Search results

The importance of financial dependence of small and medium enterprises (SMEs) on their performance is a relatively unaddressed area of research. Relatedly, whether and to what extent foreign bank penetration exerts an impact in the presence of financial dependence also remains an open question. The purpose of the paper in this regard is to exploit unit-level data on Indian SMEs and assess the independent and interactive effects of financial dependence on SME behaviour, in the presence of foreign banks.

This study uses fixed effects specification to address the issue. In subsequent analysis, this study also uses an instrumental variable approach for robustness.

The results indicate that financial dependence improves investment and employment, although there is a decline in productivity. These findings differ across size classes of SMEs. Similar is the evidence in the presence of foreign banks. In particular, foreign bank penetration leads to a decline in investment for micro and medium SMEs, although for small SMEs, the impact is found to be the opposite.

To the best of the author’s knowledge, this is one of the early within-country studies to examine the interface between SMEs and financial dependence and the role played by foreign banks in this regard.

The study investigates persistence of individuals' labour market activity with a focus on examining whether and to what extent there is genuine state dependence in six labour market states: not-in-labour-force, unemployment, self-employment, casual employment, fixed term contracts, and ongoing employment, and how the persistence and genuine state dependence of the labour market states change with education levels.

A dynamic multinomial logit model that accounts for observed and unobserved individual heterogeneity is estimated, using the first 19 waves of the Household, Income, and Labour Dynamics in Australia Survey.

While observed and unobserved individual heterogeneity plays an important role in the persistence of each of the labour market states examined, genuine state dependence is found to be present for all the states. It is also found that the persistence and genuine state dependence of unemployment is larger among those with a low education attainment than among those with higher education.

The existence of genuine state dependence of labour market states calls for early interventions to prevent people from losing jobs.

Earlier studies often focus on persistence of a particular labour market state such as unemployment, while this study examines the persistence simultaneously of six labour market states.

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.

The purpose of this study is to identify the key barriers to the adoption of blockchain technology (BCT) in the construction industry. This investigation seeks to understand the challenges that hinder the implementation of blockchain in this sector.

The study begins with a comprehensive review of relevant literature to identify the main barriers to blockchain adoption in construction. It then uses an integrated approach using total interpretive structural modeling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement to investigate the interrelationships among these barriers. In addition, the decision-making trial and evaluation laboratory (DEMATEL) method is used to establish cause-and-effect relationships between the identified barriers.

The study’s integrated TISM and DEMATEL methodology reveals several crucial barriers to blockchain adoption in the construction industry, including regulatory uncertainty, data privacy and security concerns, limited knowledge and expertise and dependence on blockchain operators. It also identifies scalability issues, uncertain benefits and lack of collaboration as dependent barriers, influenced by other identified barriers.

This study contributes original value by applying an integrated methodological approach to understand the complex interrelationships and dependencies among barriers to blockchain adoption in the construction industry. The findings provide decision-makers with insights to prioritize and effectively manage the key driving barriers, enhancing the understanding and strategic planning for blockchain implementation in this sector.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

The study examined the effect of fiscal policy on poverty in sub-Saharan Africa (SSA) while accounting asymmetric (captured by economic downturns) and spillover effects.

The study used a fixed effect (within regression) IV model to account for country-specific characteristics. The study also adopts a cross-sectional and spatial dependence-consistent model to account for the potential cross-sectional and temporal dependence in panel data modeling.

The study discovered that the effect of fiscal policy on poverty is dependent on the state of the economy. Specifically, we find that fiscal policy helps to reduce the level of poverty during an economic downturn, more than at any other time. More specifically, the findings indicate that the fiscal policy lowers the rate of poverty in SSA, following macroeconomic shocks (captured by the COVID-19 epidemic, the Global Financial Crisis, and the commodity terms of trade shocks). Our findings suggest that fiscal policy is an important policy tool to mitigate the effects of macroeconomic shocks in SSA. Further, the findings also demonstrate that there is a spillover effect of poverty in the region. This implies coordinated, constructive actions by the regional governments can help to lessen the detrimental effects of extreme poverty.

The study examined the effectiveness of fiscal policy to reduce poverty in the event of an economic downturn.

