Econometric Model Research Articles

Тенденции распределения доходов между трудом и капиталом в странах с высокой долей труда в ВВП

The work provides quantitative estimates of trends in the distribution of income between labor and capital in countries in which the share of labor in GDP exceeds the average level. The work used UN data for a set of European countries, postSoviet countries, the USA, Canada and Israel. To assess trends, linear econometric models were built depending on the share of the labor force in the GFP for the period 2012–2021. The highest level of labor share was observed in Belgium, Iceland, the Netherlands and Switzerland. The study found that Belgium, Bosnia and Herzegovina, Denmark, Spain, the Netherlands, Portugal, Finland and France have seen a decline in labor’s share of GDP. In the Netherlands and Portugal this trend is weak. Germany, Greece, Iceland, Luxembourg, the Czech Republic, Switzerland and Estonia have seen an increase in the labor share of GDP. In Germany and Switzerland this trend is weakly expressed. There are no significant trends in the redistribution of income between labor and capital in countries such as Austria, Armenia, Italy, Canada, Slovenia, the United Kingdom, the United States of America, Croatia and Sweden. Trends in the redistribution of income between labor and capital can be determined by institutional conditions in the country’s economy.

Spillover Effects of Urban Expansion on Land Green Use Efficiency: An Empirical Study Based on Multi-Source Remote Sensing Data in China

Land is an important carrier of resource elements, and improving land green use efficiency (LGUE) is the key to achieving “smart growth” in cities. This study takes 269 cities in China from 2008 to 2020 as the research object and integrates multisource remote sensing data, GIS spatial analysis, and spatial econometric models to explore the evolutionary characteristics of LGUE and the spatiotemporal effects of urban expansion on LGUE. The results show that (1) urban LGUE increases over time and has certain gradient distribution and spatial dependence characteristics; (2) urban expansion has a significant inverted U-shaped relationship with LGUE, indicating that maintaining reasonable urban expansion is the key to improving LGUE, but excessive urban expansion has a strong negative spatial effect on LGUE. In terms of spillover effect, for every 1% increase in the square term of urban expansion, the LGUE of the local city decreases by 0.0673%, but the neighboring city increases by 0.2283%; and (3) urban expansion has significant spatiotemporal heterogeneous effects on LGUE, and spatial development boundaries must be reasonably determined to achieve “smart growth”. Currently, some cities in China are expanding rapidly, and we hope to provide key support for promoting the “smart growth” of cities and improving LGUE.

Does small firm dynamics matter for occupation diversity and job quality? Evidence from Brazil

Recent studies have explored the role of firm dynamics and the connection to job reallocation. However, limited attention has been devoted to the role of micro and small firms in this process, particularly about the quality of the jobs generated. Therefore, this article aims to measure the impact of micro and small firm dynamics on occupation diversification in Brazil, with specific emphasis on job quality. The study used data from 558 microregions within the Brazilian industrial sector between 2003 and 2015. Panel data were applied to three econometric models: Feasible Generalized Least Squares (FGLS), Driscoll–Kraay (DK) and Instrumental Variable (IV) models for robustness analysis, including Two-Stage Least Squares (2SLS), Limited Information Maximum Likelihood (LIML), and the Generalized Method of Moments with Continuously Updating Estimators (GMM-CUE). Our main finding reveals that the dynamics of micro and small firms positively impact occupation diversity in Brazil, leading to the creation of a wider range of job types. Furthermore, the frequency of change of firms from microenterprises to small businesses increases the occupation diversity in the Brazilian industrial sector. Our findings are significant in providing policy recommendations for developing countries to achieve a more diverse labor market.

