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This research demonstrates the potential of machine learning for revealing the fiscal effects of the Recovery and Resilience Facility (RRF) in the European Union. It focuses on the use of a hybrid approach, based on traditional econometric methods combined with advanced data machine learning techniques. For this purpose, the following were applied: a panel data model with fixed effects, difference-in-differences analysis, correlation analysis, and machine learning, specifically, random forest regression, for the period of 2020–2024, including indicators from all 27 EU member states. The results of the conducted tests establish the effectiveness of the Recovery and Resilience Facility for fiscal stabilization, but also its high vulnerability to specific economic conditions in the individual member states. The complex relationships between the amount of funds received and the fiscal outcomes, which classical models fail to capture, were derived. A positive stabilizing effect on the indebtedness of countries with a clearly expressed imbalance in the public debt-to-gross domestic product ratio was demonstrated.

A central idea in marketing and diffusion research is that influentials—a minority of individuals who influence an exceptional number of their peers—are important to the formation of public opinion. Here we examine this idea, which we call the “influentials hypothesis,” using a series of computer simulations of interpersonal influence processes. Under most conditions that we consider, we find that large cascades of influence are driven not by influentials but by a critical mass of easily influenced individuals. Although our results do not exclude the possibility that influentials can be important, they suggest that the influentials hypothesis requires more careful specification and testing than it has received.

We estimate local fiscal multipliers and spillovers for the USA using a rich dataset based on the US Department of Defense contracts and a variety of outcome variables relating to income and employment. We find strong positive spillovers across locations and industries. Both backward linkages and general equilibrium effects (e.g., income multipliers) contribute to the positive spillovers. Geographical spillovers appear to dissipate fairly quickly with distance. Our evidence points to the relevance of Keynesian-type models that feature excess capacity.

This paper presents the bibliometrics of a Keynesian and neoclassical discussion about the multiplier-accelerator effect. Having its oldest roots in the 1930s, there was a special emphasis in the 1960s and 1970s on discussions regarding the dependence of current investment on economic growth (the accelerator effect). Through a bibliometric analysis, we also consider the Hicks-Samuelson contribution, also known as the multiplier-accelerator model. We identified, among other things, the most relevant authors on the topics, the economic areas that have been contributed to the most through keyword analysis, and the most notable contributions through citation analysis. We concluded that several areas in economics have taken advantage of the discussion around the multiplier-accelerator effect, especially the discussion on the business cycle, structural dynamics, and public finance.

As economic integration in the world continues to advance, energy consumption and carbon emissions in various regions have also become increasingly interdependent. In order to better understand the regional interaction of carbon emissions, this paper employs a three-region input-output approach to explore the linkage of CO 2 emissions among China, EU, and USA. Based on the world input-output table and environment account of World Input-Output Database (WIOD) in 2009, we calculate the carbon emission multiplier effect, spillover effect, and feedback effect and carry out the analysis from the regional and sectoral levels. The results show that, first of all, the carbon emission multiplier effect of each region is larger than the spillover and feedback effects. Second, EU and USA have higher carbon emission spillover effect on China than China’s carbon emission spillover effect on them. Finally, the carbon multiplier effects in different sectors have different characteristics.

Understanding the biogeochemical processes regulating carbon cycling is central to mitigating atmospheric CO₂ emissions. The role of living organisms has been accounted for, but the focus has traditionally been on contributions of plants and microbes. We develop the case that fully “animating” the carbon cycle requires broader consideration of the functional role of animals in mediating biogeochemical processes and quantification of their effects on carbon storage and exchange among terrestrial and aquatic reservoirs and the atmosphere. To encourage more hypothesis-driven experimental research that quantifies animal effects we discuss the mechanisms by which animals may affect carbon exchanges and storage within and among ecosystems and the atmosphere. We illustrate how those mechanisms lead to multiplier effects whose magnitudes may rival those of more traditional carbon storage and exchange rate estimates currently used in the carbon budget. Many animal species are already directly managed. Thus improved quantitative understanding of their influence on carbon budgets may create opportunity for management and policy to identify and implement new options for mitigating CO₂ release at regional scales.

