Search results

This study examines the co-integration relationships between Association of Southeast Nations (ASEAN) stock indices as a way to assess the feasibility of policy initiatives to strengthen market integration in ASEAN and identify implications for portfolio investors.

The authors employ threshold co-integration tests and a non-linear autoregressive distributed lag (NARDL) model to study the asymmetric dynamics of ASEAN equity markets. The study’s data cover the 2009–2022 period for seven member states: Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam.

The authors find evidence supporting co-integration relationships; adjustment toward equilibrium is asymmetric in the short run and symmetric in the long run for these countries. While co-movement in ASEAN equity markets seems encouraging for initiatives seeking to foster financial integration in regional economies, the benefits for international portfolio diversification appear to be neutralized.

The issue of stock market integration is important among policymakers, investors and academics. This study examines the level of stock market integration in ASEAN during the 2009–2022 period. For this purpose, advanced co-integration techniques are applied to different frequencies of data (daily, weekly and monthly) for comparison and completeness. The empirical analysis of this study is conducted using the Enders and Siklos (2001) co-integration and threshold adjustment procedure. This advanced co-integration technique is superior compared to other co-integration techniques by permitting asymmetry in the adjustment toward equilibrium.

The purpose of the paper is to explore the long-run impact of board governance and bank performance on executive remuneration. More specifically, the study addresses two objectives. First, the authors investigate the long-run relationship between pay and performance hold for the Indian banking industry. Second, the authors explore the moderating role of the board in explaining the relationship between executive pay and performance.

The study uses multivariate panel co-integration approaches, i.e. fully modified and dynamic ordinary least square, to explain the co-integrating relationship between executive pay, governance and performance of Indian banks. The analysis is conducted for the period from 2005 to 2018.

The results of co-integration tests reveal a long-run relationship between executive pay, board governance and bank performance. The long-run estimates produce evidence in favour of the dynamic agency theory, suggesting that the implications of asymmetric information can be mitigated by associating the current executive pay with the bank performance in the previous periods. The finding of this study reveals that improvements in the board quality serve as a monitoring tool to constrain excessive pay and moderate the executives’ pay. Furthermore, the interaction of performance and board governance negatively impacts pay, supporting a substitution approach. It implies that setting optimal pay packages for executives necessitates enhanced and efficient board governance practices.

The study recommends significant policy implications for regulators and the board of directors that executive pay significantly responds to the bank’s performance and good board governance practices in the long run.

This paper provides novel evidence of long-run pay-performance-governance relation using a panel co-integration approach.

This paper aims to unravel the impact of post-pandemic COVID-19 on foreign direct investment (FDI) and its determinants in the South Asian Association for Regional Cooperation (SAARC) Countries.

The study utilized four macroeconomic variables includes growth domestic product growth rate (GDPG), inflation rate (IR), exchange rate (ER), and unemployment rate (UR) to assess their impact on post-pandemic FDI, along with two variables control of corruption (CC) and political stability (PS) to measure the influence of good governance. Random effects, fixed effects, cluster random effects, cluster fixed effects and generalized method of moments (GMM) models were applied to a balanced panel dataset comprising eight SAARC countries over the period 2010–2021. To identify the random trend component in each variable, three renowned unit root tests (Levin, Lin and Chu LLC, Im-Pesaran-Shin IPS and Augmented Dickey-Fuller ADF) were used, and co-integration associations between variables were verified through the Pedroni and Kao approaches. Data analysis was performed using STATA 17 software.

The major findings revealed that the variables have an order of integration at the first difference I (1). Nonetheless, this situation suggests the possibility of a long-term link between the series. And the main results of the findings show that the coefficients of GDPG, CC and PS are positive and significant in the long run, showing that these variables boosted FDI inflows in the SAARC region as they are significantly positively linked to FDI inflows. Similarly, the coefficients of UR, IR, ER and COVID-19 are negative and significant.

