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This paper proposes a probit regression with autocorrelated errors (PAR) to estimate the reaction function of monetary policy in Taiwan using newly constructed binary monetary indicators. We develop a practical sampling scheme via the Gibbs sampling algorithm with data augmentation to make posterior inference of the binary monetary policy reaction function. In contrast to the conventional approach, our method avoids the problem of multiple integrals by directly drawing values of latent variables from the relevant full conditional density along with all the other parameters. Empirical results show that the monetary authority responds to macroeconomic conditions asymmetrically. Specifically, in the high‐inflation regime, a contractionary monetary policy is implemented to reduce the inflation rate. Once inflation is under control, that is, in the low‐inflation regime, attention is paid to stimulating the growth of the economy.

The author proposes analyzing the dynamics of income positions using dynamic panel ordered probit models. The author disentangles, simultaneously, the roles of state dependence and heterogeneity (observed and non-observed) in explaining income position persistence, such as poverty persistence and affluence persistence. The author applies the approach to Chile exploiting longitudinal data from the P-CASEN 2006–2009. First, the author finds that income position mobility at the bottom and the top of the income distribution is much higher than expected, showing signs that income mobility in the case of Chile might be connected to economic insecurity. Second, the observable individual characteristics have a much stronger impact than true state dependence to explain individuals’ current income position in the income distribution extremes.

This experimental study investigates the connotative (measured) meaning of the concept “auditor independence” within three audit engagement case contexts, including two acknowledged in the literature to represent significant potential threats to independence. The study’s research design utilises the measurement of meaning (semantic differential) framework originally proposed by Osgood et al. (1957). Findings indicate that research participants considered the concept of independence within a two factor cognitive structure comprising “emphasis” and “variability” dimensions. Participants’ connotations of independence varied along both these dimensions in response to the alternative experimental case scenarios. In addition, participants’ perceptions of the auditor’s independence in the three cases were systematically associated with the identified connotative meaning dimensions.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

The purpose of this study provide answers to the following research questions: Whether and to what extent money laundering affects the achievability and the trend of Sustainable Development Goals (SDGs)?; Does the influence of money laundering on the SDGs’ achievement differ from developing to developed countries?; How does the influence of money laundering vary among the 17 SDGs?

The paper’s analysis involves two key parts. In the first part, the authors perform a multivariate analysis to examine the influence of money laundering on the achievement of SDGs, and then in the second part, the authors make use of an ordered probit regression model to investigate the impact of money laundering on the trend of attaining each SDG.

Using a sample of 98 developed and developing countries, the regression results from multivariate analysis estimates show that money laundering has a strong inhibiting effect on the achievement of almost all the SDGs in the whole sample of countries and the sub-sample of developing countries, whereas no significant effect is observed for developed countries. However, for the SDG trends, the ordered probit estimates reveal that the harmful effect of money laundering occurs for all countries regardless their development level. In addition, perhaps surprisingly, the results from both the approaches yield also evidence advocating that money laundering activities might be associated with positive externalities on production and consumption. In fact, money laundering is found to have a significant positive influence on the achievement and the trend of SDG12 (Sustainable Consumption and Production). Overall, this study’s findings do have interesting policy implications, especially for developing countries. In these countries, prioritising the formulation and implementation of sound anti-money laundering policies is a necessary requirement for their progress towards achieving the SDGs.

The long-standing tradition of previous empirical studies examining the nexus between money laundering and sustainable development concentrates mainly on the economic dimension of sustainability (i.e. economic growth). However, little is known about the consequences of money laundering activities on the environment and the societies. Consequently, this study seeks to fill this gap by assessing the influence of money laundering on the achievement of the economic, environmental and social goals of sustainable development. To the best of the authors’ knowledge, this is the first integrated study to analyse the potential repercussions of money laundering on the SDGs’ achievement.

This study aims to examine determinants of farmers’ use of climate-smart agricultural practices, specifically improved crop varieties, intercropping, improved livestock breeds and rainwater harvesting in Wadla district, northeast Ethiopia.

A cross-sectional household survey was used. A structured interview schedule for respondent households and checklists for key informants and focus group discussants were used. This study used both descriptive statistics and a multivariate probit econometric model to analyze the collected data. The model was used to compute factors influencing the use of climate-smart agricultural practices in the study area.

The results revealed that households adopted selected practices. The likelihood of farmers’ decisions to use improved crop varieties, intercropping, improved livestock breeds and rainwater harvesting was 85%, 52%, 69% and 59%, respectively. The joint probability of using these climate-smart agricultural practices was 23.7%. The model results confirmed that sex, level of education, livestock holding, access to credit, farm distance, market distance and training were significant factors that affected the use of climate-smart agricultural practices in the study area.

The present study used the most selected locally practiced interventions for climate-smart agriculture.

An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or observed, representing competing hypotheses about the outcomes being considered. However, little attention has been applied to deciphering which specification is best supported by the data. This paper highlights the use of existing Bayesian model comparison techniques to investigate the proper specification for endogenous covariates and to understand the nature of endogeneity. Consideration of both observed and latent modeling approaches is emphasized in two empirical applications. The first application examines linkages for banking contagion and the second application evaluates the impact of education on socioeconomic outcomes.

