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The objective of this study is to construct a theoretical framework concerning wage determination, grounded in principles and supplemented by conventional theories. It discusses the Islamic perspectives on minimum wage and examines contemporary challenges and intricacies in its application.

This study uses thematic analysis to create the conceptual framework, drawing upon a review of pertinent literature such as academic papers, books and articles published up to 2023.

The framework encompasses various categories, namely, employee characteristics, job characteristics, market factors, compensation practices and Islamic principles. Each category consists of multiple variables. The resulting framework offers a holistic and ethically grounded methodology for wage determination, aligning with both Islamic and conventional perspectives. This study notes the absence of a universally agreed-upon minimum wage. Islamic economics faces challenges due to the unclear application of principles, limited awareness, legal constraints and a lack of empirical evidence on wage systems, along with complexities in their implementation.

The paper’s limited scope focuses solely on the Islamic perspective on wage determination, without comparing it to the conventional viewpoint. This may have implications for future research.

The insights on Islamic principles and wage determination guide scholars and policymakers interested in promoting just and equitable wages.

This study is distinct in its integration of various factors to propose an all-encompassing framework for wage determination, rooted in the Quran and principles, while also reinforcing the framework with conventional theories. Additionally, it adds to the growing body of literature by investigating the Quran’s stance and principles on minimum wage, as well as discusses the challenges involved in implementing an Islamic approach to wage determination, which has received limited attention in Islamic literature.

The purpose of this paper is to contribute to empirical evidence by recognizing the importance of stock markets in the financial system and consequently its causality to economic growth and vice versa.

The study used the autoregressive distribute lag model (ARDL) with bound testing procedures, the sample covered quarterly time-series data from 2001q1 to 2019q2 in Tanzania.

The results suggest that stock market development have both negative and positive causality for both short-run dynamics and long-run relationship with economic growth. Economic growth is found to only cause and relate negatively to liquidity both in the short-run and in the long-run. The results show predominantly a unidirectional causality flow from stock market development to economic growth and finds partial causality flow from economic growth to stock market development, as represented by stock market turnover which proxied liquidity.

The use of quarterly data to reflect more realistically the dynamics of the variables because yearly data may sometimes cover-up specific dynamics that may be useful for prediction and policy planning. The study uses indices to capture general aspects within the stock market against economic growth as an intuitive way to aggregate the stock market development effects.

The purpose of this paper is to investigate the potential of altmetric and webometric indicators to aid with funding agencies’ evaluations of their funding schemes.

This paper analyses a range of altmetric and webometric indicators in terms of suitability for funding scheme evaluations, compares them to traditional indicators and reports some statistics derived from a pilot study with Wellcome Trust-associated publications.

Some alternative indicators have advantages to usefully complement scientometric data by reflecting a different type of impact or through being available before citation data.

The empirical part of the results is based on a single case study and does not give statistical evidence for the added value of any of the indicators.

A few selected alternative indicators can be used by funding agencies as part of their funding scheme evaluations if they are processed in ways that enable comparisons between data sets. Their evidence value is only weak, however.

This is the first analysis of altmetrics or webometrics from a funding scheme evaluation perspective.

This paper explores the evidence of a long-run co-movement between aggregate unemployment insurance spending and the labor force participation rate in the USA. The unemployment insurance (UI) program tends to expand during an economic downturn and contract during an expansion. UI may incentivize unemployment and may also facilitate better matching in the labor market. Statistical evidence of the presence of a co-movement will thus shed new light on their dynamics.

This research applies time-series econometric approach using monthly data from 1959:1 to 2020:3 to test threshold cointegration and estimate a threshold vector error-correction (TVEC) model. The estimates from the TVEC model investigating the nature of short-run dynamics.

The Enders and Siklos (2001) test find evidence of threshold cointegration between the two indicating the presence of long-run co-movement. The estimates from the TVEC model investigating the nature of short-run dynamics find evidence that the growth in aggregate UI spending and the growth in labor force participation rate adjust simultaneously to maintain the long-run co-movement above the threshold in the short run. The author also observes the same short-run dynamics for the growth in aggregate UI spending and the growth in the labor force participation rate for females.

