Purpose The purpose of this paper is to explore the effect of monetary policy on bank risk in the banking system in some MENA countries. It explores how some economic and credit indicators affect the level of risk in the banking sector. It combines many factors that could affect banks’ risk appetite such as macroeconomic conditions, banks’ credit size and lending growth. The authors use nonperforming loans as a proxy for banking sector risks. At first, the authors have analyzed the linear relationship between monetary policy and credit risk. As mentioned above, nonlinearity is expected in the underlying relationship, and, thus, they have investigated the nonlinear relationship to deeply analyse the relationship using the dynamic panel threshold model, as stimulated by Kremer et al. (2013). Threshold models have gained a great importance in economics and finance for modelling nonlinear behaviour. Threshold models are useful in showing the turning points in the behaviour of financial and economic indicators. This technique has been applied in this study to study the effect of monetary policy on credit risk. Design/methodology/approach This paper is divided into the following sections: Section 2 which previews the recent literature; Section 3 which includes some stylized facts about the relationship between credit risk and monetary policy; Section 4 which deals with the model and methodology; Section 5 which handles the data sources and discusses the results, and finally Section 6 which is the conclusion. The paper adopts dynamic panel threshold model of Kremer et al. (2013). Findings The results show that the relationship between monetary policy and credit risk is positive and significant to a certain threshold, 6.3. If the lending interest rate is higher than 6.3, this increases the credit risk in the banking sector, because increasing the lending interest rate imposes huge burdens on the borrowers, and, therefore, the bad loans and nonperforming loans become more likely. Thus, the MENA countries need to decrease the lending interest rate to be less than 6.3 to reduce the effect of monetary policy on credit risk. Further, these results are qualitatively robust regarding the inclusion of additional control variables, using alternative threshold variables and further endogeneity checks of the credit risk, such as Risk premium and the squared term of the lending interest rate. The results of taking the risk premium and the squared term of the lending interest rate as a threshold served the analysis and confirmed the positive relationship between monetary policy and credit risk above a certain threshold. As for the risk premium, the relationship below the threshold was negative and significant. Other related research points might be a good avenue for the future research such as applying this approach to micro data of banks from different MENA countries. Also, more sophisticated approaches like time-varying panel approach to assess the relationship over the time can be applied. Originality/value The importance of this paper lies in the fact that it does not only study the effect of time, but it also focuses on the panel data about some economic and credit indicators in the MENA region for the first time. This is because central banks in the MENA region have common characteristics and congruous level of economic growth. Therefore, to study how the monetary policy affects those countries’ credit risks in their lending policies, this requires careful analysis of how the central banks in this region might behave to control default risks.
Given that carbon dioxide emissions is the main reason of environmental pollution and global warming, this paper attempts to investigate the impact of gross domestic product (GDP) per capita, electricity production from different sources (including renewable and non-renewable energy) and industrial sector share in GDP on Carbon dioxide emissions (CO 2 ) for a Euro Mediterranean panel of 25 countries using dynamic panel data model for the period 2002-2016. The paper uses the Hausman test and panel unit root test in order to verify the stationary of the variables series employed. Generalized least square estimator is used for building Panel regression model. The results indicate a significant positive impact of economic growth and electricity output from coal on CO 2 emissions In contrast, renewable resources have a negative impact on CO 2 emission.
The global transition to the Sustainable Development Goals (SDGs) has stimulated a holistic role of development planning to formulate national strategies and meet the global goals by 2030. Massive investment -particularly public investment -has crucially been regarded as a fundamental planning tool that reflects the strategies' priorities. In addition, this transition was conducive to expanding the usage of the indicators criterion to measure development: adjusted net saving (ANS) is regarded as a composite index to measure the degree of sustainable development (SD) of a country. Accordingly, as an ex-post evaluation, this work aims to empirically examine the role of allocated public investment, in different sectors, as explanatory variables in promoting the SD in Egypt throughout the period 1990-2019. In doing so, the autoregressive distributed lag model (ARDL) has been applied to test the cointegration relationship between SD and public investment. The research further employs the error correction model (ECM) to explore the short-run relationship. The empirical results revealed that while public investment in goods and social services sectors stimulated SD, it was not so for the transportation and storage sector. The short-run result ensures the same findings. Having this finding means that public investment in the transportation sector is not a supportive channel for SD achievement. Building on the empirical results, this research recommends re-allocating public investment in favour of the human capital and industrial sectors, considering them good channels to enhance the sustainability conditions in Egypt. Moreover, adopting a sustainable transportation strategy, considering clean and green energy, is a proactive measure to mitigate the side effect of the transportation projects. These findings shed light on the vital role that directing public investment toward specific channels can make in promoting the SD of a country.
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