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This study aims to analyze the long-run dynamic relationship between financial inclusion and economic growth for developing nations.

This study develops a comprehensive financial inclusion index based on the UNDP methodology for 53 developing nations. The authors use second-generation unit root tests, cointegration techniques and an advanced dynamic common correlated effects estimator model called cross-sectional augmented autoregressive distributed lags (CS-ARDL) to examine long-run dynamics among variables.

The tests confirm the presence of slope-heterogeneity and cross-sectional dependency. The second-generation panel unit root tests show the chosen variables are stationary at first difference. The bootstrap Westerlund cointegration result shows the variables are cointegrated in the long run. The CS-ARDL estimates conclude that financial inclusion positively enhances gross domestic product per capita in selected developing countries. The robustness check through augmented mean group estimation validates the findings.

The study makes three important contributions: first, it constructs a comprehensive financial inclusion index using 10 variables for a panel of 53 developing nations; second, the potential cross-section dependence and slope heterogeneity of panel data have been accounted for by applying the second-generation unit root tests; third, the study uses the dynamic common correlated effects estimator model (CS-ARDL) to examine long-run dynamics among variables.

This paper examines India's socio-economic attributes and different financial dimensions of financial inclusion (FI).

The paper uses a principal component analysis (PCA) to build indexes related to financial dimensions. It applies the logistics regression model and the Fairlie decomposition method to determine India's socio-economic and financial characteristics of FI.

Based on the logistic regression, socio-economic factors like age, gender, marital status, level of education and religion have an impact on FI. The use of financial institutions has positively contributed to the probability of FI, while the low proximity of financial service providers retards the process of FI. Fairlie decomposition concludes regional disparity and gender disparity in FI; however, the rural–urban gap in FI is not captured by the variables included in the study. The main reasons for the discrepancy are lack of education, financial literacy, the proximity of financial service providers and lack of financial institutions.

This paper makes two important contributions: first, it presents a micro-level analysis of FI across the socio-demographic strata of India, and second, it demonstrates the regional, rural–urban and gender disparity in FI in India.

Enrolling in an academic discipline or selecting the college major choice is a dynamic process. Very few studies examine this aspect in India. This paper makes a humble attempt to fill this gap using NSSO 71st round data on social consumption on education. The purpose of this paper is to use multinomial regression model to study the different factors that influence course choice in higher education. The different factors (given the availability of information) considered relate to ability, gender, cost of higher education, socio-economic and geographical location. The results indicate that gender polarization is apparent between humanities and engineering. The predicated probabilities bring out the dichotomy between the choice of courses and levels of living expressed through consumption expenditures in terms of professional and non-professional courses. Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities.

The present paper follows the same approach as that of Turner and Bowen (1999). The Multinomial regression is specified as P ( M i = j ) = ( exp ( β j × X i ) / ∑ j − 1 5 exp ( β j × X i ) ) , where P (M_i=j) denotes the probability of choosing outcome j, the particular course/major choice that categorizes different disciplines. This response variable is specified with five categories: such as medicine, engineering, other professional courses, science and humanities. The authors’ primary interest is to determine the factors governing an individual’s decision to choose a particular subject field as compared to humanities. In other words, to make the system identifiable in the MLR, humanities is treated as a reference category. The vector X_i includes the set of explanatory variables and β_j refers to the corresponding coefficients for each of the outcome j. From an aggregate perspective, the distribution of course choices is an important input to the skill (technical skills) composition of future workforce. In that sense, except humanities, the rest of the courses are technical-intensive courses; hence, humanities is treated as a reference category.

The results indicate that gender polarization is apparent between humanities and engineering. The predicated probabilities bring out the dichotomy between the choice of courses and levels of living expressed through consumption expenditures in terms of professional and non-professional courses. Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities.

Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities. This course and regional imbalance need to be worked with multi-pronged strategies of providing both access to education and employment opportunities in other states. But the predicted probabilities of medicine and science remain similar across the board. Very few research studies on the determinants of field choice in higher education prevail in India. Research studies on returns to education by field or course choices hardly exist in India. These evidences are particularly important to know which course choices can support student loans, which can be the future area of work.

The research evidence is particularly important to know which course choices can support student loans, which can be the future area of work, as well as how to address the gender bias in the course choices.

The paper has social implications in terms of giving insights into the course choices of students. These findings bring in implications for practice in their ability to predict the demand for course choices and their share of demand, not only in the labor market but also across regions. India has 36 states/UTs and each state/UT has a huge population size and large geographical areas. The choice of course has state-specific influence because of nature of state economy, society, culture and inherent education systems. Further, within the states, rural and urban variation has also a serious influence on the choice of courses.

