Hasil untuk "Economics"

R. Lyons

Paweł Niszczota, Elia Antoniou

While delegating tasks to large language models (LLMs) can save people time, there is growing evidence that offloading tasks to such models produces social costs. We use behavior in two canonical economic games to study whether people have different expectations when decisions are made by LLMs acting on their behalf instead of themselves. More specifically, we study the social appropriateness of a spectrum of possible behaviors: when LLMs divide resources on our behalf (Dictator Game and Ultimatum Game) and when they monitor the fairness of splits of resources (Ultimatum Game). We use the Krupka-Weber norm elicitation task to detect shifts in social appropriateness ratings. Results of two pre-registered and incentivized experimental studies using representative samples from the UK and US (N = 2,658) show three key findings. First, people find that offers from machines - when no acceptance is necessary - are judged to be less appropriate than when they come from humans, although there is no shift in the modal response. Second - when acceptance is necessary - it is more appropriate for a person to reject offers from machines than from humans. Third, receiving a rejection of an offer from a machine is no less socially appropriate than receiving the same rejection from a human. Overall, these results suggest that people apply different norms for machines deciding on how to split resources but are not opposed to machines enforcing the norms. The findings are consistent with offers made by machines now being viewed as having both a cognitive and emotional component.

Islem Saadaoui

This study explores the economic value of Aleppo pine forests, a unique and threatened ecosystem in the border region of central Tunisia. These forests play a vital role in supporting small rural communities, but face increasing pressures and restrictions on their use. This research aims to assign a monetary value to forest conservation, considering the region's specific socio-economic context. Strategies for empowering local residents as key actors in developing sustainable cross-border initiatives are further investigated. Employing the contingent valuation method, a survey of 350 local residents and international users was conducted to assess their willigness to pay fo forest conservation efforts. Logistic regression analysis revealed that sociodemographic factors, such as monthly income and preferred payment method, significantly influence both and the likehood of participation. These findingd highlight the feasibility and importance of reconciling economic development with ecological sustainability in this critical region.

Jose Ignacio Hernandez, Niek Mouter, Sander van Cranenburgh

Random utility maximisation (RUM) models are one of the cornerstones of discrete choice modelling. However, specifying the utility function of RUM models is not straightforward and has a considerable impact on the resulting interpretable outcomes and welfare measures. In this paper, we propose a new discrete choice model based on artificial neural networks (ANNs) named "Alternative-Specific and Shared weights Neural Network (ASS-NN)", which provides a further balance between flexible utility approximation from the data and consistency with two assumptions: RUM theory and fungibility of money (i.e., "one euro is one euro"). Therefore, the ASS-NN can derive economically-consistent outcomes, such as marginal utilities or willingness to pay, without explicitly specifying the utility functional form. Using a Monte Carlo experiment and empirical data from the Swissmetro dataset, we show that ASS-NN outperforms (in terms of goodness of fit) conventional multinomial logit (MNL) models under different utility specifications. Furthermore, we show how the ASS-NN is used to derive marginal utilities and willingness to pay measures.

David Oluseun Olayungbo, Aziza Zhuparova, Mamdouh Abdulaziz Saleh Al-Faryan et al.

The relationship between oil price movements and stock markets during the COVID-19 pandemic and the geopolitical crisis like the ongoing Russian-Ukraine war is yet unexplored extensively. This study therefore examines the return-correlation effects of oil prices on stock markets and their spillover effects in oil-exporting and European countries using daily closing data. After estimating the GARCH process, we employ the static and dynamic Markov Switching model that allow the relationship between oil price and stock market to switch between two regimes coined the COVID-19 and the Russia-Ukraine war periods. The static model shows stock price returns to respond positively and significantly to oil price returns in Italy, Germany and the US during the Covid-19 period while the response is significantly positive only for US in the Russia-Ukraine war period. As regards the volatility spillover, significant spillover is found from stock to oil market for Nigeria, vice versa for Saudi Arabia and bi-directional volatility spillover found for the US, Italy and Germany during the COVID-19 period. The policy implication is that Nigeria and Saudi Arabia should prioritize financial policy and energy policy respectively while US, Italy and Germany should adopt policy coordination to stabilize oil-stock market volatility during low oil price period like the COVID-19 period.

Tosson Elalaily, Martin Berke, Ilari Lilja et al.

