528,719 research outputs found

    Multinomial Logistic Regression Analysis of Livelihood Diversification Strategies of Rural Farm Households: A Case of Limmu District, East Wollega Zone of Oromiya Regional State, Ethiopia

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    This study investigated the factors that influence the choice of livelihood strategies in rural areas of households in Limmu District, eastern Wollega zone of Oromiya regional state, Ethiopia. 292 households’ were selected using multi stages random and purposive sampling technique. A Multinomial logit regression model was applied to identify the determinants of agricultural diversification strategies in the area. Out of the total sample household heads about 46% of the total household income was derived from on farm only, 30% from a combination of on farm and nonfarm, about 12.33% from a combination of on farm, non-farm and off-farm and 12% from a combination of on farm and off-farm activities. The multinomial logit regression analysis revealed that education level, access to credit, access to mass media, dependency ratio, access to irrigation, urban linkage, climate change, extension contact and distance to the nearest road were theoretically consistent and statistically significant effect to the likelihood choice of diversification strategies. Whereas, age of household head, sex, distance to the market, cooperative membership, crop risk and distance from market were insignificant predictor of diversification strategy at 5% significance level.  The findings of the study suggest that efforts should focus on the promotion of options, substitution between assets and activities to diversify household specific agriculture-linkage with non-farm and off farm diversification rather than focusing on the single agricultural productive farm by taking action to improve information, mobility and asset accumulation. Keywords: Multinomial Logit, Limmu, Livelihood diversification, on farm, off-farm, non-farm DOI: 10.7176/JESD/12-13-03 Publication date:July 31st 2021

    Commercial bank load loss recoveries

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    We present a new approach to analyse historical recovery rates on distressed bank assets. Our approach uses banks’ reported impaired assets and the corresponding specific provisions. The dynamics and drivers of this credit loss recovery proxy are studied for a comprehensive sample of Australian banks from 1989 to 2005. We find that macroeconomic and bank-specific factors influence banks’ estimates of loan loss recoveries, consistent with banks smoothing their earnings. In contrast with findings based on prices of distressed corporate bonds, banks record lower recoveries in years of strong economic growth

    The Effect of Taxes on Multinational Debt Location

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    We provide new evidence that differences in international tax rates and tax regimes affect multinational firms\u27 debt location decisions. Our sample contains 8287 debt issues from 2437 firms headquartered in 23 different countries with debt-issuing subsidiaries in 59 countries. We analyze firms\u27 marginal decisions of where to issue debt to investigate the influence of a comprehensive set of tax-related effects, including differences in personal and corporate tax rates, tax credit and exemption systems, and bi-lateral cross-country withholding taxes on interest and dividend payments. Our results show that differences in personal and corporate tax rates, the presence of dividend imputation or relief tax systems, the tax treatment of repatriated profits, and inter-country withholding taxes on dividends and interest significantly influence the decision of where to locate debt and the proportion of debt located abroad. Our results are robust to firm and issue specific factors and to the effect of legal regimes, debt market development, and exchange rate risk

    Number of bank relationships : an indicator of competition, borrower quality, or just size?

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    In this study the firms' choice of the number of bank relationships is analyzed with respect to influential factors like borrower quality, size and the existence of a close housebank relationship. Then, the number of bank relationships is used as a proxy to examine if bank competition is reflected in loan terms. It is shown that the number of bank relationships is foremost determined by borrower size and the existence of a housebank relationship. Loan rate spreads are not effected by the number of bank relationships. However, borrowers with a small number of bank relationships provide more collateral and get more credit. These effects are amplified by a housebank relationship. Housebanks get more collateral and are ready to take a larger stake in the financing of their customers

    Project Finance as a Risk-Management Tool in International Syndicated Lending

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    We develop a double moral hazard model that predicts that the use of project finance increases with both the political risk of the country in which the project is located and the influence of the lender over this political risk exposure. In contrast, the use of project finance should decrease as the economic health and corporate governance provisions of the borrower’s home country improve. When we test these predictions with a global sample of syndicated loans to borrowers in 139 countries, we find overall support for our model and provide evidence that multilateral development banks act as “political umbrellas”

