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The aim of this research is to analyze the impact of conditional variables—physical capital, population, and Total Factor Productivity (TFP)—on the economic convergence of the member economies of the Asia-Pacific Economic Cooperation (APEC) Forum over the period 1960–2020. This study employs a causal and correlational methodological approach, utilizing the pooled mean group (PMG) estimator within a non-experimental design framework for quantitative analysis. This methodology facilitates the estimation of conditional β-convergence, ensuring the statistical significance of estimates even in heterogeneous data panels with variables of integration order I(0) and I(1). The results indicate that physical capital, population growth, and TFP have significantly influenced the growth rates of APEC economies, contributing to economic convergence within the region during the 1960–2020 period. This study offers significant contributions by analyzing the 21 APEC economies over a 60-year period, utilizing a PMG model to estimate conditional β-convergence, and conducting comprehensive evaluations of short- and long-term trends. Consequently, the research recommends implementing policies that prioritize innovation, strengthen capital, create employment opportunities, and enhance productivity to reduce inequalities and foster sustainable growth across APEC economies.

The Strategy of Spatial Development of the Russian Federation until 2025 aims at the economic growth acceleration and reduction of the intra-regional socio-economic differences. Therefore, the factors affecting the economic growth of regions, convergence of regions, spillover effects from the neighbouring regions are of importance. Russian regions are very different and do not converge to a unique equilibrium path. 80 Russian regions were divided into the groups of poor, middle and rich regions. Three main hypotheses were considered, based on the differences in the 1) convergence speed, 2) influence of the same factors, 3) different mutual influence of regions. They were tested using a modified spatially autoregressive model for the three groups using the Russian regional data for 2000–2017. Beta-convergence was found only for the middle and rich regions, the rate of convergence was higher in the rich regions. The poor regions did not grow faster than the other regions, confirming the relevance of the Strategy of Spatial Development. The similarities and differences were identified in the factors ensuring the economic growth of regions belonging to the three groups. The growth in all regions is stimulated by the regional economy openness. The growth of rich regions can be achieved by increasing the investment and reducing the investment risk. However, the investments in the poor and middle regions are not effective. The poor and middle regions receive positive spillovers from the growth of the neighbouring regions. It is possible to expect reduced differences in the living standards between the poor and rich regions.

The power source, spatial-temporal differentiation and convergence of the growth rate of green total factor productivity in China’s agriculture were analyzed. The Malmquist index was used to measure the growth rate, and the spatial-temporal convergence was tested by σ convergence, absolute β convergence, conditional β convergence and dynamic spatial convergence. The study drew conclusions that the impetus for the intensive growth of green agriculture was insufficient, and the driving force for the growth of agricultural green total factor productivity (AGTFP) in the eastern, western and central region was green technology progress. In addition, AGTFP did not have an absolute σ convergence trend. Dynamic spatial absolute β and conditional β convergence indicated that regional differences were not completely related to regional endowment conditions, and regional green agricultural production was unbalanced. This study provides an important support for regional green development in China’s agriculture.

With growing attention being paid to the problems of sustainable development and just transition, energy poverty emerges as a focal issue to be addressed by the Common Policy. However, the dynamics of this phenomenon across the EU still appear to be insufficiently recognised. Therefore, this study deals with the problem of energy poverty convergence and aims to test it for the 27 EU countries over the period 2010–2022. Contrary to limited studies of energy poverty convergence that use a simple indicator, it uses aggregate measures based on consensual consequential indicators of energy poverty to verify the hypothesis of convergence. Absolute and conditional beta convergence are considered. Potential determinants of energy poverty are incorporated into a model of conditional convergence. The analysis confirms the existence of beta convergence of energy poverty in the EU, indicating the progressing socio-economic cohesion of the member states. The results, thus, deliver some arguments supporting an integrative approach to the energy policy of the EU. The research reveals that, among the factors influencing EP dynamics, an important role may be attributed to technological catch-up and income distribution across a society. Sustainable development should thus be supported with energy modernisation efforts of an inclusive character.

Recent economic convergence studies show that cross-country income inequalities have declined since the 1990s. However, this study finds that this episode of income convergence is unsustainable in the long run because countries' capacity to earn income diverges. Specifically, the paper analyses the convergence of per-capita Inclusive Wealth, which comprises all capital assets that contribute to the production of goods and services and the well-being of its society. Utilizing a diverse array of techniques to estimate convergence in a sample of 140 countries between 1990 and 2010, the paper demonstrates the simultaneity of unconditional convergence of GDP and unconditional divergence of Inclusive Wealth. Natural-resource-rich countries that lack human capital, in particular, appear unable to match the global per capita Inclusive Wealth growth rate. A trend emerges towards a bimodal Inclusive Wealth distribution with a substantial low-wealth peak. Thus, although swift income convergence appears promising for developing nations, I caution against optimism. When considering a more appropriate measure of future well-being, such as Inclusive Wealth, the economic outlook for many countries is bleaker than recent studies suggest.

