Analysis of Economic Aspects of Consumer Behavior

Research background: The reduction lately observed in interregional differences in the per capita income in Russia requires some clarification of the reasons. One possible way to do this could be decomposition of interregional inequality in personal incomes by income sources, i.e.: wages and salaries, property incomes, social transfers, entrepreneurship incomes and revenues from informal activities.
 Purpose of the article: The objective of this research is identification of the character, direction and degree of influence of various income sources on interregional inequality and convergence of Russian regions by their per capita income in 2001?2014. 
 Methods: We brought personal incomes in the regions to a comparable level using the relative cost of fixed consumer basket. Then we applied the population-weighted Gini coefficient, coefficient of variation, and the Theil index to measure the interregional inequality in personal incomes in dynamics. Further usage of various techniques of inequality decomposition allowed us to evaluate contribution of different types of income to the Russian regions? convergence across time.
 Findings & value added: Various types of income demonstrated different paths of interregional inequality, changes in interaction and in the shares of total income, which altogether influenced spatial inequality. Wages and salaries showed the largest and growing impact on inequality. The contribution of informal incomes to the overall inequality was the second largest, but diminishing and negatively interacting with other unevenly distributed types of income; thereby they provided more than half of the total interregional convergence. Entrepreneurship incomes revealed slightly decreasing influence on inequality, which was mainly neutralized by their reduction in the share of total income. Social transfers demonstrated the largest smoothing effect, however, their contribution to convergence was exhausted. Property incomes evidenced the greatest enhancing impact on inequality especially in the period of recovery. Additionally, informal incomes played the role of substitutes for formal incomes providing self-replicating mechanisms for reducing inequality in Russia.

Understanding the dynamics of interregional inequality in Africa is important when formulating development policies. In this paper, we investigate the convergence pattern of regional inequality within 49 African countries over the period 1992–2012 using satellite night‐light‐based inequality proxies. We find evidence of multiple equilibria in regional income inequality. Countries belonging to clubs with high regional inequality level do not show any tendency to converge toward the clubs with low regional inequality, indicating that these countries are indeed stuck in regional income inequality traps. The idea of regional income inequality traps is also confirmed by a distributional dynamic analysis that uncovered evidence of regional income inequality persistence with a limited degree of mobility between convergence clubs. From the convergence analysis, we found that the initial conditions in the economic development, natural resource rent, urbanization, political institutions, development aid programs, and infrastructure are very important determinants of regional income inequality. Our findings provide policy makers some direction for future economic policies.

This study selected five observational stations in the normal direction of Jinu-do(island) shoreline and observed water temperature, electrical conductivity and pressure from March, 2012 to January, 2013(about 11 months) and attempted to see the variation characteristics of ground watertable. This study wants to know : 1) External environment force factors(tide, climate, wave etc.) affecting ground watertable variation through time series and correlation analysis. 2) Spatial variations of ground watertable and electrical conductivity change by storm event. First, we found that the station at the intertidal zone was strongly affected by wave and tide level and the stations at sand dune and vegetation zone was affected by precipitation and tide level through time series data and correlation analysis. Second, during the storm event, we found that ground watertable and electrical conductivity are stabilized at the start line of sand dune and vegetation zone and transition zone between freshwater layer and seawater layer exists in the experiment area and is about 50~70 m from coastline of the south side of Jinu-do(island).

In the past few decades, liver disease has gradually become one of the major causes of death and illness worldwide. Hepatitis is one of the most common liver diseases in China. There have been intermittent and epidemic outbreaks of hepatitis worldwide, with a tendency toward cyclical recurrences. This periodicity poses challenges to epidemic prevention and control. In this study, we aimed to investigate the relationship between the periodic characteristics of the hepatitis epidemic and local meteorological elements in Guangdong, China, which is a representative province with the largest population and gross domestic product in China. Time series data sets from January 2013 to December 2020 for 4 notifiable infectious diseases caused by hepatitis viruses (ie, hepatitis A, B, C, and E viruses) and monthly data of meteorological elements (ie, temperature, precipitation, and humidity) were used in this study. Power spectrum analysis was conducted on time series data, and correlation and regression analyses were performed to assess the relationship between the epidemics and meteorological elements. The 4 hepatitis epidemics showed clear periodic phenomena in the 8-year data set in connection with meteorological elements. Based on the correlation analysis, temperature demonstrated the strongest correlation with hepatitis A, B, and C epidemics, while humidity was most significantly associated with the hepatitis E epidemic. Regression analysis revealed a positive and significant coefficient between temperature and hepatitis A, B, and C epidemics in Guangdong, while humidity had a strong and significant association with the hepatitis E epidemic, and its relationship with temperature was relatively weak. These findings provide a better understanding of the mechanisms underlying different hepatitis epidemics and their connection to meteorological factors. This understanding can help guide local governments in predicting and preparing for future epidemics based on weather patterns and potentially aid in the development of effective prevention measures and policies.

