Control Variables Research Articles

Do tax revenues track economic growth? Comparing panel data estimators

Determining how economic growth affects tax revenues is crucial for fiscal sustainability, economic stabilization, and policy design. The current literature on tax buoyancy presents contrasting estimates, highlighting the need for a systematic discussion of the trade-offs associated with different estimators. This paper provides new empirical evidence by reviewing a range of panel data estimators in a large sample of 172 countries from 1990 to 2019. We find evidence of lower estimates for tax responses to economic activity in the short term relative to previous literature, suggesting a limited automatic stabilization power of tax systems. The heterogeneity in our results within and across income groups underscores the importance of choosing the appropriate estimator. Our results remain broadly unchanged when we introduce new control variables to disentangle discretionary from automatic tax revenue variations, indicating that economic cycles do not significantly influence the timing of tax policies.

A confounding bridge approach for double negative control inference on causal effects

Unmeasured confounding is a key challenge for causal inference. In this paper, we establish a framework for unmeasured confounding adjustment with negative control variables. A negative control outcome is associated with the confounder but not causally affected by the exposure in view, and a negative control exposure is correlated with the primary exposure or the confounder but does not causally affect the outcome of interest. We introduce an outcome confounding bridge function that depicts the relationship between the confounding effects on the primary outcome and the negative control outcome, and we incorporate a negative control exposure to identify the bridge function and the average causal effect. We also consider the extension to the positive control setting by allowing for the nonzero causal effect of the primary exposure on the control outcome. We illustrate our approach with simulations and apply it to a study about the short-term effect of air pollution on mortality. Although a standard analysis shows a significant acute effect of PM2.5 on mortality, our analysis indicates that this effect may be confounded, and after double negative control adjustment, the effect is attenuated toward zero.

Fostering innovation through governance systems: empirical insights from South Asian countries

Purpose Innovation is necessary to ensure consistent economic growth and to meet the global competition. In view of this, the purpose of this study to check the effect of governance quality as a tool for fostering innovation performance. Design/methodology/approach The empirical analysis was arranged on 20 year’s (2000–2019) data from South Asian economies. Subject to the existence of cointegration, the authors use the fully modified ordinary least square model for regression analysis and check the robustness through robust least square model. Findings The empirical findings infer that all dimensions of governance have a positive significant impact on both research and development expenditures and trademark applications jointly known as innovation performance. In addition, the empirical analysis discloses the positive effect of all control variables, including FDI inflow, banking sector development, economic growth, ease of doing business index and government subsidies on innovation activities. The analysis confirms the “grease the wheel” role of governance in fostering innovation. Practical implications It is apparently recommended to strengthen the exercise of better governance to harvest better innovation scores. Originality/value This study advocates the positive role of individual dimensions of governance recommended by existing literature and complements the literature by jointly exploring the impact of all governance dimensions on overall innovation performance.

Factors affecting corporate cash holdings: Evidence from the energy sector of Saudi Arabia

This study investigates the impact of firm-specific components of cash holdings on the cash reserves of energy firms. Decisions related to cash management are significant and treated as confident made by financial managers to increase the value of a firm. Therefore, financial managers are obligated to hold an optimum level of cash to enhance the firm value. The study depends on secondary data from seven energy firms listed on the Saudi Arabian Stock Exchange over the period between 2014 and 2023. The study considers cash holdings as a dependent variable, leverage, networking capital, and profitability as explanatory variables, and firm size as a control variable. The study employed a linear regression model and a generalized linear regression (GLM) model with Gaussian and Gamma distributions to analyze the data. The results show that Saudi Arabian energy firms reserve approximately 7% of cash, while external financing is 51%. The pooled regression results show that the association between leverage and firms’ cash reserves was negative (–0.064) and significant at less than a 1% significance level. Further, the networking capital and profitability were positively related (0.063 and 0.113) and significant at 5% and 1% significance levels. Moreover, the firm size was positive but insignificant. The generalized linear regression model results with Gaussian and Gamma distributions were similar to the simple linear regression with minor variation. AcknowledgmentThis current project under research project number PSAU/2023/02/25767 was funded by Prince Sattam Bin Abdulaziz University.