By identifying the specific impacts of the post-pandemic FDI and its determinants, governments and policymakers can formulate targeted policies and measures to mitigate the adverse effects and enhance investment attractiveness. Additionally, investors can gain a deeper understanding of the risk factors and adapt their strategies accordingly, ensuring resilience and sustainable growth. Finally, this paper adds value to the literature on the post-pandemic impact on FDI inflows in the SAARC region.

This paper is the first attempt to trace the impact of COVID-19 on Foreign Direct Investment and its determinants in the SAARC Countries. Most of the previous studies were analytical in nature and, if empirical, excluded some countries due to the unviability of the data set. This study includes all the SAARC member countries, and all variables' data are completely available. There is still a lack of empirical studies related to the SAARC region; this study attempts to fill the gap.

This study empirically aims to examine the relationship between Bangladesh’s pharmaceutical industry growth and macroeconomic indicators such as the inflation rate, gross domestic product (GDP) growth, foreign direct investment (FDI) inflows, exchange rate and export growth through the long- and short-run relationship.

Using the time series data from 1986 to 2020, this study was developed based on the autoregressive distributed lag (ARDL) framework for co-integration. In contrast, the Toda–Yamamoto Granger Causality approach was also used for finding the direction of causality.

This study used the ARDL bounds test, which found strong co-integration among the variables, indicating a long-term relationship between them. In the long run, inflation, exchange rate and export growth significantly positively influence the pharmaceutical industry’s growth. Surprisingly, an FDI inflow has a negative impact. In the short term, the exchange rate and GDP growth were found to influence the growth of the pharmaceutical industry positively. Bidirectional causality between the growth of the pharmaceutical industry and the exchange rate was also identified using the Granger causality approach.

This paper emphasizes developing the policy as well as making concrete decisions regarding the development of the pharmaceutical industry and economic development in Bangladesh. The results also highlight the necessity for strategic macroeconomic management to support this sector’s long-term development and global competitiveness.

To the best of the authors’ knowledge, this paper is conducted to identify the short- and long-run relationship of pharmaceutical industry development with the economic indicators and progress, where no study has been found on this dimension.

The aim of the paper is to determine the asymmetric impact of tourism on the deficit in the balance of payments (BOPs).

The research uses the non-linear autoregressive distributed lag (ARDL) model to investigate the asymmetric impact of tourism on Pakistan's BOPs deficit using quarterly data from 1995 to 2019.

The finding reveals that due to the positive change in tourism, the BOPs deficit decreases by 27%, although due to the negative change in tourism, the BOPs deficit rises by 2.3%. In addition, the significance of F-statistics (10.609) confirms the existence of co-integration between tourism and the deficit in the BOPs. The Wald test confirms the asymmetric association between tourism and the deficit in the BOPs over the long term.

In order to improve tourism in Pakistan, policymakers must consider the following implications. First, there is a need for an adequate infrastructure that can help the tourist. Second, the Government must maintain a stable law and order situation as a whole and particularly at tourist destinations. Finally, the Government should develop tourism-friendly policies in order to boost tourism in Pakistan.

The research provides new evidence of the impact of tourism on the BOPs using the novel non-linear ARDL (NARDL) technique. The evidence will help policymakers to develop policies to improve tourism in order to reduce the BOPs deficit.

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 paper aims to explore the impact of corruption (CP) on income inequality (IN) by considering the size of informal sector (IFS) at different levels of percentiles.

This paper uses a panel quantile regression approach for a sample of 50 developing countries. The study also applies panel co-integration (Kao residual co-integration test) in order to examine the long-run relationship between CP and IN.

This paper using a panel quantile regression approach shows that the high incidence of IFS in an economy marginalizes CP's positive effect because it works as a source of poor peoples' livelihood and skillful individuals. The spread of IFSs in the developing economies may raise earnings among groups and individuals who remain unemployed. Moreover, the results show that CP creates asymmetry in income distribution; fascinatingly, the asymmetric income distribution is high when CP is at higher percentiles.

Due to non-availability of IFS, we restrict our analysis up to 50 developing countries.

