Wirayuda, A., Chan, M., 2021. A systematic review of sociodemographic, macroeconomic, and health resources factors on life expectance.
Asia Pac. J. Public Health 33, 335–356. https://doi.org/10.1177/1010539520983671
Yashina, N., Kashina, O., Pronchatova-Rubtsova, N., Yashin, S., Kuznetsov, V., 2022 (a). Assessment of budgetary stresses for socio-eco-
nomic development of Regions. Lecture Notes in Networks and Systems. 368, 620–631. https://doi.org/10.1007/978-3-030-
93244-2_68
Yashina, N., Kashina, O., Pronchatova-Rubtsova, N., Yashin, S., Kuznetsov, V., 2022 (b). Diagnostics of budgetary potential of regions in
order to implement the value-oriented financial policy of state. Big Data in the GovTech System. Stud. in Big Data 110, 189–197.
https://doi.org/10.1007/978-3-031-04903-3_22
Yudintsev, A., Troshkina, G., 2023. Socio-economic development of Russian federal entities in 2019: multivariate data analysis. Lecture
Notes in Networks and Systems 234, 141–150. https://doi.org/10.1007/978-3-030-75483-9_14
Список источников
Ariste, R., Di Matteo, L., 2017. Value for money: an evaluation of health funding in Canada. Int. J. Health Econ. Manag. 17, 289–310.
https://doi.org/10.1007/s10754-016-9204-6
Averin, G., Zviagintseva, A., Konstantinov, I., Shvetsova, A., 2018. Method and criteria for assessing the sustainable development. J. Soc.
Sci. Res. 1, 106–112. https://doi.org/doi.org/10.32861/jssr.spi1.106.112
Balkhi, B., Alshayban, D., Alotaibi, N., 2021. Impact of healthcare funding on healthcare outcomes in the Middle East and North Africa (MENA)
region: a cross-country comparison, 1995–2015. Front. Public Health 8, 624–962. https://doi.org/10.3389/fpubh.2020.624962
Bogoviz, A., Elykomov, V., Osipov, V., Kelina, K., Kripakova, L., 2019. Barriers and perspectives of formation of the e-healthcare system
in modern Russia. Studies in Computational Intell. 826, 917–923. https://doi.org/10.1007/978-3-030-13397-9_94
Chebyshev, I., 2021. Monitoring of cash execution of the state budget during the implementation of national projects and the choice of
economic development directions. Vestnik Universiteta 5, 134–140. https://doi.org/10.26425/1816-4277-2021-5-134-140
Endovitsky, D., Endovitskaya, E., Golovin, S., Churikov, A., 2021. Monitoring the implementation of the national healthcare project. Lec-
ture Notes in Networks and Systems 205, 871–878. https://doi.org/10.1007/978-3-030-73097-0_97
Ezangina, I., Gromyshova, O., 2020. Directions for improving the monitoring system of state programs of socio-economic development of
Russia. Finance: Theory and Practice 24(5), 112–127. https://doi.org/10.26794/2587-5671-2020-24-5-112-127
Fattakhov, R., Nizamutdinov, M., Oreshnikov, V., 2019. Assessment of the sustainability of the socio-economic development of the regions
in Russia. World of New Economy 13(2), 97–110. https://doi.org/10.26794/2220-6469-2019-13-2-97-110
Gallardo-Albarrán, D., 2018. Health and economic development since 1900. Econ. Hum. Biol. 31, 228–237.
https://doi.org/10.1016/j.ehb.2018.08.009
Ivankova, V., Gavurova, B., Khouri, S., 2022. Understanding the relationships between health funding, treatable mortality and economic
productivity in OECD countries. Front. in Public Health 10, 1–14. https://doi.org/10.3389/fpubh.2022.1036058
Khayrullina, M., 2014. Innovative territorial clusters as instruments of Russian regions development in global economy. Procedia Econom-
ics and Finance 16, 88–94. https://doi.org/10.1016/S2212-5671(14)00778-3
Kozlova, O., Nifantova, R., Makarova, M., 2017. Methods of the assessment of economic losses caused by the mortality of the population
employed in regional economy. Econ. Reg. 13, 511–523. https://doi.org/10.17059/2017-2-16
Mihalache, I., 2019. Health state of human capital in the economic theory. Postmodern Open. 10, 182–192. https://doi.org/10.18662/po/102
Mishlanova, M., 2022. Development trends of the Russian system of national projects. Transport. Res. Procedia 63, 1575–1581.
https://doi.org/10.1016/j.trpro.2022.06.170
Piskun, E., Khokhlov, V., 2019. Economic development of the Russian Federation’s regions: factor-cluster analysis. Economy of Region.
5(2), 363–376. https://doi.org/10.17059/2019-2-5
32 Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2
Yashina, N., Kashina, O., Yashin, S., Pronchatova-Rubtsova, N.
