CC BY
0DOI: 10.29141/2658-5081-2024-25-2-4 EDN: DLXTDQ JEL classification: R11, R12, R41
Svetlana G. Pyankova Ural State University of Economics, Ekaterinburg, Russia Ekaterina S. Zakolyukina ZAO Lucky Motors, Ekaterinburg, Russia
The effect of transport parameters on regional economic development: The case of the Ural Federal District
Abstract. The transport has a special role to play in the regional socioeconomic development, which changes depending on the prevailing trends for population mobility, introduction of advanced technologies, extension of economic connections, and growth in regional production. The study explores the problem of the interface between transport and regional economy on the example of the Ural Federal District. Methodologically, it relies on the theories of spatial development and regional economics. The main method of research is correlation and regression analysis. The evidence is the socioeconomic data on transport as well as values of gross regional product of the Ural Federal District for 2010-2020 sourced from the Federal State Statistics Service of the Russian Federation. The paper develops a model for forecasting GRP. The analysis indicates the presence of the relationship between the socioeconomic metrics of the transport sector and gross regional product of the Ural Federal District. In particular, there is the correlation between the indicators "density of public roads with hard surface", "deaths in road accidents per 100,000 population" and gross regional product of the Ural Federal District. The paper concludes about the need to develop a composite indicator for assessing the transport sector that will reflect the ongoing digital modernisation and introduction of innovations.
Keywords: transport sector; regional economy; gross regional product; socioeconomic indicators; digital transport infrastructure; Ural Federal District.
For citation: Pyankova S. G., Zakolyukina E. S. (2024). The effect of transport parameters on regional economic development: The case of the Ural Federal District. Journal of New Economy, vol. 25, no. 2, pp. 69-88. DOI: 10.29141/2658-5081-202425-2-4. EDN: DLXTDQ.
Article info: received December 8, 2023; received in revised form January 15, 2024; accepted January 25, 2024
Introduction
In his study of transportation and regional development Campbell noted that "In
nations of all stages of economic development, man is continuing to seek means of movement from one place to another at the least possible expenditure of time and cost" [Campbell, 1963, p. 7]. At present, transport is essential both in the daily life of every person and in the general functioning of the economy.
Globalisation increases population mobility, generates and strengthens the connections between economic agents, which is accurate both for private life and for economic processes unfolding at the level countries and regions.
In the Russian Federation, rail and pipeline transport are prioritised. The use of low-cost ground transport is also of high relevance [Pyanykh, 2020, p. 104]. Transport development contributes to the economic prosperity of Russia and its regions, reduces the asymmetry of national space [Zenkina, Kutova, 2019].
The law1 defines transport or a transport complex as objects and subjects of transport infrastructure and vehicles. The availability of efficient transport not only strengthens economic relationships and expands the production, but also improves the quality of life. Transport routes are geopolitically important, which is mirrored in the economic aspect as well (for instance, economic involvement of new territories) [Pyanykh, 2020, p. 103].
The purpose of the study is to reveal the relationships between transport and economy of a region on the example of the Ural Federal District. To accomplish this purpose, the following objectives are to be completed:
- providing a theoretical review on the role of transport in the regional economic development;
- performing a correlation and regression analysis of the relationship between socioeconomic metrics of the transport and gross regional product of the Ural Federal District.
Theoretical basis of research
Transport performs the function of delivery of ready products to consumers and thereby, finalises the whole production process. Without the functioning of transport industry, whose 'end product' is transportation, other basic industries cannot operate [Povarov, Selezneva, 2015].
Povarov and Selezneva [2015] point to a number of specificities of transport that underlie its role in the national economy:
- the product that transport produces is movement, which augments the cost of goods shipped;
1 Federal law of February, 9 2007 no. 16-FZ "On transport security". Consultant Plus. http://www.consultant.ru/ document/cons_doc_LAW_66069/. (In Russ.)
- the level of transport development significantly influences the location of productive forces across the state's territory;
- other industries' activities are difficult without the functioning of transport;
- transport finalises the production process;
- the functioning of transport needs fuel, lubricants and other auxiliary materials, but raw materials are not consumed;
- the processes of production and consumption of the transport's product occur simultaneously;
- transport industry belongs to natural monopolies;
- transport industry has a greater capital-labour ratio and capital intensity than other industries;
- production, servicing infrastructure are dispersed across the territories of different administrative units;
- the special distribution of working and rest time of transport workers.