Abstract The observation of the gate-controlled supercurrent (GCS) effect in superconducting nanostructures increased the hopes for realizing a superconducting equivalent of semiconductor field-effect transistors. However, recent works attribute this effect to various leakage-based scenarios, giving rise to a debate on its origin. A proper understanding of the microscopic process underlying the GCS effect and the relevant time scales would be beneficial to evaluate the possible applications. In this work, we observed gate-induced two-level fluctuations between the superconducting state and normal state in Al/InAs nanowires (NWs). Noise correlation measurements show a strong correlation with leakage current fluctuations. The time-domain measurements show that these fluctuations have Poissonian statistics. Our detailed analysis of the leakage current measurements reveals that it is consistent with the stress-induced leakage current (SILC), in which inelastic tunneling with phonon generation is the predominant transport mechanism. Our findings shed light on the microscopic origin of the GCS effect and give deeper insight into the switching dynamics of the superconducting NW under the influence of the strong gate voltage.

Mustafa Torun, Cüneyt Kılıç, Ahmet Tayfur Akcan et al.

Ecological footprint calculations evaluate sustainability by examining natural resources. The ecological footprint obtained by calculating the number of natural resources per person provides information about the amount of waste produced as well as the natural resources consumed and examines the sustainability of living conditions in the world in this respect. The ecological footprint is one of the frequently encountered topics in the literature in terms of the analysis of environmental impacts. This study examines the factors influencing the Ecological Footprint in Turkey. Using annual data between 1980 and 2018, the relationship of renewable energy consumption, human capital and urbanization variables to the Ecological Footprint is examined. The Fractional Fourier Augmented Dickey Fuller Unit Root Test and the Fourier Autoregressive Distributed Lag Bound Test is used in the study. There is a statistically significant relationship between the renewable energy, human capital and urbanization variables and the Ecological Footprint. When the outputs obtained in the study are examined, it is seen that the variables affect the ecological footprint. The increased value of these variables can be used to explain why the Ecological Footprint increased. Increasing industrial activities due to globalization and technological developments, increasing vehicle traffic in cities due to population growth, unplanned urbanization and destruction of green areas due to the sheltering needs of the increasing population, inability of recycling facilities to adapt to the increasing population and unplanned waste management, etc. factors can increase the ecological footprint. However, as urbanization increases, if a correct plan is drawn by taking these factors into consideration, the negative correlation between the ecological footprint and urbanization can be explained. Within the determined plan; Wastewater management, protection of green areas, prevention of unplanned urbanization and efficient use of resources are explanatory at this point. The empirical findings have important policy implications. According to these policy implications, to offset the effects on the ecological footprint, educational activities to raise environmental awareness and adopt energy-efficient lifestyles should be given due importance, various incentives and supports should be implemented and a green-based lifestyle.

Mikkel Andreas Kvande, Sigurd Løite Jacobsen, Morten Goodwin et al.

Agricultural development is one of the most essential needs worldwide. In Norway, the primary foundation of grain production is based on geological and biological features. Existing research is limited to regional-scale yield predictions using artificial intelligence (AI) models, which provide a holistic overview of crop growth. In this paper, the authors propose detecting several field-scale crop types and use this analysis to predict yield production early in the growing season. In this study, the authors utilise a multi-temporal satellite image, meteorological, geographical, and grain production data corpus. The authors extract relevant vegetation indices from satellite images. Furthermore, the authors use field-area-specific features to build a field-based crop type classification model. The proposed model, consisting of a time-distributed network and a gated recurrent unit, can efficiently classify crop types with an accuracy of 70%. In addition, the authors justified that the attention-based multiple-instance learning models could learn semi-labelled agricultural data, and thus, allow realistic early in-season predictions for farmers.

Tahseen Anwer Arshi, Joseph Wallis

Objective: The objective of the article is to provide an entrepreneurial value-based perspective that can either drive or derail circular economy (CE) adoption and related strategies. The study argued that fundamental shifts toward CE adoption require a more profound value-based change. Research Design & Methods: Existing studies have analysed several self-transcending values in advancing circular economy (CE). However, an adequate investigation is yet to occur on self-advancing values that can obstruct CE adoption and practice in an entrepreneurial context. Embedded within a norm activation model (NAM) and informed by value-belief-norm theory (VBN), the study builds on cross-lagged data (n=477) to explain the clash between dominant self-advancing entrepreneurial values and CE strategies. Findings: The SEM-based machine-learning test results predicted that entrepreneurial hedonic and egoistic values complemented by hedonic and egoistic consumption reciprocally drive linearity rather than circularity within entrepreneurship. However, awareness of the consequences of adverse CE business models on society and the environment moderates the effect of self-enhancing values on CE strategies. Implications & Recommendations: Policy instruments and macro-level societal intervention in creating, enhancing, and balancing self-transcendence values with self-advancing values can improve CE adoption across the entrepreneurial architecture. Contribution & Value Added: The study is one of the first to demonstrate entrepreneurial value-oriented barriers to circularity, derailing CE diffusion to the broader entrepreneurial landscape. It suggests measures to enhance CE adoption among entrepreneurs.                    