This model is bi-variate by construction and does not address causality.

The author argues that the UI program positively impacts the female labor market outcomes, for example, better matching. This finding may explain the upward trend in the labor force participation rate for females in the USA.

The research findings may justify the transfer programs for minority and immigrants.

This is first research that analyzes the UI programs impact on the labor force participation using a macroeconometric approach. To the best of the author's knowledge, this is the first study in this genre.

This article deals with the impact of ethnic origin on individual employability, focussing on the first stage of the hiring process. Deeply, the authors’ goal is to fathom whether there is a preference for native job candidates over immigrants, decomposing the discrimination against minority groups into its statistical and taste-based components by means of a new approach.

The authors built up a data set by means of an ad hoc field experiment, conducted by sending equivalent fictitious CVs in response to 1000 real online job openings in Italy. The authors developed the discrimination decomposition index using first- and second-generation immigrants.

The authors’ main result is that both first- and second-generation immigrants are discriminated compared to Italians. In between the two categories, second-generation candidates are discriminated especially if their ethnicities are morphologically different from those of natives (i.e. Chinese and Moroccans). This last finding is a clear symptom of discrimination connected to taste-based reasons. On the other hand, first-generation immigrants of all nationalities but Germans are preferred for hard-work jobs.

The authors develop the discrimination decomposition index to measure the proportion of the two kinds of discrimination (statistical and taste-based) over the total one and apply a probit model to test the statistical significance of the difference in treatment between the three groups of natives, first-generation and second-generation immigrants.

The purpose of this study is to investigate the impact of climate change on economic development in Bangladesh. More specifically, the research aims to figure out the influence of climate change on gross domestic product (GDP) growth rate related to different sectors such as agriculture, forest, water, health and infrastructure. It also attempts to explore the effect of climate change on the coastal economy of Bangladesh.

A set of statistical and econometric techniques, including descriptive and correlation analysis and time series regression model, was applied to address the objective of the research. Sector-wise time series economic data were collected from the World Bank for the period between 1971 and 2013. Climate data were received from the Bangladesh Agricultural Research Council online database for the period between 1948 and 2013.

The results from the statistical analysis show that climate variables such as temperature and rainfall have changed between 1948 and 2013 in the context of Bangladesh. The econometric regression analysis demonstrates that an increase by 1°C of annual mean temperature leads to a decrease in the GDP growth rate by 0.44 per cent on average, which is statistically significant at the 5 per cent level. On the other hand, the estimated coefficients of agriculture, industry, services, urbanization and export are positively associated with GDP growth rate, and these are statistically significant at the 1 per cent level. Sector-wise correlation analysis provides statistical evidence that climate change is negatively associated with various sectors, such as agriculture, forest, human health and arable land. In contrast, it has a positive relation to water access and electricity consumption. Analysis of coastal regions shows that climate change negatively affects the local economic sectors of the coastal zone of the country.

Although this study has received significant insight from the world-renowned research publication “The Economics of Climate Change: The Stern Review”, there is a dearth of research on the economic impact of climate change in the context of Bangladesh. The findings of the paper provide deep insight into and comprehensive views of policy makers on the impact of climate change on economic growth and various sectors in Bangladesh.

This study aims to investigate the occurrence of the decoy effect in stock investment decisions based on fundamental analysis.

In this study, the decoy effect was investigated by applying two questionnaires, one of them with the presence of a decoy alternative, to a set of 224 respondents with knowledge of business fundamentals, simulating investment decisions in stocks of companies listed on the Brazilian Stock Exchange. The data analysis was performed using the Fisher's exact test, Student's t-test and ANOVA. The research also aimed to detect a potential relationship between the variables gender, age, degree and professional experience with the type of decision made.

The results pointed to the occurrence of the decoy effect when analysing the general response data. However, such evidence was not confirmed when the sample was analysed by classes (gender, course, age and professional experience). There is no statistical evidence that the decoy effect influences classes.

The recent decoy effect literature is little explored in investment decision-making. This study is unique in examining the decoy effect in investment decisions in the Brazilian context.

