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• A systematic literature review of 'leading indicators (LIs) development and identification' is conducted to develop a novel analytical framework for identifying LIs. • New LIs are identified from case study incident reports and normative documents using the framework. • The study combines predominantly objectivity philosophy reflected from a critical realism position and subjectivity expressed by an interpretivist philosophical stance. • This novel research constitutes the first attempt to identify and validate LIs via the use of an analytical framework and real-life case study data. Introduction: This research implements the steps of developing or identifying leading indicators (LIs) delineated in a previously published conceptual model to test its practicality on case study data. Concomitant objectives are (a) to systematically review extant literature of 'LIs development and identification' to develop an analytical framework for identifying LIs; and to identify LIs from case study incident reports and normative documents using the framework. Method: To empirically validate the conceptual model, a two staged data analysis process was adopted: (1) a theoretical work stage, where pertinent literature was studied through systematic literature review using Scopus and Web of Science databases and a detailed framework analysis; and (2) practical work stage, where an inductively developed analytical framework and insights gained from the theoretical work stage were applied to real-life case study data and their apposite normative documents. Random sampling was used to select 12 different case studies of accidents from a private database of 97 case studies. In total, 2,423 LIs were identified from extant literature and through framework analysis using the bespoke analytical framework generated, a total of 484 LIs were identified from a combination of selected case study materials and their relevant normative documents. All these 484 newly developed LIs were contrasted with a compilation of the previously published 2,423 LIs in the literature. Results: Consequently, a total of 232 LIs out of 484 were recognized as entirely new and novel. These LIs were then thematically grouped into 19 clusters for brevity. A novel analytical framework for identifying new LIs was inductively developed. The framework enables identification of LIs from a qualitative dataset and classify them into eight types of LIs. Practical Applications: This novel research constitutes the first attempt to identify and validate LIs via the use of an analytical framework and real-life case study data. [ABSTRACT FROM AUTHOR]

This paper develops new forecasting methods for an old and ongoing problem by employing 13 machine learning algorithms to study 147 years of systemic financial crises across 17 countries. Findings suggest that fixed capital formation is the most important variable. GDP per capita and consumer inflation have increased in prominence whereas debt‐to‐GDP, stock market, and consumption were dominant at the turn of the 20th century. A lag structure and rolling window both improve on optimized contemporaneous and individual country formats. Through a lag structure, banking sector predictors on average describe 28% of the variation in crisis prevalence, the real sector 64%, and the external sector 8%. Nearly half of all algorithms reach peak performance through a lag structure. As measured through AUC, F1 and Brier scores, top‐performing machine learning methods consistently produce high accuracy rates, with both random forests and gradient boosting in front with 77% correct forecasts, and consistently outperform traditional regression algorithms. Learning from other countries improves predictive strength, and non‐linear models generally deliver higher accuracy rates than linear models. Algorithms retaining all variables perform better than those minimizing the influence of variables. [ABSTRACT FROM AUTHOR]

Crude oil prices have become volatile as geopolitical conflicts that occur in oil-exporting countries make the Indonesian government have to increase fuel prices and plan to provide cash transfers as compensation. This study analyzes the impact of fuel price rising and the direct cash assistance compensation program/Bantuan Langsung Tunai (BLT BBM) on poverty using micro data simulation. Poverty simulation is generated by modifying the poverty line (GK) and expenditure by utilizing leading indicators such as commodities inflation, household consumption expenditure growth, and population growth. The simulation results show that the presence of compensation can reduce the poverty rate compared to the absence of compensation. Without compensation, the poverty rate in September 2022 is predicted to be 10.36 percent (0.82 percent higher than in March 2022), while under the government's scheme where compensation is given to households receiving the Family Hope Program (PKH)/Non-Cash Food Assistance (BPNT), poverty is predicted to be 9.56 percent (0.02 percent higher than in March 2022). Exclusion error in the program of 55.35 percent in the lowest decile is an obstacle in poverty alleviation. Improvement and updating of the database of program recipients is needed so that the program can be more targeted to reach the poor.

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Agriculture accounts for over half of Australia's land use therefore the farmers managing this land need to be safe at work. This paper offers measuring farm safety culture as a way to overcome the stagnation in the trend of fatal farm injury burden. To work towards achieving a way to measure farm safety culture, this research reviewed the leading indicators of safety. Following PRISMA guidelines, we screened for globally significant literature in the field of methodologies to measure safety climate and safety culture. We performed a rapid review of literature resulting in nineteen articles that add to our understanding of how to create and re-adapt existing questionnaires and produce validated metrics. The leading indicators were grouped into 8 dimensions where we found a translational disconnect between safety for organisational structures and safety for family farm businesses. This paper provides recommendations for government, safety regulators, policymakers, and industry of the leading indicators that may be applicable for measuring farm safety culture for Australian farmers.

