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The discussion on international migration has become a significant part of globalization and a topical issue in international relations, especially in developing economies which mostly relies on migrant remittances. The purpose of the study is to examine whether financial market development (equity market development and banking sector development) really drives migrant remittance flow in Sub-Saharan Africa (SSA).

The study employs the dynamic heterogeneous panel data approach-the pool mean group (PMG) and the mean group (MG) techniques in analyzing the model based on data obtained from 27 SSA countries covering the period 2000–2020.

The findings of the study revealed that financial market development (equity market development and banking sector development) is a key driver of migrant remittances flows in the SSA region. In addition, the study revealed that the following macroeconomic variables such as real interest rate, unemployment rate, global growth, emigration, and economic growth are also determinants of migrant remittances flows in the SSA region.

The reviewed empirical literature revealed that several studies documents that the macroeconomic determinants of migrant remittances include inflation, GDP, interest rate, exchange rate, population growth, financial sector development and unemployment rate. Most of these studies fail to capture both equity market development and robust banking sector development (financial market development) as critical drivers of migrant remittances flow in SSA. Also, this study uses a robust measure of equity market development and banking sector development, unlike previous studies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0361

Personal remittance and ready-made garments (RMG) export incomes have emerged as the largest source of foreign income for Bangladesh's economy. The study investigates their impact on income inequality and gross domestic product (GDP) as a control variable, using time-series yearly data from 1983 to 2018.

It employs the Autoregressive Distributed Lag (ARDL) estimation and the Toda-Yamamoto (T-Y) causality approach. The ARDL estimation outcomes confirm a long-run association among the above variables and validate the autoregressive characteristic of the model.

Personal remittances positively contribute to reducing the income gap among the people of the society and declining income inequality. In contrast, RMG export income and economic growth contribute to further income inequality. The T-Y causality analysis follows the ARDL estimation outcomes and authenticates their robustness. It reveals a feedback relationship between remittance inflow and the Gini coefficient, unidirectional causalities from RMG export income to income inequality and economic growth to income inequality.

The finding has important policy implications to limit the income gaps between low and high-income groups by channeling incremental income to the lower-income group people. The policymakers may facilitate further international migration to attract further remittances and may upgrade the minimum wage of the RMG workers.

The study is original. As far as the authors' knowledge goes, this is a maiden attempt to investigate the impact of personal remittances and RMG export income on income disparity in the case of Bangladesh.

The purpose of this paper is to empirically analyze the impact of remittance inflows on sustained economic growth in India.

This study has taken a time series dataset for the period of 1976–2021, and a nonlinear autoregressive distributed lag model technique (NARDL) has been applied to check the impact of remittance inflows along with other control variables, including broad money and service sector performance, on the sustained economic growth of India.

The results of the study indicated that in both the short and long runs, any positive shock in remittance inflows has a positive impact on the economic growth of India, while negative shocks do not affect economic growth.

The economic policymakers of India can use the findings of the study by implementing remittance-friendly policies. Moreover, NITI Aayog, the body working toward achieving sustainable development goals (SDGs) in India, can also use this study as a reference while making strategies to achieve SDG.

Economic growth has always been an area of interest among economists, researchers and policymakers. However, achieving sustained economic growth requires an analysis of those factors that themselves have sustained performance over a long period of time and have the potential to sustain it over the upcoming years. This study has taken remittance inflows as one such factor and investigated its impact on the sustained economic growth of India. At present, there is an evident gap in the literature that very little attention has been given to sustained Indian economic growth. Moreover, there is no study available in which the nonlinear impact of different variables has been tested on the economic growth of India.

South Asia is one of the fastest-growing regions in the world. With its fast economic development, the energy requirement for the region has rapidly grown. As the region relies mainly on nonrenewable energy sources and is suffering from issues like pollution, the high cost of energy imports, depleting foreign reserves, etc. it is searching for those factors that can help enhance the renewable energy generation for the region. Thus, taking these issues into consideration, this paper aims to investigate the impact of macroeconomic factors that can contribute to the enhancement of renewable energy output in South Asia.

An autoregressive distributed lag methodology has been applied to examine the long-term effects of remittance inflows, literacy rate, energy imports, government expenditures and urban population growth on the renewable energy output of South Asia by using time series data from 1990 to 2021.

The findings indicated that remittance inflows have a negative and insignificant long-term effect on renewable electricity output. While it was discovered that energy imports, government spending and urban population growth have negative but significant effects on renewable electricity output, literacy rates have positive and significant effects.

Considering the importance of renewable energy, this is one of the few studies that has included critical macroeconomic variables that can affect renewable energy output for the region. The findings contribute to the body of knowledge that a high literacy level is crucial for promoting renewable energy output, while governments and policymakers should prioritize reducing energy imports and ensuring that government expenditures on renewable energy output are properly used. SAARC, the governing body of the region, also benefits from this study while devising the renewable energy output policies for the region.

The purpose of this paper is to empirically analyze the impact of social inclusion factors and foreign fund inflows on reducing gender-based unemployment in India.

A time series data set for the period of 1991–2021 has been considered, and an autoregressive distributed lag methodology has been applied to measure the short- and long-run impact of social inclusion and foreign fund inflows on reducing gender-based unemployment in India.

