Search results

The main purpose of this paper is to examine the convergence hypothesis of House Price Index (HPI) in the case of 18 major Indian cities for the period 2014–2019.

To attain the authors main goal, this study applies a clustering algorithm advanced by Phillips and Sul. This test creates a club of convergence based on the growth of the cities in terms of HPI.

The study findings show the existence of two convergence clubs and one non-convergent group. Club 1 includes the cities with high HPI growth, whereas club 2 comprises of cities with least HPI growth. Cities belonging to the non-convergent group are neither converging nor diverging.

This study findings will benefit home buyers, sellers, investors, regulators and policymakers interested in the dynamic interlinkages of house price (HP) among Indian cities.

The majority of the studies are conducted in the case of China at the province or city levels. Furthermore, in the case of India, none of the studies has investigated the HP club convergence across Indian cities. Therefore, the present study fills this research gap by examining the HP club convergence across Indian cities.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

Our paper studies a central issue with a long history in economics: the relationship between population and economic growth. We analyze the joint dynamics of economic and demographic growth in 111 countries during the period 1960–2019.

Using the concept of economic regime, the paper introduces the notion of distance between the dynamical paths of different countries. Then, a minimal spanning tree (MST) and a hierarchical tree (HT) are constructed to detect groups of countries sharing similar dynamic performance.

The methodology confirms the existence of three country clubs, each of which exhibits a different dynamic behavior pattern. The analysis also shows that the clusters clearly differ with respect to the evolution of other fundamental variables not previously considered [gross domestic product (GDP) per capita, human capital and life expectancy, among others].

Our results indirectly suggest the existence of dynamic interdependence in the trajectories of economic growth and population change between countries. It also provides evidence against single-model approaches to explain the interdependence between demographic change and economic growth.

We introduce a methodology that allows for a model-free topological and hierarchical description of the interplay between economic growth and population.

The purpose of this study is to examine the relationship between the productive structure and the size of the informal economy in Latin American countries.

The study employs econometric techniques for panel data covering the period from 2002 to 2017 and considering 17 Latin American countries. The evidence presented is based on the informal economy data generated by Medina and Schneider (2018) who estimate the size of the informal economy using a structural equation model and the share of manufacturing in total employment as a measure of the size of the manufacturing sector. Also, the study addresses the possible endogeneity bias in the relationship studied and makes the conclusions more robust, thus avoiding spurious correlations that weaken the findings.

The results indicate that most industrialized Latin American countries are associated with a smaller size of the informal economy.

The findings have important policy implications, as they suggest that Latin American economies need to switch the structure of the economy toward more sophisticated productive structures if they want to reduce the size of the informal economy. Thus, more efforts should be deployed to policies to diversify and upgrade economies.

The study contributes to the literature on the informal economy by connecting the country’s productive structure and informality. Specifically, the results show that the productive structure of countries is a plausible explanation for the size of the informal economy.

Despite the global resolves to curtail fossil fuel consumption (FFC) in favour of clean energies, several countries continue to rely on carbon-intensive sources in meeting their energy demands. Financial constraints and limited knowledge with regards to green energy sources constitute major setbacks to the energy transition process. This study therefore aims to examine the effects of financial development and human capital on energy consumption.

The empirical analysis is based on the system generalised method of moments (SGMM) for a panel of 134 countries from 1996 to 2019. The SGMM estimates conducted on the basis of three measures of energy consumption, notably fossil fuel, renewable energy as well as total energy consumption (TEC), provide divergent results.

While financial development significantly reduces FFC, its effect is positive though non-significant with regards to renewable energy consumption. Conversely, financial development has a positive and significant effect on TEC. Moreover, the results reveal that human capital development has an enhancing though non-significant effect on the energy transition process. In addition, the results reveal that resource rents have an enhancing effect on the energy transition process. However, when natural resources rents are disaggregated into various components (oil, coal, mineral, natural gas and forest rents), the effects on energy transition are divergent. Although our findings are consistent when the global panel is split into developed and developing economies, the results are divergent across geographical regions. Contingent on these findings, actionable policy implications are discussed.

The study complements extant literature by assessing nexuses between financial development, human capital and energy transition from a global perspective.

This study aims to examine the interrelationship between financial stability and economic growth with a comprehensive analysis.

The panel Granger causality testing approach is carried out to the panels of the Fragile Five (F5) and the Group of Seven (G7) countries for the period 1998–2020. To capture the different aspects of financial stability the authors use eight different indicators.

