Economic Policy Research Articles

The Impact of Economic Policy Uncertainty on Real Effective Exchange Rate and Systemic Risks --Based on TVP-VAR Modeling

Today's international situation is complex and volatile, and the impact of economic policy uncertainty on systemic financial risk has attracted much attention. In this essay, the TVP-VAR model is used to investigate the mechanism of economic policy uncertainty on systemic financial risk and real effective exchange rate. It is found that its effect on both was characterized by time-varying and nonlinear, and the impact on systemic financial risk is mainly long-term, while the impact of real effective exchange rate is mainly short- and medium-term as long as. Meanwhile, the impact of economic policy uncertainty on systemic financial risk and real effective exchange rate under some specific risk events is more lasting.

The Impacts of Financial Market, Trade and Economic Policy Uncertainties on the Import Performance of Advanced Economies

The Impacts of Financial Market, Trade and Economic Policy Uncertainties on the Import Performance of Advanced Economies

Analysis and Forecasting of Energy Data and GDP Based on Pearson Correlation Analysis-Random Forest Algorithm

The relationship between energy data and GDP is an important economic issue because energy is the foundation that supports modern economic development. This paper examines the relationship between energy data and GDP and uses Pearson correlation analysis to calculate the relationship between each variable in energy data and annual GDP for 52 regions. The results show that total electricity consumption is the factor with the highest degree of influence on GDP, followed by natural gas consumption, biomass consumption, hydroelectricity consumption, LPG consumption and geothermal energy consumption. This indicates that there is an extremely strong correlation between local energy data and GDP. To further explore this correlation, this paper attempts to predict the GDP of the locality based on the local energy data using a machine learning approach.The Random Forest algorithm is chosen as the machine learning model, and the training, validation and test sets are divided according to the ratio of 6:2:2. By using MSE, RMSE, MAE, MAPE and R to evaluate the model, it is found that the random forest machine learning algorithm can predict the local GDP well.The predicted value of GDP and the actual value of GDP are very close to each other, meanwhile, the value of MSE is 62.779.These findings show that the local energy data are closely related to economic development. The impact of energy factors on the economy should be taken into account when formulating economic policies. In addition, machine learning algorithms can provide strong support for economic forecasting and provide more accurate information for decision makers.

Economic Effectiveness and Forecasting of Scientific Activity in Azerbaijan

In the conditions of increasing international competition, sustainable development of the economy is considered to be one of the priority areas of scientific activities in the country. Since science and high technologies are the fundamental basis of innovative development, the strengthening of scientific, technical, and innovative potential for countries is considered as the goal of economic policy. In this context, the ability to achieve achievements related to scientific activity and to implement the obtained results becomes one of the factors that ensure the competitiveness of both the economy and fields and subjects in the global competitive environment. The effectiveness of scientific activity directly depends on the funding of research and development. The comparison of costs and results is closely related to the consideration of different time intervals between financing and the achievement of commercial results. Therefore, investments related to the commercialization of science appear as investments in the future. Thus, the application of the results of scientific activity to production, being considered an important factor of production intensification, ensures the increase of labor productivity, the reduction of production costs, and the improvement of the efficiency of capital investments. These, in turn, are the main conditions for the effectiveness of the integration of science and production. In the research, the effectiveness of scientific activity, the factors affecting it, the possibilities of expanding the application of scientific activity to production, and the forecast indicators of the volume of scientific and technical works shortly are determined.

Economic complexity and income inequality in Russian regions

Although economic growth leads to a reduction in poverty, the growing income inequality and its possible consequences are of some concern. The level of economic complexity has recently been increasingly viewed as an important predictor of income inequality. In this paper, using pool and models with time fixed effects, the impact of the economic complexity index on income inequality in Russian regions for 2013 and 2015 was estimated. The results of the evaluation of econometric models show that regions with more complex economies showed a higher level of income inequality compared to others. The explanations for the effect obtained include an increase in demand for highly qualified labor as a result of the development of high-tech industries and an increase in wage differentiation between high-skilled and low-skilled workers, as well as the migration of high-skilled workers to economically more complex regions. The main conclusion is that economic complexity is a significant factor of the variation in income inequality and should also be taken into account while conducting economic and social policy in Russian regions.