Regional rural inequalities in China since the reform are examined based on Theil's regional inequality index. By using data from the three zones (east, central and west) and provinces in the period 1980–2002, I find that China's rural regional inequality has an overall increasing trend with a short period of decrease during the entire reform period. However, the trends vary in different indicators and different geographical scales. The trend of income inequality does not always match that of rural economic inequality. The decomposition of Theil's regional inequality further reveals that, since 1987, interregional inequalities between the eastern, central and western regions have been more serious than provincial inequality within the regions themselves. For example, when rural non‐agriculture inequality decreased substantially within the east, it increased greatly within the west. Similarly, the income inequality increased within the west and the east but decreased within the central region. China's regional rural inequality remained at a high level and the coastal/interior income differential continued to increase.

The structural analysis of gross regional product (GRP) of Perm Krai has been conducted. The GRP has been calculated using income and expenditure approaches to determine the influence of each of its components on the growth rate of generalized indicator. The significant statistical discrepancy of GRP values, calculated with different methods, has been revealed. The GRP calculated using the expenditures approach is characterized by the greater range of fluctuation than the gross added value and the GRP calculated using the income approach. The reason of the discrepancy has been determined. It is the incomplete disclosure of interregional relations at the regional level. The authors have highlighted the challenges that arose when calculating the values, including the resulting simplification of the methods of calculation of macroeconomic indicators at regional level that are quite substantial to regions, including Perm Krai. Based on the sensitivity analysis and the results of the correlation analysis the authors have identified factors that have the significant effect on the rate of economic growth in the region. Correlation analysis has shown the high dependence of the GRP on consumer spending both in Perm Krai and in Russia. Empirical analysis has allowed to substantiate the main directions of regional economic policy and to identify the instruments of public administration. The use of these instruments together with the methods affecting the salary range, stimulating consumer demand, investment and government spending will allow the government of Perm Krai to influence economic growth in the region.

Using the most recent statistical data, two U.S.-based geographers document the changes and level of regional inequality in China for the period 1978-2006, in order to shed light on whether recent government efforts toward inequality reduction have had observable effects. The paper reveals the spatial dynamics that underlie regional inequality by decomposing interprovincial inequality into its interregional and intraregional components, and investigating the growth trajectories of regions and provinces. The authors demonstrate that interprovincial inequality declined during the 1980s, increased in the 1990s, was relatively stable from the late 1990s to 2004, and has declined thereafter. Through closer analysis, they show how the trend in interprovincial inequality up to 2004 has been shaped by countervailing patterns in (declining) intraregional inequality and (increasing) interregional inequality. Since 2004, however, the study reveals that both interregional and intraregional inequalities have declined, reflecting convergence in growth rates among provinces and among regions. Journal of Economic Literature, Classification Numbers: I31, O18, R12. 9 figures, 1 table, 77 references, 1 appendix.

Purpose: This study aimed to examine the impact of technology infrastructure, digital lending, and digital payments on real per capita income, public health, and education levels. Theoretical framework: This study is built on the relationship between technology infrastructure, digital lending, and digital payments and their impact on economic and social outcomes. The study draws on theories of technology adoption and diffusion, financial inclusion, and human development. Design/methodology/approach: This study used quantitative research to examine how technology infrastructure, digital lending, and digital payments affect income, health, and education. The sample was data from 2015-2020, cleaned and analyzed using regression analysis in E-views. Findings: The findings revealed that technological infrastructure has a significant positive effect on real per capita income and public health, but a negative and insignificant effect on the education level. Digital lending was insignificantly positive for real per capita income and negatively insignificant for public health and education levels. In contrast, digital payments had a positive and significant impact on the health and education levels but a negative and insignificant effect on real per capita income. Research, Practical & Social implications: The study suggests that governments should prioritize the development of information technology infrastructure for SMEs to increase real per capita income and public health. Furthermore, relevant laws and regulations should be improved to ensure the development of digital payments, credit investigation systems, and standardized digital payment transactions to maintain financial security and stimulate consumer demand, ultimately promoting sustainable economic development. Originality/value: The originality of this study lies in its investigation of the impact of both technology infrastructure and digital finance on income, health, and education. Additionally, the study highlights the importance of addressing regulatory issues to ensure the safe and effective use of digital finance.