The findings reveal some important implications: the relationship between financial stability and economic growth is sensitive to the financial stability indicators for both the F5 and G7 countries. The stability indicators related to the credit market contain much more causality relationship with economic growth than the indicators related to the stock market. Z-score and provisions to nonperforming loans (NPLs) are among the two variables with the highest causality relationship with economic growth. The least number of causality link is found for the Regulatory Capital Ratio and Stock Price Volatility in F5 countries and Credit Ratio, NPLs and Stock Price Volatility in G7 countries. Economic growth affects financial stability through credit market stability indicators and mostly for the F5 countries. No causal relationship is found for any of the financial stability indicators of Canada, the UK and the USA from economic growth to financial stability.

Since the linkages between financial stability and economic growth may vary due to country/group specific differences, apart from the previous studies, the authors select two different groups of countries in terms of financial stability and economic size.

The COVID-19 pandemic impacted the food and beverage sector very severely. The complete breakdown of the supply chain and lack of customers was particularly challenging for start-ups in the industry. Those that survived were the ones who made a timely and smooth transition in business models to become more technology driven. However, the issues faced and the ground realities of the extent of struggle that these start-ups went through are less understood in the scholarly literature, with most accounts being anecdotal. The purpose of this paper is to address these issues.

The present study attempts to bridge this gap by conducting a qualitative study to collect data from 35 owners/employees of food and beverage start-ups and using the grounded theory approach to code it and identify key themes.

Content analysis of the 35 responses revealed three main themes: the impact of the COVID-19 pandemic on operations of food and beverage start-ups, challenges due to the onset of the COVID-19 pandemic and combating the pandemic, divided into seven subthemes: differences in operations pre- and post-COVID, key changes experienced in operations post-COVID, problems arising in operations due to the pandemic, problems in the use of digital marketing due to the pandemic, problems in the use of technological platforms due to the pandemic, using innovative approaches and technological innovations and using disruptive technologies.

The study contributes novel insights by investigating the changes experienced by food and beverage start-ups due to the pandemic, the innovations introduced by them and the perception about the role of disruptive technologies in their postpandemic operations of food and beverage start-ups.

The purpose of this study is to verify all factors that promote renewable energy (RE) consumption. Past studies have shown that financial development (FD) and economic growth (EG) are the major drivers toward RE development, while oil prices had mixed outcomes in different regions by different studies.

Global warming effects have been the major reason of the transition by nations from fossil fuel use to RE sources that are considered as friendly to the environment. This research uses the fixed effects and random effects techniques, to ascertain the factors which impact RE development. The generalized linear model is also used to check the robustness of the Fixed Effects and Random Effects models’ results, while the Kao, Pedroni and Westerlund tests are used to check cointegration in the specified model.

The major findings of this study show the importance of EG and FD in promoting RE development. Oil prices, inflation rate and public sector credit present a negative effect on RE development, while foreign direct investment does not significantly impact RE development.

This research recommends the use of FD in promoting RE sources, as well as the stabilization of oil prices and consumer prices.

This research is important because it specifies the three proxies of FD, together with foreign direct investment inflation rate, EG and oil prices, in modeling RE. By investigating the impact of oil prices on RE in the emerging seven economies, this research becomes one of the few studies done in this region, as per the authors’ knowhow.

Ticket sales are an essential source of income for football clubs and federations. Analyzing the determinants of fans' willingness-to-pay for tickets is therefore an important exercise. By knowing the match- and fan-related characteristics that influence how much a fan wants to pay for a ticket, as well as to what extent, football clubs and federations can modify their ticket offering and targeting in order to optimize this revenue stream.

Using a detailed discrete choice experiment, based on McFadden's random utility theory, this paper formulates a Bayesian hierarchical multinomial logit model. Such models are very common in the discrete choice modeling literature. The analysis identifies to what extent match and personal attributes influence fans' willingness-to-pay for games of the Belgian men's and women's football national teams.

The results show that the strength of the opponent, the type of competition, the location of the seats in the stadium, the day and kick-off time of the match and the ticket price exert an influence on the choice of the respondent. Fans are attracted most by competitive games against strong opponents. They prefer to sit along the sideline, and they have clear preferences for specific kick-off days and times. The authors also find substantial variation between socio-demographic groups, defined in terms of factors such as age, gender and family composition.

The authors use the results to estimate the willingness-to-pay for match tickets for different socio-demographic groups. Their findings are useful for football clubs and federations interested in optimizing the prices of their match tickets.

To the best of the authors' knowledge, no stated preference methods, such as discrete choice analysis, have been used to analyze the willingness-to-pay of sports fans. The advantage of discrete choice analysis is that options and variations in tickets that are not yet available in practice can be studied, allowing football organizations to increase revenues from new ticketing instruments.