Abstract
This chapter examines the use of symbolic regression to tackle a real world problem taken from economics: the estimation of the size a country’s ’shadow’ economy. For the purposes of this chapter this is defined as a country’s total monetary economic activity after subtracting the official Gross Domestic Product. A wide variety of methodologies are now used to estimate this. Some have been criticized for an excessive reliance on subjective predictive variables. Others use predictive data that are not available for many developing countries. This chapter explores the feasibility of developing a general-purpose regression formula using objective development indicators. The dependent variables were 260 shadow economy measurements for various countries from the period 1990- 2006. Using 16 independent variables, seven basis functions, and a depth of one grammar level a search space of 1013 was created. This chapter focuses on the power conferred by an abstract expression grammar allowing the specification of a universal goal formula with grammar depth control, and the customization of the scoring process that defines the champion formula that ’survives’ the evolutionary process. Initial searching based purely on R-Squared failed to produce plausible shadow economy estimates. Later searches employed a customized scoring methodology. This produced a good fit based on four variables: GDP, energy consumption squared, this size of the urban population, and the square of this figure. The same formula produced plausible estimates for an out of sample set of 510 countries for the years 2003-2005 and 2007. Though shadow economy prediction will be controversial for some time to come, this methodology may be the most powerful estimation formula currently available for purposes that require verifiable data and a single global formula.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bagachwa, M.S.S. and Naho, A. (1995). Estimating the Second Economy in Tanzania. World Development, Vol. 23:8, pp 1387-1399.
Bhattacharyya, Dilip K. (1999). On the Economic Rationale of Estimating the Hidden Economy. The Economic Journal, Vol. 109:456, pp 348-359.
Gould, J. (2005). The New Conditionality: the politics of poverty reduction strategies. Zed Books, London UK.
Hartzenburg, G.M. and Leimann, A. (1992). The informal economy and its growth potential. In E.Adebian and Standis, B., editors, Economic Growth in South Africa, pages 187–214. Oxford University Press, Oxford UK.
Heston, A., Summers, R., and Aten, B. (2006). Penn world table version 6.2. Technical report, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.
Johnson, Simon, Kaufmann, Daniel, and Zoido-Lobatón, Pablo (1998a). Corruption, Public Finances and the Unofficial Economy - Discussion Paper. The World Bank, Washington DC. Johnson, Simon, Kaufmann, Daniel, and Zoido-Lobatón, Pablo (1998b). Regulatory Discretion and the Unofficial Economy. American Economic Review Vol. = 88:2 pp 387-392.
Kaufmann, D. and Kaliberda, A. (1996). Integrating the unofficial economy into the dynamics of post socialist economies: A framework of analyses and evidence. In Kaminski, B., editor, Economic Transition in Russia and the New States of Eurasia, pages 81–120. M.E.Sharpe, London UK.
Korns, Michael F. (2010). Abstract expression grammar symbolic regression. In Rick Riolo, Una-May O’Reilly and McConaghy, Trent, editors, Genetic Programming Theory and Practice VIII. Springer, Ann Arbor MI.
Lackó, Maria (1997a). Do Power Consumption Data Tell the Story? (Electricity Intensity and the Hidden Economy in Post-Socialist Countries) - Working Paper. International Institute for Applied Systems Analysis, Laxenberg Austria.
Lackó, Maria (1997b). The Hidden Economies of Visegrád Countries in International Comparison: A Household Electricity Approach - Working Paper. Institute of Economics - Hungary.
Lackó, Maria (1998). The hidden economies of visegrad countries in international comparison: A household electricity approach. In Wyplosz, Charles, editor, Hungary: Towards a Market Economy, pages 128–152. Cambridge University Press, Cambridge MA.
Lackó, Maria (2000). Hidden economy - an unknown quantity? comparative analysis of hidden economies in transition countries, 1989-95. In The Economies of Transition, volume 8, pages 117–149. Wiley-Blackwell.
Madzarevic, Sanja and Mikulic, Davor (1997). Measuring the unofficial economy by the system of national accounts - Working Paper. Zagreb Institute of Public Finance. Muller, K.E. (2002). Regression and ANOVA: an integrated approach using SAS software. SAS Publications Cary NC.
Nurusis, M.J. (1996). SPSS regression models 10.0. SPSS Inc., Chicago IL.
Pozo, Susan (1996). Exploring the Underground Economy: Studies of Illegal and Unreported Activity. W.E.Upjohn, Institute for Employment Research, Kalamazoo MI.
Schneider, F., Buehn, A., and Montenegro, C. (2010). Shadow Economies All over the World - New Estimates for 162 Countries from 1999 to 2007. The World Bank Development Research Group, Washington DC.
Schneider, Friedrich (1994).Measuring the size and development of the shadow economy: Can the causes be found and the obstacles be overcome? In H.Brandstaetter and Güth, W., editors, Essays on Economic Psychology, pages 193–212. Springer, Berlin.
Schneider, Friedrich (1998). FurtherEmpiricalResults of the Size of the Shadow Economy of 17 OECD-Countries over Time. Paper presented at the 54th Congress of the IIPF Cordoba, Argentina.
Yoo, I. and Hyun, J.K. (1998). International Comparison of the Black Economy: Empirical Evidence Using Micro-level Data. Working Paper 98-04. Korea Institute of Public Finance.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Truscott, P.D., Korns, M.F. (2011). Detecting Shadow Economy Sizes with Symbolic Regression. In: Riolo, R., Vladislavleva, E., Moore, J. (eds) Genetic Programming Theory and Practice IX. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1770-5_11
Download citation
DOI: https://doi.org/10.1007/978-1-4614-1770-5_11
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-1769-9
Online ISBN: 978-1-4614-1770-5
eBook Packages: Computer ScienceComputer Science (R0)