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The main purpose of this paper is to explore the transitional dynamics of housing affordability indicators of major cities in three developed countries: the USA, Canada and Australia, in the period after the global financial crisis. As the global housing markets are more interconnected today, it is essential to investigate the demographic movement pattern and their impacts on housing market dynamics.

Based on the Markov transition matrix approach and the stochastic kernel technique, a newly established framework named the mobility probability plot (MPP) is adopted to investigate the city-level trends of housing affordability in the three countries during the period 2008-2015.

The results suggest that the transitional dynamics of the USA’s housing affordability trend saliently differs from those of Canada and Australia: in the USA, MPP results reveal that when the price-to-income (P/I) ratio is higher than 3.5 times, it has a high tendency of moving downward in the next period. In Australia, housing affordability tends to continue deteriorating when the P/I ratios are in the range from 8.0 to 8.6. In Canada, the MPP analysis indicates that the P/I ratios tend to increase further when the ratios are between 5.7 and 7.0, and within the range of 8.3-9.5.

This paper adopts an innovative approach to explore the city-level trends of housing affordability in the three developed countries during the period 2008-2015. The distribution dynamics approach has several virtues: first, this approach does not merely focus on the issue of housing affordability but also includes an analysis of the underlying housing affordability distribution. Second, it can clearly show the mobility of the city-level units in terms of the P/I change. Third, it can predict the proportion of the entities in different P/I ratio bands in a number of years ahead and even in the long run.

The purpose of this paper is to study convergence and income mobility of China’s rural households.

The data of rural household income per capita are employed to compute the transitional dynamics in the rural sector. The analyses are conducted at two spatial levels, namely, the national and provincial levels. Ergodic distributions are computed to provide a forecast of future income distributions, whereas Mobility Probability Plots are constructed to offer detailed information on the transitional dynamics.

The income distributions are found to have considerable persistence. Another finding is that most of the households (except the extremely low-income households) have a tendency of moving downwards in the income distribution though they are more likely to remain in the same levels of relative income because of their high persistence. Convergence to a unimodal income distribution is possible in the long run, however, the households will converge to a value which is far below China’s per capita gross domestic product.

Since a lot of the rural households would congregate to the lower part of the income distribution if the transitional dynamics remain unchanged, therefore, it calls for government intervention.

More resources should be diverted to the rural sector.

The finding also shows that the provinces have very different transitional dynamics even if they are situated in the same economic zone. Thus, the government should formulate province-specific development polices so as to promote greater equality.

Given that no recent research has been conducted on convergence and transitional dynamics of rural household income. Therefore, this paper attempts to fill the gap in the literature by investigating the pattern and future development of rural household income in China through the use of stochastic kernel approach.

The purpose of this paper is twofold: to determine the factors contributing to farm income in the Transitional and Savanna zones of Ghana and to ascertain variations between in the same and across the two locations; and to determine the impact of credit on farm income in each of the two zones and to ascertain the variation in impact of credit across the two locations.

In order to address endogeneity and sample selection bias, the authors draw from the theory of impact evaluation in nonrandom experiment, employing the endogenous switching regression (ESR) while using the propensity score matching (PSM) to check for robustness of the results.

The results show significant mean differences between some characteristics of households that have access to credit and those that did not have access. Further, the results revealed farm size, labor; gender, age, literacy, wealth and group membership as the significant determinants of both credit access and income in the two zones. With the ESR, credit access increases households farm income by GH¢206.56/ha and GH¢39.74/ha in the Transitional and Savanna zones, respectively, but with the PSM, credit increases farm income by GH¢201.50 and GH¢45.69 and in the Transitional and Savanna, respectively.

The mean differences in characteristics of the households revealed the presence of selection bias in the distribution of household’s covariates in the two zones. The results further indicate the importance of productive resources, information and household characteristics in improved access to credit and farm income. Also, the results from both methods indicate that credit access leads to significant gains in farm income for households in both zones. However, differences exist in the results of PSM and that of the ESR results.

The presence of selection bias in the samples suggests that the use of ESR and PSM techniques is appropriate. Further, the results suggesting that enhanced credit access and farm income could be attained through improved access to household resources and information. The results also suggest the need for establishing and expanding credit programs to cover more households in both zones. The differential impact of credit between the two methods employed in each zone revealed the weakness of each model. The low values from PSM could indicate the presence of selection bias resulting from unobservable factors whiles the high values from the ESR could stem from the restrictive assumption of the model. This reinforces the importance of combining mixed methods to check robustness of results and to explore the weakness of each method employed.

The novelty of this study lies in the use of a very extensive and unique data set to decompose the determinants of credit access and farm income and as well as the impacts of credit into zones.

The purpose of this paper is to study the dynamics of the distribution of per capita income of Indian states in the post‐reform period, in order to identify trends towards convergence‐club formation, polarization or stratification during this period.

The authors adopt the “distribution dynamics” framework that involves estimating kernel density functions, stochastic kernels and ergodic distributions in order to identify these trends.

The results show that there is polarization in India in the post‐reform period and this is due to the contrary growth dynamics of the middle‐income states resulting in the “vanishing middle” of the distribution.

This is the first study that highlights the contrary growth dynamics among the middle‐income states as the driving force behind the polarization of Indian states in the post‐reform period.

