The Implication of NESREA Act and Financial Performance on Environmental Disclosure in Nigeria

The study assesses the implication of compliance and enforcement of the NESREA Act, profitability, and Growth on environmental disclosure of cement companies in Nigeria. Secondary data comprising financial and non-financial information were source from annual accounts and reports of the sample companies, spanning a period of five years (2015 – 2019). Panel regression models were considered in assessing the implication of the variables under study. The findings reveal that NDI has a significant P-value which signifies that compliance with NESREA Act increases environmental disclosure by 2.9%. ROA also exerts a significant impact on environmental disclosure. This implies that a 1% increase in the profitability of the sample companies will increase environmental disclosure by 1.4%. Firm Size is also positive and exerts significant impact, by implication, the result suggested that an increase in the total revenue will lead to about 9% increase in environmental disclosure. Hence the study recommends among others that measuring, treatment, disclosure, and reporting of environmental activities need to be standardized and mandated to give a true and fair view of environmental management practices. These will not only protect the environment but will also enhance the firm's competitiveness and subsequently lead to high corporate performance.

On Pooled OLS and Panel Regression Models for Assessing the Contributions of Electronic Payment System on Commercial Banks Profitability

This study examined the impact of electronic payment system on the profitability of commercial banks in Nigeria. Pooled OLS and Panel regression models were fitted on the data extracted from the banks’ annual reports, Nigerian interbank settlement scheme, and central bank of Nigeria website. The assessment of the contribution of the various electronic payment systems considered were measured using Breusch and Pagan Lagrangian Multiplier (LM) Test, the Hausman Test, Stationarity Test, The Schwarz Criterion, and the Akaike Information Criterion. Results obtained showed that the random effect model was more appropriate than the fixed effect model for all the electronic payment systems considered in this study. Furthermore, it was discovered that there exists a positive relationship between the electronic payment systems and profitability of the commercial banks in Nigeria.

Relationships between Renewable Energy and the Prevalence of Morbidity in the Countries of the European Union: A Panel Regression Approach

The main objective of the presented study was to examine the associations between the use of renewable energy sources in selected sectors (transport, electricity, heating, and cooling) and the prevalence of selected groups of diseases in the European Union, with an emphasis on the application of statistical methods considering the structure of data. The analyses included data on 27 countries of the European Union from 2010 to 2019 published in the Eurostat database and the Global Burden of Disease Study. Panel regression models (pooling model, fixed (within) effects model, random effects model) were primarily used in analytical procedures, in which a panel variable was represented by countries. In most cases, positive and significant associations between the use of renewable energy sources and the prevalence of diseases were confirmed. The results of panel regression models could be generally interpreted as meaning that renewable energy sources are associated with the prevalence of diseases such as cardiovascular diseases, diabetes and kidney diseases, digestive diseases, musculoskeletal disorders, neoplasms, sense organ diseases, and skin and subcutaneous diseases at a significance level (α) of 0.05 and lower. These findings could be explained by the awareness of the health problem and the response in the form of preference for renewable energy sources. Regarding statistical methods used for country data or for data with a specific structure, it is recommended to use the methods that take this structure into account. The absence of these methods could lead to misleading conclusions.

Changes in Unemployment Affect Sickness Absence and Disability Retirement Rates: A Municipality-Level Panel Study

To enhance understanding of the interplay between unemployment and sickness absence and disability retirement, the aim of this study was to examine how changes in area-level unemployment rates are associated with changes in sickness absence and disability retirement rates in a longitudinal setting. Municipality-level time-series data were collected on unemployment, sickness absence, disability retirement and covariates from databases for Finnish municipalities for years 2003–2017 (n = 4425 municipality–year observations). Fixed effects panel regression models were used to analyse how changes in unemployment rates predict changes in sickness absence and disability retirement rates when comparing consecutive years. The results showed that when examining yearly cross-sections, a higher level of unemployment in the municipality was associated with higher sickness absence and disability retirement rates. However, longitudinal assessment of consecutive years with panel regression models showed that a one percentage point increase in the municipality-level unemployment rate was associated with a decrease both in the sickness absence rate (−1.3%, p < 0.001) and in the disability retirement rate (−2.1%, p = 0.011), adjusted for simultaneous changes in demographic and socio-economic covariates, morbidity and economic situation of the municipality. The results indicate that unemployment and disability benefits partly act as substitutes for each other. Unemployment and disability rates should be assessed together to reach a more complete understanding of the level of non-employment overall and in different areas.

Financial Sustainability of Microfinance Institutions at Growth Stage: Do Lending Terms Matter?

The financial sustainability of microfinance institutions (mfi s) is crucial if their benefits are to be enjoyed in the long run. This study investigated the determinants of mfi s’ financial sustainability at growth stage. The study aimed to address the following questions: are factors influencing financial sustainability at maturity equally important at growth stage? What influence do lending terms have on financial sustainability at growth stage? The study used panel regression models and four-year survey data from 106 rural mfi s in Tanzania. Decomposition of lending types was adopted to unveil the contribution of lending terms to financial sustainability. We found that most factors influencing financial sustainability at maturity stage are equally important in influencing sustainability at growth stage. In addition, two factors appear to affect financial sustainability at growth stage only. Moreover, lending terms matter in determining financial sustainability at growth stage. The study provides insights on how lending terms can be used to influence financial sustainability at growth stage.

