Korruption und Schattenwirtschaft: die Bedeutung guter Regeln

Rudolph, Alexandra / Axel Dreher
Externe Publikationen (2011)

in: Theresia Theurl (Hrsg.), Gute Regeln oder Wirtschaftslenkung?: Europas neue Herausforderungen (Schriften des Vereins für Socialpolitik 333), Berlin: Duncker & Humbolt (online only)

The paper discusses various possible ways to empirically investigate the relation between corruption, other aspects of institutional quality and the size of the shadow economy. Corruption is measured by using perceptions-based indices. The most widely spread measure of the size of the shadow economy is based on the structural equation approach and exists for many countries and a number of years. Using such indices Dreher et al. (2009) find a negative effect of corruption on the size of the shadow economy. This is in line with their model, showing that the existence of the shadow economy can prevent corrupt bureaucrats to extract bribes (reducing corruption). Rather than using existing indices, the degree of corruption and the size of the shadow economy can both be estimated as latent variables in a structural equation model. This can be done separately as in Dreher/ Schneider (2010), where the authors use data for corruption from Dreher et al. (2007) and data for the shadow economy from Schneider (2005a), both derived using a (DY)MIMIC model. The results show that corruption and the shadow economy are complements in poor countries, but that there is no robust relationship between them in rich countries. Corruption and the shadow economy can also be analyzed as latent variables in the same model. Using such a model, Dreher et al. (2011) find that corruption reduces the size of the shadow economy (in a sample of 18 OECD countries). Finally, Dreher et al. (2010) find that the impact of corruption on per capita GDP is mitigated once the size of the shadow economy is taken account of, while its impact on total factor productivity even becomes completely insignificant.

Über die Autorin

Rudolph, Alexandra

Ökonomin

Rudolph

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