Shcherbanin [2011] notes that there are different approaches to what comes first:
the economy impacting on transport or transport stimulating the economy. With regard to this, he speaks of two divergent views within transport economic community. According to the first one, the development of transport favours economic growth, while the second one denies the connection between the development of transport and economic growth. Despite this, certain aspects of this connection can be identified: the level of transport development allows judging about the state's spatial development and make conclusions about transport accessibility of regions, resources, and production facilities.
The importance of transport for the economic development proceeds from its multiple connections with different forms of human activities [Kozlak, 2017]. The complication in understanding these connections consists in that it is difficult to identify the cause-effect mechanisms. Many studies contain conclusions about positive effects generated by the development of transport infrastructure, however, there is no consensus about the directions and strength of these effects.
In line with Limani [2016], transport can be considered to be the vital factor in the economic development and growth (Table 1).
In many developed states, investment in transport industry and improvement of technologies in the 21st century have brought the transport costs down, what stimulated countries' economic growth and development. At the same time, despite the significance of transport for the development generally, its particular impact depends on many factors. Since these factors are explored insufficiently, there is high probability that the investment in transport industry may not produce the results expected [Berg et al., 2017]. Nevertheless, none type of transport has been reported to be solely responsible for economic growth. The complexity of the relationships between the
Table 1. Factors affecting the relationship between transport and the economy
Factors The economic assessment of the effects of transport factors Other impact of transport
Positive Negative Social Environmental
Infrastructure Tax revenues. Productivity. Mobility. Accessibility Deterioration Level of sufficient access to transport system diversity and basic activities Land fragmentation. Land take
Mobility Movement of goods and peo-ple-increased productivity - Noise. Psychological pressure Air pollution. Energy resources depletion
Congestion - Time waste, operational cost increase Psychological and physical pressure Air pollution. Energy resources depletion
Energy use Tax revenues. Productivity increase - - Air pollution. Energy resources depletion
Safety and security (accidents) - Costs of crashes Injuries Congestion
Equity Equal share of transport costs - Level of sufficient access to transport system diversity and basic activities Equal accessibility distribution
Accessibility Increased mobility broadening product distribution, service, and labour markets - Access for people with disabilities -
Source: [Limani, 2016].
transport industry and economic development consists in the diversity of potential effects [Nistor, Popa, 2014].
Recently, the science and technology progress has transformed transport, and information technologies have taken on a more influential role. There arises the question whether the effects produced by modern transport infrastructure are exclusively positive. For example, uberization (derived from Uber company name) may negatively affect the economy [Pyankova, Zakolyukina, 2022].
Vukic, Mikulic and Kecek [2021] have studied the impact of transportation on Croatian economy and qualitatively measured its multiplier effect for 2004-2015 using input-output analysis. They found that multiplier effects of the transportation sector in Croatia were significant in the observed period, especially for the air transport sector. In general, utilization of modern and efficient transportation exerts significant impact on the growth of other types of economic activities and socioeconomic development of Croatia.
Macheret [2020] focuses on commodity exchange, i.e., trade and transport, as a contributing factor in economic development. He proposes a theoretical model of influence of transport costs on production and sale of goods that reveals the economic relationships between commodity exchange and production activities. This model proves that reducing transport costs is a catalyst for economic growth, while poor development of transport leads to conservation of the technical and technological situation and low production efficiency.
Litvinova and Ponomarev [2016] state that the development of road infrastructure affects positively the total factor productivity. In particular, they argue that a 1 % increase in its density causes an increase of total factor productivity by 0.26 %, ceteris paribus.
Postnikov and Butorina [2014] attempt to establish a correlation between the following indicators: GDP per capita, level of automobilization, public road network size. The scholars report that there is the correlation between them, which also confirms the influence of the transport development on the economy.
