Search results

Credit spread change is a key issue for investors to earn the excess return from corporate bonds. Generally, there are two kinds of different effects that support the changing of credit spread: asset allocation effect and credit risk change effect. The existing literature based on US data supports credit spread change is driven by credit risk change; however, this issue based on Chinese market data has not been investigated clearly. This paper seeks to address this issue.

The authors adopt Markov regime switching model to investigate the changing mode of the credit spread in the Chinese bond market. They select three kinds of variables: the credit risk variables, the asset allocation variables and the liquidity condition variables. With ML estimators, the authors can find the changing mode and further they study the relationship between the regime switching and some observed variables, such as macro economy variables and turnover of stock market.

The authors find it is driven by asset allocation effect in most time and by credit risk change only in shorter period. Empirical results show that the switching of dominance from one effect to another isn't closely related with macro‐economy variables, but related with the turnover of stock market.

These results indicate that in China the credit risk of corporate bonds is relatively low and the corporate bonds are still auxiliary asset for investors.

In this paper, the authors have the following two contributions: first, they discuss the asset allocation effect in the Chinese bond market and introduce the stock market variables and bank credit variable to describe the asset allocation effect; second, based on Chinese bond market data, they find different findings from the existing literature about US and European bond markets, showing that the changing of credit spread is mostly related with asset allocation effect but not credit risk change.

Like the cross-country convergence or divergence analysis in incomes to address the global phenomenon, the same analysis is also required to be done in the case of a group of states within a national territory. Further, it is also required to see whether convergence or divergence in incomes of the states is attributable to the convergence or divergence in their allocations of bank credits. Thus, this chapter aims at examining whether the selected major states in India are converging or diverging in the allocations of bank credit, and if so, what will be the magnitudes of decreases or increases in the level of disparities and inequalities in credit allocations. This study concludes that there is a clear diverging tendency of credit allocations of the states of India during the post-reform period so far as the absolute convergence hypothesis of the neoclassical theory is concerned. Further, in terms of the framework of σ convergence, the study observes that all phases of the Indian economy have produced converging paths of the inter-state credit allocations, and the path becomes diverging during the post-reform phase. Based on the quantifications of the magnitudes of disparities and inequalities in terms of CV, C4 concentration, HHI and Gini values, this study thus reveals that there are significant increases in the levels of disparities and inequalities in the allocations of credit to the states from the pre-reform to the post-reform phases. Therefore, the persistence of divergence in income or rising income inequality during the phase of the major reform program in India may be due to the persistence of divergence and rising inequality in the allocation of bank credit.

Risk capital is an important input for management functions. Capital structure decisions, capital budgeting, and ex post performance measurement require different measures of risk capital. While it has become common to estimate risk capital using VaR models, it is not clear that VaR‐based capital estimates are optimal for applications to management functions (e.g. risk management, capital budgeting, performance measurement, or regulation). This article considers three typical problems that require an estimate of credit risk capital: an optimal equity capital allocation; an optimal capital allocation for capital budgeting decisions; and an optimal capital allocation to remove moral hazard incentives from a compensation contract based on ex post performance. The optimal credit risk capital allocation is different for each problem and is never consistent with a credit VaR estimate of unexpected loss. The results demonstrate that the optimal risk capital allocation depends on the objective.

A new financial asset (Allotment Trading Unit or ATU) that allows a firm to pollute was issued to a number of Chicago firms in 2000 as part of a cap‐and‐trade model to reduce emissions in the Chicago area. A model of this market was developed to enable us to: 1.) Estimate equilibrium tradable credit prices and quantities and calculate compliance costs for comparison with traditional environmental regulation; 2.) Estimate the consequences for prices and quantities of introducing changing emitter costs; and 3.) Estimate the impacts on prices and quantities of changing market features such as auctioning tradable credits instead of a free allocation, introducing spatial constraints, and changing the emissions cap. The model's results on the price determination of this new financial asset are of interest to accountants and financial analysts. A dated bankable ATU credit has a one‐year life expectancy, but future tradable credits can be bought or sold for use at the appropriate future date. It is an intangible asset that should be disclosed, measured and valued. The valuation to place on this asset is an important research topic in finance and accounting and various valuation approaches are discussed to handle the short‐term and long‐term price paths.

The linkages between credit allocation and productivity have particular relevance in Portugal. This study aims to investigate whether credit extended by the Portuguese banking system has been allocated to the most productive firms within each sector.

