Good for the goose, bad for the gander? Corruption and income inequality.
We examine the relationship between corruption and income inequality across countries. While previous studies have explored this association at both an international and within‐country level, we expand on this literature in two distinct ways. First, along with the most commonly utilized measure of i...
| Publicado en: | Southern Economic Journal Vol. 91; no. 3; pp. 850 - 881 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Jan2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=183867436&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 183867436 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00384038 SEJ jtl: Southern Economic Journal issn: 00384038 maglogo: N pubinfo: dt: Jan2025 vid: 91 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 183867436 10.1002/soej.12733 ppf: 850 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 3.2MB tig: atl: Good for the goose, bad for the gander? Corruption and income inequality. aug: au: Pavlik, Jamie Bologna Callais, Justin T. affil: Department of Agricultural and Applied Economics, Texas Tech University, Lubbock Texas,, USA Department of Economics and Finance, University of Louisiana at Lafayette, Lafayette Louisiana,, USA su: Corruption Income inequality Informal sector Income distribution Elite (Social sciences) Gini coefficient Multiple comparisons (Statistics) Per capita sug: subj: Corruption Income inequality Informal sector Income distribution Elite (Social sciences) Gini coefficient Multiple comparisons (Statistics) Per capita keyword: causal inference corruption income deciles inequality matching methods causal inference corruption income deciles inequality matching methods ab: We examine the relationship between corruption and income inequality across countries. While previous studies have explored this association at both an international and within‐country level, we expand on this literature in two distinct ways. First, along with the most commonly utilized measure of inequality (Gini coefficients), we also examine income per‐capita at each decile, along with top 1% and 5%, and the associated income shares. Second, we employ an empirical strategy that differs from the existing literature. Our primary results are estimated using matching methods, but we also supplement these results with a "doubly robust" difference‐in‐difference design. We find that a reduction in corruption increases incomes of the top 80% but does not significantly impact incomes of the bottom 20%, or the top 1% and 5%. We find some evidence of income growth amongst the top 1% and 5% following increases in corruption, but these results are inconsistent across estimations. Our results also suggest that accounting for the size of the informal sector matters a great deal in understanding the relationship between corruption and the distribution of income. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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