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The purpose of this paper is to empirically test the economic convergence that operate between five selected Asian countries (namely Thailand, Singapore, Malaysia, the Philippines and Indonesia). In particular, it seeks to investigate how increased economic integration has impacted the inter-country income levels among the five founding members of ASEAN.

A new Machine Learning (ML) approach is applied along with a panel data analysis (GMM), and the application of KOF Globalization Index.

The Generalized Method of Moments (GMM) results highlight that the endogenous growth theory seems to be supported for the selected Asian countries, indicating evidence of diverging forces resulting from unequal growth and polarization dynamics. Overcoming the technical issues raised by the econometric approach, the new ML algorithm brings contrasted but interesting results. Using the KOF Globalization Index, the authors confirm how the last phase of globalization set the conditions for an economic convergence among sample members.

Using the KOF Globalization Index, the authors confirm how the last phase of globalization set the conditions for an economic convergence among sample members. As a matter of fact, the new LSTM algorithm has provided consistent evidence supporting the existence of converging forces. In fact, the results highlighted the effectiveness of the experiments and the algorithm we chose. The high predictability of the authors’ model and the absence of self-alignment in the values showed a convergence be-tween the economies.

This study aims to explore the relationship among energy consumption, real income, financial development and oil prices in Italy over the period 1960-2014.

Different econometric techniques – such as the General Methods of Moment (GMM) or the AutoRegressive Distributed Lags (ARDL) bounds test – are usually used in the empirical analysis. Moreover, both the Toda and Yamamoto causality tests and the Granger causality tests are applied to the data.

The results of unit root and stationarity tests show that the variables are non-stationary at levels, but stationary in first-differences form, or I(1). The ARDL bounds F-test reveals an evidence of a long-run relationship among the four variables at 1% significance level. Moreover, an increase in real GDP and oil prices has a significant effect on energy consumption in the long run. The coefficients of estimated error correction term are also negative and statistically significant. In addition, the paper explores the causal relationship between the variables by using a VAR framework, with Toda and Yamamoto but also Granger causality tests, within both multivariate and bivariate systems. The findings indicate that energy consumption is affected by real GDP.

The study also filled the literature gap of applying ARDL technique to examine this relevant issue for Italy.

In this paper, the heterogeneity of the linkages among financial development, productivity and growth across income groups is emphasized.

An empirical analysis is conducted with an illustrative sample of 130 economies over the period 1991–2019 and classified into four subsamples: Organisation for Economic Co-operation and Development (OECD), developing, least developed and net food importing developing countries. Forecast error variance decompositions and panel vector auto-regressive estimations are computed, with insightful findings.

Higher levels of output stimulate the economic development in the agricultural sector, mainly via the productivity channel and, in the most developed economies, also through access to credit. Differently, in developing and least developed economies, the role of access to credit is marginal. The findings have practical implications for stakeholders involved in the planning of long-run investments. In less developed economies, priorities should be given to investments in technology and innovation, whereas financial markets are more suited to boost the development of the agricultural sector of developed economies.

The authors conclude on the credit–output–productivity nexus and contribute to the literature in (at least) three ways. First, they assess how credit access, agricultural output and agricultural productivity are jointly determined. Second, they use a novel approach, which departs from most of the case studies based on single-country data. Third, they conclude on potential causality links to conclude on policy implications.

This paper aims to investigate the influence of air and rail transportation on pollution in Turkey from 1970 to 2020.

Fourier Autoregressive Distributive Lags (ADL) and Fourier Fractional ADL cointegration tests (Banerjee et al., 2017; Ilkay et al., 2021) are employed to analyze the relationship be-tween the variables. Cointegration tests that take into account soft transitions under structural changes are implemented. Structural change issues are crucial for this topic since the changes in countries’ environmental policies and transportation habits are shaped by the decisions taken in relation to environmental regulations. Finally, for robustness purposes, we tested the estimated equation with a completely different methodology. Thus, a Machine Learning (ML) analysis is conducted, through a Ridge Regression (RR).

The findings obtained by applying Fourier Autoregressive Distributive Lags (FADL) and Fourier Fractional ADL cointegration tests, which can control for structural changes, reveal the existence of a long-term relationship between the variables. In addition, FMOLS estimates emphasize that economic growth and air transport can lead to increased pollution in the long run, while rail transport reduces it. Moreover, the statistically significant trigonometric terms indicate the existence of a smooth structural change among the variables. Robustness checks are performed through a Machine Learning (ML) analysis, which roughly confirms the previous results.

To our knowledge, existing research in Turkey focuses mainly on road transport, while the impact of rail and air transport on pollution has not yet been investigated. As such, this study will be a significant addition to the academic literature.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

This paper explores international trade of the Chinese manufacturing industries through the lenses of network analysis (NA) to visualise the world trade network of the Chinese economy, describe its topology and better explain the international organisation of Chinese manufacturing industries.

The authors built a dataset of 40,550 Chinese companies and their 107,026 subsidiaries in 118 countries from Orbis-BVD and used a NA to investigate the connection between China and other countries. In particular, the authors studied the connections between Chinese companies and their subsidiaries in order to build a network of Chinese industries.

The authors found that the network of Chinese companies is ramified but not wide and it can be divided into two clusters. Moreover, the relations between China and other peripheral countries are strongly mediated by a few leading locations (e.g. Hong Kong and the USA).