Komarova et al. [2021] emphasise the relationship between the quality of transport infrastructure and level of economic development. They note that effective transport can bring about positive multiplier effects, for instance, increase employment and improve access to markets, etc. This relationship is identified by them on the example of metropolises. Gross regional product is employed as an indicator of the level of economic development. Their findings confirm that transport is one of the tools accelerating the economic development, in particular, that of metropolises.
Luz et al. [2016] examined the relationship between transport infrastructure and economic development based on the linear regression analysis of the Logistic Performance Index (LPI) of the World Bank and GDP of the top ten economies for
2010-2014. The results showed that there is no relationship between the two variables, but at the same time suggest that this relationship is positive when considering the GDP per capita.
Lithuanian researchers Sevcenko-Kozlovska and Ciziuniene [2022] looked at the impact of transport sector on GDP of neighboring countries on the example of the Baltic states. They have identified a combination of key factors in the Lithuanian transport sector that affect differences in the level of real GDP per capita.
Shibata, Yano and Kosaka [2010] investigated the historical impact of high-speed transportation development on Japan's economic growth. They found that Japan's economic growth is positively correlated with the development of transport, in particular, of a highway system, and also discovered problems associated with economic disparities among regions, in particular, centralization (concentration of people and goods) and decentralization occurred in core and local regions, respectively.
Zhang and Cheng [2023] explored the said relationship in the Great Britain considering a longer period of 1970-2017 and found differences in the mutual impact on each other between short and long terms. In the long term, transport infrastructure has a positive effect on the economy, while in the short run, it fails to foster economic development.
Indian researchers Ghosh and Dinda [2019] considered the relationship between transport infrastructure and economic growth in India in 1990-2017. According to them, road and air transports have significant positive contribution to economic growth in the long-run while rail transport is insignificant.
A research of the relationship between transport infrastructure and foreign direct investment (FDI) in explaining economic growth from the road and air transport perspectives was performed for Ethiopia by Badada et al. [2023]. They established that transport infrastructure has a significant long-term economic effect, while the short-run dynamics demonstrates the speed of adjustment is corrected by 81 % each year toward the long-run path, and thereby transport infrastructure attracts FDI in the country.
The higher the transport quality and efficiency are, the greater are a country's opportunities for economic growth. The scholarly literature points to various benefits obtained from highly developed transport industry, including from the creation of efficient transport services and reduction in transport costs [Heldmann, 1973].
Transformations in transport industry impact on the accessibility of regions, which affects the attractiveness of different regions, employment possibilities, land prices [Mullen, Marsden, 2015]. Efficient functioning of transport produces such positive outcomes as improved access to markets, higher employment, attracted investments, while its poor development can lead to additional economic costs the waste of opportunities, and impaired quality of life [Stankovic, 2021].
The availability of efficient transport infrastructure and vehicles facilitates the distribution of goods, which favours price stability. In line with Djajasinga, the goal of transport is to deliver services, which are needed by society every day [Djajasinga 2021, p. 347].
Efficient flow of goods largely depends on transport [Okechukwu, Madonsela, Adetunla, 2020]. Investment in transport industry is a tool of regional development [Nistor, Popa, 2014].
From the perspective of the regional economic development, transport is one of the key factors in the formation of social and economic space. The sensitivity of various industries to changes in transport is heterogeneous. For instance, Efimova [2009] identifies different reactions of groups of industries depending on changes in transportation: the transformation of transportation conditions causes the redistribution of the commercial activity between these and other regions; such changes stimulate higher growth rates; the effect of the transport industry in minimal (business targets exclusively the local market).
Figure 1 presents the main methods for assessing the impact of transport industries on regional economic development.
Fig. 1. Methods of assessing the impact of transport on regional economic development
While studying this topic Pokharel, Bertolini and Brömmelstroet [2023] have determined the next key aspects:
- the impact of transport on regional development is complex, systemic, and dynamic;
- interregional transport infrastructure determines the possibilities for the emergence and growth cities;
- urban transport infrastructure guides how populous and expansive a city can be;
- timing, scale, and location of transport investments shape cities and regional GDP;
- regional inequality is highly influenced by the location and size of cities.
Wei [2021] emphasizes that agglomeration has recently become the major factor in regional economic development. Transport development is believed to be the key tool in urbanisation of a region [Maparu, Mazumder, 2017].