This study examines whether international tourism demand in the Visegrád countries is influenced by countries' risk rating on environmental, social and governance (ESG) factors, as non-economic factors relating to ESG risks have been ignored by previous researches on determinants of international tourism demand.

The study investigates panel data for the Visegrád countries comprising the Czech Republic, Hungary, Poland and Slovakia over the period 1995–2019. Recently developed techniques of augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators are employed so as to take care of cross-sectional dependence, nonstationary residuals and possible heterogeneous slope coefficients.

The regression estimates suggest that besides economic factors, the perception of international tourists regarding ESG risk is another important determinant of international tourism demand in the Visegrád countries. The study also established that income levels in the tourists' originating countries are the most critical determinant of international tourism demand to the Visegrád countries.

The research outcomes of the study include the need for the Visegrád countries to direct policies towards further mitigating their ESG risks in order to improve future international tourism demand in the area. They also need to ensure exchange rate stability to prevent volatility and sudden spikes in the relative price of tourism in their countries.

The study examined the roles of fiscal and monetary policy in reducing poverty in sub-Saharan Africa (SSA), while accounting for macroeconomic disruptions. In particular, the study examined the complementarity of fiscal and monetary policy to mitigate shocks and reduce poverty in SSA.

The study adopts the fixed effect (within regression) model to account for country-specific characteristics, and a cross-sectional dependence – consistent model to control for the potential cross-sectional in panel data modelling. The study used the dummy variable approach to account for the macroeconomic shocks. The authors assigned 1 to the following years – 2008, 2014 and 2020; and 0 otherwise to take care of the global financial crisis, commodity terms of trade shocks and the COVID-19 pandemic respectively.

The study found that fiscal policy (particularly, government spending on health and education) has the greater capacity to reduce the level of poverty in SSA. The results also indicate that fiscal policy and monetary policy can work in tandem to reduce the negative effects of a pandemic. However, the study found an optimal threshold level of monetary policy beyond which monetary policy reduces the effectiveness of fiscal policy to reduce poverty in SSA. The research and policy implications are discussed.

The study, unlike previous studies, accounts for the impact of macroeconomic shocks in the monetary/fiscal policy and poverty literature.

The paper contributes to the literature on investment and poverty in sub-Saharan Africa (SSA). Specifically, the study examined the separate role of private and public investment in poverty reduction in a panel of 40 sub-Saharan African countries.

For robustness, the study adopts a variety of estimation techniques. These include the fixed effect (within) regression model, the two-step system generalised method of moments (GMM) and the pooled OLS with Driscoll-Kraay robust standard errors to account for the well-known problems of endogeneity, heterogeneity and cross-sectional dependence inherent in panel data.

The empirical results show that the reducing impact of public investment on poverty is marginal, while private investment has a significant reducing impact on poverty. The study also found that access to social services, such as water and sanitation, and credit are important determinants of investment in SSA. The research and policy implications are discussed.

The study investigated the separate effect of private and public investments on poverty in SSA, unlike the existing studies that adopted total investment.

The surge in internet usage has generated widespread speculation and optimism regarding its potential impact on the accessibility to financial services. The aim of this study is to investigate the effect of internet penetration on the accessibility of banking services in developed and developing countries.

Panel data regression methods are used to estimate the impact of internet penetration on accessibility to banking services in a sample of 74 countries from Global Findex survey waves of 2011, 2014, 2017 and 2021. To mitigate potential issues related to heteroscedasticity, autocorrelation and cross-sectional dependence, the study has implemented cluster robust standard errors testing. Furthermore, as a sensitivity check, the sample has been segregated into developed and developing country groups.

The study finds a significant positive correlation between internet penetration and banking access in full sample. Subsample analysis reveals that this relationship is statistically significant in developed countries, but not in developing ones, despite being positive. The research discusses the implications of these findings for both country groups.

Research to date has largely investigated the link between information and communication technology (ICT) and financial inclusion, often treating internet penetration as one component of ICT, which obscures its individual influence. This study, however, isolates internet penetration to specifically analyze its distinct effects on banking accessibility across developed and developing countries.