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Despite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period.

Various estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality.

The bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth.

Existing studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.

The purpose of the study is to find out the impact of Digital Financial Inclusion (DFI) on economic growth [(Industrial Production Index (INDP)] of Bangladesh.

Using the monthly data over the period 2018 M12 to 2021 M12, this study applied the Auto-regressive Distributed Lag (ARDL) model to assess the effect of DFI indicators on INDP. The secondary data was collected from the Bangladesh Bank and CEIC Global Economic Data.

The study found that the majority of DFI indicators are positively associated with INDP. From the short-run ARDL, it is seen that one unit positive increase in Point of Sales Transactions (POST) can increase the INDP by 0.055 units. From the long-run ARDL, it is seen that POST and e-commerce transactions (ECOMT) have a significant positive impact, while Automated Teller Machine Transactions (ATMT) have a significant negative effect on INDP. One unit increase in POST and ECOMT increases INDP by 0.13544 and 0.11611 units, respectively.

During the era of the fourth industrial revolution, the findings will be beneficial for policymakers, financial technology service providers, manufacturers, consumers, corporations and investors as they pave the way for a more inclusive approach to financial transactions for economic growth.

The study’s novelty is that it explored the influential DFI indicators and shed light on both short-run and long-run relationships between the indicators and macro-economy from the context of a developing nation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0306

In this article, we assess the impact of inflation on the current account balance in the case of Tunisia, covering the period 1976–2022.

The study utilizes a threshold regression approach proposed by Hansen (2001) in a bid to identify inflation threshold values.

The results show two inflation threshold values (3.87% and 8.41%), which determine three inflation regimes in the case of Tunisia. In lower inflation regimes, inflation has a positive and statistically significant impact on the current account balance. However, in higher inflation regimes, where inflation rates exceed 3.87%, there is a negative and statistically significant correlation with the current account balance, resulting in a deficit.

The research suggests the need for a new policy approach that considers these threshold levels to address high inflation rates, which currently stand at approximately 11%, and aims to restore them to the targeted rate of 4%. This necessitates coordinated monetary and fiscal measures.

It is widely argued that money laundering (ML) is not a new phenomenon and the pervasiveness of ML is associated with some severe economic, social and political costs. Due to the lack of studies on the ML’s issue in the UAE, this study aims to examine the determinants of ML in the country between 1975 and 2020.

The autoregressive distributed lag bounds testing results demonstrate the presence of long-run relationship between ML and the selected macroeconomics variables. The analysis is validated by the dynamic ordinary least squares, the fully modified ordinary least squares and the canonical co-integration regression estimators.

The estimation result reveals that while the real estate market, outflow of money, arms procurement and size of the underground economy influences the size of ML positively, gold trade, the level of financial development and the size of economic activities are negatively associated with ML, both in the short- and long-run.

Up to date from a country-level analysis, no study has been devoted to the ML in UAE, except for Aljassmi et al. (2023). To the best of the authors’ knowledge, this study is the first to investigate the determinants of laundered money in the UAE economy. Based on these outcomes, strategies and measures which will deter the laundering of illicit funds through the real estate and gold market, remittance system, financial system and arms procurement contracts in the UAE are recommended.

The study investigates the impact of various factors on the number of active investors in digital health startups. Through nine hypotheses, we examine the influence of metrics such as patents, online presence, financial aspects and company valuation on investor interest. The results reveal positive associations between these metrics and investor numbers, highlighting their role in signaling strength and attracting investment. This research enhances the understanding of investor valuation in digital health startups, emphasizing the importance of credible signals for building trust and securing funding. However, we acknowledge limitations in data analysis methods and suggest future research to explore industry signals, longitudinal trends and failed startups for comprehensive insights.

This study delves into the design methodology and approach, aiming to fill gaps in understanding investor roles in valuing digital health ventures. We focus on deciphering factors driving valuations for these startups to secure growth financing. Using signaling theory, we investigate how entrepreneurs communicate their latent strengths to bridge information gaps, aiding investment decisions. We analyze a sample of 482 healthcare startups from the Pitchbook database using Poisson regression in SPSS.

This research sheds light on the factors driving investor interest in digital health startups. Despite the critical role of entrepreneurs in patient care innovations, the relationship between investor characteristics and funding for digital health technologies still needs exploration. We examine factors influencing investor valuation in healthcare startups and identify patents, social followers and financial disclosures as pivotal elements shaping investor interest. The findings show that all factors for active investors are significant for all variables except similar unique visitors.

These results significantly enhance our understanding of investor decision-making in digital health startups. They confirm the importance of various signals, like patent activity, online presence and financial performance, in attracting investor attention. We utilize unique data sources, offering insights into investors' behavior across different funding stages. In conclusion, these findings underscore investors' crucial role in the growth and funding of healthcare tech startups, emphasizing the need for robust signals to attract investment.

This study aims to examine the effect of structural transformation on poverty alleviation in Sub-Saharan Africa (SSA) countries with a higher share of services as a percentage of gross domestic product (GDP). The study specifically focuses on the value-added share as a percentage of GDP in the agricultural, manufacturing, industrial, and service sectors using time series data from 1988 to 2019.

