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The purpose of this paper is to fill the momentous gap by explicitly investigating the asymmetric effects of monetary policy (MP) on non-bank financial intermediation (NBFI) assets.

The authors utilized panel data from 29 countries for the period of 2012–2020 and used the quantile regression estimation. In addition to simultaneous quantile regression (SQR), the authors also employ quantile regression with clustered data (Parente and Silva, 2016) and the generalized quantile regression (GQR) method (Powell, 2020).

The empirical results show a significant heterogeneous impact of MP. While there is a positive relationship between MP and NBFI assets (“waterbed effect”) at lower quantiles of NBFI assets, at middle and higher quantiles, MP has a negative impact on NBFI assets (“search for yield” effect). The authors further find that negative impact strengthens as the quantile levels of NBFI assets rise from mid to high. Findings also reveal that “procyclicality” (except higher quantile) and “institutional demand” hypotheses hold. However, regarding “regulatory arbitrage,” mixed results are observed indicating the impact of Basel III requirements.

Previous empirical studies have concentrated on either the Dynamic Stochastic General Equilibrium (DSGE) framework or conditional mean regression approaches and delivered mixed findings of the MP effects on NBFI. The current paper takes a step toward dealing with this issue by deploying quantile regression methodology, which shows the impact of MP on NBFI at different conditional distributions (quantiles) of NBFI assets instead of just NBFI's conditional mean distribution.

Micro, small and medium-sized enterprises (MSMEs) are the backbone of economic development for every economy. They contribute to local economic development through household wealth creation, employment generation and poverty reduction. Despite this pivotal role, MSMEs lack access to finance, and scholarship on the enabling role of financial inclusion on micro, small and medium-sized enterprises' performance is scant. The authors contribute to closing the knowledge gap by examining the enabling effect of financial inclusion on MSMEs using the FinScope MSME 2017 survey for the Kingdom of Eswatini. This paper aims to discuss the aforementioned objective.

The study used the re-centered influence function regression framework to estimate unconditional quantile regressions and the generalized ordered logit model to analyze the data.

The findings from the unconditional quantile regression revealed that small changes in access to bank accounts, saving for business, formal saving, stokvel and informal saving at the 50th and 75th percentiles have a positive and statistically significant effect on microenterprises' annual turnover profit. Conversely, small changes in formal insurance have a mixed effect on annual turnover profit. At the 10th and 25th percentiles, a small increment in insurance reduces annual turnover profit but increases microenterprise annual turnover profit at the 75th percentile. Meanwhile, the evidence from the generalized ordered logit model showed that financial inclusion reduces the likelihood of microenterprises being classified as least developed and increased the chances of microenterprises falling into emerging and developed business categories.

This study makes use of a cross-sectional survey dataset, as a result, it does not infer causal relationships over the long term, but rather an association between the independent and dependent variables.

Overall, formal and informal financial inclusion enhances the annual turnover profit for microenterprises, particularly at the 50th and 75th percentiles in the Kingdom of Eswatini. The authors recommend a specialized institution such as a micro, small and medium-sized partial credit guarantee scheme to improve the quality and affordability of credit for microenterprises, and a mix of financial and non-financial supports depending on the development stage to boost a sustainable microenterprises' sector.

The study uses two advanced cross-sectional techniques, the recentered influence function framework and the generalized ordered logit model to analyze the data. The paper is original and contributes to the discussion of the role of financial inclusion in enabling microenterprises' success in Africa, using the FinScope 2017 survey of microenterprises in Eswatini as a case study.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2020-0689.

Expansion of the consumption of renewable energy is a significant issue for reducing global warming, to cope with climate change and achieve sustainable development. This study aims to examine how research and development expenditure (R&D) affects renewable energy development in developed G-7 countries over the period from 2000 to 2019. Variables of trade liberalization and CO₂ emissions are considered control variables.

This study has adopted a panel quantile regression. The impact of the variables on renewable development has been examined in quantiles of 0.1, 0.25, 0.5, 0.75 and 0.9. Also, a robust examination is accomplished by applying generalized quantile regression (GQR).

The empirical findings reveal a positive and significant relationship between R&D and the consumption of renewable energy in 0.1, 0.25, 0.5 and 0.75 quantiles. Also, the findings describe that the expansion of trade liberalization and CO₂ emissions can significantly increase the development of renewable energy in G-7 countries. Furthermore, GQR verifies the main outcomes.

These results have very momentous policy consequences for the governments of G-7 countries. Therefore, investment and support for the R&D section to promote the development of renewable energy are recommended.

