Raising the Accuracy of Shadow Economy Measurements

This article estimates the size of the shadow economy in a Spanish region (Navarre) for the period 1986- 2016. To this end, we employ indirect macro-econometric methods such as the Currency Demand approach, Electricity Consumption (Physical Input) methods and the multiple indicators multiple causes (MIMIC) approach. A differential feature of our empirical analysis is that we incorporate various methodological innovations (e..g. Bayesian Model Averaging, a Time-Varying Parameter model, normalization of the latent variable) to refine and increase the measurement accuracy of each of the indirect methods considered. The temporal pattern of the shadow economy’s size that emerges from the different approaches is similar, which suggests that the estimates obtained are robust and capture the underlying dynamics of the hidden sector. After quantifying the shadow economy, we analyze its determinants by means of Bayesian Model Averaging techniques. We find that the evolution of the shadow economy in Navarre can be explained by a small and robust set of factors, specifically the tax burden, the share of employment in the construction sector, the inflation rate, euro area membership and the ratio of currency outside the banks to M1.

Exchange of Information and Bank Deposits in International Financial Centres

This paper assesses the impact of exchange of information on foreign-owned bank deposits in international financial centres (IFCs). IFC deposits declined globally by 24% or USD 410 billion during 2008 to 2019. The commencement of automatic exchange of information is associated on average with a 22% reduction in IFC bank deposits held by non-IFC jurisdictions. Increasing multilateral expansion of exchange of information on request seems to diminish marginal gains of new bilateral treaties. IFC jurisdictions specialising in banking activities have been mostly affected by increasing tax transparency. A comprehensive multilateral approach is thus fundamental for successfully increasing international tax transparency.

Use of Social Network Analysis for Tax Control in Spain

The Spanish Tax Agency is an experienced user of big data and has now deployed social network analysis (SNA) tools. SNA tools have led to a qualitative leap in such wide-ranging areas as tax collection, enforcement, control of ultra-high-net-worth individuals, and money laundering. This paper presents a comprehensive overview of the different lines of research, strategies and results of nine projects over the last five years, including the lessons learned. We present the best practices in pattern discovery, the tools developed for the control of big fortunes and the strategy developed to create a bridge between expert knowledge and SNA technologies. We highlight the results of investigating interposed entities used to channel personal remuneration, complex corporate structures, and opaque companies.

The Impact of Plastic Money Use on VAT Compliance: Evidence from EU Countries

This paper examines the impact of credit and debit card usage on VAT compliance using annual national level data for 26 European Union countries from 2000 to 2016. Exploiting spatio temporal variation in plastic money use along with an instrumental variables approach, we find that a 1% increase in card payments reduces VAT gaps 0.51 percentage points whereas a 1% increase in cash withdrawals increases VAT gaps by 0.6 percentage points. Our contribution lies in using more adequate measures of VAT compliance gap and in accounting for potential confounders such as the ex-ante enforcement capacity of tax administrations.

Segmentation of Potential Fraud Taxpayers and Characterization in Personal Income Tax Using Data Mining Techniques

This paper proposes an analytical framework that combines dimension reduction and data mining techniques to obtain a sample segmentation according to potential fraud probability. In this regard, the purpose of this study is twofold. Firstly, it attempts to determine tax benefits that are more likely to be used by potential fraud taxpayers by means of investigating the Personal Income Tax structure. Secondly, it aims at characterizing through socioeconomic variables the segment profiles of potential fraud taxpayer to offer an audit selection strategy for improving tax compliance and improve tax design. An application to the annual Spanish Personal Income Tax sample designed by the Institute for Fiscal Studies is provided. Results obtained confirm that the combination of data mining techniques proposed offers valuable information to contribute to the study of tax fraud.