    The Determinants of Credit Ratings in the United Kingdom Insurance Industry

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    Executive Summary The Determinants of Credit Ratings in the United Kingdom Insurance Industry Academic researchers have devoted a considerable amount of attention to the activities of credit rating agencies over the past 20 years, focusing in particular on the agencies’ potential role in overseeing corporate financial strength and promoting the efficient operation of financial markets. Examinations of credit rating practices has recently extended to the insurance industry, where the complex technical nature of market transactions leads to policyholders, investors and others facing particularly acute information asymmetries at the point-of-sale. Published credit ratings are therefore seen as helping to alleviate imperfections in insurance markets by providing a third party opinion on the adequacy of an insurer’s financial health and the likelihood of it meeting obligations to policyholders and others in the future. Although the United Kingdom (UK) insurance market is now one of the five largest in the world, relatively little is known about the practices of the major firms and policy-makers which influence its operations. In particular, whilst the determinants of rating agencies’ assessments of United States (US) insurers is well documented, published studies have yet to provide comprehensive evidence about insurance company ratings in the UK. This study attempts to fill this gap by examining the ratings awarded by two of the world’s leading agencies – A.M. Best and Standard and Poor (S&P) – and establishing the extent to which organizational variables can help predict: (i) insurance firms’ decision to be rated; and (ii) the assigned ratings themselves. Our sample of UK data comprises ratings made by A.M. Best and S&P over the period 1993-1997 for both life and property-liability insurers. The panel data we use is ordinal in nature and is therefore analysed using an ordered probit model. However, because neither A.M. Best or S&P rate the full population of UK insurance firms our data set is potentially subject to selfselection bias and we therefore extend the model to correct for such problems. In particular, the paper examines the effect of eight firm-specific variables (namely, capital adequacy, profitability, liquidity, growth, size, mutual/stockowner status, reinsurance level, and short/long-term nature of business) on the ratings awarded by the two agencies, as well as on insurance firms’ decisions to volunteer for the ratings in the first place. In general terms, our evidence concurs with earlier US findings, and suggests that although the decision to be rated by either of the agencies is largely influenced by a common set of factors, the determinants of the ratings themselves appear to differ. Specifically, our first main finding is that insurers’ decisions to be rated by either A.M. Best or S&P is positively related to surplus growth, profitability and leverage. Second, while we find that A.M. Best’s ratings are positively linked to profitability and liquidity, as well as being generally higher for mutual insurers, the findings for S&P differ substantially. Although liquidity again exerted a positive influence on assigned ratings, the only other statistically significant variable was financial leverage, which had a negative sign. We believe that the results of our research are of potential importance for companies operating in insurance markets as well as for policy-makers, brokers and others. For example, the evidence that mutual insurers are generally assigned higher ratings than stock insurers suggests that certain publicly-traded insurers, in particular new entrants, might not possess sound financial strength and may require closer regulatory scrutiny than other, more established, insurance firms. In addition, the finding that liquidity has a significantly positive effect on ratings assigned by two of the world’s leading credit agencies should provide a measure of confidence about the robustness of the ratings to industry regulators, policyholders and investors in the UK. This could imply that external ratings might eventually play a role in substituting for costly industry regulation. The study concludes that although the factors influencing the decision to be rated by A.M. Best or S&P are broadly the same, a degree of variability exists in the variables which influence the actual ratings themselves. Insurance company managers should be aware of this when contemplating whether to seek an independent rating and which agency to choose for the assessment. We therefore believe that this study fills an important gap in the literature about key players in the important UK insurance market and provides a basis for the conduct of future research

    Credit losses in Australasian banking

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    We analyse determinants of bank credit losses in Australasia. Despite sizeable credit losses over the past two decades, ours is the first systematic study to do so. Analysis is based on a comprehensive dataset retrieved from original financial reports of 32 Australasian banks (1980- 2005). Credit losses rise when the macro economy is weak. Asset markets, particularly the equity market, are also important. Larger banks provide more for credit losses while less efficient banks have greater asset quality problems. Strong loan growth translates into significantly higher credit losses with a lag of 2-4 years. Finally, the results show strong evidence of income smoothing activities by banks
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