As they say, “Change is the only constant.” Thriving and surviving during a period of extraordinary collision of technological advances, globalization, and climate change can be daunting. At any given point in one’s life, a transition can be interpreted in terms of the magnitude of change (how big or small) and the individual’s ontological experience of change (whether it disrupts an equilibrium or adapts an emergent way of life). These four quadrants represent different ways to live in a highly dynamic and complex world. We share the resulting four-quadrant framework from a quantitative and a mixed methods study to examine responses to various ways we respond to transitions. Contingent upon these two dimensions, one can use a four-quadrant framework to mobilize resources to design a response and hypothesize a desired outcome. Individuals may find themselves at various junctions of these quadrants over a lifespan. These four quadrants provide “requisite variety” to navigate individual ontology as they move into and out of fluid spaces we often call instability during a time of transition. In this chapter, we identified social, cognitive, psychological, and behavioral factors that contribute to thriving transition experiences, embracing dynamic stability. Two new constructs were developed, the first measures the receptivity to change, Transformation Quotient (TQ) and second measures the range of responses to transitions from surviving to thriving, Thriving Transitional Experiences (TTE). We hope our work will pave the way for Thriving to become a “normal” outcome of experiencing change by transforming the lexicon and expectation of engaging with transitions.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

The purpose of this paper is to investigate how credit access affects the welfare of households and sheds light on how household characteristics influence the decision to take credit and the efficiency in credit use.

This study uses data from the fourth round of the Ethiopian Rural Household Survey conducted in 2009, and examines factors that determine the decision to take credit and the effect of such decision on household welfare. The household welfare variable is measured by the food security indicator and total food expenditure. The study employs endogenous Regime Switching model to account for endogeneity in access to credit and self-selection bias in the decision to participate in credit.

The result from the kernel distribution shows households with access to credit have more consumption expenditure than those without access to credit. The ordinary least square regression shows that access to credit increases total consumption by 12 percent without considering self-selection bias. Participation in non-farm activity increases the demand for credit by 17 percent. Land holding, household size, and participation in saving associations increase the probability of getting credit by 5, 11, and 20 percent, respectively. Access to credit appears to have a positive impact on food security in both actual and counterfactual cases for the current credit receivers.

This study provides a thorough analysis of the impacts of access to credit on household welfare in Ethiopia. The study contributes to the debate on the link between access to credit and household welfare and provides valuable input for policy makers.

There is ample empirical evidence to show that larger banks are more efficient than smaller banks in developed countries. However, there is very little empirical evidence to show that in small developing economies, such as Vietnam, bank size is associated with increased risk, especially credit risk. This paper aim to provide empirical evidence to fill in this gap. This paper employs a slack-based directional distance function using the intermediation approach in measuring the inefficiency of banks in Vietnam during the period 2006–2015. Non-performing loans are used as an undesirable output to capture credit risk. The results show that small banks are more efficient than large banks at the mean level and across the entire distributions of inefficiency of the two groups. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies. The study’s empirical results contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to enhance the performance of Vietnam's banking system as a whole.

This paper uses the non-radial slack-based directional technology distance function developed by Färe and Grosskopf (2010) to estimate the efficiency of banks using the data envelopment analysis technique. Data for 44 commercial banks are used.

The empirical results of the paper contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to improve the performance of Vietnam's banking system as a whole.

This paper extends the extant literature by examining whether efficiency is associated with size in a typical transitional developing economy. The classic Cournot model, the structure-conduct-performance and the efficiency structure hypotheses state that larger banks are more efficient than smaller banks (Bikker and Bos, 2008). Empirical studies of Berger (2003), Mester (2005), Wheelock and Wilson (2012) lend support to the statement in developed countries. However, not much empirical literature focuses on small developing economies such as Vietnam to show that bank size is associated with increased risk, especially credit risk. The study’s empirical results show that size enlargement is not positively associated with risk-adjusted efficiency. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies.

Aims to provide an analytical framework investigating the accumulation of human capital in an OLG framework characterized by a continuous interplay between human capital distribution and individual choice of accumulation. This leads to a wide variety of dynamics. Generally, more equal economies tend to accumulate a higher human capital but other cases are possible. The accumulation is characterized by bimodality or multimodality in the human capital distribution and by an endogenous poverty trap.

In scientific works on forecasting price volatility (of which the overwhelming majority, in comparison with works on price forecasting) for energy products: crude oil, natural gas, fuel oil, the authors compared the effectiveness of forecasting models of generalized autoregressive heteroscedasticity (Generalized Autoregressive Conditional Heteroscedastic model, GARCH) with regression of support vectors for futures contracts. GARCH models are a standard tool used in the literature on volatility, and the vector machine nonlinear regression model is one of the machine learning methods that has been gaining huge popularity in recent years. The authors have shown that the accuracy of volatility forecasts for energy and aluminum prices significantly depends on the volatility proxy used. The model with correctly defined parameters can lead to fewer prediction errors than GARCH models when the square of the daily yield is used as an indicator of volatility in the evaluation. In addition, it is difficult to choose the best model among GARCH models, but forecasts based on asymmetric GARCH models are often the most accurate. The work is based on a model with a representative investor who solves the problem of optimizing utility in a two-period model. The key assumption of the model is the homogeneity of energy and aluminum investor preferences, that is, preferences do not change over time. There are also works with an attempt to solve this problem in a continuous state space. A completely new theory has been put forward that allows predicting the movement of the underlying asset without using historical data, so this topic is very relevant.