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Capital structure decisions are important decisions for any business activity because they have considerable influence on the worth and cost of companies. Most previous studies in Ethiopia were primarily focused on identifying and measuring problems in banking sectors and other sectors and paying little attention to the construction sector. The purpose of this study is mainly to fill the gap by examining the effects of capital structure on the profitability of construction firms in Ethiopia.

To test hypotheses of the study, time series secondary data were gathered from the sample of 30 grade one construction companies in Ethiopia during the 2011–2015 period. To examine the correlation among capital structures and its determinants, random effect multiple regression models were used.

From the regression outcomes, the study indicates that capital structure measured by debt to equity and long-term debt to total assets has a significant positive correlation with return on equity (ROE) and return on assets (ROA) of sampled construction companies. However, the capital structure measured by debt to assets has a significant negative correlation with ROE and ROA of sampled construction companies in Ethiopia.

This paper is the author’s original work and assures that the paper was not undertaken anywhere and is also not published in any journal before.

This study investigates important factors that should be used by lenders in risk‐rating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt‐to‐asset ratio as a major predictor of repayment ability. In addition, the rate of asset turnover and family living expenses are strong predictors of farm performance. The results are tested over several time periods to verify the robustness of the predictors.

Purpose – This research studies how the discipline of option-like personal equity portfolio and the market discipline of debt jointly affect executive compensation design.

Design/methodology/approach – A theoretical model is proposed based on the moral hazard problem of Holmstrom and Milgrom (1987) by integrating firm financial leverage, executive equity holding, and profit-sharing rule. Subsequently, a panel data set of executive compensation is analyzed to provide empirical evidence.

Findings – The discipline of option reduces the need of performance-based compensation. The discipline of debt reduces the use of incentive pay for lowly leveraged firms, but increases the use of incentive pay for highly leveraged firms. These two disciplines can be either complements or substitutes on affecting optimal contracts depending on firm leverage.

Research limitations/implications – The present study provides a starting point for further study of optimal compensation that is not only the conventional one of mainly aligning managerial interests with that of shareholders but also the one of reinforcing the joint discipline of debt and option.

Originality/value – This new perspective produces several results characterizing firms that the discipline of debt and the discipline of option can be either complements or substitutes on affecting incentive compensation design.

In recent years, socially disadvantaged farmers and ranchers have increased their usage of nontraditional lending nearly converging to levels of usage observed for nonsocially disadvantaged groups. The purpose of this research is to explore explanations for this trend in lending utilization by socially disadvantaged farmers and ranchers by examining factors that influence credit usage and credit choice.

A multinomial logit is used to estimate the probability of loan choice given characteristics of the producer and farm.

While not a causal analysis, the results suggest that farm characteristics, which differ between socially disadvantaged and nonsocially disadvantaged producers, are associated with a lower likelihood of credit usage by an average socially disadvantaged farmer. For those that have loans, socially disadvantaged producers exhibit higher debt-to-asset ratios and lower current ratios, characteristics that are typically associated with higher than observed probability of usage of loans other than nontraditional. Socially disadvantaged producers also have lower value of assets which is associated with a higher probability of nontraditional loan usage.

This research is among the first to examine loan usage of socially disadvantaged producers using nationally representative data.

The purpose of this research is to evaluate the financial performance measures calculated and reported by the Economic Resource Service (ERS) from Agricultural Resource Management Survey (ARMS) data. The evaluation includes the calculation method and the underlying assumptions used in obtaining the reported values. Recommendations for improving the information reported are proposed to ERS.

The financial measures calculated and reported are compared with those recommended by the Farm Financial Standards Council (FFSC). The underlying assumptions are identified by analyzing the software code used in calculating the values reported. The values reported by ERS are duplicated and alternative methods for calculating the financial performance measures are considered. The values obtained from the various calculation methods are compared and contrasted.

Recommendations for ERS include: calculate and report the financial measures recommended by FFSC, note values that are imputed, periodically update and validate assumptions used in calculating imputed values, review its policy for flagging estimates as statistically unreliable, report medians and other select percentiles, and consider reporting the percent of farm businesses that have values within critical zones.

A total of four methods for calculating financial performance measures are compared and contrasted. These are the aggregate mean, sample mean, sample median, and percentage of farm businesses with values in critical zones.

The purpose of this paper is to examine the use of single vs multiple lenders by Kansas farms. Previous studies suggest that as the risk level of the firm changes, borrowers desire to enhance the probability of obtaining credit at the lowest possible cost may cause them to use multiple lenders.

A model is adopted from the banking literature to describe farm behavior in obtaining credit from a single vs multiple lenders. Using farm-level data from the Kansas Farm Management Association, an empirical model analyzes how farm characteristics affect the number of lending relationships. A model is developed to analyze the number of lending relationships effect on the profitability of the farm.

