Empirical Illustration Research Articles

Investigating water sustainability towards indicators: An empirical illustration using country-level data

This paper contributes to the academic debate on the construction of synthetic indices with respect to water-based sustainability analysis. In this regard, the United Nations Sustainable Development Goal (SDG) 6.4 specifically considers the progress on water-use efficiency within the target of ensuring access to water and sanitation (SDG 6). Our contribution investigates the levels of water sustainability reached by countries around the world considering a suitable measure of the goal obtained by aggregating variables available at the national level, i.e. water efficiency (SDG 6.4.1) and stress (SDG 6.4.2).The construction of a composite index for SDG 6.4 is discussed through the usual steps of normalization, aggregation, and weighting, also highlighting the pros, cons and issues encountered in each step. Fundamental issues faced in the construction of synthetic indices are also discussed: different methods for performing the above steps are compared, and uncertainty analysis is carried out to understand which methods could be reasonably suitable to measure the synthetic indicator of the entire target. As a further theoretical contribution, we illustrate some arguments in favor of the application of categorical scales for normalization, even if the results of some countries seem to be particularly sensitive to the choice of weights in the aggregation phase.Results, obtained after examining univariate and multivariate descriptive statistics, show that the best performances can be observed in small and medium European countries and sub-Saharan African nations (such as Congo, Gabon, and Angola), while the lowest levels appear to be located in Central Asia, the Middle East, and North Africa.A natural implication of this research is its contribution to valorizing the social capital of a regional area, stimulating new approaches to environmental issues, informing public debate, and influencing policy-making.

Bouncing back from life's perturbations: Formalizing psychological resilience from a complex systems perspective.

Experiencing stressful or traumatic events can lead to a range of responses, from mild disruptions to severe and persistent mental health issues. Understanding the various trajectories of response to adversity is crucial for developing effective interventions and support systems. Researchers have identified four commonly observed response trajectories to adversity, from which the resilient is the most common one. Resilience refers to the maintenance of healthy psychological functioning despite facing adversity. However, it remains an open question how to understand and anticipate resilience, due to its dynamic and multifactorial nature. This article presents a novel formalized framework to conceptualize resilience from a complex systems perspective. We use the network theory of psychopathology, which states that mental disorders are self-sustaining endpoints of direct symptom-symptom interactions organized in a network system. The internal structure of the network determines the most likely trajectory of symptom development. We introduce the resilience quadrant, which organizes the state of symptom networks on two domains: (1) healthy versus dysfunctional and (2) stable versus unstable. The quadrant captures the four commonly observed response trajectories to adversity along those dimensions: resilient trajectories in the face of adversity, as well as persistent symptoms despite treatment interventions. Subsequently, an empirical illustration, by means of a proof-of-principle, shows how simulated observations from four different network architectures lead to the four commonly observed responses to adversity. As such, we present a novel outlook on resilience by combining existing statistical symptom network models with simulation techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Testing in GARCH‐X models: boundary, correlations and bootstrap theory

In this article, we consider the so‐called Fixed Shrinkage (FS) bootstrap for the class of GARCH models with explanatory variables (GARCH‐X). Under the assumption of stationary covariates, the proposed FS bootstrap does not require modeling the covariates, as these are kept fixed in the bootstrap generating process. Our main focus is on testing whether one or more of the covariates can be excluded in the GARCH‐X model. As is well‐known the limiting distribution of the likelihood‐ratio (LR) statistic in this setting is non‐standard and depends in particular on whether nuisance parameters are on the boundary or in the interior of the parameter space. In particular, and as detailed here, the non‐standard limiting distribution depends on correlations, or dependence, between the explanatory variables. The FS bootstrap takes the presence of nuisance parameters into account by implementing shrinking as proposed in Cavaliere et al. (2022) for pure ARCH models. We establish asymptotic validity of the FS bootstrap for GARCH‐X models, and demonstrate by simulations that the bootstrap‐based test performs extremely well even when nuisance parameters lie on the boundary of the parameter space. The empirical illustration amplifies that the presence of nuisance parameters (especially whether or not on the boundary) are vital for interpreting the dynamics of conditional volatility in financial stock market indices.

Statistical inference for Hicks–Moorsteen productivity indices

AbstractThe statistical framework for the Malmquist productivity index (MPI) is now well-developed and emphasizes the importance of developing such a framework for its alternatives. In this paper, we try to fill this gap in the literature for another popular measure, known as the Hicks–Moorsteen productivity index (HMPI). Unlike MPI, the HMPI has a total factor productivity interpretation in the sense of measuring productivity as the ratio of aggregated outputs to aggregated inputs and has other useful advantages over MPI. In this work, we develop a novel framework for statistical inference for HMPI in various contexts: when its components are known or when they are replaced with non-parametric envelopment estimators. This will be done for a particular firm’s HMPI as well as for the simple mean (unweighted) HMPI and the aggregate (weighted) HMPI. Our results further enrich the recent theoretical developments of nonparametric envelopment estimators for the various efficiency and productivity measures. We also examine the performance of these theoretical results for both the unweighted and weighted mean of HMPI for a finite sample, using Monte-Carlo simulations and also provide an empirical illustration along with the computation code.