Kataeva [2013] applies correlation analysis to justify the relationship between transport and regional economy. In the analysis, she uses socioeconomic indicators of regional development (GRP, population, etc.), and data on transport infrastructure development (number of motor vehicles, length of road network, number of road traffic accidents, etc.). The greatest impact on GRP and GP (gross product created by the group of economic activities "Transport and communications") is exerted by the next metrics: freight transported by road transport; rail passenger traffic; density of railway lines. The scholar underscores that the major problem in the management of transport infrastructure of a region is lack of adequate tools for measuring the level of its development.
Berezhnaya [2019] points to the mutual influence of transport and regional economy on each other. Problems existing in a region may adversely affect the functioning of its transport infrastructure. However, the researcher specifies that there is no scientific unanimity about the influence of transport infrastructure on the socioeconomic indicators of a region. Transport can accelerate regional development, while its inefficiency creates obstacles for it [Djajasinga, 2021].
Milewski and Zaloga [2013] highlight that transport development may influence a region both directly and indirectly. They have developed a model reflecting this influence that accounts for possible direct and indirect effects. The scholars especially consider direct effects, which include time, costs, traffic bottlenecks, and safety.
Among the plurality of effects produced by transport and distribution centres operating in the socioeconomic system of a region, Kaznacheev [2012] points to the effect for regional economy that is related to the transformation of potential and latent clusters in stable and strong ones.
Prus and Sikora [2021] undertook a study on the impact of transport infrastructure on the sustainable socioeconomic development of a region considering the case of the Walcz Lake District. They confirmed this impact and also have shown existing differentiation in both the development of infrastructure and the economic attractiveness of urban and rural areas.
We need to stress that currently the importance of transport as a driver of the socioeconomic development is becoming stronger urged by globalization processes. The colossal progress in the technical component of transport vehicles and means of communication has led to that the world has got smaller [Heldmann, 1973]. Yet the only availability of transport infrastructure and sufficiency with transport vehicles is not enough for economic growth [Lenz, Skender, Mirkovic, 2018]. There is the growing importance of introducing digital, information and communication technologies
in ensuring the transport sector's competitiveness. At present, efficient movement of people and freight creates the competitive advantage for the economy, therefore, high-quality transport infrastructure that meets modern requirements is needed. The socioeconomic development and prosperity of a region or a nation under globalisation is undoubtedly linked with this or that level of movement of people and exchange of goods, and the intensity of using advanced technologies.
Based on the literature review on the problem under consideration we can conclude the following:
- transport is significant for the economy, socioeconomic system in general as well as for other industries;
- there is no consensus on the nature of the relationship between transport and the economy. There are divergent views on the answers to many questions: What is the root cause? Does transport impact on the economy or vice versa? Is there a relationship between transport and economic growth? What potential effects can occur?;
- some studies point to different impact of transport over short- and long-term time horizons.
We believe that the weak link in the chain is the lack of uniformity of the methods for identifying the relationship between transport and economic processes. While GDP and GRP are traditionally taken as the indicators of economic development, the transport indicators involved in the analysis are rather diverse: transport costs, the level of automobilization, length of public road network, logistic performance index, indicators characterising particular type of transport, number of roads, road traffic accidents, etc. Consequently, the findings of various research may contradict each other. Moreover, the complication in revealing this relationship consists in highly dynamic development of transport. Currently, this process is connected with the science and technology progress, a trend towards the introduction of digital, information and communication technologies in the transport sector, uberization etc., thus, the reliance on such indicators as freight transported and passengers caried is insufficient for the assessment.
Materials and methods
The theoretical review allows concluding that when studying the relationship between transport and economic development researchers apply various statistical methods, among which the most frequently used is correlation and regression analysis. At this, GDP and GRP serve as economic growth indicators.
In our study we examine the relationship between the socioeconomic indicators of transport and economic development of the Ural Federal District1. To identify the
1 Regions of Russia. Socioeconomic indicators: Statistical Yearbook. Moscow: Rosstat. https://rosstat.gov.ru/ folder/210/document/13204. (In Russ.)
relationship between predictor and response variables we employ multiple analysis. The analysis period is 2010-2020, for we have decided not to take on comparing the results for short- and long-term term. The response variable y is GRP of the Ural Federal District. Figure 2 presents the dynamics of GRP in the Ural Federal District for 2010-2020.
