In this book linear panel data estimators are employed to investigate the relationship between life insurance and economic growth. This study contributes to previous studies by using Maximum likelihood estimation of dynamic panel that was not used in previous studies concerning the aforementioned relationship; by controlling for number of factors thought to influence economic growth; by referring to a much larger number of countries and by exploring the relationship between life insurance and economic growth while controlling for the degree of financial sector development, as well as for the regional and income disparities. Sixteen models that explore the impact of control variables integrated singly in the equations and an integrated model that controls for the impact of all key variables are estimated. Empirical results reveal a significant positive relationship between life insurance and economic growth in models -. Education is reported to have a positive impact on economic growth. Government spending is found to have a negative impact on economic growth, while model reports that inflation has a negative impact on economic growth. Trade openness is not reported to have a significant impact on economic growth in model. Model reveals a significant positive impact of banking sector on economic growth; significant negative impact of non-life insurance sector while stock market is not reported to have a significant impact. An integrated model that controls for the impact of all key variables gives a strong support to the results obtained in models -. Results of models - that attempt to control for the importance of regional disparities indicate that a significant positive relationship between life insurance and economic growth is reported for all regions but South Asia and North America. Models - that additionally control for differences in levels of development reveal a significant positive relationship between life insurance and economic growth in high-, middle- and low-income countries.
Table of Contents
1. INTRODUCTION
1.1. Description of the research
1.2. Research objectives
1.3. Research question and hypotheses
1.4. Research methodology
2. LINEAR PANEL DATA ESTIMATORS
2.1. Introduction to panel data
2.2. Advantages of panel data
2.3. Limitations of panel data
2.4. Sources of bias
2.4.1. Heterogeneity bias
2.4.2. Selectivity bias
2.4.3. Simultaneity bias
2.5. Static panel data model specification
2.5.1. Pooled regression
2.5.2. The fixed effects model
2.5.3. The random effects model
2.6. Linear static panel data estimators
2.6.1. Ordinary least squares estimator
2.6.2. Least squares dummy variables estimator
2.6.3. Generalized least squares estimator
2.7. Analysis of models estimated using linear static panel data estimators
2.7.1. Specification test
2.7.2. Violations of Gauss-Markov assumptions in the classical linear regression model
2.8. Linear dynamic panel data estimators
2.8.1. Arellano and Bond estimator
2.8.2. Arellano and Bover estimator
2.8.3. Maximum likelihood estimator
2.9. Analysis of models estimated using linear dynamic panel data estimators
3. THEORETICAL OVERVIEW OF THE RELATIONSHIP BETWEEN LIFE INSURANCE AND ECONOMIC GROWTH
3.1. Growth theory
3.2. Brief history of life insurance
3.3. Main characteristics of life insurance
3.4. Actuarial basis of life insurance
3.4.1. Determinants of life insurance development
3.4.2. Types of life insurance products in actuarial mathematics literature
3.4.3. Gross premium
3.4.4. Actuarial provision
3.5. Financial intermediaries and economic growth
3.5.1. Life insurance companies as financial intermediaries
3.5.2. Causality between life insurance activity and economic growth
3.5.3. Joint role of banks and insurers in contributing to economic growth
3.6. Overview of the empirical studies
3.7. Theoretical development of Solow-Swan model
3.7.1. Solow-Swan model
3.7.2. Revised Solow-Swan model
4. STATIC PANEL DATA ANALYSIS
4.1. Data and modeling exogenous relationship between life insurance activity and economic growth
4.1.1. Exogenous relationship between life insurance and economic growth
4.1.2. Variables
4.1.3. The specification of models
4.1.4. The sample selection
4.2. Simultaneity bias in modeling the relationship between life insurance activity and economic growth
4.3. Results of the research
4.3.1. Descriptive statistics
4.3.2. Basic model
4.3.3. First extended model
4.3.4. Second extended model
4.3.5. Third extended model
4.3.6. Fourth extended model
4.3.7. Fifth extended model
4.3.8. Integrated model
4.3.9. Models that test for the impact of regional disparities
4.3.10. Models that test for the impact of income disparities
5. DYNAMIC PANEL DATA ANALYSIS
5.1. Data and modeling endogenous relationship between life insurance activity and economic growth
5.1.1. Endogenous relationship between life insurance and economic growth
5.1.2. Variables
5.1.3. The specification of models
5.2. Results of the research
5.2.1. Basic, five extended and integrated models - Arellano-Bond estimator
5.2.2. Models that test for the impact of regional disparities - Arellano-Bond estimator
5.2.3. Models that test for the impact of income disparities - Arellano-Bond estimator
5.2.4. Models estimated using Arellano-Bover estimator
5.2.5. Models estimated using Moral-Benito framework
5.3. Discussion
6. CONCLUDING REMARKS
6.1. Summary of the most important empirical findings
6.2. Contribution to knowledge
6.3. Limitations and recommendations for future research
Research Objectives and Themes
The primary objective of this book is to model the relationship between life insurance and economic growth by addressing deficiencies in previous studies through the application of linear dynamic panel data estimators. The research focuses on identifying the most relevant estimators while rigorously controlling for endogeneity issues and incorporating additional economic determinants.
- Analysis of the relationship between life insurance activity and economic growth using dynamic panel data methods.
