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on Informal and Underground Economics |
| By: | Prichard, Wilson |
| Abstract: | The concept of ‘tax morale’ seeks to capture an individual’s willingness (or unwillingness) to pay taxes. The study of tax morale in lower-income countries is significant for understanding “quasi-voluntary†tax compliance, popular support for tax reform programs, and the broader character of social contracts. While interest in tax morale research has surged over the past decade, the use of the concept in research has often been relatively broad and imprecise. This risks a lack of comparability across studies. More importantly, insufficiently nuanced research risks telling an incomplete or misleading story. As part of a broader effort for greater conceptual precision, this paper highlights the importance of distinguishing between conditional and unconditional understandings of tax morale |
| Keywords: | Development Policy, Finance, Governance, |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:idq:ictduk:18086&r=iue |
| By: | Francesco Menoncin; Andrea Modena; Luca Regis |
| Abstract: | We study tax evasion in a dynamic macroeconomic model where utility-maximizing entrepreneurs use capital to produce or buy bonds, depending on their firm’s stochastic productivity. The government provides productivity-enhancing public goods financed through taxes and bond issuance. Entrepreneurs can increase their income by evading taxes at the risk of being audited and fined. Lower productivity boosts evasion incentives, exacerbating capital misallocation because unproductive entrepreneurs accumulate wealth at their peers’ expense. Consistently with OECD data, the model predicts a negative relation between tax evasion and productivity in the aggregate but heterogeneous signs and magnitudes across productivities. Public goods provision affects these outcomes ambiguously. |
| Keywords: | dynamic tax evasion, financial frictions, general equilibrium, misallocation |
| JEL: | E25 E26 H23 H26 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2023_453&r=iue |
| By: | Leenders, Wouter; Lejour, Arjan (Tilburg University, School of Economics and Management); Rabate, Simon; Riet, Maarten van ‘t |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:dc537319-6adc-4b3a-8f9f-525161d428e2&r=iue |
| By: | Joao Felix; Michel Alexandre, Gilberto Tadeu Lima |
| Abstract: | The use of machine learning models and techniques to predict economic variables has been growing lately, motivated by their better performance when compared to that of linear models. Although linear models have the advantage of considerable interpretive power, efforts have intensified in recent years to make machine learning models more interpretable. In this paper, tests are conducted to determine whether models based on machine learning algorithms have better performance relative to that of linear models for predicting the size of the informal economy. The paper also explores whether the determinants of such size detected as the most important by machine learning models are the same as those detected in the literature based on traditional linear models. For this purpose, observations were collected and processed for 122 countries from 2004 to 2014. Next, eleven models (four linear and seven based on machine learning algorithms) were used to predict the size of the informal economy in these countries. The relative importance of the predictive variables in determining the results yielded by the machine learning algorithms was calculated using Shapley values. The results suggest that (i) models based on machine learning algorithms have better predictive performance than that of linear models and (ii) the main determinants detected through the Shapley values coincide with those detected in the literature using traditional linear models. |
| Keywords: | : Informal economy; machine learning; linear models; Shapley values |
| JEL: | C52 C53 O17 |
| Date: | 2023–08–28 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2023wpecon10&r=iue |
| By: | Antonis Adam; Thomas Moutos |
| Abstract: | Using information from all IMF conditionality programs from 1990 to 2018, we implement a dynamic Augmented Inverse Probability Weighting Regression Adjustment approach to examine the effects of programs, including public sector dismissals, on the size of the shadow economy. The estimated effect five years after the policy intervention indicates an increase in the share of the shadow economy to GDP by about 1.3 percentage points. More importantly, this change involves a sizable reallocation of private economic activity from its formal to its informal part, i.e., the size of the formal private sector relative to the size of the informal sector decreases by seven percentage points. We interpret these findings through the lens of a two-sector model in which there is interdependence between worker incomes and the allocation of product demand across the formal and informal sectors. |
| Keywords: | shadow economy, public sector employment, IMF programs, informality |
| JEL: | O17 J45 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10614&r=iue |
| By: | Schmidt, Verena,; Webster, Eddie,; Mhlana, Siviwe,; Forrest, Kally, |
| Abstract: | Abstract. |
| Keywords: | informal economy, social dialogue, workers representation |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ilo:ilowps:995278893302676&r=iue |