With a data set covering 95% of total outstanding credit to non-financial corporations recorded in the Portuguese credit register, the authors investigate whether outstanding loans by resident banks to 64 economic sectors have been granted to the most productive firms. First, the authors estimate a baseline, reduced-form model of credit reallocation, where the parameter of interest gives the response of total credit granted to each firm to its level of productivity. Second, the authors assess how this response is affected by the share of credit allocated to unproductive firms. Third, the authors redo the analysis with credit granted to each firm by each banking group, instead of by the entire banking system, so that bank indicators can be taken on board.

The authors find evidence of misallocation, which reflects the joint effects of credit supply and credit demand decisions taken over the course of time, and the adverse cyclical developments following the accumulation of imbalances in the Portuguese economy for a protracted period. In 2008–2016, the share of outstanding credit granted to firms with very low productivity (measured or inferred) was always substantial, peaking at 44% in 2013, and declining afterwards with the rebound in economic activity and the growing allocation of new loans towards lower risk firms and away from higher risk firms. Furthermore, the authors find that misallocation is associated with slower reallocation. The responsiveness of credit growth to firm relative productivity is much lower in sectors with relatively more misallocated credit and when banks have a high share of such credit in their portfolios.

Banking system distortions are often mentioned as potential or likely culprits for capital misallocation, but they are not empirically analysed with credit data. The ability to explicitly analyse bank credit and link it to variables pertaining to both firms and banks is a novel feature relative to most previous studies, which largely rely on firm-level or sectoral data alone.

The aspect of economic contrast seen in developing countries shoots primarily from the structure of loan allocation to the micro, small and medium enterprises (MSME) sector, which is essential for job creation and understanding the role of microfinance institutions (MFIs) in meeting the credit demands of the vulnerable but vital sector of less developed economies. The study demonstrates the impact of MSME protection in terms of both fixed and adjustable factor coefficient settings, creating a model of a small open economy with three sectors: a skill-intensive export sector; a capital-intensive import competing sector; and a labour-intensive import competing and intermediate products producing sector. It analyzes the types of protection that aid in the expansion of credit and the alleviation of capital constraints, which further highlights the insufficiencies of tariff protection for the organized sector and simple credit guarantee policies to provide adequate credit flow and thus continued MSME growth. Finally, it considers the importance of priority sector lending policies in ensuring adequate credit distribution to this sector. The results show that protection helps in enhancing flow of credit and thereby works to relax the capital constraint. However, the tariff protection for organized sector may positively or negatively affect the non-traded unorganized sector.

Philanthropy is taken as a strategic behavior by private enterprises to obtain financial resources from governments. This paper aims to examine the relationship between private enterprise philanthropy and the debt finance, further investigating the way by which governments exchange resources with private enterprises.

The paper opted for an empirical study using a sample of 1,489 Chinese private-listed companies from 2007 to 2010. The study analyzed the relationship between philanthropy and debt finance based on the resource dependence theory and social exchange theory and tested the moderating effect of political connection.

Philanthropy can help private enterprises to get the debt finance, and this effect occurs mainly among the political connected private enterprises; the higher degree of credit allocation marketization is, the less philanthropy can affect the debt finance and the less influence political connection can exert on that relationship. Philanthropy contributes to debt financing mainly because it can help obtain more long-term loan, and this effect is more obvious for politically connected private enterprises in regulated industries.

This paper verifies the action logic of private enterprises philanthropy from the perspective of exchange behavior, which is helpful to understand the motive and influence of private enterprises philanthropy.

The purpose of this study is to elaborate the theoretical mechanism of rural credit input affecting the urban–rural income gap from the perspective of labor transfer, and use a dynamic panel mediation model to test the transmission mechanism of rural credit input affecting the urban–rural income gap through labor transfer, so as to provide an empirical basis for narrowing the urban–rural income gap in China.

This paper constructs a mechanism analysis framework for rural credit input affecting the urban–rural income gap. From the perspective of resource allocation and labor transfer, the authors expound the transmission path of rural credit input to the urban–rural income gap and analyze the theoretical mechanism of rural credit input that affects the urban–rural income gap through labor transfer. Based on this, this paper uses the dynamic panel mediation model to test the effect relationship between rural credit input, labor transfer and urban–rural income gap in 31 provinces of China from 2009 to 2018.