Expanding on the real-world financial market framework and considering the current market turmoil, with cryptocurrencies (where contracts for difference (CFDs) are extremely common) (Hasso et al., 2019) displaying unprecedented volatility, the authors aim to test in an online laboratory setting whether displaying a risk warning message is truly effective in reducing the level of risk taken and whether the placement of this method makes a difference.

To explore the impact of risk disclosure framing on risk-taking behavior, the authors conducted an online pair-wise lottery choice experiment. In addition to manipulating risk awareness through the presence or absence of risk warning messages of varying intensity, the authors also considered dynamic inconsistency, cognitive ability and questionnaire-based financial risk tolerance (FRT) scores. The authors aimed to identify potential relationships between these variables and experimentally elicited risk aversion. The authors' study offers valuable insights into the complex nature of risky decision-making and sheds light on the importance of considering dynamic inconsistency in addition to risk awareness and aversion.

The authors' results provide statistical evidence for the efficacy of informative and very salient messages in mitigating risky decision, hinting at several policy implications. The authors also provide some statistical evidence in support of the relationship between cognitive abilities and risk preferences. The authors detect that individual with low cognitive abilities scores display great risk aversion.

This study investigates the impact of risk warning messages on investment decisions in an online laboratory setting – a unique approach. However, the authors go beyond this and also examine the potential influence of dynamic inconsistency on decision-making, adding further value to the literature on this topic. To ensure a comprehensive understanding of the participants, the authors collect data on cognitive ability and FRT using questionnaires. This study provides a simple and cost-effective framework that can be easily replicated in future research – a valuable contribution to the field.

The present paper aims to understand the influence of consumer's functional, psychological and emotional barriers to the use of digital banking services.

The authors carried out a quantitative study in which data were collected through a self-administered online questionnaire. A final sample of 202 Brazilian adults, with and without experience in using digital banking services, enabled the test of research hypotheses by means of a structural equation modeling approach.

The authors found statistical evidence that supports the hypothesis that psychological barriers, emotional barriers and user experience positively influence the resistance to the use of digital banking services. However, there is no empirical evidence supporting that the influence of functional barriers affects the resistance to the use of digital banking services.

Efforts to understand the mechanisms that lead consumers to adopt or reject innovative products or services are important to prevent investments in these innovations, avoiding revenue failures. The results provide managerial implications by favoring the creation of communication programs capable of reducing the possibilities of innovation failure.

The main theoretical contribution of this work is the identification of the predominant influence of emotional barriers, in comparison to functional barriers, on the resistance to innovation in digital banking services. Currently, the models that illustrate resistance to innovation tend to focus solely on functional aspects; however, these models can be improved by incorporating emotional aspects.

This study aims to investigate, for the first time in the literature, the stochastic properties of the US aggregate health-care price inflation rate series, using the data on health-care inflation rates for a panel of 17 major US urban areas for the period 1966-2006.

This goal is undertaken by applying the first- and second-generation panel unit root tests and the panel stationary test developed recently by Carrion-i-Silvestre et al. (2005) that allows for endogenously determined multiple structural breaks and is flexible enough to control for the presence of cross-sectional dependence.

The empirical findings indicate that after controlling for the presence of cross-sectional dependence, finite sample bias, and asymptotic normality, the US aggregate health-care price inflation rate series can be characterized as a non-stationary process and not as a regime-wise stationary innovation process.

The research findings apply to understanding of health-care sector price escalation in US urban areas. These findings have timely implications for the understanding of the data structure and, therefore, constructs of economic models of urban health-care price inflation rates. The results confirming the presence of a unit root indicating a high degree of inflationary persistence in the health sector suggests need for further studies on health-care inflation rate persistence using the alternative measures of persistence. This study’s conclusions do not apply to non-urban areas.

The mean and variance of US urban health-care inflation rate are not constant. Therefore, insurers and policy rate setters need good understanding of the interplay of the various factors driving the explosive health-care insurance rates over the large US metropolitan landscape. The study findings have implications for health-care insurance premium rate setting, health-care inflation econometric modeling and forecasting.

Payers (private and public employers) of health-care insurance rates in US urban areas should evaluate the value of benefits received in relation to the skyrocketing rise of health-care insurance premiums.

This is the first empirical research focusing on the shape of urban health-care inflation rates in the USA.