This study examines the relationship between firm‐level factors including corporate social responsibility (CSR) performance and financial constraints, and the firm‐level risk of corruption. Facing a measurement challenge referring to firm‐level risk of corruption, we define a corruption score based on corporate disclosures of corrupt activities. We show that, first, CSR performance is negatively related to the risk of corporate corruption. Second, a firm's vulnerability to financial constraints is positively related to the risk of corporate corruption. Third, the effects are particularly strong for firms with low board independence. Finally, we contribute to prior literature by developing the first firm‐level corruption score that is not only robust to prevalent corruption indices but is also applicable to any dataset and can be used for future research on firm‐level corruption. Moreover, the study provides new empirical evidence on firm‐level determinants that relate to corporate corruption risk.

According to the concept of microfinance, financial institutions ought to contribute to sustainable economic and financial systems development by offering access to credit for clients who are usually excluded from the formal banking system. However, in recent decades microfinance institutions (MFIs) have often focused on their profitability rather than the support of their poor clients. In order to empirically examine this mission drift and its consequences for MFIs' performance, we propose a model of MFIs' contribution to sustainable development as dependent on their outreach and profitability focus measured by percentages of female borrowers and profit margins, respectively. Utilizing a large transnational panel data set comprised of institutional and country‐specific data, we provide preliminary empirical evidence indicating that both extensive outreach and profitability are negatively related to development. The model further highlights that the problem of a mission drift is especially pronounced for non‐profit‐oriented MFIs. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

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The purpose of this study is to investigate the so far underexamined statistical causality of the relationship between microfinance and economic development. For a representative transnational dataset covering the period 1995 -2012 we instrumentalize pairwise vector autoregressive (VAR) estimation models and the Granger approach. We utilize prevalent microfinance institutions' (MFI) performance indicators as measures of microfinance as well as relevant economic development indicators that not only measure economic and capital growth but also poverty, income inequality and labor participation. We find bidirectional causal interactions between both MFIs' social and financial performance and economic development. Based on our results important implications for microfinance theory, research and practice can be derived. Future empirical research should account for the statistical causality between microfinance and economic development. In practice, purposeful and progressive action that considers the directions of causality between microfinance and economic development verified within our study should be taken to promote economic growth and poverty alleviation.

Purpose A credit rating, as a single indicator on one consistent scale, is designed as an objective and comparable measure within a credit rating agency (CRA). While research focuses mainly on the comparability of ratings between agencies, this paper additionally questions empirically how CRAs meet their promise of providing a consistent assessment of credit risk for issuers within and between market segments of the same agency. Design/methodology/approach Exhaustive and robust regression analyses are run to assess the impact of market sectors and rating agencies on credit ratings. The examinations consider the rating level, as well as rating downgrades as a further measure of empirical credit risk. Data stems from a large global sample of Bloomberg ratings from 11 market sectors for the period 2010-2018. Findings The analyses show differing effects of sectors and agencies on issuer ratings and downgrade probabilities. Empirical results on credit ratings and rating downgrades can then be attributed to investment grade and non-investment grade ratings. Originality/value The paper contributes to current finance research and practice by examining the credit rating differences between sectors and agencies and providing assistance to investors and other stakeholders, as well as researchers, how issuers’ sector and rating agency affiliations act as relative metrics.