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The study examines whether the growth effect of government spending is contingent on the level of institutional environment prevalent in Economic Community of West African States (ECOWAS).

The study adopts the more refined and more appropriate dynamic threshold panel by Seo and Shin (2016) and made applicable be Seo et al. (2019). The technique models a nonlinear asymmetric dynamics and cross-sectional heterogeneity simultaneously in a dynamic threshold panel data framework.

The results show that there is a threshold effect in the government spending-growth relationship. Specifically, the authors found that the impact of government spending on economic growth is positive and statistically significant only above a certain threshold level of institutional development. Below that threshold, the effect of government spending on growth is insignificant and negative at best. The findings suggest that government spending-growth nexus is contingent on the level of Institutional quality.

Unlike previous studies that adopt the linear interaction model which pre-impose a priori conditional restrictions, this study adopts the dynamic threshold panel framework which allows the lagged dependent variable and endogenous covariates.

This study aims to explore the relationship between the growth threshold effect on renewable energy consumption (REC) in the major oil-producing countries in sub-Saharan Africa (SSA) over the period 1990–2018.

This article used a dynamic panel threshold regression model introduced by Hansen (1996, 1999 and 2000) threshold (TR) models. The procedure is achieved using 5,000 bootstrapping replications and the grid search to obtain the asymptotic distribution and p-values. For the long-run relationship among our variables, the author followed the process in Pesaran et al. (1999) pooled mean group (PMG) for heterogeneous panels. Furthermore, for the robustness of our empirical results due to the sensitivity of the results to outliers, the author used the approach by Cook (1979) distance measure. The author applied quantile (QR) regression to explore the distribution of dependent variables following Bassett and Koenker (1982) and Koenker and Bassett (1978) approaches.

The results from the threshold effect test and threshold regression revealed a significant single threshold effect of growth level on REC. Furthermore, the result from the PMG estimation showed the growth of the variable, energy intensity, consumer prices and CO₂ emissions play a significant role in REC in major oil-producing countries in SSA. The growth threshold estimation results indicated one significant threshold value of 1.013% at one period lagged of real growth. The outlier’s sensitivity detention greatly influenced our empirical results.

The article filled the literature gap by applying a combined measure that is robustness to detect outliers in the data, which none of the studies in the literature addresses hitherto. Further, the article extends the quantile regression to growth – REC literature.

The purpose of this paper is to examine the relationship between the quality of different dimensions of institutional and economic growth in a panel of 15 member ECOWAS.

The study adopts Driscoll and Kraay′s nonparametric covariance matrix estimator, and the spatial error model to account for cross-section dependency, cross-country heterogeneity and spatial dependence inherent in empirical modelling, which has largely been ignored in previous studies. This is because, the likelihood that corruption and human capital cluster in space is very high because factors that affect these phenomena disperse across borders. Similarly, to test the threshold effect, the study adopts the more refined and more appropriate dynamic panel data which models a nonlinear asymmetric dynamics and cross-sectional heterogeneity, simultaneously, in a dynamic threshold panel data framework.

The empirical evidence supports findings by previous researchers that better-quality political and economic institutions can have positive effects on economic growth. Similarly, our results support a nonlinear relationship between political institutions and economic institution, confirming the “hierarchy of institution hypothesis” in ECOWAS. Specifically, the findings show that economic institutions will only have the desired economic outcome in ECOWAS, only when political institution is above a certain threshold.

Unlike previous studies which assume cross-sectional and spatial independence, the authors account for cross-section dependency and cross-country heterogeneity inherent in empirical modelling.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2019-0630

This research investigates the threshold effects of the interplay between finance, development and carbon emissions across 97 countries, including 50 low-income and 47 high-income countries, during the period from 1991 to 2019.

Employing various econometric modeling techniques such as dynamic linear regression, dynamic panel threshold regression and in/out of sample splitting, this study analyzes the data obtained from the World Bank's world development indicators.

