What drives the hidden side of Uzbekistan’s shadow economy?

M. Ishaq Bhatti, Nicolas Hamelin and Temurbek Saidov

Post-Communist Economies, 2024, vol. 36, issue 3, 281-297

Abstract: This study examines factors influencing Uzbekistan’s shadow economy rate, with a focus on GDP, corruption index, tax rate (TR), and the unemployment rate. Using a quantitative approach, we analyse secondary data to test our research hypotheses. The results indicate positive correlations between GDP and corruption index with the shadow economy rate, while tax and unemployment rates exhibit negative correlations. An expansionary fiscal policy contributes to the positive GDP-shadow economy correlation, while corruption motivates participation in the informal sector. Conversely, changes in tax law discourage formal sector involvement, leading to a negative correlation. Similarly, the unemployment rate negatively correlates due to partial income reporting for tax evasion. Our study provides original insights into the drivers of Uzbekistan’s shadow economy, which can inform policymakers when addressing this issue. Paper emphasises the impact of fiscal policy, corruption, taxation, and unemployment on shadow economy growth, aiding informed policy decisions.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14631377.2023.2287757 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:pocoec:v:36:y:2024:i:3:p:281-297

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CPCE20

DOI: 10.1080/14631377.2023.2287757

Access Statistics for this article

Post-Communist Economies is currently edited by Roger Clarke

More articles in Post-Communist Economies from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().