GDP Growth Rate Research Articles

Asymmetric effect of consumption expenditure on import function in Saudi Arabia

We study the asymmetric effect of consumption expenditure on Saudi imports from 1991 to 2020. We found a direct relationship between consumption expenditure and imports both in short and long-run periods, however, the positive shocks in consumption expenditures have a dominant impact in the long run. We included several control variables, i.e. non-oil GDP growth, export, public debt, and real effective exchange rate (REER) as a proxy of relative price. We found that income elasticity for imports is elastic in the short run. While Exports impact imports positively in the short and long run, the Public Debt hurts it. Exchange rate appreciation induces the demand for imports. Moreover, imports were price-inelastic in the short and long run. The next-level analysis also confirmed the consumption expenditure as one of the key factors in determining Saudi imports. It further identified the bi-directional causalities between these macroeconomic variables, i.e. import causes consumption expenditure, consumption expenditure causes import, export causes consumption expenditure, non-oil GDP growth causes consumption expenditure, and non-oil GDP growth causes import. These findings are validated by the relevant diagnostics tests. Our empirical findings can be useful as a reference for future research and policy implications.

Regime‐Switching Density Forecasts Using Economists' Scenarios

ABSTRACTWe propose an approach for generating macroeconomic density forecasts that incorporate information on multiple scenarios defined by experts. We adopt a regime‐switching framework in which sets of scenarios (“views”) are used as Bayesian priors on economic regimes. Predictive densities coming from different views are then combined by optimizing objective functions of density forecasting. We illustrate the approach with an empirical application to quarterly real‐time forecasts of the US GDP growth rate, in which we exploit the Fed's macroeconomic scenarios used for bank stress tests. We show that the approach achieves good accuracy in terms of average predictive scores and good calibration of forecast distributions. Moreover, it can be used to evaluate the contribution of economists' scenarios to density forecast performance.

Uncertainty, Skewness, and the Business Cycle Through the MIDAS Lens

ABSTRACTWe employ a mixed‐frequency quantile regression approach to model the time‐varying conditional distribution of the US real GDP growth rate. We show that monthly information on financial conditions improves the predictive power of an otherwise quarterly‐only model. We combine selected quantiles of the estimated conditional distribution to produce novel measures of uncertainty and skewness. Embedding these measures in a VAR framework, we show that unexpected changes in uncertainty are associated with an increase in (left) skewness and a downturn in real activity. Business cycle effects are significantly downplayed if we consider a quarterly‐only quantile regression model. We find the endogenous response of skewness to substantially amplify the recessionary effects of uncertainty shocks. Finally, we construct a monthly frequency version of our uncertainty measure and document the robustness of our findings.

Predictive Modeling of US Stock Market and Commodities: Impact of Economic Indicators and Geopolitical Events Using Machine

Comprehending the dynamics of the stock market is pivotal for investors, policymakers, and economists in the USA. The United States stock market being one of the most influential financial markets in the world, contributes significantly to shaping the world economy. This research project aimed to bridge the important gaps in understanding the interrelationship between economic indicators and geopolitical events concerning the performance of the US stock market and commodities. The prime objective was to assess how different economic indicators have an impact on stock market performance over a specific period. In this regard, the development of machine learning models facilitated the ability to forecast stock market and commodities trends. These models utilized economic indicators and geopolitical events from historical data to predict future movements in the market with higher accuracy than the traditional forecasting technique followed. The study considered several different datasets to comprehensively analyze the effects that economic indicators and geopolitical events have on the stock market and commodity performances. The key datasets used in this analysis involve historical stock market indices such as the S&P 500, Dow Jones Industrial Average, and NASDAQ, as well as commodity prices for gold, oil, and silver. These datasets were collected from reputed financial databases such as Bloomberg and Federal Reserve Economic Data-FRED, for metrics including GDP growth rates, unemployment rates, inflation figures, and interest rates. The researchers also gathered data on geopolitical events: elections, trade wars, and military conflicts, using usually reliable news archives like Reuters, Bloomberg News, and The New York Times. Linear Regression, Random Forest, and XG-Boost algorithms were selected to capture various facets of the data. The performance metrics used to evaluate the models in this study included Mean Absolute Error, Root Mean Squared Error, and R-squared. Random Forest Regressor outperformed the other models with the lowest RMSE, showcasing its ability to model complex relationships. XG-Boost Regressor equally delivered a strong balance between performance and computational efficiency, achieving similar accuracy to Random Forest. Results from this study therefore can inform policy makers, especially in regards to stabilizing the financial markets during periods of heightened economic or geopolitical uncertainty. Key recommendations include developing proactive policy measures that could dampen the effect of adverse economic indicators and geopolitical events on market stability.