Baseline Econometric Model and Solar Energy Requirement for Potential Hydrogen Demand in Tarlac, Philippines

This study aims to build a baseline econometric model for the possible development of green hydrogen production in Tarlac. Scenario analysis will consider hydrogen produced from renewable solar energy as an alternative energy carrier in the fuel mix for electricity generation in Tarlac to achieve sustainable economic growth and improve energy security while minimizing carbon emissions and improving resource efficiency. Tarlac is the chosen area to be considered in this study because this is where New Clark City is being established. New Clark City is a 9450-hectare disaster-resilient, environment-friendly, integrated urban development metropolis. It is important to build the baseline econometric model before proceeding with scenario generation. The parameters used are determined from available historical data through statistical procedures. The basic assumption is that the relationships between the independent and dependent variables that existed in the past will continue to be true in the future. The energy demand data and the continuous predictors were used to build a baseline econometric model for potential hydrogen demand in Tarlac, Philippines. Model equations were obtained using stepwise regression analysis. These model equations were used to forecast the energy demand up to 2032. These forecasted energy demands will be used in scenario generation to integrate green hydrogen production. Preliminary assessments of green hydrogen scenarios that are based on the assumption of renewable energy surplus generated from solar power plants in Tarlac are discussed. The forecasted demand will be used to assess the scenario generation in integrating green hydrogen in New Clark City. The proposed energy management strategy of storing surplus renewable energy, such as green hydrogen, for the power sector has the potential to impact various stakeholders, including industries and the government, significantly. This strategy aligns with transitioning to cleaner and more sustainable energy sources, contributing to national and international environmental targets. Adopting green hydrogen storage can contribute to energy security by diversifying the sources of power generation.

The Impact of Green Innovation on CO2 Emissions in China: Evidence from Spatial Regression Model

Using data from USPTO, CEADs, the Chinese Statistical Yearbook, and spatial econometric models for the panel of the 30 Chinese provinces for the period 2000∼2019, this study explores and compares the impact of green and non-green innovation on CO2 emissions. The findings reveal a significant spatial correlation in CO2 emissions across provinces, validating the appropriateness of spatial econometric models for this analysis. The results indicate that both green and non-green innovations significantly reduce CO2 emissions, with green innovation having a more pronounced effect despite its lower prevalence. Additionally, patents and their citations notably enhance environmental quality in neighboring provinces through indirect effects. The empirical results highlight that China should focus on: (1) spatial spillover effects of technological, especially green, innovation; (2) removing barriers to green patent invention and citation; (3) transitioning from an energy-driven to a green innovation-driven economy; and (4) enhancing environmental quality by mitigating CO2 emissions.

CORRUPTION AND ECONOMIC GROWTH IN NORTH AFRICA AND THE MIDDLE EAST: A CROSS-COUNTRY ANALYSIS

Purpose: This article examines the impact of corruption on economic growth in North African and Middle Eastern countries. It aims to uncover the extent to which corruption influences economic performance and to provide policy recommendations for mitigating these effects. Methodology: A comprehensive literature review was conducted, analyzing existing studies on corruption and economic growth in the specified regions. Empirical data from various sources were used to perform a quantitative analysis, employing econometric models to identify correlations and causations. Results: The findings indicate a significant negative relationship between corruption and economic growth. High levels of corruption are found to stifle investment, reduce government efficiency, and deter foreign direct investment, thereby hampering economic development. The results highlight that countries with lower corruption levels tend to exhibit higher economic growth rates. Practical Implications: The study suggests several practical measures for policymakers, including strengthening anti-corruption laws, improving transparency and accountability in government operations, and fostering a culture of integrity within public and private sectors. The recommendations aim to create an environment conducive to sustainable economic growth. Originality/Value: This article contributes to the existing literature by providing a detailed analysis of the North African and Middle Eastern regions, which have been relatively under-researched in this context. The study's unique focus on these regions offers valuable insights and tailored recommendations for combating corruption and promoting economic growth.

A hybrid model for predicting the carbon price in Beijing: a pilot low-carbon city in China

Beijing is one of the earliest pilot low-carbon cities in China. It was one of the first cities in China to establish a pilot carbon market to achieve this goal. As an emerging market, China’s carbon pricing mechanism is not yet complete. In this context, it is crucial for market managers and companies to predict carbon prices. This study uses a Prophet-EEMD-LSTM model to predict the carbon price in the Beijing carbon market, which significantly improves prediction performance. The advantage of this hybrid model is that it considers the particularities of carbon prices including trends, cyclical changes, and volatility. Considering that the carbon market has multiple complex characteristics, the carbon price is decomposed into multiple simple sequences using the Prophet and EEMD models. These simple sequences were predicted using an LSTM model. The hybrid model outperformed both econometric and single-machine learning models in terms of carbon price prediction. Based on the findings of this study, market managers and companies can take appropriate measures to prevent carbon price risks. These findings are conducive to the smooth operation of the carbon market, thereby providing sustainable support and guidance for the development of low-carbon cities.

