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The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

This paper aims to examine the impacts of unstable off-farm employment on the probability and stability of farmland rent-out and explore its mechanisms.

The paper adopts Ordinary Least Squares (OLS), Probit, Tobit, Order probit models with two-way fixed effects to conduct empirical analysis based on the balanced panel data collected in 2016 and 2023 with a national representativeness sample of 1,206 rural households in 100 villages across 5 provinces in China.

The empirical results showed that unstable off-farm employment had negative effects on the probability of farmland rent-out, but it had no effects on the stability of farmland rent-out. The mechanism analysis showed that unstable off-farm employment affected the probability of farmland rent-out by decreasing the probability of purchasing houses in city and endowment insurance with high pension. Heterogeneity analysis indicated that the negative effect of unstable off-farm employment was much larger for the households with higher share of labor engaging in off-farm employment outside home county, elder members in the households and those located in the villages of mountain areas.

This paper is the first to define the unstable off-farm employment from the perspective of incontiguous off-farm employment for several years, which could capture the normality rather than particular case in a certain year of off-farm employment among rural labors. Using these new measurements of unstable off-farmland, this paper examined the impacts and mechanisms of share of unstable off-farm employment on the probability and stability of farmland rent-out.

We investigate the role of fiscal policy, through several measures of government revenues and expenditures and redistribution, on disposable and market income inequality and economic growth as well as the interaction between inequality and growth for 31 European countries from 1995 to 2019.

We use a simultaneous equations model to assess the linkage between economic growth, inequalities and fiscal policy variables.

(1) While disposable income inequality has a negative effect on all fiscal policy variables, market income inequality has a mixed effects; (2) for Eastern European countries, public consumption and direct taxation positively influence economic growth; conversely, for Western European countries, the effects are negative; (3) disposable and market income inequality have a positive effect on growth for Eastern European countries, and a negative influence on growth for Western European countries; (4) growth contributes to the increase of disposable and market income inequality for Eastern European countries; for Western European countries, the effects are opposite; and (5) fiscal policy allows for the attenuation of disposable income inequality.

The different results between the role of market and disposable income inequality levels lead us to suggest tax progressivity as an important feature to consider when analyse the trivariate relationship between inequalities, fiscal policy and growth. Furthermore, there are different dynamics between inequality and growth, and the role of fiscal policy, on both Eastern and Western European countries.

The paper aims to analyse the impact of economic and governance factors on remittance inflows to India from the UK, USA and UAE. India is globally recognised as the largest recipient of remittances.

Using a comprehensive time series data set spanning 1996 to 2022, the authors use an innovative non-linear autoregressive distributed lag model approach to examine the influence of economic growth, corruption control and employer availability in the three source countries on remittance inflows to India.

The results indicate that in the UAE, changes in economic growth and corruption control directly affect remittance outflows. However, the presence of employers in the UAE has minimal impact on remittance outflows to India. Regarding the UK, fluctuations in economic growth primarily drive remittance outflows to India. The effect of corruption control and employment opportunities on remittance outflows is marginal. In the USA, economic growth does not notably impact remittance outflows, whereas corruption control and employment opportunities significantly influence the outflows to India.

These findings have important implications for policymakers. Analysing macroeconomic factors from key remittance-sending nations offers valuable insights for Indian policymakers and their international counterparts to enhance remittance inflows. The study focuses on three countries that collectively contribute to about 50% of India's remittances, providing a unique contribution compared to the usual country-specific or regional focus in existing literature. Finally, leveraging these findings, NITI Aayog, an organisation dedicated to achieving India's sustainable development goals, can effectively monitor macroeconomic indicators related to significant remittance-sending countries.

This research examines the impact of the National Rural E-commerce Comprehensive Demonstration Project (NRECDP) on poverty reduction and income growth in rural China.

The study develops a theoretical framework, which considers the role of geographical, technological, institutional and cultural factors for the e-commerce poverty alleviation (e-CPA) model. Empirically, this study applies the difference-in-differences (DID) model and the event study approach to evaluate the effectiveness of NRECDP on the basis of large-scale county-level and household-level panel data spanning 2010 to 2020.

The study found that the NRECDP, as a government-led, information and communication technology (ICT)-enabled, market-based program, has led to a significant increase in per capita output of primary industry employees, as well as in the disposable income of rural residents, especially those in national-level poverty-stricken (NP) counties. The interventions of the NRECDP achieved these positive outcomes through transportation and Internet infrastructure improvement, ICT adoption and human capital accumulation in impoverished towns and villages in remote rural areas. These effects are larger in the eastern region of China, followed by the central region, whereas the weakest effects were found in the western region. However, we found little evidence of the NRECDP increasing household developmental expenditure.

The study findings have important practical and policy implications for rural e-commerce development and self-sustained poverty alleviation solutions. The research revealed the significance of government NRECDP interventions for increasing rural income, reducing living costs, and empowering the rural population in its multiple social roles, namely, as consumers, producers, employees and microentrepreneurs. The local cultural context may also play a role in ICT adoption and entrepreneurship cultivation with a downstream effect on the effectiveness of e-CPA practices. Policymakers would need to ensure a supportive entrepreneur-friendly environment for rural e-commerce development and continue implementing progressive policies for poverty alleviation.

This study explores poverty alleviation issues in China by developing for the first time a multi-faceted framework that is subsequently tested by both county-level and household-level large-scale observations. Also, it is the first study to provide nationwide empirical evidence on the effectiveness of e-CPA in narrowing down the spatial and digital divides in China. In addition to the impact of geography, technology and governmental support, this study also sheds light on the role of culture in the adoption and diffusion of digital technologies and as a source of local entrepreneurial opportunities.