Dynamic capabilities and competitiveness: Relationship among industrial policy, structural transformation, and economic growth in Indonesian regional industries

The complex interactions of industrial Policy, structural transformation, economic growth, and competitive strategy within regional industries are examined in this research. Using a dynamic capabilities framework, the study examines the mediating roles of organizational innovation and adaptability in the link between competitiveness and macroeconomic variables. A two-way fixed effects model is used in this study to examine the influence of structural transformation (ST) on Industrial Policy (IP). Using regional data covering the years 2010 to 2022, the research undertaken in this paper explores the dynamics of the Indonesian economy by empirically assessing the consequences of structural change on industrial Policy. In order to establish a comprehensive model that clarifies the mechanisms through which industrial policies and structural shifts impact the development of dynamic capabilities, ultimately influencing competitiveness strategies, this research draws on a large amount of empirical data and integrates insights from seminal works. Our research adds to our knowledge of strategic management in regional industries by providing detailed information on how economic development and policy interventions influence businesses’ ability to adapt and gain a competitive edge. In addition to advancing scholarly discourse, this study offers business executives and politicians valuable insights for managing the intricacies of global economic processes.

Debt on the Ground: The Scholarly Discourse of Bankruptcy and Financial Precarity

A rich literature uses law and social science methods to better understand household financial distress and overindebtedness both inside and outside of bankruptcy. This scholarship contributes to several ongoing scholarly conversations, such as those on income and wealth disparities across race and class, how people live in circumstances of financial precarity, why people turn to the legal system to solve their problems, and how to improve access to justice so people can get the help they need. We first review the current literature about who files bankruptcy, the contributors to people's need to file bankruptcy, what happens to them in bankruptcy court, and what happens after their bankruptcy cases conclude. We then outline a research agenda of low-hanging fruit that will contribute to broader sociological and sociolegal research agendas, including economic mobility, aging, gender studies, health studies, family studies, social psychology, and policy work.

How does economic policy uncertainty impact CO2 emissions? Investigating investment's role across 22 economies (1997–2021)

Economic policy uncertainty (EPU) has significant implications for both economic growth and CO2 emissions through investment and consumption. Existing research has produced mixed findings on the impact of EPU on CO2 emissions. Nevertheless, there is a need for further empirical investigation into the role of investment in the relationship between EPU and CO2 emissions in various economies. This article contributes to the literature by examining the investment channel in the EPU-CO2 emissions nexus in 22 economies using annual panel data from 1997 to 2021. The analysis was conducted using eight specifications to classify the empirical estimates of the direct and indirect impacts of EPU on CO2 emissions through the investment channel. The CD test, CADF test, and CIPS test were used to test for cross-sectional dependence, unit root, and cointegration, respectively. The GMM estimation technique was used to test for the long-run relationship. The empirical results suggest that both direct and indirect EPU stimuli on CO2 emissions through the investment channel are positive, and the EKC hypothesis holds. Furthermore, findings show that fossil-fuel energy usage leads to an increase in CO2 emissions. Given the significant impact of economic policy uncertainty on CO2 emissions, policymakers should consider the environmental implications of their policy decisions. Second, reducing fossil fuel energy usage in the production process should be a priority to mitigate the impact of EPU on CO2 emissions.