Analysis of Factors Affecting the Import and Export of New Energy Products

With global warming and climate change becoming more and more serious, countries around the world have begun to pay attention to emission reduction and environmental protection, and the concept of ‘dual carbon’ has further penetrated into people's hearts, bringing new opportunities for the export of power lithium batteries from China. This paper aims to conduct a digital analysis of the export trade of the top seven countries (regions) in terms of the volume of power lithium battery trade in China, and to explore the influencing factors of the export volume of power lithium batteries from China. This paper first conducts a contour analysis based on the data fluctuations of five indicators in 32 provinces (excluding Hong Kong, Macao and Taiwan) from 2019 to 2021: total carbon dioxide emissions (mt), lithium iron phosphate production (10,000 tons), lithium battery electrolyte (10,000 tons), two-wheeled electric vehicles (100,000 vehicles), and lithium battery separators (10,000 tons). The final conclusion is that the ‘dual carbon’ background has a significant impact on China's power lithium battery industry. Furthermore, based on a panel data regression model, the factors affecting trade between China and the major countries (regions) exporting power lithium batteries are explored in depth. The results of the analysis found that the power generation, economic freedom, number of charging piles, population size, and per capita GDP of the exporting countries significantly affect the export volume of power lithium batteries in China. Among them, the population size and per capita GDP growth rate of the exporting countries have a negative impact, while the rest have a positive impact on the export volume of power lithium batteries in China.

Bank market power and its determinants: evidence from listed conventional commercial banks from Indonesia

PurposeOver the past three decades, financial deregulation and various reforms have significantly transformed the competitive environment for banks in Indonesia. These changes have introduced new challenges for banks to retain their market power and ensure their survival. In light of this, the article aims to assess the current levels of market power held by Indonesian banks and explore the factors that influence it.Design/methodology/approachThe paper measured the degree of market power and identified its impacting factors for 22 listed commercial banks using the Adjusted Lerner Index (ALI) and appropriate regression technique over a period of 2011–2023.FindingsThe empirical findings reveal that banks in Indonesia enjoy high market power, and factors such as capitalization, diversification, operational inefficiency, asset quality and GDP growth rate significantly impact banks’ market power. Additionally, the findings contradict the structure-conduct-performance paradigm, which advocates that a concentrated banking system impairs competition.Research limitations/implicationsThe study suggests that regulatory authorities should closely monitor the market power levels and promote strategies to enhance competition within the banking sector. Additionally, banks should prioritize implementing measures to reduce operational costs and improve the quality of assets.Originality/valueThis research represents one of the early attempts to gauge the market power of publicly listed conventional commercial banks in Indonesia by employing the Adjusted Lerner Index. Additionally, it introduces “technology adoption” as a novel variable to the analysis alongside other established variables.

