nep-iue New Economics Papers
on Informal and Underground Economics
Issue of 2020‒02‒10
five papers chosen by
Catalina Granda Carvajal
Universidad de Antioquia

  1. Informality : Why Is It So Widespread and How Can It Be Reduced? By Loayza,Norman V.
  2. Health, wealth, and informality over the life cycle By Julien Albertini; Xavier Fairise; Anthony Terriau
  3. The effect of reporting institutions on tax evasion:Evidence from the lab By Kaisa Kotakorpi; Satu Metsälampi; Topi Miettinen; Tuomas Nurminen
  4. Tax Compliance in the RentalHousing Market: Evidence from aField Experiment By Essi Eerola; Tuomas Kosonen; Kaisa Kotakorpi; Teemu Lyytikäinen
  5. Tax dredger on social networks: new learning algorithms to track fraud By D. Desbois

  1. By: Loayza,Norman V.
    Abstract: In a typical developing country, about 70 percent of workers and 30 percent of production are informal. Informality is a cause and a consequence of the lack of economic and institutional development. It implies productive inefficiency and a culture of evasion and noncompliance. Informality, however, exists because it offers the advantages of flexibility and employment in economies with low labor productivity and an excessive regulatory burden. Under these conditions, if there were no informality, there would be greater unemployment, poverty, and crime. A well-conceived formalization strategy should seek to make formality more attractive. As the causes of informality are complex and interrelated, the reforms to reduce it must include all relevant areas. A formalization strategy should consist of making labor markets flexible, reforming social protection, increasing labor productivity, making the regulatory framework and the justice system efficient, and rationalizing the tax system.
    Keywords: Labor Markets,Social Protections&Assistance,Employment and Unemployment
    Date: 2018–12–19
  2. By: Julien Albertini (GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - ENS Lyon - École normale supérieure - Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - Université de Lyon - CNRS - Centre National de la Recherche Scientifique); Xavier Fairise (GAINS - Groupe d'Analyse des Itinéraires et des Niveaux Salariaux - UM - Le Mans Université); Anthony Terriau (GAINS - Groupe d'Analyse des Itinéraires et des Niveaux Salariaux - UM - Le Mans Université)
    Abstract: How do labor market and health outcomes interact over the life cycle in a country characterized by a large informal sector and strong inequalities? To quantify the effects of bad health on labor market trajectories, wealth, and consumption, we develop a life-cycle heterogeneous agents model with a formal and an informal sector. We estimate our model using data from the National Income Dynamics Study, the first nationally representative panel study in South Africa. We run counterfactual experiments and show that health shocks have an important impact on wealth and consumption. The channel through which these shocks propagate strongly depends on the job status of individuals at the time of the shock. For formal workers, bad health reduces labor efficiency, which translates into lower earnings. For informal workers and the non-employed, the shock lowers the job finding rate and increases job separation into non-employment, which results in a surge in non-employment spells. As bad health spells persist more for non-employed than for employed individuals, the interaction between labor market risks and health risks generates a vicious circle.
    Keywords: Health,Wealth,Life cycle,Informality
    Date: 2020
  3. By: Kaisa Kotakorpi (VATT, Tampere University); Satu Metsälampi (University of Turku); Topi Miettinen (Hanken School of Economics); Tuomas Nurminen (Hanken School of Economics)
    Abstract: We study the effects of different tax reporting mechanisms in experi-mental double auction markets in the laboratory. The sales tax is paidby the seller, and we compare market outcomes in a no-tax conditionto cases where (i) tax evasion is impossible, (ii) taxes can be evaded butthere is an exogenous (low) audit probability, or (iii) there is double-reporting by both the buyer and the seller, and the seller’s audit prob-ability is endogenously increased if her tax report is inconsistent withthe buyer’s report. The latter case mimics the use of so called third-party reporting in tax enforcement. We find that third-party reportingeffectively deters evasion, and deterrence also has real effects on mar-ket outcomes: market clearing prices, quantities and overall efficiencyreturn to the levels observed when tax evasion was impossible. Whenreporting is costly to buyers, they report significantly less trades. Taxcompliance by sellers however remains at a relatively high level, eventhough payoffs would be maximized for both parties if no trades werereported. This suggests that the mere possibility of the existence ofthird party information may be a fairly effective deterrent on tax eva-sion, and tax administrators might consider making their informationsources more widely publicized.
    Keywords: Tax Evasion, Tax Incidence, Third-Party Reporting, Double Auction, Experiment
    JEL: H21 H22 H26 D40 D44 D91
    Date: 2019–05