Million rubles
:
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Fig. 2. GRP of the Ural Federal District, 2010-20201
The predictor variables X1—X12 are the following statistical indicators: x1 is public rail, goods transported (million tonnes); x2 is public rail, passengers carried (thousand persons); x3 is motor vehicles of organisations of all types of economic activities, goods transported (million tonnes); x4 is motor vehicles of organisations of all types, goods transported (million tonne-km); x5 is public buses, passenger carried (million persons); x6 is public buses, passengers carried (million passenger-km); x7 is the proportion of paved (hard) roads in the total length of public roads (%); x8 is the proportion of hard roads with improved surface in the total length of public paved roads (%); x9 is the density of public roads with hard surface, (km per 1,000 km2); x10 is public buses per 100,000 population (units); x11 is road traffic accidents per 100,000 population (units); x12 is deaths in road accidents per 100,000 population (persons). These indicators were chosen from the open access socioeconomic statistics on the development of Russia's regions.
Results and discussion
In the course of the analysis we obtained pair correlation coefficients presented in Table 2.
Correlation coefficients are needed for further analysis of the relationship. In the matrix this coefficient is located at the intersection of the variables. The closer its value to 1 or -1, the stronger the correlation. As we can see from the matrix obtained, there is strong correlation between the majority of the predictor variables, therefore, there is multicollinearity, which can result in less reliable statistical inferences (when predictor variables are strongly correlated, even minor changes in the data cause substantial changes in the values of coefficients).
1 Regions of Russia. Socioeconomic indicators: Statistical Yearbook. Moscow: Rosstat. https://rosstat.gov.ru/
folder/210/document/13204. (In Russ.)
Table 2. Correlation matrix
x3 X4 x5 x6 x7 x8 Xg *10 Xu x12 7
Xi 1
x2 -0.53172 1
x3 0.178258 0.632084 1
%4 0.731655 -0.12636 0.474217 1
x5 -0.49408 0.960376 0.717067 -0.04882 1
Xtj -0.51221 0.966637 0.662462 -0.14597 0.979774 1
x7 -0.90579 0.69305 0.035132 -0.48007 0.652875 0.665414 1
x$ -0.74619 0.87824 0.433918 -0.27149 0.88151 0.878694 0.902029 1
Xt) 0.746019 -0.90147 -0.45546 0.256058 -0.89467 -0.88956 -0.90204 -0.98255 1
*io -0.37894 0.85197 0.626592 -0.28107 0.814528 0.855254 0.424475 0.627167 -0.68196 1
Xn -0.08724 0.761896 0.911224 0.16209 0.83781 0.826183 0.258303 0.63131 -0.62258 0.734863 1
X\2 -0.24127 0.844028 0.845749 0.13259 0.868522 0.859073 0.428076 0.715613 -0.759 0.773181 0.914906 1
y 0.498936 -0.81052 -0.70482 0.121501 -0.85972 -0.84903 -0.62998 -0.85368 0.869731 -0.71819 -0.84814 -0.91322 1
For there is no uniform method for fixing multicollinearity, to proceed further with analysis we will remove some of the highly correlated variables by sorting them. When sorting out the variables, we will take the highest correlation coefficients between each pair of the x and compare it with correlation coefficient of each x in the pair with y.
The variable x1 has the strongest correlation with the variable x7. Compared to other variables, y is correlated more strongly with x7. Therefore, we remove the variable x1.
The variable x2 has the strongest correlation with the variable x6. Among these variables, y is correlated more strongly with x6. Therefore, we remove the variable x2 from the model.
The variable x3 has the strongest correlation with the variable x11. Among these variables, y is correlated more strongly with x11. Therefore, we remove the variable x3 from the model.
The variable x4 has rather weak correlation with y, therefore, we remove this predictor variable from the model.
The variable x5 has the strongest correlation with the variable x6. Among these variables, y is correlated more strongly with x5. Therefore, we remove the variable x6 from the model.
The variable x7 has the strongest correlation with the variables x1, x8, x9. We removed the variable x1 earlier, and the variables x8, x9 are correlated with y more strongly than the variable x7. Therefore, we remove the variable x7 from the model.