Alzheimer's disease (AD) is a neurodegenerative disease with a long incubation period. While extensive research has led to the construction of long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) regulatory networks, which primarily derived from differential analyses between clinical AD patients and control individuals or mice, there remains a critical knowledge gap pertaining to the dynamic alterations in transcript expression profiles that occur with age, spanning from the pre-symptomatic stage to the onset of AD. In the present study, we examined the transcriptomic changes in AD model mice at three distinct stages: the unaffected (un-) stage, the pre-onset stage, and the late-onset stage, and identified 14, 57, and 99 differentially expressed mRNAs (DEmRs) in AD model mice at 3, 6, and 12months, respectively. Among these, we pinpointed 16 mRNAs closely associated with inflammation and immunity and excavated their lncRNA-mRNA regulatory network based on a comprehensive analysis. Notably, our preliminary analysis suggested that four lncRNAs (NONMMUT102943, ENSMUST00000160309, NONMMUT083044, and NONMMUT126468), eight miRNAs (miR-34a-5p, miR-22-5p, miR-302a/b-3p, miR-340-5p, miR-376a/b-5p, and miR-487b-5p), and four mRNAs (C1qa, Cd68, Ctss, and Slc11a1) may play pivotal roles in orchestrating immune and inflammatory responses during the early stages of AD. Our study has unveiled age-related AD risk genes, and provided an analytical framework for constructing lncRNA-mRNA networks using time series data and correlation analysis. Most notably, we have successfully constructed a comprehensive regulatory ceRNA network comprising genes intricately linked to inflammatory and immune functions in AD.

Objective: to determine the problems of settling the overdue debts of banks in arbitration proceedings and ways to solve them. Methods: methods of vertical and horizontal time series analysis, correlation analysis, grouping methods, and abstract-logical method were used in the research.Results: the problem of collecting bad debts of banks within bankruptcy procedures has actually not been studied in the scientific literature. The paper considers the main aspects of modern research in the field of bankruptcy of physical and juridical persons, and emphasizes the need to systematize the practice of settling bank receivables in arbitration courts. The article presents the results of the analysis of the Bank of Russia statistics on the structure of overdue debts of the banking system, as well as the main results of bankruptcy procedures according to the Federal Register.The analysis of operations of one of the largest regional banks allowed identifying and systematizing the main problems of regulation and control of overdue debts, provision for impairment on loans and interest on loans, and stipulated a low efficiency of the bank’s participation in bankruptcy cases.Scientific novelty: the paper systematizes and analyzes the data on the practice of bankruptcy creditors’ participation in bankruptcy proceedings, and formulates the main directions for solving the problems of low efficiency and high costs of participation in such procedures.Practical significance: the research results can be used in the practical activities of banks for managing and settling accounts receivable, including on the basis of judicial recovery, as well as for developing the main directions of the monetary regulation of banking activity.

It is the hypothesis of imbalance vibration monitoring and dynamic balance to separate the imbalance vibration signal accurately. The compositive method of accurate imbalance vibration signal separation was given based on some methods, such as signal forecasting and extension of time series model, EMD and correlation analysis etc. The principle and procedure of the method were introduced in detail based on radial vibration signal analysis of the motor spindle. In order to test the validity of the method, further unbalance experimentation and signal separation were carried out.