  4. By: Essi Eerola (VATT Institute for Economic Research and CESifo); Tuomas Kosonen (Labour Institute for Economic Research and CESifo); Kaisa Kotakorpi (VATT, University of Turku and CESifo); Teemu Lyytikäinen (VATT)
    Abstract: We study rental income tax compliance using a large-scale randomizedfield experiment and register data with third-party information on theownership of apartments. We analyze the responses of potential land-lords to treatment letters notifying them of stricter tax enforcement. Wealso study spillover effects of tax enforcement within the household andbetween landlords within local rental markets. We find an increase inreported income after an enforcement letter is sent to landlords. We alsofind positive reporting spillovers between spouses, as well as betweenlandlords in a subgroup of more likely evaders.
    Keywords: tax compliance, tax enforcement, field experiment, rental housing markets
    JEL: H26 H83 R31
    Date: 2019–05
  5. By: D. Desbois (ECO-PUB - Economie Publique - INRA - Institut National de la Recherche Agronomique - AgroParisTech)
    Abstract: In France, estimates of tax evasion vary between 2 and 80 billion euros (€ bn) according to the parliamentary report of Bénédicte Peyrol. This would explain the injunction addressed by President Emmanuel Macron to the Court of Auditors on April 25 to shed light on this controversial issue in a context of tensions over public finances and a decline in tax compliance. In a letter sent on May 9 to Didier Migaud, president of this institution, Prime Minister Édouard Philippe indicates that "the time has come to take stock of the scale of tax fraud in the country and to assess the action state services and the tools that are put in place. " A recent interview with Gérald Darmanin, Minister of Action and Public Accounts, has just revealed the French government's plan to use machine learning algorithms to better target tax audits based on the information that taxpayers disclose to them. - even on social networks. Illegal trade and false tax domiciliations are particularly targeted by article 57 of the 2020 finance bill, which provides for the use of artificial intelligence in the service of the fight against tax fraud, adopted on November 13 by MEPs 3. Thus, this project plans to strengthen the IT resources to improve the targeting of tax audit operations thanks to an investment of 20 million euros by 2022. Artificial intelligence, often invoked to discuss technologies Numeric, is a misleading term, because it evokes the capacities of machines fantasized by the works of science fiction popularized by the seventh art. In tax matters, nothing like this: among the myriad of behaviors observed, the use of machine learning techniques aims to detect recurrent ones that are specific to certain types of fraud (VAT, bleaching, false domiciliation and illicit optimization). However, the tax administration concedes a weak point: "Today, nearly one audit in four results in only a small recovery. "
    Abstract: En France, les estimations de fraude fiscale varieraient entre 2 et 80 milliards d'euros (Md€) selon le rapport parlementaire de Bénédicte Peyrol. Ce qui expliquerait l'injonction adressée par le Président Emmanuel Macron à la Cour des comptes, le 25 avril dernier, pour faire la lumière sur cette question controversée dans un contexte de tensions sur les finances publiques et de baisse du consentement à l'impôt. Dans un courrier adressé le 9 mai à Didier Migaud, président de cette institution, le Premier ministre Édouard Philippe indique que « le moment est venu de dresser un bilan de l'ampleur de la fraude fiscale dans le pays et d'évaluer l'action des services de l'État et les outils qui sont mis en place ». Une interview récente de Gérald Darmanin, ministre de l'Action et des Comptes publics, vient de révéler le projet du gouvernement français d'utiliser des algorithmes d'apprentissage automatique pour mieux cibler les contrôles fiscaux sur la base des informations que les contribuables dévoilent eux-mêmes sur les réseaux sociaux. Le commerce illicite et les fausses domiciliations fiscales sont particulièrement visés par l'article 57 du projet de loi de finances 2020, qui prévoit un usage de l'intelligence artificielle au service de la lutte contre la fraude fiscale, adopté le 13 novembre dernier par les députés 3. Ainsi, ce projet prévoit de renforcer les moyens informatiques pour améliorer le ciblage des opérations de contrôle fiscal grâce à un investissement de 20 millions d'euros d'ici à 2022. L'intelligence artificielle, souvent invoquée pour discourir sur les technologies numériques, est un terme trompeur, car il évoque les capacités de machines fantasmées par les oeuvres de science-fiction popularisées par le septième art. En matière fiscale, rien de tel : parmi la myriade des comportements observés, l'utilisation de techniques d'apprentissage automatique a pour objectif de détecter ceux récurrents qui seraient spécifiques à certains types de fraudes (à la TVA, au blanchiment, à la fausse domiciliation et à l'optimisation illicite). Cependant, l'administration fiscale concède un point faible : « Aujourd'hui, près d'une vérification sur quatre n'aboutit qu'à un redressement peu élevé. »
    Date: 2019–12–12

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