Abedalghani Halahlah, Felix Abik, Heikki Suhonen et al.

Wood hemicelluloses have been used as a wall material for spray-dried microencapsulation of polyphenols. Nevertheless, their incomplete water solubility could negatively impact their encapsulation efficiency (EE) and the formation of a complete protective layer, which might be alleviated synergistically by combining them with carboxymethylcellulose (CMC). Here, we explored the effects of CMC addition (0.5–3.0 %, w/w of WM) on the capacity of galactoglucomannans (GGM) and glucuronoxylans (GX) to retain bioactive compounds of bilberry during spray drying; and its contribution to the formation of wall thickness. The results revealed that EE of GGM and GX increased by 4–8 % with the CMC addition at 0.5 %, but significantly declined at higher CMC concentrations. Adding CMC improved the microcapsules' antioxidant activities, surface smoothness, and solubility, but had no effect on their particle size, thermal properties, amorphous structure, or moisture content. The majority of the GGM and GX microcapsules had a hollow internal structure surrounded by continuous wall matrix with a thickness of about 2.3–2.6 μm, which increased to 3.1–3.5 μm with the addition of 0.5 % CMC. Therefore, using CMC at an optimized proportion as a co-encapsulant improved wood hemicelluloses' ability to protect bioactive compounds during spray drying and enhanced microcapsule wall formation.

Sita Acharya, Ujjal Tiwari, Rishi Ram Kattel et al.

Dairy production is one of the risky businesses, which seeks effective risk management strategies. Adoption of a livestock insurance scheme is one of the most effective risk management strategies for dairy entrepreneurs. However, in Nepal, insurance coverage is very low in the dairy sub-sector. The study aimed to assess the dairy farmer’s willingness to pay for the livestock insurance scheme. The study was carried out in Kavrepalanchowk district of Nepal in 2022. The simple random sampling technique was used to select 146 dairy farmers. Double Bounded Dichotomous Choice Contingent Valuation technique was used to estimate Willingness to Pay (WTP). About 93% of the respondent farmers were the member of cooperatives which was the major source of information flow for livestock insurance scheme to them. The result revealed that number of cattle reared, awareness about livestock insurance scheme, and experience of livestock loss have significant positive influences in the decision regarding the adoption of livestock insurance. Farmers are willing to pay more than the current premium rate for livestock insurance. Hence, only increasing the subsidy might not be the solution in expanding the livestock insurance adoption rate. Rather, alternative approach like mobilizing institutions (cooperative) in expanding the insurance scheme is required.

Florian Gunsilius, David Van Dijcke

Sharp, multidimensional changepoints-abrupt shifts in a regression surface whose locations and magnitudes are unknown-arise in settings as varied as gene-expression profiling, financial covariance breaks, climate-regime detection, and urban socioeconomic mapping. Despite their prevalence, there are no current approaches that jointly estimate the location and size of the discontinuity set in a one-shot approach with statistical guarantees. We therefore introduce Free Discontinuity Regression (FDR), a fully nonparametric estimator that simultaneously (i) smooths a regression surface, (ii) segments it into contiguous regions, and (iii) provably recovers the precise locations and sizes of its jumps. By extending a convex relaxation of the Mumford-Shah functional to random spatial sampling and correlated noise, FDR overcomes the fixed-grid and i.i.d. noise assumptions of classical image-segmentation approaches, thus enabling its application to real-world data of any dimension. This yields the first identification and uniform consistency results for multivariate jump surfaces: under mild SBV regularity, the estimated function, its discontinuity set, and all jump sizes converge to their true population counterparts. Hyperparameters are selected automatically from the data using Stein's Unbiased Risk Estimate, and large-scale simulations up to three dimensions validate the theoretical results and demonstrate good finite-sample performance. Applying FDR to an internet shutdown in India reveals a 25-35% reduction in economic activity around the estimated shutdown boundaries-much larger than previous estimates. By unifying smoothing, segmentation, and effect-size recovery in a general statistical setting, FDR turns free-discontinuity ideas into a practical tool with formal guarantees for modern multivariate data.

