Economic Decisions Research Articles

How Does Mental Accounting Influence Financial Decision-Making Among New Employees in Indonesia?

Mental accounting is a concept that explains how individuals categorize, group, and evaluate their economic decisions. The study hypothesizes that income and expense categorization, as well as allocation preferences, influence the financial decision-making of new employees. The research uses a quantitative approach with a descriptive method, collecting primary data through a questionnaire distributed to 200 new employees in Indonesia. The results show that both income and expense categorization and allocation preferences significantly influence the financial decision-making of new employees.

Application of correlation-based recurrent neural network in porosity prediction for petroleum exploration

Abstract Porosity prediction serves as a key metric to determine the characteristics of reservoirs and to reduce the exploration efforts, which in turn lead to enhancing the production planning and making economic vital decisions. In this sense, one can use low costly and low time-consumptive method by conducting experimental tests on various soil samples. Predicting porosity can be effectively utilized for identifying suitable drilling locations for petroleum exploration, thus reducing the need for extensive laboratory experiments beforehand. The seismic behavior has a direct impact on porosity features. This study works to find a mapping relationship between well-log porosity and seismic attributes. A Pearson correlation coefficient method has been proposed based on diverse scales to determine the most significant seismic attributes and to reduce data dimensionality. The recurrent prediction neural network models have been proposed for learning and prediction. Three neural network models are introduced, represented by Nonlinear Autoregressive (NARX) network, the Long Short-Term Memory (LSTM) network, and the Gated Recurrent Unit (GRU) network. All these proposed models are trained to associate the nonlinear relationship between the selected attributes and porosity. The seismic attributes are assigned as the inputs to the neural models, while the porosity represents the target of neural structure. Moreover, the correlated and uncorrelated strategies are involved to test the effective attributes based on seven scenarios with the proposed neural structures. The results showed that the correlated NARX network consistently delivers better prediction accuracy and minimum errors as compared to other models for different correlation scales. Consequently, correlated NARX network is the best choice for porosity prediction.

Measuring the impact of the economic balance goals on the economic growth of the Iraqi economy for the period (1990-2023)

Achieving economic balance is a multifaceted and ongoing goal. Its achievement requires effective policy making and strategic investments capable of adapting to changing economic conditions. It is a dynamic goal that governments and policy makers around the world seek to achieve for the benefit of their countries by implementing a set of strategies and plans that will Achieving a more balanced and resilient economy, ultimately leading to greater prosperity and improved quality of life for the community. The research aims to determine the impact of the objectives of economic balance (national income, unemployment rate, inflation rate) on economic growth in the Iraqi economy for the period (1990-2023), and to determine the impact of inflation, unemployment, and national income as objectives of economic balance and how they affect economic growth. The researcher relied on the descriptive analytical method, The study also recommended the need for coordination and harmonization between monetary and financial policy so that there is no contradiction between the goals and the possibility of achieving stability, reducing inflation and unemployment rates, and achieving economic growth through testing the limits of co-integration within the autoregressive methodology of distributed time lag based on the existence of a long-term equilibrium relationship between the rate of economic growth and national income. And the rate of unemployment and inflation, although the decline in economic growth was mainly caused by wrong policies and the state’s control over the overall economic activity and the accompanying mismanagement and the inability to diagnose the effective factors in achieving development and the loss of the standards necessary to make appropriate economic decisions, which was reflected in the overall economic activity and was accompanied by that. High unemployment rates in the Iraqi economy.

Analyzing the Relationship Between Inflation and Unemployment in Afghanistan (2003-2021)

Inflation and unemployment are two key elements in any market economy, and their relationship significantly impacts socio-economic conditions. This study examines the connection between Afghanistan's unemployment and inflation, providing crucial information for macroeconomic policymakers. The study explores the applicability of the Phillips Curve in Afghanistan's specific economic context. This study employs the Ordinary Least Squares (OLS) method to analyze data gathered from 2003 to 2021, aiming to identify patterns and relationships that may guide economic policy decisions. The results indicate a positive relationship between inflation and unemployment. However, this relationship is statistically insignificant, and there is no proof to substantiate the presence of a Phillips Curve in Afghanistan. This lack of alignment with traditional economic models underscores the necessity for tailored economic policies to address the specific challenges faced by the Afghan economy. The study highlights the importance of understanding local economic dynamics and suggests that policymakers should explore alternative approaches to managing inflation and unemployment, ultimately fostering sustainable economic growth.