This article is devoted to checking the possibility of using business survey data surveys to nowcasting of Russian manufacturing output: analysis of business cycles and short-term forecasting (1 month ahead). The study uses data from business surveys of the Federal State Statistics Service, the Russian Union of Industrialists and Entrepreneurs and S&P Global. The results of these surveys are published promptly (20 days or more ahead of the release of official statistics) and are freely available. The study shows that the indicators of the Federal State Statistics Service (economic situation, number of employees, business confidence index, output, demand, export) allow to increase the accuracy of forecasts by 21–39 % and have a high correlation with business cycles of the manufacturing industry. In general, the use of most business survey indicators improves short-term forecasts of manufacturing output, more than half of the indicators have a correlation coefficient greater than 0,8 with the business cycles of this sector of the economy.

For the greater part of the twentieth century, business forecasting has been a struggle against time. Forecasters needed reliable, comprehensive, and up-to-date data that would allow them to extrapolate trends for the months ahead. The collection and analysis of economic data, however, were laborious processes, and became even more so as the mass of statistical data grew. Focusing on the history of business cycle research conducted at the National Bureau of Economic Research (NBER), the article investigates how the introduction of large-scale computers in the early 1950s altered the field of business cycle research and forecasting. While the advent of large-scale electronic computers promised to solve the problem of timeliness, making it, for the first time, possible to process economic data within minutes, electronic computers brought about new problems of time. The complex and highly time-consuming task of programming and the scarcity of machine time pushed business cycle researchers to aim for ever greater standardization in the use of electronic computers. While this did indeed allow for an unprecedented acceleration in data processing, it also limited the possibilities of exercising trained judgment. As this article argues, this ultimately challenged the researchers’ concepts of economic change and called into question the very project of forecasting itself.

The impact of climate change and the dynamic nature of environmental conditions underscore the critical need to enhance resilience of systems and process safety considerations. The efficacy of such efforts primarily depends on how resilience is measured. Among the myriad efforts to quantify resilience, composite indicators have emerged as promising tools. However, these indicators typically employ statistical methods to derive weights for aggregation and rely on statistical homogeneity among indicators which can limit their scope and fidelity. In this study, we propose an alternative novel resilience index derived from a system's structure and the essential conditions for safe operation during and after disruptions. The proposed measure reflects the systems' ability to resist and respond to failures by addressing possibilities of impact propagation to other infrastructure systems. Moreover, it eliminates the need for weights and allows for compensability among its leading indicators. Using a case study based on the on-site wastewater treatment and disposal systems (OSTDS) in South Florida that faces increasing risks due to rising sea levels, we investigate the validity of the proposed index and perform a comparative analysis with statistically-driven measures. Furthermore, we demonstrate the adaptation of the proposed index for decision making within a generalized optimization framework. [ABSTRACT FROM AUTHOR]

• Hazard control and prevention in safety management aim to eliminate or disable hazards. • Presented the hazard control and prevention practices in a coal-fired power plant as a case study. • Empirical validated the influence of hazard control and prevention practises on safety outcomes through safety compliance. • Prove that driven leading indicators affect observant leading indicators before affecting safety outcomes. Introduction: The electrical utility industry, which plays a vital role in sustaining other sectors, contributes to high occupational accident rates in the utility industries. The high accident rate shows that there has been insufficient effort made to control unsafe actions and conditions in the workplace. This study aims to examine the influence of hazard control and prevention as leading indicators of safety behaviors and outcomes in coal-fired power plants in Malaysia. Methods : This quantitative research was conducted by distributing survey questionnaires randomly to five coal-fired power plants in Peninsular Malaysia. A total of 340 respondents were involved in this research. Partial least squares structural equation modeling (PLS-SEM) analysis was performed using SmartPLS to validate and examine the relationship of the proposed model. Results : The results validate the construct of hazard control and prevention consisting of planning, action, managing, and verifying, while the safety outcomes construct consists of occupational accidents, fatal accidents, near misses, and lost time injuries. The results indicate that hazard control and prevention significantly relate to safety compliance, safety participation, safety motivation, and safety knowledge. Moreover, safety outcomes were influenced negatively by hazard control and prevention through safety compliance. Conclusion : The model provides a better understanding of the influence of hazard control and prevention on safety behavior and outcomes. Practical Applications: The model can be used as guidance for practitioners and researchers in planning and implementing hazard control and prevention to improve health and safety in the workplace. [ABSTRACT FROM AUTHOR]

