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Means, medians and SD for available socio‐economic status (SES) black‐white differences are here substituted for those of IQ in a between‐groups model published by the author over a decade ago. The goodness of fit of the SES variables used is compared with that for the earlier IQ data. Even when SES variables are relatively successful this can be viewed as additional evidence of the importance of IQ differences to black‐white differences in delinquency.

The purpose of this paper is to identify the factors that affect the Canadian credit unions' financial performance which play an important role in providing financial services to the agriculture sector.

We surveyed the literature to identify different performance metrics of credit unions and a set of possible factors that might affect their performance. We collected data related to different dependent and independent variables from financial statements and balance sheets of 189 credit unions and from general websites like Statistics Canada and Bank of Canada. Then, we imputed the missing data and developed fixed effect and random effect panel data regression models. First, we used return on asset as the main dependent variable. Afterwards, we used six performance metrics to check the robustness of our models.

From an initial list of 16 possible factors that might affect the financial performance of a credit union, we were able to narrow the factors down to the nine most significant ones. It was observed that credit unions in the prairies were more likely to perform well financially as compared to other provinces. Membership size, the size of a credit union in terms of total assets, capital adequacy ratio, market penetration, diversification of income, inflation rate and provincial GDP and interest rates were significant. The cross-sectional analysis performed confirmed the findings of the fixed effect panel data models.

This study has a limitation concerning the number of years included into the time series analysis. Only ten years worth of data were available.

Results provide credit union management, service providers for credit unions and market analysts with a current understanding of how different internal and external factors might affect return on assets, return on equity, delinquency, cash ratio, efficiency ratio, asset growth and loan growth. Our models can be used to predict financial performance of credit unions based on the defined significant variables.

Although there is a wide body of literature that studies performance of banks, not many studies focus on credit unions. Moreover, the existing studies are based on credit unions in United States or Europe, and literature on Canadian credit unions is scarce. The data collected covered 189 Canadian credit unions. To our knowledge this is the first study that looks at the various internal, external and regulatory factors together that affect the credit unions in various jurisdictions of Canada.

This paper aims to answer how effective the loan-to-value (LTV) regulation has been since 2011 for conventional and Islamic (participation) banks in Turkey in terms of curbing mortgage loan growth and delinquency[1].

The authors first use unit root tests and tests of difference in loan and property price data in pre-LTV and post-LTV period. Second, the authors follow Chow test and ordinary least squares regression analyses to test for a structural break when sensitivity of mortgage loan and delinquency growth changes to property price changes considered.

The authors find that two periods are statistically different, while the significance level is lower for Islamic banks. Moreover, loan growth has become less responsive to property price increases; delinquency sensitivity to property price changes has significantly increased in the post-LTV period for conventional banks, while this is not the case for Islamic (participation) banks.

This paper not only increases empirical evidence regarding the effectiveness of LTV ratio policy but also fills the gap in the literature by providing a comparison between conventional banks and Islamic (participation) banks.

The late 2000s Great Recession led to a surge of bank failures in the USA with nearly 300 banks failing from 2009 to 2010. Recalling the farm crises of the 1980s where the farm sector was pinpointed as one of the major precursors of such crises, this study is an attempt to validate if the agricultural sector can once again be considered as a major instigator of the current financial crises.

An early warning model is developed based on factors that may cause bank failures, with special attention given to the role of the agricultural lending portfolios of commercial banks. The model will have several time period versions that will determine the length of time prior to the actual bank bankruptcy declarations that early warning signals could be detected.

The empirical results indicate that credit exposure to the farm sector does not necessarily enhance a bank's tendency to fail or its probability of success or survival. This lends support to the reality that agricultural loan delinquency rates are consistently below the banks' overall loan delinquency rates, thus confirming that agricultural lenders are in relatively stronger financial health. This study instead finds that costly funding arrangements, increasing interest rate risk, and declining asset quality can be possible early warning signals that can be detected as far back as two or three years before eventual bank failure.

This study differentiates itself from previous studies by its special focus on the role of the agricultural finance industry in the ensuing economic crises. This study's early warning model also presents an extended version of previous empirical models as it accounts for measures of capital adequacy, asset quality, management risk, profitability, liquidity risk, loan portfolio composition and risk, funding arrangement, structural and macroeconomic variables.

