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Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.

An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.

This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.

The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.

This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.

This study aims to identify the key enablers for the adoption of Industry 4.0 (I4.0) in the automobile industry of India, which has been severely impacted by COVID-19. Adopting I4.0 will provide organizations greater flexibility and resilience during the COVID-19 pandemic.

Based on the literature review and experts’ opinions, 21 enablers were identified. Further, contextual relationships among the identified factors and a hierarchical digraph was developed by using the total interpretive structural modelling (TISM) technique. Finally, fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was conducted to classify the enablers into different categories based on their dependence and driving power.

The results indicate that top management support, clarity on government policy, strategic vision on I4.0 and development of new industrial policy are the most influential factors, with the highest driving power placed at the bottom of the TISM hierarchical model. Furthermore, agile workforce, smart HR practices and IT standardization and security are identified as linkage enablers with the most driving and dependency power.

The hierarchical TISM model and fuzzy MICMAC approach provide a comprehensive understanding of the I4.0 implementation process through a visual, logical structure to the managers. It will help the researchers and practitioners understand the contextual relationship among various enablers in fostering the I4.0 adoption process and digital reorganization in the automobile industry during the COVID-19 pandemic.

This study provides a holistic TISM hierarchical framework on I4.0 adoption that will elevate the next maturity level of innovation adoption and may act as a blueprint for automobile industries during the COVID-19 pandemic.

The challenge of predicting changes in aggregate income and stock prices is one that has occupied the research agendas of economists. This paper aims to use the consumption–income ratio and the dividend–price ratio to predict future income and stock prices.

To examine the stability of the consumption–income ratio and the dividend–price ratio, the authors run a two-variable, two-lag reduced-form VAR in the vein of Cochrane (1994), using a lag of each respective ratio as exogenous to the VAR. Additionally, the authors estimate an AR(4) model for income and prices.

The consumption–income ratio and the dividend–price ratio remain key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. The dividend–price ratio does not significantly predict future price growth. Consumption and dividend shocks have lasting impacts on income and prices.

The consumption–income ratio and the dividend–price ratio are still key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. However, the dividend–price ratio does not significantly predict future price growth, a change from previous research from the 1990s, despite the increasing complexity of stock markets. Consumption and dividend shocks have lasting impacts on income and prices and appear to be significant drivers in both the short- and long-run variance in income and prices.

This study aims to investigate the main drivers of private saving in Egypt (2005–2020).

It employs an autoregressive distributed lag (ARDL) approach for quarterly data on private saving, lagged private saving, real gross domestic product (GDP) growth, public saving, inflation, real interest rate, money supply, current account deficit and unemployment.

Private saving in Egypt displays persistency and public saving depresses private saving in the short run and long run. Real interest rate, inflation and unemployment have negative and statistically significant impacts on private saving in the short run and long run. The current account deficit displays a negative effect on private saving but is significant only in the short run. Other incorporated variables, like real GDP and money supply, are not statistically significant. This could be attributed to the high consumption rather than saving motive of the Egyptian population and their tendency to rely more on other informal saving channels.

Findings are of policy relevance as unleashing the determinants of private saving guides policymakers in formulating the appropriate sustainable development policies. It also assists in identifying the main obstacles hindering the promotion of private saving and hence major areas for policy intervention, like financial inclusion, poverty eradication, employment generation and structural reforms.

This study contributes to the literature: (1) it tackles private saving figure rather than aggregate saving figure that is covered by similar studies due to lack of consistent data, (2) given the relatively low quality, unavailability and inconsistency of data on private saving in developing countries, investigating the determinants of private saving should be carried out on an individual country basis which is done by this study, (3) this study fulfills the gap in literature related to the lack of up-to-date studies on private saving in Egypt and (4) it relies on quarterly data that could produce more reliable results.

The purpose of this study is to analyze the nonlinear relationship between electricity consumption (EC) and electricity transmission losses (ETL) in Ghana. Also, we examined how ETL moderate the effect of EC on economic growth in Ghana from 1980 to 2021.

We used timeseries data from 1980 to 2021 within an autoregressive distributed lag framework to analyze the links among ETL, EC and economic growth in Ghana.

Findings show the existence of an asymmetric long-run relationship between EC and ETL. Also, the negative effects of ETL on EC are bigger in the long run. In addition, ETL and EC combine to reduce economic growth, in the long run, providing evidence for the energy-led growth theory in Ghana. Population and inflation were also found to have a significant effect on economic growth in Ghana.

We examined the nonlinear nexus of EC and ETL, which extant studies have ignored in discussing the link between EC and economic growth. Again, we showed that ETL reduces EC causing a reduction in economic growth.

The purpose of this study is to estimate the housing wealth effect on non-durable consumption using data from the Spanish Survey of Household Finances (Encuesta Financiera de las Familias, SHF) for the period 2002–2017.

The authors aim at identifying the effect of anticipated and unanticipated housing wealth changes on consumption with the sample of homeowners, following Paiella and Pistaferri (2017).

Results of this study lead us to conclude that there exists a strong housing wealth effect on consumption for the Spanish households.

The authors provide evidence against the permanent income model. They also analyse how the results change with income expectations, age and the household indebtedness rate. Finally, they detect a strong excess sensitivity to income.