There has been a growing interest in research on how to define and build indicators of resilience to address challenges associated with sea-level rise. Most of the proposed methods rely on lagging indicators constructed based on the historical performance of an infrastructure sub-system. These indicators are traditionally utilized to build curves that describe the past response of the sub-system to stressors; these curves are then used to predict the future resilience of the sub-system to hypothesized events. However, there is now a growing concern that this approach cannot provide the best insights for adaptive decision-making across the broader context of multiple sub-systems and stakeholders. As an alternative, leading indicators that are built on the structural characteristics that embody system resilience have been gaining in popularity. This structure-based approach can reveal problems and gaps in resilience planning and shed light on the effectiveness of potential adaptation activities. Here, we survey the relevant literature for these leading indicators within the context of sea-level rise and then synthesize the gained insights into a broader examination of the current research challenges. We propose research directions on leveraging leading indicators as effective instruments for incorporating resilience into integrated decision-making on the adaptation of infrastructure systems. [ABSTRACT FROM AUTHOR]

This paper applies a Qual VAR approach to generate a continuous banking crisis indicator from an underlying latent variable using a Markov Chain Monte Carlo algorithm. Four decades of banking crises are assessed by accounting for the evolutionary nature of precursors, as measured through periodic, regional, and developmental effects using a representative sample of countries. Aggregate results from forecast error variance decomposition show that banking sector variables explain nearly half of total variation, external sector a third and real sector a fifth. Findings suggest that recursive out-of-sample forecasts up to 12-months preceding a banking crisis render vital early warning signals, and as based on quarterly data, support expeditious response times. In out-of-sample forecasting, the Qual VAR outperforms a probit model. Improved forecasting performance may assist banking oversight departments and support remediation efforts of policymakers to adequately and timeously respond to banking crises.

• A systematic literature review is undertaken to identify the main constructs of leading indicators (both passive leading indicators and active leading indicators). • Interpretivism and critical realism philosophical stances are utilised together with inductive reasoning to generate new perspectives and insight in leading indicators. • A culmination of research conducted leads to the generation of a guidance note for leading indicator implementation (as a conceptual model). • Notably, the model proposes continuous learning through a perpetual loop of development and application of leading indicators in the workplace. Introduction: Leading indicators represent an invaluable tool that offer organizations the capability to: track health and safety performance, not just failures and accidents; measure effectiveness of safety efforts adopted; and focus on undesired precursors, rather than undesired occurred events. Despite these palpable advantages associated with their adoption, leading indicator's definition, application, and function are mostly ambiguous and inconsistent within literature. Therefore, this study systematically reviews pertinent literature to identify the constructs of leading indicators and generates guidance for leading indicator implementation (as a conceptual model). Method: The overarching epistemological design adopted interpretivism and critical realism philosophical stances together with inductive reasoning to analyze 80 articles retrieved from the Scopus database, plus 13 more publications supplemented by the snowballing technique. Analysis of the safety discourse within literature (as secondary data) was undertaken in two stages, namely: (1) a cross-componential analysis identified the main features of leading indicators in comparison to lagging indicators; and (2) content analysis revealed prominent constructs of leading indicators. Results and conclusion: Analysis results identify that the definition, types, and development methods represent the main constructs for understanding the concept of leading indicators. The study identifies that ambiguity around the definition and function of leading indicators is due to the lack of differentiation of its types, namely passive leading indicators and active leading indicators. Practical application: As a practical contribution, the conceptual model, which introduces continuous learning through a perpetual loop of development and application of leading indicators, will help adopters create a knowledge repository of leading indicators and to continuously learn and improve their safety and safety performance. Specifically, the work clarifies their difference in terms of the timeframe passive leading indicators and active leading indicators take to measure different safety aspects, the functions they serve, the target they measure and their stage of development. [ABSTRACT FROM AUTHOR]

In today's society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people's favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly. [ABSTRACT FROM AUTHOR]

The paper proposes a chronology for the Egyptian economy by detecting phases of expansions and recessions during the period (2002–2019). It examines the cyclical behavior of variables that are considered potentially useful in measuring or predicting aggregate economic activity. To do so, we combine the National Bureau of Economic Research (NBER) approach together with time series analysis techniques to select the variables best suitable to play the role of coincident and leading indicators. This methodology is found to be the most appropriate when there is no well-established reference chronology as a benchmark for the cyclical analysis, which is the case of Egypt. As a result, two composite indexes are constructed: 1) the composite index of coincident economic indicators (CEI), which can be considered an adequate measure for the Egyptian business cycle; 2) the composite index of leading economic indicators (LEI) that shows good performance in anticipating aggregate economic activity in Egypt. Moreover, the empirical results indicate that total employment, consumption, and investment move coincidently with the reference cycle, while exchange rate and interest rate variables lead the reference cycle and have strong predictive power for economic activity in Egypt. [ABSTRACT FROM AUTHOR]

