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Foreign Direct Investment (FDI) is an important means of boosting the agricultural sectors of developing economies. The first necessary step to formulate effective public policies to encourage agricultural FDI inflow to a host country is to develop a comprehensive understanding of the main determinants of FDI inflow to the agricultural sector, which is the main objective of the present study.

In view of this, we take a comprehensive approach to exploring the macroeconomic and institutional determinants of FDI inflow to the agricultural sector by examining a large panel data set on agricultural FDI inflows of 37 countries, investigating both groups of developed and developing countries, incorporating a large list of potentially relevant macroeconomic and institutional variables, and applying panel-data econometric models and estimation structures, including pooled, fixed-effects and random-effects regression models.

The general pattern of our findings implies that the degree of openness of an economy has a negative effect on FDI inflows to agricultural sectors, suggesting that the higher the degree of openness in an economy, the lower the level of agricultural protection against foreign trade and imports, and thus the less incentive for FDI to inflow to the agricultural sector of the economy. Additionally, our results show that economic growth (as an indicator of the rate of market-size growth in the host economy) and per-capita real GDP (as an indicator of the standard of living in the host country) are both positively related to FDI inflows to agricultural sectors. Our other results suggest that agricultural FDI tends to flow more to developing countries in general and more to those with higher standards of living and income levels in particular.

FDI inflow has not received much attention with respect to the identification of its main determinants in the context of agricultural sectors. Additionally, there are very few panel-data studies on the determinants of FDI, and even more surprisingly, there are no such studies on the main determinants of FDI inflow to the agricultural sector. We have taken a comprehensive approach by studying FDI inflow variations across countries as well as over time.

This paper aims to examine the customer-based and supplier-based trade credit gaps for USA firms from 1970 to 2020.

The authors' study examines USA companies from 1970 to 2020. The authors begin with an analysis of the trends in aggregate working capital, the capital's components and the trade credit gaps. Various regression models are used to estimate the impacts of identified firm characteristics and unidentified sources on customer-based and supplier-based trade credit gaps over time. The authors then decompose the impacts of firm characteristics to further understand whether changing firm characteristics and/or changing sensitivity to firm characteristics drive the variation in trade credit gaps.

There is a gradual reduction in the customer-based trade credit gap and a substantial expansion in the supplier-based trade credit gap. Though identified firm characteristics have dominant impacts on observed trade credit gaps, there is evidence of the effects of time and unobservable factors. The main source of changes in customer-based and supplier-based trade credit gaps lies in changes in sensitivity to firm characteristics. In addition, the authors find that firm age is the factor with the largest average effect on both trade credit gaps when examining the full sample period. However, different firm characteristics appear to be the key driver of variations in trade credit gaps over time and across the two types of trade credit gaps. The authors also find that financial distress has the least impact on both customer-based and supplier-based trade gaps. There are variations in the firm characteristics with the largest impacts when evaluating decade-long evaluation periods.

To the authors' knowledge, this is the first paper to examine the customer-based and supplier-based trade credit gaps. The connection between trade credit and the trade credit's corresponding inventory (INV) component extends prior literature on the joint management of trade credit and INV. The authors analyze both identified firm characteristics and unidentified sources in the search for explanations of the trade credit gaps. Furthermore, the authors' study explores the channels through which firm characteristics affect different types of trade credit gaps. The authors' findings help identify relevant and irrelevant risk factors of corporate working capital policy.

Professional sports teams employ highly paid managers and coaches to train players and make tactical and strategic team decisions. A large literature analyzes the impact of manager decisions on team outcomes. Empirical analysis of manager decisions requires a quantifiable proxy variable for manager decisions. Previous research focused on manager dismissals, tenure on teams, the number of substitutions made in games or the number of healthy players on rosters held out of games for rest, generally finding small positive impacts of manager decisions on team success.

The authors quantify manager decisions by developing a novel measure of game-specific coaching decisions: the Herfindahl–Hirschman Index (HHI) of playing-time across players on a team roster over the course of a season.

Evidence from two-way fixed effects regression models explaining observed variation in National Basketball Association team winning percentage over the 1999–2000 to 2018–2019 seasons show a significant association between managers’ allocation of playing time and team success. A one standard deviation change in playing-time HHI that reflects a flattened distribution of player talent is associated with between one and two additional wins per season, holding the talent of players on the team roster constant. Heterogeneity exists in the impact across teams with different player talent.

This is one of the first papers to examine playing-time concentration in the NBA. The results are important for understanding how managerial decisions about resource allocation lead to sustained competitive advantage. Linking coaching decisions to wins can help teams to better promote this core product.

Today, the sustainability and outreach of microfinance institutions (MFIs) are crucial to the success of microfinance and the sector’s potential to make a lasting impact. The ability of MFIs to operate financially well without sacrificing their social goals has come under scrutiny. This study aims to identify the kind of relationships between the two objectives of MFIs in Ethiopia.

This study investigated the association between the outreach and financial sustainability of Ethiopian MFIs from the years 2012 to 2021 using a balanced set of panel data. The study used secondary data and employed a descriptive research design and a quantitative research approach. To this end, random and fixed effects estimation models, as well as three-stage least squares, with the model of seemingly unrelated regression (SUR) are used.

