s2fieldsofstudy:
Economics
Computer Science

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Statistical analysis of the processes of intensification of the introduction of information and communication technologies in the socio-economic sphere of the Russian Federation

The article analyzes the effectiveness of the development of digitalization processes in the socio-economic sphere of the Russian Federation and its territorial entities. Based on the information arrays of the Federal statistical observation on the use of information technologies and information and telecommunications networks by the population, the level of use of information and communication technologies by the population of the Russian Federation in everyday life is studied. In order to deepen the analysis, the authors produced a ranking of regions of southern Federal district by key indicators, characterizing the use information and communication technologies in 2019 by its population, allowing to allocate and justify the leading factors in the development of the information society and the intensification of the processes of digitalization of economy and social sphere of regions. The authors formulated a number of recommendations for the further development of the information society in the Russian Federation and its territorial entities.

Introduction
One of the most important tasks of implementing the process of digitalization of the economy is to improve the quality of life of the population by opening new directions for development and increasing human capital, expanding the accessible environment for people with disabilities. In turn, the digital economy also imposes new requirements for the individual: the ability to work with information and communication technologies (ICT) and to apply innovative products for professional and personal needs. In this regard, there is a pressing need for statistical analysis and assessment of the readiness of the population to move to a qualitatively new level of social development.
In terms of the high degree of territorial differentiation in the Russian Federation, it is very difficult to assess the readiness of the transition to the digital economy at the regional level. This is due to the fact that many of the indicators that characterize the process of digitalization of the economy and are calculated at the country level as a whole are not used in the formation of regional information resources.
Along with the above, today, the solution of the problem of obtaining an assessment of the compliance of regional development with the challenges of the digital economy is relevant and significant. First, diversification of the processes of regional informatization unable to restrain the total transition to the digital economy of the country, and second, the developed methodology for assessment of compliance of the regions to the challenges of the digital economy is necessary for maximizing the economic benefits from the use of this type of information technology.

Materials and methods
In this study, information resources of the Federal state statistics service ("Rosstat") were used, in particular, data from the sample Federal statistical observation on the use of information technologies and information and telecommunications networks by the population (hereinafter referred to as the "ICT survey") [1].
The most important area of application of the information arrays of the abovementioned survey is the analysis of the results of implementation of major state programmes aimed at the development of the information society and the digital economy, monitoring the implementation of Goals of sustainable development and the realization of inter-territorial comparisons, including ratings of the level of ICT development.
In this study, the authors used data from a sample survey of ICT to rank the regions of the southern Federal district based on non-parametric methods of analysis and evaluation, in particular, on the basis of the Pattern method. This method is based on the estimation of a multidimensional average value based on the formula of the arithmetic mean simple, calculated from the relative values of comparison of territorial entities by the best value of the indicator for each component of the information system under study [2].
Thus, the Pattern method allows to bring various indicators to a single comparison base, calculate the integral indicator of the studied area and on this basis make a rating assessment of territories, which will allow to identify areas of sustainable growth or insufficient development of the phenomenon under study, which is the basis for developing recommendations for improving the situation within a specific territorial entity.

Results and Discussion
In order to conduct statistical analysis and evaluation of the processes of digitalization of economy and social sphere, as well as the intensification of ICT use in the everyday life of the population of the Russian Federation and southern Federal district (SFD), the authors developed an algorithm comprising the following steps: − selecting of indicators from the information base of the ICT surveys in 2019 as characteristics of information society development in Russia; − conducting a comparative assessment of the selected indicators for the Russian Federation and the southern Federal district; − implementation of the rating assessment of the entities of the southern Federal district based on the main criteria that characterize the intensity of the population's use of the Internet in their daily lives; − evaluation of use of state and municipal services by the population of the southern Federal district via the Internet, as the most important factor in the intensification of the introduction of ICT in the socio-economic sphere of territorial entities; − identifying points of growth and slowing down the intensification of digitalization processes in the regions of the southern Federal district. According to the authors, the use of this algorithm will enable the analysis and make a conclusion about the nature and intensity of development of information society and the speed of introduction of ICT in socio-economic sphere of the territorial entities of the Southern Federal district, that will expand the information base of regional statistics in this area and will enable governments to make effective decisions in terms of achieving the goals of sustainable development.
Based on the data from the ICT survey for 2019, authors selected the main indicators that characterize the degree of use of the Internet by the population of the Russian Federation and the southern Federal district (see figure 1). We should also notice a rather high level of Internet users in the age group of 60-69 years, which indicates the involvement of citizens of pre-retirement and retirement age in the processes of digitalization of the socio-economic sphere of the country and its regions. According to the ICT survey, in 2019, in most cases, the population of the Russian Federation and the Southern Federal district aged 15 years and older used the Internet at home (96.1% and 97.4% of the total population of the surveyed age who used the Internet, respectively), at work (46.1% and 42.3%, respectively), in public access points (hotels, airports, cafes, etc.) (31.5% and 26.3%, respectively).
According to the results of the ICT survey, the majority of the population of the Russian Federation and the southern Federal district in 2019 preferred to use a smartphone to access the Internet via a cellular telephone network (65.5% and 60.5%, respectively) and via wireless networks (40.1% and 35.5%, respectively). Other types of mobile devices (laptops and tablets) were used by 5-8 % of the population of the Russian Federation and the Southern Federal district.
According to the ICT survey, the largest part of the population of the Russian Federation and the southern Federal district in 2019 faced such problems as unauthorized mailing (22.2% and 21.6%, respectively) and infection of devices with virus components (7.5% and 6.5%, respectively).
Thus in 2019, population's of Russia and Southern Federal district the most common means of information protection were antivirus programs (75,5 % and 76.4%, respectively) and anti-spam filters (18.4% and 20.7% respectively).
The results of the ICT survey allowed authors to identify the most common reasons for the refusal to use the Internet by the population of the Russian Federation and the Southern Federal district in 2019. These include: lack of need or desire (75.4% and 74.9%, respectively), insufficient level of Internet skills (34.1% and 36.0%, respectively), sufficiently high cost of connecting to the Internet (13.2% and 13.1%, respectively), the presence of certain technical difficulties associated with connecting to the Internet (4.1% and 4.7%, respectively), ensuring the security of personal data (3.0% and 3.1%, respectively) [1].
The next stage of the authors' algorithm for studying the processes of digitalization of the socio-economic sphere of the Russian Federation and its territorial entities is ranking of the Southern Federal district regions by the main indicators of ICT use by the population in 2019 based on the Pattern method, which includes the following set of criteria: − the percentage of households connected to the Internet from a personal computer [1]; − the percentage of the population that uses the Internet at work and in everyday life [1]; − the percentage of the population using mobile devices to connect the Internet [1]; − the percentage of the population facing it security problems [1]; − the percentage of the population using various information security tools [1]; − the percentage of the population that ordered goods and/or services via the Internet [1]; − the percentage of the population that interacted with state and local authorities via the Internet [1]; − the percentage of the population who used mobile devices in obtaining state and municipal services through official websites and portals [1]; − the percentage of the population who faced problems in obtaining state and municipal services through official websites and portals [1]; − the percentage of the population who fully satisfied with the quality of state and municipal services provided in electronic form [1]. The results of ranking the regions of the Southern Federal district based on the multidimensional average of the indicators calculated using the Pattern method for 2019 are shown in figure 3.
Based on the data in figure 3, it is clear that the Republic of Adygea is the leader among the regions of the Southern Federal district in terms of ICT use, mainly due to a significant proportion of the population using mobile devices connected to the Internet, and a high overall degree of satisfaction with the quality of state and municipal services provided in electronic form due to the minimum number of problems in obtaining this type of services through electronic portals. Also, we should note the small proportion of the population of the Republic of Adygea, faced with information security problems. The group of regions that have a fairly high level of ICT use by the population includes the Astrakhan, Rostov, Volgograd and the Krasnodar regions. These territories are generally characterized by a high proportion of the population using the Internet, including for state and municipal services and using mobile devices connected to the Internet, as well as using information security tools. The regions with an average level of ICT use by the population are Sevastopol and the Republic of Crimea. In these subjects, the proportion of the population using the Internet, including from a personal computer, is quite high. In the Republic of Crimea in 2019, the largest share of the population who used information security tools was recorded, and in Sevastopol -the population who ordered goods or services via the Internet.
The Republic of Kalmykia is in the last place in this rating, due to the fact that in 2019 there was a fairly high proportion of the population that faced problems with it security and receiving state and municipal services via the Internet, as well as a low proportion of the population that ordered goods and services via the Internet.
In General, the so-called Electronic government services (E-government services) are developing quite actively, allowing the population to get a huge range of state and municipal services without leaving home via the Internet. So, in 2019, the most popular E-Government services received by the population of the Southern Federal district via the Internet were the following: making an appointment and receiving other services by healthcare organizations (52.4%), making tax payments and fees (37.4%), services provided by the Ministry of internal Affairs and the State road safety Inspectorate (28.4%), services of housing and communal services (19.7%), services of educational organizations (12.6%), services for obtaining a passport/registration (11.9%), services of organizations, providing social security for the population (8.9%). [1].
In most cases, the population of the Southern Federal district applied to E-Government services to obtain the necessary data through official websites and portals of state and municipal services (72.8%), make an appointment (59.8%), make mandatory payments and fees (48.3%), download standard forms of necessary documents (40.6%), send electronic forms of documents (37.1%), and receive results of providing E-Government services (36.2%) [1].
At the same time, most of the problems faced by the population of the Southern Federal district regions in the process of obtaining E-Government services via the Internet in 2019 were related to technical problems (16.3%). And only 5.8% of respondents noted difficulties in obtaining reliable information, while 2.7% of respondents could not get explanations and assistance in obtaining a particular electronic service [1].
In General, the study of ICT use by the population of the Russian Federation indicates a high degree of ICT coverage and the quality of information provided, the availability of a particular type of service on the electronic portals of state and municipal services. However, the public authorities should pay attention to improving the level of technical support for these services.