Does a Company’s Profitability Influence the Level of CSR Development?

The objective of this paper is to analyze the effect of economic and financial performance on Corporate Social Responsibility (CSR). For this reason, we have used the data from a sample made up of 662 companies, 146 registered as medium-sized or large and 516 as small or micro, highlighting the significant weight of small companies in the sample. CSR has been measured using an indicator estimated from the data gathered by way of a questionnaire containing information related with the economic, environmental, and social dimensions. The analysis has been conducted by estimating panel regression models with robust errors. The results show a negative relationship between economic performance and more CSR activities implemented, supporting the Managerial Opportunism Hypothesis. Furthermore, large companies under the pressure of stakeholders are more prone to implementing certain CSR actions than small ones, meaning that a minimum size is essential according to this research.

Influence of tree cover and urban area on streamflow in the United States using multiple statistical attribution techniques

&lt;p&gt;There is still limited quantitative understanding of the effects of tree cover and urbanisation on streamflow at large scales, making it difficult to generalize these relationships. We use the globally consistent European Space Agency (ESA) Climate Change Initiative (CCI) Global Land Cover dataset to estimate the relationships between streamflow, calculated as high (Q0.99), median (Q0.50), and low (Q0.01) flow quantiles, and urbanization or tree cover changes in 2865 catchments between the years 1992 through 2018. We apply three statistical modelling approaches and examine the consistencies and inconsistencies between them. First, we use distributional regression models -- generalized additive models for location, scale, and shape (GAMLSS) -- at each site and assess goodness-of-fit. Model fits suggested a strong association between land cover, especially urban area, and low and median flows at sites with statistically significant trends in streamflow. We then examine the sign of the distributional regression model coefficients to determine whether the inclusion of a land cover variable in the regression models results in a relative increase or decrease in flow, regardless of the overall direction of trends in streamflow. Finally, we use fixed effects panel regression models to estimate the average effect across all sites. Panel regression results suggested that a 1% increase in urban area corresponds to between a &lt; 1% and 2.1% increase in streamflow for all quantiles. Results for the tree cover panel regression models were not significant. We highlight the value of statistical approaches for large-sample attribution of hydrological change, while cautioning that considerable variability exists across catchments and modelling approaches.&lt;/p&gt;

Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015

This study statistically identified the association of remotely sensed environmental factors, such as Land Surface Temperature (LST), Night Time Light (NTL), rainfall, the Normalised Difference Vegetation Index (NDVI) and elevation with the incidence of leptospirosis in Thailand based on the nationwide 7,495 confirmed cases reported during 2013–2015. This work also established prediction models based on empirical findings. Panel regression models with random-effect and fixed-effect specifications were used to investigate the association between the remotely sensed environmental factors and the leptospirosis incidence. The Local Indicators of Spatial Association (LISA) statistics were also applied to detect the spatial patterns of leptospirosis and similar results were found (the R2 values of the random-effect and fixed-effect models were 0.3686 and 0.3684, respectively). The outcome thus indicates that remotely sensed environmental factors possess statistically significant contribution in predicting this disease. The highest association in 3 years was observed in LST (random- effect coefficient = -9.787, p<0.001; fixed-effect coefficient = -10.340, p = 0.005) followed by rainfall (random-effect coefficient = 1.353, p <0.001; fixed-effect coefficient = 1.347, p <0.001) and NTL density (random-effect coefficient = -0.569, p = 0.004; fixed-effect coefficient = -0.564, p = 0.001). All results obtained from the bivariate LISA statistics indicated the localised associations between remotely sensed environmental factors and the incidence of leptospirosis. Particularly, LISA’s results showed that the border provinces in the northeast, the northern and the southern regions displayed clusters of high leptospirosis incidence. All obtained outcomes thus show that remotely sensed environmental factors can be applied to panel regression models for incidence prediction, and these indicators can also identify the spatial concentration of leptospirosis in Thailand.

Do larger boards improve shareholder value creation? – Effects of the board size on business performance in Eastern Central Europe

The aim of this study is to analyse the impact of board size on a firms' operational and market performance at the largest East Central European listed non-financial, non-public utility firms. The literature debates the effects of the size of the board. While the resource dependency theory supports a positive effect, the agency theory supports a negative impact on firm value. This question is rarely investigated in two-tiered corporate governance models. This paper estimates the effects of management board and supervisory board size, between 2007 and 2016. The results indicate that the effect of management board size depends heavily on the size of the observed company. In both fixed effects and GMM-type dynamic panel regression models, using Tobin's Q, market-to-book ratio, total shareholder value and ROA as firm performance measures, increase in management board size has a significant positive impact on firm performance; however, in the case of larger firms, the effect is significantly negative. Moreover, the increase in the ratio of outside directors has a positive impact on the firm's performance in all dynamic panel regression models and this effect is even more significant in Tobin's Q and market-to-book ratio models. This can indicate the effective monitoring role of the supervisory board.