Economic Growth with Public Finance: A Theoretical Model for Equilibrium Dynamics and Analytic Solution| Chapter 9 | Contemporary Research in Business, Management and Economics Vol. 9

 

This work analyses the equilibrium dynamics of a growth model with public finance where two different allocations of public resources are considered, namely "institutional" spending and “traditional” core productive spending. Both components of government expenditure are complementary with private production. The model we propose simultaneously determines the optimal proportion of consumption, capital accumulation, taxation and composition of the two different public expenditures which maximize a representative household's lifetime utility in a centralized economy. The model supplies a closed-form solution. Moreover, with one restriction on the parameters (a = s) we fully determine the solution path for all variables included in the analysis and establish the conditions for balanced growth.

 

 

Author(s) Details

 

Oliviero A. Carboni

DiSEA and CRENoS, University of Sassari, Italy.

Paolo Russu

DiSEA, University of Sassari, Italy.

 

 

Please see the link:- https://doi.org/10.9734/bpi/crbme/v9/501

Economic Growth with Public Finance: A Theoretical Model for Equilibrium Dynamics and Analytic Solution | Chapter 9 | Contemporary Research in Business, Management and Economics Vol. 9

This work analyses the equilibrium dynamics of a growth model with public finance where two different allocations of public resources are considered, namely "institutional" spending and “traditional” core productive spending. Both components of government expenditure are complementary with private production. The model we propose simultaneously determines the optimal proportion of consumption, capital accumulation, taxation and composition of the two different public expenditures which maximize a representative household's lifetime utility in a centralized economy. The model supplies a closed-form solution. Moreover, with one restriction on the parameters ( = ) we fully determine the solution path for all variables included in the analysis and establish the conditions for balanced growth.

 

Author (s) Details

 

Oliviero A. Carboni (Associate Professor),
DiSEA and CRENoS, University of Sassari, Italy.

 

Paolo Russu (Senior Professor),
DiSEA, University of Sassari, Italy.

 

Please see the book here:- https://doi.org/10.9734/bpi/crbme/v9/501

An Overview of Statistical Techniques for Analysis of Data in Agricultural Research | Chapter 11 | Emerging Issues in Agricultural Sciences Vol. 8

 The significance of statistical analysis is paramount in research, particularly in situations involving the collection, classification, analysis, and interpretation of numerical data. Statistical principles find widespread application in various types of experimental studies and serve as a critical component in agricultural research endeavors. The inherent variability present in commonly used experimental materials within agricultural research necessitates the utilization of statistical methods, leading to numerous advancements and innovations in the field of statistics. The selection of an appropriate tool for data analysis and subsequent processes involving statistical components has become a matter of concern. This chapter delves into the diverse statistical techniques essential for the analysis of agricultural data and the derivation of valid conclusions.

Author(s) Details:

Rahul Banerjee,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Bharti,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Pankaj Das,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Varun Srivastava,
Department of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, India.

Ankita,
Department of Agricultural Statistic and Computer Application, Birsa Agricultural University, Kanke, Ranchi, India.

Suraj Kataria,
Department of Anthropology, University of Delhi, Delhi, India.

Bulbul Ahmed,
Department of Agriculture, Galgotias University, Greater Noida, India.

Nitin Varshney,
Department of Agricultural Statistics, Navsari Agricultural University, Navsari, India.

Please see the link here: https://stm.bookpi.org/EIAS-V8/article/view/12877

Stochastics Growth Model | Chapter 1 | Theory and Practice of Mathematics and Computer Science Vol. 10

 The goal is to create a mathematical model that takes into account genetic defects while evaluating the development rate of roan antelopes in Kenya's Ruma National Park.

Methodology: To estimate the population growth rate of roans, this work modified Oksendal and Lungu's stochastic logistic model by integrating genetic defects not included by Magin and Cock. Vortex version 9.99, a computer simulation programme used to mimic the extinction process, was adjusted appropriately.

Inbreeding has a high-level impact on population expansion (survival) in tiny communities, according to the findings. Juvenile and adult roans were supplemented to promote population survival for a longer period of time.

Conclusion: To combat inbreeding, which is a serious concern to tiny populations, this paper proposes supplements rather than predator control because of the unknown implications to the environment and conflicts with wildlife management plans in protected areas. Supplementation should be done in stages so that social groupings are not disrupted. Genetic research should be conducted to determine the extent to which inbreeding influences population increase, according to this study.

Author(s) Details

Daniel Ochieng Achola
Department of Mathematics, Kabarak University, Private Bag-20157, Nakuru, Kenya.

View Book :- https://stm.bookpi.org/TPMCS-V10/article/view/1071