Conclusions
The study of the processes of intensification of ICT in socio-economic development of the Russian Federation and the subjects of the Southern Federal district in 2019 has allowed to draw some conclusions: − information base of the ICT survey allows to provide statistical analysis and evaluation of the processes of the digitalization of the socio-economic sphere both in the territorial and temporal aspects; − the study showed a high degree of use of ICT by the population of the Russian Federation as a whole and the Southern Federal district, in particular, in their daily lives; − the most active Internet users in 2019 in the Russian Federation and the Southern Federal district were the population aged 25-39 years, however, the share of Internet users in the pre-retirement and pension age was also high. Consequently, public authorities should consider features of this age group during the implementation of the concept of digitalization of economy and social sphere, to develop assistance programs to expand the opportunities of e-services to this category of population; − the results showed a high degree of use of mobile devices by the population, in particular mobile phones, for accessing the Internet, purchasing goods and receiving various electronic services. Therefore, in the future it is necessary to expand the network of cellular stations, thereby improving the quality and speed of the Internet, as well as to reduce the cost of using the Internet via mobile phones. An important aspect is the development and improvement of special mobile versions and applications for electronic portals that provide state and municipal services to the population; − the study revealed a small proportion of the population faced information security problems and a fairly high degree of loyalty of the population of the Russian Federation to domestic information security protection tools, which creates prospects for the active development of the Russian market of computer and information security products, thereby minimizing the losses of possible sanctions measures from other countries aimed at this area; − the ranking of Southern Federal district's territorial entities of the ICT use in 2019 showed that the leading factors in the development of the information society and the intensification of the processes of digitalization of the economy and the social sphere were the use state and municipal Internet portals, including via mobile devices by the population, and high degree of it information security. Therefore, it is necessary to transfer the most part of the state document flow to an electronic format, and in the future -fully provide this type of service via the Internet. Along with this, there is a need to develop mobile versions of websites of state and municipal authorities in order to facilitate access to them for all users, to fill them with high-quality content and to ensure the safety of users ' personal data.


Sustainable Development Indicators in Benchmarks of Russia’s Regional Policy

We have considered the problem concerning the substantiation of sustainable development indicators and the improvement of the standard of living of the population in Russian regions. To quantify the standard of living of the population, we have used a common indicator such as the Human Development Index. The authors present an approach to managing the region in the form of optimization problems aimed at improving the standard of living of the region’s population by increasing the probability of its classification as a region with a higher standard of living. The optimization problem is solved based on the identified relationship between the standard of living of the region’s population and its socioeconomic indicators.

Introduction
Determining practical measurable indicators of the sustainable development concept is of essence for its implementation. There is a wide range of approaches and indicators offered by leading international organizations (UN, World Bank, OECD, European Commission, etc.). These indicators are used in the program-based approach to territory management as markers determining the system sustainability.
Russian regions are developing their systems of sustainable development indicators using the current database of the official Russian statistics which, according to S.N. Bobylev [1], makes it much easier to obtain necessary information and provides an adequate assessment of the regional progress in sustainable development. Since 2012, the general Russian regional development strategy has been determined by the so called ''May Decrees'' that contain clear benchmarks in such areas as education, science, healthcare, economy, demography and utilities. The goal of this research is to understand whether the target indicators of the regional policy are well justified, and to what extent their accomplishment will promote the region's sustainable development and improve the standard of living of its population.

Building a logistic regression by the criterion of maximum likelihood
where i x is the vector of values of the i-th object = ( ; m is the number of features of each object; n is the number of observations. The classification is performed using the function taking values in the range of (0; 1). The threshold value is (2) sets a separating linear border described with the hyperplane equation Let us introduce the function Let us set the D1 area of possible x values for the first class as , and for the second class as 2 = { : ( ) > 0}. Then ∀ ∈ 1 ℎ( ) < 0.5 and ∀ ∈ 2 ℎ( ) > 0.5. If x belongs to the hyperplane (3), then . Consequently, for the arbitrary observation * x , the probability of its placement into the first class is equal to ( * ∈ 1 ) = 1 − ℎ( * ), and to the second class - . It is established [2] that the maximization of the likehood logarithm is equivalent to the minimization To assess the coefficient vector b, iteration descent algorithms are used to solve extremum problems, e.g. the Newton-Raphson iteration algorithm is offered [2][3]. It involves the following. As a zero approximation, the classification problem can be accomplished using the multivariable linear regression Then the iteration process starts, on the k-th step of which the coefficient vector bis specified (k) : , where Q′(b (k) ) is a vector of first derivatives (gradient) of the Q(b) functional in the b (k) point, Q′′(b (k) ) is a matrix of second derivatives (hessian) of the Q(b) functional in the b (k) point,andhk is a step value that can be equivalent to 1, although its selection at each step can increase the convergence rate. The Newton-Raphson method is described in a number of textbooks on optimization methods [4]. However, the minimization problem (5) is not generally accomplished as, with a correct classification of all precedents, it has the lower bound at infinity equal to zero [5]. The quality of the built separating the border with a formal descent will not be related to the value of the target function Q(b) any more. The growth in the component of the b vector results in the unconstrained growth of values -yib T xi that can take very large values, which causes inherent computational errors or even stoppage of the algorithm due to memory overflow [6]. For this reason, the descent strategy needs to be adjusted. Gradient methods are intended for a descent to a local minimum. As the target function has a lower bound at infinity and there can be points, in which the gradient is close to zero, we deem it reasonable to proceed to zero-order methods. In [5], the authors offer an iteration descent mechanism. It comprises a sequence of zero-order minimization problems based on random searching. At every k-th step, the length of the coefficient vector b (k) is fixed. It is gradually increased until the target function reaches the required value.

