Adoption des Politiques de Protection de l'Environnement et performance des Entreprises Camerounaises

The objective of this paper is to evaluate the effect of the environmental protection policies of by Cameroonian firms on their performance. It uses the endogenous switching regression technique and propensity scores applied to micro-data from 639 firms in Cameroon. The results show that only 17% of firms adopt these measures, while on average 85% of firms produce solid, gaseous or liquid waste. The results also indicate that the adoption of these environmental protection policies increases operating costs while significantly improving the turnover and the performance of the productive capacity of the company. These increases are 39.11%, 58.6%, and 38.63% for operating costs, turnover and return on productive capacity of the company, respectively. However, firms can also suffer significant losses resulting from the non-adoption of environmental policies. In fact, firms that do not adopt environmental protection policies have their performance reduced by an average of 1.625 percentage points.     

Electoral uncertainty and financial volatility: Evidence from two‐round presidential races in emerging markets

We study how the predictability and the decisiveness of electoral outcomes affect financial volatility. We argue that traders’ optimal investment strategies depend on their ability to make accurate electoral forecasts and the prospective losses associated with placing a bet on the wrong candidate. Using a triple difference‐in‐difference approach and data from two‐round presidential elections in five Latin American countries between 1999 and 2018, we find that financial volatility is greatest in the days immediately following unpredictable, decisive, elections. Postelectoral volatility also occurs following predictable, indecisive elections. The effect of learning the identity of the winning candidate on financial volatility is null when the election is unpredictable and indecisive, as well as when the election is decisive, but the outcome is predictable. These findings offer insights into investors seeking to hedge price risk around elections. They also have important implications regarding the relationship between public opinion polls and postelectoral financial volatility.     

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.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

State‐Led Financial Regulation and Representations of Spatial Fixity: The Example of the Spanish Real Estate Sector

This article offers a bottom‐up contribution to the fixity–motion literature. It aims to unravel the apparent contradiction of real estate spatial fixity in Spain, which is portrayed both as a barrier to accumulation and as a unique source of investment by different capitalist actors. Empirically, it describes the shifts in real estate ownership and changes in profit‐making strategies that have taken place across the real estate sector during the crisis years, and the role of the state in these shifts. The article asserts that the idea of spatial fixity representing a spatial barrier for accumulation does not necessarily apply in the Spanish case. It further claims that the tensions in capital circulation through real estate are not only to be found in the action of time, but in different state strategies pursued by various actors. The opposing representations of fixity are the result of state regulation of interest rates, taxation and risk weighting. The state also increasingly promotes land rents as a source of liquidity creation.     

Higher Intensity,Higher Profit? Empirical Evidence from Dairy Farming in New Zealand

Over the last two decades there have been significant and well‐documented changes in the nature and structure of New Zealand dairy production. One particular feature has been a marked shift in the ‘input intensity’ of dairy farming systems through increased use of supplementary feed. These changes have generated debate about the impact of dairy farm intensity on the performance of farm businesses and the competitiveness of the New Zealand dairy sector. Using a novel econometric approach, we assess statistically the impact of three types of dairy farming systems on milk production and financial performance, using farm business data provided by DairyBase^®. Our empirical results show that higher input systems perform significantly better physically than lower input systems, but not financially. The disaggregated analysis suggests that the average treatment effects differ by region and performance quantiles.     

Efficiency,economy and effectiveness—but what about ethics? Supreme audit institutions at a critical juncture

Ethics is increasingly recognized as one of several important dimensions of performance. Yet, performance audit, or value-for-money audit, as conducted by supreme audit institutions (SAIs), does not typically include this dimension. Instead, the focus is on the classical ‘three Es’ (efficiency, economy and effectiveness). The reluctance to address issues of ethical misconduct has taken the audit practice of SAIs to a critical juncture, where the legitimacy of these audits ultimately is at stake. This paper explains why SAIs need to add a fourth ‘E’— ethical audit. Possible implications are discussed.     

The major determinants of logistic performance in a global perspective: evidence from panel data analysis

The global logistics industry has grown significantly and logistics has become an important sector of the business economic system and a major global economic activity in recent years. Logistics activities accelerate economic and productivity growth. Efficient logistics is also important to a country’s competitiveness and source of employment. The purpose of this paper is to uncover and understand the major determinants of logistics performance (LP) to further lift the LP of countries. Using unbalanced panel data of 93 selected countries from 2007 to 2014, the present research attempts to critically investigate the major determinants of LP. In estimating the model, this study prefers to use static panel data approach owing to limited data. The findings of the present study reveal that (a) countries with low level of corruption and stable political environment are likely to yield a high level of LP; (b) improvement in resources supply such as infrastructure, technology, labour, and education also have a significant positive effect on LP. Therefore, institutional reforms and upgrading resources will effectively accelerate LP.     

The effect of mandatory CSR disclosure on firm profitability and social externalities: Evidence from China

We examine how mandatory disclosure of corporate social responsibility (CSR) impacts firm performance and social externalities. Our analysis exploits China's 2008 mandate requiring firms to disclose CSR activities, using a difference-in-differences design. Although the mandate does not require firms to spend on CSR, we find that mandatory CSR reporting firms experience a decrease in profitability subsequent to the mandate. In addition, the cities most impacted by the disclosure mandate experience a decrease in their industrial wastewater and SO₂ emission levels. These findings suggest that mandatory CSR disclosure alters firm behavior and generates positive externalities at the expense of shareholders.