This paper, in comparison to other research, used panel quantile regression to investigate the impact of factors affecting renewable energy consumption. Also, to the best of the authors’ knowledge, no study has perused the effect of R&D along with trade liberalization and carbon emissions on renewable energy consumption in G-7 countries. Also, in this paper, as a robustness check for panel quantile regression, the GQR has been used.

The purpose of this paper is to examine the effect of bank capital on liquidity creation. Especially, the authors test two competing hypotheses: the “risk absorption” hypothesis and the “financial fragility-crowding out” hypothesis that describe such association in the context of UK and French banking industry.

The authors use data collected from Bankscope for commercial banks pertaining to the aforementioned countries. The sample period ranges from 2000 to 2014. Liquidity creation was measured using a novel approach proposed by Berger and Bouwman (2007). This study uses the quantile regression (QR) and the instrumental variables QR, along with classical ordinary least squares (OLS) and panel regression, to deal with the mixed results reported by previous papers.

Using OLS and panel regression, the authors first find that bank capital negatively affects liquidity creation which supports risk absorption hypothesis. Second, the result from QR confirms the negative association between the aforementioned variables and shows that the effect is homogenous across quantiles of liquidity creation distribution. The result remains unchanged when using the QR with instrumental variables to address the potential problem of endogeneity.

This paper sheds more lights on the relationship between bank capital and liquidity creation by using a novel estimation approach based on the QR methodology.

Interdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting structure among industries. One of the reasons is that the interdependencies show a different pattern in tail events. This paper aims to investigate industry interdependency with the tail events.

General predictive model of Rapach et al. (2016) is extended to an interdependency model via least absolute shrinkage and selection operator quantile regression and network analysis. A dynamic network approach was applied on the Fama–French industry portfolios to study the time-varying interdependencies.

A denser network with heterogeneous central industries is found in tail cases. Significant interdependency varieties across time are shown under dynamic network analysis. Market volatility is identified as an influential factor of industry connectedness as well as clustering tendency under both normal and tail cases. Moreover, combining dynamic network with prediction direction information into out-of-sample industry return forecasting, a lower tail case is obtained, which gives the most accurate prediction of one-month forward returns. Finally, the Sharpe ratio criterion prefers high-centrality portfolios when tail risks are considered.

This study examines the industry portfolio interactions under the framework of network analysis and also takes into consideration tail risks. The combination of economic interpretation and statistical methodology helps in having a clear investigation of industry interdependency. Moreover, a new trading strategy based on network centrality seems profitable in our data sample.

This paper aims to examine house price drivers in Dubai, addressing nonlinearity and heterogeneity.

The study applies a combination of linear and nonlinear, as well as quantile regression, specifications to address these concerns and better explain the real-world phenomenon.

The study shows the double-log quantile regression approach is an overarching description of house price drivers, confirming that not only the price of housing and its determinants are non-linearly related but also that their relationship is heterogeneous across house price quantiles. The findings reveal the prevalence of sub-market differentials in house price sensitivity to house attributes such as size (in square meters), location and type of house, as well as government laws. The study also identifies the peaks and deflation, as well as the rebounding nature of the house price bubble in Dubai.

The data used are limited, in that information on only a few house attributes was available. Future research should include data on other house attributes such as house quality, zip codes and composition.

The findings of this study are expected to suggest results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution.

This study allows policy makers, developers and buyers of higher-priced houses to behave differently from buyers of lower-priced or medium-priced houses.

Methodologically, it demonstrates alternative linear and nonlinear, as well as quantile regression, specifications to address two increasing concerns in the house price literature: nonlinearity and heterogeneity. Unlike most other studies, this study used a rich data (140,039 day-to-day transactions of 10 years’ pooled data). The Dubai housing market presents an interesting case. UAE (Dubai, in particular) is named as the second-hottest marketplace for global residential property investors, ahead of Singapore, the UK and Hong Kong (Savills plc, 2015).

The main purpose of this paper is to look at the link between chief executive officer (CEO) behavior and corporate social responsibility (CSR) engagement with the moderating role of bank risk-taking behavior.

Based on a 13-year data set (2007–2019), the authors applied the feasible generalized least squares with panel data to test the hypotheses.

The findings reveal a positive and significant link between CEO behavior and CSR engagement. Based on these findings, it can be argued that the characteristics of the CEO of the banks would improve the CSR strategies. Furthermore, the study suggests a moderating effect of bank risk-taking in the link between psychological bias and corporate social responsibility engagement (CSR engagement).

As CEO behavioral characteristics are essential to understanding CSR practice, boards of directors should consider the behavioral traits of dominant and overconfident CEOs while designing CSR practices.