This paper contributes to the literature in several ways. First, the authors provide empirical evidence on the magnitude and ramifications of Chinese enterprises in the world. The existing studies generally focus on applying NA to sectoral insights (Mao and Yang, 2012; Shaikh et al., 2016; Zheng et al., 2016; Wanzenbö ck, 2018; Krichene et al., 2019), whereas in this work the authors take a comprehensive view of the entire Chinese manufacturing system. Second, this paper complements the existing literature identifying the difference between cluster levels in Chinese manufacturing (Wu and Jiang, 2011) by proposing a cluster centralisation method to analyse the international network of Chinese firms rather than just the national network. Finally, the results also shed light on the international trade relationship between China, Hong Kong and the USA.

This study aims to investigate the stationarity and convergence of CO₂ emissions series in MENA countries. The stationarity and unit root properties of per capita carbon dioxide (CO₂) emissions series are explored by an increasing amount of studies, which use different methodologies. Examining the time series properties of energy and environmental series is crucial for both researchers and the policymakers, given the close link between energy, environment and the real economy. In fact, if energy exhibits the presence of a unit root, this suggests that this series does not revert to its equilibrium level after being hit by a shock.

The contribution of this work is twofold. First, to the author’s knowledge, a very little attention has been paid to the topics of stationarity and convergence of CO₂ emissions in the case of Middle East and North Africa (MENA) member states, especially in a panel context. Convergence analyses of CO₂ emissions for MENA countries can improve the knowledge of energy and environmental scenario of the area, giving some ideas for appropriate future policies. Second, this is the first study that jointly analyzed time series and panel data properties of emissions series for these countries.

The author finds that relative per capita CO₂ emissions in the 19 MENA countries are a mixture of I(0) and I(1) processes and there is a weak evidence to support the stationarity of CO₂ emissions. After having verified the presence of cross-sectional dependence in the series, the panel unit root tests in presence of cross-section dependence show strong evidence in favor of non-stationarity. In addition, after performing tests for ß-convergence, it is also found that per capita CO₂ emissions are converging on average in 11 out of 19 sample’s countries, while s-convergence analysis reveals that the variance of per capita CO₂ emissions decreased over time, which is an indication of convergence.

Important policy implications emerge from the empirical results. Sustainable environmental and energy policies rely heavily on the CO₂ series’ properties. In this regard, determining whether shocks to CO₂ emissions are permanent or transitory is important for setting feasible goals for sustainable environmental policies. Given that per capita CO₂ emissions are essentially associated with a quality of life, the issues of their reduction have been the leading agenda in energy and environmental management over the past two decades.

The aim of the paper is to evaluate fiscal adjustments that have occurred in the Economic and Monetary Union (EMU) countries in the last 35 years, and their consequences on the economic growth process by using the mean group (MG) estimators.

Our emphasis is on the effects of different composition of fiscal stimuli and consolidations. We compare the effects on the economic growth rate of different compositions of major fiscal changes. We use a cyclically adjusted value of the fiscal variables to leave aside variations of the fiscal variables induced by business cycle fluctuations.

Our empirical research of the effects of large changes in fiscal policy, both in case of a fiscal consolidation and of fiscal stimulus in the 18 EMU countries from 1980 to 2015, shows that adjustments by cutting current expenditures, rather than by tax increases are more likely to boost economic growth. It also shows that cuts of investment expenditures may reduce GDP growth. During fiscal stimulus episodes, tax cuts and public investments are more likely to increase growth than current public expenditure.

This is the first study devoted to the EMU countries. It should be underlined that the results obtained as for EMU countries are not necessarily applicable to other countries, as the different government size as well as different market institutions may influence the results.

The purpose of this paper is to assess the relationship among fiscal variables (net lending, government expenditure and revenue) and economic growth in Sub-Saharan African countries.

Using yearly data for the period between 1980 and 2011 in 15 Economic Communities Of West African States (ECOWAS) countries, the relationship among fiscal variables, economic growth and trade is investigated, through various econometric techniques.

Government expenditure and revenue show pro-cyclical effects in West African Economic and Monetary Union (WAEMU) and ECOWAS countries, while fiscal balance has a pro-cyclical nature for WAEMU during the years 1999-2011. Moreover, a weak long-run relationship between government expenditure and revenue emerge, but only in the case of West African Monetary Zone (WAMZ) countries. Granger causality analysis showed mixed results for WAEMU countries, while for four out of six WAMZ countries (Gambia, Liberia, Nigeria, and Sierra Leone) the “tax-and-spend” hypothesis holds, since government revenue would drive the expenditure. Finally, in the last three decades, cyclical component of economic growth has reduced its fluctuations, both for WAEMU and WAMZ member States.

This is the first study on the effects of fiscal policies in the ECOWAS countries.

The purpose of this paper is to examine the counterfeiting process in Italy, at a subnational level.

The paper uses panel data estimators and mixture models regression.

The paper finds that homogeneous clusters of regions could be derived, as a result of economic and geographical reasons. Moreover, household and public administration expenditure, indirect taxation, foreigners/population ratio and the number of ports have a positive impact on the counterfeiting diffusion index.

The paper is practical as a source of reference in contrasting counterfeiting process.

The paper uses new data applying recent econometric techniques to find homogeneous groups of regions on counterfeiting index.