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The purpose of this paper is to construct, for the first time, composite index for Egypt that measures the economic and social rights fulfillment (ESRF) based on socioeconomic surveys at the household/individual levels.

The paper highlights some of the statistical debatable issues about composite indices and focuses mainly on six of them. Those issues are indicators selection, handling missing data, identification of and dealing with outliers, scale of measurement, computing the margin of error, weights assigned for indicators and domains and aggregation method. Handling these problematic issues gave rise to a rigorous index.

The quality of economic and social rights fulfillment index (ESRFI) is judged by its bootstrap standard error. Based on these margin of errors, confidence intervals can be computed and rigorous comparisons across all disaggregation levels of the ESRFI can be made. The results shows that the overall index is accurate and representative in measuring the ESRF in Egypt. Comparisons between rural and urban regions indices show that the rural areas are always worse than the urban areas in all levels of dimensions, especially for the Right to Education and Adequate Housing.

The ESRFI is not very current because it is based on the 2010 Egyptian Household Conditions Observatory Survey (EHCOS), which is the latest published version of the survey with complete variables for the index data. When the next EHCOS becomes available, an updated ESRFI can be easily and quickly constructed.

The ESRFI could strengthen policy formulation that takes into account ESRF, especially by highlighting the situation in different regions and disaggregation levels.

The proposed ESRFI would strengthen policy formulation that takes into account ESRF, especially by highlighting the situation in different regions and different disaggregation levels.

The paper emphasizes the importance of recognizing and handling of the six problematic issues that arise when constructing composite indices. The paper presents the first ESRFI for Egypt and demonstrates the rigor of its construction.

This paper demonstrates an application of the Arbitrage Pricing Theory using canonical analysis as an alternative to the conventional factor analysis. Following the traditional view that asset prices are influenced by unanticipated economic events, the systematic effects of the major composite economic indices on a wide spectrum of industry returns are explored. The main conclusion is that profitability may be considered as the single most important factor that influences security returns. Also, the composite lagging economic indicators appear to be more useful to investors in forming market expectations than the composite leading economic indicators. Finally, it is argued that the composite index of coincident economic indicators do not exhibit any significant influence in the pricing of capital assets.

This study uses an event study approach which is the development of the efficient market hypothesis theory. First, the random walk test was conducted on the Jakarta Composite Index (JCI) to test the efficiency in the weak form. Furthermore, event study analysis was carried out on JCI and nine sectoral indices to determine the impact of COVID-19 related events on price movements. The study found that JCI prices follow a random walk pattern so that the stock market in Indonesia is efficient, at least in a weak form. In the event study testing, only events related to the first confirmed case of COVID-19 and the implementation of large-scale social restriction in Indonesia affected the composite index. From a sectoral point of view, only the event of Jakarta’s call center had no impact on price changes in the sectoral index. Thus, each index had a different effect throughout the event. The reaction seen from the movement of prices for the composite and sectoral index to the public information explains that the condition of the Indonesian capital market is efficient, at least in semi-strong form.

To introduce the systemic approach towards measuring globalization of the economy that is based on the theory of systemic framework of national competitiveness and economic growth.

Construction of a composite index of global economy by the principal components analysis (PCA) on the basis of 83 individual indicators, linked into the seven spheres of national competitiveness in the period of contemporary globalization. The weights of single indicators in the composite index are determined by two levels: on the first level, the weights of the indicators in the subindices are determined, whereas on the second level the weights of the sub‐indices in the joint composite index of global economy are determined. Reliability of the composite indices is verified by Cronbach's α.

The results of the PCA indicate the high reliability of the joint composite index on the basis of five subindices, whereas the inclusion of the remaining two subindices – international business activities and government role – has lowered the reliability of the joint composite index. This finding is combined with many statistical limitations in the field of measuring globalization of economies.

The unavailability and incomparability of statistical data in the field of international business activities presented great obstacles to deeper analysis. Such analysis would be attainable above all by the international harmonization of hard data collecting and greater emphasis on development of qualitative surveys in the field of activities of transnational enterprises.

This paper develops the concept for monitoring and benchmarking globalization of economies which is founded on the comprehensive theory and solid methodology. It offers a useful tool for national policymaking.

This work aims to establish the relationship between painting art and sustainability, which allows for highlighting implications likely to improve sustainability for humanity's welfare.

To achieve this objective, painting art is measured by a composite index aggregating the quantity and quality represented by the market value. As for sustainable development, it is represented by a composite index comprising three variables: the climate change performance index (ecological dimension), the wage index reflecting distributive justice (social dimension) and the gross domestic product (economic dimension). The composite indices were determined through adjusted data envelopment analysis. In addition, two other methods are used in this work: correlation analysis and a neural network method. These methods are applied to data from 2007 to 2021 across the world.

The correlation method highlighted a perfect positive correlation between painting art and sustainability. As for the neural network method, it revealed that the quality of painting has the greatest impact on sustainability. The neural network method also showed that the most positively impacted variable of sustainability by painting art is the social variable, with a pseudo-probability of 0.90.