The article aims to outline approaches to the study of a harmonized model of forecasting, strategic planning and expectations of business communities. In the Introduction, the idea of transformation of planning to the dominance of the network approach instead of the dominance of centralization is substantiated. Materials and methods. The key aspects of the expediency of applying the experience of centralized planning under state ownership, providing information resources and applying modern high-tech methods for collecting, processing and analytically presenting materials to improve the effectiveness of preventive decisions, introducing a backbone component of leading indicators into network planning are considered. Results and discussion. The article includes innovative proposals for using the technology of leading indicators and increasing the role of horizontal links in planning with their help. It is concluded that it is necessary to shift the semantic emphasis in discussions on the development of planning to the harmonization of planning, forecasting and development parameters expected by business communities.

• Identification of important indicators for process safety. • A comparative study of process safety indicators in developing and developed countries. • Measuring the relative importance of the indicators using the Fuzzy Best Worst Method. Introduction: The importance of safety in high-risk industries such as oil and gas facilities has been reported previously. Process safety performance indicators can provide insight into improving the safety of process industries. This paper aims to rank the process safety indicators (metrics) by Fuzzy Best-Worst Method (FBWM) using the data gathered through a survey. Method: The study uses a structured approach considering the UK Health and Safety Executive (HSE), the Center for Chemical Process Safety (CCPS), and the IOGP (International Association of Oil and Gas Producers) recommendations and guidelines to generate an aggregate set of indicators. It calculates the level of importance of each indicator based on the opinions of experts from Iran and some Western countries. Results: The findings of the study demonstrate that some lagging indicators such as the number of times processes do not proceed as planned due to insufficient staff competence and the number of unexpected disruptions of the process due to failure in instrumentation and alarms are important in process industries in both Iran and Western countries. Western experts identified process safety incident severity rate as an important lagging indicator, whereas Iranian experts considered this as relatively unimportant. In addition, leading indicators such as sufficient process safety training and competency, the desired function of instrumentation and alarms, and proper management of fatigue risk play an important role in enhancing the safety performance of process industries. Experts in Iran viewed permit to work as an important leading indicator, while experts in the West focused on fatigue risk management. Practical Applications: The methodology used in the current study gives a good view to managers and safety professionals in regard to the most important indicators of process safety and allows them to focus more on important process safety indicators. [ABSTRACT FROM AUTHOR]

Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospitalisations across SARS-CoV-2 waves in England. This analysis includes an evaluation of internet search volumes from Google Trends, NHS triage calls and online queries, the NHS COVID-19 app, lateral flow devices (LFDs), and the ZOE app. Data sources were analysed for their feasibility as leading indicators using Granger causality, cross-correlation, and dynamic time warping at fine spatial scales. Google Trends and NHS triages consistently temporally led admissions in most locations, with lead times ranging from 5 to 20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 app, and LFD testing, which diminished with spatial resolution, showing cross-correlation of leads between –7 and 7 days. The results indicate that novel surveillance sources can be used effectively to understand the expected healthcare burden within hospital administrative areas though the temporal and spatial heterogeneity of these relationships is a key determinant of their operational public health utility. [ABSTRACT FROM AUTHOR]

• Project management aspects positively impact safety management performance in the construction industry. • Leadership and the functioning of teams are seen as key organisational aspects that impact safety management. • Safety management should be integrated in all management activities, not operating as a disconnected sub-system. • The relationships between project managment, safety management and safety performance are explored. • Project performance data and incident data from contruction projects across the industry are used in the analysis. The significance of general project management performance for safety performance in construction projects is studied in a sample of Norwegian construction projects. The data used are from a benchmarking tool for performance assessment and incident data from construction projects across the industry. Statistical analysis through independent-sample Mann–Whitney U -tests is performed to test for and identify significant relationships between project management, safety management and safety performance. The analysis confirms that good overall management of a construction project positively impacts safety management. The results advocate for applying safety management as an integral aspect of all management activities in a project rather than as a disconnected sub-system. The work is novel by empirically demonstrating the impact of project management aspects on safety management in construction projects. [ABSTRACT FROM AUTHOR]