Governance and Fiscal Decentralisation in Latin America: An Empirical Approach

The objective of this study was to establish the relationship between certain institutional variables and the effectiveness of fiscal decentralisation in Latin America. To fulfil this objective, we took a sample of 15 Latin American countries for the years 1996 to 2020 to estimate the logarithm of GDP per capita based on the level of fiscal decentralisation, as well as its interaction with six institutional variables plus three control variables. The results show that institutional variables always modulate the effects of fiscal decentralisation, in most cases significantly and negatively, the exceptions being accountability with a positive result and government effectiveness with a non-significant result. It was concluded that in the presence of weak regulations, political conflicts, and corruption, fiscal decentralisation can worsen social or economic circumstances in Latin America.

A soft actor-critic reinforcement learning framework for optimal energy management in electric vehicles with hybrid storage

The efficient energy management of electric vehicles (EVs) equipped with hybrid energy storage systems (HESS) poses a significant challenge due to its vast search space, numerous control variables, and intricate driving conditions. In response to this challenge, this paper introduces a novel deep reinforcement learning (DRL) algorithm, specifically the soft actor-critic (SAC), tailored for optimizing energy distribution within EVs equipped with battery-supercapacitor (SC) HESS. The proposed SAC-based energy management system (EMS) is designed to address inherent limitations present in most existing DRL algorithms, such as slower convergence rates, discretization errors, unstable training dynamics, and suboptimal optimization effects. The SAC-based EMS undergoes training in a continuous action space through self-play, utilizing a complex driving cycle and a novel reward function. The algorithm demonstrates its efficiency by rapidly maximizing cumulative rewards and refining its decision-making policies. Afterward, extensive experiments showcase the superiority of the proposed SAC-based EMS over rule-based (RB) techniques, deep deterministic policy gradient (DDPG), and battery-only configurations across various driving cycles. Notably, the approach effectively allocates high-power surges and braking energy to the SC, reducing the frequency of charge/discharge cycles and thereby extending the battery's lifespan. Additionally, the learned SAC-based EMS policy achieves substantial reductions in electricity consumption by 39.6 %, 47.87 %, and 45 % compared to battery-only configuration, and 42.56 %, 47.87 %, and 45.83 % compared to RB technique under the NYCC, LA92, and FTP cycles, respectively. In contrast, the DDPG algorithm tends to rely heavily on the SC to deliver the total power required, even under normal driving conditions.

Finite-Time Convergence Guidance Law for Hypersonic Morphing Vehicle

Aiming at the interception constraint posed by defensive aircrafts against hypersonic morphing vehicles (HMVs) during the terminal guidance phase, this paper designed a guidance law with the finite-time convergence theory and control allocation methods based on the event-triggered theory, achieving evasion of the defensive aircraft and targeting objectives for a morphing vehicle in the terminal guidance phase. Firstly, this paper established the aircraft motion model; the relative motion relationships between HMV, defensive aircraft, and target; and the control equations for the guidance system. Secondly, a guidance law with finite-time convergence was designed, establishing a controller with the angle between the aircraft–target–defense aircraft triplet as the state variable and lift as the control variable. By ensuring the angle was non-zero, the aircraft maintained a certain relative distance from the defense aircraft, achieving evasion of interception. The delay characteristic of the aircraft’s flight controller was considered, analyzing its delay stability and applying control compensation. Thirdly, a multi-model switching control allocation method based on an event-triggered mechanism was designed. Optimal attack and bank angles were determined based on acceleration control variables, considering different sweep angles. Finally, simulations were conducted to validate the effectiveness and robustness of the designed guidance laws.

Addressing brain drain and strengthening governance for advancing government readiness in artificial intelligence (AI)

PurposeThis study aims to investigate the impact of brain drain on government AI readiness in EU member countries, considering the distinctive governance characteristics, macroeconomic conditions and varying levels of ICT specialists.Design/methodology/approachThe research employs a dynamic panel data model using the System Generalized Method of Moments (GMM) to analyze the relationship between brain drain and government AI readiness from 2018 to 2022. The study incorporates various control variables such as GDP per capita growth, government expenditure growth, employed ICT specialists and several governance indicators.FindingsThe results indicate that brain drain negatively affects government AI readiness. Additionally, the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness.Research limitations/implicationsMajor limitations include the focus on a specific region of countries and the relatively short period analyzed. Future research could extend the analysis with more comprehensive datasets and consider additional variables that might influence AI readiness, such as the integration of AI with emerging quantum computing technologies and the impact of governance reforms and international collaborations on AI readiness.Practical implicationsThe theoretical value of this study lies in providing a nuanced understanding of how brain drain impacts government AI readiness, emphasizing the critical roles of skilled human capital, effective governance and macroeconomic factors in enhancing AI capabilities, thereby filling a significant gap in the existing literature.Originality/valueThis research fills a significant gap in the existing literature by providing a comprehensive analysis of the interaction between brain drain and government AI readiness. It uses control variables such as ICT specialists, governance structures and macroeconomic factors within the context of the European Union. It offers novel insights for policymakers to enhance AI readiness through targeted interventions addressing brain drain and fostering a supportive environment for AI innovation.

