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1 – 10 of over 1000Kumar Shaurav, Abdhut Deheri and Badri Narayan Rath
The purpose of this research is to evaluate corruption in the context of India, spanning the period between 1988 and 2021. Additionally, it aims to provide an in-depth…
Abstract
Purpose
The purpose of this research is to evaluate corruption in the context of India, spanning the period between 1988 and 2021. Additionally, it aims to provide an in-depth comprehension of the factors that drive its prevalence and to propose policy directives for addressing these underlying issues.
Design/methodology/approach
The study instead of relying on perception-based measures, takes a distinct approach by formulating a corruption index derived from reported instances, thus ensuring a more objective assessment. Furthermore, we employ stochastic frontier analysis to tackle the issue of under-reporting within the corruption index based on reported cases. Subsequently, an auto regressive distributed lag (ARDL) methodology is applied to ascertain the principal drivers of corruption, encompassing both long and short factors.
Findings
This study reveals that corruption in India is notably influenced by economic growth and income inequality. Conversely, government effectiveness and globalization display a tendency to mitigate corruption. However, our rigorous analysis demonstrates that financial development does not wield a substantial influence in our study. Moreover, our inquiry uncovers a nonlinear relationship between economic growth and corruption. Additionally, we ascertain that the long run and short run impacts of corruption remain relatively stable across both models utilized in our study.
Originality/value
This study differs from previous research in the subsequent manners. Primarily, we employed an objective measure to formulate the corruption index, coupled with addressing the underreporting issues via stochastic frontier analysis. Moreover, this study pioneers the identification of a non-linear relationship between corruption and economic growth within the Indian context, a facet unexplored in previous investigations.
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Arif Gulzar Hajam, Shahina Perween and Mushtaq Ahmad Malik
Tourism–economy relationship in India has been studied extensively in the past literature using a single equation approach. However, the present paper diverted from this trend and…
Abstract
Purpose
Tourism–economy relationship in India has been studied extensively in the past literature using a single equation approach. However, the present paper diverted from this trend and examined the tourism–economy relationship using the specific to general modelling approach over the 1990–2018 time period. The study also accounts for the influence of merchandise trade, capital formation, foreign investment inflows and inflation on economic growth to achieve the robustness of the coefficient estimates.
Design/methodology/approach
To achieve the objective, the study utilised a specific to general modelling strategy. First, the regression equation includes only three core variables: gross domestic product (GDP), international tourist receipts and international tourist expenditures. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegration among the variables. As for the estimation technique, the authors employed autoregressive distributed lag (ARDL) approach.
Findings
The paper's findings highlight that tourism receipts and expenditures exert a positively significant impact on economic growth. Moreover, including the additional independent variables does not substantially change the tourism and economic growth relationship. The existence of one-way causality from tourism expenditures to economic growth supports the tourism-led growth hypothesis. These findings highlight the rationale for intervention by the government and policymakers to promote tourism potential and facilities to accelerate the overall growth performance of the country. While the existence of one-way causal effect from economic growth to tourism revenues supports the growth-led tourism development hypothesis, implying that economic expansion is necessary for tourism development.
Research limitations/implications
This research article tried to present a comprehensive picture of India's tourism–economy relationship. However, the present study is organised as an aggregate economy-level analysis. It assumed that the aggregate tourism sector is homogenous. However, different tourism sectors exert different levels of influence on the economy. The authors expect future research can take the disaggregated analysis of the tourism–economy relationship.
Practical implications
This study provides valuable insights into the tourism-led growth hypothesis in India. The study highlights comprehensive intervention by the government and policymakers for accelerating tourism development to invigorate the overall growth performance of the country over the long run. The principal recommendation emerging from the present research is that the tourism growth potential can be depended upon to stimulate the economic performance of the Indian economy.
Originality/value
The present study diverted from the previous empirical studies by following a specific to general modelling strategy. First, the regression model includes only three core variables such as economic growth, tourism receipts and tourism expenditure. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegrating relationship among the variables. GDP growth rate is used as a dependent variable in all five specifications. The idea is to expand the model to capture every feature of the data generating process.
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Cathrine Banga, Abraham Deka, Salim Hamza Ringim, Abubakar Sadiq Mustapha, Hüseyin Özdeşer and Hasan Kilic
The current study aims to ascertain the association between tourism development, economic growth and environmental quality by using the short-run and long-run autoregressive…
Abstract
Purpose
The current study aims to ascertain the association between tourism development, economic growth and environmental quality by using the short-run and long-run autoregressive distributive lag model.
Design/methodology/approach
Tourism development has a major role to play in improving a nation’s economic growth. However, it is also blamed for exacerbating environmental pollution because of its massive use of energy (non-renewable energy).
Findings
The major findings of this research show that renewable energy (RE) use and gross domestic product (GDP) negatively impact carbon dioxide (CO2) emissions in South Africa. Tourism arrivals and CO2 emissions negatively impact GDP, while capital positively impacts GDP in the long run.
Practical implications
This research recommends the use of RE, since it reduces carbon emissions, and capital, as it remains the major driver of economic growth.
Originality/value
The originality of the current research is that it uses long-period annual time series data from 1971 to 2019 of South Africa, one of the largest tourist nations in Africa. To the best of the authors’ knowledge, no studies have examined South Africa in this context and minimal research has been conducted to ascertain the impact of the tourism industry on the environment, despite the accusations directed toward it.
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Corey Fuller and Robin C. Sickles
Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The…
Abstract
Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.
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As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of…
Abstract
Purpose
As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.
Design/methodology/approach
To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.
Findings
This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.
