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Despite the vulnerability of rapidly developing and emerging market economies, researchers have paid less attention to the determination of the size of money laundering (ML) in these economies, including the United Arab Emirates (the UAE). Therefore, this paper aims to estimate the magnitude of ML in the UAE between 1975 and 2020 based on the currency demand approach (CDA).

The study uses the Gregory–Hansen cointegration technique alongside the autoregressive distributed lag bounds testing procedure to estimate the CDA model.

The results illustrate that an amount equivalent to about 19.034% of the GDP is laundered in the UAE between 1975 and 2020, on average, with the value lying between 15.129% and 23.121%. In addition, the results demonstrate the importance of the real estate market, gold trade, remittance channels and the size of the underground economy in facilitating the laundering of illicit funds in the country.

To the best of the authors’ knowledge, the study is the pioneering attempt at estimating the amount of illicit funds laundered in the UAE. Besides, the adoption of a novel, yet robust, approach based on the modification of the CDA technique also sets the study apart as it ensures a correct, clear, unambiguous and indisputable estimate of the magnitude of ML is obtained. In addition, it is expected that the outcome of the study will expand the frontiers of knowledge among policy makers and relevant agencies and ensure the adoption of the most efficient and effective measures to curb the ML menace in the country.

The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.

Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, the authors develop a three-stage model that accounts for the salesperson’s price decision within the limits of the latitude provided by the firm; the firm’s decision to approve or not approve a sales application; and the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, the authors compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e. Broyden–Fletcher–Goldfarb–Shanno algorithm).

The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives leads to double-digit lifts in firm profits. Moreover, the authors find that the high-risk customer segment is less price-sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.

Substantively, to the best of the authors’ knowledge, this paper is the first to empirically investigate the profitability of analytics-driven segment-level (i.e. discriminatory) centralized pricing compared with sales force price delegation in indirect retail channels (i.e. where agents are external to the firm and have access to competitor products), taking into account the decisions of the three key stakeholders of the process, namely, the consumer, the salesperson and the firm and simultaneously optimizing sales commission and centralized consumer price.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The conflict between institutionalism and neoclassicism in the 20th century has been investigated by scholars over the years. Many of them believe that in the postwar period, neoclassicism triumphed while institutionalism largely disappeared. The present chapter takes a very different view. The late 20th century represents a broad synthesis of neoclassical and institutional themes in a methodology we call pragmatic empiricism. That approach combines the mathematical model building and theoretical formalism of neoclassical economics with the institutional economist’s data-driven statistical analysis and concern for developing institutional forms. We use as a case study the history of American locational economics from the 1930s to the present. The mixing of institutional and neoclassical themes is quite evident in the work of three young scholars at Harvard who effectively initiated American locational economics. In the postwar period, we find a series of outstanding, well-published papers that capture the spirit of the “founders.” These papers do use more modeling, but they also focus on major institutional developments. A broader review of locational works is consistent with the pragmatic empiricism label. The history of locational economics supports the claim that institutionalism, far from disappearing, continues to provide fundamental questions and techniques for modern pragmatic empiricism.

Bipolar and depression disorders are some of the most common mental health disorders affecting millions of people in low-and middle-income countries, including those in Africa. These disorders are therefore major contributors to the burden of diseases and disability. While an enhancement in income is seen as a major approach towards reducing the burden of these disorders, empirical evidence to support this view in the African context is lacking. This study therefore aims to examine the effect of per capita income growth on bipolar and depression disorders across African countries.

The study uses data from secondary sources comprising 42 African countries over the period, 2002–2019, to achieve its objective. The prevalence of bipolar and major depressive disorders (depression) are used as the dependent variables, while per capita income growth is used as the main independent variable. The system Generalised Method of Moments regression is used as the estimation technique.

In the baseline, the authors find per capita income growth to be associated with a reduction in the prevalence of bipolar (coefficient: −0.001, p < 0.01) and depression (coefficient: −0.001, p < 0.1) in the short-term. Similarly, in the long-term, per capita income growth is found to have negative association with the prevalence of bipolar (coefficient: −0.059, p < 0.01) and depression (coefficient: −0.035, p < 0.1). The results are similar after robustness checks.

This study attempts at providing the first empirical evidence of the effect of per capita income growth on bipolar and depression disorders across several African countries.

This study aims to assess the impact of participation in self-help group (SHG) on the psychological empowerment of rural tribal women.

Primary data was collected using multistage random sampling from the rural women of Jharkhand, India. The propensity score matching method was adopted using the psmatch2 command in STATA.

The results show a significant positive change in women’s self-esteem, self-confidence, self-efficacy, autonomy, knowledge and skills, reduction in agony and quality of life after participation in SHG. This reveals that participation in SHG has a significant positive impact on the psychological empowerment of rural tribal women.

Despite the numerous studies on rural women’s empowerment, there is little evidence of literature focusing on the impact of participation in SHG on psychological empowerment, specifically in the tribal context. This study primarily focuses on women belonging to the scheduled tribe category of Jharkhand, one of the poorest states of India.