Wildfires have been increasing in length and frequency since the mid-1980s, which emit pollutant matter that can adversely impact human health, specifically exacerbating negative respiratory impacts (Lipner et al., 2019). Although many studies examine the respiratory impacts of worsened wildfire conditions, a limited number take place outside of the western United States, occur over prolonged periods, and look at how air quality is changing due to warming climates. The objective of this study is to investigate how air quality health index (AQHI) values have changed over a period of two decades (January 1, 2001 to December 31, 2021) in the Great Plains Air Zone (GPAZ) in southern Saskatchewan and the potential respiratory implications of AQHI changes. The study uses a mixed methods approach: time series analysis, correlation analysis, and regression analysis to analyze AQHI and a literature review to study respiratory implications of AQHI increases. We found a significant increase in AQHI over time of 19.3% (p < 0.0001, n = 7308) and three main outcomes for respiratory data: (1) individuals with respiratory conditions have increased susceptibility to worsened air conditions, (2) elevated AQHI values cause increases in healthcare admittances for respiratory conditions such as asthma and COPD, and (3) women and individuals in the age ranges of 15-65 are particularly susceptible to respiratory outcomes from wildfire smoke. The study provides areas for future research, including the implementation of apps to track respiratory outcomes, the impact of other risk factors on respiratory health, and the effects of AQHI on Indigenous populations.

Coronavirus disease 2019 (COVID-19) is a respiratory ailment that can spread from individual to individual, first recognized during an outbreak in Wuhan, China. Possibility of getting COVID-19 is greater for people who are in contact of someone known to have COVID-19, for example clinical professional, or family member. Peoples at higher peril for sickness are the those who live in or have starting late been in a zone with advancing spread of COVID-19. A few patients have pneumonia in the two lungs, multi-organ failure and sometimes death. In this work we are predicting the impact of COVID-19 cases in India based on time series analysis, correlation analysis, Granger Test, and Group Method of Data Handling (GMDH). We have compared the prediction of four algorithms namely combinatorial (quick), stepwise forward, stepwise mixed, and GMDH neural network (GMDH-NN) for predicting the future of India. Out of that stepwise mixed method gives good prediction for confirmed cases but GMDH-NN gives better prediction in case of death and recovered cases. As this disease is declared as an epidemic, the present study will help researchers to understand the impact of this outbreak. We have used combinatorial (quick), stepwise forward selection, stepwise mixed selection, and GMDH neural network to predict the spread of disease in India.

The aim of this paper is to examine and understand the impact of various green economy indicators on economic growth in Poland from 2010 to 2021. The study focused on a select number of indicators, including the final energy intensity of the economy, expenditures on environmental fixed assets in relation to gross domestic product (GDP), the share of renewable electricity in gross final electricity consumption, primary energy intensity of GDP, and the percentage of urban green areas. The relationship between green economy indicators and economic growth, expressed as gross domestic product per capita, was examined using regression analysis, time series analysis and correlation analysis. Data for the analysis were taken from the Central Statistical Office. Time trend analysis showed that the economy’s final energy intensity and the share of renewable energy improved steadily over the period under review, with a positive impact on GDP per capita. Correlation analysis demonstrated a robust inverse correlation between energy intensity and GDP per capita, along with a positive association between the share of renewable energy and economic growth. In linear and multiple regressions, the economy’s final energy intensity, expenditures on environmental fixed assets, and the share of renewable energy exhibited a significant impact on GDP per capita. The study’s conclusions underscore the necessity of sustained and intensified efforts towards a green economy in Poland, which is pivotal for ensuring long-term economic growth and sustainable development.

Drought is one of the most important factors affecting vegetation and natural resources in arid and semi-arid regions. This study aimed to investigate changes in vegetation and analyze its relationship with climatic parameters in the Saripol watershed during the period 2001–2020. The data used included MODIS sensor images, Enhanced Vegetation Index (EVI), Vegetation Condition Index (VCI), Land Surface Temperature (LST), and PERSIANN Dynamic Infrared–Rain Rate data (PDIR-Now). Time series analysis, drought indices (SPI), and correlation analysis were used to analyze the data. Spatial data preparation and statistical analyses were performed in ArcGIS and Excel. The results showed that the highest vegetation cover occurred in years with higher rainfall and lower temperatures than average, while dry and hot years showed the lowest vegetation cover. EVI showed a negative correlation with VCI (r = -0.768) and a positive and significant correlation with rainfall and SPI, indicating a direct effect of drought and soil moisture on plant growth. Although temperature affected vegetation, no significant correlation was observed with climatic indices. The findings of this study emphasize the importance of using remote sensing data for long-term monitoring of vegetation and natural resource management in arid regions and can be effective in agricultural planning and drought mitigation policies.