Effective online methods and pedagogical insights in digital creativity for literature education

The transition to distance learning during the pandemic has highlighted the imperative to adapt all academic disciplines, including literature, to an online format. The aim of this study is to ascertain the most efficacious pedagogical approaches for the remote teaching of literature courses, with a particular focus on the distinctive requirements of artistic interpretation, which has traditionally benefited from real-time interaction. The methodology comprised surveys and experimental analysis conducted with 300 students at the Kazakh National University. The research compared traditional online teaching methods with innovative techniques, including game-based learning, problem-based learning, case studies, brainstorming, multimedia technologies, information literacy skills, and programmed learning. The results demonstrate that these contemporary strategies markedly enhance students’ comprehension and engagement, as evidenced by elevated academic performance and a more profound grasp of the course material. The integration of advanced technologies in distance education provides students with the opportunity to experiment with new ways of expressing their literary understanding. The findings of this study indicate that distance learning in literature can be highly effective when tailored pedagogical strategies and technological tools are employed. However, the management of these systems requires economic decision-making and further social and technological adaptations, particularly for disciplines like literature where a nuanced approach to artistic content is essential. This study provides recommendations for refining online literature courses to optimise student engagement and comprehension in virtual learning environments.

Performance evaluation of Neural Network models applied to the classification of mineral imagery from the Katanga mining region

Nowadays, the need for a powerful model in the task of accurate image classification is increasing. This paper focuses on evaluating and comparing the effectiveness of three main neural network models namely the multilayer perceptron model, convolutional neural networks and the transfer learning model for the classification of mineral and rock images in the mining region of Katanga. The main problem addressed is the identification of the most efficient and accurate machine learning techniques in the specific classification of mineral images, with a particular focus on the constitution of the dataset, the analysis of mineral image data and their associated labels. Parameters such as the burial lot, the number of cycles, the size of convolution filters, the precision and the loss have been taken into account. The results show that the transfer learning-based model significantly outperforms the multilayer perceptron models and convolutional neural networks in terms of accuracy and robustness, achieving a classification accuracy of 97.8% compared to 75% for the multilayer perceptron and 96% for the convolutional neural networks designed from scratch. These remarkable results demonstrate the importance of deep learning in processing complex images and open new perspectives for the use of these techniques in the mining sector of the Greater Katanga mining region in the identification of mineral resources. The broader implications of this study include an innovation in mining exploration strategies through faster and more accurate classification of minerals, thus influencing both economic decision-making and environmental policies associated with mining in the region.

Estimating self-performance when making complex decisions

Metacognition, the ability to monitor and reflect on our own mental states, enables us to assess our performance at different levels – from confidence in individual decisions to overall self-performance estimates (SPEs). It plays a particularly important part in computationally complex decisions that require a high level of cognitive resources, as the allocation of such limited resources presumably is based on metacognitive evaluations. However, little is known about metacognition in complex decisions, in particular, how people construct SPEs. Here, we examined how SPEs are modulated by task difficulty and feedback in cognitively complex economic decision-making, with reference to simple perceptual decision-making. We found that, in both types of decision-making, participants’ objective performance was only affected by task difficulty but not by the presence of feedback. In complex economic decision-making, participants had lower SPEs in the absence of feedback (compared to the presence of feedback) in easy trials only but not in hard trials, while in simple perceptual decision-making, SPEs were lower in the absence of feedback in both easy and hard trials. Our findings suggest that people estimate their performance in complex economic decision-making through distinct metacognitive mechanisms for easy and hard instances.

