Distributional Implications of Tax Evasion: Evidence from the Lab

Doerrenberg, Philipp; Duncan, Denvil

doi:10.2139/ssrn.2160528

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Fewer studies have focused on the distributional implications of tax non-compliance (e.g. Johns and Slemrod, 2010;Doerrenberg and Duncan, 2013), some in combination with microsimulation modelling (Benedek and Lelkes, 2011;Leventi et al, 2013). For reviews of theoretical and empirical literature on tax evasion, see Andreoni et al (1998), Slemrod (2007) and Alm (2012).…”

Section: Modelling Non Take-up and Non-compliancementioning

confidence: 99%

Microsimulation and Policy Analysis

Figari

Paulus

Sutherland

2015

Handbook of Income Distribution

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ISER Working Paper Series www.iser.essex.ac.uk Microsimulation and Policy AnalysisNon-technical summary This paper is prepared as a chapter forthcoming in the Handbook of Income Distribution, Volume 2 (edited by A. B. Atkinson and F. Bourguignon, Elsevier-North Holland). Microsimulation methods are increasingly used to study the effects of policies on the income distribution contributing both to policy debates and academic literature. In general terms, microsimulation refers to modelling changes in the state or behaviour of micro-level units. We provide an overview of approaches that address questions related to the impact of taxbenefit policies on household income distribution. We first discuss how microsimulation modelling contributes to the analysis of the income distribution more broadly, pointing out that it can enrich survey and administrative data by checking its consistency, adding further details and enhancing analytical flexibility. In addition, it provides the basis for calculating indicators that cannot otherwise be estimated directly from micro-data such as measures of work incentives, net support for children and automatic stabilisers embedded in tax-benefit systems. We then focus more specifically on the role of microsimulation in policy evaluation. Taxbenefit models can provide information on household disposable income and its components under various policy scenarios and as such constitute a unique tool for ex ante policy analysis, though equally can be used for ex post analysis. We review the formal framework underlying such microsimulation-based decomposition analysis of income distribution and suggest a broader perspective to accommodate a wider range of applications. Our main emphasis is on static modelling methods, though we also consider extensions accounting for behavioural reactions and links with macroeconomic models. As microsimulation models are often concerned with current and future policies, we discuss various approaches to predict the income distribution given that data providing information on the current situation becomes available with a time lag, distinguishing between nowcasting and forecasting. We emphasise the role of comparative analysis as cross-country differences in tax-benefit systems and population structures provide a basis to assess the robustness of results and generalise conclusions. We provide empirical illustrations drawing mainly on analysis using the EU-wide tax-benefit model EUROMOD. Covering 27 countries and made generally accessible this is now one of the most widely used models. There are many remaining challenges to providing estimates of the effects of policy and policy changes that can be used with confidence within policy analysis and we consider three major issues: reconciling the simulated income distribution and that measured using the original micro-data; accounting for benefit non take-up and tax non-compliance, as well as validating and assessing the statistical reliability of microsimulation estimates. While our main focus is on the distributio...

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Section: Modelling Non Take-up and Non-compliancementioning

confidence: 99%

Microsimulation and Policy Analysis

Figari

Paulus

Sutherland

2015

Handbook of Income Distribution

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“…The slider task has also been used in other studies includingHetzel (2010),Eacret et al (2011), Hammermann et al (2011,Dolan et al (2012),Kimbrough and Reiss (2012),Monahan (2012),Breuer (2013),Das et al (2013),Djawadi and Fahr (2013),Fahr and Djawadi (2013),Ibanez and Schaffland (2013),Riener and Wiederhold (2013),Djawadi et al (2014),Doerrenberg and Duncan (2014a),Doerrenberg and Duncan (2014b),Evdokimov and Rahman (2014),Fan and Zhao (2014),Friedl et al (2014),Gärtner and Sandberg (2014),Gerhards and Siemer (2014),Khadjavi (2014),Kirchler et al (2014),Lezzi (2014),Lindner (2014),Schmitz (2014),Straub et al (2014),Teng et al (2014),Avram (2015),Bonein and Denant-Boèmont (2015),Zeppenfeld (2015),Cettolin and Riedl (2016),Dorrough et al (2016),Imas et al (2016),Riener and Wiederhold (2016),Smith (2016),Brown et al (2017), Gee et al (2017,Meissner and Pfeiffer (2017),Kesternich et al (2018) andMalik et al (2018).3 Gill and Prowse (2012) embed heterogeneous effort co...…”

mentioning

confidence: 99%

Measuring Costly Effort Using the Slider Task

Gill¹,

Prowse

2018

SSRN Journal

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Distributional Implications of Tax Evasion: Evidence from the Lab

Cited by 2 publications

References 26 publications

Microsimulation and Policy Analysis

Microsimulation and Policy Analysis

Measuring Costly Effort Using the Slider Task

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