Trading-off or trading-in? A critical political economy perspective of green growth’s policy framing

ABSTRACT The trade-off policy framing has been a central feature of green growth since the 1980s, employed to frame the countervailing spheres of social, environmental, and economic policies purported to ensure the sustainable development of the global economy. This article argues that so central has this framing become that IPE scholars have tended to focus on different types of ‘greenable’ growth observable within capitalism, rather than question the prospect of greening growth itself. Far from value-free, the trade-off framing is ultimately determined by the structural imperative for economic growth, veiling the disciplines anthropocentric ontology in a normative or objective guise. To account for the tacit prioritization, the trade-in policy framing – the compromising of environmental objectives to accommodate the growth imperative – is advanced as an alternative framing. The trading-in of environmental policies is legitimized through political-industrial narratives, of which three, the (i) consequential, (ii) allay, and (iii) finance are outlined in this analysis.

A news-based economic policy uncertainty index for Nigeria

AbstractIn this study, we develop the first daily news-based Economic Policy Uncertainty (EPU) index for Nigeria, which was previously not covered in recent EPU indices. The need to track economic uncertainties in Nigeria becomes crucial for investment and policy, especially with the renewed interest in the country as an important investment destination. To construct the EPU index, we use relevant keywords from articles in prominent newspapers in the country, covering the aftermath of the global financial crisis and the COVID pandemic, with a data scope of January 2010 to November 2022. We evaluate the predictability of the index by examining its connection with economic and financial variables like exchange rates, stock prices, and inflation in Nigeria. The results are robust to alternative model specifications, data frequencies, and multiple forecast horizons. We hope to extend this exercise to other useful indices, including Geopolitical Risk, Financial Stress Indicators, and Monetary Policy Uncertainty, which are not readily available for Africa, including Nigeria.

Does global economic and climate policy uncertainty affect private investment? Evidence from sub‐Saharan Africa

AbstractThis study investigates how uncertainty in climate and global economic policies affects private investment in sub‐Saharan Africa (SSA). Using panel data from 41 countries over the period 2000–2022, the study employs a dynamic panel model to estimate the effects of these two types of uncertainty on the private investment‐to‐gross domestic product ratio. The study finds that both global economic policy uncertainty (EPU) and climate policy uncertainty have a negative and significant influence on private investment, implying that higher levels of uncertainty discourage private investors from undertaking long‐term projects in the sub‐region. The study also finds that the effect of uncertainty on climate policy is stronger than that of EPU, suggesting that private investors are more sensitive to the lack of clarity and coherence in the global climate policy framework. The findings are robust to different estimation techniques. The study concludes that reducing policy uncertainty, especially in the area of climate change, is crucial for enhancing private investment and promoting sustainable development in SSA.

Predicting Indian GDP with Machine Learning: A Comparison of Regression Models

In recent years, the application of machine learning algorithms in economic forecasting has gained significant traction. This study focuses on predicting the Gross Domestic Product (GDP) of India by harnessing advanced machine learning techniques. We assembled a comprehensive dataset incorporating time series analysis and inflation rates from diverse sources. Our investigation involved a comparative analysis employing both linear and polynomial regression methods to discern the most accurate predictive model. Our findings underscore the superiority of the polynomial regression model, which excelled in capturing non-linear relationships between independent variables and GDP. Specifically, the polynomial regression model achieved an impressive prediction accuracy rate of surpassing the accuracy achieved by the linear regression model. This study underscores the critical role of advanced machine learning algorithms in economic forecasting. By emphasizing the significance of high-quality datasets and the application of techniques like polynomial regression, we illustrate how these factors can substantially enhance the precision of economic forecasts. These insights hold substantial implications for policy makers and businesses alike, aiding informed decision-making and effective economic policy development. This study serves as a valuable reference for future research, highlighting the importance of advanced machine learning methods and robust data sources in economic forecasting.

