Tax compliance with uncertain income: a stochastic control model

Gaetano T. Spartà () and Gabriele Stabile
Additional contact information
Gaetano T. Spartà: Sapienza-Università di Roma

Annals of Operations Research, 2018, vol. 261, issue 1, No 13, 289-301

Abstract: Abstract This paper examines the compliance behaviour of a taxpayer endowed with a stochastic income, taking into account dynamical factors as public and private investments, within a stochastic control framework. Assuming logarithmic utilities and thanks to a suitable rewrite of the problem, we provide an existence and uniqueness result for the solution of the Hamilton–Jacobi–Bellman equation associated to the control problem, and we rely on a symbolic and numerical algorithm to study its solution. Moreover, we implement a Monte Carlo simulation in order to determine an estimate of the mean and the variance of the total declared income together with a confidence interval. To illustrate how the method works, we present a computational example where we assign values to the parameters. In this case we perform a sensitivity analysis, showing how the total declared income is affected by public and private investments, probability of being discovered, fine, tax rate and income uncertainty.

Keywords: Tax evasion; Stochastic income; Stochastic control; HJB equation (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-017-2618-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:annopr:v:261:y:2018:i:1:d:10.1007_s10479-017-2618-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-017-2618-9

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().