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The purpose of this study is to provide new insights into the relationship between fiscal policy and total factor productivity (TFP) while accounting for several economic and econometric issues of the phenomenon like non-stationarity, fiscal feedback effects, persistence in productivity, country heterogeneity and unobserved global shocks and local spillovers affecting heterogeneously the countries in the sample.

The paper is empirical. It builds an Error Correction Model (ECM) specification within a dynamic heterogeneous framework with common correlated effects and models both reverse causality and feedback effects.

The results of this study highlight some new findings relative to the existing related literature. The outcomes suggest some relevant evidence at both the academic and policy levels: (1) the causal effects going from fiscal deficit/surplus to TFP are heterogeneous across countries; (2) the effects depend on the time horizon considered; (3) the long-run dynamics of TFP are positively impacted by improvements in fiscal budget, but only if the austerity measures do not exert slowdowns in aggregate growth.

The main originality of this study is methodological, with possible extensions to related phenomena. Relative to the existing literature, the gains of this study rely on the way econometric techniques, recently proposed in the literature, are adapted to the economic relationship of interest. The endogeneity due to the existence of reverse causality is modelled without implying relevant performance losses of the models. Moreover, this is the first article that questions whether the effects of fiscal budget on productivity depend on the impact of the former on aggregate output growth, thus emphasising the importance of the quality of fiscal adjustments.

The purpose of this paper is to examine the effect of digital financial innovation on financial depth and economic growth in Kenya.

The study utilized autoregressive distributed lag (ARDL) model, which is preferable over other time series methods as the model allows application of co-integration tests to time series with different integration orders and is flexible to the sample size including small and finite.

The main findings of this paper are as follows: first, there is evidence of a positive relationship between digital financial innovation and financial depth with the strongest impact emanating from Internet usage and mobile financial services and the lowest impact from bank branches; second, the results reveal a significant positive impact of financial depth on economic growth consistent with the supply-leading finance theory.

The results of the study imply a need for investment in technology-enabling infrastructure for digital financial services (DFS) and a redesign of strategies to avoid further financial exclusion of low-income earners due to the unaffordability of digital devices and financial and digital illiteracy.

The study is original and important for policymakers as the study provides insights on the components of financial innovation that are growth-enhancing in Kenya, considering that some aspects of innovation can be growth-retarding as was demonstrated during the global financial crisis.

This study aims to investigate the relationships between loan growth, loan losses and net income after the 2008 global financial crisis. This study further conducts a comparative analysis by considering the period of COVID-19.

This study uses panel data models such as one-step system GMM, random effects, fixed effects and OLS, with a data set of 131 Chinese commercial banks from 2009 to 2020.

The study finds no significant relationship between loan growth and future loan losses. However, after adjusting loan loss by net interest income (NII-adjusted loan loss), the study reveals that loan growth in the subsequent year decreases if NII-adjusted loan loss increases. The study also demonstrates the positive effect of loan growth on net income as newly expanded loans are funded at similar costs but offered at a lower rate compared with existing loans. During COVID-19, loan growth and net income were higher than in previous years.

The findings suggest that Chinese banks can increase lending to support the economy without sacrificing loan quality, emphasizing the importance of maintaining and enhancing credit policies and practices. Chinese banks should also continue to refine their pricing strategies for loans and deposits. The findings also imply that China's policy responses to the impact of COVID-19 could serve as lessons for future policy decisions.

The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).

To determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.

The study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).

This study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.

This study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.

This paper aims to examine the influence of Covid-19, current war and other factors on the dynamics of real estate prices in Ukraine from 2019Q2 to 2022Q4. More specifically, the authors examine the extent of the influence of Covid-19 and war on the real estate market in Ukraine.

The authors monitor and accumulate information flows from the existing real estate market with their subsequent in-depth math-stat processing to examine dynamics and drivers of Ukrainian real estate prices evolution.

The study finds that the Ukrainian residential property market has experienced an average growing trend from June 2019 to December 2022, despite the strong influence of pandemic and war. The analysis shows that the impact of these factors varies across different regions and property types, with some areas and property types being more affected than others. The study also identifies the main drivers of the market evolution, including cost-sensitive factors such as floor level, overall area, housing conditions and geographical location.

This research is oriented to analyze evolution of residential property market in Ukraine in 2019–2022 years characterized by influence of such disturbing factors as pandemic and military actions.

Results gained are essential for any type of Ukrainian residential market analytics implementation including but not limited to investment analysis, valuation services, collateral, insurance and taxation purposes, etc. In broader sense, it can be also useful for comparison with same type market development in other geographical arears.

Initial data base collected and constantly monitored covers all different regions of the country that gives a broad view on the overall market development influenced by pandemic and war.

The lack of a reliable database of the purchase and sale of residential properties remains one of the biggest obstacles in obtaining reliable data on their market value. This considerably complicates the process of carrying out a valuation and reduces the accuracy and reliability of the results of such work. This is especially important for market which evolves in times of unrest being influenced by such strongly disturbing factors as pandemic and military actions. The originality of the study lies in the development of a complete probabilistic processing of the initial database, which provides a reliable and accurate assessment of the market evolution. The results achieved could be used by various stakeholders, such as property owners, investors, valuers, insurers, regulators and other interested customers, to make informed decisions and mitigate risks in the turbulent Ukrainian real estate market.

In this paper, the author aims to investigate the relationship between economic growth and unemployment in six Arab countries from Middle East and North Africa (MENA) zone including Tunisia, Egypt, Morocco, Lebanon, Jordan and Oman through the implementation of Okun's law using quarterly dataset covering the time period 2000: 1–2014: 4.

In this paper, static and dynamic linear and nonlinear models are used to test the linkage between cyclical unemployment and cyclical growth rate.