Economic policy uncertainty and volatility of corporate bond credit spread: Evidence from China and the United States

In the prevailing situation of economic instability, it is customary for governments to enact economic policies aimed at influencing the decision-making processes of corporations. Nevertheless, the uncertainty surrounding economic policies inevitably impacts the capital structure of firms, consequently giving rise to fluctuations in corporate bond credit spreads through various transmission channels. Therefore, this study examines the impact of economic policy uncertainty (EPU) on the volatility of corporate bond credit spread in China and the United States (U.S.) from January 2014 to July 2021. Developing a one-sided filtering single-factor and multi-factor GARCH-MIDAS model in which monthly EPU and daily corporate bond credit spread are used as variables with different frequencies, the total volatility of corporate bond credit spread is decomposed into long- and short-term volatility components. Utilising the EPU data, encompassing indices delineating EPU in the U.S. (Baker et al., 2016) and China (Huang and Luk, 2020), alongside the corporate bond credit spreads in both the Chinese and American markets, we find that the level and volatility of EPU positively impact the long-term volatility of corporate bond credit spread with different ratings. Moreover, the EPU level contributes to more than 12% to the total volatility of corporate bond credit spread in the Chinese samples and more than 4.31% in the U.S. samples. While the variance of EPU contributes to over 5.19% to the total volatility of corporate bond credit spread in the U.S. samples, which is more than that of the Chinese samples.

Front-line guardian of the status quo: Japan under the Kishida government

Abstract Prime Minister Fumio Kishida and his government have implemented bold defence, diplomatic and economic policies against the challenges to the status quo order posed by Russia's invasion of Ukraine and increased Chinese assertiveness. What do these policies unveil about Japan's strategy and response to the transformed strategic environment? This article argues that the Kishida government has upgraded Japan to a front-line guardian of the status quo order. To achieve its front-line role, the Kishida government has consolidated Japan's deterrence policy, namely focusing on two main pillars—military and rules. To ensure Japan's frontline role is sustainable, this policy paper makes three recommendations: Japan must develop strategic clarity and clear operational plans on how to respond to a regional contingency, such as the Taiwan Strait; Japan must resolve domestic problems that prevent the increase in defence spending to 2 per cent in the period to 2027. Japan's role as a front-line state to protect the status quo order is very much dependent on the availability of resources that would be provided by this increase in defence spending; Japan should re-energize its efforts towards east Asia through a sustained diplomatic, economic and security means. This is important in securing support for Japan's preferred vision of regional and global order from not only like-minded partners and friends in east Asia, but also China. Japan's approach should underscore how the extant status quo order benefits all states, including China, especially in terms of spreading economic prosperity, development and connectivity.

Crude credit: The political economy of natural resource booms and sovereign debt management

Oil, gas, and minerals have notoriously adverse effects on institutional quality. But when global liquidity is high, risk-tolerant investors are more willing to lend to all borrowers, even resource-rich countries with low-quality institutions. Despite the availability of cheaper credit during commodity booms, we argue that countries do not increase current borrowing to mitigate future revenue shortfalls during commodity busts. Instead, they rely on resource windfalls to meet their current financing needs, fearing they would otherwise forfeit national policy discretion to global financial markets. We leverage primary evidence from extensive field research across five Latin American countries to show that national economic officials (i.e. finance ministers and central bank governors) are wary of high indebtedness, after past commodity booms ended in cycles of lofty spending, borrowing, and default. For sovereign borrowers, high bond market indebtedness often reduces government discretion over economic policy, whereas windfalls increase it; all else equal, governments will favor the latter. Using data on 22 Latin American and Caribbean countries from 1996 to 2020, we find that governments issue bonds less frequently, in smaller amounts, as their oil and gas production or GDP share from resource rents increases. These findings make an important contribution to our understanding of how commodity cycles affect global capital markets: sovereign borrowers do not fully leverage commodity booms to expand their fiscal space to finance more spending over time.