Financial inclusion and stability in Ethiopia using bank-level data: A two-step system GMM estimation

Background This paper examines the impact of financial inclusion on bank stability within Ethiopian context, using panel data from 17 commercial banks over the period 2015-2023. Given the scarcity of research focused on the relationship between financial inclusion and bank stability in Ethiopia, this paper seeks to address a crucial gap by analyzing both conventional and digital aspects of financial inclusion in relation with bank stability. Methods A two-stage principal component analysis (PCA) was conducted to construct a composite financial inclusion index, integrating 10 conventional and 5 digital indicators. The study applied a two-step robust system generalized method of moments (GMM) to examine the effects of financial inclusion on bank stability, complemented by Granger causality testing to examine the directionality of this relationship. Results The result reveals a significant positive effect of financial inclusion on bank stability and Granger causality tests confirms a bi-directional relationship between financial inclusion and stability, indicating that improvements in financial inclusion foster greater stability and vice versa. Our results also highlight that while bank efficiency and GDP growth rate positively effect stability, total assets and income diversification exhibit detrimental effects. Conclusions It is essential to capitalize policy synergies to promote bank stability and to enhance financial inclusion through conventional and digital channels, while carefully managing the implications of risks associated with income diversification and asset distribution. Ensuring inclusive financial system is vital for maintaining bank stability, thus positioning it as a key priority for financial institutions.

Determinants of Inflation in Pakistan: A Comprehensive Analysis of Macroeconomic Variables (1991–2022)

This research paper investigates the determinants of inflation in Pakistan from 1991 to 2022, focusing on key macroeconomic variables such as imports, GDP growth rate, GDP per capita, military expenditure, population growth, total debt service, and unemployment. The main objective is to analyse the impact of these variables on inflation and provide policymakers with actionable insights. Utilizing annual time series data from reliable source World Bank WDI, the study employs descriptive statistics, correlation analysis, ADF unit root tests, regression analysis, and the Autoregressive Distributed Lag (ARDL) model. The findings reveal significant interdependencies among the examined variables. The correlation analysis shows strong positive relationships between inflation and imports and military expenditure, while GDP per capita and population growth negatively correlate with inflation. OLS results confirm that imports, GDP growth, and total debt service significantly raise inflation, whereas GDP per capita, military expenditure, and population reduce inflation. The ARDL model reveals that, in the long run, imports, GDP growth, and debt service positively affect inflation, while GDP per capita, population growth, and unemployment exert downward pressure. Short-run dynamics indicate that inflation quickly adjusts to economic shocks, with imports and GDP growth having an immediate impact. These findings highlight the importance of managing imports and debt, while promoting economic growth to maintain price stability. The policy implications emphasize the need for targeted monetary policies to manage imports and optimize debt service strategies, as well as policies aimed at enhancing economic growth and increasing per capita income. Effective debt management and strategic military spending are also vital for maintaining price stability. Despite its comprehensive analysis, the study identifies gaps such as the need for further exploration of structural breaks and non-linear effects in inflation dynamics, and the impact of external factors like global oil prices. The significance of this research lies in its contribution to understanding the complex economic interactions influencing inflation in Pakistan, providing a crucial resource for policymakers to develop effective strategies for economic stability in developing economies.

Macroeconomic Conditions and Private Equity in Morocco: A Bounds-Testing Cointegration Approach

Private equity plays a pivotal role in fostering entrepreneurship, innovation, and job creation across many economies. In Morocco, the private equity sector is in its developmental stage and faces significant macroeconomic challenges that hinder its growth. This study seeks to address the gap in the literature by examining how key macroeconomic factors—including GDP growth, exchange rates, government spending, inflation, gross domestic savings, interest rates, and industrial production—affect private equity fundraising in Morocco between 1990 and 2019. Employing the autoregressive distributed lag (ARDL) model, we investigate both short- and long-term relationships between these macroeconomic variables and private equity fundraising. The results reveal that GDP growth and interest rates significantly influence private equity fundraising in both the short and long run, while inflation and government spending have a more pronounced long-term effect. These findings highlight the need for policies focused on stabilizing macroeconomic conditions to attract private equity investments. Policymakers should prioritize fostering a stable economic environment characterized by sustainable growth, low inflation, and moderate interest rates to create a favorable investment climate.