The variable x8 has the strongest correlation with the variable x9. Among these variables, y is correlated more strongly with x9. Therefore, we remove the variable x8 from the model.
The variable x10 has the strongest correlation with the variable x12. Among these variables, y is correlated more strongly with x12. Therefore, we remove the variable x10 from the model.
The variable x11 has the strongest correlation with the variable x12. Among these variables, y is correlated more strongly with x12. Therefore, we remove the variable x11 from the model.
The variable x5 also has strong correlation with the variables x9 and x12, -0.89 and 0.86 respectively. At this, the variable x5 is less correlated with y, than x9 and x12 Therefore, we remove the variable x5 from the model.
The variables x9 and x12 are enough correlated with y, their pair correlation coefficient is -0,7, which we will take as a threshold value.
Thus, to proceed with the regression model further we will use the predictor variables x9 and x12. Figures 3 and 4 show the regression relationships between these variables and the response variable y.
M
1=1 O
a a
Ph" Pi
O
14 12 10 8 6 4
20 30 40 50
Density of public roads with hard surface, km per 1,000 km2
Fig. 3. Regression relationship between Ural Federal District's GRP and density of public roads with hard surface, 2010-2020
14
<L>
■§ 12
)H
Ö 10
o
•»H 8
ti
Ph 6
Pi
Ü 4
ê
i
• •
•
10 15 20
Deaths in road accidents per 100,000 population
25
Fig. 4. Regression relationship between Ural Federal District's GRP and deaths
in road accidents, 2010-2020
The next stage of the analysis involves the calculation of coefficients for the multiple linear regression model:
y = fl0 + a9x9 + «12*12, (1)
The obtained equation takes the form:
y = 9868583 + 153632.2x9 + (-422246)x12. (2)
Calculation results are given in Tables 3-4.
Table 3. Regression statistics
Parameter Value
Multiple R 0.9526
R-squared 0.9075
Adjusted R-squared 0.8844
Standard error 925510.1547
Observations 11
Table 4. Dispersion analysis
Parameter Regression Residual Total
df 2 8 10
SS 6.73E+13 6.85E+12 7.41E+13
MS 3.36E+13 8.57E+11 -
F 39.26046 - -
Значимость F 7.31E-05 - -
Parameter y-intersection %9 x12
Coefficients 9868583.2 153632.18 -422246.35
Standard error 3747319.9 60894.38 116779.28
i-statistic 2.63 2.52 -3.62
p-value 0.0300 0.0356 0.0068
Lower 95 % 1227248 13209.48 -691539.85
Upper 95 % 18509918 294054.88 -152952.86
The obtained model can be considered reliable. The value of the coefficient of determination R2 equals 0.9, therefore, the model describes the data accurately. Table 5 presents the predicted values of the response variable y in line with the suggested model. We also calculated mean error of the model, which amounts to 7.67 %, which is acceptable.
Table 5. Forecasted value of the response variable
Observations Values of the variables
%9 x12 7 Уса\с. Error, %
1 23 18.4 5,118,918 5,632,790 10.04
2 24 18.2 6,314,341 5,870,872 7.02
3 32 20.4 7,098,364 6,170,987 13.06
4 38 18.7 7,568,240 7,810,599 3.20
5 39 17.3 8,119,343 8,555,376 5.37
6 39 14.5 9,063,072 9,737,666 7.44
7 40 12.5 9,770,443 10,735,791 9.88
8 41 11.6 10,983,195 11,269,445 2.61
9 43 10.8 13,035,608 11,914,506 8.60
10 43 10.9 13,272,019 11,872,282 10.55
11 44 9.9 11,674,931 12,448,160 6.62
Mean error of the model 7.67
Based on the obtained model we can forecast the changes in GRP. Should the indicator "density of public roads with hard surface (km per 1,000 km2)" (the variable x9) increase by 1 % and the indicator "deaths in road accidents per 100,000 population" (the variable x12) decrease by 1 %, we can record an increase in GRP on average by 0.54 % for the period under consideration.