Development of per Capita GDP Forecasting Model Using Deep Learning: Including Consumer Goods Index and Unemployment Rate

In the 21st century, the increasing complexity and uncertainty of the global economy have heightened the need for accurate economic forecasting. Per capita GDP, a critical indicator of living standards, economic growth, and productivity, plays a key role in government policy-making, corporate strategy, and investor decisions. However, predicting per capita GDP poses significant challenges due to its sensitivity to various economic and social factors. Traditional methods such as statistical analysis, regression, and time-series models have shown limitations in capturing nonlinear interactions and volatility of economic data. To address these limitations, this study develops a per capita GDP forecasting model based on deep learning, incorporating key macroeconomic variables—the Consumer Price Index (CPI) and unemployment rate (UR)—to enhance predictive accuracy. This study employs five deep-learning regression models (RNN, LSTM, GRU, TCN, and Transformer) applied to real and placebo datasets, each incorporating combinations of CPI and UR. The results demonstrate that deep learning models can effectively capture complex, nonlinear relationships in economic data, significantly improving predictive accuracy compared to traditional models. Among the models, the Transformer consistently achieves the highest R-squared and lowest error values across various metrics (MSE, RMSE, and MSLE), indicating its superior ability to model intricate economic patterns. In addition, including CPI and UR as additional predictors enhances model robustness, with the TCN and Transformer models showing particularly strong performance in capturing short-term economic fluctuations. The findings suggest that the deep learning models, especially the Transformer, offer valuable tools for policymakers and business leaders, providing reliable GDP forecasts that support economic decision-making, resource allocation, and strategic planning. Academically, this study advances the understanding of deep learning applications in economic forecasting, particularly in integrating significant macroeconomic variables for enhanced predictive performance. The developed model is a foundation for informed economic policy and strategic decisions, offering a robust and actionable framework for managing economic uncertainties. This research contributes to theoretical and applied economics, providing insights that bridge academic innovation with practical utility in economic forecasting.

Evaluating the Cost-Effectiveness of Antenatal Screening for Major Structural Anomalies During the First Trimester of Pregnancy: A Decision Model.

To assess the cost-effectiveness of modifying current antenatal screening by adding first trimester structural anomaly screening to standard of care second trimester anomaly screening. Health economic decision model. National Health Service (NHS) in England and Wales. Pregnant women attending for first trimester antenatal screening. The decision model estimated pregnancy outcomes (maternal and foetal) and 20-year costs for current screening practice and for a policy adding a protocol screening for eight major structural anomalies to the current first trimester ultrasound scan. Event probabilities, costs, and outcomes for the model were informed by meta-analyses, published literature, and expert opinion. Expected numbers of pregnancy outcomes, healthcare costs, and maternal quality-adjusted life years (QALYs). Estimation of the incremental cost-effectiveness ratio (ICER), likelihood of cost-effectiveness, and a value of information (VoI) analysis assessing if further research is needed before making a decision about screening. First trimester anomaly screening increased mean per woman costs by £11 (95% CI £1-£29) and maternal QALYs by 0.002065 (95% CI 0.00056-0.00358). The ICER was £5270 per QALY and the probability of cost-effectiveness at a willingness to pay value for a QALY of £20 000, exceeded 95%. VoI analysis showed further research would be unlikely to represent value for money. The protocol would likely lead to a reduction in infant healthcare costs and QALYs. A protocol to screen for eight major structural anomalies during the first trimester appears to represent value for money for the NHS. The opposing implications for mothers and infants, however, raise complex, challenging, and sensitive issues.

Destructive Behaviour, Judgement, and Economic Decision-making under Thermal Stress

Destructive Behaviour, Judgement, and Economic Decision-making under Thermal Stress

Karma-related influences on moral obligation and empathy: an ethical perspective

PurposeThis study aims to explore how the three dimensions of karma – karmic duty orientation, indifference to rewards and equanimity – influence empathy and moral obligation, with implications for social economics.Design/methodology/approachThe research was conducted on a valid sample of 401 university students in Vietnam, using hierarchical regression to test formulated hypotheses.FindingsThe analysis reveals that karmic duty orientation and indifference to rewards significantly enhance empathy and moral obligation, indicating that individuals with a strong sense of moral duty and intrinsic motivation are more likely to engage in ethical and pro-social behaviors. Equanimity was not found to impact empathy or moral obligation significantly.Practical implicationsOrganizations and policymakers should focus on cultivating moral duties, intrinsic motivations and resilience in ethical behavior to promote social responsibility and sustainability and ensure long-term social and economic stability.Originality/valueThis study contributes to social economics by integrating ethical dimensions of karma into the analysis of moral behavior, offering a fresh perspective that challenges traditional economic models centered on self-interest. The research provides a comprehensive framework for understanding how moral principles influence economic decisions and social outcomes.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0571