The study utilizes the autoregressive distributive lag (ARDL) bound test framework for estimation, based on the conclusions drawn from the augmented Dickey-Fuller and Phillips–Perron unit root tests, which provide evidence of a mixed order of integration.

The result reveals that agriculture value-added (AVA), manufacturing value-added (MVA), industrial value-added (IVA), and services value-added (SVA) have a positive and significant impact on poverty alleviation in both the short and long run. However, the agriculture sector is found to be more effective in reducing poverty compared to the other sectors examined in this study. Additionally, this study challenges the notion that SSA countries have undergone an immature structural transformation. Instead, it reveals a pattern of stagnant structural transformation, as indicated by the lack of growth in the industrial and manufacturing value-added shares of GDP.

To enhance productivity and reduce poverty, SSA economies should adopt a development strategy that prioritizes heavy manufacturing and industrial sectors, leading to a transition from the agricultural to the secondary and tertiary sectors.

The study contributes to the emerging literature on structural transformation by investigating which sector is more efficient in reducing poverty in SSA countries, using the value-added share as a percentage of GDP for agricultural, manufacturing, industrial, and service sectors. The study also aims to determine if SSA countries have experienced immature structural transformation due to the growing share in the service sector.

This study presents time-series data estimations on the association between the indicators of institutional environment and inward foreign direct investment (FDI) in India utilizing a comprehensive data set from 1996 to 2021.

The study employs the nonlinear autoregressive distributive lag (NARDL) model. The asymmetric ARDL framework evaluates the existence of cointegration among the factors under study and highlights the underlying nonlinear effects that may exist in the long and short run.

The significance of coefficients of negative shock to “control of corruption” and positive shock to “rule of law” is greater when compared to “government effectiveness, regulatory quality, political stability/absence of violence.” The empirical outcomes suggest the positive influence of rule of law, political stability and government effectiveness on FDI inflows. A high “regulatory quality” is observed to deter foreign investment. The “voice and accountability” index and negative shocks to the “rule of law” are exhibited to have no substantial impact on the amount of FDI that the country receives.

This study empirically examines the institutional determinants of FDI in India for a comprehensive period of 1996–2021. The study's findings imply that quality of the institutional environment has a significant bearing on India's inward FDI.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0375

The present study reviews the theoretical and empirical literature about the significance of international migrants' remittance to empirically analyse the effect of remittance on the productivity growth of developing countries using a panel dataset from 1991 to 2021.

The study utilised the data envelopment analysis (DEA)-based Malmquist Productivity Index (MPI) to measure nationwide production efficiencies. It first performed a unit root test, cointegration test and pool mean group autoregressive distributed lag (PMG-ARDL) technique. To assess the robustness of the findings, the study also uses dynamic ordinary least squares (DOLS) and fully modified OLS (FMOLS) estimators.

The results demonstrated that remittances are a significant source of funding that promotes innovation [i.e. technological progress (TEC)] and hastens the country's total factor productivity (TFP) growth. However, the study needed to have established the effect of inward remittances on the nation's technical efficiency (EFF).

As remittances encourage innovation and TFP growth (TFPG), the concerned governments must create favourable and enabling economic environments to increase remittance inflows, which will have far-reaching growth repercussions.

The present study emphasises the connection between remittances and productivity growth, the disintegration of TFP, advanced econometric techniques and contribution to research policy. Despite prior literature exploring the effect of remittances on economic growth, a dearth of literature exists on how remittances affect a country's productivity. The output-based MPI methodology used in this study offered a nuanced understanding of how remittances affect many facets of productivity growth in developing nations.

This study aims to provide an empirical investigation into rising capital flight and the role of institutional quality to mitigate its effect on the real sector in sub-Saharan Africa (SSA).

The study uses the system generalized method of moments and uses data spanning from 1989 to 2020 from 26 SSA countries.

The findings show that capital flight has no direct impact on the real sector while institutional quality adversely impacted the agricultural and industrial sectors. The study also found that institutional quality is unable to mitigate the effect of capital flight on the industrial sector.

This study investigates if institutional quality mitigates the impact of capital flight on the real sector proxied by industrial value-added and agriculture value-added.

The purpose of this study is to investigate the interplay between fiscal dominance and monetary policy in South Africa from 1960 to 2023.

The study employs a structural vector autoregression (SVAR) medel to analyze the relationship between fiscal dominance and monetary policy. Short-term and long-term shocks of government borrowing and deficits are examined to understand their impact on inflation dynamics.

Fiscal dominance has a significant effect both in the short and long run. There is evidence that government debt and deficits increase inflation, overriding the effects of monetary policy aimed at maintaining price stability. On the other hand, the study reveals that money supply shocks have a greater effect in reducing fiscal dominance compared to interest rate shocks. The variance movement on inflation is significantly explained by government debt and deficits. This emphasizes the persistence of inflationary pressures associated with fiscal dominance, highlighting the importance of effective policy interventions to mitigate inflationary risks.

This study contributes to the existing literature by providing insights into the dynamics of fiscal dominance in South Africa. Moreover, this study extends the theoretical framework of the fiscal theory of the price level (FTPL) and the government budget constraint. This study contributes valuable insights into the dynamics of fiscal dominance in South Africa and offers guidance for policymakers in formulating strategies to safeguard economic stability.