2018
DOI: 10.3390/ijfs6040082
|View full text |Cite
|
Sign up to set email alerts
|

Does Credit Composition have Asymmetric Effects on Income Inequality? New Evidence from Panel Data

Abstract: This paper studied the effects of credit to private non-financial sectors on income inequality. In particular, we focused on the distinction between household and firm credits, and investigated whether these two types of credit had adverse effects on income inequality. Employing cross-section augmented cointegrating regressions and using balanced panel data for 30 developed and developing countries over the period from 1995 to 2013, we showed that firm credit reduced income inequality, whereas there was no sig… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
3
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
7
1

Relationship

0
8

Authors

Journals

citations
Cited by 9 publications
(4 citation statements)
references
References 46 publications
1
3
0
Order By: Relevance
“…Particularly, slope heterogeneity test, LM bootstrap test of Westerlund and Edgerton (2007), Pesaran (2004) CIPS unit root test, Pedroni (2001) cointegration test, Westerlund's (2008) cointegration test, robustness checks of D-CCEMG estimator, FMOLS, and DOLS were critically utilized. The result justify that the individual Northern African countries have similar economic dynamics, which is in line with the studies by Seven et al (2018), andCavatorta andDurac (2013) and consistent with the authors` expectations. After detecting cross-sectional dependence in the estimated models, the next step is to check for the slope homogeneity test, as is emphasized by the study of Blackburne and Frank (2007).…”
Section: Empirical Findingsupporting
confidence: 92%
“…Particularly, slope heterogeneity test, LM bootstrap test of Westerlund and Edgerton (2007), Pesaran (2004) CIPS unit root test, Pedroni (2001) cointegration test, Westerlund's (2008) cointegration test, robustness checks of D-CCEMG estimator, FMOLS, and DOLS were critically utilized. The result justify that the individual Northern African countries have similar economic dynamics, which is in line with the studies by Seven et al (2018), andCavatorta andDurac (2013) and consistent with the authors` expectations. After detecting cross-sectional dependence in the estimated models, the next step is to check for the slope homogeneity test, as is emphasized by the study of Blackburne and Frank (2007).…”
Section: Empirical Findingsupporting
confidence: 92%
“…∅ is the estimated coefficient of the CCEMG estimator. As rightly noted by Seven et al (2018), the CCEMG estimator provides consistent estimates even when there is correlation between the observed regressors and the common factors. In addition, it is robust to heterogeneity of slope parameters, endogeneity, cross-sectional dependence, non-stationarity and structural breaks.…”
Section: Panel Long-run Estimatorsmentioning
confidence: 77%
“…In the literature, it has been widely conceived that there is another variant to this technique known as the Common Correlated Effects Mean Group (CCEMG) developed by Pesaran [ 42 ] that can estimate series when there is a presence of correlation between the observed explanatory variables and the common factors [see [ 43 , 44 ]]. Despite this uniqueness, the superiority of the AMG framework over the CCEMG estimator can be seen in this regard, first, the former provides meaningful economic interpretation to the unobserved factors within the panel framework as compared to the latter which only produces nuisance parameters.…”
Section: Methodsmentioning
confidence: 99%