According to the study, outreach performance enables MFIs to achieve sustainability/financial performance. On the other side, MFI that are financially sound improve social performance. There was therefore no trade-off between the two objectives.

As Ethiopia’s microfinance sector shifts away from government and non-government backing and toward commercialization, such research is crucial. This aspect of the Ethiopian microfinance industry has gotten little consideration in research. The SUR model was used in the study together with random and fixed effect estimators, and the most reliable estimation result was chosen based on the necessary tests.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk.

A study of 236 companies from the S&P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results' reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised.

Stakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions.

This study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon.

This study identifies and examines psychological and environmental factors that influence the long-term orientation of top management teams (TMTs).

Data on S&P 500 companies from 2011 to 2020 are collected from the Compustat database. Additional variables were measured through content analysis of earnings conference calls. This study used two-stage least squares regression with fixed effects to analyze the data and test the hypotheses. Appropriate diagnostic tests were conducted to ensure validity and eliminate endogeneity.

The results indicate that a chief executive officer’s (CEO) promotion focus positively and significantly influences the TMT's long-term orientation. However, the influence of prevention focus is statistically insignificant. Furthermore, the results indicate that environmental hostility moderates both relationships.

The TMT's long-term orientation can be improved through the insights provided by this study.

To the authors’ knowledge, this is the first study to examine the collective effects of psychological and task environmental factors on the long-term orientation of the TMT. Additionally, this study sheds light on the internal dynamics of the top-management team.

This study aims to examine whether a firm's demand for high-quality auditors is influenced by multiple large shareholders (MLS). As one type of ownership structure, MLS have gained popularity in China recently and have different types of large shareholders, including large institutional shareholder, large foreign shareholder and large state shareholder. The authors also examine whether different types of MLS have heterogeneous impacts on appointing high-quality auditors.

With a sample of 27,131 firm-year observations from Chinese public companies from 2003 to 2018, the authors use multivariate regressions to examine the effect of MLS on auditor choice. Heckman two-stage analysis, a firm fixed effects model, propensity score matching and difference-in-differences test are used as robustness checks.

This paper finds that the presence and power of MLS increase the likelihood of appointing high-quality auditors. With regard to the types of MLS, large institutional shareholders and foreign shareholders have significant positive effects on appointing high-quality auditors, while the presence of state-owned large shareholders has no effect on auditor choice. Further analyses reveal that the positive effect of MLS on high-quality auditor choice is more pronounced in firms with severe agency problems and information asymmetry. Taken together, these results suggest that MLS play a monitoring role by demanding high-quality auditors.

This paper contributes to the literature on the determinants of auditor choice. While prior studies primarily focus on the impact of concentrated ownership structure, corporate governance and the pressure from stakeholders on auditor choice, this paper complements the literature by providing evidence from the heterogeneous effects of different types MLS. This paper also extends the literature on the consequences of MLS from the perspective of auditor choice.

This study aims to investigate the effect of digitalization on bank profitability among Vietnamese banks.

The research employs fixed-effects regression on a panel data of 32 banks in Vietnam during the period 2010–2021.

The study reveals a positive impact of digitalization on bank profitability. The result is robust to different measures and empirical settings. Not surprisingly, small banks and banks with high percentage of state ownership experience lower profitability than their peers. However, digitalization helps improve the profitability of these banks. This study explains the effect by showing that digitalization significantly reduces bank cost in terms of cost to income ratio and increases bank non-interest income through diversification into non-traditional products and services. In addition, the current stage of bank digitalization in Vietnam does not reduce banks’ employment costs since it requires staffs to support and operate the new system.

The research findings are motivations for bankers and policy-makers in designing appropriate strategies toward digitalization. Investors can also consider highly digitalized banks as valuable investment.

This research extends the current literature on the relationship between digitalization and bank profitability, with a focus on commercial banks in Vietnam. Given the high involvement of the government and the dominance of several large banks in the banking system, the study also explores whether the effect of digitalization on bank profitability varies with the bank’s size and state ownership. Last but not least, the channels in which digitalization affects bank profitability are also examined.

Off-farm employment is an important factor associated with fertility transition in many developing countries. The purpose of this paper is to investigate the impact of female off-farm employment on their fertility desire in rural China.

Based on the data from the China Labor-force Dynamics Survey, the authors adopt an instrumental variable approach to address the endogeneity issue. Desired number of children and desire for a second child are used to measure fertility desire.

The results show that off-farm employment participation significantly reduces women's desired number of children and the likelihood of their desire for a second child. Moreover, off-farm employment reduces women's fertility desire mainly through pathways including the weakening of son preference and a decrease in job autonomy, rather than the changes in leisure hours. Further evidence suggests that social health insurance plays an important role in moderating the adverse relationship between off-farm employment and the desire for a second child. The fertility-reducing effects are more pronounced among younger women, among those participating in off-farm wage employment and among families with only wives' participation in the off-farm labor market.

This paper contributes to the existing research by investigating the causal impact of off-farm employment on fertility desire in a rural developing context and the possible underlying mechanisms responsible for this relationship. This study provides important insights on this topic in developing countries and may have important implications for theory and practice.