The empirical results from considered models confirm an inverse linkage between unemployment rate and economic growth, as the Okun's law suggests (except for Oman). In a nonlinear autoregressive dynamic linear (NARDL) framework and gap specification, statistically significant Okun's coefficients indicate that output growth can be translated into employment gains. Absolute effect of an economic contraction is significantly larger than that of an expansion in Tunisia, Egypt, Morocco and Lebanon. The opposite is true for Jordan and Oman.

Empirical finding provides then an additional proof that Okun's law could exist in a developing countries such as Tunisia, Egypt, Morocco, Lebanon and Jordan. Hence, any attempt to increase gross domestic product (GDP) through some economic fiscal and/or monetary policies in these countries would reduce unemployment rate.

Based on asymmetric specification, the author can conclude with precision that an economic upturn of 3.37, 2.98 and 2.5%, respectively, in Tunisia, Morocco and Egypt reduces unemployment by 1%, whilst the downturn of 5.03 and 2.43% (and about 12%), respectively, in Tunisia and Morocco (and Lebanon and Jordan) achieves the opposite.

This study aims to investigate the factors contributing to the low adoption rate of mobile money services (MMS) in the Middle East and North Africa (MENA) region compared to other regions. The study focussed on socio-demographic factors and macro-level determinants in several selected MENA and Sub-Saharan African (SSA) regions where MMS have been successful.

This study analysed 23 countries across MENA and SSA to establish the correlation between socio and macroeconomic factors and MMS adoption using a quantitative approach. The analysis used the generalized least square (GLS) method.

The study revealed that gender and income are factors that positively influence the adoption of MMS in MENA and SSA regions. Additionally, the study found that the affordability index, which measures macroeconomic indicators, correlates with MMS adoption in both regions but in an inversed way. On the other hand, political stability appears to have a positive correlation with MMS adoption in the MENA region. The correlation between the regulatory index and MMS adoption positively impacts the entire study group, although it is insignificant in the SSA region.

Future studies should assess market competition among MMS providers and the psychological aspect of user adoption behaviour. Additionally, conducting a focus group discussion with stakeholders in the MMS industry can assist in uncovering potential factors contributing to low MMS adoption in the MENA region.

This study contributes to understanding the role of the socio-demographic and macroeconomic determinants in promoting digital transformation through adopting MMS.

This study investigates the effect of intellectual capital (IC) and its components on the technical efficiency of Indian commercial banks after controlling the influence of bank-specific and macroeconomic variables.

The study selects a sample of 37 listed Indian commercial banks from 2005 to 2019 and uses the two-step data envelopment analysis (DEA) approach. Banks' technical efficiency scores are first estimated, while the relationship between IC and technical efficiency is examined in the second stage using the panel data Tobit model.

This study's findings suggest a fluctuating trend in the technical efficiency of Indian banks. Notably, from 2015 onwards, a declining technical efficiency trend is observed for all banks. However, private-sector banks outperform public-sector banks in terms of technical efficiency. This study's regression analysis indicates a positive relationship between IC and banks' technical efficiency scores. Further, by decomposing IC into its components like human capital, structural capital and capital employed, the study's findings show that human capital and structural capital enhance banks' technical efficiency. Notably, capital employed reduces technical efficiency. Moreover, bank size, diversification, capitalization, net interest margin and the country's growth rate significantly drive Indian banks' efficiency. In contrast, their operating cost ratio and the country's inflation negatively influence the same.

This study makes a novel endeavor to examine the IC and bank's technical efficiency nexus in the Indian context, encompassing a period of landmark banking reforms.

The social media industry has entered a new stage with intensifying competition and heightened uncertainty about future directions. The purpose of this paper is to provide analyses of the current challenges and to identify industry-wide trends that may offer a roadmap for the future.

Drawing on publicly available key performance metrics, company reports and press reports, this paper offers critical analyses of the challenges facing the major social media platforms and new trends in the social media industry.

This study identified five major trends in the current social media industry: 1) content is king, and that content is moving to visual; 2) artificial intelligence is key to competitive advantage; 3) network effects still matter, but business model innovation can overcome that barrier; 4) the need to broaden revenue sources; and 5) the strive for the everything app. In this changing environment, social media companies need to adapt and innovate their business models proactively to stay ahead.

This paper not only sheds light on the current challenges of individual social media platforms but also identifies industry-wide trends that may apply across all platforms. Taken together, these insights paint a comprehensive picture of the current industry landscape, as well as offer clues about its future directions.

The purpose of this paper is to explore and analyse the dynamic relationship between remittances inflows of Egyptians working abroad and asymmetric oil price shocks.

This study uses a vector autoregressive (VAR) model to explain the impulse response functions (IRFs) and the forecast error variance decomposition (FEVD). The rationale behind using these tools is its ability to examine the dynamic effects of our variables of interest.

The impulse response functions confirmed that remittance inflows have various responses to asymmetric oil price shocks. For instance, inflowing remittances increase in response to positive oil price shocks, while it decreases in response to negative oil price shocks. Also, the results indicate that the responses are significant in the short and medium-run and insignificant in the long run. The magnitude of these responses reaches its peak or trough in the third year. Further, the variance decomposition reveals that oil price decreases are more influential than oil price increases.

This means that remittances inflows in Egypt are pro-cyclical with oil price shocks. That explained by the fact that more than one-half of those remittances sent from GCC countries where real economic growth is very pro-cyclical with the oil prices. This empirical assessment will help policymakers to determine the behaviour of remittances and highlights the impact of different kinds of oil prices shocks on remittances. Unlike the little existing literature, this study is the first study applied the VAR model using a novel dataset spanning 1960-2016.