Time of Siberia

A theoretical and practical substantiation of the need for a new economic policy in Siberia is carried out. The basis for this research and analysis was the Krasnoyarsk Economic Forum, which took place from March 1 to March 3, 2023, the St. Petersburg International Economic Forum, which took place in June 2023, and speeches by experts, analysts, and economists during the past and also the current year. At these, as well as many other economic forums, participants, experts, and analysts shared their opinions on changing the economic course in our country. Also, they discussed the development of new logistics and the so-called turn to the East. In this process, it was noted that Siberia is playing a pivotal role, becoming a logistics center and a creator of the economy. Why Siberia and not some other regions of Russia? Why is it now, despite numerous risks, that the time has come for Siberia? What conditions are necessary for regular successful business and economic development in general? This study is aimed at finding answers to these and other questions. There are many reasons for this, but the main ones are economic. The time has come to urgently change priorities in developing our economy to ensure the transition to our high-tech manufacturing economy using scientific intelligence and robotization. Only Siberia and our other regions can provide the Russian economy with all the necessary natural resources, such as water, forest, energy, and other resources. Siberia at present, and in the future for external and internal reasons, is safe for solving all economic problems. Due to its geographical location, Siberia allows us to favorably and quickly activate all supply chains with our partners, not only in Asia and the East. It is essential for the economy and business that Siberia, in the conditions of the Northern Military District, has no threat to development. The industrial production index should be the final indicator demonstrating the development of the Siberian economy. The first practical results are already there; based on the results of the past 2023, it was noted that the GDP in our country amounted to more than 3.6%, and Siberia plays an essential role in this.

Role of IT in Banking

Up until 1990, Indian banks operated in a relatively safe and secure environment; however, economic policies have forced them into fierce competition. As technology advances, the banking industry in India is also attempting to awaken from slumber and become more proactive. Banks have long used technology to enhance their offerings and operational effectiveness. These days, technology is altering not just the surroundings but also the interactions with clients. In addition to removing numerous barriers, technology has produced better goods and distribution methods. This has increased attention on customer relationships. It is also seen as a tool for cutting expenses and facilitating efficient communication between entities and individuals involved in the banking industry.

Time-frequency connectedness between electricity prices in Romania and its determinants in the competitive markets

PurposeIn this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.Design/methodology/approachOur study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.FindingsWe discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.Originality/valueWe combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).

Research on economic growth in smart cities based on wireless sensor networks

With the acceleration of urbanization, the concept of smart city is rising gradually. Wireless sensor network as an important technical support of smart city, its application in environmental monitoring and water resources management has a profound impact on economic growth. Water resource is one of the most dependent resources for human beings. With the growth of world population and the rapid development of economy, water resource crisis is constant, water pollution, water shortage and water waste coexist. How to build a perfect water resource economic policy is a worldwide problem at present. At present, the formulation of water resources policies is often based on experience or the knowledge system of decision makers. Due to the dynamic nature of water resources utilization and the incomplete information of decision makers, there are often policy failures, which affect economic growth. Based on this, this paper uses system dynamics model to study the mechanism of water resources management policies affecting economic growth by taking Gansu, Tianjin and Zhejiang as three qualitatively representative arid areas, transitional areas and water-rich areas. The research results show that under the same water resources policy coupling, different regions also have different eco-economic effects. The effect of coupled water resources policy is better than that of single water resources management policy.

Understanding China’s Grand Strategy Through the Belt and Road Initiative

China’s transition from a country striving for self-reliance in the 1950s to the world’s dominant power in 2020 is remarkable. The economic and foreign policies it followed during various phases of development helped the country to achieve the targets and goals that it set up for itself. The ambitious Belt and Road Initiative by President Xi Jinping to rebuild old Silk routes and to build new connectivity projects containing road, rail and sea transport corridors include in China’s Grand Strategy. That is to achieve self-sufficiency of up to 70% in advanced technology sectors by 2025 and to reach a preeminent position in the world by 2049. This includes protecting security interests as well as the flow of energy resources required for a steady progress. The article provides an overview of China’s progress and plans from Mao’s regime to Xi Jinping’s presidentship. In the process, it explains China’s changed priorities, interests and how foreign policy also changed according to the changing economic policies.