How Does Social Capital Reduce the Size of the Shadow Economy?

Lee, Dongwon

doi:10.1080/1226508x.2013.833846

Cited by 16 publications

(17 citation statements)

References 31 publications

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“…In this paper, social capital is the primary explanatory variable. As suggested by Lee (2013), we use two measures of social capital, which are social trust and tax morale. Thus, model 1 is written into models 2 and 3 as follows:…”

Section: Methodology and Model Specification 31 Modelmentioning

confidence: 99%

“…Moreover, to investigate the research objective, we follow a principal-agent framework based on Aidt (2003), Bjørnskov (2010) and Lee (2013). Specifically, Aidt (2003) constructs an agency model of corruption for collecting taxes.…”

Section: Related Literature 21 Definition and Theoretical Considerationsmentioning

confidence: 99%

“…Likewise, Lee (2013) analyzes a sample of 65 countries between 1999 and 2007 to uncover the relationship between social capital and the shadow economy. The author posits that two important indicators of social capital are social trust and social norms (i.e.…”

Section: The Determinants Of the Shadow Economymentioning

confidence: 99%

“…tax morale). Particularly, Lee (2013) reveals that both social trust and tax morale have a detrimental effect on the size of the shadow economy. Moreover, the study indicates that social networks are not significantly related to the size of the shadow economy.…”

Section: The Determinants Of the Shadow Economymentioning

confidence: 99%

“…Notably, Arrow (1970, p. 20) argues that there exists "a whole set of customs and norms which might be similarly interpreted as agreements to improve the efficiency of the economic system (in the broad sense of satisfaction of individual values) by providing commodities to which the price system is inapplicable." Indeed, some studies show that this dimension can significantly affect the size of the shadow economy in many countries, even after controlling different influences (Torgler and Schneider, 2009;D'Hernoncourt and M eon, 2012;Lee, 2013;Williams and Horodnic, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Social capital and the shadow economy: a Bayesian analysis of the BRICS

Nguyen

2021

AJEB

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PurposeThe paper aims to examine the impact of social capital on the size of the shadow economy in the BIRCS countries over the period 1995–2014.Design/methodology/approachThe authors employ the Bayesian linear regression method to uncover the relationship between social capital and the shadow economy. The method applies a normal distribution for the prior probability distribution while the posterior distribution is determined using the Markov chain Monte Carlo technique.FindingsThe results indicate that the unemployment rate and tax burden positively affect the size of the shadow economy. By contrast, corruption control and trade openness are negatively associated with the development of this informal sector. Moreover, the paper's primary finding is that social capital represented by social trust and tax morale can hinder the size of the shadow economy.Research limitations/implicationsThis study is limited to the case of the BRICS countries for the period 1995–2014. The determinants of the shadow economy in different groups of countries can be heterogeneous. Moreover, social capital is a multidimensional concept that may consist of various components. This difficulty of measuring the social capital calls for further research on the relationship between other dimensions of social capital and the shadow economy.Originality/valueMany studies investigate the effect of economic factors on the size of the shadow economy. This paper applies a new approach to discover the issue. Notably, the authors use the Bayesian linear regression method to analyze the relationship between social capital and the shadow economy in the BRICS countries.

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Section: Methodology and Model Specification 31 Modelmentioning

confidence: 99%

Section: Related Literature 21 Definition and Theoretical Considerationsmentioning

confidence: 99%

Section: The Determinants Of the Shadow Economymentioning

confidence: 99%

Section: The Determinants Of the Shadow Economymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Social capital and the shadow economy: a Bayesian analysis of the BRICS

Nguyen

2021

AJEB

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An estimation of the informal economy in the agricultural sector in the EU‐15 from 1996 to 2019

2022

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This study is the first to calculate the size and development of the informal/shadow economies in the agricultural sector for 15 “old” EU countries over 1996–2019. The multiple indicators multiple causes model was used for the estimation of levels and development of informal economies of these countries. The results show higher levels of informal economies in agriculture compared with those in the overall economies. One main contributing factor to this development was a change in the subsidization regime, namely, decoupling Common Agricultural Policy payments from quantities of products produced, introduced in 2003. The effects of taxation, share of imports, and factor income in agriculture are also important for the size of the informal agricultural economy. Finally, we disaggregate the informal work into subcategories, such as self‐employed, family and nonfamily, and household work. The biggest potential for the development of informal economy comes from taxation, regulation, and self‐employment, followed by family work and unreported employment. [EconLit Citations: Q11, Q14, Q19].

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Rebmann¹

2015

Palgrave Dictionary of Emerging Markets and Transition Economics

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How Does Social Capital Reduce the Size of the Shadow Economy?

Cited by 16 publications

References 31 publications

Social capital and the shadow economy: a Bayesian analysis of the BRICS

Social capital and the shadow economy: a Bayesian analysis of the BRICS

An estimation of the informal economy in the agricultural sector in the EU‐15 from 1996 to 2019

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