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The purpose of this paper is to examine the market integration and direction of causality of wholesale and retail prices for the chickpea legume in major chickpea markets in India.

In this paper, the authors employ the Johansen co-integration test, Granger causality test, vector autoregression (VAR), and vector error correction model (VECM) to examine the integration of markets. The authors use monthly wholesale and retail price data of the chickpea crop from select markets in India spanning January 2003–December 2020.

The results of this study strongly confirm the co-integration and interdependency of the selected chickpea markets in India. However, the speed of adjustment of prices in the wholesale market is weakest in Bikaner, followed by Daryapur and Narsinghpur; it is relatively moderate in Gulbarga. In contrast, the speed of adjustment is negative for Bhopal and Delhi, weak for Nasik, and moderate for retail market prices in Bangalore. The results of the causality test show that the Narsinghpur, Daryapur, and Gulbarga markets are the most influential, with bidirectional relations in the case of wholesale market prices. Meanwhile, the Bangalore market is the most connected and effective retail market among the selected retail markets. It has bidirectional price transmission with two other markets, i.e. Bhopal and Nasik.

This paper calls for forthcoming studies to investigate the impact of external and internal factors, such as market infrastructure; government policy regarding self-reliant production; product physical characteristics; and rate of utilization indicating market integration. They should also focus on strengthening information technology for the regular flow of market information to help farmers increase their incomes.

Very few studies have explored market efficiency and direction of causality using both linear and nonlinear techniques for wholesale and retail prices of chickpea in India.

The main purpose of this paper is to analyse the influence of capital efficiency on the economic growth of Bangladesh using the Harrod-Domar (H-D) model.

We use annual data from 1980 to 2019 for this paper. Three steps are taken in the data analysis. First, to check the existence of a unit root, we use the augmented Dickey-Fuller (ADF) test and to determine co-integration among the variables, we use the Johansen-Juselius co-integration test. Next, for long-run estimation, we use the dynamic ordinary least square (DOLS) estimator. The sensitivity of the long-run estimations is further checked by the fully modified OLS (FMOLS) and autoregressive distributed lag (ARDL) estimators. Lastly, we use the Granger causality test to determine the long-run causality among the variables.

The long-run co-integration test validates the co-integrating relationship among the variables. DOLS estimations reveal that the economic growth of Bangladesh is negatively associated with the incremental capital output ratio (ICOR), validating the notion that capital efficiency matters for achieving higher economic growth. On average, an increase in ICOR by a unit tends to reduce economic growth in the long term by 0.75 percent. Our results also reveal no significant relationship between savings and economic growth when the model is extended. Finally, causality results indicate unidirectional causality between ICOR and economic growth.

Based on the results obtained, we argue that the enhancement of capital productivity could bring efficiency because ICOR is an inverse of capital productivity. Since Bangladesh’s capital productivity is considerably low compared with other neighbouring countries, it is suggested that firms should gradually move towards technological advancement and enhance economies of scale, etc. in the long run. Moreover, policies in favour of continuous skill development programmes could be highly effective in increasing capital productivity given that capital follows a vintage structure.

This is the first paper to analyse the economic growth pattern of Bangladesh using the traditional H-D model by incorporating variables such as savings and ICOR and also by relaxing the assumption of time-invariant (i.e. fixed) data of the variables. Moreover, this paper extends the traditional H-D empirical model by introducing key indicators and time breaks for Bangladesh’s economy through a stepwise regression process.

This article investigates the long-run impact of remittance inflows on agricultural productivity (AGP) in emerging Asian economies (Bangladesh, Sri Lanka, Malaysia, India, Nepal, Philippines, Pakistan, and Vietnam), employing a panel dataset from 2000 to 2018.

This study initially applies cross-sectional dependence (CSD), second-generation unit root, Pedroni, and Westerlund panel co-integration techniques. Next, it uses the augmented mean group (AMG) and common correlated effect mean group (CCEMG) methods to investigate the long-term impact of remittance inflows on AGP while controlling for several other important determinants of agricultural growth, such as cultivated area, fertilizers, temperature change, credit, and labor force.

The empirical findings are as follows: The results first revealed the existence of CSD and long-term co-integration between AGP and its determinants. Second, remittance inflows significantly boosted AGP, indicating that remittance inflows played a crucial role in improving AGP. Third, global warming (changes in temperature) negatively impacts AGP. Finally, additional critical elements, for instance, cultivated area, fertilizers, credit, and labor force, positively affect AGP.