• A novel mixed-frequency spillover method of Cotter et al. (2023) is adopted. • The spillover effects between monthly leading indicators and weekly financial market prices in China is studied. • Some monthly leading indicators, i.e., CMI and CPMI, can lead the monthly performance of financial markets. • All the monthly leading indicators fail to lead the weekly performance of financial markets. • The mixed-frequency approach yields much higher spillover measures than the common-frequency method. • Stock and commodity markets are spillover transmitters to other financial markets in both CF and MF conditions. • CMI and CPMI are more effective leading indicators than the CCCI. Leading indicators, usually measured at low frequency (e.g., monthly or quarterly), are widely used as pioneering indices to foresee the future perspective of macro economy and financial markets. However, it is not clear whether these low-frequency leading indicators really lead the actual performance of financial markets, which are usually measured at high frequency (e.g., daily or weekly). This paper uses a novel mixed-frequency spillover method of to examine the spillover effects between monthly leading indicators and weekly financial market prices in China, aiming to assess the pioneering function of these leading indicators. The empirical results suggest that, first, some monthly leading indicators can lead the monthly performance of financial markets, but all of them fail to lead the weekly performance of financial markets. Second, the mixed-frequency approach yields much higher spillover measures than the common-frequency method of Diebold and Yilmaz (2012). Third, China's stock and commodity markets are spillover transmitters to other financial markets in both common and mixed frequency conditions. Finally, the China Monetary Index (CMI) and the Manufacturing Purchasing Managers' Index (CPMI) are more effective leading indicators than the China Consumer Confidence Index (CCCI). These findings help investors and policymakers to adjust risk management strategies in a timely manner and facilitate more accurate financial forecasting. [ABSTRACT FROM AUTHOR]

Water cycle has been intensified by global warming, leading to frequent extreme climate events. Drought is an extreme climate phenomenon. Runoff decrease and human water demand increase aggravate the water shortage of regional ecosystems, affecting regional water and land ecosystems and causing ecological drought, river cutoff and water pollution. Finally, the reverse succession and the imbalance of regional ecological structures take place. The clarification of the concept of ecological drought for effective evaluation of regional ecological drought degree has become an urgent important scientific issue to be resolved. Therefore, in this paper, the typical region of the Yellow River estuary was studied for the analysis of characteristics of regional ecological changes and the definition of the concept and connotation of ecological drought. Based on the representative monitoring and early warning indices to ecological drought, the evaluation method and the classification standard of regional ecological drought were proposed. The regional ecological drought includes four levels: I (Severe), II (General), III (Weak) and IV (None). The indicator thresholds of river runoff, biodiversity and vegetation coverage on different ecological drought levels were quantified. The research results can be technically beneficial for the improvement of global ecological drought emergent support capacity and reducing loss due to drought. HIGHLIGHTS The concept of ecological drought in areas with important ecological functions is discussed.; A model with certain applicability for ecological drought assessment is proposed.; The regional ecological drought can be divided into four levels.; The thresholds of leading indicators to different ecological drought degrees are quantified.;

The construction industry is one of the hazardous industries all over the world. There have been continuous safety efforts on post-facto safety investigations and related corrective actions. However, to prevent future accidents, post-facto reports may not be able to provide comprehensive insights. Also, past literature pointed at limited proactive efforts to measure the current level of safety at the project. Therefore, this study introduced a leading indicator-based jobsite safety inspection (JSI) method to measure the project's safety performance. A total of 781 observations were reported in baseline and follow-up JSI on a real construction site. The study's findings highlighted an increase in safe behavior and conditions (SB&C) by reducing unsafe behavior (UB) and unsafe conditions (UC). Results confirmed a significant improvement in the safety performance index (SPI) of the case study from 39.07 to 67.47%. This study also investigated hazardous locations and weak safety dimensions for further SPI improvement. [ABSTRACT FROM AUTHOR]

There has been a growing interest in research on how to define and build indicators of resilience to address challenges associated with sea-level rise. Most of the proposed methods rely on lagging indicators constructed based on the historical performance of an infrastructure sub-system. These indicators are traditionally utilized to build curves that describe the past response of the sub-system to stressors; these curves are then used to predict the future resilience of the sub-system to hypothesized events. However, there is now a growing concern that this approach cannot provide the best insights for adaptive decision-making across the broader context of multiple sub-systems and stakeholders. As an alternative, leading indicators that are built on the structural characteristics that embody system resilience have been gaining in popularity. This structure-based approach can reveal problems and gaps in resilience planning and shed light on the effectiveness of potential adaptation activities. Here, we survey the relevant literature for these leading indicators within the context of sea-level rise and then synthesize the gained insights into a broader examination of the current research challenges. We propose research directions on leveraging leading indicators as effective instruments for incorporating resilience into integrated decision-making on the adaptation of infrastructure systems.

