纳税信用评级结果披露与研发投入

以纳税信用评级披露作为一个天然外生冲击,并基于2013—2016年1214家A股上市公司的微观数据构造准自然实验,使用双重差分法系统评估纳税信用评级结果披露对上市公司研发投入的影响。结果发现:纳税信用评级结果披露显著增加了上市公司的研发投入。基于PSM-DID方法的估计结果与上述结论无明显差异。稳健性检验也表明上述结论的正确性。机制检验表明,纳税信用评级结果披露通过降低企业的融资约束,进而促进企业增加研发投入。此外,分样本回归发现纳税信用评级结果披露只能对中小型企业和民营企业的研发投入产生促进作用。     

Remittances and rural credit markets: Evidence from Senegal

This study investigates the relationship between remittances and credit markets in Senegal, focusing on rural areas where financial constraints are more challenging. Using a household fixed effects model, the findings show that remittances and credit markets are complements; namely, the receipt of remittances is positively associated with the likelihood of having a loan in a household. This means that migrants can increase the reliability of their family members and close relatives back home through their remittances, insuring them vis‐à‐vis lenders for their credit contracts. They are the collateral or the “element of trust” in the credit contract between the borrower and the lender, representing a potential alternative in case of non‐repayment. This result is robust to alternative models and various robustness tests mitigating the potential endogeneity of remittances. A detailed analysis also shows that the relationship between remittances and credit markets is mainly driven by loans taken for consumption and food, in particular, as well as loans provided by informal institutions.     

State-owned banks in the market for corporate control

We study the pre-deal characteristics of state-owned banks acquiring other companies, relative to their private counterparts. We build a unique international data-set of 3682 deals in the years 2003–2013. Econometric results highlight that those state-owned banks that are acting as acquirers have an ex-ante performance similar to their private benchmarks. The results are driven by the role of development banks. This new finding points to the recent evolution of some types of contemporary state-owned financial players.     

Bibliometric and Scientometric analysis on CSR practices in the banking sector

This paper provides a bibliometric and Scientometric analysis of the corporate social responsibility (CSR) in banking sector. Our study analyzes 551 articles from the Scopus database to find out the relationship between CSR and banking. A bibliometric method was used to visualize the results using R-studio and VOS viewer software. The Scientometric analysis was conducted to determine the findings and mappings of the research themes, directions of current and future research, impact, co-occurrence, co-citations and impact and collaboration trends. We explore how CSR literature has evolved over the years in the banking sector between 1993 and 2021. We find that publication in the CSR and banking domain has increased significantly during 2017 and 2021. Social aspects, board of directors, CSR, environment, competitions, Islamic banking, sustainability, disclosure, ethics, legitimacy theory, sustainable banking, loyalty, and brand equity are the popular research trends and collaboration trends identified. We also provide further scope of the study based on the extensive review of the past literature. Our findings may provide help to future researchers, bankers, and regulators in understanding the current trends and future research progression in the CSR and banking sector.     

Tax evasion by tax deferment: Sham litigation with an informal credit market

We show theoretically how tax evasion is facilitated by informal credit market through tax deferment. Our model is empirically based. Using sham litigation, tax evaders earn a higher rate of return than the stipulated penalty rate for tax evasion while the government loses tax revenue. We propose an upfront part–payment of the disputed amount of tax as a solution to the form of tax evasion we describe.     

Does Targeted Monetary Policy Matter? A Perspective from China

Several major central banks have experimented with targeted monetary policy to improve credit resource allocation. This policy only applies to ‘eligible’ banks. For example, The People's Bank of China conducted seven targeted reductions of reserve requirements during 2014–15. This article documents the phenomenon of targeted monetary policy and evaluates its effects. The results show that, in the case of China, this policy has generated an extra significant, positive return on the stocks of eligible banks, amounting to 1.2–1.3 per cent in a four‐day treatment period. This substantial return gives commercial banks an extra incentive to align with the policy goals of central banks.     

Bank lending channel in a dual banking system: Why are Islamic banks so responsive?

We examine the interest rate sensitivity of both deposits and credits at Islamic and conventional banks in Turkey. We find that the bank lending channel is especially operative for Islamic banks. Impulse responses for conventional and Islamic banks reveal that Islamic bank depositors’ sensitivity to policy rate changes is substantially larger than that of conventional bank depositors. Next to heavily dependence on deposit funding, we consider that inertia in Islamic bank deposit rates impedes these banks to keep those depositors who consider the opportunity cost of monetary policy rates is unbearable. On the lending side, we obtain similar results, implying that tight monetary policy leads to a larger contraction in Islamic bank credits. This finding is a reflection of the favourable attitude of Islamic banks towards small and medium‐sized enterprise (SME) financing. When similar relationships are analysed for currency and inflation shocks, we again find larger responses for Islamic banks showing the cyclical nature of SME credits.     

Assessing the Reliability of Self‐reported Income Information in Informal Small Business Lending through a Bogus Pipeline Experiment

Coping with asymmetric information plays a major role in successful small business lending. Our purpose is to determine if small business applicants report their income information correctly when requesting a loan. We use a randomised controlled trial bogus pipeline experiment, established during a typical cash‐flow analysis of a bank for small businesses in the Philippines. The bogus pipeline approach is commonly applied in social science and aims to increase the rate of truth telling by informing participants that answers will be verified by a lie detector. The experimental data, which include 243 observations of credit clients that are mainly from the agricultural and food value chain, served to identify asymmetric information. Additionally, debtors’ repayment behaviour for approved loans was observed by the bank. Our results indicate that loan applicants of the treatment group report lower incomes, an effect which is most pronounced in lower income quantile. Our analyses also reveal higher loan delinquencies in the control group.     

基于DEA模型的建筑业环境行为效率评价研究

对建筑企业进行环境行为效率评价是促进我国建筑行业健康可持续发展的重要手段。根据建筑行业特点及我国现有的企业环境信用标准,构建了建筑企业环境行为评价的投入产出指标体系,将改进的数据包络分析方法模型运用到企业环境行为效率评价领域,解决了现有企业评价研究主观性突出的问题,得出建筑企业评价分析方法。在此基础上对某代表性建筑企业2010—2017年环境行为数据进行了评价分析。分析结果表明该企业环境行为总体效率良好,但鉴于出现了奇数年和偶数年的效率波动情况,仍需提高环境行为效率。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.