This study suggests that policymakers of emerging Asian economies should develop an exclusive remittance-receiving system and introduce remittance investment products to utilize foreign funds and mitigate agricultural production risks effectively.

This is the first empirical examination of the long-term impact of remittance flows on agricultural output in emerging Asian economies. This study utilized robust estimation methods for panel data sets, such as the Pedroni, Westerlund, AMG, and CCEMG tests.

This paper examines the effect of bond market development on economic growth of selected developing countries from 1990 to 2020. Previous studies provide inconsistent results on the effect of bond market development on economic growth. Some results reveal positive effects while others show negative effects of bond market development on economic growth. These conflicting findings have motivated research.

The autoregressive distributed lag (ARDL) and co-integration methods are used for analysis. The gross domestic product per capita proxies economic growth while government bond capitalisation and corporate bond capitalisation measure bond market development.

The findings unveil a long-term effect within the series. The results disclose that government bond capitalisation, trade openness and inflation positively affect economic growth while corporate bond capitalisation and domestic credit to the private sector presents negative effects on economic growth.

The results propose that the governments should issue more bonds to raise funds for long-term economic growth initiatives. The governments should promote bond market development such that the corporate bonds issued boost economic growth by limiting lengthy documentations and bottlenecks in the bond market listing and issue procedures. The policymakers and regulatory authorities should implement policies which attract investors and encourage companies' listing in the countries' bond markets.

The study’s findings add value that government bond capitalisation positively impacts economic growth, while corporate bond capitalisation negatively affects economic growth in developing countries.

When the factors affecting health expenditures are examined in the literature, it is seen that one of the most important factors is income. In this context, the purpose of this study is to investigate the relationship between out-of-pocket health expenditures and disposable personal income and revealing the income elasticity of health expenditures.

Therefore, short/long-term coefficients were obtained by analyzing Westerlund (2007) co-integration analysis and pooled mean group (PMG) regression methods for 22 European Union (EU) member states during the period 2003–2017. In addition, a comparison of the long-term coefficients for each country was obtained with augmented mean group (AMG) estimator.

The results of the AMG and PMG tests show that the long-term coefficients between disposable personal income and health expenditures are 0.83 and 0.97, respectively. These results imply that there is a significant relationship between the variables, and that health care should be categorized in the group of normal goods. However, the fact that the long-term coefficient is very close to 1, despite being classified in the category of necessity goods, requires more care to be taken in evaluating whether health services are luxury goods or necessity goods.

The use of second generation econometric tests on both cross-sectional dependence and heterogeneity demonstrates the value of the study. On the other hand, obtaining similar results by investigating the relationship between variables using different appropriate econometric models reveals the importance of the methodology used in this study. It reveals important details in terms of the literature regarding the long-term and short-term results obtained in this study.

This paper aims to explore factors influencing apartment price volatility in the two biggest cities in Vietnam, Hanoi and Ho Chi Minh City.

The study uses the supply and demand approach and provides a literature review of previous studies to develop four main hypotheses using four determinants of apartment price volatility in Vietnam: gross domestic product (GDP), inflation rate, lending interest rate and construction cost. Subsequently, the Vector Error Correction Model (VECM) is used to analyze a monthly data sample of 117.

The research highlights the important role of construction costs in apartment price volatility in the two largest cities. Moreover, there are significant differences in how all four determinants affect apartment price volatility in the two cities. In addition, there is a long-run relationship between the determinants and apartment price volatility in both Hanoi and Ho Chi Minh City.

Limitations related to data transparency of the real estate industry in Vietnam lead to three main limitations of this paper, including: this paper only collects a sample of 117 valid monthly observations; apartment price volatility is calculated by changes in the apartment price index instead of apartment price standard deviation; and this paper is limited by only four determinants, those being GDP, inflation rate, lending interest rate and construction cost.

The study provides evidence of differences in how the above determinants affect apartment price volatility in Hanoi and Ho Chi Minh City, which helps investors and policymakers to make informed decisions relating to the real estate market in the two biggest cities in Vietnam.

This paper makes several recommendations to policymakers and investors in Vietnam to ensure a stable real estate market, contributing to the stability of the national economy.

This paper provides a new approach using VECM to analyze both long-run and short-run relationships between macroeconomic and sectoral independent variables and apartment price volatility in the two biggest cities in Vietnam.