• We summarize insights from developing six industry-specific safety climate scales. • Safety climate has been inconsistently operationalized and specific issues are identified. • The conceptualization of safety climate dimensions differs within industries. • Best practice guidelines to develop robust safety climate scales are offered. Introduction: Researchers are finding merits in utilizing industry-specific safety climate scales that capture the nuances of context, and tend to show stronger associations with safety behavior and outcomes like incidents. Yet, to date, guidance around the practicalities of developing and validating such industry-specific scales is lacking in the safety science literature. Method: In this paper we outline our experiences developing six industry-specific safety climate scales and highlight strengths and limitations of our approach. We also briefly review the industry-specific safety climate literature and offer highlights for consideration when developing such scales. Our method to develop industry-specific safety climate scales followed an established best practice structure: literature review of existing published industry scales, collation and review of existing scale items, consultation interviews with industry experts, item drafting, exploratory and confirmatory statistical analyses, and finally, a real-world ecological validity test. Results: Our research highlighted the diversity of safety climate dimensions (both the conceptual and content domains of each dimension) when it is considered at an industry level. Also, the literature reviews revealed a dearth of industry-specific safety climate scales in the areas we engaged with, so our project filled a glaring gap in research and practice. Best practice safety climate scale development methods are provided to stimulate further research. Conclusions: We conclude with reflections on the nature of safety climate within and across industries, and offer suggestions for future lines of research across other contexts (e.g., national culture, geography, and regulatory settings). We suggest that industry-specific safety climate scales have a specific use case, such as identifying specific areas to improve and evaluating the impact of safety interventions. Practical Applications: This article provides applications for both applied researchers (to improve capabilities in safety climate scale development) and practitioners who wish to measure organisational safety climate and design effective interventions. Engaging with regulators to build safety climate scales is powerful because their personnel have rich experiences to share across multiple workplaces. Organisational researchers can engage with survey panels to build robust scales. Finally, industry-specific nuances can lead to richer insights into an organisation's safety climate. [ABSTRACT FROM AUTHOR]

This paper presents a coincident and a leading composite monthly indicator for the world business cycle—the Global Economic Barometers. Both target the world's output growth rate and consist of economic tendency surveys results from many countries around the world. The calculation of these indicators comprises two main stages. The first consists of a variable selection procedure, in which a pre-set correlation threshold and the targeted leads to the reference series are used as selection criteria. In the second stage, the selected variables are combined and transformed into the respective composite indicators, computed as the first partial least squares factor with the reference series as response variable. We analyse the characteristics of the two new indicators in a pseudo real-time setting and demonstrate that both are useful additions to the small number of indicators for the global business cycle published so far. Finally, yet importantly, the Barometers were quick to plunge in the beginning of March 2020 and have since then given a reliable real-time reflection of the economic consequences of the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

Industrialization, which is currently taking place in the world under new technological conditions, guarantees successful economic development. In this regard, analysts, entrepreneurs, government officials, politicians at the local and national levels are interested in timely and reliable information about the state and prospects for the development of the national industry. To get signals about changes in economic activity in the near future, economists use leading indicators -- dynamic data series that demonstrate a fairly stable connection with the basic data series of the macroeconomic development cycle in a particular country. However, in connection with the peculiarities of development, each state has to find its own solutions in this subject area. The article focuses on leading indicators of industry development. The objectives of the work are to generalize the accumulated experience in the field of short-term forecasting of industry based on the use of the dynamics of indicators that have a leading connection with industrial production, and to substantiate recommendations regarding the possibilities of their use for short-term forecasting of the industrial development of Ukraine. As a result of the generalization of the world experience of using leading indicators, considering the national industrial specifics, two working hypotheses were suggested in the research process. The first one is based on the fact that: when forecasting the turning points of economic activity in the national industry, it is necessary to focus on general and specific leading indicators of countries that are the main partners of Ukraine, especially those to which goods of the industrial group are exported; the list of candidate indicators should include world energy prices, taking into account the significant dependence of national production on hydrocarbon imports; in addition to the real money supply, the dynamics of industrial value added are influenced by other monetary factors, namely -- the hryvnia exchange rate to the dollar and the index of the US dollar, which is explained by the openness of the national economy, its small size, specialization in the export of raw materials and significant dependence on the import of industrial goods, necessary to create products with high value added. For further verification of this hypothesis, it is offered to divide the relevant indicators into four groups: those based on the results of enterprise surveys, indicators of financial activity of enterprises, monetary and socio-economic indicators. At the same time, the availability of data and promptness of updating (at least quarterly) is a key condition for their inclusion in the selection of candidates. The second working hypothesis is that the proposed indicators can serve as ones to provide early signals about the dynamics of the value added created in the industry. In order to verify it in practice, it is necessary to create a database of indicators that can be used to predict the dynamics of gross value added in industry, perform mathematical calculations (build quantitative dependencies of value added on selected indicators considering the time lag), integrate individual indicators into a composite index of industrial development. The subject of further research is solving these problems. [ABSTRACT FROM AUTHOR]