If the bank behaves in a socially responsible manner, direct and indirect stakeholders may be able to evaluate the level of risk-taking in more detail.

This research highlights the importance of CEO behavior characteristics for CSR, which is a crucial application that supports the upper echelons theory; and fills a gap in literature research. It is one of the few studies examining the interaction between risk-taking, CEO behavior and CSR engagement.

The objective is to determine if consumer preferences for grocery purchasing are impacted by the severity of the COVID-19 pandemic and whether these preferences differ by demographic and psychographic characteristics.

The authors conduct an online discrete choice experiment (DCE) with 900 U.S. consumers to assess grocery shopping preferences under various scenarios of the COVID-19 pandemic (i.e. decreasing, constant and increasing cases). The attribute of interest is the purchasing method (i.e. in-store purchase, in-store pickup, curbside pickup and home delivery) with minimum order requirements, time windows and fees as secondary attributes. Heterogeneity in individual-level willingness-to-pay (WTP) estimates for the main attribute is analyzed by means of mixed logit and quantile regression techniques.

The mixed logit model reveals heterogeneity in WTP estimates for grocery purchasing methods across participants. According to estimates from quantile regressions, the heterogeneity is partly explained by the severity of the COVID-19 pandemic. For example, the home delivery purchasing method is less preferred when the number of cases is decreasing. The results also show that consumer preferences for grocery shopping methods are affected more by psychographic characteristics than demographic characteristics. Consumers who comply with COVID-19 directives (e.g. wear face coverings) have stronger preferences for curbside pickup and home delivery, particularly at the tails of the WTP distributions.

Although there is much data on food consumer behavior during the COVID-19 pandemic at the aggregate level, there are few analyses of grocery shopping preferences at the individual level. The study represents a first attempt to relate individuals' demographic and psychographic characteristics to their grocery shopping preferences during the COVID-19 pandemic, thus yielding numerous recommendations in terms of consumer segmentation.

A financial perspective of farmer cooperative performance is assumed by conceptualizing the cooperative as an independent firm. The purpose of this paper is to explore variability in the financial performance of the largest 1,000 US farmer cooperatives with emphasis on efficiency, productivity, and leverage.

Cooperative performance is analyzed by means of the extended DuPont identity, an accounting tool which decomposes return on equity into five ratios of efficiency, productivity, and leverage. The extended DuPont identity is applied empirically with quantile regression, which allows estimation of the statistical interrelationship of the DuPont components across the full response distribution.

Per the results, variability in the financial performance of US farmer cooperatives is for the most part associated with the operating profit margin, which confirms prior findings of cost inefficiency in the empirical literature. Therefore, US farmer cooperatives may improve financial performance by emphasizing sales and operating costs. Specifically, recommendations include placing emphasis on bargaining power, product differentiation, and scale economies. Supply cooperatives may also consider issuing non-qualified equity and securing long-term debt access as additional possibilities to improve financial performance.

The empirical application of the extended DuPont identity with quantile regression facilitates a novel investigation of cooperative performance by placing emphasis on the efficiency, productivity, and leverage of cooperatives with various degrees of performance.

Since competitiveness is crucial in international trade, this paper contributes to the literature by interrogating the information and communication technology (ICT)-trade nexus on competitiveness in Eastern and Western European countries. Does ICT usage promote or hinder the impact of trade openness on competitiveness? This study attempts to answer two questions: (1) is the interaction of trade and ICT significant in promoting competitiveness? (2) Is the effect significantly different by European classification?

With data on 17 European countries from 2007 to 2020 and using mobile phones and fixed telephone usage as ICT indicators, the study engages the bootstrapped ordinary least squares (BOLS) and method of moments quantile regression (MM-QR) techniques to probe the discourse.

The empirical findings reveal that (1) the interaction of trade and ICT boost competitiveness; (2) the effect of mobile phone is consistent across the full, East, and West European samples; (3) the interaction effect is also significant across the conditional distribution of competitiveness and (4) mobile phones and fixed broadband usage reveal “leapfrog” effect across the quantiles. Overall, the study submits that ICT usage will enhance the impact of trade, and thus, ICT is a critical enabler of competitiveness in Europe; policy recommendations were discussed.

To the best of the authors' knowledge, this is the first study examining the interaction effect of trade openness and ICT usage on competitiveness in Europe. In other words, the authors attempt to analyze how ICT usage influences trade-competitiveness dynamics. To fill the gap in the literature, the authors' use a sample of 17 European countries from 2007 to 2020. The variables of interest are the competitiveness index, trade openness, and four ICT indicators (mobile phone, fixed telephone subscriptions, fixed telephone subscriptions, and Internet users).