CP devastates the effectiveness of institutions over time. Therefore, the government should have to take bold steps to reduce CP in society. Another policy implication of this study is that the government should reduce CP to decrease IN in less developing countries. Moreover, to increase the net base, the authorities need to bring IFS under the umbrella of regulation to avoid inequality in society. In developing economies, a higher part of labor force is related to IFS; therefore, our findings suggest a dire need to reduce labor exploitation in IFS. The policymakers can reduce labor exploitation by reducing the size of IFS, which ultimately reduces IN.

On the basis of the authors’ findings, this paper further suggests that it is mandatory for government to reduce CP in order to reduce IN. Moreover, to reduce IN, one needs to reduce the size of IFS.

This study is unique as it is the first that examined the role of IFS in establishing the effect of CP on IN for developing countries at different percentiles.

Education had proven to be one of the main determinants of economic growth, and it is a reason of the variations in economic growth levels between developed and developing countries. One of the main dimensions in studding the relationship between economic growth and education is the gender dimension or the importance of gender equality or female education in achieving economic growth. This chapter aims to test the hypothesis of the existence of a positive relationship between female education and economic growth in Egypt since 1990.

To address this question, Auto Regression Distributed Lag (ARDL) Bound test approach is conducted to analyze the co-integration between female education and economic growth using Egyptian Data for the period 1990–2022. The Empirical analysis for Egypt suggests the existence of positive significant relationship both in the short run and long run and that the impact of female education on economic growth is larger than the impact of education in general on growth. This could be explained by the existence of gender gap in Egypt, labor market, and thus, more educated girls able to enter the labor market will affect the economic growth more than the education of both sexes, in other words, there is still a room for improvement in the female labor market opportunities than for both sexes. The chapter also confirms the existence of a direct link between education in general and economic growth and thus confirms the hypothesis of the positive impact of education economic growth.

The purpose of this paper is to examine key macroeconomic determinants on Cameroon's economic growth from 1970 to 2018.

Data were obtained from the World Development Indicators and applied on time series data econometric techniques. The auto-regressive distributed lag (ARDL) bounds model analyzed the data since the variables had different order of integration.

The results showed long and short runs’ positive and significant connection between economic growth in Cameroon and government expenditure; trade openness, gross capital formation and exchange rate. Human capital development, foreign aid, money supply, inflation and foreign direct investment negatively and significantly affected economic growth in the short and long-runs. Hence, the macroeconomic indicators are not death.

The present research paper has tried to capture the impact of nine macroeconomic determinants on economic growth such as the government expenditure (LNGOVEXP), human capital development (LNHCD), foreign aids (AID), trade openness (LNTOP), foreign direct investment (LNFDI), gross capital formation (INVEST), broad money (LNM2), official exchange rate (LNEXHRATE) and Inflation (LNINFLA). However, these variables have the tendency to affect each other in a unidirectional or bidirectional manner. Further, the present research paper is unable to capture the impact of other macroeconomic variable due to the unavailability of data.

The study recommends that Cameroon should use proper planning and strategic policy interventions to achieve higher sustainable economic growth with human capital development, foreign aid, money supply, foreign direct investment and moderate inflation.

Macroeconomic indicators, if managed well, increase economic growth.

This paper to the best of the researcher's knowledge presents new background information to both policymakers and researchers on the main macroeconomic determinants using econometric analysis.

This paper examines the short- and long-run effects of foreign direct investment (FDI) on tax revenue in Ghana.

The paper adopts the autoregressive distributed lag approach to estimate FDI’s long-run and short-run effects on tax revenue. The study uses time-series data from 1983 to 2019 for Ghana, mainly obtained from The Bank of Ghana, the World Bank and the IMF.

The results show that, in the short-run, FDI has no significant effect on direct tax revenue and total tax revenue but significantly hurts indirect tax revenue. In the long run, however, the results show that FDI has significant positive effects on indirect tax revenue and total tax revenue but no significant effect on direct tax revenue.

Empirical studies often fail to analyse the short-run and long-run effects of FDI on tax revenue. This study contributes to the mixed literature by analysing the short-run and long-run effects of FDI on tax revenue in an emerging market context. Additionally, this study employs three tax revenue measures in analysing the nexus.