State transitions are changes in ecosystem structure and self-reinforcing feedbacks that are initiated when an exogenous driver variable crosses a threshold. Reversing state transitions is difficult and costly. While some state transitions are relatively rapid, many take years to decades. Outside of theoretical models, very little is known about slower state transitions and how they unfold in time and space. We quantified changes in spatial variance as a mesic grassland ecosystem shifts to a shrub-dominated state, using long-term experiments and simulations that maintain grasslands with annual fires or initiate a state transition to shrub dominance by decreasing fire frequency. In the experiments, the susceptibility to state transitions varied substantially in space. In the less frequent fire treatment, some plots became shrub-dominated around year 20 and grass extirpations began in year 25, but a third of the plots were still grass-dominated in year 37. Variable rates of state transition resulted in increasing spatial variance of grass cover over time, whereas shrub cover variance decreased. In the annually burned treatment, grasses remained dominant and the spatial variance of grass cover declined. In a separate experiment, less frequent fires were maintained for 23 years and then switched to annual fires. The switch to annual fires occurred shortly after grass variance started to increase and a majority of these plots quickly returned to a grass dominated state. In simulations, spatial variance remained low and average grass cover was high under frequent fires. If fire frequency decreased below a threshold, the ecosystem transitioned to shrubland, with a transient increase in the spatial variance of grass cover during the transition between states. Synthesis. Spatial variability in the rate and susceptibility to state transitions is indicative of a system with a patchy spatial structure, high spatial heterogeneity and low connectivity between patches. Increases in spatial variance can serve as an indication that some patches have begun a state transition and that management interventions are needed to avoid widespread transitions. This is one of the first empirical examples where altering management after an increase in spatial variance prevented state transitions.

• This research presents new theoretical insight which will augment the promulgation of leading indicators' use in safety management. • An extensive and thorough literature analysis is conducted using two consecutive data analysis methods viz. thematic and content analyses. • A new theoretical explanation of leading and lagging indicators is proffered which incorporates safety management dynamics. • The work serves to engender renewed polemic debate and discussion on how to create a step change in safety performance improvement in the workplace. With the aim of developing a new theoretical insight which will augment the promulgation of leading indicators' use, this present research studies pertinent literature. Associated study objectives are to: 1) generate insight into potential challenges, use and benefits of adopting leading indicators in safety management from pertinent literature; and 2) elucidate upon existing abstruseness between safety leading and lagging indicators. For an extensive and thorough literature analysis, two consecutive data analysis methods are adopted (viz., thematic and content analyses). Through thematic analysis, 13 clusters (each representing different uses of leading indicators) emerged, from which the 'health and safety' cluster was selected for a deeper analysis in content analysis. In addition, by reviewing the compendium of leading indicator examples collected from extant safety leading indicator literature, novel types of dichotomous leading indicators were identified, viz., generic and specific leading indicators. Subsequently, a synthesis of challenges and benefits of safety leading indicator adoption was generated through deductive content analysis. Furthermore, a new theoretical explanation into the relationship of safety leading and lagging indicators is proffered which also incorporates safety management dynamics representing different levels of safety maturity. This study constitutes a first attempt to: provide a structured synthesis of safety leading indicators' functions along with their challenges in development, implementation and adoption stages; shed a new light into the theoretical explication of safety lagging and leading indicators' relationship; and introduce safety management dynamics that establish different levels of safety maturity. [ABSTRACT FROM AUTHOR]

This study draws on machine learning as a means to causal inference for econometric investigation. We utilize the concept of transfer entropy to examine the relationship between the US National Association of Home Builders Index and the S&P CoreLogic Case‐Shiller 20 City Composite Home Price Index (SPCS20). The empirical evidence implies that the survey data can help to predict US house prices. This finding extends the results of Granger causality tests performed by Rodriguez Gonzalez et al. in 2018 using a new machine learning approach that methodologically differs from traditional methods in empirical financial research. [ABSTRACT FROM AUTHOR]

