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A Re-examination of the 'Underground Economy' in the United States

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... While indirect models are based on macroeconomic indicators, direct approaches use income audits (Clotfelter, 1983;Feige, 1986;Feinstein, 1991) or survey data (Zienkowski, 1996). The main advantage of these methods is the detailed information they can provide about the structure of the shadow economy. ...
... Survey data Zienkowski (1996) Kim (2003) Results from survey data are sensitive and mostly depends on the formulation of questionnaire, and also the respondents' willingness to cooperate. Income audits Clotefelter (1983) Feige (1986) Feinstein (1991 Appendix 2. Descriptive statistics Table 1. Variable definition ...
... The log-log specification also violates the theoretical assumption that the shadow and non-shadow demand for cash is separate and additive (see, e.g. Feige 1986). Further, we do not include the lagged dependent variable in the model (in contrast to, e.g. ...
... This assumption may both underestimate or overestimate the share of the SE in the total economy. If the SE is mostly present in the service sector, which often involves fewer intermediate transactions than does the nonservice sector, one may underestimate the part of GDP related to the SE(Feige 1986). On the other hand, if the SE mostly includes simple economic activities with a relatively low value added per unit of turnover, an overestimation of the SE is likely. ...
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Model-based econometric techniques of the shadow economy estimation have been increasingly popular, but a systematic approach to getting the best of their complementarities has so far been missing. We review the dominant approaches in the literature—currency demand analysis and MIMIC model—and propose a hybrid procedure that addresses their previous critique, in particular the misspecification issues in CDA equations and the vague transformation of the latent variable obtained via MIMIC model into interpretable levels and paths of the shadow economy. We propose a new identification scheme for the MIMIC model, referred to as ‘reverse standarization’. It supplies the MIMIC model with the panel-structured information on the latent variable’s mean and variance obtained from the CDA estimates, treating this information as given in the restricted full information maximum likelihood function. This approach allows avoiding some controversial steps, such as choosing an externally estimated reference point for benchmarking or adopting other ad hoc identifying assumptions. We estimate the shadow economy for up to 43 countries, with the results obtained in the range of 2.8–29.9% of GDP. Various versions of our models remain robust as regards changes in the level of the shadow economy over time and the relative position of the analysed countries. We also find that the contribution of (a correctly specified) MIMIC model to the measurement of trends in the shadow economy is marginal as compared to the contribution of the CDA model, confirming the scepticism of some previous literature towards this method.
... J., 1997;Ahumada, H. et al. 2008;Sardà, J., 2011, 2015;González-Fernández, M. and González-Velasco, M.C., 2015). Entre sus limitaciones, destacan (a) el que no todas las operaciones de economía sumergida se pagan en efectivo; (b) que estos modelos presuponen que la carga impositiva es el único factor que provoca la economía sumergida, obviando otros como la regulación, el comportamiento de los ciudadanos o la moral fiscal; y (c) que la velocidad de la circulación del dinero no es igual en ambas economías (Thomas, J.J., 1986(Thomas, J.J., , 1992(Thomas, J.J., , 1999Feige, E.L., 1986). http://dx.doi.org/10.12795/rea.2017.i34.16 ...
... (vii) Método de aproximación fiscal o auditorías fiscales (Simon, C.P. and Witte, D.A., 1982;Clotfelter, C.T., 1983;Feige, E.L., 1986;Battiston, P. et al, 2016;Mazzolini, G. et al., 2017): con este método se recopila información y posteriormente se analiza para ver si existen discrepancias entre los ingresos declarados y los observados. La principal desventaja de este método es que la selección de la población seleccionada para ser auditada no es aleatoria, pues pretende detectar el fraude (Dallago, B., 1990;Thomas, J.J., 1992;Schneider, F. and Enste, D.H., 2000). ...
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Este estudio analiza el trabajo no declarado como pieza fundamental en la economía sumergida en la UE-28, haciendo especial énfasis en su cuantificación y evolución, estructura, variabilidad por países y determinantes. Con este objetivo, se aplican métodos de regresión lineal y modelos de elección discreta con variable dependiente binaria sobre la base de los microdatos correspondientes a los Eurobarómetros especiales dedicados al análisis del fenómeno del trabajo no declarado en los años 2007 y 2013. Los resultados muestran la importancia que tienen sobre el trabajo no declarado (i) el grado de aceptación individual del propio fenómeno; (ii) la percepción del riesgo de sanciones; (iii) la situación laboral del sujeto; y (iv) la propia situación macroeconómica. Las implicaciones de estos resultados para el espectro de políticas disponibles para combatir la economía sumergida son también discutidas en este trabajo.
... Їм належить розробка моделей ігрової взаємодії, в яких кожний агент (держава, підприємець, найманий працівник) конкурує з іншими за реалізацію власних інтересів. Розробці методик і створенню інструментів для вимірювання тіньової економіки присвятили свої праці Ф. Шнайдер [10], С. Джонсон, Д. Кауфман та П. Зойдо-Лобатон [11], A. Каліберда [12], Е. Фейдж [13]. ...