Uncertainty in banking and debt financing of firms in Vietnam.

This study explores the effects of banking uncertainty on firms' debt financing. Employing data from 2007 to 2022 of Vietnam-a bank-based economy, we document that banking uncertainty negatively impacts corporate debt. The impact firmly holds across various debt maturities and sources, with the most predominant driver witnessed in bank debt. We also investigate the potential underlying mechanism linking banking uncertainty to debt financing, thereby validating the working of three crucial channels, including increased costs of debt, substitution of trade credit, and contractions in firm investment. Furthermore, conducting extended analysis, we find that debt financing exhibits more pronounced reactions to banking uncertainty for firms with closer ties to banks or during macroeconomic shocks, as captured by the financial crisis and the COVID-19 pandemic. Our findings survive after robustness checks by alternative measurement, static and dynamic econometric models, and endogeneity controls.

The Impact of Factor Price Change on China’s Cotton Production Pattern Evolution: Mediation and Spillover Effects

Cotton is an important agricultural crop and strategic resource. China is currently the country with the largest global cotton production and consumption, but few studies have systematically analyzed the dynamic relationship between factor price change (FPC) and cotton production pattern evolution (CPPE). Based on provincial panel data from the main cotton planting areas from 1985 to 2021, this paper used spatial econometric models to empirically analyze the impact of FPC including labor price, production material cost, and mechanical cost on CPPE from the perspective of mechanical substitution difficulty. The findings are that (1) FPC significantly affected CPPE, specifically the rise of labor price induced the demand for mechanical substitution, resulting in a significant cotton agglomeration effect; (2) spatial econometric analysis found that FPC had a significant spatial spillover effect on CPPE in adjacent regions; and (3) the transfer analysis found that higher mechanical substitution difficulty exerted an inhibitory effect on cotton planting, leading to a gradual shift of the main cotton planting areas from the Yangtze River Basin and Yellow River Basin with high labor prices to the Northwest Inland region with lower labor prices. Updating the production technology and improving the efficiency of cotton specialization are effective strategies. The results are valuable for policy making related to the development of China’s cotton industry.

Exploring the Influence of the Digital Economy on Marine Pollution Mitigation: A Spatial Econometric Study of Coastal China

This work employs the spatial econometric model to explore the potential relation between the condition of marine ecosystems and the rapid development of the digital economy (DE), focusing on the coastal area of China. While the environmental benefits of the DE are well studied regarding the land and atmospheric pollution, its influence on marine pollution (MP) remains underexplored, and this work aims to fill in this gap. By analyzing panel data from 42 coastal cities in China using a spatial Durbin model to examine both the direct and indirect impacts of the DE on MP, the results highlight the positive role that the DE plays in reducing MP, benefitting not only the local marine environments but also those of neighboring areas through non-negligible spatial spillover effects. In addition, we find a non-linear, inverted U-shaped relationship between the DE and MP. These results are further confirmed through extensive robustness tests. This work enriches the field of environmental economics by reporting the first empirical study on the marine benefits of the DE and offers policy recommendations to optimize digital technologies for marine environmental preservation.

Modeling and Forecasting Historical Volatility Using Econometric and Deep Learning Approaches: Evidence from the Moroccan and Bahraini Stock Markets

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

Bus Basis Model Applied to the Chilean Power System: A Detailed Look at Chilean Electric Demand

This paper presents a methodology to forecast electrical demand for the Chilean Electrical Power System considering a national, regional, district and bus spatial disaggregation. The methodology developed was based on different kinds of econometric models and end-use models to represent the massification of low carbon emission technologies such as electromobility, electric heating, electric water heating, and distributed generation. In addition, the methodology developed allows for the projection of the electric demand considering different kinds of clients as regulated and non-regulated clients, and different economic sectors. The model was applied to forecast the long-term electricity demand in Chile for the period 2022–2042 for 207 districts and 474 buses. The results include projections under the base case and low carbon scenarios, highlighting the significant influence of new technologies on future demand.

The effect of the exit of an insurer, due to government liquidation, on access to health care: evidence from Colombia.