In this article we construct a number of early housing-market indicators from daily-scraped list-price data and investigate how they inform on housing-market trends prior to the release of official transaction data. We minimize the impact of non-market conform list prices by our data selection- and cleaning routine and thereby improve the overlap between list and transaction data. In addition to some well-known real-estate indicators (price and rent indices, price-rent ratios, market volume, time-on-market, and market turnover) we develop a new market sentiment indicator which we construct from the direction and frequency of online price changes for individual listings. We then use this group of indicators to investigate how housing markets in London and Vienna react to the COVID-19 pandemic during 2020. For London these indicators show high volatility during much of 2020 and we find that the sales and rental markets drift apart: The sales market shows an overall positive price trend, while the rental market becomes significantly weaker after the onset of the COVID-19 crisis. For Vienna we find that all indicators signal positive market developments for the first eight months after the start of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Information on the annual variability in abundance and growth of juvenile groundfish can be useful for predicting fisheries stocks, but is often poorly known owing to difficulties in sampling fish in their first year of life. In the Western Gulf of Alaska (WGoA) and Eastern Bering Sea (EBS) ecosystems, three species of puffin (tufted and horned puffin, Fratercula cirrhata, Fratercula corniculata, and rhinoceros auklet, Cerorhinca monocerata, Alcidae), regularly prey upon (i.e., "sample") age‐0 groundfish, including walleye pollock (Gadus chalcogramma, Gadidae) and Pacific cod (Gadus microcephalus, Gadidae). Here, we test the hypothesis that integrating puffin dietary data with walleye pollock stock assessment data provides information useful for fisheries management, including indices of interannual variation in age‐0 abundance and growth. To test this hypothesis, we conducted cross‐correlation and regression analyses of puffin‐based indices and spawning stock biomass (SSB) for the WGoA and EBS walleye pollock stocks. For the WGoA, SSB leads the abundance of age‐0 fish in the puffin diet, indicating that puffins sample the downstream production of the WGoA spawning stock. By contrast, the abundance and growth of age‐0 fish sampled by puffins lead SSB for the EBS stock by 1–3 years, indicating that the puffin diet proxies incoming year class strength for this stock. Our study indicates connectivity between the WGoA and EBS walleye pollock stocks. Integration of non‐traditional data sources, such as seabird diet data, with stock assessment data appears useful to inform information gaps important for managing US fisheries in the North Pacific. [ABSTRACT FROM AUTHOR]

Abstract The shipping industry has been quite successful in reducing the number of major accidents in the past. In order to continue this development in the future, innovative leading risk indicators can make a significant contribution. If designed properly, they enable a forward-looking identification and assessment of existing risks for ship and crew, which in turn allows the implementation of mitigating measures before adverse events occur. Right now, the opportunity for developing such leading risk indicators is positively influenced by the ongoing digital transformation in the maritime industry. With an increasing amount of data from ship operation becoming available, these can be exploited in innovative risk management solutions. By combining the idea of leading risk indicators with data and algorithm-based risk management methods, this paper firstly establishes a development framework for designing maritime risk models based on safety-related data collected onboard. Secondly, the development framework is applied in a proof of concept where an innovative machine learning-based approach is used to calculate a leading maritime risk indicator. Overall, findings confirm that a data- and algorithm-based approach can be used to determine a leading risk indicator per ship, even though the achieved model performance is not yet regarded as satisfactory and further research is planned.

Abstract This paper studies the predictive power for recessions of the slope of the Swiss term structure using monthly data for 1974–2017. Dynamic probit models indicate that the term structure contains information useful for predicting recessions for horizons up to 19 months. Whether the economy is currently in recession or not is also useful for forecasting recessions. These relationships prove stable over the sample. Robustness tests indicate that the KOF business course indicator and some monetary aggregates contain different information from the term structure which can improve the in- and out-of-sample fit of the model.

Companies that implement Integrated Management Systems (IMS) require approaches that optimize resources and results. In the case of IMS of a new or emerging nature, the use of dynamics risk analysis approaches and the integration of real-time monitoring data in the risk assessment process offers news perspectives. The objective of this work is to identify and classify leading indicators that facilitate the dynamic analyses of emerging risks in an IMS for quality, environment, and safety. For it, such indicator analysis has been based on a bibliographic analysis. Regarding results, firstly, a structure of indicators emerges configured of three categories organized in two levels. At the first level, it is established by the indicators of the IMS which can be integrated. The second level is configured of two categories of interrelated indicators, that is, process integrity indicators and occupational risks indicators. In turn, each of these three categories has two dimensions. The first dimension represents the direction of the indicator, leading or lagging indicator. The second dimension represents the risk nature, emerging or traditional risk. Secondly, a classification of the leading indicators is derived according to the categories of the indicators and the risk nature. This classification shows the direction of the leading indicators as well as qualitative graduation of the potential associated consequences. Said theoretical framework has been applied to a case study configured by a manufacturing process. Thus, a conceptual scheme has been developed that represents the first step towards a more in-depth and detailed development that allows the identification and definition of specific leading indicators within an IMS from a dynamic and emerging risk perspective. [ABSTRACT FROM AUTHOR]