ICT diffusion, E-governance, and sustainability in the digital era

This study analyses the impact of information and communications technology (ICT) diffusion and e-governance on sustainability in 148 Belt and Road Initiative (BRI) partner countries. It considers control variables such as institutional quality, urbanization, research and development (R&D), and the COVID-19 effect as dummy variables. Two step-generalised method of moments analysis of a panel dataset of 148 BRI partner countries from 2013 to 2021 reveals that ICT diffusion and e-governance benefit sustainability, with R&D and institutional quality also making positive contributions, while urbanization and the pandemic negatively impact sustainability. The findings were validated through D-K regression checks and support the technology spill-over effect, innovation, triple bottom line, endogenous growth, and stakeholder theories. They highlight the significance of investing in ICT infrastructure and digital literacy programs, improving institutional reforms and governance practices, putting sustainable urban planning into practice, and supporting R&D projects to advance sustainability in BRI partner nations. This study clarifies the potential problems brought forth by the pandemic and makes suggestions for reducing its negative effects on sustainability. It contributes to evidence-based decision-making, guiding sustainable practices in BRI partner nations.

Globalization and financial development contributions toward economic growth in Sudan

The main objective of this study is to evaluate how globalization and financial development contributed to Sudan’s economic growth using data from 1978 to 2021 by applying the autoregressive distributed lag (ARDL) model. Furthermore, a model to calculate total factor productivity and the contributions of the underlying variables is being developed. The findings from ARDL indicate that globalization and capital display positive and beneficial impacts on Sudan’s economic growth in the long run, implying that an increase of one percentage point in Sudan’s globalization index will, over time, boost economic growth by 1.50408 % and 0.156301 %. Government spending as a control variable was found to influence economic growth positively. Financial development shows a detrimental effect on growth in Sudan. The average productivity growth rate is negative, regardless of the positive contributions of globalization and financial development. This outcome suggests that globalization and capital have been gainful to Sudan’s economy. Therefore, this study advocates that capital accumulation triggers investment, encouraging more global market openness making it easier to access international markets, setting up a strong and sophisticated financial system to assist Sudan’s economic development, and enhancing Sudan’s spending policy to support the productive sectors and encourage domestic production will significantly stimulate economic growth post-war, taking into account additional measures, such as regulations, investment laws, institutional quality, the integration of the banking system, and identifying expenditure prioritizations.

The Nexus Between Financial Inclusion and Economic Growth in The Organization of Islamic Cooperation (OIC) Countries

This study aims to examine the relationship between financial inclusion and economic growth in 22 OIC countries from 2005-2018 using a panel regression model, especially the REM model. There are three financial inclusion indicators as independent variables considered in this research: the financial inclusion index, the financial outreach index, and the financial usage index. We use GDP per capita as a proxy measure for economic growth. Using a random effect model (REM), our empirical results show that financial inclusion positively and significantly affects national economic growth in OIC countries. It is proven by all the proxy variables of financial inclusion, i.e., the financial inclusion index, financial outreach index, and financial usage index, positively correlate with the GDP per capita as a proxy for national economic growth. Other empirical results from control variables show that inflation is found significant to decrease OIC's economic growth. Trade also has significant effects on economic growth in OIC countries. Besides, in this study, unemployment has positively effects to increase economic growth in OIC countries. The main hypotheses in this study are accepted. Therefore, it could be concluded that financial inclusion positively contributes to increasing economic growth in Muslim countries. Furthermore, this finding will implicate the government to enhance and promote financial inclusion programs massively and provide access to financial services formally, such as insurance, saving, or credits/financing for the underprivileged community, especially for SMEs in all rural areas in OIC countries.

Enhance business performance in the naval and maritime sectors through the implementation of environmental management focused on human capital

ABSTRACT Organizations are shifting focus from profits to people and planet. Sustainable human capital management is key to this transition. Bio-energy is a potential solution to reduce oil dependency. Additionally, strong Corporate Social Responsibility (CSR) is essential for business reputation and success. With an eye towards achieving the Sustainable Development Goals (SDGs), this work’s goal is to measure the relationship among HC, Environment Management (EM) and CSR on the Business Performance (BP) across a diverse range of companies from the naval and maritime sector. Two control variables have been added: the age and the size of the companies. In order to quantify the linkages, a survey comprising questions was distributed to managers. The data obtained from this survey was then analysed by RStudio and SmartPLS. A positive influence has been got in five of the six relationships established and one of the control variables has an impact on the constructs studied.