The results indicate that low-income countries require a minimum financial development threshold of 0.354 to effectively reduce carbon emissions. Conversely, high-income countries require a higher financial development threshold of 0.662 to mitigate finance-induced carbon emissions. These findings validate the presence of a finance-led Environmental Kuznet Curve (EKC). Furthermore, the study highlights those high-income countries exhibit greater environmental concern compared to their low-income counterparts. Additionally, a minimum GDP per capita of US$ 10,067 is necessary to facilitate economic development and subsequently reduce carbon emissions. Once GDP per capita surpasses this threshold, a rise in economic development by a certain percentage could lead to a 0.96% reduction in carbon emissions across all income levels.

This study provides a novel contribution by estimating practical financial and economic thresholds essential for reducing carbon emissions within countries at varying levels of development.

The purpose of this study is to seek to re-examine the threshold effects of public debt on economic growth in Africa.

This study applies panel smooth transition regression approach advanced by González et al. (2017). The method allows for both heterogeneity as well as a smooth change of regression coefficients from one regime to another.

A debt threshold in the range of 62–66% is estimated for the whole sample. Low debt is found to be growth neutral but higher public debt is growth detrimental. For middle-income and resource-intensive countries, a debt threshold in the range of 58–63% is estimated. As part of robustness checks, a dynamic panel threshold model was also applied to deal with the endogeneity of debt, and a much higher debt threshold was estimated, at 74.3%. While low public debt is found to be either growth neutral or growth enhancing, high public debt is consistently detrimental to growth.

The findings of this study show that there is no single debt threshold applicable to all African countries, and confirm that the debt threshold level is sensitive to modeling choices. While further analysis is still needed to suggest a policy, the findings of this study show that high debt is detrimental to growth.

The novelty of this study is twofold. Contrary to previous studies on Africa, this study applies a different estimation technique which allows for heterogeneity and a smooth change of regression coefficients from one regime to another. Another novelty distinct from the previous studies is that, for robustness checks, this study divides the sample into low- and middle-income countries, and into resource- and nonresource intensive countries, as debt experience can differ among country groups. Further, as part of robustness checks, another estimation method is also applied in which the threshold variable (debt) is allowed to be endogenous.

Insurance companies play a significant role in every economy; hence, it is essential to investigate and understand the factors that propel their profitability. Unlike previous studies that present a linear relationship, this study provides initial evidence by exploring the non-linear impacts of the determinants of profitability amongst life insurers in South Africa.

The study uses a panel dataset of 62 life insurers in South Africa, covering 2013–2019. The generalised method of moments and the dynamic panel threshold estimation technique were used to estimate the relationship.

The empirical results from the direct relationship reveal that investment income and solvency significantly predict life insurance companies' profitability. On the other hand, underwriting risk, reinsurance and size reduce profitability. Further, the dynamic panel threshold analysis confirms non-linearities in the relationships. The results show that insurance size, investment income and solvency promote profitability beyond a threshold level, implying a propelling effect on life insurers' profitability at higher levels. Below the threshold, these factors have an adverse effect. The study further points to underwriting risk, reinsurance and leverage having a reduced effect on life insurers' profitability when they fall above the threshold level.

The findings suggest that insurers interested in boosting their profit position must commit more resources to maintain their solvency and manage their assets and returns on investment. The study further recommends that effective control of underwriting risk is critical to the profitability of the life insurance industry.

The study contributes to the literature by providing first-time evidence on the determinants of life insurance companies' profitability by way of exploring threshold effects in South Africa.

The purpose of this article is to investigate the asymmetric impact of exchange rate pass-through (ERPT) on employees' wages via consumer prices in 15 major oil-exporting countries from sub-Saharan Africa over the period 1996-2017 using the panel threshold regression model.

The methodology used in this article was built on non-linear panel threshold regression models developed by Hansen (1996, 1999) threshold regression. The authors first tested for the existence of threshold-effect in ERPT and wage nexus using 1,000 bootstrap replications and 400 grid searches to obtain an optimal threshold. We also estimated that asymmetric ERPT on employees' wages reacts differently when the inflation-threshold exceeds beyond a 15.12% threshold level.

Our findings showed that asymmetric ERPT is incomplete and indicates that an increase by one standard deviation in real exchange rate causes a decline in employees' wages by 2.69%.

