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The purpose of this paper is to understand the distributional impact of house price increases on consumption in the context of the energy transition.

This study draws from two micro cross-sectional datasets, the English Housing Survey (EHS) and the Living Costs and Food Survey (LCFS) to study the Marginal Propensity to Consume (MPC) out of changes in house prices. By employing pseudo-panel regressions, the paper examines the impact of house price changes on consumption among diverse household types.

This paper finds varying consumption responses to house price changes across age and tenure groups. Older homeowners tend to increase consumption when house prices rise. In contrast, middle-aged individuals, often renters or mortgage holders, reduce consumption in response to price increases. The youngest age group also experiences increased consumption but to a lesser degree than the oldest group. Energy-efficient homes are related to lower consumption across all tenure levels. However, when interacted with house prices and age, the estimates are positive, pointing to an unequal accrual of property premiums depending on housing market positions.

The main limitations stem from data constraints. First, using a pseudo-panel approach hinders control for unobservable selection bias. Additionally, while robust under cross-validation and specifications tests, the energy efficiency variable imputation results in a low number of energy-efficient homes. Due to heterogeneous responses to rising house prices, this paper contends that an energy transition model that subsidises homeowners’ renovation is likely to produce a negative impact on consumption among younger and middle-aged households.

This paper contributes to the MPC literature by incorporating energy efficiency as a key variable. It draws from recent data to obtain new estimates. By highlighting shifts in consumption patterns the paper contributes to a well-established body of literature with renewed policy relevance regarding housing retrofit.

The current study sought to better understand the factors that contribute to whether officers value procedurally just interaction techniques and contribute to the limited research examining how the effects of warrior and guardian mentalities may vary based on individual officer characteristics.

Survey data collected from patrol officers in two geographically different and ethnically diverse United States police departments allowed for an examination of the generality of warrior and guardian orientations on perceptions of procedural justice across gender, race and/or ethnicity, military service, education, and experience.

There was a largely invariant effect of the mentalities on officer attitudes toward procedural justice, except for officers of color. In this sample, the guardian effect on prioritizing procedural justice was stronger for officers of color than for White officers.

This study sheds light on our theoretical understanding of the warrior/guardian framework and offers practical implications for police leaders and policymakers in their effort to improve police-community relations.

Our objective was to evaluate the cost-utility of a health and social care integration programme for frail older adults in Buenos Aires, Argentina.

Based on a study of the programme’s effectiveness, a Markov model was conducted to assess its cost-utility. The active intervention was the health and social care integration programme, and the control was the best standard of care so far. The setting was the patients' home of residence. A third-party payer perspective and a lifelong time horizon were adopted. All transition probabilities, quality-adjusted life years (QALYs) and costs were estimated from the effectiveness study. A discount rate of 3.5% was applied to costs and benefits. Costs are expressed in international dollars (Int$), calculated according to the International Monetary Fund’s purchasing power parity rate. Different sensitivity analyses were performed. The model was built in Excel 365. Construct validity, verification during model construction and internal consistency of the results were assessed.

The programme had an average cost of Int$18,768.22/QALY, and the control Int$42,609.68/QALY. In the incremental analysis, the programme saved Int$26,436.10 and gained 0.81 QALYs over the control. In the sensitivity analyses, in 99.96% of cases, the programme was less costly and more effective.

The cost savings can facilitate the scalability.

The health and social care integration programme for frail older adults was more effective and less costly than the best standard of care to date. This study contributes to the scarce evidence on the efficiency of integrated care strategies for frail older persons.

This study aims to describe and empirically explore a new method for bank anti-money laundering (AML) systems using machine learning models. Current automated money laundering detection systems are notorious for flagging many false positives, causing bank employees to spend unnecessary time manually checking transactions that do not constitute money laundering. Decreasing the number of false positives can free up resources for investigating money laundering.

This study uses unique bank data on small- and medium-sized enterprises (SMEs) to examine how various client risk classification models can predict future suspicious transactions. This study explores various sources of client risk data and machine-learning approaches.