Impact of External Shocks on Peru's Economic Growth, 2000-2021

Objective: To analyze the impact of China's GDP growth and an international oil price shock (external shocks) on Peru's growth rate, terms of trade, private investment, exports and unemployment for the period analyzed. Originality/Value: This study contributes to the literature on the dependence of a small economy on external factors, verifying through an econometric analysis the implications of an external shock that will be relevant for economic policy decision makers, academia and globalized economic culture. The relevance and value of this research are evidenced by quantifying the impacts of a large economy like China on variables such as terms of trade, private investment and Peru's GDP growth rate. Method: It is quantitative and exploratory with 4 variables, China's GDP growth rate, terms of trade, private investment and Peru's GDP growth rate; It is exploratory because it will give us an overview or knowledge of the behavior of China's GDP growth rate, terms of trade and private investment and their impact on Peru's growth rate from 2000 to 2021. The variables explained are: China's GDP growth rate (tCreChin), terms of trade (tInt), private investment (inv Priva). Explanatory variable: Peru's GDP growth rate (tCrePerú). The design is non-experimental of a correlational type, the population includes data from the 04 variables for the years 2000-2021, each with 22 observations that were extracted from the 21 years analyzed, extracted from institutional secondary sources BCRP and World Bank (WB). Subsequently, tests will be carried out to verify correlation, stationarity and heteroscedasticity in the variables and the estimation of the model parameters for several alternatives or possible combinations of factors. In each case, the “t” test, the “F” test will be performed and the coefficient of determination R2 will be calculated, to determine the best fit. And finally, the best model will be selected for forecasting purposes. Results: The response of GDP to a shock of the GDP variable of China has a positive impact from period 1-3, on the other hand the IT variable has a positive impact from period 1. Another relevant aspect achieved is that in 10 years, 51% of the variability of Peru's economic growth depends on the variability of China's growth, 2.20% of the variability of Peru's economic growth depends on the variability of the IT variance and 1.48% of the variability of Peru's economic growth depends on the variability of private investment. Discussion: Our results find similarity with studies by Guevara and Yamaca (2021) and Chávez (2020) et.al., since an external shock is positive and explains a high percentage of historical variability. A shock to China's GDP growth has a greater effect on Peru's GDP growth; and financial shocks reduce domestic GDP growth. On the other hand, Peruvian monetary policy is synchronized with the behavior of the American FED to mitigate the adverse effects of external factors. In our study we use a VAR demonstrating the influence of China's economic growth, the terms of trade, Peruvian GDP and private investment. Finally, the findings indicate that the impacts of external shocks on economic growth are more long-term than short-term, and also reveal the existence of fragility in the international economy that can be opportunities on the side of raw material prices.

Industrialization and innovation strategy of event resources in ice and snow sports under the background of big data

This article examines the industrialization and innovative strategy of event resources in ice and snow sports in the context of big data. Based on this analysis, the article proposes a path and development strategy for the Chinese sports industry, which includes encouraging the industrialization of the nation’s ice and snow sports industry. It is important to evaluate ice and snow sports in order to improve competitive results and optimize training techniques using big data technologies. Despite using a variety of techniques to evaluate performance, their inability to fully capture the complex patterns found in ice and snow sports events presents difficulties. In order to assess and enhance sports talents, therefore we proposed a new method using ISSE-Apriori algorithms. The pre-processed data gathered before extracting the most important and relevant features. The experiment results section analyzes the resources of ice and snow sports cities. The results examine sports event strategies, taking into account predictions and actual accuracy results, the GDP growth rate, the efficiency of industrial development, and a comparison of Apriori before and after enhancement. The experimental result is validating using measures like recall, accuracy, and precision. In addition, we conducted a comparative analysis with the current approaches to confirm the efficiency and robustness of the proposed methodology. The Suggested approach is implemented with Python Software. The Suggested Approach’s performance is measured in terms of RMSE (0.3524), MAE (0.1832), MAPE (4.24) with large dataset. The results stated that the proposed methodology has provided an accuracy of 98.42%.