Research on the Sheepskin Effect in China: Factors Influencing Educational Returns and Heterogeneity Analysis

Using sample data from the China General Social Survey (CGSS2021), this study employs econometric methods based on the basic cost-benefit economics perspective. It investigates the relationship between educational investment and labor income in China in recent years, utilizing the Mincer earnings model as well as an extended model incorporating additional control variables. An Ologit model is constructed to analyze the impact of gender, ethnicity, family economic status, and regional registration on educational attainment among the sample population. Furthermore, the study compares educational returns across different genders, regions, workplaces, and job positions. Finally, based on empirical analysis, suggestions are proposed to enhance individual income levels and optimize the allocation of social educational resources from both individual and environmental perspectives.

Surrogate gradient methods for data-driven foundry energy consumption optimization

In many industrial applications, data-driven models are more and more commonly employed as an alternative to classical analytical descriptions or simulations. In particular, such models are often used to predict the outcome of an industrial process with respect to specific quality characteristics from both observed process parameters and control variables. A major step in proceeding from purely predictive to prescriptive analytics, i.e., towards leveraging data-driven models for process optimization, consists of, for given process parameters, determining control variable values such that the output quality improves according to the process model. This task naturally leads to a constrained optimization problem for data-driven prediction algorithms. In many cases, however, the best available models suffer from a lack of regularity: methods such as gradient boosting or random forests are generally non-differentiable and might even exhibit discontinuities. The optimization of these models would therefore require the use of derivative-free techniques. Here, we discuss the use of alternative, independently trained differentiable machine learning models as a surrogate during the optimization procedure. While these alternatives are generally less accurate representations of the actual process, the possibility of employing derivative-based optimization methods provides major advantages in terms of computational performance. Using classical benchmarks as well as a real-world dataset obtained from an industrial environment, we demonstrate that these advantages can outweigh the additional model error, especially in real-time applications.

Alcohol and Cannabis Use by Adolescents with Special Educational Needs: A Systematic Review Focused on Students with Emotional and Behavioral Disorders

Aim: As most substance use prevention programs are offered at school, it is important to explore which group of students might be at increased risk. Both researchers and educators have often maintained that students with special educational needs due to emotional and behavioral disorders (SENs-EBD) are particularly vulnerable. Hence, the aim of the current systematic review was to examine the alcohol and cannabis use by students with SENs-EBD in comparison to students without SENs or other types of SENs. Methods: Adhering to the PRISMA guidelines, the review was registered on PROSPERO (CRD42023375799) and search terms were entered in Web of Science, Scopus, ERIC, and PubMed in December 2022. Publications were required to report on empirical quantitative studies (primary data) that assessed alcohol and/or cannabis use by adolescent students with SENs-EBD and a comparison group, and be published in English-language journal articles, conference papers, or book chapters from 2000 onwards. The QuADS tool was used for quality assessment. Results: Based on these criteria, six articles (sample sizes N = 61 to N = 122,180) were included for the thematic narrative synthesis. The studies revealed that the percentage of students with SENs-EBD consuming alcohol and cannabis was higher compared to students who did not have SENs or attended mainstream schools. Conclusion: The small number of heterogeneous studies, with limitations concerning the sample size, missing effect sizes, and influence of control variables, demonstrates the need for more research. The systematic review provides guidance for such future research endeavors, as well as preliminary notions for practical implications.

Peers in puppy love and student academic performance in middle school: quasi-experimental evidence from China

ABSTRACT In the ongoing debate regarding the impact of puppy love on adolescents’ personal development, the existing literature has paid insufficient attention to the externalities of students experiencing puppy love in classrooms. By using a nationally representative survey of middle school students in China, we employed random student-classroom assignment as a quasi-experiment to investigate the causal effect of classroom peers experiencing puppy love on students’ academic performance. We found that classroom peers in puppy love had an adverse impact on students’ academic performance, which is proven to be reliable by a series of robustness checks, including instrumental variable（IV）estimation, removing extreme observations, placebo tests, and adding other control variables. Mechanism analysis indicated that this negative peer effect was caused by students’ lower learning effort, lower expectations for the future, further peer effect of student friends and teachers’ lack of responsibility and patience. Furthermore, this negative effect was more pronounced among boys, students with high family income, and medium or low cognitive ability.