This study aims to investigate the interplay between renewable energy development, unemployment and GDP growth within Bangladesh, India, Pakistan and Sri Lanka. The research underscores the significant role of renewable energy plays in stimulating economic growth and mitigating unemployment, offering crucial policy insights for sustainable growth in South Asia.

Utilizing the autoregressive distributive lag (ARDL) framework and Toda Yamamoto causality through the vector autoregressive (VAR) approach, the study analyzes the long-term and short-term impacts of these variables from 1990 to 2019.

This study reveals a significant co-integration among renewable energy consumption, unemployment and GDP growth in selected South Asian countries. The long-term estimation shows renewable energy consumption influences negatively economic progression in Bangladesh, with no notable correlation with unemployment. In contrast, Sri Lanka demonstrates an optimal relationship among all the variables. Short-run assessments reveal a significant positive relationship between renewable energy consumption and economic growth in India, while an inverse relationship is evident in Pakistan. Moreover, the relationship between unemployment and economic progression, the result shows a negative and significant relationship in India and Sri Lanka.

The study emphasizes the need for policy development concerning renewable energy development, unemployment reduction and sustainable economic growth in South Asia. While limitations exist, future research can expand upon this work by incorporating varied data, additional countries or alternative modeling techniques.

This research offers a unique exploration into the multidimensional impacts of renewable energy consumption, unemployment and economic growth in the South Asian context, an area previously unexplored in such depth.

This study aims to test the Kuznets curve between economic growth and child labor, along with the influence of exports, household size and rural population in the context of Pakistan.

To achieve the research objective, this study applied the unit root test, bound co-integration test, and autoregressive distributive lags (ARDL) method for the period of 1972–2021.

The findings show an inverted U-shaped relationship between economic growth and child labor indicating that at the beginning stage of economic development, child labor increases due to lower per capita household and subsequently, in the long-run of economic development, child labor decreases due to the higher per capita households. Moreover, the results also show that exports, household size and rural population have a positive influence on increasing child labor.

The policymakers and government of Pakistan need to focus on long-term economic growth policies, ensure free quality education and cheap equipment which practices minimum manpower to reduce the threat of child labor.

Having long-run economic growth, the government of Pakistan need to equally benefit the households and the poor population to reduce child labor and enhance the social welfare of society.

To the best of the authors’ knowledge, this is the first study that investigates the Kuznets curve relationship between economic growth and child labor in the context of Pakistan. Moreover, this study contributes to the reduction in child labor through long-term economic growth in the context of Pakistan.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0387

The principal aim of this study is to examine the relationship between financial development and income inequality in India using the financial Kuznets curve (FKC) hypothesis.

This study uses the autoregressive distributed lag (ARDL) model and the Toda–-Yamamoto causality test to investigate the long-run and short-run relationship and causality between financial development and income inequality. In addition, this study employs a principal component analysis (PCA) to construct a comprehensive financial development index.

The study found a long-run relationship between financial development and income inequality in India for the period under consideration. Trade is found to improve the income distribution, while inflation worsens income distribution. Moreover, the empirical results revealed a feedback causality between financial development and income inequality. The study results confirm an inverted U-shaped relationship between financial sector development indicators and income inequality, thus validating the FKC hypothesis for the Indian economy.

The study draws attention of the government and policymakers, urging them to focus on building a strong financial sector by improving its efficiency. This, in turn, will lead to enhanced financial stability and a reduction in income inequality. They should prioritise the development of high-quality and sustainable financial products and services to ensure the robust growth of the financial sector.

To the best of our knowledge, this study is the latest of its kind to empirically test the financial development on income inequality and the FKC hypothesis simultaneously for the Indian economy using financial proxy variables from financial institutions (FIs) and financial markets (FMs) for the measurement of financial depth.

This paper aims to determine causal relationships between the level of productive credit, real deposits and money demand – all of them in real terms – and Gross National Product between 2006 and 2020.

The vector autoregressive technique (VAR) was used, where data from real macroeconomic aggregates published by the Central Bank of Ecuador (BCE) are correlated, such as productive credit, gross domestic product (GDP) per capita, deposits and money demand.

The results indicate that there is no causal relationship, in the Granger sense, between GDP and financial activity, but there is between the growth rate of real money demand per capita and the growth rate of total real deposits per capita.

The study shows that bank credit mainly finances the operations of current assets and/or liabilities. In addition, economic agents use the banking system mainly to carry out transactional and precautionary activities.