According to the study’s findings, both social inclusion and foreign fund inflows are critical factors for reducing male unemployment. However, in the case of female unemployment, only social inclusion factors play an important role, whereas foreign fund inflows have no role in it.

Analyzing the factors that affect gender-based unemployment has always been a grey area in literature. There are very few studies that capture gender-based unemployment in India, making this study a novice contribution. Second, it examines the relationship between foreign fund inflows, social inclusion and unemployment, which is another novel area of investigation. Finally, this study provides comprehensive and distinct results for both male and female unemployment that can help policymakers devise gender-based unemployment policies.

This paper aims to measure the extent of digital financial inclusion (DFI) and examine the effect of socioeconomic characteristics on using government remittances and the adoption of digital financial services (DFS) during the COVID-19 pandemic.

The World Bank Global Financial Inclusion (Global Findex) database 2021 is used in this study, with a sample size of 3,000 Indian individuals. The study measured the demand-side analysis of DFI, namely, accessibility and usage of DFS with selected socioeconomic characteristics such as gender, age, income, education, being in the workforce and residential status of respondents. The dependent variable is binary in nature; therefore, the logistic regression model is used for the data analysis.

The results of the study reveal that individuals’ socioeconomic factors, such as female, all the age groups, tertiary education, third- and fourth-income quintile and workforce, are found to have a significant association with “accessibility,” an exogenous variable of DFS. Besides, respondents’ socioeconomic attributes, namely, female, tertiary education, income for all quintiles and workforce, are more likely to use DFSs in the COVID-19 pandemic. The study also finds the residential status of individuals is influencing the accessibility and usage of DFS.

The findings of the study provide valuable insights to the service providers and policymakers regarding the rapid expansion of DFS by digital infrastructure, simplifying the banking procedures and highlighting the importance of digital financial literacy to accomplish government goals through serving the unbanked population and also design strategies for achieving the objectives of Digital India: “Faceless, Paperless, and Cashless” of DFI across the country.

Notable studies used World Bank Findex survey data to explore the determinants of financial inclusion in general. This research is one among the few studies to explore the determinants of India’s DFI. Moreover, this study measured the effect of individual socioeconomic attributes on the adoption of DFSs during the COVID-19 pandemic, which has not been included in prior studies. Therefore, this study has added value to the existing literature on financial technology innovation and DFS for the sustainable development of emerging nations.

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.

Even if digital financial services have a positive impact on financial inclusion, it creates a digital as well as gender divide within and across countries, creating regional disparity even within developing nations. Though pandemic has initiated digitalization of various services, there has been scanty research on whether digital transfer of income can improve digital financial inclusion in post-pandemic era, especially in developing countries. The purpose of the current study is to explain the regional disparity within developing countries from three regions East Asia Pacific, South Asia and Sub-Saharan Africa, using latest World Findex data, 2021.

The author takes an instrumental variable approach to run bivariate probit model to find the factors that motivate the users to make digital payments.

The study observes that electronic transfer of wages, government transfers and remittances can motivate individuals to make use of digital mode of transactions and mobile. The practice of formal saving and borrowings are the prerequisites. However, this mechanism holds good for East Asia Pacific and not for South Asia and Sub-Saharan Africa, which are poor in information and communication technology infrastructure. Women are lagging behind men, but digital transfer of wages motivate them to make digital transaction.

Digitalization of all government services and provision of affordable mobile network and internet services are necessary for regions like South Asia and Sub-Saharan Africa. In East Asia Pacific region, data protection, data governance and better regulatory framework are required. Higher female labor force participation with digital transfer of wages and empowerment with smartphones are key to reducing the Gender gap.

The current study corrects for the possible endogeneity issue, which the extant literature has not paid attention to, and provides region-specific and gender-specific policy recommendations for an improved digital inclusion.

The purpose of the study is to examine the available literature to comprehend what the underground banking system is, for what purposes this method is used and how policymakers should proceed to address this issue.

The approach involved conducting this study and combining it with a critical analysis of the available literature as well as the available historical data. The sources the study has explored include government documents, public hearings, academic articles, case studies and articles available on the internet.

The study revealed prevention of illegal proceeds is critical, still regulating one entity, is a phenomenon of Boyle’s law, “squeezing the balloon”. If one end of the balloon is squeezed, making the volume smaller, the pressure inside increases, making the un-squeezed part of the balloon expand out. The real issue is not how criminals transfer their illegal earnings; it is the criminals themselves. Hence, the policy decision on this issue needs to be carefully considered.

Due to the complexity of the operating system of Undiyal, this study had a number of limitations, as do many others. Firstly, there are no records of the Undiyal agents or the volumes of transactions publicly available. However, comprehending the scope of the underground operation that exists in the country is quite difficult.

The study contributes to the academic researchers grasping what type of future research should be focused on in this area according to the study.

From the point of view of its practical application, the study seeks to resolve social issues that the middle-class population experiences on a daily basis and that have a huge and adverse impact on GDP.

To the best of the authors’ knowledge, this is an original contribution.

This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.

This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.

The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.

The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.

The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.