scholarly journals Effect of inventory management on profitability: evidence from the Polish food industry: Case study

2020 ◽  
Vol 66 (No. 5) ◽  
pp. 234-242
Author(s):  
Zbigniew Gołaś
Keyword(s):  
Panel Data ◽  
Financial Performance ◽  
Inventory Management ◽  
Regression Models ◽  
Food Industry ◽  
Panel Regression ◽  
Management Efficiency ◽  
Causative Link ◽  
Panel Regression Models

The main purpose of this study is to verify the causative link between inventory performance and profitability of food companies. This was done using the panel data methodology at the level of Polish food industry sub-sectors. The study takes account of the inventory mix, which includes the stocks of raw and other materials, work-in-progress, finished products and commodities. As shown by the analysis, the 2005–2017 period witnessed a decline in the share of inventories in total assets and in current assets. That trend was accompanied by an improvement in inventory management efficiency. The study also found that the days sales of inventory for total stocks clearly tends to become shorter due to a reduction in the days in inventory ratio for materials and finished products. Based on panel regression models, this study demonstrated that an improvement in inventory management efficiency is positively correlated with financial performance, measured as the return on operating assets.

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 Modeling the consequences of the transition to parenthood: Applications of panel regression methods

2019 ◽  
Vol 36 (11-12) ◽  
pp. 4005-4026 ◽  
Author(s):  
Francisco Perales
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Regression Models ◽  
Transition To Parenthood ◽  
National Sample ◽  
Panel Regression ◽  
Cross Sectional ◽  
Time Dynamics ◽  
Advantages And Disadvantages ◽  
Panel Regression Models

The transition to parenthood is a topic of substantial interest to family researchers across the social sciences, and many theoretical paradigms have been invoked to understand how it affects men’s and women’s lives. While early empirical scholarship on the transition to parenthood relied on cross-sectional data and methods, the increasing availability of panel data has opened up new analytical pathways—including the possibility to track the same individuals over time as they approach and experience parenthood and their children grow older. By making full use of longitudinal data, researchers can both improve estimation of the consequences of parenthood, as well as advance knowledge by testing more nuanced and complex theoretical premises involving time dynamics. In this article, I present an overview of panel regression models, a family of specifications that can be leveraged for these purposes. In doing so, I discuss the data requirements, advantages and disadvantages of different models, pointing to useful examples of published research. The approaches considered include random effects and fixed effects panel regression models, specifications to model linear and nonlinear time dynamics, and specifications to handle dyadic data structures. The use of these techniques is exemplified via an application considering the effect of motherhood on time pressure using long-running panel data from an Australian national sample, the Household, Income and Labour Dynamics in Australia Survey ( n = 68,911 observations; 10,734 women).

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>

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