Logistic regression as a mathematical model of relationship between the human development index and the socioeconomic indicators of the region
To characterize the standard of living of the region's population, we use the Human Development Index (HDI). Let us consider the values of the informative socioeconomic indicators of thei-th region of Russia as the vector xi. We have formed two classes of regions: the first class comprises regions with a low HDI (HDI < D), and the second class includes regions with a high HDI (HDI  D), where D is a threshold value. For the first D1 class, let the value of the binary variable membership be y = -1, and for the second class D2, let it be y = 1. Let us consider a region with a set of actual values of its socioeconomic indicators as the vector x*. According to (3), we are assessing W(x*).
, the region will fall into the first class with the probability , the region will fall into the second class with , the case is uncertain: the region can be placed both into the first class and into the second class with an equal probability of 0.5. Thus, a probabilistic assessment of the standard of living of the region's population can be made. The region can be classified among regions with a high standard of living with the probability h(x*), and among regions with a low standard of living with the probability can be used as the value of the target function: the higher its value, the more likely it is that the region's population will be deemed as having a high standard of living.
The features of the object in question are determined by the location of the point x* relative to the hyperplane (3). They can be numerically described, for example, using the distance between the point x* and the hyperplane (3) is always orthogonal to the hyperplane (3), its direction is set by the vector b, and its length -by the value q(x*). As the coefficient vector sets the hyperplane (3) equation with the accuracy up to the multiplier, it is expedient to normalize it for definiteness purposes when solving Let us assume that By equating the differential and multiplying both parts of the latter formula by 100%, , the coefficient bk with the variable Xk shows the percentage, by which the ratio of the probability of the region's classification as one with a high standard of living (D2) to the probability of classifying the region among regions with a low standard of living (D1) will change, if Xk increases by one unit, with the fixed values of other variables. The derivative of the function Z(x) for the variable Xk is directly proportionate to the coefficient bk. The growth (reduction) of the function Z(x) is equivalent to the growth (reduction) of h(x). Accordingly, to increase the value h(x), with positive values bk the increments k x  must also be positive, or negative, if the values are negative.
One of the main issues related to the use of logistic regression is the ability to establish a statistically reliable HDI-based classification of regions. Let us consider the classification of regions in two classes [7]: with a low HDI (y=-1) and with a high HDI (y=1). Based on the statistics for 2013, two classes of precedents (training samples) were formed. The first class of regions with a low HDI (maximum 0.84) included 28 subjects of the Russian Federation. The second class of regions with the HDI of over 0.85 included 31 subjects of the Russian Federation. To improve the reliability of results, we removed from the total array of statistical data 24 constituent entities of the Russian Federation with very low and very high HDIs, as well as those whose HDI was close to the average value, and those with data gaps. As a result, we obtained a system of statistically significant indicators which enabled us to accurately classify the observations for the training samples. The coefficients of the separating hyperplane (3) equation built on the basis of the training sample are given in Table 1 . This is quite reasonable and consistent with Keynesian theory. One of the main requirements for the model is its adequacy to the described system or value. The coefficients of the hyperplane (2) equation were built using the data for 2013 (Table 1). We examined the adequacy of the diagnostic model (1) based on the statistical data released by Rosstat for the next year of 2014, using the case of the subjects of the Ural Federal District. Table 2 presents the actual HDI values resulting from the assessment of the h(x) probabilities over 2013-2014. It should be noted that in 2013 the h(x) probabilities were based on training samples; and in 2014 -on test statistics available under the formula (2). When establishing the quantitative values of attributes X4, X5, X12, and X13 for 2014, we recalculated the monetary indicators taking into account the inflation estimated through the consumer price index. The results presented in Table 2 indicate the adequacy of the model. The increase in the HDI values for the Kurgan, Sverdlovsk and Chelyabinsk Regions correlates with the increase in the h(x) probabilities for all the three regions. The Tyumen Region has the maximum value of its HDI in both cases, which corresponds to the maximum probability h(x) that is practically equal to 1.

Optimization models based on logistic regression
Direct consequences of the set effect on the object are estimated by the value of the target function in the problems (6)- (8). Indirect consequences can be evaluated in each particular case based on the value of the vector of socioeconomic indicators received from the accomplishment of the optimization problem. The main disadvantage of the problems (7) and (8) is the complexity of setting the cost functions vj(j). We can circumvent this restriction by presenting the problem as follows: The essence of the problem (9) is as follows. It is necessary to achieve the transition of the region to the state h(x) = p0 with a minimum mean square relative change in the values of the components of the socioeconomic indicators vector. This problem definition does not require explicit specification of the labor intensity of the changes in the Xj indicators. The weighting coefficients j allow us to take into account the peculiarities of changing the variables, e.g. labor intensity of the change, the importance of the change in each of the variables. If the a priori information about the Xj indicators included in (2) is missing, we take all weighting coefficients j equal to 1. The zero value of the weighting coefficient can be used where the index of the bj coefficient has an opposite direction as compared to the expected one (e.g. in this problem, it is the coefficient b1=-2,381<0 with the aggregate birth rate coefficient), which makes it possible to record the actual value of Xj. It should be noted that this can be attributed to the following dependency: the birth rate is higher in regions with a higher standard of living as compared to regions with a low standard of living. This issue cannot be solved within the framework of the optimization problems offered in this publication, as the coefficient vector is determined based on the training sample and reflects the current socioeconomic environment in the country.
Additional restrictions in (10) allow us to factor in the specifics of the changes in the socioeconomic indicators Xj. Let us consider the possibilities of using optimization problems by the example of (9), assuming ρj = 1, j = 1, 2, …, m. We will solve the problem (9) using the penalty function method [3]. We will test the logistic regression as the management model for three subjects of the Ural Federal District, namely the Kurgan Region, the Sverdlovsk Region and the Chelyabinsk Region. According to their HDI values, each of these regions has a different standard of living (low, high and medium, respectively). The solution of the optimization problem (10) allowed us to calculate the values of socioeconomic indicators, by reaching thereof the optimum is attained, i.e. the best state of the system ensuring the improvement in the standard of living of the population.
The Kurgan Region can be ranked among the regions with a low HDI. The probability of its classification as a region with a high HDI: h(x 0 ) = 0.005. Table 3 provides the results of management aimed at reaching the threshold level (h(x * ) = 0.5) for the Kurgan Region, which were obtained by solving the problem (9). To compare the estimate values of socioeconomic indicators, Table 3   improve the standard of living of its population. Therefore, the estimate (optimal) values of indicators cannot serve as the new proposed benchmarks, but should be taken into consideration when optimizing the costs and specifying the priorities of further development. For the Kurgan Region, such priorities in improving the standard of living of its population should include: (1) Increasing life expectancy (X2) up to 70 years (in general, this level was achieved in Russia as early as in 2012, but for this region, with its initially low indicator, this is an optimal and realistic target); (2) Developing mortgage lending (X3), which has been significantly reduced in recent years as a result of macroeconomic instability; (3) Increasing the investment activity in the region (X4), which would help kick-start the manufacture, attract free labor and improve the paying capacity of the population; (4) Reducing the unemployment rate (X10) to 6.7%.
The Sverdlovsk Region has a relatively high HDI. The solution results of the optimization problem, which provided for the attainment of a higher HDI corresponding to the increase in the h(x) probability of up to 0.95, are presented in Table 4. According to the calculations, an important condition for probable improvements in the standard of living of the population in the Sverdlovsk Region is economic growth, which is impossible without additional investments (X4). Development of mortgage lending (X3) will help raise investments in the construction sector, a driver of the region's economic growth. The Chelyabinsk Region is classified as a region with a high HDI with a probability h(x 0 ) = 0.805, which is average for Russian regions, but lower than in the Sverdlovsk region. When solving the optimization problem for improving the standard of living of its population, we considered three options of increasing the probability h(x). The results of solving the problem for three probabilities h(x 1 ) = 0.805, h(x 2 ) = 0.9, and h(x 3 ) = 0.95 are provided in Table 5.


A Topological Analysis of Trade Distance: Evidence from the Gravity Model and Complex Flow Networks

As a classical trade model, the gravity model plays an important role in the trade policy-making process. However, the effect of physical distance fails to capture the effects of globalization and even ignores the multilateral resistance of trade. Here, we propose a general model describing the effective distance of trade according to multilateral trade paths information and the structure of the trade flow network. Quantifying effective trade distance aims to identify the hidden resistance information from trade networks data, and then describe trade barriers. The results show that flow distance, hybrid by multi-path constraint, and international trade network contribute to the forecasting of trade flows. Meanwhile, we also analyze the role of flow distance in international trade from two perspectives of network science and econometric model. At the econometric model level, flow distance can collapse to the predicting results of geographic distance in the proper time lagging variable, which can also reflect that flow distance contains geographical factors. At the international trade network level, community structure detection by flow distances and flow space embedding instructed that the formation of international trade networks is the tradeoff of international specialization in the trade value chain and geographical aggregation. The methodology and results can be generalized to the study of all kinds of product trade systems.

Introduction
International trade is one of the most important economic activities among countries, and its effect on one country's economic growth is self-evident. Forecasting international trade flows is critical to assessing economic growth. International trade data, as a non-monetary tool to measure the complexity of national economies, have greatly enriched the assessment and prediction of the growth performance of individual economies [1][2][3][4][5][6]. For example, Hausmann, the renowned economist, proposed the concept of economic complexity in the dimension of international trade to quantitatively evaluate a country's future economic growth potential: the gap of countries' economic growth depends on the diversity of their exports, which also causes that the national development has the path-dependence property. This makes it possible to rely on historical data to predict short-term economic growth, and there is a correlation among investments, performance, and economic growth [7,8].
For the long term, the gravity model describes the pattern of total bilateral trade flows [9,10]: trade flows are proportional to the size of a country's economy and inversely proportional to the distance between them, because of the cost of transportation. Although the role of economic size has been well understood in various theoretical contexts, the interpretation of the role of distance has been controversial [11], especially with some counter-intuitive empirical results. For example, Head and