A growing number of econometric examinations show that works councils substantially shape the personnel policy of firms in Germany. Firms with works councils make greater use of various human resource management (HRM) practices. This gives rise to the question of whether employers view the shaping of personnel policy positively or negatively. Against this background, the purpose of this paper is to examine the influence of works councils on employer attitudes toward HRM practices.

Using data from manufacturing establishments, multivariate and recursive multivariate models are applied to estimate the determinants of employer attitudes toward HRM practices.

The incidence of a works council increases the probability of positive employer attitudes toward the incentive effects of performance pay, profit sharing, promotions, further training and worker involvement in decision making. However, it decreases the probability of positive employer attitudes toward high wages. The results suggest that works councils play a redistribution role in wages and a collective voice role in the other HRM practices.

The study complements examinations focusing on the influence of works councils on the formal presence of HRM practices. There are two potential limitations of focusing solely on formal HRM practices. First, the formal presence of a practice does not necessarily mean that the practice is effectively used. Second, a firm may informally use HRM practices even though the practices have not been formally adopted. The study provides insights into the question of whether or not works councils influence employers’ support for the various practices. This support can be important for the effective use of the practices, regardless of whether they are of formal or informal nature.

The purpose of this study is to explore the potential and challenges of biogas utilization as an alternative and sustainable energy source in the Gurage zone of Southern Ethiopia, where traditional energy sources such as firewood and charcoal are widely used.

The study adopts a mixed-methods approach to collect and analyze data from different sources and perspectives. The research collects quantitative data from structured interviews with 200 rural households who use biogas or other energy sources, and qualitative data from key informant interviews and focus group discussions with biogas experts, local authorities and community leaders. Socioeconomic analysis is conducted to assess the importance of biogas in terms of income, expenditure, health and environmental benefits, and a multivariate probit model is used to identify the factors influencing biogas energy adoption among rural households.

The findings indicate that biogas users are more likely to substitute traditional energy sources with biogas for cooking, lighting and heating purposes. The model reveals that age, sex, education level, land size and livestock quantity influence biogas energy adoption, whereas income, distance to market and access to credit do not have a significant effect. The findings also show that biogas users have higher income, lower expenditure, better health and lower greenhouse gas emissions than nonusers.

The study concludes that the socioeconomic impact of biogas varies among households based on location and lifestyle. The study also highlights the need for further research on the technical, institutional and behavioral aspects of biogas utilization in different contexts.

To address the challenges faced by biogas users and their energy choices, such as lack of awareness, maintenance, quality control and affordability, the study suggests exploring biogas energy to meet the diverse needs of cattle owners in different regions. The study also recommends enhancing the capacity of local stakeholders, promoting public–private partnerships, and developing supportive policies and regulations for biogas development in Ethiopia.

The study implies that biogas utilization can contribute to social development by improving the living standards, health status and gender equality of rural households. The study also suggests that biogas utilization can foster social cohesion and empowerment by creating opportunities for collective action, knowledge sharing and income generation among biogas users and their communities.

The study provides a comprehensive and empirical analysis of the socioeconomic landscape of biogas utilization and the determinants of energy choice in the Gurage zone of Southern Ethiopia. The study also offers valuable insights and recommendations for policymakers, practitioners, researchers and other stakeholders involved in biogas development in Ethiopia and other developing countries.

Using data from the Survey of Income and Program Participation (SIPP) matched to administrative records, we examine mortality risk and participation in the Disability Insurance (DI) and Supplemental Security Income (SSI) disability programs from a long-term perspective. Over a period of 14 years, we analyze the effect of self-reported health and disability on the probability of death and disability program entry among individuals aged 18–48 in 1984. We also assess DI and SSI programs from a life-cycle perspective. Self-reported poor health and severe disability at baseline are strongly correlated with death over the 14-year follow-up period. These variables also are strong predictors of disability program participation over the follow-up period among non-participants at baseline or before, with increasing marginal probabilities in the out-years. Our cross-sectional models are consistent with recent studies that find that the work-prevented measure is useful in modeling DI entry. However, once self-reported health and functional limitations are accounted for, the longitudinal entry models provide conflicting DI results for the work-prevented measure, suggesting that, contrary to claims based on cross-sectional or short-time horizon application models, the work-prevented measure is an unreliable indicator of severity. The risk of SSI and DI participation is significantly greater for individuals who die, suggesting that future mortality captures the effect of case severity and deterioration of health during the follow-up period. From a life-cycle perspective, a substantially greater proportion of individuals participate in SSI or DI at some point in their lives compared to typical cross-sectional estimates of participation, especially among minorities, people with less than a high school education, and those with early onset of poor health and/or disabilities. Cross-sectional estimates for the Social Security area population indicate SSI and DI participation rates of no more than 5% combined in 2000. In contrast, for individuals aged 43–48 in 1984, we observe a cumulative lifetime SSI and/or DI participation rate of 14%. The corresponding figure is 32% for individuals in that age group who did not graduate from high school, suggesting the need for human capital investments and/or improved work incentives.