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This paper takes Chinese A-share listed companies from 2007 to 2021 as research samples to investigate the influence of supplier concentration on debt maturity structure and its mechanism. It further analyzes whether the relationship between the two is different in the case of different monetary policies, collateral assets, and total debt. The research conclusion is of practical significance for enterprises to construct a balanced debt maturity structure and prevent financial risks.

This paper adopts the empirical research method. The data came from the CSMAR database, which eliminated ST and *ST and companies with missing data, resulting in a sample of 20,328. Stata16 was used for statistical analysis.

There is an inverted U-shaped relationship between supplier concentration and debt maturity structure, and market position and trade credit play an intermediary role. In the case of tight monetary policy, fewer collateral assets, and higher total debt, the inverse U-shaped relationship is more significant.

This paper examines the relationship between supplier concentration and debt maturity structure from a non-linear perspective for the first time, providing theoretical support for enterprises to form a reasonable debt structure, and deepening the theoretical cognition of the relationship between supplier concentration and corporate debt maturity structure.

Considerable debate centres around the use of debt finance as opposed to new equity and internally generated funds for the financing of new investment projects. The favourable corporate tax treatment of debt interest payments compared to equity returns appears to be a government incentive to debt finance. In addition, the differential tax treatment of financial institutions' income and individual investors' income under the tax code, all leads to the idea, that debt financing may increase the market value of a firm beyond the expected value of its operational cash flows.

This paper aims to examine the nexus between hedging, which reduces the volatility of corporate assets, and the anomaly of debt overhang, whereby corporate management is motivated to reject positive net present value (NPV) projects. The question of whether hedging ameliorates or aggravates debt overhang is addressed.

The Black–Scholes isomorphism between common shares and call options is exploited to determine the allocation of a project’s NPV between debt- and stock-holders. The effect of hedging on this NPV-partitioning is then gauged to determine the resulting likelihood of debt overhang.

If the volatility of corporate assets is below a critical maximum, hedging ameliorates debt overhang consistent with extant theoretical research. However, above that critical value of volatility, hedging aggravates debt overhang.

The novel result of this note, namely, hedging may exacerbate debt overhang, is demonstrated both analytically and intuitively. The latter is explained by allusion to a second agency-theoretic conflict between debt- versus stock-holders, namely, risk shifting. The disparate effects of hedging on debt overhang imply a non-monotonic relationship between metrics for these two variables, which is a phenomenon that extant empirical studies have failed to take into account.

This paper examines the appropriate term of the risk free rate to be used by a regulator in price control situations, most particularly in the presence of corporate debt. If the regulator seeks to ensure that the present value of the future cash flows to equity holders equals their initial investment then the only choice of term for the risk free rate that can achieve this is that matching the regulatory cycle, but it also requires that the firm match its debt duration to the regulatory cycle. Failure of the firm to do so leads to cash flows to equity holders whose net present value will tend to be negative, and will also inflict interest rate risk upon equity holders. This provides the firm with strong incentives to match its debt duration to the regulatory cycle.

Now that debt has replaced equity as the preferred source of finance for many UK companies, the correct calculation of the cost of debt assumes even greater importance than it has done formerly. While financial management textbooks are in agreement on how to calculate the pre‐tax cost of debt, there is much less agreement on how to calculate the after tax cost of debt. The different approaches taken by different authors leave students and practitioners confused and unsure as to how they should proceed. This article explores the calculation of the after tax cost of debt in order to help both students and practitioners to understand the interaction of tax and debt in the current UK environment and to be aware of the limitations of the various simplifications which are made, explicitly or implicitly, in the textbooks.

The purpose of this paper is to provide empirical support for micro‐economic theory respecting debt capacity and develop a practically useful model for assessing debt capacity for firms seeking to minimize credit risk and the cost of debt (interest rate).

Theoretically important factors explaining the variation in debt capacity are identified and tested, namely: the proportion of property, plant and equipment over total assets, industry group (highlighting asset specificity), sales variability, and the depreciation method. Data were collected from the SEC Disclosure Database. Using the SPSS software, this paper's theoretically based constructs were tested by developing a linear regression model.

The regression results indicate that the theoretical model explains a statistically significant portion of the variation across firms in the proportion of debt to total assets a firm is willing (and is allowed by the financial market) to carry. However, a major portion of the variation in debt capacity is not explained. Future research can identify and test other factors to develop a better explanatory model.

Subject to the above limitation, the model developed provides a basis for firms to assess their debt capacity. Firm's whose actual debt to asset ratio is less than their debt capacity can borrow more if needed and if additional leverage is justified. Creditors can also use the estimated debt capacity when deciding the terms (including the interest rate) of extending credit. Investors can shy away from companies with very little or no unused debt capacity to reduce their portfolio risk.