Literature Review
The role of trade costs in the international economic system has been extensively discussed and widely used in many fields [15,16]. Especially, physical distance is regarded as the most common proxy for international trade costs [12,17]. In the earliest contribution of this issue, the authors modified the gravity model by adding different explanatory variables [18,19], e.g. bilateral distance effects measured by the fixed costs. For example, Manova and Zhang fixed costs that are increasing with distance [20] or firm search costs for buyers [21]. Yi also argued that fragmentation can explain the nonlinear effects of trade costs on trade [22].
However, the drawback is that the estimated results fail to capture the effect of globalization, which is defined as the "missing globalization puzzle": it remains constant over time. Many economic studies have attempted to resolve this issue with mixed success [23][24][25][26]. This approach requires the addition of more explanatory items when more factors are considered, which may lead to gravity models extremely bloated. Especially, some variables have strong economic significance, but are highly correlated and pose multicollinearity problems.
Additionally, the effects of globalization should also be reflected in increasing estimates of the effects of contiguous borders, which reflect the effects of globalization that may travel through the middle trade, i.e., global value chains may first be established regionally, thereby reinforcing trade with neighboring countries in gravity estimations. For example, Marek Maciejewski illustrated that attractiveness can be expressed as the common border of many factors [27]. Moreover, intermediate merchandise trade has a different impact on the final products in the aspect of product and factor prices. Research shows that intermediate merchandise trade can promote exports, increase R&D investment, and positively affect innovation [28,29], thereby improving productivity and promoting the quality of export products [30]. Quantitative analysis of the impact of the real distance between the import and export of commodities on trade is relatively rare, especially the export to domestic sales, intermediate products trade, etc. because measuring these trade distances is complicated. Quantifying the distance cost of intermediate trade is very complicated, we thus need to find new research paradigms to address this problem.
The elasticity of distance may change in both directions as the market size changes [12], and an empirical model of scale effects can be explained by the relevant export-dynamic network (International Trade Network, ITN). The significant difference of ITN from the gravity model is that many dimensions of trade information have been embedded virtually in ITN, rather than infinitely added explanatory variables because ITN is formed by the interaction of national behaviors and multiple factors that have already covered politics, culture, and other information. Another stream of literature has examined how complex network measures affect key international trade, which mainly focuses on multi-scale models of multilateral trade based on a network topology from the real trade data.
In that case, how can we define the "effective trade distance"? There are already different distance measures proposed based on network theory [31,32]. Despite the success of these distances for analyzing practical issues, they suffer from defects in studying trade problems. The most obvious shortcomings is that network distance, such as hyperbolic distance and shortest path distance, seldom consider the trade flow information. Additionally, although Brockman's effective distance subsumes the information of the topology and the flux on edges, this method only calculates the distance of the most possible (maximum likelihood) paths between nodes but does not count all paths. Conversely, with the rise of the global industrial chain, trade flows can be recognized as part of a longer value chain. This information is encoded into all the paths along with the trade flow network. It is not enough if we only consider the most possible path, and economic behavior is often not driven by probability. On the contrary, with the rise of global industrial chains, trade flows can be considered as part of a longer value chain, and this information is encoded into all paths of the trade flow network, rather than the most possible path. Therefore, the definition of effective distance should hybrid trade networks and full path constraints.
Following the existing literature, which provides a good foundation for setting up the empirical model, this paper aims to make the following contributions to construct the "effective trade distance". It can identify the real trade distance-flow distance-which emphasizes the networks of trade relations under multilateral interactions and the structure of the global production value chain.

Data
All of the data used in this article are from open source databases that can be downloaded from corresponding websites. The data for international trade come from the United Nations commodity trade statistics database. The data we use are 97 categories of product data with 2-bite customs codes from 1996 to 2016, covering the information on trade relations and trade volume among countries. The geographic distance data come from the CEPII database (http://www.cepii.fr/). The geographic distance between national capitals is used as the distance variable of the gravity model. GDP, energy net export, s and domestic energy use were obtained from the world bank from 1996 to 2016 (https: //data.worldbank.org.cn/).

Construction of International Trade Flow Networks
As a supplementary and typical example, we build the international trade flow network and make empirical analyses using the latest achievements of complex network theory. We employ international trade data that are available from the United Nations commodity trade statistics database, which contains data on the value of trades between countries and their trading partners. The dataset provides both import and export trade data. Here, the ITN can be described as a directed weighted network, where nodes and directed links represent all of the countries and their imports and export relationships, respectively. From the perspective of network science, a country can be abstracted into a node and export or outport trade relationships can be abstracted into a link in the international trade networks.
The framework of complex networks study, enabling us to understand international trade as a whole, offers a new perspective of connection to explore the issue of trade systems with more information, such as the core/periphery structure [33,34], the role of WTO [35], the robustness of trade systems (stability of the international trade system) [34,36], problems of critical propagation in ITN [37,38], and so on. Additionally, ITN can be regarded as a space where international trade is formed by the interaction of national behaviors and multiple factors that have already covered politics, culture, and other information. This understanding may help us to reconsider the distance effect on the gravity model, capturing the features of effective trade distance, including time-varying, multilateral resistance, and network effects.
However, the most obvious shortcomings is that network distance, such as hyperbolic distance and shortest path distance, seldom consider the intrinsic mechanism of trade flow information and trade distance. The international trade system is constructed by exchanging the flow of commodities [39] and restricted by the natural resources, technological level, and production capacity of each country. From the perspective of trade flow, the international trade system is an open system that describes how the energy, substance, products, and money exchange between resources of the external environment and trade relations of an inter-system [40]. Thus, we use an open system approach to characterize international trade, known as the International Trade Flow Network (ITFN) [41][42][43].
The international trade flow network F is a weighted directed network with N + 2 nodes :N countries and two special nodes (i.e., the source (labeled "0") and the sink (labeled "N + 1"), as shown in Figure 1A. The most significant difference from complex network models is that flow network models have two special nodes which represent the effect of the environment on the international trade systems. Specifically, the outflow from the source to a country is the domestic production within this country, and the inflow to the sink from a country is its domestic consumption, as shown in Figure 1A by the dashed arrows.
In the ITFN system, the relationships between a country and the two special nodes are defined by network balance principle: the total in-flow of each node equals its total outflow except for the sink and the source, i.e., ∑ N j=0 f ij = ∑ N+1 j=1 f ji (1 ≤ i ≤ N). In some cases, the data for domestic productions and consumptions are not available. We replace these flows as the non-balanced trade flows. If a country's total import value is larger than the total export (i.e., ∑ N j=1 a ij < ∑ N j=1 a ji ), the net import can be the surrogate as the domestic consumption: the flow from i to the sink f i,N+1 = ∑ N j=1 a ji − ∑ N j=1 a ij (i.e., orange dashed lines in Figure 1A). The net export represents the countries' production: the flow from the source to i f 0,i = ∑ N j=1 a ij − ∑ N j=1 a ji (i.e., green dashed lines in Figure 1A). By doing this, the element f ij in the adjacency matrix of the flow trade network F is obtained, as follows: where a ij is calculated from the raw trade data: if a country i exports to a country j with trade volume w ij , the element of adjacency matrix is set as a ij = w ij , otherwise a ij = 0. According to to the raw trade data, the adjacency matrix F is shown in Figure 1A. Sink Black solid lines denote the raw trade data, and dashed lines represent the trade gap between imports and exports. The source node and sink node make the country's import and export trade in the balanced state, which helps the definition of a Markov matrix during the derivation of flow distances. (B) The plane rectangular coordinate system consisting of flow distance (FD) and geographic distance (GD). y = x is the case where the flow distance is equal to the geographic distance. The black area in the picture is the range of standard deviation ±σ of a straight line y = x. Blue and red represent the data points beyond the standard deviation range.

Flow Distances
Bilateral trade, such as commodity trade and capital flows, may increase due to the reduction in spatial distance costs, and the relationship between trade costs and trade flows remains a significant issue. International trade is not only subject to economic scale and geographical distance, but also the result of searching for trade information in various economies. The network information provides new perspectives and ideas for further detailed estimation of trade costs and trade flows. Based on the structure of international trade networks and trade flows, we employ flow distance to quantify effective distance in energy trade systems. Flow distance l ij measures the relative trade flows between two countries. More specifically, following the insights of Guo [44], we briefly review how to get flow distance based on the flow network structure. First, all possible paths t ij from i to j can be defined as where p k ij is the probability that nodes (i.e., countries) transfer from i to j after k steps and satisfies ∑ ∞ k=1 p k ij = 1. Thus, p k ij can be expressed as where, the total flow can be calculated as ρ ij = φ 0i u ij [45], the inverse of M's Laplacian is U = 1 + M + M 2 + ... = (I − M) −1 , u ij is the element of the matrix U, and φ 0i is the first-passage flow from the source to i. The number of nodes that jump from i to j along all possible paths with k steps is M is a Markov matrix and its the element m ij is where m ij represents the probability of nodes jumping to j if they are at i. Thus, providing Equations (2)-(4), we have, Note that all possible paths t ij from i to j may contain the path t jj from j to j during the random walk. Therefore, the definition of flow distance is represented as c ij = t ij − t jj and the symmetric flow distance can be finally determined by

