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

Afrika Focus ◽  
2021 ◽  
Vol 34 (1) ◽  
pp. 50-74
Author(s):  
Ganka Daniel Nyamsogoro
Keyword(s):  
Regression Models ◽  
Growth Stage ◽  
Financial Sustainability ◽  
Maturity Stage ◽  
Panel Regression ◽  
Microfinance Institutions ◽  
Factors Influencing ◽  
Long Run ◽  
Two Factors ◽  
Panel Regression Models

Abstract 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.

scholarly journals IMPACT OF STOCK MANAGEMENT ON THE PROFITABILITY OF MILK PROCESSING COMPANIES

2019 ◽  
Vol XXI (3) ◽  
pp. 82-92
Author(s):  
Zbigniew Gołaś
Keyword(s):  
Regression Models ◽  
Food Industry ◽  
Panel Regression ◽  
Stock Management ◽  
Micro Data ◽  
Return On Assets ◽  
Milk Processing ◽  
Management Efficiency ◽  
Long Run ◽  
Panel Regression Models

This paper analyzes the relationships between the productivity of stocks and the return on assets of milk processing companies. Productivity of stocks was measured as the Days Sales of Inventory (DSI) for materials, intermediate products, work-in-progress, finished products and commodities, and as the DSI for total stocks. The study was based on corporate micro-data from 2007–2016 retrieved from the EMIS database. Based on panel regression models, it was concluded that an improvement in stock management efficiency, measured with the Days Sales of Inventory, is positively correlated to the return on assets of milk processing companies. Although it was considerably shorter in the milk sector than in the entire food industry throughout the study period, it grew longer each year. This evolution means a deterioration in the efficiency of stock management of finished products which, in the long run, may adversely affect the financial performance of milk producers. The parameters of the estimated regression models clearly confirm it is reasonable to reduce DSIs.

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

2021 ◽  
Vol 18 (12) ◽  
pp. 6548
Author(s):  
Robert Stefko ◽  
Beata Gavurova ◽  
Miroslav Kelemen ◽  
Martin Rigelsky ◽  
Viera Ivankova
Keyword(s):  
European Union ◽  
Renewable Energy ◽  
Statistical Methods ◽  
Regression Models ◽  
Kidney Diseases ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Panel Regression ◽  
The European Union ◽  
Panel Regression Models

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.

scholarly journals Does ESG Disclosure Create Value to Firms? The Malaysian Case

2018 ◽  
pp. 515-521
Keyword(s):  
Social Norms ◽  
Regression Models ◽  
Financial Data ◽  
Panel Regression ◽  
Tobin’S Q ◽  
Publicly Traded ◽  
Tobin's Q ◽  
Publicly Traded Firms ◽  
Governance Disclosure ◽  
Panel Regression Models

We examine whether ESG (Environmental, Social and Governance) disclosure creates value to Malaysian firms. Based on the dataset of 37 Malaysian publicly traded firms, our results obtained from various panel regression models show that the overall ESG disclosure score and its environmental and governance pillars are positively associated with Tobin’s Q. This implies that Malaysian firms which act in accordance to social norms will be rewarded by the market. The outcomes of this research highlight the importance of non-financial data disclosure in Malaysian market.

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

2021 ◽  
Author(s):  
Bailey Anderson ◽  
Louise Slater ◽  
Simon Dadson ◽  
Annalise Blum
Keyword(s):  
Land Cover ◽  
Urban Area ◽  
Regression Models ◽  
The United States ◽  
Additive Models ◽  
Tree Cover ◽  
Panel Regression ◽  
Distributional Regression ◽  
Panel Regression Models ◽  
Modelling Approaches

<p>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 < 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.</p>

League Ranking Mobility Affects Attendance: Evidence From European Soccer Leagues

2020 ◽  
Vol 21 (8) ◽  
pp. 808-828
Author(s):  
András Gyimesi
Keyword(s):  
Regression Models ◽  
Panel Regression ◽  
Competitive Balance ◽  
Uncertainty Of Outcome ◽  
Panel Regression Models

Ranking mobility belongs to the indicators of dynamic long-term competitive balance, as it is based on season to season changes in league rankings. If the uncertainty of outcome hypothesis holds at the league level, ranking mobility might increase demand for league games. This assumption is tested by using panel regression models on data of 19 European domestic soccer leagues. Ranking mobility is found to significantly affect average stadium attendance per game, particularly if only the top 5 ranking positions are considered. Results suggest that the closeness of competition across seasons is more important for the fans than within a season.

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