scholarly journals Financial development and governance: A panel data analysis incorporating cross-sectional dependence

2021 ◽  
pp. 100855
Author(s):  
Usman Khalid ◽  
Muhammad Shafiullah
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Financial Development ◽  
Panel Data Analysis ◽  
Cross Sectional ◽  
Cross Sectional Dependence

scholarly journals Cross-Sectional Dependence in Panel Data Analysis

2011 ◽  
Vol 31 (5) ◽  
pp. 483-531 ◽  
Author(s):  
Vasilis Sarafidis ◽  
Tom Wansbeek
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Cross Sectional ◽  
Cross Sectional Dependence

Credit Market Depth and Income Inequality in Vietnam: A Panel-Data Analysis

2016 ◽  
pp. 5-18
Author(s):  
Hoi Le Quoc ◽  
Hoi Chu Minh
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Income Inequality ◽  
Financial Development ◽  
Inflection Point ◽  
Panel Data Analysis ◽  
Credit Market ◽  
Method Of Moment ◽  
Market Depth ◽  
Generalized Method Of Moment

Financial development could exert various effects on income distribution of a country. By employing Generalized Method of Moment, this paper aims at examining the impacts of credit market depth, one of most used financial development barometers, on income inequality in Vietnam. The empirical findings show that expanding credit market in the country could lead to higher income inequality. We have not found evidence that supports the hypothesis of an inverted U-shaped relation ever introduced by Greenwood and Jovanovich, although this hypothesis may still hold in a sense that Vietnam has not reached to the inflection point to generate such a curve alike.

An Examination of Audit Report Lag for Banks: A Panel Data Approach

2000 ◽  
Vol 19 (2) ◽  
pp. 159-174 ◽  
Author(s):  
B. Charlene Henderson ◽  
Steven E. Kaplan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Explanatory Power ◽  
Omitted Variables ◽  
Audit Report ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
The Cross ◽  
Sectional Analysis

This study investigates the determinants of audit report lag (ARL) for a sample of banks. Researchers have been interested in the determinants of ARL, in part, because it impacts the timeliness of public disclosures. However, prior ARL research has relied exclusively on regression analysis of cross-sectional samples of companies from many industries. In addition to focusing exclusively on banks, panel data analysis is introduced and compared with cross-sectional analysis to demonstrate its power in dynamic settings and its potential to improve estimation. Results reveal important differences between cross-sectional analysis and panel data analysis. First, bank size is negatively related to ARL in cross-section but positively related to ARL using panel data analysis. The cross-sectional size estimate is subject to omitted variables bias, and furthermore, cross-sectional analysis fails to capture variation in size over time in relation to ARL. Panel data analysis both accounts for omitted variables and captures the dynamics of the relationship between size and ARL. As well, the panel data model's explanatory power far exceeds that of the cross-sectional model. This is primarily due to the panel model's use of firm-specific intercepts that both capture the role of reporting tradition and eliminate heterogeneity bias. Thus, panel data analysis proves to be a powerful tool in the analysis of ARL.

Financial development and self-employment

2020 ◽  
Vol 19 (3) ◽  
pp. 339-357
Author(s):  
Papar Kananurak ◽  
Aeggarchat Sirisankanan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Financial Development ◽  
Panel Data Analysis ◽  
Statistical Significance ◽  
Random Effect Model ◽  
Direct Examination ◽  
Content Type ◽  
Self Employment ◽  
The Impact

Purpose There are several different factors that can influence self-employment. However, there is little evidence stemming from direct examination of the impact of financial development (FD) on self-employment. This study aims to formulate empirical specification models to examine the effect of FD on self-employment. Design/methodology/approach Panel data analysis of 136 sample countries was performed during the period from 2000 to 2017. This study initially implemented the new financial index developed by the International Monetary Fund (IMF) to examine the impact of FD on self-employment. Panel data analysis including the pooled model, fixed effect and random effect model has been carried out. Findings The empirical results show that the financial institutions index has a negative significant impact on self-employment by a considerable magnitude, whereas the financial markets index does not show any statistical significance. The results also find that the government effectiveness index is negative and statistically significant on self-employment. Originality/value There are several different factors which can influence self-employment. Nevertheless, there is little evidence for the direct examination of the impact of FD on self-employment. This study investigated the impact of FD on self-employment by using the new FD index created by the IMF. The finding may help policymakers to implement FD along with other institutional policies to control self-employment.

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