Collaborative climate labs: A youth-led methodology for co-creating community responses to climate change

This article presents the Collaborative Climate Laboratories (CiCli-Labs), a participatory research methodology that bridges citizenship, science and politics. This methodology is grounded on young people’s knowledge and experience about their communities and seeks to develop a strategy for co-designing climate solutions with local stakeholders. Carried out in schools, the CiCli-Labs engage scientists, activists, business owners, and politicians in discussing climate problems identified by young people. By bringing together different positionalities that emerge from people’s diverse social roles, the CiCli-Labs aim to foster conditions for democratic citizenship and climate justice, embracing the necessarily conflicting yet binding dimensions of politics that can produce transformative and reciprocal learning. Additionally, it responds to the growing demand within educational research for community-based approaches capable of tackling the shortcomings in promoting youth agentic power in participatory designs, often marked by one-off events and youth-washing practices. The CiCli-Labs’ methodology builds on community profiling, through which students identify climate-related issues that work as catalysts for broader dialogues with local actors, challenging traditional adult-centric power dynamics. The methodology evolves through a sequential design based on the adaptation of participatory tools and methods: i) Climate Problem Tree; ii) Climate Social Cartography; iii) Pros-and-Cons Circuit; iv) Solutions Funnel; v) Climate Speed Dating; vi) Hands on Earth and Eyes on Clouds. The methodology was implemented in eight schools in northern Portugal, involving more than 300 students (grades 8 to 12). We discuss the rationale and practicality of the CiCli-Labs while mobilising empirical illustrations of the methodological process and participants’ perceptions about it, thereby contributing to furthering the problematisation of democratic research practices with young people.

Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production

AbstractNational Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML solutions, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld as codified, e.g., in the Quality Framework for Statistical Algorithms (QF4SA; Yung et al. 2022, Statistical Journal of the IAOS). At the same time, a growing body of research focuses on fairness as a pre-condition of a safe deployment of ML to prevent disparate social impacts in practice. However, fairness has not yet been explicitly discussed as a quality aspect in the context of the application of ML at NSOs. We employ the QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness. We thereby extend the QF4SA framework in several ways: First, we investigate the interaction of fairness with each of these quality dimensions. Second, we argue for fairness as its own, additional quality dimension, beyond what is contained in the QF4SA so far. Third, we emphasize and explicitly address data, both on its own and its interaction with applied methodology. In parallel with empirical illustrations, we show how our mapping can contribute to methodology in the domains of official statistics, algorithmic fairness, and trustworthy machine learning.Little to no prior knowledge of ML, fairness, and quality dimensions in official statistics is required as we provide introductions to these subjects. These introductions are also targeted to the discussion of quality dimensions and fairness.

Birge ratio method for modeling dark uncertainty in multivariate meta-analyses and inter-laboratory studies

In the paper, we introduce a new approach for combining multivariate measurements obtained in individual studies. The procedure extends the Birge ratio method, a commonly used approach in physics in the univariate case, such as for the determination of physical constants, to multivariate observations. Statistical inference procedures are derived for the parameters of the multivariate location-scale model, which is related to the multivariate Birge ratio method. The new approach provides an alternative to the methods based on the application of the multivariate random effects model, which is commonly used for multivariate meta-analyses and inter-laboratory comparisons. In two empirical illustrations, we show that the introduced multivariate Birge ratio approach yields confidence intervals for the elements of the overall mean vector that are considerably narrower than those obtained by the methods derived under the multivariate random effects model.

Likelihood-ratio test for technological differences in two-stage data envelopment analysis for panel data

This study explores the question of adapting a likelihood-ratio test in the two-stage data envelopment analysis (DEA) framework, where DEA estimates are regressed against external factors. We focus on the hypotheses of testing the technological difference across time periods (or groups) and propose two bootstrapping procedures. Our Monte Carlo (MC) simulation shows that the proposed test has a substantially better-estimated size for the case of smoothing the ‘spurious ones’, rather than removing them. MC simulation also confirms the curse of dimensionality when the input and output variables increase in the production model. Finally, the proposed test is demonstrated in an empirical illustration.