Economic Status and Decision Making Power of Tribal Women: An Overview of Backyard Poultry Rearer in Khagrachari District of Bangladesh

Economic Status and Decision Making Power of Tribal Women: An Overview of Backyard Poultry Rearer in Khagrachari District of Bangladesh

Artesanos, precariedad económica e inestabilidad política neogranadina: de una sociedad estamental a una sociedad de clases

This article will provide an overview of the first two decades of New Grenada Republic's existence, when the artisans began to gain prominence in political spheres and wield influence on New Grenada's economic decisions. By contextualizing political, economic and social characteristics of this period, this article will illuminate the environment that led to the emergence of artisan societies in the city of Bogota and demonstrate how the appointed society of Bogota transitioned from an estate society to a class society.

Using the natural capital framework to integrate biodiversity into sustainable, efficient and equitable environmental-economic decision-making.

One of Georgina Mace's many transformational research contributions was to provide a universally applicable framework for incorporating any or all elements and connections of the natural environment within conventional economic decision-making. We apply this natural capital framework to consider the overall effects of a suite of land-use policy options intended to promote the conservation and renewal of biodiversity. Options considered include sharing, sparing, three-compartment sparing, rewilding and organic farming regimes. Each is assessed in terms of its impact on both domestic and global biodiversity. Reinforced by an empirical application considering land use in Great Britain, we show that while policy has prioritized sharing options, evidence supports land sparing and three-compartment approaches as more efficient, sustainable and equitable alternatives.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.

The Impact of 2018 Election on Economic Decision-Making: A Case Study of District Chitral, Khyber Pakhtunkhwa

This study analyzes the socio-economic effects triggered by the general elections held in 2018 on the people of Chitral District, Pakistan. It employs a mixed-methods strategy that integrates the quantitative analysis of questionnaires obtained from 300 participants and qualitative commentary derived from focus group discussions. The study addresses shift that occurred in the patterns of consumption, labor market and investments, and perception of economic opportunities after the elections. Political changes have outspread consequences for economic behavior. There was an increase in the households spending in education, healthcare, and transportation, owing to the great hopes the people had of the Pakistan Tehreek-e-Insaf (PTI) Government. Such changes also encouraged women into work to increase economic empowerment and then election also boosted the labor force participation. Nonetheless, the political, social, and economic challenges impacting infrastructure and access to markets continue to impede the progress in economic growth of the area. Political stability strengthens the economic welfare of the communities, societies, and regions. This study among others demonstrates that economic interventions in marginalized regions with good political policies discern positive economic growth. Portions of such policies would include effective policy formulation and successful implementation. Actions include finalizing infrastructure projects, implementing specific employment schemes, enhancing outreach to financial service providers and improving systems for policy monitoring. These measures aim for sustainable development and translate political pledges into real benefits for the people of District Chitral. It also adds to the body of existing knowledge on political and economic process relationships and how such may influence policy advising and development practice within specific contexts, because the region that was targeted by this study enjoys peculiar economic and geographic conditions.

A distinct hypothalamus-habenula circuit governs risk preference.

Appropriate risk evaluation is essential for survival in complex, uncertain environments. Confronted with choosing between certain (safe) and uncertain (risky) options, animals show strong preference for either option consistently across extended time periods. How such risk preference is encoded in the brain remains elusive. A candidate region is the lateral habenula (LHb), which is prominently involved in value-guided behavior. Here, using a balanced two-alternative choice task and longitudinal two-photon calcium imaging in mice, we identify risk-preference-selective activity in LHb neurons reflecting individual risk preference before action selection. By using whole-brain anatomical tracing, multi-fiber photometry and projection-specific and cell-type-specific optogenetics, we find glutamatergic LHb projections from the medial (MH) but not lateral (LH) hypothalamus providing behavior-relevant synaptic input before action selection. Optogenetic stimulation of MH→LHb axons evoked excitatory and inhibitory postsynaptic responses, whereas LH→LHb projections were excitatory. We thus reveal functionally distinct hypothalamus-habenula circuits for risk preference in habitual economic decision-making.