This paper's academic and practical contributions, respectively, are to empirically test debt capacity's theoretical constructs and provide a practically useful and theoretically based model for assessing debt capacity by creditors, investors, and the companies.

In previous research, Friend and Hasbrouck theorized that managerial insiders (officers and directors) have a personal incentive to cause the firm to use less than the optimal amount of debt in its capital structure. They suggested this occurs because officers and directors have a large proportion of their personal wealth invested in the firm in the form of common stock holdings and firm‐specific human capital. This makes managerial insiders reluctant to use the optimal amount of debt financing for the firm because of the additional bankruptcy risk higher levels of debt engender. I test FH’s theory and find evidence that supports it. Specifically, the amount of debt in our sample firms’ capital structures declines as the percentage of the firm’s common stock held by the CEO and other officers and directors increases. A direct relationship is found between blockholder share ownership and our sample firms’ debt/equity ratio. This suggests that monitoring by blockholders is effective in controlling the suboptimal debt usage agency problem. Further, for any given level of blockholder share ownership, the greater the number of blockholders a firm has the less effective blockholders are in raising the amount of debt in the firm’s capital structure. Lastly, some weak evidence was found suggesting that a dual leadership structure was effective in increasing the amount of debt in a firm’s capital structure.

This paper seeks to describe a new service developed by national debt charity Consumer Credit Counselling Service (CCCS) aimed at identifying clients within its online debt counselling tool who may be suffering from stress and anxiety and then referring them for advice and support, including computer‐based cognitive behavioural therapy (CCBT).

Since December 2010, clients using CCCS Debt Remedy, the charity's online debt counselling tool, have been asked four trigger questions which indicate whether the user is suffering from depression and/or anxiety. Clients who show these signs, after they receive a recommendation about how to deal with their debt, are offered the opportunity to complete a more comprehensive assessment known as CCCS Wellbeing. The CCCS Wellbeing assessment consists of 16 questions, nine relating to depression and seven to anxiety. The depression questions are based on the medically endorsed depression screener, PHQ‐9, and the anxiety questions are based on the similarly medically endorsed anxiety screener, GAD‐7. These two screeners are also the source of the four original trigger questions.

Of the 36,618 clients who were counselled by CCCS Debt Remedy between the launch of the new service in December 2010 and the end of May 2011, 65 percent obtained a recommendation to undertake CCCS Wellbeing. The vast majority of clients who obtained a CCCS Wellbeing recommendation through the online debt counselling tool were showing signs of both depression and anxiety (74 percent).

The high propensity for people to be recommended to CCCS Wellbeing demonstrates the need for the service. This will inform future service development by CCCS, which is studying new ways to further identify and refer for help its clients who are struggling with their mental health.

Developing countries tend to be characterised by relatively high levels of indebtedness. The proceeds from debt can potentially have positive effects on growth, if the funds are employed to expand the productive capacity of the nation. The paper aims to investigate whether debt can be used to finance growth.

The paper utilizes meta‐analysis approach to address the issue. This approach allows researchers to combine the results from both published and unpublished research to gain insights regarding the directional and statistical significance of the relationship between the two variables.

The results suggest that there exist a positive relationship between debt and economic growth.

The findings from the paper suggest that future research should be conscious of the effect model specification can have on the results on their studies. Indeed, when external debt, the fiscal balance, debt relief where included in the econometric specification the relationship between debt and growth was weakened.

Economic growth should be stimulated, if the proceeds from debt issues are utilized to finance investment in productive areas of economic activity.

While the relationship between debt and growth has stimulated a number of research papers, seminars and conferences, to date, however, no clear answer to the question is available. Through the use of meta‐analysis, this paper allows the reader to glean the main findings from this body of research.

To identify periods of severe stress, and potentially take action to avoid or dampen their negative effects, lenders and policymakers need accurate and reliable data on US farm debt supply and credit needs. The purpose of this paper is to assess the current availability of information on US farm debt as well as its accuracy.

A review of the farm debt information and survey methodology of the Agricultural Resource Management Survey (ARMS).

This manuscript examines several potential issues involving the debt and lender data within ARMS. First, the empirical results indicate that there is an informational break in ARMS beginning in 2000. Second, the paper presents evidence that the overall level of debt reported by USDA is not consistent with information reported by lenders for other regulatory sources. Finally, the paper proposes a modification of the debt question to improve the data collection.

The paper offers an external review of farm debt information in ARMS.