An adaptation of Random Forest to estimate convex non‐parametric production technologies: an empirical illustration of efficiency measurement in education

AbstractThis paper presents a novel approach to conduct non‐parametric estimations of production technologies that adhere to the basic assumptions of production theory axioms, including free disposability in inputs and outputs and convexity. The methodology is rooted in adapting the highly effective machine learning techniques associated with Random Forest and the use of splines. The new method features a piecewise linear estimator analogous to data envelopment analysis (DEA); however, it distinguishes itself by addressing DEA's overfitting and lack of robustness via randomization of data and input variables in the construction of the models. In this paper, the virtues of employing machine learning techniques for assessing the efficiency of public services, particularly in the realm of educational institutions, are underscored. The new approach has the capability to predict outputs based on inputs, even for units not included in the observed sample. Furthermore, it enables the identification of the most relevant inputs in relation to output production. To demonstrate the advantages of our method, an estimation of the educational production function is conducted for Spanish regions utilizing data sourced from the Program for International Student Assessment.

Using necessary condition analysis to complement multigroup analysis in partial least squares structural equation modeling

With the growing importance of partial least squares structural equation modeling (PLS-SEM) in marketing and consumer research, the use of Multigroup Analysis (MGA) for discovering observed heterogeneity (i.e., differences in relationships between variables for subgroups of the population under investigation) and deriving relevant operational results has become of great interest. However, these analyses are based exclusively on an additive sufficiency logic and do not permit researchers to test and validate hypotheses drawing on a necessity logic, the latter having been the focus of recent significant developments. Addressing this concern, the present paper offers guidelines for combining the use of Necessary Condition Analysis (NCA) and MGA performed with PLS-SEM. Taken together, these analyses can explore and improve knowledge about predefined subgroups of interest, enhance the understanding of relationships, refine the role of specific key antecedents by discovering meaningful necessary conditions, and therewith, contribute to theorizing. An empirical illustration drawing on the relationship between corporate social responsibility and customer loyalty is developed in a step-by-step fashion to provide marketing researchers with the guidelines to conduct the MGA and NCA, and finally report and interpret the results in accordance with both the sufficiency and the necessity logics. This integrative procedure contributes to the advancement of PLS-SEM applications. By delivering a better understanding of the group-specific results of a PLS-SEM–based MGA in a necessity logic, it promotes the complementary usage of sufficiency and necessity logics and therefore helps researchers to uncover novel theoretical and practical results when evaluating the data.

Standard Errors for Two-Way Clustering with Serially Correlated Time Effects

Abstract We propose improved standard errors and an asymptotic theory for two-way clustered panels. Our theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods. Our asymptotic distribution theory is the first which allows for this level of inter-dependence. Under weak conditions, we demonstrate that OLS is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal. The results extend to two-way fixed-effect models; we argue that two-way clustering is still necessary even if two-way fixed effects are included. Simulation and empirical illustration are provided.

EFFICIENCY IN ESTIMATION UNDER MONOTONIC ATTRITION

Attrition is monotonic when agents leaving multi-period studies do not return. Under a general missing at random (MAR) assumption, we study efficiency in estimation of parameters defined by moment restrictions on the distributions of the counterfactuals that were unrealized due to monotonic attrition. We discuss novel issues related to overidentification, usability of sample units, and the information content of various MAR assumptions for estimation of such parameters. We propose a standard doubly robust estimator for these parameters by equating to zero the sample analog of their respective efficient influence functions. Our proposed estimator performs well and vastly outperforms other estimators in our simulation experiment and empirical illustration.

On the decomposition of the extensions of the Gini index that are based on the ‘metallic’ sequences of number theory

AbstractThe present paper extends the work of Subramanian (Econ Bull 41(4):2309–2319, 2021), as well as that of Creedy and Subramanian (Exploring a new class of inequality measures and associated value judgements: Gini and Fibonacci-type sequences, 2022), by showing how this new extension of the Gini index may be decomposed by income sources, income classes and population subgroups. It also gives an empirical illustration applying this extension of the Gini index to the analysis of the inequality in expenditure on social protection among European countries in 2018. In the decomposition by benefit function it appears that the housing function consistently exhibits the smallest contribution, due to its low expenditure weight, while the old-age function has the lowest inequality but the highest contribution, due to its substantial expenditure weight. The family/children function's contribution increases when a higher weight is given to lower expenditures, highlighting a pronounced inequality at lower expenditure levels. Conversely, the old-age function shows higher inequality among countries with higher expenditures. In the breakdown by welfare systems, we observe greater inequality between systems than within them.