This study aims to propose a non-linear model to describe the effect of macroeconomic shocks on delinquency rates of three kinds of bank loans. Indeed, a wealth of literature has recognized significant evidence of the linkage between macro conditions and credit vulnerability, perceiving the importance of the high amount of bad loans for economic stagnation and financial vulnerability.

Generally, this linkage was represented by linear relationships, but the strong dependence of bank loan default on the economic cycle, subject to changes in regime, could suggest non-linear models as more appropriate. Indeed, macroeconomic variables affect the performance of bank’s portfolio loan, but such a relationship is subject to changes disturbing the stability of parameters along the time. This study is an attempt to model three different kinds of bank loan defaults and to forecast them in the case of the USA, detecting non-linear and asymmetric behaviors by the adoption of a Markov-switching (MS) approach.

Comparing it with the classical linear model, the authors identify evidence for the presence of regimes and asymmetries, changing in correspondence of the recession periods during the span of 1987–2017.

The data are at a quarterly frequency, and more observations and more extended research periods could ameliorate the MS technique.

The good forecasting performance of this model could be applied by authorities to fine-tune their policies and deal with different types of loans and to diversify strategies during the different economic trends. In addition, bank management can refer to the performance of macroeconomic conditions to predict the performance of their bad loans.

The authors show a clear outperformance of the MS model concerning the linear one.

Using a unique dataset of a commercial microfinance institution (MFI) in Tanzania, the purpose of this paper is to investigate first whether agricultural firms have a different probability to get a loan and whether their loans are differently volume rationed than loans to non‐agricultural firms. Second, the paper analyzes whether agricultural firms repay their loans with different delinquencies than non‐agricultural firms.

The authors estimate a Probit‐Model for the probability of receiving a loan, a Heckman‐Model to investigate the magnitude of volume rationing for all loan applications and an OLS‐Model to examine the loan delinquencies of all microloans disbursed by the MFI.

The results reveal that agricultural firms face higher obstacles to get credit but as soon as they have access to credit, their loans are not differently volume rationed than those of non‐agricultural firms. Furthermore, agricultural firms are less often delinquent when paying back their loans than non‐agricultural firms.

Even if the authors can show that access to credit and loan repayment is different for agricultural firms, the current regional focus of the MFI only allows for lending to agricultural firms in the greater Dar es Salaam area. Thus, these results might change in a rural setting. Besides general differences of the rural economic environment, the production type of agricultural firms might also differ in rural areas. Also, these results might change in different country contexts.

The findings suggest that a higher risk exposition typically attributed to agricultural production must not necessarily lead to higher credit risk. They also show that the investigated MFI overestimates the credit risk of agricultural clients and, hence, should reconsider its risk assessment practice to be able to increase lending to the agricultural sector. In addition, the results might indicate that farmers qualify less often for a loan as they do not fit into the standard microcredit product.

To the authors' knowledge, this is the first paper which simultaneously investigates access to credit and the repayment behavior of agricultural firms.

In recent decades, research on consumer debt and well-being is emerging. However, research on the potential effect of debt portfolios on family financial well-being is limited. The purpose of this study is to fill this research gap by examining the potential effect of debt portfolios on family financial well-being, measured by three indicators of progressive financial burdens. These indicators include debt pressure (debt payment to income ratio >40%), debt delinquency (60+ days late for debt payments) and insolvency (total liability > total asset). Debt portfolios refer to various combinations of mortgage, credit card, vehicle, education and other loans.

With data from the 2019 Survey of Consumer Finances in the USA, multivariate logistic regressions are used to identify specific debt types, consumer backgrounds and financial capability factors that are significantly associated with debt burden indicators. The findings are used to create a table demonstrating warning debt portfolios that may lead to undesirable financial outcomes.

Holdings of different types of debts are associated with different financial burdens. Specifically, holdings of three types of debts (mortgage, vehicle and other debts) tend to increase debt pressure; holdings of two types of debts (education and other debts) tend to increase debt delinquency; and holdings of four types of debts (mortgage, credit card, education and other debts) tend to increase insolvency. These results are used to construct warning debt portfolios that show greater chances of undesirable financial outcomes. Among them, the top warning portfolio for debt pressure is the combined holding of mortgage-vehicle-other debts; for debt delinquency is the holding of education-other debts; and for insolvency is the holding of mortgage-credit card-education-other debts.