Total Debt Service and Economic Growth in Nigeria: The Empirical Nexus

This study examines the relationship between debt service and economic growth in Nigeria in the period 1990-2022, utilizing time series data from the World Development Indicator and CBN Statistical Bulletin. The issue of debt servicing has become difficult for developing countries like Nigeria, and has not only stifle economic growth, but also increased the rate of poverty and unemployment in these countries. The study explores the empirical nexus between total debt service and economic growth in Nigeria, employing GDP growth rate as the dependent variable, while the explanatory variables involved; are total debt serving, inflation rate and capital expenditure. The variables were estimated with autoregressive distributed lag (ARDL) method, among other, after the stationarity tests conducted on them Indicate that the variables were of mixed integration. Evidence suggesting a long-term equilibrium relationship among the variables was also established given the cointegration result. The ARDL results reveal that the inflation rate has an inverse and significant impact on GDP growth. Total Debt services variable also exhibited a similar impact on GDP growth. However, the results indicated that capital expenditure variable has a direct and significant impact on GDP growth. Based on the findings, this study recommends that the Nigerian government should implement robust monetary policies aimed at controlling inflation and revisit its debt management strategy to ensure sustainability. It is also recommended that the government should not only increase capital expenditure but also ensure that these investments are directed towards sectors with the highest economic growth potentials. Keywords: Debt service, economic growth, inflation, capital expenditure and GDP growth

Total Debt Service and Economic Growth in Nigeria: The Empirical Nexus

This study examines the relationship between debt service and economic growth in Nigeria in the period 1990-2022, utilizing time series data from the World Development Indicator and CBN Statistical Bulletin. The issue of debt servicing has become difficult for developing countries like Nigeria, and has not only stifle economic growth, but also increased the rate of poverty and unemployment in these countries. The study explores the empirical nexus between total debt service and economic growth in Nigeria, employing GDP growth rate as the dependent variable, while the explanatory variables involved; are total debt serving, inflation rate and capital expenditure. The variables were estimated with autoregressive distributed lag (ARDL) method, among other, after the stationarity tests conducted on them Indicate that the variables were of mixed integration. Evidence suggesting a long-term equilibrium relationship among the variables was also established given the cointegration result. The ARDL results reveal that the inflation rate has an inverse and significant impact on GDP growth. Total Debt services variable also exhibited a similar impact on GDP growth. However, the results indicated that capital expenditure variable has a direct and significant impact on GDP growth. Based on the findings, this study recommends that the Nigerian government should implement robust monetary policies aimed at controlling inflation and revisit its debt management strategy to ensure sustainability. It is also recommended that the government should not only increase capital expenditure but also ensure that these investments are directed towards sectors with the highest economic growth potentials. Keywords: Debt service, economic growth, inflation, capital expenditure and GDP growth

Environmental concerns and their impact on inbound tourism revenue: does the growth rate of the nation’s matter?

PurposeEnvironmental concerns are gaining traction with every passing day. Moreover, post Covid 19, similar to many sectors, the tourism sector is also trying to revive itself. Theoretically, the environment and tourism complement each other. However, empirical vetting is not adequate. This study is motivated to determine how the environment impacts tourism. In addition, the moderating influence of the growth rate of the nations on the impact of the nations on tourism is also investigated.Design/methodology/approachWe have gathered clear and balanced panel data on tourism and the environment for 106 nations for 10 years. The difficulty in measuring environment status is managed by estimating environment efficiency using Data Envelopment Analysis (DEA).FindingsSurprisingly, we find a significant impact of environmental efficiency on inbound tourism across the nations used in the study. Such findings are rarely observed in the earlier studies as very less studies look for the association of environmental efficiency with tourism. However, the findings are supportive of the principles of the Faro convention and ICOMOS (“International Council on Monuments and Sites”) charter to promote environmental quality for tourism attraction. The current research findings can change the future course of action regarding the environment for tourism. The findings of the study establish financial materiality for the tourism sector. These findings give a boost to the theory of sustainable tourism.Research limitations/implicationsThe study’s inconsistent outcome (as the literature finds significant association) regarding the insignificant influence of GDP growth rate is a limitation of the study. The insignificant association needs to be further investigated. This limitation can be a future scope on the topic.Originality/valueThe authors do not find many studies on the environment’s impact on inbound tourism. In addition, a few studies on the topic, which exist provide contradictory outcomes. Above all, the literature does not observe the moderation of the GDP growth rate on the environment’s impact on inbound tourism. This lack of studies in literature, to the best of our knowledge, is the unique contribution of the current study.