Research limitations/implications
The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.
Practical implications
A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.
Originality/value
The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.
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Haoze Cang, Xiangyan Zeng and Shuli Yan
The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high…
Abstract
Purpose
The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.
Design/methodology/approach
First, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.
Findings
The predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.
Originality/value
Crude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.
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Ketki Kaushik and Shruti Shastri
This study aims to assess the nexus among oil price (OP), renewable energy consumption (REC) and trade balance (TB) for India using annual time series data for the time period…
Abstract
Purpose
This study aims to assess the nexus among oil price (OP), renewable energy consumption (REC) and trade balance (TB) for India using annual time series data for the time period 1985–2019. In particular, the authors examine whether REC improves India's TB in the context of high oil import dependence.
Design/methodology/approach
The study uses autoregressive distributed lags (ARDL) bound testing approach that has the advantage of yielding estimates of long-run and short-run parameters simultaneously. Moreover, the small sample properties of this approach are superior to other multivariate cointegration techniques. Fully modified ordinary least square (FMOLS) and dynamic ordinary least squares (DOLS) are also applied to test the robustness of the results. The causality among the series is investigated through block exogeneity test based on vector error correction model.
Findings
The findings based on ARDL bounds testing approach indicate that OPs exert a negative impact on TB of India in both long run and short run, whereas REC has a favorable impact on the TB. In particular, 1% increase in OPs decreases TBs by 0.003% and a 1% increase in REC improves TB by 0.011%. The results of FMOLS and DOLS corroborate the findings from ARDL estimates. The results of block exogeneity test suggest unidirectional causation from OPs to TB; OPs to REC and REC to TB.
Practical implications
The study underscore the importance of renewable energy as a potential tool to curtail trade deficits in the context of Indian economy. Our results suggest that the policymakers must pay attention to the hindrances in augmentation of renewable energy usage and try to capitalize on the resulting gains for the TB.
Social implications
Climate change is a major challenge for developing countries like India. Renewable energy sector is considered an important instrument toward attaining the twin objectives of environmental sustainability and employment generation. This study underscores another role of REC as a tool to achieve a sustainable trade position, which may help India save her valuable forex reserves for broader objectives of economic development.
Originality/value
To the best of the authors’ knowledge, this is the first study that probes the dynamic nexus among OPs, REC and TB in Indian context. From a policy standpoint, the study underscores the importance of renewable energy as a potential tool to curtail trade deficits in context of India. From a theoretical perspective, the study extends the literature on the determinants of TB by identifying the role of REC in shaping TB.
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Fredrick Otieno Okuta, Titus Kivaa, Raphael Kieti and James Ouma Okaka
This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This…
Abstract
Purpose
This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.
Design/methodology/approach
This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.
Findings
The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.
Practical implications
The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.
Originality/value
The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.
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This paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on…
Abstract
Purpose
This paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on well-developed financial markets, and little is researched about financial professionals, such as institutional investors, portfolio managers, investment advisors, financial analysts, etc., in emerging markets.
Design/methodology/approach
An expert-validated questionnaire measure four prominent behavioral biases and Indian financial professionals' rational decision-making process. The final sample consists of 274 valid responses using the purposive sampling technique. IBM SPSS and AMOS structural equation modeling (SEM) software are used to build measurement and structural models, multivariate analysis including regression, factor analysis, etc.
Findings
The results provide empirical insights into the relationship between behavioral biases and the decision-making process. The results suggest that the structural path model closely fits the sample data. The presence of behavioral biases indicates that financial professionals' forecasting and decision-making is not always rational but bounded rational or irrational due to these factors. Furthermore, these biases (except overconfidence bias) have a markedly significant and positive relationship with irrational decision-making.
Research limitations/implications
It is critical to eradicate these psychological errors, but awareness and attentiveness toward behavioral biases may help financial professionals to make informed decisions. Investors can improve their portfolio decisions and investments by recognizing their judgment errors and focusing on specific investment strategies to mitigate the impact of these biases. It is necessary to incorporate behavioral insights while developing training techniques for financial professionals. Rules of thumb, visual tools, financial coaching and implementing social-cultural elements in training programs enable financial professionals to develop simple, engaging, appealing and customized approaches for their clients.
Originality/value
This novel study is the first of this kind of research that examines the relationship between financial professionals' behavioral biases and rational decision-making process. This study significantly and remarkably provides insights into irrationality in financial professionals' decision-making.
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Imran Khan and Darshita Fulara Gunwant
The purpose of this paper is to empirically analyze the impact of social inclusion factors and foreign fund inflows on reducing gender-based unemployment in India.
Abstract
Purpose
The purpose of this paper is to empirically analyze the impact of social inclusion factors and foreign fund inflows on reducing gender-based unemployment in India.
Design/methodology/approach
A time series data set for the period of 1991–2021 has been considered, and an autoregressive distributed lag methodology has been applied to measure the short- and long-run impact of social inclusion and foreign fund inflows on reducing gender-based unemployment in India.
Findings
According to the study’s findings, both social inclusion and foreign fund inflows are critical factors for reducing male unemployment. However, in the case of female unemployment, only social inclusion factors play an important role, whereas foreign fund inflows have no role in it.
Originality/value
Analyzing the factors that affect gender-based unemployment has always been a grey area in literature. There are very few studies that capture gender-based unemployment in India, making this study a novice contribution. Second, it examines the relationship between foreign fund inflows, social inclusion and unemployment, which is another novel area of investigation. Finally, this study provides comprehensive and distinct results for both male and female unemployment that can help policymakers devise gender-based unemployment policies.
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