Throughout the 21st century, US households have experienced unprecedented levels of leverage. This dynamic has been exacerbated by income shortfalls during the COVID-19 crisis. Leveraging and deleveraging decisions affect household consumption. This study investigates the effect of the dynamics of household leverage and consumption on the stock market.

The authors explore the relation between household leverage and consumption in the context of the consumption capital asset pricing model (CCAPM). The authors test the model's implication that leverage has a negative risk premium by transforming the asset pricing restriction into an unconditional linear factor model and estimate the model using the general method of moments procedure. The authors run time-series regressions to estimate individual stocks' exposures to leverage, and cross-sectional regressions to investigate the leverage risk premium.

The authors show that shocks to household debt have strong and lasting effects on consumption growth. The authors extend the CCAPM to accommodate this effect and find, using various test assets, a negative risk premium associated with household deleveraging. Looking at individual stocks the authors show that the deleveraging risk premium is not explained by well-known risk factors.

This paper contributes to the literature on the role of leverage in economics and finance by establishing a relation between household leverage and spending decisions. The authors provide novel evidence that households' leveraging and deleveraging decisions can be a fundamental and influential force in determining asset prices. Further, this paper argues that household leverage might explain the small, persistent, and predictable component in consumption growth hypothesised in the long-run risk asset pricing literature.

The purpose of this study is to find out the effect of consumption tax on savings behavior especially on the people who are close to their retirement.

The authors analyze the response in spending and retirement saving using a difference-in-differences regression methodology. The authors use the year since the Public Provident Fund (PPF) enrollment date for each individual as a random assignment to identify the service tax policy's causal impact. Therefore, this variable is a continuous variable defined as an individual's age until the end of the restrictions when people can withdraw money from their retirement savings account PPF without any penalty. The treatment variable is the service tax shock (increase in service tax) that happened effective 1st April 2015.

The authors find a significant effect of a change in the service tax rate on individuals' spending and PPF saving behavior. On average, individuals lower their consumption by about 14% and increase their PPF savings by 16% in response to the increase in the service tax rate. The authors find substantial heterogeneity in effect across different types of individuals. The effect is more pronounced for people closer to their retirement and needy people (defined as individuals with low traditional savings account balances).

The authors studied the effect of consumption tax on one category of savings (PPF) only. There are other savings instruments available in India. The data for those were not available to us.

This paper not only throws light on the consumption and savings behaviour of the individuals, but will also help the policy maker for framing appropriate fiscal policy.

Using a unique and proprietary data from a large bank in India, the authors analyze the effect of a tax policy change on households' consumption and retirement savings behavior. The authors find that households reduce their consumption by 14% and increase their voluntary retirement savings (Public Provident Fund aka PPF) by 16% in response to an increase in the service tax policy. Individuals close to their retirement age (55 years of age and above) and without any withdrawal restrictions from their PPF account tend to reduce their expenditures more and save more. Individuals with financial constraints and withdrawal restrictions do not reduce their expenditures significantly. To the best of the authors’ knowledge no study was done on this.

The recent COVID-19 is forcing governments to implement policies on a large scale to counter its spread. A central issue in the economic debate is the effective quantification of the impact that the policies may implicitly have on the economy. This study quantifies the effects of lockdown in the United States.

The study uses a dynamic computable general equilibrium (DCGE) model calibrated on a social accounting matrix (SAM). The lockdown policy is applied on the supply side, by using a reduction in the production according to the closing time of each industry. The reduction in the demand is also applied, throughout the contraction of the household consumption that is diversified by the commodities. In order to analyse the pure effect of the lockdown policy, the interventions by the policy makers are not considered in this study.

The results show an important contraction of productivity in the food industry, the real estate activities, the constructions and the general services.

The contraction produces a fall of the GDP for the whole period analysed, traced by the investments, which includes repercussions on the whole productive system, employment and income of the institutional sectors.

Scholars have not agreed with each other on how people would behave after experiencing a catastrophic event. They could save more as a precautionary action for future difficulties or save less with a carpe diem attitude. This study aims to attempt to shed light on this debate with empirical observations on how the Covid-19 pandemic has affected household saving decisions.

The two waves of the survey data allowed us to investigate both instantaneous and ongoing effects of Covid-19 on household saving decisions. The instantaneous effect refers to the immediate impact of the crisis, while the ongoing effect refers to the lasting impact of the pandemic when economic recovery had started. The variation in the number of confirmed cases across cities during the two waves provides the source of power for identification. The authors extend their analyses of the impact of Covid-19 on the household saving decision by using ordinary least squares models. Due to the ordered nature of survey responses, the authors also rerun all baseline models using the ordered probit regression method.

This paper studied the impact of the Covid-19 pandemic on household saving decisions in China. This study found that households in the most affected cities would save more during the Covid-19 but tend to save less when the disaster started fading away. Combining findings in Kun et al. (2013) and Filipski et al. (2015), people do become more pessimistic during and after the Covid-19, possibly driving their observed precautionary and cape diem behaviors during the two points of time. Heterogeneity analysis shows that specific households would dramatically change their saving behavior. These observations might be useful for policymakers who concern the economic recovery after this pandemic disaster.

Understanding how the Covid-19 pandemic would affect household consumption vs saving decisions is important for the economic recovery after this disaster comes to an end. The analyses presented in this research could be useful for policymakers who concern appropriate policies aiming to boost consumption and economic activities after Covid.