Mathematical modeling of Ethiopia's energy demand by sectors and energy types, with forecasts for the next 30 years

Due to the scarcity of economic resources, it is vital to optimize everything so that the supply and demand lines intersect in an optimized quantity, with no shortage or surplus of a provided item or service. Energy supply contains both surplus and shortage, thus estimating the amount of projected energy demand is a key work that must be completed. The objective of this paper is generating a mathematical model based on the actual data for thirty years forecasting. To create a mathematical model using actual data from the last fifteen years, a model that can represent the past trend and be used for future forecasting. This study provides a general overview of Ethiopia's current energy requirement with different energy type as well as sector-specific energy demand and estimates for different economic growth scenarios up to 2052. This model was created using a linear regression polynomial fit through Origin graphic and analysis software, and an econometric model was also applied. GDP was utilized as an independent variable in the economic model to determine the trend of energy consumption. Another input-output model is also used for multilinear regression to evaluate the change of four variables, GDP, population growth, urbanization growth rate, and general inflation rate, which was quantitatively linked with total energy requirement using Weka software. The mathematical model developed through linear and multilinear regression has been validated by using a different assumption on GDP growth based on past growth rate as low, medium and high growth rate and using the mathematical formula to generate an energy demand trend that can be compared with the actual trend; as a result, all the mathematical model that are generated has been found to be valid for the purpose of the intended work. Based on the generated mathematical model and different GDP growth rate scenario as low, medium, business as usual (BAU) and high a future energy demand was forecasted up to 2052 for thirty years. The model's results can help energy planners ensure that the country's supply capacity keeps up with predicted energy demand growth.

Do governance indicators have a role in remittances-growth nexus in Egypt?

Purpose ― Drawing on annual data from 1996 to 2022, this study aims to examine the effect of Egypt’s real GDP growth. Methods ― Autoregressive Distributed Lag (ARDL) and Cointegration technique are applied by first examining the stationarity of the series by utilizing the Augmented-Dicky-Fuller (ADF) unit root test. The bound cointegration test is then implemented to evaluate the existence of cointegration among the variables. Findings ― The results indicate that the GDP growth rate has a long-run positive relationship with remittance inflows in Egypt. This paper also finds that the average governance indicators in Egypt, which include government effectiveness, political stability, control of corruption and regulatory quality, voice and accountability, and the rule of law, could help facilitate the long-run effect of remittances on GDP growth. Implication ― The findings imply that maintaining high levels of governance indicators is essential for helping Egypt benefit from remittances and enhancing its economic growth.Originality/value ― The study is a pioneer in including the average governance indicators in the remittance-growth nexus study for Egypt. Its purpose is to assess whether governance quality affects the nexus and whether there exists a threshold for the average governance indicators below which the flow of remittances does not encourage economic growth.