In theory, increasing rural credit input can ease the financial constraints on the development of “agriculture, rural areas and farmers” and provide capital accumulation for the development of rural non-agricultural industries. The development of rural non-agricultural industries can provide more jobs for rural surplus labor, thereby increasing the labor rate of return in rural areas, and ultimately conducive to narrowing the urban–rural income gap. Further, increasing rural credit input can improve the development level of rural non-agricultural industries, thereby promoting the transfer of agricultural labor. At the same time, rural credit input based on the intermediary variable of labor transfer has a significant inhibitory effect on the urban–rural income gap.

This study mainly focuses on the relationship between rural credit input, labor transfer and urban–rural income gap, so it is impossible to use micro-level data to further verify the impact of rural credit input on labor transfer. At the same time, the collection of indicators of rural credit investment in the China Financial Yearbook only started in 2009, which limited the number of samples to a certain extent.

This paper assumes that the economy is mainly composed of urban and rural economic sectors. Therefore, labor can flow freely between urban and rural areas. However, in the near future, China's rural secondary and tertiary industries may develop rapidly, especially with the in-depth implementation of rural revitalization strategy, it is very important to pay attention to the current situation of rural industrial structure and incorporate the factors such as rural industrial structure into the existing model.

This study attempts to provide a new perspective and inspiration for rural credit input, the optimal allocation of labor force and narrowing the urban–rural income gap under China's rural revitalization strategy.

Based on the analysis framework of neoclassical economic theory, this paper uses the constant elasticity of substitution production function to establish an urban–rural two-sector nested model that includes credit supply variables and analyzes the mechanism of rural credit input affecting the urban–rural income gap through labor transfer.

The purpose of this paper is to explore the factors that affect farmer’s decision to allocate credit for livestock production. The results are expected to contribute to the understanding of what motivates smallholders to allocate credit to agricultural production in general and livestock production in particular. A better understanding of the farmers’ behavior in allocating credit for livestock would provide useful information for project implementers and financial institutions that work with small-scale livestock producers.

A cross-section data set collected in 2014 from 5,000 households and 497 rural communities in the major highland regions of Ethiopia is examined. The authors developed a conceptual framework for credit allocation decision. Percentiles, means, and standard deviation as well as t, χ² and Fisher’s exact tests for association and Cramer’s V measure for strength of association have been used to describe the status of farmer’s access to credit and analyze credit utilization, while a three-stage probit model with double sample selection is used to identify factors that affect household’s decision to allocate credit for livestock production.

After controlling for potential selection biases, sex and literacy status of household head, land size, wealth and access to livestock centered extension service are found to have a statistically significant effect on farmers’ decision to allocate credit to livestock production. The results showed female-headed households, wealthy farmers, farmers with small plot of land and farmers that have access to livestock centered extension services are more likely to allocate the credit for livestock production. The results suggest that policies aimed at improving access to credit together with access to livestock focused extension service are more effective in increasing livestock production.

The study’s findings should be viewed with perspective and caution, as only households with excess demand for credit were the subject of the research.

The contribution of this paper is twofold. First, it is one of a very few empirical studies that try to identify factors that affect households credit allocation to livestock in systematic way that removed confounding effects using three-stage probit models. Given the emphasis on financial constraints in livestock development, new empirical insights on household credit allocation are essential to better inform development interventions. Second, the analysis relies on a comprehensive data set that represents the major agricultural system of the country.

Recalling that the introductory chapter (Chapter 1) wanted to carry out similar types of analysis for the major states in India. Thus, the present chapter tries to examine the trends of a bank branch, deposit, credit, the credit–deposit ratio, sectoral shares of credit, magnitudes of banking transactions, credit concentration, etc., for the selected 15 states and Delhi as the only union territory for the period 1972–2019. The study period covers the pre-reform period from 1972 to 1992 and the post-reform period 1993–2019. The observations show that the branch, deposit and credit did not grow significantly during the post-reform period. As a result, the credit–deposit ratio did not increase significantly during the reform period. But, the magnitude of banking transactions increased in most of the states during the reform period. Regarding the sector-wise share of credit, AP, Maharashtra, UP and TN are the leading states in agricultural credit, WB, Gujarat and Maharashtra are in industrial credit and Kerala, Assam and Delhi are in the service sector. On the other hand, the study finds rising magnitudes credit concentrations of the states during the post-reform period in contrast to the declining concentration in the pre-reform period. Maharashtra is the state which holds around 25 per cent of all states’ credit throughout the entire period of 1972–2019. Hence, there are the notions of rising disparity and inequality in credit as well as incomes of the states and all India levels.