Quantifying Trade Barriers Based on Trade Flows
In a broad sense, measuring the effective trade distance is an approach to study international trade barriers. Many Non-Tariff Trade Barriers (NTBs) cannot be directly quantified, including technical regulations, quotas, subsidies, and some anti-dumping measures. Some authors attempted to quantify these NTBs by utilizing the price wedge method [46], but they suffered from difficulties in data acquisition as well as inconsistency of price data [17]. The most direct impact of NTBs is to modify the production conditions and costs in import and export countries and intermediate product manufacturers, thereby affecting the relative enterprises. It causes the change of products' comparative advantage, due to the eventual alternation of advantageous industries and technologies. The alternation will be reflected in international trade [2,3]. Thus, some studies used trade flows to quantify barriers, e.g., quantitative tool method [47]. The international trade system is complex under the interaction of politics, trade barriers, and the games of national interests. Complex network methods are one of the most important tools in the study of a complex system. Such a method provides a global perspective to model the international trade system. The international trade network is stable [33], which offers the possibility for predicting future trade behavior by historical data. Intuitively, trade data are a process of coevolution between trade status and trade barriers. Just as the river shapes the bed while the bed, in turn, guides the flow, they constitute a complex system of coevolution. Because of the dependence on the path and the lock-in effect (i.e., stability over time) of the international trade network, the national trade behavior will be affected by the inherent network effect when the trade network space is formed. Trade behavior in the past determines the basic framework of the whole trade network space, which constrains the formation of the future space. Therefore, network constraints provide an internal basis for depicting trade barriers using historical data.
National trade behavior constructs an international trade network with the characteristics of complexity, nonlinearity, and time-varying under the constraints of all kinds of barriers. The potential information of trade resistance is thus hidden in trade network data, which makes it possible to quantify trade barriers. The quantification of effective trade distance aims to extract hidden resistance information from trade network data, including multilateral resistance and network effect. It needs to be reiterated that the restriction of economic behavior by resistance or barriers makes national trade behavior controlled by multilateral resistance, and accordingly the network effect. In general, the economic network is also understood as the result of a game between the subjects of competition and cooperation [48]. Thus, the trade network contains not only trade factors but also multi-dimensional information. In this paper, flow distance is adopted as the general measure quantify the trade barrier and multilateral resistance.

Econometric Methodology: Gravity Model
To understand the role of trade distance, it is mandatory to analyze all kinds of products trade and establish trade gravity models to compare. To examine the effectiveness of the flow distances, we employ the gravity model to compare the performance of geographical and flow distance. We select d GDP and distance (flow distance and geographical distance) as explanatory variables. The logarithm equation is converted into a linear form firstly, and the trade gravity model is expressed as follows: Model 1 (classical gravity model) and Model 2 (modified gravity model).
Model 1 is expressed as follows: where X ij measures the volume of bilateral trade from country i to country j, D ij is the geographical distance between countries, Y i denotes GDP of country i, and β are fitting parameters. Similarly, Model 2, the flow gravity model, is expressed as follows: where l t,ij is the flow distance among countries. Considering endogeneity, we adopt the rolling window method to consider the distance measure, that is, the flow distance generated by the historical trade data, to predict the current trade flows. τ ∈ {1, 5, 10} is a time delay parameter, and flow distances of t − 1 and t − 5 predict the trade flow at time t.

The Economic Meaning of Flow Distances
With the improvement of global value chain systems and the coupling interaction of international division of labor and transnational corporations, the national attributes of product sources are becoming increasingly vague, and it is difficult to identify the real producer from imported products and even to quantify such trade distances. From the perspective of the theory of the global value chain, the more complex the products are, the more processes are involved in the production, which makes trade distances in the commodity trade flow also contain potential multilateral paths.
Broadly speaking, the process of summation in this way reveals the potential trade distance of multi-path fusion behind the trade, which is fundamentally different from geographical distances. By contrast, the geographical distance of the classical gravity model cannot identify multilateral distance and the complexity caused by network effects, and the mechanism of trade flow cannot be described completely. More significantly, definition of distance which ignores the multilateral distance will seriously underestimate (that is, the blue area in Figure 1B) or overestimate (that is, the red area in Figure 1B) real trade distances.
Next, we illustrate the potential impact of multilateral goods trade on the "effective trade distance" with a simplified diagram of international trade relations in Figure 1A. First, geographical distances may understate effective trade distances. For instance, when measuring the trade distance between countries A and C, geographical distances only focus on the direct distance. However, there are three potential routes for commodities: A exports to C directly; A exports to C via B; and A exports transfer selling internally and re-exports to C. In doing so, the trade distance between A and C in Figure 1A is completely depicted as l AC = ∑ 3 k=1 kp k ij ≈ 2.875, which includes intermediate products imported from A. Second, geographical distances may overestimate effective trade distances, especially if A and E have important import and export relations with each other. When measuring trade distances between A and E, it cannot be avoided that E imports products from A and re-exports them to A. Thus, l AE ≈ 1 is calculated by Equation (6).

The Role of Flow Distance in the International Trade
In this section, a detailed analysis focuses on the performance of flow distances to predict trade flows. To avoid endogeneity, we use a lagging distance variable (that is, flow distances with time delay feature) to replace the geographical distance in the classical gravity model. In doing so, flow distances at t − τ time are used as effective trade distance to predict trade flow at t time, and the flow distance under different delay coefficients are compared with the regression results of geographical distance. A more intuitive comparison scheme is first adopted. We plot logarithmic distance both for geographical ( Figure 2D) and flow distances with τ ∈ {1, 5, 10} (Figure 2A Here, for the sake of presentation, we mainly show the empirical results of the energy trade. When the delay variable is one or five years, the result of the forecast in Model 2 is significantly better than the measurement of geographical distance, i.e the scatters formed by the distance and non-distance terms are in the vicinity of a straight line. The result of the GDP regression coefficient is stable and significant under different flow distances with different time lags, and regression coefficients of flow distances in regression equations are always larger than the geographical distance, showing that effective distance (flow distance) has a stronger negative effect on trade flow than geographical distance. More generally, we do an empirical analysis of different products in different years and we can obtain similar results. With the increasing value of time delay variable, the effect of flow distance will gradually degenerate into the role of geographic distance, which indicates that flow distance covers a wider range than geographic distance. In rough terms, the regression effect of geographic distance in the classical gravity model is equivalent to flow distances measured by trade networks over a 10-year delay, which has an obviously huge impact on trade flow forecasts, because the international situation is changing rapidly. Due to the path dependency and lock-in effect in the international trade system, when the new network space is formed, countries' trade behaviors will be affected by the network. Thus, the network space and the trade flows co-evolve: past trade behaviors will determine the entire trade network space and future trade flows will be created by the existing trade network space. This understanding may help us revise the distance itself as the "effective distance" in the network space, rather than patching up the gravity model by adding factors.
Moreover, we also analyze the goodness of fit (R 2 ) in the regression model. In Table 1, the result shows that the gravity model with flow distance is accompanied by a higher R 2 . Specifically, taking the energy trade as an example, R 2 is between 0.50 and 0.53 in Model 2 with τ = 1, while it is between 0.25 and 0.30 in Model 1. Flow distance has a better performance in all cases, which means that the gravity model can still explain international trade, but the geographical distance is insufficient to identify effective trade distance under multilateral trade resistance and multi-path effects. The regression results of different products and years are qualitatively consistent.  In this section, we show that better performance of gravity model can be derived by replacing the geographical distance with this new effective distance, which means the bilateral trade flows and the flow distances have much stronger correlations than the geographical distances. This phenomenon is robust against different products and different periods.

The Relationship between Flow Distance and Geographical Distance
Time lag effect (that is, flow distances can degenerate into the effect of geographic distance with an adequate value of time delay) indicates that geographic information is hidden in flow distances from another perspective. It is a common understanding that trade distances embed geographical information and other information. Placed in the historical perspective, effective trade distance can be explored further by analyzing the dynamical Pearson correlations between flow distance and geographical distance in all products trade (a total of 97 products). The result is shown in Figure 3; for the most commodities trade, we can observe that the Pearson correlation coefficients are not significant in all periods, and their values are less than 0.5. This also indicates that the effective trade distance is mainly affected by trade structures and other network factors.
To highlight this effect, we further analyze the more detailed product classification of trade data. Illustrating by the case of energy, similar results have been captured by empirical data. The result is shown in Figure 3. It is worth mentioning that there is a weak sign that there aew geographical effects in the electricity trade. In the 1990s, the correlation between flow and geographical distance was as high as 90% in the electricity trade network, which indicates that geographical was the main influencing factor for trade flow. However, it is clear that the correlation continued to drop, which reveals that some other factors of distance gradually occupy the main position in affecting the trade volume.
In this section, we further discuss the relationship between flow distance and geographical distance. The results show that the trade distance generated by the embedding space includes geographical factors. Similar conclusions have been verified on different types of trade products.

