Nexus Between Good Governance and Economic Growth: Econometric Evidence from India| Chapter 5 | New Advances in Business, Management and Economics Vol. 11

 

The concept of good governance refers to the way authority is exercised to manage a country’s economic, social and political resources. It highlights the importance of dynamic interactions between the state, civil society and citizens in processes of decision-making, implementation and accountability. When governments prioritise key sectors such as education, health and social protection, the outcomes directly enhance individual well-being and foster inclusive growth. This study investigates whether good governance helps drive economic growth in India, using annual data from 1996 to 2022. Economic growth is measured by per capita GDP, while governance is captured through three key indicators: control of corruption, political stability and voice and accountability. To get a clearer picture, other important factors such as the Human Development Index (HDI), government consumption, trade openness, foreign direct investment (FDI) and gross fixed capital formation (GFCF) are also included.

 

The analysis applies the Autoregressive Distributed Lag (ARDL) bounds testing approach, which is well-suited for exploring both short-run and long-run relationships, when variables are integrated of mixed order. The results confirm that good governance plays a significant role in supporting India’s long-term economic growth. Specifically, stronger control of corruption, political stability and voice and accountability are all linked with higher economic growth. Among the control variables, human development, government consumption and trade openness also show a positive impact on economic growth. On the other hand, FDI is found to have a negative but statistically insignificant impact, while GFCF has a negative but significant impact, suggesting that capital investments in India may not be translating efficiently into economic growth, possibly due to institutional or structural weaknesses.

 

Overall, the findings highlight that improving governance and investing in human development are crucial for India’s sustained progress. Policymakers should therefore focus on strengthening institutions, ensuring accountability and enhancing the quality of governance systems so that investments and reforms can produce stronger and more inclusive economic outcomes.

 

 

Author(s) Details

Ayushi Vashistha
Department of Economics, IIS (Deemed to be University), Jaipur, India.

 

C.R. Bishnoi
Department of Economics, IIS (Deemed to be University), Jaipur, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/nabme/v11/6497

Modeling the Cointegration Dynamics between COVID-19 Infection Rates, Death Rates, and the Population in the US | Chapter 7 | Science and Technology - Recent Updates and Future Prospects Vol. 4

This chapter is an attempt to examine the short and long-run relationships between the total population, the cumulative number of new COVID-19-infected cases, and the cumulative number of deaths due to COVID-19 in different states in the US. The bounds testing approach to cointegration and error correction models, developed within an autoregressive distributed lag (ARDL) framework is applied to the cumulative number of COVID-19 infections and deaths as of October 25, 2023, starting on February 15, 2020. The ARDL (1,4,3) model is found suitable for investigating the short-run relationships between the study variables. The model is highly significant, and the value of the coefficient of determination is R2 = 96%. The result of the bounds test indicates that there is a stable long-run relationship between these variables. In addition, the CUSUM and CUSUMSQ tests confirm the stability of the model parameters.

 

Author(s)details:-

 

Dr. Rajarathinam Arunachalam
Department of Statistics, Manonmaniam Sundaranar University, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/strufp/v4/596

A Study on Dynamic Relationships between the Area and Production of Food Grain Crops Based on ARDL Model and Bound Cointegration Test | Chapter 3 | An Overview on Business, Management and Economics Research Vol. 4

 The autoregressive delivered lag model (ARDL), which handles complex relationships and strong autocorrelation in agricultural production dossier, is employed in the current study. Mapping and listening tools are imperative in determining agricultural orders and guiding future decision-making to safeguard feed security. This chapter shows that, from 1950 to 2018, skilled was a substantial enduring association between the region and output of bread grain crops cultivated in India. The balance of the model parameters are examined. The cumulative sum of circular residuals test and the accruing sum of recursive leftover squares tests are employed to evaluate the constancy of the model parameters.  Cointegration equatings such as the fully reduced ordinary smallest squares (FMOLS), dynamic ordinary slightest squares (DOLS), and canonical cointegration reversion (CCR) are applied to check the long-run elasticities in the concerned connection.

Author(s) Details:

Rajarathinam A.
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli – 267 012, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/AOBMER-V4/article/view/12236

Development of an Autoregressive Distributed Lag Model of COVID-19 Infected Cases and Deaths| Chapter 3 | Research Highlights in Mathematics and Computer Science Vol. 3

 The plurality of research used various regression and period-series models to analyse COVID-19 cases cause these models are widely used to analyse the progress or development of afflictions. The main goals of the current study search out examine the short- and unending cointegration relationships between the accruing number of new COVID-19 infections (X) and the accruing number of COVID-19-related grave (Y), to examine the long-run balance relationship middle from two points these variables using an autoregressive delivered lag model and bounds cointegration tests, and to check the stability of the supposed model.  In order to evaluate the thickness of the model parameters, two tests-the accruing sum of circular residuals test and the accruing sum of recurrent residuals squares test-are utilised.

Author(s) Details:

Rajarathinam Arunachalam,
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli–627 012, Tamil Nadu State, India.

Tamilselvan Pakkirisamy,
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli–627 012, Tamil Nadu State, India.

Please see the link here: https://stm.bookpi.org/RHMCS-V3/article/view/8875