- Evaluation of financial development impacts including banking, stock markets, and non-life insurance activities.
- Application of rigorous robustness and sensitivity analyses to validate model stability.
- Exploration of regional and income-based disparities in life insurance-growth dynamics.
Excerpt from the Book
1.1. Description of the research
Significant positive impact of life insurance on economic growth is reported by (Webb et al., 2002; Arena, 2006; Ćurak et al., 2009; Avram, 2010; Azman and Smith, 2010; Ege and Bahadır, 2011; Chen et al., 2011; Hou et al., 2012; Verma and Bala, 2013; Cristea et al., 2014 and Dhiab and Jouili, 2015). However, Patrick (1966) and Beck and Webb (2003) provide robust evidence that the economic growth has significant positive impact on life insurance. Taking into account these results, endogeneity problem is expected to occur when analyzing the life insurance-economic growth relationship.
Since linear static panel data estimators do not provide consistent estimates if it is expected that changes in dependent may cause changes in independent variable (Arellano and Bond, 1991), in this book an attempt is made to analyze and identify the most relevant estimator for modeling the relationship between life insurance and economic growth that controls for endogeneity. Both types of estimators are used (linear static and dynamic) since linear dynamic panel data estimators lead to the decrease in the number of observations (number of observed years) because of calculating first differenced and lagged values of all variables analyzed. Linear static panel data estimators are proposed as well as appropriate tests to explore whether the endogeneity problem really exists and no matter what the results indicate on the potential reverse causality issue, they would be contrasted and discussed in great detail with those obtained using linear dynamic panel data estimators.
In addition, research aims to examine the mechanisms by which life insurance may contribute to economic growth. Life insurance refers to all the policies that come to payment of insured sum (benefit) in the case of termination or duration of life of one or more persons insured (Chen et al., 2011). Life insurance can be also defined as a contract between an insurance policyholder and an insurer where the insurer promises to pay a sum of money (the benefit) to designated beneficiary in exchange for a premium, upon the death of insured person (Kozarević, 2010).
Summary of Chapters
1. INTRODUCTION: Outlines the research context, highlighting the role of life insurance in economic growth and the necessity for robust econometric modeling to address endogeneity.
2. LINEAR PANEL DATA ESTIMATORS: Provides a theoretical and methodological foundation for panel data analysis, discussing static and dynamic estimators along with specification tests.
3. THEORETICAL OVERVIEW OF THE RELATIONSHIP BETWEEN LIFE INSURANCE AND ECONOMIC GROWTH: Explores growth theories, the history of life insurance, and how life insurers function as financial intermediaries.
4. STATIC PANEL DATA ANALYSIS: Details the empirical application of linear static panel data estimators to model the relationship, including data selection and tests for bias.
5. DYNAMIC PANEL DATA ANALYSIS: Presents the application of dynamic models to address endogenous relationships, comparing various GMM and MLE approaches to achieve consistent estimates.
6. CONCLUDING REMARKS: Synthesizes the main empirical findings, discusses the research's contribution to existing literature, and notes limitations with suggestions for future work.
Keywords
Life Insurance, Economic Growth, Panel Data, Dynamic Estimators, Simultaneity Bias, Endogeneity, Arellano-Bond, Arellano-Bover, Maximum Likelihood, Financial Intermediaries, Econometrics, Solow-Swan Model, Robustness Analysis, Financial Development
Frequently Asked Questions
What is the core focus of this research?
The research examines the relationship between life insurance activity and economic growth, specifically aiming to resolve causal ambiguities and estimation challenges found in prior studies.
What are the primary themes covered in this book?
The book covers growth theory, the economic role of financial intermediaries, econometric modeling of panel data (both static and dynamic), and specific analyses of regional and income disparities.
What is the primary goal of the study?
The goal is to determine the most relevant linear panel data estimator to model the life insurance-economic growth relationship while effectively controlling for the endogeneity problem.
Which scientific methods are utilized?
The research employs a quantitative approach using linear static panel data estimators, linear dynamic GMM estimators (Arellano-Bond, Arellano-Bover), and Maximum Likelihood Estimation (MLE) of dynamic panels.
What is discussed in the main body of the work?
The main body includes a thorough review of growth theories, a critical analysis of empirical literature, detailed specifications of static and dynamic panel models, and rigorous robustness checks of empirical results.
Which keywords characterize this work?
The study is characterized by keywords such as Life Insurance, Economic Growth, Dynamic Estimators, Endogeneity, and Financial Intermediation.
How does this book improve upon previous empirical studies?
It improves upon previous work by utilizing advanced dynamic panel data estimators and conducting exhaustive robustness and sensitivity analyses to address simultaneity bias, which was frequently overlooked in prior literature.
What is the significance of the Arellano-Bover estimator in this study?
The Arellano-Bover estimator is used as a more efficient GMM framework compared to the standard Arellano-Bond approach, particularly for addressing instrument weakness in dynamic models.
Why is the Maximum Likelihood Estimation (MLE) included as a framework?
MLE is utilized to address potential failures in GMM specification tests, offering a reliable alternative that avoids common issues such as the "incidental parameters" problem in certain panel settings.
- Citation du texte
- Elma Satrovic (Auteur), 2018, Merits of Life Insurance, Munich, GRIN Verlag, https://www.grin.com/document/439587