Habtamu Hawaz, Mestawet Taye, Diriba Muleta

Food safety remains the main health concern in the developing countries. Thus, the major purpose of the present study was to characterize and determine antibiotic susceptibility patterns of Listeria monocytogenes from raw milk samples collected from southern Ethiopia. Two hundred and forty raw cow milk samples were collected from dairy farms and smallholder dairy producers using a simple random sampling technique and analyzed by cultural and multiplex PCR methods. The antimicrobial susceptibility profile of L. monocytogenes was evaluated using the standard disk diffusion method. Over 28% of the samples were found positive for Listeria spp., of which 17 (7.08%) isolates were identified as L. monocytogenes after morphological and biochemical confirmation. The prevalence of L. monocytogenes was 6.02% in Hawassa city, 5.56% in Dale district, and 9.41% in Arsi Negele district. L. monocytogenes was higher in the wet season (9.32%) than in the dry season (4.92%). The gene for Listeria specific 16S rRNA was detected in all the 17 examined isolates, while hlyA and iapA were only found in 11 of them. Furthermore, no isolate was identified to have the prfA, actA, or plcA genes. Antimicrobial resistance profiling revealed that all the L. monocytogenes isolates were resistant to nalidixic acid (100%), followed by erythromycin (88.24%). However, all the L. monocytogenes isolates were sensitive to vancomycin, gentamicin, and sulfamethoxazole. Raw cow milk is a potential source of L. monocytogenes and it poses a threat to human and animal health. Therefore, it is crucial that dairy producers and vendors of raw milk in the study areas should take considerable precautions to prevent Listeria species from contaminating raw fresh milk.

Lan Thi Phuong Nguyen, Wisdom Kalabeke, Saravanan Muthaiyah et al.

Background - Since 2016, the Securities Commission (SC) in Malaysia has given licenses to only eleven P2P lending platforms. Such lending platforms are expected to disrupt the lending services of traditional lenders in the coming years. However, being still in their infant stages, it is essential to know the extent to which such platforms are made known to potential investors out there. This study aims to examine the awareness level of the eleven P2P lending platforms among Malaysian adults. The study also explores if past investment experience and financial knowledge would influence such awareness from Malaysian adults.  Methods - A sample of 335 Malaysian individuals was used for this study. An online questionnaire was designed with three main parts: demographic, financial literacy, and P2P lending awareness. Using IBM SPSS Statistics 26, frequency, descriptive, normality, Pearson coefficients and multiple regression analyses were carried out.  Results - Although seven out of ten respondents have good knowledge in three areas of finance: compounding rate, inflation and diversification, only 14.33% had a good and excellent awareness level of P2P lending. Thus, one would expect lesser awareness about P2P lending among Malaysian adults whose financial literacy is poor or zero. Test results from multiple regression analysis suggest that past lending experiences positively affect the awareness of P2P lending in Malaysia, but not the financial literacy. Conclusions - the awareness about P2P lending among Malaysian adults is too low, despite their high level of education and financial literacy. No investing experience and not knowing any existing P2P lending in the country may be the reason for this low awareness. Therefore, for P2P lending to thrive in Malaysia, the eleven P2P lending platforms need to be promoted aggressively in various social media outlets.

Yuhki Hosoya

We present a necessary and sufficient condition for Alt's system to be represented by a continuous utility function. Moreover, we present a necessary and sufficient condition for this utility function to be concave. The latter condition can be seen as an extension of Gossen's first law, and thus has an economic interpretation. Together with the above results, we provide a necessary and sufficient condition for Alt's utility to be continuously differentiable.

T. Brown, L. Reichenberg

Although recent studies have shown that electricity systems with shares of wind and solar above 80% can be affordable, economists have raised concerns about market integration. Correlated generation from variable renewable sources depresses market prices, which can cause wind and solar to cannibalise their own revenues and prevent them from covering their costs from the market. This cannibalisation appears to set limits on the integration of wind and solar, and thus to contradict studies that show that high shares are cost effective. Here we show from theory and with simulation examples how market incentives interact with prices, revenue and costs for renewable electricity systems. The decline in average revenue seen in some recent literature is due to an implicit policy assumption that technologies are forced into the system, whether it be with subsidies or quotas. This decline is mathematically guaranteed regardless of whether the subsidised technology is variable or not. If instead the driving policy is a carbon dioxide cap or tax, wind and solar shares can rise without cannibalising their own market revenue, even at penetrations of wind and solar above 80%. The strong dependence of market value on the policy regime means that market value needs to be used with caution as a measure of market integration. Declining market value is not necessarily a sign of integration problems, but rather a result of policy choices.