We seek to quantify the relationship between urbanization and economic growth in China using recent advances in econometric techniques.

This study adopts a smoothed instrumental variables quantile regression (SIVQR) estimator to obtain consistent estimates of the effects of urbanization on economic growth in China. Our approach accounts for the differential impacts of urbanization across the conditional distribution of economic growth while allowing for an identification strategy that addresses the endogeneity of urbanization. Our main findings reveal that ignoring urbanization endogeneity leads to inconsistent estimates of urbanization effects. Further, we find a positive relationship between urbanization and growth resembling an inverted U-shape. This supports the hypothesis that the beneficial effects of urbanization intensify at initial stages while diminishing beyond a certain threshold, due perhaps to weakening scale economies.

Our main results indicate that the individual productivity gains brought by urbanization outweigh the negative effects thereof that impede productivity, thus contributing to the economic growth in China. Further, we find that ignoring differential impacts of urbanization underestimates the beneficial effects of urbanization for provinces whose quality of governance is in the vicinity of the center of quality distribution. Ignoring the endogeneity of urbanization generates inconsistent estimates of the elasticity of economic growth with respect to urbanization. Finally, we estimate an inverted U-shape resembling relationship between urbanization and growth.

First, future studies would benefit from incorporating more data as provinces further east on the mainland become more urbanized and urbanization runs its course. Second, controlling the barriers to rural-urban mobility would contribute to the robustness of the estimated relationship between urbanization and growth once such data became available. Unveiling the impact of government-imposed barriers is key to designing optimal policies that help fuel economic growth in the country. Finally, future research could benefit from information on urbanization sources not considered here such as inter-provincial migration, as such data become publicly available.

Quantifying the beneficial effects of urbanization on economic growth can help guide the government in China to further fuel the growth through a set of relevant policy tools that promote urbanization.

Rural-urban migration in China lays the groundwork for economic advancement in recipient cities and economies, as it may induce scale economies. This can benefit both the economy at large and the migrants.

The SIVQR estimator accounts for potential heterogeneous effects of urbanization across the entire conditional distribution of growth while allowing for an identification strategy that addresses the endogeneity of urbanization. An additional distinguishing feature of the current study is our use of the most recent novel, provincial-level data obtained from the National Bureau of Statistics of China. Our focus on a single country allows sidestepping issues arising from the inconsistency of the definition of urban across different countries while accounting for intra-country urbanization drivers intrinsic to China, such as natural features and geographic characteristics. Therefore, our approach has the potential to sidestep the bias resulting from the differences in mechanisms behind urbanization-growth relationships across different countries.

The purpose of this study is to quantify the impact of electricity power outages on the local housing market in South Africa.

This study uses the autoregressive distributive lag (ARDL) and quantile autoregressive distributive lag (QARDL) models on annual time series data, for the period 1971–2014. The interest rate, real income and inflation were used as control variables to enable a multivariate framework.

The results from the ARDL model show that real income is the only factor influencing housing price over the long run, whereas other variables only have short-run effects. The estimates from the QARDL further reveal hidden cointegration relationship over the long run with higher quantile levels of distribution and transmission losses raising the residential price growth.

Overall, the findings of this study imply that the South African housing market is more vulnerable to property devaluation caused by power outages over the short run and yet remains resilient to loadshedding over the long run. Other macro-economic factors, such as real income and inflation, are more influential factors towards long-run developments in the residential market.

To the best of the authors’ knowledge, this is the first study to examine the empirical relationship between power outages and housing price growth.

This chapter examines the impact of sectoral indebtedness on GDP in Czechia, initially a low-indebted small open economy in which debt dynamics are becoming a major concern. The impact of household debt, non-financial corporation debt and public debt is analysed with the use of local projections based on instrumental variable estimations. The results show a more pronounced influence of household debt compared to non-financial corporation and government debt. Initially, increasing household debt stimulates short-run economic activity, but in the medium run, it limits household consumption and negatively affects output. This negative impact gradually turns into a positive effect in the long run. Non-financial corporation debt has a negative short- to medium-run impact but can have a small positive effect in the long run due to the prevalence of tradable industries. Public debt initially has a short-run negative impact, but then gradually becomes positive. Overall, the findings have implications for macroeconomic policies and the importance of monitoring financial stability.

While today's customer steadily adapts to various modes of shopping, their beliefs around fluency through each shopping channel, and personal factors such as income level, can impact their intention to patronage or purchase from omnichannel department stores. Hence, this study analysed the customers of omnichannel fashion department stores, using perceived fluency and income as indirect factors that help understand customers' patronage intention and purchase intention.

The overarching framework for this research is the theory of reasoned action, in which patronage and purchase intentions represent the specific likelihood-of-performance behaviours. A Seemingly Unrelated Regression model was empirically used to analyse the relationships between generational cohorts, income, and perceived channel fluency and the behaviours that lead to patronage intention and purchase intention. Researchers conducted a survey among 552 omnichannel fashion department store consumers to examine today's retail environment.

The results of this study suggest that (1) consumers between the ages of 50 and 69 years, including older Generation X and younger Baby Boomers, who earn between $60,000 and $79,999 in annual salary show a significantly positive relationship with both patronage and purchase intentions through perceived fluency and (2) consumers between the ages of 38 and 49 years, including older Millennials and younger Generation X, who earn between $80,000 and $99,999 in annual salary show a significantly positive relationship with purchase intention through perceived fluency

This study analyses correlations between a generational cohort, perceived fluency as moderated by income and the relationship between these variables and customers' patronage and purchase intentions, which has not been studied before.