Search results

This chapter is a radical critique of the neoclassical growth theory, justifying ways out of mainstream economics. It has three parts. The first one analyzes growth theories from the Classical representation to the endogenous growth models. The second part demonstrates that the “new growth theory” is not a break with Solow's formalization. To prove it, we build an original Solowian endogenous growth model. Then, this neoclassical macrodynamic framework is technically, deeply critized in a third part. We show that both exogenous and endogenous neoclassical models prove to be incapable to explain growth in the long period. We concentrate on the ambiguities surrounding the hypothesis of single agent, as well as on the role of the state, in particular when it is considered as a “planner” by the neoclassicals. Endogenous growth models do not correspond to macrodynamization of the Walrasian general equilibrium, nor have solid microeconomic bases. We advocate in favor of rehabilitating state's intervention in social areas and of reactivating Marxist theoretical reflections regarding social planning and class analysis in the current time of structural crisis of the capitalist world system.

This chapter considers the lag structures of dynamic models in economics, arguing that the standard approach is too simple to capture the complexity of actual lag structures arising, for example, from production and investment decisions. It is argued that recent (1990s) developments in the the theory of functional differential equations provide a means to analyze models with generalized lag structures. The stability and asymptotic stability of two growth models with generalized lag structures are analyzed. The chapter's penultimate section includes a speculative discussion of time-varying parameters.

The greatest challenge facing most economies today is how to grow their economies and reduce over-dependence on imports in the midst of increasing integration of world economies. Addressing this challenge seems to be difficult despite all efforts by policymakers at different times to salvage the situation, the problem persists as evident in the global financial crisis of 2008 and the Eurozone crisis of 2012 which were generally viewed as a glaring illustration of limitless pursuit of economic integration and governance failure at the expense of carefulness, prudence, due diligence, and regulation. It also reflects the lack of proper coordination and lack of proper economic integration facing most emerging market economies of the world. Against this background, this study focuses on the reexamination of the impact of trade openness (TOP) and financial openness (FOP) on economic growth in emerging market economies. The direct and interaction effect of the both openness variables on economic growth in these markets is investigated using data from 2000 to 2017 adopted from World Development indicators of the World Bank. Over 30 emerging market economies covering Asia, Latin America, and Europe are included in the study. For empirical analysis, the study uses one measure of FOP: de facto (total capital flow) variables following Aizenman and Noy (2009) and a measure of TOP as total trade–GDP ratio. The study applies the Dynamic Panel Approach, that is, the Arellano–Bond GMM estimation technique and Granger Causality Test to address the objectives. The results of this study show that TOP has a positive and significant impact on all the countries studied, whereas FOP has positive but no significant impact on economic growth of these countries, implying that these countries have not harnessed the benefit of financial liberalization and integration. It is recommended that the emerging market economies should open not only their economies to trade but also open their economies to finance so as to reap the benefits of FOP and integration.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

The purpose of this study is to empirically examine the impact of financial development on poverty reduction in Egypt. The paper also investigates whether financial development affects poverty via gross domestic product (GDP) growth.

This study uses the autoregressive distributed lag approach to estimate two specifications. The first is dependent on poverty by the ratio domestic credit to the private sector (percentage of GDP) and the second is dependent on the poverty by the ratio liquid liabilities to GDP or M3/GDP. The data are annual and cover the period from 1980 to 2015.

In long run, the study finds that relationship between economic growth and poverty is bidirectional. Financial development and poverty (household final consumption expenditure per capita) are complementary as bidirectional (in Granger sense). In short run, the study finds the bidirectional causality between financial development (real domestic credit to private sector per capita) and poverty reduction.

The findings suggest that governments should remove policies that impede the ability of banks to offer loan products or undermine the commercial incentive structure for banks or borrowers. It is crucial to enhance the role of specialized state-owned banks in financial intermediation.

Several attempts have been made to investigate the relationship between financial development and other macroeconomic variables, but few studies have examined the impact of financial development on poverty reduction. Furthermore, the majority of the previous studies are based on Asia and Latin America – affording Egypt very little or no coverage at all.

The purpose of this paper is to provide a quantitative assessment of the factors contributing to India's growth acceleration since 1970, based on the neoclassical growth model.

A feature of neoclassical growth models is that capital accumulation is induced by both productivity growth and increases in investment rates. The paper uses a growth decomposition method based on that of Robertson. The method reconstructs India's actual growth path exactly, then decomposes the growth using counterfactual simulations, holding investment rates constant and productivity growth constant. The role of human capital is also discussed.

An increase in the productivity growth rate from 1970 accounts for 68per cent of India's post 1970s growth and the rise in the investment rate accounts for 30 per cent. Hence an upward trend in productivity growth has been more than twice as important as the doubling of the investment rate. A similar conclusion applies for the post 2000 era, where a rise in investment from 25 per cent to 37 per cent of GDP, only adds about 0.7 percentage points of growth to the 4.5 per cent annual growth rate over this period.

The paper provides quantitative estimates of the role of investment and productivity to India's growth based on the neoclassical growth model. It thus improves upon existing growth accounting studies by allowing for the induced effect of productivity growth on capital accumulation. It also improves upon existing development accounting techniques that rely on steady state restrictions, and which would therefore be inappropriate for evaluating India's recent transitional growth.

This paper takes the finance-growth nexus further by looking at the relationship between bank competition, financial innovations and economic growth in Ghana. The purpose of this paper is to find the causality among bank competition, financial innovations and economic growth in Ghana.

The relationship between bank competition, financial innovations and economic growth was established through the framework of the endogenous growth model. In addition, the paper employed the bound testing ARDL cointegration procedures to enable us to establish both short-run and long-run relationship between bank competition, financial innovations and economic growth. Granger causality test were also estimated to determine the direction of causality.

The results showed that, in the long run, bank competition is positively related to economic growth while financial innovation is negatively related to economic growth. In the short run, bank competition is negatively related to economic growth. By the same token, financial innovation is positively related to economic growth in the short run. In terms of causality, the results showed that there is unidirectional Granger causality from bank competition to economic growth. However, there is bidirectional Granger causality between financial innovation and economic growth.

The study therefore, recommends for more regulations toward a more competitive banking system with more innovative products tailored toward mobilization of savings and investment to growth induced sectors of the economy.

This paper provides a time series perspective to the finance-growth nexus and highlights the potential contribution of effective banking development to the economic welfare of the Ghanaian citizens.

This study examines the moderating effect of institutional quality on the finance-growth nexus in South Africa from 1986 to 2015.

This study adopts unit root tests, cointegration test and autoregressive distributed lag (ARDL) model.

The findings reveal that institutional quality constitutes a drain to the growth benefits of financial development (FD) in South Africa in the short-run while FD and institutional quality converge to enhance growth process of the country in the long-run. Also, the threshold of institutional quality beyond which institution stimulates strong positive impact of finance on growth is estimated to be 6.42 on a 10-point scale.

This study, therefore, suggests that institutional quality matters in the way FD influences economic growth in South Africa. Hence, stakeholders are encouraged to trace and block lapses and loopholes in the institutional framework guiding financial system in South Africa so as to maximize growth benefits of FD.

This study contributes to the extant studies by introducing a country-specific analysis into the empirical examination of how institutional quality influences the impact of FD on economic growth. Also, this study deviates from other studies by determining the threshold of institutional quality beyond which FD stimulates strong positive effect on economic growth in South Africa

Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.