Client risk classification models can accurately predict suspicious future transactions. Adding accounting data and credit score information to client risk classification dramatically improves accuracy. This makes it easier to balance the risk of missing suspicious transactions with the need to reduce the number of false positives.

The suggested approach with readily available data sources and a focus on classifying client risk in a dynamic model can help banks significantly improve their efficiency by targeting their AML efforts toward the riskiest clients.

To the best of the authors’ knowledge, this study is the first to empirically explore machine learning in client risk classification, document how machine learning in client risk classification can significantly reduce false positives by incorporating novel, but readily available sources, such as credit risk and accounting data.

This paper aims to analyse gender wage gaps by university majors along the entire wage distribution in Spain before and after the 2008 financial crisis.

The authors perform unconditional quantile regressions to estimate the gender wage gap and use the Oaxaca–Blinder approach to decompose the gender gap.

The observed gender gap among graduates hides significant differences across various fields of study, and both the gap and its unexplained part are highly dependent on the position in the distribution. Engineering and Experimental sciences are the fields with the highest wage differences, and the gap size worsens with the crisis. Health and Humanities, the majors with the highest women presence, show a higher proportion of unexplained part at the bottom tail of the wage distribution, especially after the crisis, suggesting that discrimination against low-paid women has aggravated in these majors.

The paper adds to the existing knowledge by analysing the role that educational decisions play in shaping the wage gap, the variability of the gap along the wage distribution and its response to a change in macroeconomic conditions.

Given the scientific and practical difficulties inherent in measuring and comparing socioeconomic deprivation (SED), and the further complexity added in cross national measurements, the main aim of this paper was to check the validity of SED measures used in PISA 2018 dataset. The SED measure used in PISA 2018 was the PISA index of economic, social and cultural status abbreviated as the ESCS index. This assessment was important as PISA analysis is based on variables derived from this instrument and the ESCS index and these reports influence and reflect international and comparative education policies and practice.

This study critically evaluates the socioeconomic status measures in the PISA 2018 dataset, focusing on their convergent validity and cross-national comparability. Using responses from over 600,000 students in 73 countries, it examines the validity of SES indicators and their comparability across countries. The study employs principal component analysis to construct local SES measures and compares them with the existing Economic, Social, and Cultural Status (ESCS) index. It explores the relationship between these SES measures and academic achievement in reading, science, and mathematics, aiming to understand their predictive validity in diverse educational settings. Statistical analyses were conducted using the IEA’s IDB Analyser and SPSS, ensuring robustness and generalisability across the diverse participant countries.

Our research findings challenge the assumed superiority of local measures over broader constructs like the Economic, Social, and Cultural Status (ESCS). It suggests that standardised measures like ESCS may provide more reliable predictions of academic achievement across various educational contexts, underscoring the complex relationship between SES measures and academic performance.

Our novel analysis shows that local and cross-national SED measures are poorly correlated. Our findings raise questions about the measures' validity while acknowledging the methodological challenges. We provide empirical evidence to support ongoing debates on the topic.

Adolescents with harmful sexual behaviors (AHSB) who drop out of treatment are more likely to continue offending than are those who complete treatment; therefore, it is important to identify factors that heighten the risk of dropout, so they can be detected early. The purpose of this paper is to present the predictors of treatment dropout derived from a community sample of AHSB in New Zealand.

Pretreatment data on 100 males (aged 12–16) in community-based treatment for harmful sexual behavior were analyzed. Data on 50 adolescents who dropped out were matched by age and ethnicity to 50 adolescents who completed treatment. Pretreatment variables were identified using the Estimate of Risk of Adolescent Sexual Offence Recidivism. The degree to which these variables influenced treatment dropout was tested using logistic regression.

Compared to those who completed treatment, adolescents who dropped out were more likely to have a prior history of personal victimization, to deny or minimize their behavior, to have been mandated to attend treatment and to have engaged in noncontact offences.