Pushkarev, A., 2018. Cluster analysis of regional innovation activity in Russia in 2010–2015. R-ECOMONY 4(1), 10–17.
https://doi.org/10.15826/recon.2018.4.1.002
Revnyakov, G., 2017. Analysis of the regional financial cluster strategies implementation. Russian J. Ind. Econ. 10(1), 82–88.
https://doi.org/10.17073/2072-1633-2017-1-082-088
Romanova, T., Andreeva, O., Sukhoveeva, A., Kaptsova, V., 2019. Targeting the principal implementation in the system of social support.
Int. J. Econ. Bus. Admin. 7(2), 52–62. https://doi.org/10.35808/ijeba/370
Shah, M., Rehman, A., Zeeshan, M., Afridi, F., 2021. Public health funding and health outcomes in Pakistan: evidence from quantile autore-
gressive distributed lag model. Risk. Manag. Healthc. Policy 14, 3893–3909. https://doi.org/10.2147/RMHP.S316844
Sharma, R., 2018. Health and economic growth: evidence from dynamic panel data of 143 years. PLoS ONE 13(10), 1–20.
https://doi.org/10.1371/journal.pone.0204940
Soofi, M., Matin, B., Karyani, A., Rezaei, S., Soltani, S., 2021. Health-care determinants of mortality and recovered cases from COVID-19:
do heath systems respond COVID-19 similarly? J. Educ. Health Promot. 10, 34485557. https://doi.org/10.4103/jehp.jehp_1509_20
Wirayuda, A., Chan, M., 2021. A systematic review of sociodemographic, macroeconomic, and health resources factors on life expectance.
Asia Pac. J. Public Health 33, 335–356. https://doi.org/10.1177/1010539520983671
Yashina, N., Kashina, O., Pronchatova-Rubtsova, N., Yashin, S., Kuznetsov, V., 2022 (a). Assessment of budgetary stresses for socio-economic
development of Regions. Lecture Notes in Networks and Systems. 368, 620–631. https://doi.org/10.1007/978-3-030-93244-2_68
Yashina, N., Kashina, O., Pronchatova-Rubtsova, N., Yashin, S., Kuznetsov, V., 2022 (b). Diagnostics of budgetary potential of regions in
order to implement the value-oriented financial policy of state. Big Data in the GovTech System. Stud. in Big Data 110, 189–197.
https://doi.org/10.1007/978-3-031-04903-3_22
Yudintsev, A., Troshkina, G., 2023. Socio-economic development of Russian federal entities in 2019: multivariate data analysis. Lecture
Notes in Networks and Systems 234, 141–150. https://doi.org/10.1007/978-3-030-75483-9_14
The article was submitted 01.08 2023, approved after reviewing 28.08 2023, accepted for publication 02.09.2023.
Статья поступила в редакцию 01.08.2023, одобрена после рецензирования 28.08 2023, принята к публикации
02.09.2023.
About authors:
1. Nadezhda Yashina, Doctor of Economics, Head of the Department of Finance and Credit, Lobachevsky State Uni-
versity of Nizhny Novgorod, Nizhny Novgorod, Russia. [email protected], https://orcid.org/0000-0002-0630-7949
2. Oksana Kashina, PhD in Economics, Associate Professor of the Department of Finance and Credit, Lo-
bachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia. [email protected],
https://orcid.org/0000-0002-7256-4628
3. Sergey Yashin, Doctor of Economics, Head of the Department of Management and Pub-
lic Administration, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
[email protected], https://orcid.org/0000-0002-7182-2808
4. Natalia Pronchatova-Rubtsova, Lecturer, Department of Finance and Credit, Lobachevsky State University of
Nizhny Novgorod, Nizhny Novgorod, Russia. [email protected], https://orcid.org/0000-0002-8924-2991
Информация об авторах:
1. Надежда Яшина, доктор экономических наук, заведующий кафедрой финансов и кредита, Нижегородский
государственный университет им. Н.И. Лобачевского, Нижний Новгород, Российская Федерация.
[email protected], https://orcid.org/0000-0002-0630-7949
2. Оксана Кашина, кандидат экономических наук, доцент кафедры финансов и кредита, Нижегородский
государственный университет им. Н.И. Лобачевского, Нижний Новгород, Российская Федерация.
[email protected], https://orcid.org/0000-0002-7256-4628
3. Сергей Яшин, доктор экономических наук, заведующий кафедрой менеджмента и государственного
управления, Нижегородский государственный университет им. Н.И. Лобачевского, Нижний Новгород,
Российская Федерация. [email protected], https://orcid.org/0000-0002-7182-2808
4. Наталия Прончатова-Рубцова, преподаватель кафедры финансов и кредита, Нижегородский
государственный университет им. Н.И. Лобачевского, Нижний Новгород, Российская Федерация.
[email protected], https://orcid.org/0000-0002-8924-2991
Sustain. Dev. Eng. Econ. 2023, 3, 2. https://doi.org/10.48554/SDEE.2023.3.2 33