The indicators underlying the assessment of transport in the Russian Federation, do not reflect all the aspects. For instance, within the transport infrastructure, introduction of innovations in transport is not accounted for in general statistical bulletins, though innovation activity is a priority development direction in the Russian Federation [Bodrunov, 2012, p. 221].
The efficiency of transport functioning is a necessary condition for economic development of the state in general, and regions in particular. Current transformations occurring in transport are quite self-evident. The issue of the common uberization is being actively studied, which was mentioned earlier in the work. This process is connected with the use of online services of all kinds (delivery, taxi, etc.), which cannot but impact on the transport sector, because they reduce the load on the transport infrastructure [Pyankova, Zakolyukina, 2022]. Moreover, transport is influenced by the general trends of digital transformation unfolding in all spheres of the public life. The examples of modernisation against this background are tracking systems introduced in transport infrastructure. In view of this, it is necessary to perform thorough assessments of all transformations, as well as the transport sector as a whole, including of the level of digital transport infrastructure development [Pyankova, Zakolyukina, 2023].
The scholars from the Lomonosov Moscow State University have prepared an analytical report that includes an original method for calculating the Urban Transport Development Index. Based on the index calculation, the authors of the report provide comparative analytics on the development of the world's largest cities1. The structure of the proposed index is presented in Figure 5. The sub-indices present in the structure of the index consist of around 200 indicators both relative and absolute. It is noteworthy that the index takes into consideration such indicators as "Wi-Fi availability in metro", "presence of car sharing services in a city", etc.
Fig. 5. Structure of the Urban Transport Development Index
1 Urban Transport Development Index. Analytical Report. Moscow: Lomonosov Moscow State University, 2020. 116 p. https://www.msu.ru/upload/pdf/2020/Transport_Index_MSU_2020.pdf. (In Russ.)
With regard to the US experience, its transport policy relies on the economic indicator Transportation Services Index. This indicator was created by the U.S. Department of Transportation and Bureau of Transportation Statistics1. The indicator allows tracing the trends in transport highlighting its role in the economy. The Transportation Services Index combines the data on freight and passenger transport taking into account seasonal fluctuations, and is a monthly indicator of the output of transport services. This index shows changes in the demand for goods and services. In the times of economic recovery, the demand for transport goes up, which is reflected by the index.
From our perspective, the refinement of the transport sector assessment is the necessary source of its improvement at the regional level. This involves the development of a composite indicator that allows for all aspects of the index and assesses the level of the digital transformation of transport infrastructure. Given the mutual impact of transport and economic development, a proper evaluation of the dynamics of transport indicators will allow us to give a realistic assessment of either positive or negative effect within the economic development generally. Such assessment may underlie the adjustment of the strategic socioeconomic development plans for both the state and its regions.
Conclusion
The study established the relationship between the indicators of the transport sector and economic development of the Ural Federal District (in particular, its GRP). The obtained regression model can be used in practice, for instance, to identify general trends in regional socioeconomic development, to validate the priority directions for transport development, to design regional development strategies, etc. With regard to further research, a more comprehensive assessment of the transport sector development is needed, which should take into account all aspects of its functioning. This can be achieved by designing a composite indicator.
The developed transport sector and modern transport infrastructure allow maintaining efficient, uninterrupted movement of products, people, and resources. In the Russian Federation, the transport sector plays a special role in the economic growth and development of the state in view of the size of the country's territory and its unique geographical location. At the same time, the transport sector requires constant attention to identify the degree of its influence on economic processes and quick responses to any changes through the adjustment of strategic plans.
1 Transportation as an Economic Indicator: Transportation Services Index. U. S. Department of Transportation. Bureau of Transportation Statistics. https://data.bts.goV/stories/s/TET-indicator-1/9czv-tjte.
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Information about the authors
Svetlana G. Pyankova, Dr. Sc. (Economics), Associate Prof., Prof. of Regional, Municipal Economics and Governance Dept. Ural State University of Economics, Ekaterinburg, Russia. E-mail: silen_06@list.ru
Ekaterina S. Zakolyukina, Head of Customer Service Dept. ZAO Lucky Motors, Ekaterinburg, Russia. E-mail: k_zako@mail.ru
© Pyankova S. G., Zakolyukina E. S., 2024