Community Structure Detection by Flow Distances
Community structure is not only an important property for the complex network but also an important approach to depict the structure of economic systems. A network can be divided into several groups so that each group is tightly knit with many links inside, and there are only a few edges between each group [49]. The early research structure indicates that the community structure has the geographic aggregation feature, while the role of globalization has led to an unprecedented division of labor and links in the trade market, which in turn has produced heterogeneity of products, namely that differences in the added value of products [39]. Specifically, some high-end products, such as cars, can be split into different parts (intermediate products) and coordinated by various countries for production and assembly, which requires intermediate products, not for final consumption, but high value-added and export reproduction.For some agricultural and sideline products, on the contrary, imported products are mainly used for domestic consumption and their added value may be lower than some intermediate products [50,51]. From this view, we want to further discuss i the role of flow distance by considering multipath features in reshaping the international trade structure. Does international trade specialize in the production value chain or geographic aggregation? We expect to see communities form around international specialization and geographic aggregation, respectively.
Next, we introduce a network flow space embedding model to detect the international trade network of community structures. The community division method by flow space embedding is based on the above two special flow distances to embed the entire trade network into a two-dimensional plane with the two kinds of distances as the axes (the country-from-the-source distance (s2c) as the horizontal axis, and the to-the-sink distance (c2s) as the vertical axis). It is noted that there are two special distances for each country. If a country has a shorter flow distance from the source, it is more likely to be a supplier of the energy product. Otherwise, the smaller is the flow distance to the sink, the higher is the propensity for the country to be a consumer of the energy product. Figure 4 shows that not all countries are on the diagonal of the s2c-c2sdistance plane. Namely that the national energy trade's consumption and production status are not equal and the diversity exists in the form of a contribution to the energy value chain. Specifically, if the country's y coordinate (c2s) is smaller, it is more likely that a consumer for the energy product locates at the high end of the energy production chain (i.e., the colorful dot points below the diagonal in Figure 4). Correspondingly, the lower is the x coordinate, the smaller is the s2c distance, and the greater is the propensity of the country to be an energy producer, and, thus, it locates at the low end of the energy production chain (i.e., colorful points above the diagonal in the Figure 4).
In doing so, three communities can be obtained by identifying the national energy consumption and industrial development level, including resource-driven community, deep-processing community, and mixed community. Note that Countries in the mixed community (related balance of trade) are also referred to as the trade balance or the international trade balance, which shows that the national status of import and export are roughly equal. BOT country corresponds to the black point in Figure 4. More precisely, countries above the diagonal line are natural resources abundant, such as Canada, Russia, and Saudi Arabia, and most of them are mainly exporters, who can maintain national economic stability by exporting energy. Their distances to the source are less than to the sink, therefore they locate at the bottom of the production chain. On the contrary, some countries are located below the diagonal, which are mainly energy importers. Their distances to the sink are lower than to the source; therefore, they are at the top of the production chain. Such countries tend to be lack of natural resources, such as Japan, and primary energy can be further processed to improve product competitiveness through importing. Finally, there are a few countries located near the diagonal, such as Britain.
To illustrate the validity, we furthermore provide the community structures of economic data by energy market share and demand share in Figure 4A. The results show that the flow distances plane can categorize the characteristics of international status clearer than the market and demand share. The energy market share and demand share are measured by the value of energy net exports and domestic energy use (kg of oil equivalent per capita), respectively. Especially, Russia and Canada fail to be effectively identified in the resource-driven community. Although the input-output structure and relative magnitude of production among sectors are not explained by trade status only, the theory of complex economy points that there are many unmeasurable factors, including infrastructure, education, vocational skills, and other factors, hidden behind economic behaviors. Those non-monetary metrics, based on trade network structures, have greatly enriched the theory of economy. Those unmeasurable factors are thus reflected in the country's export capacity, i.e., trade data.
Actually, the s2c-c2s distances plane also provides us with a representation of the entire trade systems. This method shows the different community nature from that of traditional methods. The flow distances abstracted from trade networks embodies strong and stable explanatory power in describing multilateral trade distance to uncover the intrinsic structure of trading systems. Each type of international specialization in the production value chain also reflects the aggregation of national energy roles, rather than geographical clustering.

Resource-driven community
Deep-processing community Mix community

Identifying National Status in Trade Value Chain
In the analysis above, we verify that flow distance has better performance in explaining trade volume and representing hierarchical structures. From the perspective of the flow distance, the community structure of trade networks is driven by the coevolution of product specialization and geographic distance. Flow space embedding method not only can detect the community structure but also can reveal the position of the countries on the energy production chain according to their production chains and the level of added-values. To show this, because energy production, processing, and consumption are so important for countries in the sense of economics and politics, let us take the energy products as examples to show the flow distances.
In terms of countries with rich energy resources, such that they are major energy export countries, including Russia, Canada, Saudi Arabia, etc., their industrial structure is highly dependent on energy trade, therefore they often have some problems such as monotonous industries, lagging industrialization, and weak manufacturing development. For the oil trade market, in particular, resource-driven statehood makes the country become an important exporter of oil in the world. As an "oil country", Saudi Arabia's oil reserves and production are adequate in the world, and the petroleum and petrochemical industries are the blood of the country's economy, and thus its contribution to the energy value chain is low.
Beyond that, we can identify countries' capabilities with deep processing and high value-added product contribution. On the one hand, the deep-processing country would balance national demand through importing enough energy, which can be further processed to primary energy to produce higher value-added products. We can find that the developed countries have higher c2s distances in most commodity trades with countries, such as Japan, Germany, France, etc. These countries are located above the diagonal, which shows they can produce higher energy value. On the other hand, owing to resource emptiness, energy policies, etc., some countries flood into the energy consumption market. For instance, the United States has always had a protection policy for its domestic energy sources, relying on large amounts of imports to maintain domestic demands. The situation of coal is on the contrary. The United States and Australia are the world's largest coal exporters, which confirms that the results of our energy flow distance can be used to identify the country's position. China's coal trade has similar phenomena.
Based on our results, we conclude that the distances of open flow systems can characterize the international status and value-added contribution capacity of a given country, which reveals that flow distances have economic significance. When the energy needs more complex production processes and energy demand, more countries must be imported from to form a long c2s distance. Thus, a longer national energy value chain is formed. As a method to quantify the energy national status, the flow distance can provide a new study perspective on related research.

The Evolution of the National Status in Energy Value Chain
According to the observations, we know that flow distance is the basic property of national status and contributions of the products in the value chain. We expect countries with smaller c2s distance can produce more diversified products. Based on the evolution of c2s distance, we can trace the national development process. Similarly, for the evolution of s2c distance, we can find the development trend of national status and potential contributions. Therefore, the features of the evolution of national status are the key point of our analysis in the s2c-c2s distance plane. This section shows how the global production and consumption of energy trade evolve and the results are shown in Figure 5. The three major energy resources, namely coal, oil, and natural gas, are in the core positions in the energy trade system. As an example, we analyze China's evolution of production and consumption in the following part (that is, the red line in Figure 5). The c2s distance of coal has a huge reduction in 2008 and has been in a low state, which reveals that China's coal processing level is relatively higher in the upstream industry chain. Meanwhile, it coincides with a significant increase in China's coal inventories, which may confirm that China is moving to the "source". Furthermore, China's s2c distance is higher than the c2s distance in the oil trade flow network, and the flow distance from the source node is closer. Especially, China had reversed the way of the trade from oil exporter to oil importer country in 1996, after which China's oil imports and dependence on foreign oil is gradually increasing. Besides, the natural gas trade in Germany and the United States fluctuates, while China, Russia, Japan, and Saudi Arabia are relatively stable.
Another interesting finding is that studies based on flow distance have detected that China, Britain, Africa, and other places inject new vitality into the energy market. For example, the black line in Figure 5 shows that the UK's energy consumption capacity has gradually increased since 2005, and the main reason is the depletion of North Sea oil fields, which led to the failure of the UK energy industry's self-reliance. More importantly, Britain' s2c distance of electricity trade has been higher than c2s distance since 2008. It is obvious that, as the first country to complete the industrial revolution, Britain also changes from the direction of energy consumption to clean energy in the process of development.
In this section, taking the energy trade as an example, we explore the evolution of the national status by flow distance using the trade flow network model. The results show that the trade distance of network structure and multi-path coupling not only predicts trade flow well but also has an important meaning to shape the trade network structure and the position of the country in the trade value chain.