The relationship between painting art and sustainability is underexplored, in particular in terms of statistical analysis. Therefore, this research intends to fill this gap. Moreover, analysis of the relationship between both using composite indices computed via an original method (adjusted data envelopment analysis) and a neural network method is nonexistent, which constitutes the novelty of this work.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0006

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

Most composite indicators of national performance limit their scope to only economic performance criteria and aggregate primitive performance data using subjective fixed weight values applied uniformly to all countries. This paper proposes a weighting method to correct for biases inherent in the use of fixed and uniform weights, and it presents a composite performance indicator that encompasses both economic and non‐economic performance criteria.

The paper presents a method that endogenously determines country‐specific weights that explicitly take account of a country's own choices and achievements across primitive dimensions of performance. The method is then used to construct a composite inclusive index that combines economic performance with two other performance dimensions: environmental sustainability and governance.

Comparison of the endogenous weight method with the method of using fixed and uniform weights indicates a bias in the latter that penalizes countries, in terms of indicating lower relative performance, which are more diverse in their achievements among primitive performance dimensions. When the endogenous weight method is used, the performance ranking of countries is altered such that countries with greater diversity improve their relative performance while the relative performance of countries having less diversity may either rise or fall.

The weighting method discussed in this paper: is applicable at any level of analysis (e.g. nations, companies, business units, etc.), obviates objections about the “importance” of alternative primitive dimensions that can arise when subjective fixed weights are used; and indicates more accurately relative performance since each unit of analysis is first allowed to obtain its best performance before relative performance is assessed. The method can therefore assist policy makers, companies, etc. to more accurately benchmark performance and it can, in particular, assist companies to respond to perceptions of low performance or compliance among different performance dimensions when performance has been determined using the traditional method of fixed and uniform weights.

A major consequence of global environmental change is projected to be the alteration in flood periodicity, magnitude, and geographic patterns. There are a number of extant methods designed to help identify areas vulnerable to these consequences, the construction of composite vulnerability indices prominent among them. In this paper we have implemented the Order Rated Effectiveness (ORE) model (Klimberg & Ratick, 2020) to produce composite flood vulnerability indicators through the aggregation of six constituent vulnerability indicators future projected for 204 hydrologic subbasins that cover the contiguous US. The ORE aggregation results, when compared with those obtained using the Weighted Linear Combination and Data Envelopment Analysis, provided a more robust and actionable distribution of composite vulnerability results for decision-makers when prioritizing Hydrologic Unit Codes for further analysis and for effectively and efficiently implementing adaptation and mitigation strategies to address the flooding consequences due to global climate change.

The purpose of this study is to develop an index for financial constraints, specifically for restaurant firms, and to further validate the developed financial constraint index.

This study used logistic regression with a composite criterion based on the dividend payout ratio, KZ index and Cleary index to estimate restaurant firms’ financial constraints. Then, a fixed-effects regression was used to verify the validity of the measurement of restaurant firms’ financial constraints.

A restaurant firm’s operating profit, financial leverage, asset tangibility, sale of fixed assets and percentage change in number of employees are critical indicators for identifying financial constraints. The results indicated that in cases with positive operating cash flows, the effect of operating cash flow on capital investments continuously decreased as restaurant firms’ financial constraints increased.

This study is unique in that the specific financial and operational characteristics of restaurant firms were included in the model to determine financial constraint indicators, such as sale of fixed assets and percentage change in number of employees.

This research has two primary goals: first, to develop a composite index that evaluates the degree to which Asian–Pacific economies are prepared to engage in public–private partnerships (PPPs), and second, to investigate the factors that have been most influential in the formation of PPP arrangements in the nations' infrastructure over the course of the period 1995–2016.

The study constructs sectoral and overall index of possible determinants of PPP. Subsequently, it examines each constructed index's role in PPP investment. The author also conducted a panel data analysis to understand the role of each of the potential determinants on PPP projects and investments. This paper analyzes the author’s empirical models using a range of cross-section and panel estimators, including Poisson, zero-inflated Poisson and fixed effect.

The study’s results based on cross-section analysis suggest that regulatory and institution quality, institutional arrangement and regulatory frameworks, financial market development and macroeconomic stability positively impact investment in PPP. Moreover, the results depict that financial market development has the most substantial impact on PPP investment, followed by macroeconomic stability and prior experience with PPPs. The panel data analysis shows that per-capita income, financial development, inflation, debt, resource import and fuel export are crucial determinants of PPP in Asian–Pacific economies.

Governments of the countries should promptly amend the important policies outlined in this study and adopt a more robust strategy to foster a competitive PPP environment. This will aid in maintaining transparency and gaining the confidence of investors. The study’s findings may assist policymakers in focusing on specific areas in need of improvement. Social welfare and industrialization are ultimately enhanced by the formulation of such policies and by attracting additional infrastructure investment.

This is the first attempt to rank countries on the basis of PPP enablers. Unlike previous studies, this study examines the role of a large number of indicators in determining PPP investment and projects in cross-section as well as panel data framework. The study also investigates the effects of PPP specific provisions and rules. Furthermore, the focus is specifically on Asian–Pacific countries, which are a mix of third-world, emerging, developing and developed countries. Focusing on Asia–Pacific is also crucial because the region is home to most of the world's population, and the region's infrastructure outcomes significantly impact their lives.