Analyzing Nigeria Monthly Consumer Price Index Using the ARIMA Model

Consumer Price Index (CPI) serves as economic vital indicator that helps decision makers to apply the right approach to investment areas in the midst of limited resources. CPI measures the changes in the broad spectrum level of prices pof consumer goods and services that households employ for utilization. This paper seeks to investigate the fluctuations of Nigeria Consumer Price Index from 2009-2024 using an Autoregressive Integrated Moving Average (ARIMA) model to predict future values of Nigeria CPI based on past values. The data was analyzed to make well informed economic decisions as it indicates the change in consumers’ purchasing power. The forecast based on the ARIMA (3,2,2) model shows a continuous upward trend in CPI, with the point forecasts suggesting rising inflation rates throughout 2024 and 2025. The Seasoned Autoregressive Integrated Moving Average, SARIMA (3,2,2) (2,0,0)12 model was identified as the best fitted model for the CPI. The study therefore concluded that the model has been shown to adequately explain the variation in the monthly CPI.

EUROPEAN CONCRETE ROAD STANDARDS AND PRACTICES

The scientific article "European Concrete Road Standards and Practices" by Raymond Sharp provides a comprehensive overview of the current standards and practices in the construction of concrete roads across Europe, focusing on twelve countries: Austria, Belgium, Czechoslovakia, Denmark, France, Great Britain, Italy, the Netherlands, Spain, Sweden, Switzerland, and West Germany. The research synthesizes data from the Permanent International Association of Road Congresses (PIARC) and the European Cement Association, including detailed synoptic tables prepared for previous European symposia on concrete roads. This study delves into the various factors influencing pavement design, including traffic levels, sub-grade strength, and pavement materials, discussing the complexities of predicting future traffic and the insensitivity of pavement costs to small traffic variations. It examines the philosophical and economic decisions that guide pavement thickness and highlights the standardization in pavement design across different nations, emphasizing the small changes in design thickness due to the proven success and economic efficiency of existing systems. Furthermore, the paper explores the influence of non-technical factors such as governmental policies and competition between construction methods on pavement design. It also discusses the impact of local material availability and construction practices on the cost and quality of road construction. The research concludes that while concrete pavements generally perform well and offer long life spans, there are multiple viable approaches to pavement design influenced by local conditions and historical practices. It suggests that imposing a uniform design approach across different environments and traditions is impractical, advocating for flexible, context-specific solutions in pavement engineering. (Abstract generated by AI tool ChatGPT 4)

Unlocking Drivers of Country-Specific Sensitivities of Atmospheric Greenhouse Gas Accumulation: Preparing for Future Pandemic Management.

The global atmospheric greenhouse gas (GHG) accumulation trajectory has been subjected to fluctuations in the context of the COVID-19 pandemic. Country-level virus prevalence and geography conditions added complexity to understanding atmospheric GHG accumulation sensitivities in terms of the growth rate. Here, extensive data sets were comprehensively analyzed to capture historical and projected fate of atmospheric GHG concentrations. Although a temporary slowdown was observed during the lockdown, global atmospheric GHG growing rates exhibited a sharp rebound during the early economic recovery after COVID-19, which would threaten climate goals without proactive measures. Despite this consistent global trend, various countries demonstrated differential relative changes in growth rates, representing their specific responses to the pandemic crisis. After systematic consideration of socio-economic and demographic factors and employment of optimal regression models, transportation and industry variables emerged as the strongest predictors for country-specific GHG accumulation sensitivities during lockdown and recovery phases, respectively. Addressing global health and climate change issues would necessitate sustainable government actions and economic decisions in anticipation of future pandemic-related events.

Cognition, economic decision-making, and physiological response to carbon dioxide

Cognition, economic decision-making, and physiological response to carbon dioxide