Discussion
We propose trade distances by focusing on multilateral trade path and network effects emerging from trade relations to formulate a general model describing the effective distance of trade. By studying the properties of the flow distances, we find that structure-based flow distance can better explain the motivation of trade volume in the gravity model. Flow distance can be considered as an effective distance of trade to quantify the diversity of distance in bilateral trade.
In the first analysis, the gravity law can be supported by the empirical data if the geographical distance is replaced by flow distance. Regression results of flow distances are all significantly greater than those for geographical distance, which shows that the flow distance is more suitable for quantifying the effective distance of international trade. The correlation between flow distance and geographical distance also reveals that trade distances encode more than purely geographical information. Noteworthy, compared with hyperbolic distance and Brockman's effective distance, flow distances reflect the heterogeneity of the international trade network by incorporating the trade network topology and the flow information of the nodes together. Hyperbolic distance is defined as "effective distance" by incorporating the trade network topology and the GDP information of the nodes together [31,52]. Although it obtains a better description for the international trade network, the trade flow information is not contained in this distance; therefore, it is difficult to represent the rich information of the multi-lateral trades encoded in paths. Brockman's effective distance only calculates the distance of the maximum likelihood paths between nodes [32], but does not count all paths. Importantly, trade distances may consider how national behavior across the trade value chain acts on international trade, including direct and indirect effects.
In the second analysis, experimental evidence finds that flow distance can collapse to the predicted results of geographic distance with the appropriate time lagging variable. Due to the path dependence and lock-in effect in the international trade system, when the new network space is formed, countries' trade behaviors will be affected by the network. Past trade behaviors will determine the entire trade network space and future trade flows will be created by the existing trade network space. This understanding may help us to revise the distance itself as the "effective distance" in the network space. Some advantages of the effective distance, which is defined by using the historical trade network data, are as follows. From the perspective of topological structure, the international network is relatively stable within a certain range [53], which reflects the hypothesis of statistical stationarity in economics.
Especially, the trade structure may not be changed suddenly by trade policies in the short term, because the role of economic policies has the property of time lag [54]. It provides the basis for the flow distance prediction of short-term trade. Additionally, the complex network model is more intuitive to portray multilateral trade resistance, since it considers international trade as a whole. With the increase of the time lag parameter in Equation (7), the prediction of the flow distance to trade flows gradually degenerates into the effect of geographical distances. Furthermore, the mechanism of flow distances has yet to be explored. How flow distances reshape trade architecture and organizations can be discussed.
In the third analysis, using the theoretical model of international trade, the forming mechanism of the international trade network is further discussed. The global value chain is changing the world trade pattern, and the flow network model also shows the formation of the trade value chain of commodities at the macro and micro scales in international trade. In this paper, we use the flow-space distance embedding scheme to detect the community structure in international trade networks. The classic community detection algorithm is to classify countries through the maximum modularity function Q [55,56]. A larger value of Q indicates a better division effect. Different from previous literature, the community division method by flow distances embedding highlights the function of the country in the energy value chain. From an evolutionary perspective, as shown in Figure 5, the countries within the community are relatively stable, but some large countries have changed greatly. The community structure obtained by the optimization method has the effect of geographical agglomeration and has a prominent position in Asia over time [13,57]. However, this community division method is coarse-grained. How to explore a more refined algorithm may be an important field in future studies.

Conclusions
Complex network models provide a snapshot of international trade systems, enabling us to understand international trade as a whole and analyze the issues of trade in a multi-level view. For trade distances, a simple and effective method is used by us to detect the national status and analyze the trade distances through the mobility of international trade. In this paper, we first construct trade distances by focusing on multilateral trade path and network effects emerging from trade relations according to the trade flow network structure and commodity flows. Next, we verify that the flow distance defined above is reasonable and effective, using an econometric methodology, namely gravity model. Finally, we link the effective distance to the economy. The results show that flow distance is an effective classification of the country's position in the energy production value chain.

Conflicts of Interest:
The authors declare no conflict of interest.


Determinants of Banks Profitability in a Developing Economy: Empirical Evidence from Palestine

: This study analyzes bank-specific, industry-specific and macroeconomic determinants that affect bank profitability. Dynamic panel analysis is applied on the sample of 14 banks in the Palestinian banking system in the period between 2008 and 2018. Regression model was used to do the empirical analysis and develop the econometric model consisted of one dependent variable and nine independent variables. According to the obtained results, among bank-specific factors of bank profitability, the most important one is operating expense management. Regarding the external variables, economic growth show significant effect on bank profitability in Palestine.

INTRODUCTION
Efficient financial system should have solid and robust intermediation process is in fact a revenue-generating activity for most banks, show profitability improvements and better quality services for consumers (Sufian and Habibullah, 2009). According to the theoretical and empirical works bank profitability is determined by various firm specific, industry specific and macroeconomic factors which requires studying and analyzing these factors and determining their importance in influencing profits. The increased competition among financial firms, after the liberalization of trade in services as well as compliance with Basel requirements, challenges and risks facing the banking sector have grown which may lead to insolvency of the bank as a result of a temporary shortfall in liquidity, poor risk management and unplanned expansion in some operations (Atikogullari, 2009). Despite the importance of the banking sector and its remarkable development in recent years, still the identification and analyses of the bank profitability determinants, knowing the factors and variables that affect profits has a huge matter, especially, in this competitive environment. Furthermore, identifying the profitability determinants is important to the bank's owner as well the regulatory maker as they can assess and adjusts the performance of banks and regulation to maximize profit.

Aims, questions and hypothesis Research objectives:
The main aim of this study was to find out and identify the factors affecting the profitability of banks operating in Palestine.
Address the causes that lead to losses or reduce their profitability and going out with recommendations would preserve the profits of banks, improve profitability and address the causes that lead to losses or reduce their profitability.

Research questions:
Despite the importance of the banking sector and its remarkable development in recent years, still the identification and analyses of the bank profitability determinants, knowing the variables that affect profits has a huge matter, especially, in this competitive environment. Furthermore, identifying the profitability determinants is important to the bank's owner as well the regulatory maker as they can assess and adjusts the performance of banks and regulation to maximize profit. Against this background and problem formulation, the major research question is the following: what are the most important factors and variables that affect the profitability of banks operate in Palestine?
Research hypothesis: In the light of the research objectives, the researcher sought to test the following hypotheses: C H 1 : Asset Size (AS) has a positive impact on bank's profitability C H 2 : Asset's structure (LAT) has a positive impact on bank's profitability C H 3 : Asset's quality (LLP) has a positive impact on bank's profitability C H 4 : Capital Adequacy (CAR) has a positive impact on bank's profitability C H 5 : Liquidity (LTD) has a negative impact on bank's profitability Int. Business Manage., 13 (9): 392-396, 2019 C H 6 : operating expense management (CIR) has a positive impact on bank's profitability C H 7 : Activity Mix (AM) has a positive impact on bank's profitability C H 8 : Market Share (MS) has a positive impact on bank's profitability C H 9 : Economic Growth (EG) has a positive impact on bank's profitability Literature review Theoretical and conceptual framework: According to the theoretical and empirical works, bank profitability is determined by various firm specific, industry-specific and macroeconomic variables. First, there is a group of determinants of profitability that are specific to each bank and that in many cases are the direct result of managerial decisions (asset structure, asset quality, capitalization, cost efficiency, size and income diversification). The second group of determinants includes factors relating profitability to the industry structure and to the macroeconomic environment within which the banking system operates such as industry concentration and economic growth. This section explores the previous literature related to the scope of this research and the determinants suggested by several studies in which conducted locally and globally, regarding the profitability of banks.

Overview of the Palestinian banking sector:
The banking sector has great impact on the Palestinian economy as it is the main funding channel for the private and public sector and the main source of credit in the local market due to the capital market weakness and limitation and the lack of the ability to provide adequate sources of financing to investors. The Palestinian banking sector consists of domestic and foreign banks. Fourteen banks operate in Palestine, seven of them are domestic (three of them are Islamic) and the rest are foreign with a total of 343 branches and offices. The banking sector has total assets reaching USD 16.125 billion, deposits of US$ 12 billion and credit facilities of US$8 billion at the end of year 2018.

Determinants of bank profitability:
There are various factors affecting the profitability of banks. Generally, these factors are categorized as bank specific, industry specific and macroeconomic factors. Bank specific factors such bank size, asset quality, capital adequacy, liquidity and overhead expense management. These variables within the control of the bank and which are mainly influenced by the bank's management decisions. Industry-specific determinants demonstrate industry conditions such concentration, ownership and market share. Macroeconomic factors such as inflation, GDP and market capitalization reflect the economic environment where banks operate and management has no control over.