The policy implications of our results are drawn from the significant threshold estimates. However, a significant threshold value of 15.12 is an inflation-threshold estimates that split our 330 observations into the lower (upper) regimes. Further, an inflation rate beyond the threshold value is likely to have an asymmetric ERPT on employees' wages in the 15 major oil-exporting sub-Saharan African (SSA) countries.

The practical implication of the study is when ERPT exceeds the threshold, the effect of real exchange rate variations is passed on to employees' wages. It is widely believed that labor productivity increase with increased minimum wages. Nevertheless, there is contention as regards the effects on employment and poverty. As rising goods prices make the minimum wage increased homogeneous of degree zero.

Considerable increased ERPT on imported goods reduces employees' wages purchasing ability from import-dependent countries through import prices. Once it has documented, this also reduces welfare via deteriorations of marginal propensity to consume (MPC) and marginal propensity to savings (MPS).

This article integrates labor purchasing power into the analysis of ERPT using non-linear dynamic panel heterogeneous threshold regression. It extends the Hansen (1996, 1999) dynamic panel threshold models to exchange rate pass-through in SSA economies.

The purpose of this paper is to examine the relationship between urbanization and economic growth in ASEAN countries for the period 1993-2014.

The Granger causality test and the regression estimation method with static and dynamic panel data (FE, RE, Driscoll and Kraay, D-GMM and PMG) were used. The sample includes seven ASEAN countries: Brunei, Cambodia, Indonesia, Malaysia, Philippines, Thailand and Vietnam.

The results show that at least a causal relationship exists between urbanization and economic growth and urbanization positively impacts economic growth. However, the relationship between urbanization and economic growth is non-linear. The urbanization reaches a threshold after which it may impede the economic growth. The estimated threshold is 69.99 percent for the static model and 67.94 percent for the dynamic model.

The evidence from this study suggests that there is a non-linear relationship between urbanization and the economic growth. Urbanization has the potential to accelerate the economic growth, and this potential will depend on the establishment of favorable institutions and investments in appropriate public infrastructure.

The decision on the model of urbanization needs to be based on social and environmental considerations as well as market-based economic efficiency. The quality of urbanization manifests in the way that people and businesses perceive when they come to cities and their position in the labor market, urban housing, niche commodity markets, supply chain, collaborative network and physical space for the operation of the business. Most ASEAN countries have not yet reached a high level of urbanization, despite having a number of policies for promoting urbanization to contribute to the economic growth. However, policymakers should find ways to facilitate the development of urbanization that contributes to economic growth, employment growth, environmental sustainability, rather than the pursuit of speeding up the process of urbanization.

Between urbanization and economic growth at least a causal relationship exists. Urbanization positively impacts economic growth. However, the relationship between urbanization and economic growth is non-linear. The urbanization reaches a threshold after which it may impede the economic growth. The estimated threshold is 69.99 percent for the static model and 67.94 percent for the dynamic model.

The aim of this article is to research on forestry green total factor productivity and explore the impact of financial support on forestry green total factor productivity.

The methods used in this study are super efficiency SBM model of undesired output and empirical model. SBM model is a kind of Data Envelopment Analysis (DEA). The SBM model with non-expected outputs (slacks-based measure) can be used to deal with the problem of efficiency measurement with multiple input and output variables and can be used to analyze the efficiency of green development of forestry economy.

First, the overall green total factor productivity of the authors’ country's forestry has shown a trend of first decline and then an increase from 2008 to 2018, and there are significant spatiotemporal differences; second, financial support has a significant positive impact on forestry green total factor productivity; third, environmental regulation has a significant threshold effect in the process of financial support on forestry green total factor productivity, and the role of financial support shows a trend of first increasing and then decreasing.

Secondly, taking the data of 30 provinces and cities in the authors’ country from 2008 to 2018 as the research object, using the super-efficiency SBM-Malmquist index to measure the country's forestry green total factor productivity and analyze its temporal and spatial changes; finally, a dynamic panel model was established to explore the impact of financial support on forestry green total factors quantitative impact on productivity, and adding environmental regulation as a threshold variable to establish a dynamic threshold regression, and found that financial support has a nonlinear impact on forestry green total factor productivity.