This study is limited by using only cross-sectional survey data to examine associations between debt portfolios and financial burdens. To examine the causality of debt portfolios on financial burdens, appropriate panel data are necessary, which is a direction for future research. In addition, this study used data from only one developed country. In future research, data from more countries, including both developed and developing countries, should be analyzed to verify if similar relationships exist among families in other countries.

Results of this study have implications for practitioners in banking and other financial institutions. The study presents a comprehensive list of debt portfolios in the order from high risk to low risk in terms of financial burdens. Banking and other financial service professionals can use the information to help their clients make informed borrowing decisions, predict their debt burdens and offer early preventions based on their clients' debt portfolios. Marketing strategists can use the information for effective segmentation and promotion purposes.

This study utilizes a new concept, debt portfolios and examines its associations with family financial burdens. Financial burdens include three indicators that are seldom used together in previous research. These indicators conceptually indicate various severity levels of debt burdens. This study also presents a conceptual discussion on the association between debt portfolios and financial burdens and provides a better understanding of consumer debt behavior and its consequences. The warning debt portfolios constructed based on the findings have direct managerial implications for banking and other financial service professionals.

The primary objective of the paper is to demonstrate the importance of borrower‐specific characteristics as well as local situation factors in determining the demand prospect as well as the risk of credit loss on residential housing loan repayment behavior in India.

Using 13,487 housing loan accounts (sanctioned from 1993‐2007) data from Banks and Housing Finance Cos (HFCs) in India, this paper attempts to find out the crucial factors that drive demand for housing and its correlation with borrower characteristics using a panel regression method. Next, using logistic regression, housing loan defaults and the major causative factors of the same are examined.

In analyzing the housing demand pattern, some special characteristics of the Indian residential housing market (demographic and social features) and the housing loan facility structure (loan process, loan margin, loan rate, collateral structure etc.), that have contributed to the safety and soundness of the Indian housing market have been deciphered. The empirical results suggest that borrower defaults on housing loan payments is mainly driven by change in the market value of the property vis‐à‐vis the loan amount and EMI to income ratio. A 10 percent decrease in the market value of the property vis‐à‐vis the loan amount raises the odds of default by 1.55 percent. Similarly, a 10 percent increase in EMI to income ratio raises the delinquency chance by 4.50 percent. However, one cannot ignore borrower characteristics like marital status, employment situation, regional locations, city locations, age profile and house preference which otherwise may inhibit the lender to properly assess credit risk in home loan business, as the results show that these parameters also act as default triggers.

This study contributes on the micro side of the housing market in India, since it uses unique and robust loan information data from banks and HFCs. The empirical results obtained in this paper are useful to regulators, policy makers, market players as well as the researchers to understand housing market demand and risk characteristics in an emerging market economy such as India.

The purpose of this paper is to investigate the balanced role of internal and external compliance in risk evaluation process of specialized agriculture financing. The authors examine the adaptive behavior of risk managers to determine the role of proposed transformation for risk monitoring (RM) and control process in risk mitigation and avoidance of agriculture credit failure.

A self-administered survey was conducted to collect data from 353 risk-related officers and managers in Zarai Taraqiati Bank Limited (ZTBL) Pakistan. The authors used a previously tested scale for the main constructs. The descriptive analyses were used to gauge the model capacity for determining the strength of proposed risk patterns in agriculture risk management.

The results reveal that risk evaluation process in ZTBL is reasonably efficient in mitigating risks. Given the sensitive nature of farm credit, there is a need of fundamental reforms in risk policy manuals in line with central bank’s agriculture prudential regulations and Basel-III standards. The results fully support H1 and H2, while H3 is partially validated. The result patterns indicate serious issues in risk evaluation process in agriculture finance that is causing higher delinquency in farm credit.

Based on highlighted issues, the authors recommend valuable guidelines in the RM review system for agriculture financing products at ZTBL.

The authors propose remodeling of agriculture risk management and offer valuable insights to the agriculture financial regulators and government in taking policy initiatives in the pre-and-post agriculture risk evaluation process. The proposed model enables RM process to improve farm credit delinquency, particularly in ZTBL and other agriculture banking networks in commercial banks.

This is the first study to empirically investigate RM evaluation process in agriculture risk management of ZTBL in Pakistan, thus, offers new horizon of farm credit regulatory compliance in agricultural sector of Pakistan.