Bank-specific profitability determinants; asset's size:
Asset size is the total market value of the securities in the bank's portfolio. It can also be referred to assets under management. The asset's size of a bank can be important for investors to consider for a few reasons. Firstly, the greater the asset's size the more trust the investors have in the bank's management. Secondly, larger assets size tends to be more actively traded in the market with higher average daily trading volume providing for greater market liquidity.
Asset's structure: Assets are mainly composed of liquid assets and profitable assets in which liquid assets are kept for meeting customer's withdrawal demands which generates faith and credibility to the public. On the other hand, profitable assets are used to make reasonable profit by operating and investing the customer's deposits as loan and long-term loans. Thus, banks should keep a balanced assets structure; a combination of both types of assets.
Asset's quality: The asset's quality ratio reflects the amount of existing and potential credit risk associated with loans. Furthermore, the evaluation of asset quality should consider the adequacy of the allowance for loan losses. Assets quality is an important factor to investors because it's one of the main sources of the bank's earnings.
Capital adequacy: Capital adequacy refers to the sufficiency of the amount of equity to absorb any shocks that the bank may experience. Capital adequacy is a measure of the financial strength of a bank; it is commonly expressed as capital to its assets ratio.
Several ratios can be used for measuring capital adequacy. The researcher chose the ratio of total shareholder's equity/net assets. This has been used as an indicator of capital adequacy.
Liquidity: Liquidity risk reflects the probability of bank inability to meet its obligations which can eventually lead to bank failure even though, technically, it may still be solvent. The exposure to liquidity risk is usually measured by many ratios as ratio of loans to deposits and cash assets and government securities to total assets (Kosmidou et al., 2004). In order to reduce the insolvency problems, bank holds the right amount of immediately spendable funds when they are required and its ability to raise liquidity at timely fashion. However, liquid assets usually have lower rates of return. Therefore, higher liquidity would imply lower profitability.
Operational expense management: CIR can be defined as the ratio between the business non-interest expenses and its net interest and commission income. With this ratio, the bank can see how much of its interest and commission income is spent on various expenses not including interest expense. By calculating this ratio, the Int. Business Manage., 13 (9): 392-396, 2019 bank can manage the amount of non-interest expenses they have and generate strategies to reduce it to have a lower ratio, meaning fewer expenses are being spent and more income is preserved.

Activity mix:
The bank activity mix is also an important proxy for the overall level of risk undertaken by banks to the extent that different sources of income are characterized by different credit risk. A bank's activity mix is represented by the share of non-interest income in the form of fees, commissions and trading income to its total revenues. The activity mix comes from the wide range of financial activities that banks offer.
Industry-specific profitability determinants; market share: Market share is a firm's percentage of an industry's total sales. It is calculated as total assets of the bank as a percentage from total assets of the whole system. Market share is usually used to express competitive position used as the proxy for market power.
Macroeconomic profitability determinants; economic growth: Bad economic conditions can worsen the quality of the loan portfolio; generating credit losses and increasing the provisions banks need to hold, thus, reducing bank profitability. In contrast, an improvement in economic conditions in addition to improving the solvency of borrowers, increases demand for credit by households and firms with positive effects on the profitability of banks (Athanasoglou et al., 2008).

MATERIALS AND METHODS
For achieving the aim and objectives, the study was based on the descriptive method in order to identify the most important factors and variables that affect the profitability of banks operate in Palestine.

Study population:
Palestinian banking sector has been chosen for conducting this study which comprised from fifteen banks commercial and islamic from 2008 until 2018. The necessary data were collected from each bank's annual audited financial statements, the association of banks in Palestine and from Palestinian Monetary Authority (PMA)'s annual reports. In the light of the elements and the dimensions of the problem, the researcher built a study model on the basis of the relationship between the independent variables of banking sector (expressed in terms of the asset's size, asset's structure, asset's quality, capital adequacy, liquidity, operating expense management, activity mix, market share and economic growth) and the dependent variable (Return on Assets) ( Table 1).

Data collection:
The study data has been collected from sampled banks annual reports which is mainly represented in the financial statements of the banks (2008-2017) and published annual financial reports and its notes as well as the statistics issued by the Palestinian Monetary Authority (PMA). Also, for theoretical background of this study and its literature review, books and academic papers published on the subject of this current study were collected from libraries, relevant websites and available database to cover this part of the study.

Model of the study:
Based on the light of the elements and the dimensions of the problem the researcher employed the regression model presented below to highlight the relationship between the independent variables on the one hand and the dependent variables (ROA) on the other hand. The following mathematical equations illustrate the model (Table 2):

RESULTS AND DISCUSSION
One of the functions of linear regression that examines the strength of the relationship between independent variables and dependent variable. In addition to that it works on framing this relationship in a mathematical model used for prediction (Table 3). Table 3 presents regression results for the profitability equation for the model where ROA is dependent variable. It showed that Asset Size (AS) has positive insignificant impact on ROA, thereby, it has little impact on profitability. The positive insignificant relationship, indicating that the more banks assets and investments the more profitable they become. This finding is consistent with the research hypothesis. The insignificance of bank size is also found by Goddard et al. (2004); Haron (2004); Khan (2012); Zeitun and Tian (2007). The table also shows that asset's structure (LAT) has a positive significant impact on ROA, indicating that the more banks hold income generating assets, especially, loans the more profitable they become. The result is consistent with the research hypothesis and previous findings of Yang and Chan (2010).
The results also showed that asset's quality (LLP) affected ROA negatively. Since, LLP ratio was negative but insignificant. It is clear that there was a negative relationship between poor asset quality and profitability. CAR has negative significant impact on ROA, indicating that the negative relationship was due to the volume of negative reserves of banks which produce poor profitability results, furthermore, this negative relationship also attributes to the weak capital base by most of commercial in the study sample. The result is not consistent with the research hypothesis and previous findings of Bikker and Hu (2002) and Goddard et al. (2004). LTD (the ratio that measures liquidity) affected ROA negatively. Since, LTD ratio was negative but insignificant. It is clear that there was a negative relationship between liquidity and profitability because Palestinians banks comprise more income generating assets than liquid assets in their investment portfolio. The result is consistent with the research hypothesis and previous findings of Albertazzi and Gambacorta (2010).
The results also showed that a negative change in operating expense management will have a positive impact on ROA. A major contributor to insufficient level of profitability is a weak or poor expense management.
As it is expected it shows negative sign. Operating expenses could be affected by level of the productivity (the low levels of productivity and high levels of inefficiency) in the Palestinian banking sector. Further, newly established banks have high costs in the first years of their business, since, most of banks operate in Palestine are newly established. Namely, they are mainly focused on increasing their market share that would results in higher expenses. Thus, the banks should focus more on management of these expenses. As the result, the improved efficiency will increase bank profitability. The result is consistent with the research hypothesis and previous findings of Berger et al. (2000) and Khan (2012).
As we expected, Activity Mix (AM) was positive. However, it is insignificant. Thus, bank non-credit activities are not at the level of importance in the Palestinian banking sector at which they influence the bank profitability. According to the Palestinian Monetary Int. Business Manage., 13 (9): 392-396, 2019 Authority (PMA) reports, almost 13% of banks income is from non-credit activities. The result is consistent with the research hypothesis and previous findings of Meslier et al. (2014).
Bank's Market Share (MS) has insignificant positive effect on bank's profitability which indicate, the larger the market share of a bank, the higher the profitability earned by the bank. As it mentioned before, the increased number of bank branches do not necessarily increased profitability if it has high fixed and operational costs which needs to be managed and controlled well. The result is consistent with the research hypothesis and with the Berger who found the positive effect of the market share on bank efficiency.
As it is expected, Economic Growth (EG) positively and significantly affects bank profitability. Favorable economic conditions in the terms of growing economic activities, means increasing of household savings and demand for financing. The result is consistent with the research hypothesis.

CONCLUSION
This study analyzed bank-specific, industry-specific and macroeconomic determinants that affect bank profitability. Dynamic panel analysis is applied on the sample of 15 banks in the Palestinian banking system in the period between 2008 and 2018. Since, the internal determinants are controlled and influenced by management decisions, banks have means to influence them. The most important bank-specific determinant of bank profitability is operating expense management. Thus, there is need to undertake the activities that reducing administrative and personnel costs and in that way improvement of the cost efficiency. Among other internal factors affecting profitability is the asset structure variable, indicating that the more banks hold income generating assets, especially, loans the more profitable they become. Thus, there is need to undertake more investments in credit activities, since, loans to deposit ratio in Palestine (65%) still low compared to the countries in the region. Also banks need to involve more in productive investments and not to be conservative in their investments which results in sustainable growth. Also there is a need to increase and diversify fee-based services and to generate new source of income. Regarding external variables, beside of market share GDP growth has the highest importance.