Determinants of Non-Hydro Renewable Energy Consumption in China’s Provincial Regions

This study investigates the determinants of non-hydro renewable energy (NHRE) consumption across 31 provincial regions in China, spanning from 2015 to 2022. Utilizing fixed effects and moderating effects, the research analyzes the impacts of research and development intensity (RDI), urbanization (UR), and human capital (HC) on NHRE consumption (NHREC). Additionally, the moderating roles of industrial structure (IS) and tax (TA) are examined, along with control variables such as informationization and economic development. The findings reveal that increased RDI significantly boosts NHREC by enhancing technological advancements. UR also positively affects NHREC, particularly in rapidly urbanizing regions, while HC provides a skilled workforce that drives renewable energy projects. However, the study identifies that concentrated industrial structures and high taxes can negatively moderate these positive impacts, highlighting the complex interplay of these factors. Policy recommendations include creating “Renewable Energy Innovation Hubs” in underdeveloped regions to attract startups and researchers, developing “Solar Cities” with mandated solar panels on all buildings, and introducing a “Carbon Offset Lottery” to incentivize investments in renewable energy.

Using the tripartite model of place attachment to understand bequest intentions among family forest landowners in Georgia, United States

ABSTRACT Family forest landowners own 54% of forestland in Georgia. However, most forest landowners are older than 65 years, and it is unclear what will happen to these forests when they can no longer care for their land. We apply the tripartite model of place attachment to assess the roles of person, place, and process dimensions concerning forest landowners’ future bequest intentions. Using data from 1,143 family forest landowners in Georgia, we find that the person dimension is the strongest predictor for wanting to keep the forestland in the family. Being involved in the process of forestland management is also a significant but weaker predictor, while the role of the place itself seems less important. Place attachment dimensions are more robust predictors for bequest intentions than any other control variable. These findings shed new light on bequest intentions and could inform future developments of family forest landownership.

Nutrient Dynamics and Resource-Use Efficiency in Greenhouse Strawberries: Effects of Control Variables in Closed-Loop Hydroponics

The importance of implementing recirculating drainage for greenhouse strawberries is often overlooked because of the low electrical conductivity (EC) of drainage and transpiration despite the large area of cultivation in Korea. In this study, we analyzed the growth of strawberry crops and their water and individual nutrient use efficiency when using closed-loop hydroponics in greenhouses. The study consisted of two parts: Experiment (Exp) 1 and Exp 2, each of which employed a different closed-loop hydroponic control method. In Exp 1, the system was controlled solely based on the EC of the drainage mixed with raw water. In Exp 2, the nutrient solution (NS) was corrected according to the ion concentrations in the drainage, with correction intervals of 2 weeks, 4 weeks, and no correction, while the control treatment was open-loop. In Exp 1, a prolonged increase in drainage EC resulted in an imbalance in NS ion composition and reduced fruit yield. In Exp 2, the NS composition was stabilized through periodic nutrient correction, and the fruit yield of the closed-loop treatments did not differ from the open-loop treatment. However, the closed-loop with 2- and 4-week correction in Exp 2 showed 94% and 88% higher nutrient use efficiency (NUE), respectively, than an open-loop system. Among the closed-loop treatments, the 2- and 4-week correction intervals had 36% and 32% higher NUE than the no-correction treatment. Furthermore, the 2-week correction interval showed a 3% improvement in NUE compared to the 4-week interval. These findings highlight the importance of considering a shorter ion correction interval for optimal NUE and normal plant growth in recirculating hydroponic systems of strawberry cultivation.

Self‐organizing cooperative hunting for unmanned surface vehicles with constrained kinematics

SummaryThe article aims at solving a cooperative hunting problem for multiple unmanned surface vehicles (USVs) subject to constrained kinematics. In order to cooperatively trap the evader into the hunting domain, a velocity model with control variable for the pursuers is firstly proposed according to the Apollonius circle. Then, a flexible self‐organizing control strategy is developed, which enables the pursuers to approach the evader while forming an encirclement. The pursuers can dynamically adapt their strategies in real‐time by choosing the optimal control variable. Additionally, take into account the limitation imposed on the vessel's motion, the optimal control variable with constraint can be obtained by using the particle swarm optimization with log‐barrier method. The simulation results ultimately demonstrate the validity and superiority of the proposed cooperative hunting algorithm.