We argue that the alternative hypotheses about cross-country convergence dynamics, namely the Conditional Convergence Hypothesis, Absolute Convergence Hypothesis, and Club Convergence hypothesis, build upon different modelling choices and answer different empirical questions. Hence, results favoring one hypothesis are not necessarily evidence against the other hypotheses. We apply several modelling approaches to a sample of world economies to support our argument, and present empirical evidence that yields controversial conclusions if the hypotheses about convergence are taken as competing. However, the controversy disappears as we note that there are neither theoretical nor empirical reasons to take evidence in favor of the Absolute Convergence Hypothesis as necessarily being against the Club Convergence Hypothesis, and vice versa. We present results for the world economies where the two processes co-exist. We conclude by arguing that when analysis is conducted at the regional level, the two processes are more likely to co-exist because regions share the same institutions, culture, natural resources, and other fundamental causes of growth. Consequently, a test for convergence that studies whether clusters converge or diverge once eventually emerged in the distribution of incomes is still needed. Finally, we argue that to study convergence dynamics, it is necessary to model relationships between economies.

Researchers interested in studying whether convergence dynamics are in place among regions within the same country have adopted both statistical tools and empirical frameworks developed when studying convergence across different economies. We show that this approach is risky, because when an analysis is conducted at the regional level, the absolute and club convergence processes are more likely to co-exist than in the case of world economies. We propose an empirical approach where the two hypotheses are not taken as competing. Our procedure uncovers periods of convergence and periods of divergence for the three samples we studied: Italy observed at both the regional and provincial levels; EU regions; and world economies. We find a process of absolute convergence for Italian regions from 1951 to 1999, and that their convergence process ends in 1971 after a period which we define as clustering convergence. We also find a process of convergence across European regions from 1977 to 1993; that ends in 1985 in favor of a process of clustering and divergence. Finally, our procedure uncovers a process of absolute convergence from 1964 to 1975 and divergence from 1975 to 1999 in the case of world economies.

This article investigates the impact of both the Common Agricultural Policy and structural policies on European regions by estimating a conditional growth convergence model. The Common Agricultural Policy influences the convergence process by affecting regional aggregate productivity, eventually conflicting with the structural policies designed to promote growth in lagging regions. The conditional convergence model is specified in a dynamic panel data form and applied to 206 regions observed from 1989 to 2000. A GMM estimation is applied in order to obtain consistent estimates of both the convergence parameter β and the impact of the conditioning variables, policy measures in particular.

Ever-increasing computational power, along with ever-more sophisticated statistical computing techniques, is making it possible to fit ever-more complex statistical models. Among the more computationally intensive methods, the Gibbs sampler is popular because of its simplicity and power to effectively generate samples from a high-dimensional probability distribution. Despite its simple implementation and description, however, the Gibbs sampler is criticized for its sometimes slow convergence, especially when it is used to fit highly structured complex models. Here we present partially collapsed Gibbs sampling strategies that improve the convergence by capitalizing on a set of functionally incompatible conditional distributions. Such incompatibility generally is avoided in the construction of a Gibbs sampler, because the resulting convergence properties are not well understood. We introduce three basic tools (marginalization, permutation, and trimming) that allow us to transform a Gibbs sampler into a partially collapsed Gibbs sampler with known stationary distribution and faster convergence.

In this paper, we investigate the (in)-consistency of different bootstrap methods for constructing confidence intervals in the class of estimators that converge at rate $n^{1/3}$ . The Grenander estimator, the nonparametric maximum likelihood estimator of an unknown nonincreasing density function f on [0, ∞), is a prototypical example. We focus on this example and explore different approaches to constructing bootstrap confidence intervals for f(t₀), where t₀ ∈ (0, ∞) is an interior point. We find that the bootstrap estimate, when generating bootstrap samples from the empirical distribution function ${\\Bbb F}_{n}$ or its least concave majorant $\\tilde{F}_{n}$ , does not have any weak limit in probability. We provide a set of sufficient conditions for the consistency of any bootstrap method in this example and show that bootstrapping from a smoothed version of $\\tilde{F}_{n}$ leads to strongly consistent estimators. The m out of n bootstrap method is also shown to be consistent while generating samples from ${\\Bbb F}_{n}$ and $\\tilde{F}_{n}$ .