The present study is a value addition on three counts. First, the choice of courses includes the recent trends in the preference over market-oriented/technical courses such as medicine, engineering and other professional courses (chartered accountancy and similar courses, courses from Industrial Training Institute, recognized vocational training institute, etc.). The choice of market-oriented courses has been examined in relation to the choice of conventional subjects. Second, the socio-economic background of students plays a significant role in the choice of courses. Third, the present paper uses the latest data on Social Consumption on Education.

This study aims to attempt to investigate the time-varying causality and price spillover effects between crude oil and exchange rate markets in G7 economies during the COVID-19 and Russia–Ukraine crises.

This study uses time-varying Granger causality test and spillover index.

This study finds a time-varying causality between exchange rate returns and oil prices, implying that crude oil prices have the predictive power of the foreign exchange rate markets in G7 economies in their domain. Furthermore, the total spillover index is estimated to fall significantly around COVID-19 and war events. However, this index is relatively high – more than 57% during the first wave of COVID-19 and decreasing slightly during the Russia–Ukraine conflict.

This outcome supports the hypothesis that the majority of the time-varying interaction between exchange rates and oil prices takes place in the short term. As a result, the time-varying characteristics provide straightforward insight for investors and policymakers to fully understand the intercorrelation between oil prices and the G7 exchange rate markets.

First, this study has reexamined the oil–exchange rate nexus to highlight new evidence using novel time-varying Granger causality model recently proposed by Shi et al. (2018) and the spillover index proposed by Diebold and Yilmaz (2012). These approaches allow the author to improve understanding of time-varying causal associations and return transmission between exchange rates and oil prices. Second, compared to past papers, this paper has used data from December 31, 2019, to October 31, 2022, to offer a fresh and accurate structure between the markets, which indicates the unique experience of the COVID-19 outbreak and Russia–Ukraine war episodes. Third, this study analyzes a data set of seven advanced economies (G7) exhibiting significant variations in their economic situations and responding to global stress times.

The main purpose of this paper is to investigate the relationships among a set of human resources management (HRM) practices, entrepreneurial traits (ET) and corporate entrepreneurship (CE) in an emerging market such as the Egyptian one.

This research is explanatory in nature where it explains the causal relationships between the variables. Data was collected from 230 human resources (HR) managers and professional serving in top Egyptian financial institutions. Data was analyzed using structural equation modeling with relevant goodness-of-fit statistics.

The results of this study indicate that HRM practices have significant impact on the development of ET of personnel currently operating in the Egyptian financial institutions. Besides, the results confirm the effect of HRM practices on ET, which in turn have significant impact on CE.

This research provides an appropriate direction for HR managers on how to best design HR programs directed toward the development of strong CE orientation. It also highlights the core ET needed to enhance CE in emerging economies. The validated hypothetical relationships of this model serve as the baseline for those managers to plan, execute and measure the consequences of those HRM programs.

Although HRM is widely considered as a critical driving force for CE, there is a remarkable scarcity of empirical research examining the role of ET. The conceptual model tested in this research typically deepens both HRM scholars and managers’ understanding of how they can best connect HRM practices to ET and CE in emerging economies. The findings of this study open the door for a new venue of research in the HRM area of study, particularly in emerging markets that search for a fast-growing rate of economic prosperity. The findings of this study lend support for HRM as an antecedent to CE rather than vice versa.

The objective of this paper is to investigate the validity of stock valuation theories and their forecasting ability by conducting an empirical study. It employs four most commonly used theories which are then tested using 19-year banking-firm market data. The usefulness of these models demonstrates with promising results.

This paper conducts a multi-country study using the multi-model testing approach to evaluate validity of theories and forecast accuracy of banking firms. It employs four methodology models used in finance literature; (1) P/E multiples model, (2) accounting-information-based clean surplus model, (3) theoretical model based on Gordon and Shapiro (1956) method and (4) the Damodaran-Kottler Free Cash Flow or FCF theory based on discounting model.

The tests show that the four theories under tests have a significant fit with actual price formation. The explained variation ranges from 72 to 92%, so the explanatory power of the theories accounting for variations in bank prices over 19-year period is substantial. The models fit suggest that the P/E model has superior predictive power followed by the RIM, DDM and FCFE. These findings shed new lights on the relative performance of valuation models.

The study is limited in terms of the sample period size for 1999–2019. The availability of essential financial data prior to 2000 is very limited, so one can understand interpretation of statistical results under certain assumptions.

The paper suggests that one-factor model is better than the two-factor model.

The work done in this paper is unpublished and original contribution to banking and finance literature and also not under consideration for publication in any other journal.

Domestic refrigerators could be considered as one of the major potential sources of food-borne diseases, in addition limited data are available regarding the level of contamination of domestic refrigerators in Iran. The purpose of this paper is to detect some of bacterial contamination in domestic refrigerators.

In total, 104 households were randomly selected from ten health centers in five areas of Tehran, Iran. Visual inspection and temperature evaluation of the households’ refrigerators were done. In addition, the refrigerators were swabbed and analyzed for contaminants using polymerase chain reaction (PCR) method. DNA was isolated and purified by the proposed standard protocol.