It is found that highly leveraged farms seek additional lending relationships supporting the theoretical model and that additional lending relationships correlate to a decrease in profitability. Roughly, 50 percent of Kansas farmers that borrow use a single lender. Roughly 48 percent use from two to four lenders, with the remaining 2 percent using more than four lenders.

Provides empirical results to support developed theoretical framework on the number of lending institutions. This study helps understand factors correlated to a farmer's decision to use multiple lenders. Analyzing the number of lending relationships helps understand how farmers manage their debt to maintain access to credit when needed at the lowest possible cost.

The purpose of this paper is to provide evidence of the effect of the differences related to reporting inventory, property plant and equipment, intangible assets, and development costs between International Financial Reporting Standards (IFRS) and US Generally Accepted Accounting Principles (US GAAP) companies.

Both univariate tests (t‐tests) and multivariate tests (ANOVA, probit and logit analyses) are used to compare the ratios between IFRS and US GAAP companies.

Results consistently show that IFRS‐country firms have a significantly higher current ratio, a significantly lower asset turnover ratio, and a significantly lower debt‐to‐asset ratio.

This paper only focuses on inventory, property plant and equipment, intangible assets, and development costs. Other financial variables are not considered.

The results are useful for individuals who are interested in reporting and investing in countries using different financial reporting systems.

This paper is a timely examination of the recent emphasis of mandating IFRS.

The purpose of this study is to examine how US farm sector performed in 2020, the first year of the pandemic. There were significant supply and demand shocks due to the pandemic. Furthermore, there was significant fluctuation in commodity prices and record high government payments in 2020. This study aims to examine the performance and position of US farm sector (financially) to system (and global economy) wide shocks.

The authors examine 2020 values for farm sector financial ratios before and after the onset of the Coronavirus (COVID-19) pandemic using the data from the United States Department of Agriculture to understand the financial position and performance of the US farm sector.

The authors find solvency ratios (which are indicators of the sector's ability to repay financial liabilities via the sale of assets) worsened in 2020 relative to pre-pandemic expectations. Efficiency ratios (which evaluate the conversion of assets into production and revenue) and liquidity ratios (which are indicators of the availability of cash to cover debt payments) showed mixed outcomes for the realized results in 2020 relative to the pre-pandemic forecasts. Four profitability ratios were stronger in 2020 relative to pre-pandemic expectations. All solvency, liquidity and profitability ratios plus 2 out of 5 efficiency ratios for 2020 were weaker than their respective average ratios obtained from 2000 to 2019 data.

This research is one of the first papers to use financial ratios to examine how the US farm sector performed in 2020 compared to expectations prior to the pandemic.

The pandemic COVID-19 has affected every sector of an economy in every possible way. Banking sector of Bangladesh has been affected by it badly. The purpose of this paper is to find out the impact of COVID-19 on the liquidity and financial health of the listed banks in Bangladesh.

Liquidity ratios are calculated to measure the liquidity condition of the banks and revised Altman's Z-Score Model for non-manufacturing companies is used to measure the financial health. The ratios are compared before and during the COVID-19 periods to assess the impact.

The findings of this study indicate a deterioration of liquidity position and financial health of the listed banks after the emergence of this pandemic. Though the banks have poor liquidity ratios and financial health prior to the emergence of this pandemic, they have decreased more in the second quarter of 2020. Most of the banks have poor liquidity ratios and cash position. The listed Islamic Banks have poor financial health than the listed Commercial Banks and all the banks belong to the red zone in all the quarters.

The results of this study will have policy implications for companies and regulators of money market.

This paper is a pioneer initiative in assessing the impact of COVID-19 pandemic on liquidity and financial health based on empirical data.

The purpose of this paper is to investigate a farm manager’s personal characteristics (personality, age, education, objectives, experience, etc.) as drivers of debt payback success and rates. Traditionally bankers have used historic business statistics, and equity levels, to assess loans and credit worthiness. It is hypothesised that a managers’ personal characteristics are likely to be a better predictor of future debt payback performance.

The literature was searched to isolate the managers’ personal variables likely to determine debt payback. The information led to defining a quantitative model based on the theory of planned behaviour (TPB) which was hypothesised as determining payback rates where a choice was available. A postal random stratified survey of NZ owner operator farm managers provided the data to test the model and define its parameters using regressions, structural equation modelling and statistical comparisons.

The modelling results make it clear a manager’s personal characteristics are highly correlated with debt payback and, logically, are very likely to be the drivers. Four random effects equations and a comparison of high- and low-debt payback managers led to this conclusion.

Bankers should use the managers’ personal characteristics, as defined in the regressions, alongside traditional measures when assessing farm business loan requests. This approach is opposite to the traditional methods using mainly historic data.

The use of the TPB in assessing debt payback is a new and novel approach showing how enduring personal characteristics can be used in assessing proposals, and particularly, entrepreneurs’ adventurist investments in situations where historic data are not available.