Contemporary Demographic and Economic Processes in Russia: A Statistical Analysis of Their Interaction

The article analyses the relationships between economic and demographic processes in contemporary Russia.Using index analysis, the author concluded that, in 2006–2018, a decline in the working-age population would have reduced, ceteris paribus, almost one-third of the GDP growth rate if the negative impact of this factor had not been fully compensated by an increase in the employment rate of the working-age population during this period. Growth of fertility in 2007–2015 would have led, if the influence of the situation on the labor market was eliminated, to a decrease in the number of employees by 1.2% (caused by the decline in female labor force participation rate), while fertility decrease after 2015 would have led to an increase in this number by 0.4%. Based on this estimation, it was concluded that the changes in fertility can, in the short term, have any significant impact on employment only in those types of economic activity in which the women of reproductive age make up the overwhelming majority of the employees.It has been suggested that the accumulation of socio-psychological prerequisites for a decline in fertility has led the demographic system, both in Western countries and in Russia, into a state of unstable equilibrium. The global financial crises of 2008–2009 and the 2014–2015 financial crisis in Russia upset this equilibrium and launched a mechanism for fertility decline. It is argued that in the period under consideration, the Russian economy adapted to the turbulence of the external environment more successfully than the demographic sphere

Macroeconomics Factors Affecting Net Exports of Oil Palm Industry in Malaysia

This quantitative study investigates the impact of oil palm production, oil palm pricing, inflation, and GDP growth rate on the net exports of Malaysia's oil palm industry. This research is aimed at analyzing how the net export of oil palm relates to certain macroeconomic variables. This study has employed multiple regression analysis through Ordinary Least Squares for 26 years of time series data from 1996 to 2020 in Malaysia. The findings from this study improve our understanding of the major factors contributing to net exports in the oil palm industry. Palm oil production, price, as well as GDP growth, drive net export of palm oil in Malaysia found during this research. An increase in production leads to the reduced amount of palm oil exported, all other things remaining constant. Conversely, inflation and GDP growth are major drivers behind the increased net export of palm oil.

The Impact of Government Expenditure on Economic Growth in Malaysia

This study experimentally investigates the relationship between economic growth and government spending in Malaysia from 1990 to 2022 using time series data. The study analyzes the impact of total government spending on economic growth, covering housing, healthcare, social services, military, and education while identifying which sector has the most effect on economic growth. The Autoregressive Distributed Lag (ARDL) method is employed to evaluate the effects of these expenditures. The findings demonstrate that, in the short run, all variables significantly affect economic growth, hence refuting the null hypothesis. However, only trade openness and labor spending positively impact long-term economic growth. Research indicates that housing and education expenditures are the primary long-term drivers of economic growth in Malaysia. The data suggest that the Malaysian economy contradicts the Keynesian theory, as less government spending appears to improve the real GDP growth rate. The inverse relationship between government spending and economic growth indicates that government expenditure may not be the primary catalyst for economic growth, hence supporting Wagner’s law. These findings provide policymakers with essential insights for optimizing government spending to improve economic development results.

Impact on Local Economy from Zhoushan National New Area

To empirically study the policy impact of a National New Area on the local economy, this paper evaluates the effect of the Zhoushan Archipelago New Area on local GDP growth rate and economic efficiency. Firstly, the economic efficiency of 20 prefectural-level cities in Jiangsu, Zhejiang and Anhui provinces is estimated by data envelopment analysis with input and output data from 1995 to 2015. In selecting some cities except Zhoushan as a control group, we construct some counterfactuals of Zhoushan by a panel data approach. The difference between the actual and counterfactual values for the GDP growth rate and economic efficiency of Zhoushan is calculated and compared to conclude the treatment effect of the National New Area. The research shows that in the first four years, the policy of the National New Area promoted Zhoushan's economic quality by raising its efficiency, but relatively reduced its economic quantity by negatively affecting the local GDP growth rate. Then the policy influence on Zhoushan’s GDP growth rate and economic efficiency gradually disappeared after four years. Combining some other research on our study, we find that the policy effect on the GDP growth rate is related to the level of economic growth in these areas. The policy of the National New Area has less influence on GDP growth if approved in the developed region rather than in the undeveloped region. We think that it is better to set up a National New Area in a relatively undeveloped area.