Our study evaluates the liquidation effect of a health insurer from a subsidized scheme, with the largest number of members in Colombia, on restrictions to future access to user care. Based on the information regarding complaints and judicial claims about healthcare, the effect of this government decision is estimated using a dynamic econometric model of differences in differences. Our results suggest that the liquidation of the Health-Promoting Entity (EPS, its acronym in Spanish) CAPRECOM has a negative effect, specifically, it led to an increase of 0.32 and 0.21 in complaints rates per 1,000 members in the receiving EPSs during the first and second quarters after the intervention, respectively. However, this effect does not persist over time and becomes diluted in the following quarters. There is no evidence of a relationship between the magnitude of the effect and the EPSs performance ranking. Additionally, we do not find significant effects on judicial claims. Our results are important concerning the design and implementation of public policies for EPSs liquidation. We demonstrate the necessity of implementing actions to incorporate guidelines and strategic plans during the transition period. Such actions would enable safeguarding the right to health for the affected population in a liquidation insurer case.

Advanced Machine Learning Techniques for Predicting Gold and Silver Futures

This research focuses on predicting the future values of gold and silver futures by employing advanced machine learning algorithms. Traditional econometric models often struggle with commodity prices’ non-linear and dynamic nature. To address this, the study evaluates the performance of four unconventional machine learning algorithms: Gaussian Processes, Quantile Regression Forests, Extreme Learning Machines, and Support Vector Regression with an RBF kernel. The dataset used includes monthly trading data for gold and silver futures. The research findings indicate that these machine- learning models significantly enhance prediction accuracy. Support Vector Regression with an RBF kernel demonstrated the highest accuracy for gold futures predictions, while Extreme Learning Machines performed competitively for silver futures. This study highlights the potential of advanced machine learning techniques in financial forecasting, providing valuable insights for traders and investors in optimizing their strategies.

Impact of technological advancement and greener energy on sustainable agriculture in Asia: Evidence from selected Asian countries

AbstractRegardless of major advancements in food production, Asia continues to confront severe food security challenges. Sustainable agriculture presents entirely new prospects by prioritizing the productive worth of human, social, and natural capital—all of which are abundant in Asian nations or can be replenished at a relatively low financial expense. This paper sets out to explore the role of technological innovation, renewable energy use, financial development, globalization, and institutional quality on the environmental sustainability of agriculture, measured by the greenhouse gas emissions from the agricultural sector for top 10 agricultural economies of Asia from 1990 to 2019. To attain the above objective, we employ a variety of econometric models capable of accounting for cross‐sectional dependence, including the CS‐ARDL model and the Dumitrescu‐Hurlin Panel Granger Causality tests. The result indicates that technological innovation as well as the use of renewable energy can reduce the greenhouse gas emissions from the agricultural sector and thus contribute towards enhancing environmental performance of this sector in short and long run. Although globalization result is revealed to be positive, it turns out to be insignificant in both short and long run. Financial development exerts positive and significant effects on agricultural emissions while the institutional quality is found to be increasing the agricultural environmental performance. Finally, we provide policy recommendations based on the results of the study.

Spatial spillover effect of the enterprise sector on local residents’ income in Vietnam

AbstractThis study uses a panel data set from all 63 provinces in Vietnam and spatial regression models to investigate the spatial spillover effect of the enterprise sector on local residents’ income. The results show that expanding the enterprise sector can directly and indirectly increase local income. Although this spillover effect is positive and significant, it is smaller than the direct effect and gradually diminishes when the distance between provinces increases. For example, the estimation results applying the spatial Durbin model holding other variables constant show that a 10% increase in the enterprise capital volume in Ho Chi Minh City can raise local total income in this city, and first- and second-order neighbours by 0.53%, 0.22%, and 0.17%, respectively. This study provides novel evidence on the spatial spillover effect of the enterprise sector on local incomes using spatial econometric models. The findings offer valuable information for policymakers in planning enterprise sector development to improve local residents’ income in Vietnam.

The effect of provincial policies on seismic disaster mitigation in China: An empirical study.