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The article analyzes some components of the shadow economy using the example of modern Ukraine. The purpose of article is to predict the behavior of the economically active population when choosing an employment option using an agent-based simulation model. The work uses the methods of theoretical generalization, retrospective analysis – to study the theoretical aspects of socio-economic relations at the enterprise; system analysis, agent-based modeling – to develop a decision-making model in the labor market. The proposed algorithm of taxpayer behavior is based on studies of domestic and foreign scientists in the field of shadow economy and informal employment. Particular attention is paid to the national economic mentality as an important factor which determines the trajectory of the country’s development. As a substantiation of the subject’s behavior on the labor market, it is proposed to use the model of decision-making by a taxpayer, developed by M. Ellingham. When constructing an algorithm for the behavior of an employee, the demographic situation in Ukraine, the causes and consequences of labor migration, and the mental characteristics of the population were analyzed. The novelty of the proposed model lies in the fact that it takes into account the dependence of the taxpayer’s utility curve on the taxpayer’s age. The developed model was applied to forecasting of the demographic situation in Ukraine, and to estimation of the population participation in the informal economy sector.
... w f or t = s and zero otherwise. (5) It is also assumed that E (ut ws) = o for all the t and s. The parameter β4 is unknown but expected to be near unity. ...
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In this study, the volume of dirty money in Iran was estimated. The data belonged to the period of 1997-2019, and was taken from the Central Bank of Iran (website: https://www.cbi.ir). Fuzzy logic was used to estimate the underground economy. Fuzzy theory can mathematically formulate many variables that are imprecise and ambiguous concepts. This theory is appropriate for reasoning, inference, control, and decision-making under uncertainty. This approach works in conditions of uncertainty. In cases in which the variables are inaccurate, this method is used. Fuzzy set theory is a generalization of the set theory. The underground economy is important in estimating the amount of dirty money and has a positive effect on this amount. The effect of the underground economy was investigated using the vector autoregressive (VAR) and vector error correction (VECM) models. •In this article applied the fuzzy logic, to estimate the underground economy. •The method presented in this article can be useful for Researchers and managers in the monetary trend of economics. •The fuzzy method is the best way to estimate the size of the underground economy because it is a measure of uncertainty.
... Using an estimate of the money velocity, he translated this surplus into the size of the underground economy (in % of GNP). As a response to this work (Feige, 1986) proposed his 'transaction method' that was derived from the equation of exchange. This method requires the knowledge of the value-added and the stock of money in the economy, as well as the level of the underground economy itself in some baseline period. ...
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The existence of an underground economy may hide the official number of unemployed persons, their share in the labour force, and wages. It also gives rise to the economic and social conditions of individuals, household and countries, which are evaluated in a biased way if one relies on the official statistics. In this paper, we use the currency demand model of Tanzi to estimate the underground economy in Palestine over the period: (2008–2017). Toward that end, a group of econometric techniques of time series data was applied. The main empirical results show that the underground economy reached up to 28.6% in 2010 of the GDP with about USD 2676.227 million. The empirical study results also reveal that the explanatory variables listed in the study model suggested by economic theory and previous research have a significant impact on the dependent one, except the variable ratio of governmental wages and salaries to GDP. However, the money received by the government employees does not contribute to the underground economy.
... This however, might be as a result of the rural dwellers in Nigeria who have limited access to banking service and still resorted to barter system of transaction in most of their services. This supports the claim by Thomas (1986Thomas ( , 1999 and Feige (1986) who argue that cash in circulation only give a rough indication of informality because it is not all informal transactions that are performed in cash. Lastly, in line with the study of Fasanya and Onakoya (2012), who found that human capital positively affect unemployment in Nigeria, this study also supported the evidence that human capital had a positive effect on unemployment in the long-run in Nigeria where a 1% increase in human capital in Nigeria caused increased unemployment of 0.29%. ...
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While many marginalised groups, including women and poor and low-income families, are working in the informal economy (Adom, 2015), these actors remain absent in major public policies. While policymakers have come to an understanding about the role of the informal economy and the imperative behind extending social protection to informal workers (Surrender and Walker, 2013), recent literature provided hints of economic growth as the unspoken mission of being on the side of the informal actors, (Joshi, Prichard, and Heady, 2014; Schneider and Klinglmair, 2004; Schneider, Buehn, and Montenegro, 2010). Example of such mission includes the rising trend of documenting the informal economy – which is less likely to bring many benefits to the informal actors, compare to firms in the formal economy that get a direct splash of the economic growth. Therefore, a previous hypothesis that data is all that is required to bring equality to the informal economy is inaccurate. This article aims to unwrap the role of data on the informal economy. More specifically, by looking at recent data innovation, the article criticised the growth narrative behind documenting the informal economy, which hinders improvement in informal actors' livelihood conditions. This article relies on the capability approach first developed by Amartya Sen (see: Sen, 1994, 2001, 2004) and later Martha Nussbaum (see: Nussbaum, 2003, 2011) to evaluate the current usage of statistics to measure the magnitude and nature of the informal economy, in addition to elaborating the ‘ideal’ of how to define objectives of documenting the informal economy through data innovation.
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