Dharani Dhar Burra, Sriganesh Lokanathan

Big data sources provide a significant opportunity for governments and development stakeholders to sense and identify in near real time, economic impacts of shocks on populations at high spatial and temporal resolutions. In this study, we assess the potential of transaction and location based measures obtained from automatic teller machine (ATM) terminals, belonging to a major private sector bank in Indonesia, to sense in near real time, the impacts of shocks across income groups. For each customer and separately for years 2014 and 2015, we model the relationship between aggregate measures of cash withdrawals for each year, total inter-terminal distance traversed by the customer for the specific year and reported customer income group. Results reveal that the model was able to predict the corresponding income groups with 80% accuracy, with high precision and recall values in comparison to the baseline model, across both the years. Shapley values suggest that the total inter-terminal distance traversed by a customer in each year differed significantly between customer income groups. Kruskal-Wallis test further showed that customers in the lower-middle class income group, have significantly high median values of inter-terminal distances traversed (7.21 Kms for 2014 and 2015) in comparison to high (2.55 Kms and 0.66 Kms for years 2014 and 2015), and low (6.47 Kms for 2014 and 2015) income groups. Although no major shocks were noted in 2014 and 2015, our results show that lower-middle class income group customers, exhibit relatively high mobility in comparison to customers in low and high income groups. Additional work is needed to leverage the sensing capabilities of this data to provide insights on, who, where and by how much is the population impacted by a shock to facilitate targeted responses.

Dimitri J. Papageorgiou, Francisco Trespalacios, Stuart Harwood

Discretely-constrained Nash-Cournot games have attracted attention as they arise in various competitive energy production settings in which players must make one or more discrete decisions. Gabriel et al. ["Solving discretely-constrained Nash-Cournot games with an application to power markets." Networks and Spatial Economics 13(3), 2013] claim that the set of equilibria to a discretely-constrained Nash-Cournot game coincides with the set of solutions to a corresponding discretely-constrained mixed complementarity problem. We show that this claim is false.

Sonja Radosavljevic, L. Jamila Haider, Steven J. Lade et al.

Most of the world poorest people come from rural areas and depend on their local ecosystems for food production. Recent research has highlighted the importance of self-reinforcing dynamics between low soil quality and persistent poverty but little is known on how they affect poverty alleviation. We investigate how the intertwined dynamics of household assets, nutrients (especially phosphorus), water and soil quality influence food production and determine the conditions for escape from poverty for the rural poor. We have developed a suite of dynamic, multidimensional poverty trap models of households that combine economic aspects of growth with ecological dynamics of soil quality, water and nutrient flows to analyze the effectiveness of common poverty alleviation strategies such as intensification through agrochemical inputs, diversification of energy sources and conservation tillage. Our results show that (i) agrochemical inputs can reinforce poverty by degrading soil quality, (ii) diversification of household energy sources can create possibilities for effective application of other strategies, and (iii) sequencing of interventions can improve effectiveness of conservation tillage. Our model-based approach demonstrates the interdependence of economic and ecological dynamics which preclude blanket solution for poverty alleviation. Stylized models as developed here can be used for testing effectiveness of different strategies given biophysical and economic settings in the target region.

A. V. Dyrrdal, K. Isaksen, J. K. S. Jacobsen et al.

<p>A number of seaside communities in Troms, northern Norway, are vulnerable to sudden weather-induced access disruptions due to high-impact weather and dependency on one or few roads. In this paper we study changes in winter weather known to potentially cause access disruptions in Troms, for the present climate (1958–2017) and two future periods (2041–2070; 2071–2100). We focus on climate indices associated with snow avalanches and weather that may lead to for example slippery road conditions. In two focus areas, the most important results show larger snow amounts now compared to 50 years ago, and heavy snowfall has become more intense and frequent. This trend is expected to turn in the future, particularly at low elevations where snow cover during winter might become a rarity by 2100. Strong snow drift, due to a combination of snowfall and wind speed, has slightly increased in the two focus areas, but a strong decrease is expected in the future due to less snow. Events of heavy rain during winter are rather infrequent in the present winter climate of Troms, but we show that these events are likely to occur much more often in all regions in the future.</p>