PurposeThe tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about it is limited in Indonesia, especially at the provincial level and after the COVID-19 crisis. This study aims to estimate the TME in all provinces of Indonesia, test its differences in priority and non-priority areas before and after the COVID-19 crisis, analyze its spatial distribution and examine the determinant factor of TMEDesign/methodology/approachThis study applies an input-output model to measure the TME of all provinces in Indonesia, an independent sample t-test to examine the similarity of TME in priority and nonpriority areas, a paired sample t-test to examine the similarity of it before and after the COVID-19 crisis, and spatial analysis to check its spatial relationship.FindingsThe result shows that regional TME ranges from 1.25 to 2.05 in 2019, which changed slightly over time. The empirical result shows the TME difference before and after the COVID-19 crisis, and there is a spatial correlation in terms of TME with the hot spots are clustered in the eastern region of Indonesia, However, there was a slight change in the position of hot spots during the COVID-19 crisis. Moreover, the spatial model shows that value-added and employment in agriculture, manufacturing, trade and transportation affect the size of TME.Originality/valueThis study contributes to the academic literature by providing the first estimate of the TME at the provincial level in Indonesia, comparing the it in priority and non-priority areas before and after the COVID-19 crisis, and mapping its spatial distribution.

Background Compensation mechanisms and multiplier effects may affect productivity losses due to illness, disability, or premature death of individuals. Hence, they are important in estimating productivity losses and productivity costs in the context of economic evaluations of health interventions. This paper presents a systematic literature review of papers focusing on compensation mechanisms and multiplier effects, as well as whether and how they are included in health economic evaluations. Methods The systematic literature search was performed covering EconLit and PubMed. A data-extraction form was developed focusing on compensation mechanisms and multiplier effects. Results A total of 26 studies were included. Of these, 15 were empirical studies, three studies were methodological studies, two studies combined methodological research with empirical research, four were critical reviews, one study was a critical review combined with methodological research, and one study was a cost–benefit analysis. No uniform definition of compensation mechanisms and multiplier effects was identified. The terminology used to describe compensation mechanisms and multiplier effects varied as well. While the included studies suggest that both multipliers as well as compensation mechanisms substantially impact productivity cost estimates, the available evidence is scarce. Moreover, the generalizability as well as validity of assumptions underlying the calculations are unclear. Available measurement methods for compensation mechanisms and multiplier effects differ in approaches and are hardly validated. Conclusion While our review suggests that compensation mechanisms and multiplier effects may have a significant impact on productivity losses and costs, much remains unclear about their features, valid measurement, and correct valuation. This hampers their current inclusion in economic evaluation, and therefore, more research into both phenomena remains warranted.

As a far-reaching initiative in China’s air pollution control and energy transition efforts, the clean heating policy has sparked considerable debate in both academia and practice regarding its effectiveness in reducing carbon emissions. This study uses panel data from 15 prefecture-level cities in northern China from 2013 to 2023 and constructs a multi-period difference-in-differences model to empirically examine the impact of the clean heating policy on regional carbon emissions. The results are summarized as follows: (1) The policy effectively promotes the reduction of regional unit GDP and per capita carbon emission intensity in Northern China, but it has no evident effect on regional total carbon emissions. (2) The policy can exert the multiplier effect of the central government funds and structural effect to facilitate regional low-carbon transformation, but no significant Porter effect has been observed. (3) The carbon reduction effects exhibit significant regional heterogeneity. The policy has a more significant effect on carbon emissions of nonprovincial capital cities, coal-resource cities, and regions without coal power output, but it may significantly increase emissions in coal power-exporting regions. The clean heating policy should continue to be vigorously implemented, but its implementation strategy should be optimized by strengthening the transmission mechanism and addressing regional differences.

This research investigates the multiplier effect of ferry transportation development on the medium-term economy of Samosir Island from 2016 to 2022. The study will provide insights into the implications of ferry transportation for the economies of rural or remote regions like Samosir Island. The analysis was conducted by comparing the multiplier effects before and after the development, utilizing the input-output approach, which encompassed the output multiplier, the multiplier on gross value added (GVA), and the household income multiplier. The findings from the input-output analysis indicate that the average output multiplier for 37 industries on Samosir Island has declined by 0.84% annually, with the average output multiplier recorded at 1.80 in 2016, decreasing to 1.71 by 2022. This suggests that, overall, the advancement of ferry transportation in the medium term is comparatively ineffective in promoting economic growth in rural or remote regions such as Samosir Island. Conversely, the average GVA multiplier rose by 1.04% annually. Similarly, the household income multiplier index experienced an increase of 1.91% each year. This indicates that ferry transportation seems to exert a notable influence on both GVA and the household income multiplier, albeit the effect is comparatively modest.