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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 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 identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.

This study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.

The empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.

This study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.

The findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

The purpose of this paper is to develop an index for measuring urban house price affordability that integrates spatial considerations and to explore the drivers of housing affordability using the post-least absolute shrinkage and selection operator (LASSO) approach and the ordinary least squares method of regression analysis.

The study is based on time-series data collected from 2005 to 2021 for 256 prefectural-level city districts in China. The new urban spatial house-to-price ratio introduced in this study adds the consideration of commuting costs due to spatial endowment compared to the traditional house-to-price ratio. And compared with the use of ordinary economic modelling methods, this study adopts the post-LASSO variable selection approach combined with the k-fold cross-test model to identify the most important drivers of housing affordability, thus better solving the problems of multicollinearity and overfitting.

Urban macroeconomics environment and government regulations have varying degrees of influence on housing affordability in cities. Among them, gross domestic product is the most important influence.

The paper provides important implications for policymakers, real estate professionals and researchers. For example, policymakers will be able to design policies that target the most influential factors of housing affordability in their region.

This study introduces a new urban spatial house price-to-income ratio, and it examines how macroeconomic indicators, government regulation, real estate market supply and urban infrastructure level have a significant impact on housing affordability. The problem of having too many variables in the decision-making process is minimized through the post-LASSO methodology, which varies the parameters of the model to allow for the ranking of the importance of the variables. As a result, this approach allows policymakers and stakeholders in the real estate market more flexibility in determining policy interventions. In addition, through the k-fold cross-validation methodology, the study ensures a high degree of accuracy and credibility when using drivers to predict housing affordability.

Recent evidence on the impact of the crisis on developed countries shows that changes in income inequality and poverty have been relatively small in spite of the macroeconomic heterogeneity of the recession across different economies. However, when evaluating individual perceptions linked to the crisis any changes in the chances to scale up or lose ground in the income ladder are also crucial. Our aim in this paper is to analyze to what extent the recession has had an impact on individual equivalent incomes and, in particular, on the prevalence of downward mobility in two developed countries where job losses have been large. We find that income losses have increased, particularly in Spain, and while age and education are key determinants of the probability of experiencing an income loss in both countries, the presence of children only increases the probability of an income loss in Spain.

The purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.

A model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.

This modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.

The research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.

The study implies that transport operations would actually need to be re-organized so as to deal with lowering CO₂ footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).

The growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.

Despite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

This paper documents how gender differences in occupational status (defined by earnings, education, and returns to skills) have evolved over time and across generations. The paper finds a persistent gender earnings gap, a reversal of the education gap, and a convergence in starting salaries and returns to experience. Divergent occupational choices might explain part of the persistent gender gaps and women’s failure to reach parity with men in the earnings distribution. Women choose more flexible jobs than men. But whereas men dominate women in high-powered occupations, they are also more likely to be in low-skilled low-pay occupations. Differential effects of children and time spent keeping house explain most of the gender gap in high-powered occupations but cannot explain fully why women choose more flexible occupations.

The paper aims to investigate performance benefits associated with adopting a mobile wireless sensor network (WSN). Sensor nodes are generally energy constrained due to the latter being acquired from onboard battery cells. If one or more sensor nodes fail, possible coverage holes may be created which could invariantly lead to a reduced network lifetime. The paper proposes that instead of rendering the entire WSN inoperative, sensor nodes should physically change position within the region of interest thus adaptively altering the WSN topology with a view of recovering from failures. This type of motion will be referred to as “self healing”.

This paper presents a mobility scheme based on Bayesian networks for predictive reasoning (BayesMob) which is essentially a distributed self healing algorithm for coordinating physical relocation of sensor nodes. Using the algorithm, sensor nodes can predict the performance of the WSN in terms of coverage given that the node moves in a given direction. The evidence for this hypothesis is acquired from local neighborhood information.

The paper compares BayesMob with an alternative algorithm – Coverage Fidelity Algorithm – and shows that BayesMob maintains a higher level WSN coverage for a greater percentage of failures, thus increasing the useful lifetime of the WSN.

The physical relocation of sensor nodes will incur energy overhead, therefore the tradeoffs between all application criteria should be investigated before implementation.

This paper presents a Bayesian network based motion coordination algorithm for WSN which repairs coverage holes caused by energy exhaustion and/or abrupt node failures.

The purpose of this paper is to investigate the extent and the human‐capital determinants of low‐wage mobility for labour market entrants in the UK and Germany.

Using panel data for the UK (BHPS) and Germany (GSOEP), a competing‐risks duration model is applied that allows the study of transitions from low pay to competing destination states: higher pay, self‐employment, unemployment and inactivity. Unobserved heterogeneity is tackled by a non‐parametric mass‐point approach.

It is found that low pay is only a temporary state for most young job starters. However, there is a small group of job starters that is caught in a trap of low pay, unemployment or inactivity. In the UK, job starters escape from low pay mainly by developing firm‐specific skills. In Germany, involvement in formal vocational training and the attainment of apprenticeship qualifications account for low pay exits.

Over the past decades, unemployment and low‐wage employment have emerged as major challenges facing young labour market entrants. While most empirical studies focus exclusively on the transition from low pay to high pay, the paper shows that a significant percentage of young entrants are caught in a low‐pay‐non‐employment trap. Moreover, it is shown that, depending on the institutional context, different types of human capital investments can account for a successful low‐pay exit.