Screening for a prior history of personal victimization, denial or minimization, mandated treatment and noncontact offences may facilitate the prediction of dropout risk more confidently. Addressing these pretreatment risk variables has the potential to improve treatment completion rates.

To the best of the authors’ knowledge, this paper is the first to highlight treatment dropout predictors in a New Zealand community sample.

The conventional customer lifecycle fails to acknowledge the “sleeping” stage between regular patronage and churn, particularly prevalent in the hospitality industry. This study constructs an awakening model to regain “sleeping” guests.

342 questionnaires from Macau using partial least squares-structural equation modeling (PLS-SEM) were analyzed. The model was compared across different membership levels through multigroup analysis.

The results indicate that the point policy can awaken “sleeping” guests by influencing their perceived value, regret, and integrated satisfaction with a shorter “sleeping” period. Two path coefficients showed significant differences among basic and elite members.

Companies with loyalty programs should implement a transitional period before resetting points, leveraging altruistic point policies to awaken “sleeping” guests via direct communication. This strategy mitigates the negative impact of finite point expiration policies, enhancing customer re-engagement and point utilization.

Our study focuses on a crucial facet of hotel marketing—customer regain strategies. By identifying customer segments who have not revisited the hotel group for more than twelve months, we confirm the concept of “sleeping” guests. This term offers a nuanced perspective, distinguishing “sleeping” guests from generic lost customers. The “sleeping” guest segment provides valuable insights for enhancing targeted and effective marketing activities in the highly competitive hotel industry.

Student engagement evaluation is considered to be connected to many aspects of the management of higher education, but outside Western higher education, research and evaluation on student engagement and experiences has been limited so far. Our study focuses on the underexplored aspects of Palestinian higher education with the aim of gaining an actionable understanding from the overall student engagement situation to enhance the management and development of local teaching and learning practices.

A quantitatively oriented, sequential mixed-methods design was adopted. With the applied and validated engagement measurement we collected 946 engagement questionnaire responses from Palestinian university students. Quantitative data were analysed using structural equation modelling, K-means cluster analysis and chi-squared tests. Inductive and deductive thematic analysis was employed for the open answers.

With the three validated student engagement dimensions, the applied cluster analysis allowed three different engagement profile groups to be distinguished: strongly, moderately and loosely engaged. In the subsequent statistical and qualitative thematic analyses, these three engagement clusters differ in the degree to which they had a clear vision of a future profession or in their academic engagement with their studies. Moreover, qualitative analysis brought up many shared concerns regarding theoretically oriented studies and uncertain professional and career prospects in the Palestinian higher education context.

This study is one of the first attempts to develop tools for student engagement management in Palestinian higher education. The study findings are particularly significant for developing micro- and meso-level management practices in Palestinian higher education institutions.

This study aims to explore the asymmetric effects of macroeconomic factors on the profitability of large-cap companies in an emerging country like Colombia, using the Morgan Stanley Capital International (MSCI) Colombia index as the basis.

We employ a combination of singular spectrum analysis (SSA) and principal component analysis (PCA) to identify and estimate four key macroeconomic factors that account for approximately 47.8% of Colombia's macroeconomy. These factors encompass indicators related to inflation and cost of living, foreign trade and exchange rate, employment and labor force and trade and production in Colombia. We utilize the distributed lag nonlinear model (DLNM) to analyze the asymmetric relationships between these factors and corporate profitability, considering different scenarios and lags.

Our analysis reveals that there are indeed asymmetric relationships between the identified macroeconomic factors and corporate profitability. These relationships exhibit variability over time and lags, indicating the nuanced nature of their impact on corporate performance.

This study contributes to the existing literature by applying a novel methodology that combines SSA and PCA to identify macroeconomic factors within the Colombian context. Additionally, our focus on asymmetric relationships and their dynamic nature in relation to corporate profitability, using DLNM, adds original insights to the research on this subject.