With the development of earthquake disaster reduction efforts in China, the content of earthquake disaster reduction policies has become increasingly enriched and improved. Particularly, multiple provincial governments have proposed earthquake disaster reduction planning policies. It is important to explore whether these policies can affect disaster mitigation. Therefore, this paper summarizes the earthquake disaster reduction plans and factors influencing seismic resilience. Panel data from 24 provinces between 2012 and 2021 were collected, and a difference-in-differences approach was used to construct an econometric model to evaluate the policy effects and analyze the enhancement of seismic resilience. The results show that the implementation of earthquake disaster reduction policies has a positive impact on earthquake monitoring, evacuation, and emergency relief capabilities, and the estimated policy effects are statistically significant. Moreover, a series of tests were conducted. The conclusions are as follows: (1) Earthquake disaster reduction policies have a positive impact on the improvement of seismic resilience in provinces. (2) Provinces with a higher number of earthquakes experience more significant effects from earthquake disaster reduction policies. (3) Provinces with higher seismic peak ground acceleration values exhibit more pronounced improvements in seismic resilience.

Dynamics of innovation and income inequality mediated by human capital and financial development: a panel data study of Asia-Pacific countries

ABSTRACT Income inequality is one of the most important among the grand challenges faced by the global economy. The hyper-globalization phase and rising digital innovations have accentuated income inequalities within countries. Asia-Pacific countries have shown a continuous rise of income inequalities and surge of innovations. This study attempts to examine the relationship between income inequality and innovation mediated by human development and financialization. For unravelling the dynamics of income inequality, impact of innovation, human capital and financial development of 15 Asia-Pacific countries covering the period 1990–2022 are examined. It employs panel cointegration econometric models for empirical estimation. Findings revealed that innovation has a significant adverse impact on income distribution. Human capital as a mediating variable of innovations has reduced income inequalities, whereas financial development widens income inequalities. Important finding of the study is that the income dis-equalizing effect of financial development outweighs the income equalizing effect of human capital in the presence of improved technological innovations. Public policy lesson emerged from this study is that the policymakers should enact those policies that foster technological innovations with expansion of human development, inclusive financial development, institutional quality and enhancement of macroeconomic stability that can ensure reduction of income inequality.

Dynamics of innovation and income inequality mediated by human capital and financial development: a panel data study of Asia-Pacific countries

ABSTRACT Income inequality is one of the most important among the grand challenges faced by the global economy. The hyper-globalization phase and rising digital innovations have accentuated income inequalities within countries. Asia-Pacific countries have shown a continuous rise of income inequalities and surge of innovations. This study attempts to examine the relationship between income inequality and innovation mediated by human development and financialization. For unravelling the dynamics of income inequality, impact of innovation, human capital and financial development of 15 Asia-Pacific countries covering the period 1990–2022 are examined. It employs panel cointegration econometric models for empirical estimation. Findings revealed that innovation has a significant adverse impact on income distribution. Human capital as a mediating variable of innovations has reduced income inequalities, whereas financial development widens income inequalities. Important finding of the study is that the income dis-equalizing effect of financial development outweighs the income equalizing effect of human capital in the presence of improved technological innovations. Public policy lesson emerged from this study is that the policymakers should enact those policies that foster technological innovations with expansion of human development, inclusive financial development, institutional quality and enhancement of macroeconomic stability that can ensure reduction of income inequality.

Unraveling the impact of energy demand and exports on environment and economy: A case study of South Asian Economies

<p class="MsoNormal" style="margin-top: 10pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This study employs empirical analysis using an econometric model that examines the interdependence among environmental degradation, exports, and economic development with energy use. It also provides an environmental Kuznets curve (EKC) for selected South Asian economies utilizing time-series data. The findings reveal a long-term, stable equilibrium link between energy demand and pollution. There exists a positive relationship between structural factors and pollution. Moreover, this study constructs a model of exports and pollution from an interdependent perspective. The three perspectives are tested: the scale and structure of energy consumption considering the twin constraints of export-trade and pollution, and the scale of pollution in export-trade constraint. These results show that the increase in energy use leads to higher CO2 emissions amidst export volume. However, in the presence of income, the scale of effect lowers a little. The analysis also supports the presence of Kuznets curve for south-Asian economies. The results imply substantial scope for development in the energy use and pollution structure within South Asia's current export trade process. This development can be attained by regulating energy use and enhancing system efficacy without necessitating changes to the scale effect or structural effect.</span></p>