Screening of the domestic refrigerators by PCR method showed that 51.7 percent of the samples were positive for pathogens as follows: L. monocytogenes 41.6 percent, S. aureus 5.5 percent, Salmonella spp 4.6 percent, and E. coli O157:H7 0 percent; consequently, none of mentioned pathogens were detected in 48.3 percent of the refrigerators. Results of the visual inspection indicated that 57 percent of the refrigerators were on desirable, 36.5 percent were acceptable, and 7 percent were weak conditions. Most of the refrigerators about 44 percent had desirable temperatures. There were no significant correlations between the visual inspection scores, temperature and frequency of isolation of specific pathogens in the domestic refrigerators. A significant correlation was observed between contamination and education of parents (p < 0.05).

Determination of the bacterial contamination and evaluating the temperature of domestic refrigerators in Iran can be considered as a novel approach of current study. These findings could be employed in designing and implementing appropriate educational interventions to promote food safety and diminish the risk of food-borne illnesses. Also, obtained results might be applied as introduction for further investigations.

The aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2) to investigate co-movements between the ten developing stock markets, the sentiment investor's, exchange rates and geopolitical risk (GPR) during Russian invasion of Ukraine in 2022, (3) to explore the key factors that might affect exchange market and capital market before and mainly during Russia–Ukraine war period.

The wavelet approach and the multivariate wavelet coherence (MWC) are applied to detect the co-movements on daily data from August 2019 to December 2022. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are used to assess the systemic risks of exchange rate market and stock market return in the developing market.

Results of this study reveal (1) strong interdependence between GPR, investor sentiment rational (ISR), stock market index and exchange rate in short- and long-terms in most countries, as inferred from (WTC) analysis. (2) There is evidence of strong short-term co-movements between ISR and exchange rates, with ISR leading. (3) Multivariate coherency shows strong contributions of ISR and GPR index to stock market index and exchange rate returns. The findings signal the attractiveness of the Vietnamese dong, Malaysian ringgits and Tunisian dinar as a hedge for currency portfolios against GPR. The authors detect a positive connectedness in the short term between all pairs of the variables analyzed in most countries. (4) Both foreign exchange and equity markets are exposed to higher levels of systemic risk in the period of the Russian invasion of Ukraine.

This study provides information that supports investors, regulators and executive managers in developing countries. The impact of sentiment investor with GPR intensified the co-movements of stocks market and exchange market during 2021–2022, which overlaps with period of the Russian invasion of Ukraine.

The aim of this research is to re-examine the dynamic linkages between macroeconomic variables and the stock market indices in Malaysia following some transformational changes in the policies and the exchange rate regime.

Using monthly data points for all the economic variables and the stock market index (KLCI Index), the authors applied vector autoregression (VAR) model to examine the relationship. The authors also used impulse response function (IRF) in order to explore the effect of one-unit shock in “X” on “Y” under the VAR environment.

The authors' study finds a significant relationship between all the macroeconomic variables and the stock market index of Malaysia. The cointegration results indicate a long-term relationship, whereas the vector autoregressive-based impulse response analysis suggests that the Malaysian stock index (KLCI) responds negatively to the money supply, inflation and producer price index (PPI). However, the authors' results indicate a positive response from the stock index to the exchange rate.

The authors' study's results are based on selected macroeconomic variables and the VAR model. Researchers may find other variables and methods more useful and may provide findings accordingly.

Since the results are quite asymmetric, it would be interesting for the market players, policymakers and regulators to consider the findings and explore appropriate opportunities.

While the relationship between macroeconomic variables and stock market indices has been widely examined, a significant gap in the literature remains concerning the role of exchange rate variable on the stock market in an emerging economy context.

The purpose of this paper is to calculate the financial inclusion index and analyze its dynamics in developing countries.

The authors use the two-stage principal component analysis (PCA) method and consider financial technology innovations to improve the accuracy of the financial inclusion index.

The authors found a downward trend in the financial inclusion index in most developing countries over the study period. The authors also found that a high financial inclusion index is linked to high scores in the Doing Business and high business climate regulation ranking. In addition, the authors observed that the rates of low financial inclusion in developing countries are due to low utilization of and unequal access to financial services.

The analysis suggests that policymakers in developing countries could invest in digital infrastructure to extend access to financial services in remote areas. They could also encourage financial innovation, particularly in financial technologies, by adopting flexible regulatory frameworks. Promoting the financial inclusion of marginalized groups through targeted initiatives tailored to their needs is another solution. They could also encourage the use of financial services by raising awareness and educating populations through training programs. Finally, to improve the business climate, governments could simplify administrative procedures and promote transparency and legal stability.

Unlike previous studies, the use of the two-stage PCA method and the consideration of financial technology (Fintech) innovations such as mobile money in the determinants of the financial inclusion index improve the accuracy of the index.