The logistical governance of vertical commuting in the central business district

The centre of major cities is a focus of commuting patterns, and this article sets out how major cities use calculative practices to guide commuters through a complex, multiplanar, volumetric city. It examines how public transport officials, consultants, city planners and property developers interact to move commuters through inter-locking public and private spaces on a journey between underground, surface and high-rise commercial structures. Using a case study of Sydney’s central business district, it presents three areas where the governance of this movement can be observed. First, it considers how underground rail planning has adopted new modes of organising capacity, especially in terms of the use of behavioural psychology in organising platform and escalator crowd behaviour. Second, the article discusses navigation at ground level, where rail commuters emerge onto the pavement to continue their journey. Urban planners, along with specialists in wayfinding and people movement, calculate capacity and make behavioural interventions to influence movement up, down and across surfaces. Third, it explores the relationship between elevator technology, vertical people flow analysis and the floorplate design of offices. The article’s contribution is in its conceptual and empirical illustration of how the rhythms of urban crowds are tracked, calculated and structured by a range of experts; in turn, we can see how these experts have emerged as significant agents in maximising the ability to extract value from the built volume of cities.

An ‘all-world ageing’ perspective and its wider ethics of care: An empirical illustration

Recent commentaries have proposed ‘all-world ageing’ as a new perspective for social scientific ageing research. It is based on the theoretical observation that the ageing process involves all forms of entities co-ageing relationally with each other, and with their surrounds. Its disciplinary implications hence being that what we categorize as ageing in social scientific ageing research should not be limited to human bodies, and that ageing non-humans should be brought under its purview. To empirically illustrate these theoretical and disciplinary assertions, and explore their implications, the current paper reports a study of how people co-age with non-humans they interact with in their daily lives. Sixteen people aged 66–90 were interviewed, ten of them also being observed at those times. The findings show some intricate and diverse relations that involve their co-ageing with varied biological entities and nature surrounds (such as plants, domestic animals and green spaces) and varied non-biological entities and non-nature surrounds (such as materials, technologies, accommodations, organizations and infrastructures). Meanwhile, important crosscutting themes – including lifespan, function and aesthetics – emerge as objectives of care, valued and exercised in broad terms. This empirical reconnaissance shows the potential for an all-world ageing perspective to engage diverse societal challenges and inform diverse areas of practice as part of a wider ethics of care. From it, a number of important considerations and undertakings arise for future scholarship.

Leadership in Neoliberal Organizations: Theory and Empirical Illustrations

Leadership in Neoliberal Organizations: Theory and Empirical Illustrations

Environmental multipliers for circular flow–positive-profit economies: formulation, implications and empirical illustration

Environmental multipliers for circular flow–positive-profit economies: formulation, implications and empirical illustration

Empirical illustrations of student’s perception of a preferred employer brand

PurposeTo examine the employer brand attributes that attract students to various organisations. This paper studies the perceived importance levels of students in the context of employer brand dimensions.Design/methodology/approachThis paper is based on a well-administered and structured questionnaire with survey results to test the effectiveness of the employer brand model. The researcher received a convenience sample of 416 filled questionnaires out of the 550 distributed questionnaires. Independent sample and paired sample t-tests were run to test the hypotheses.FindingsDevelopment value emerged as the most potent factor, whereas interest value emerged as the least favoured characteristic of an employer brand. Further analyses revealed no significant differences regarding the perceived levels of importance for dimensions of employer brand in relation to gender. Distinct significance levels are associated with various dimensions of employer brand, so companies should carefully emphasise and facilitate those dimensions. The managers could design their job advertisements to attract skilled employees based on the essential values depicted in this study.Originality/valueThe study contributes valuable suggestions for organisations to formulate an effective employer brand for successful recruitment strategies.

Reading the Candlesticks: An OK Estimator for Volatility

Abstract We propose an Optimal candlesticK (OK) estimator for the spot volatility using high-frequency candlestick observations. Under a standard infill asymptotic setting, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. Its estimation error can be coupled by a Brownian functional, which permits valid inference. Our theoretical and numerical results suggest that the proposed candlestick-based estimator is much more accurate than the conventional spot volatility estimator based on high-frequency returns. An empirical illustration documents the intraday volatility dynamics of various assets during the Fed chairman’s recent congressional testimony.

Selection effects in education and implications for educational opportunity: state of the field and future directions

ABSTRACT Whether intended by education policy (e.g., via school entrance examinations) or unintended (e.g., via social stratification), selection effects in education (who goes where, gets what, and how much) shape educational opportunities – influencing the life chances of individuals and groups, and the structure of societies. However, within the quantitative approach to educational research, current statistical methods can struggle to simultaneously evaluate both the presence and impacts of these effects. In turn, this methodological limitation impedes efforts to facilitate equality of educational opportunity. This paper responds with a critical overview of types of selection effects in education, their consequences for educational opportunity, and the statistical methods used for their identification. Two empirical illustrations show how a new statistical method (“airbag moderation”) can enable better detection and evaluation of selection effects in education and help direct future research into selection effects in education with a focus on opportunities for equality of educational opportunity.