Mathematics and Computer Science
Volume 3, Issue 2, March 2018, Pages: 54-66
Received: Apr. 18, 2018;
Accepted: May 3, 2018;
Published: Jun. 1, 2018
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Sergio Augusto Para Bittencourt Neto, Federal District's Revenue Department, Brasilia, Brazil
Simone Borges Simao Monteiro, Department of Production Engineering, University of Brasilia, Brasilia, Brazil
Joao Carlos Felix Souza, Department of Production Engineering, University of Brasilia, Brasilia, Brazil
Ricardo Matos Chaim, Department of Production Engineering, University of Brasilia, Brasilia, Brazil
This study presents a practical methodology developed in the R software language, which makes use of Data Envelopment Analysis, in the Constant Returns of Scale model, to measure the tax collection efficiency of the ICMS taxpayers (Brazilian tax on commercial operations related to the movement of goods and interstate and inter-municipal transportation and communication services), using as input the component variables of the tax calculation function found in the amounts recorded in the Electronic Invoices (purchases and sales) and in billing obtained with sales made with Card (credit and debit mode). The data corresponding to a fiscal year are obtained in the databases of the Brazilian revenue agencies, tabulated and submitted to the DEA calculation (multipliers and the envelope models). Thus, in a process of monitoring taxpayers belonging to the same economic sector, the lower relative efficiency performances of the companies will raise suspicion and serve to identify those that deserve to be audited (fiscal audit). Two examples of application of the explained methodology are demonstrated (Department Stores sector and Retailing of Footwear sector), where it is possible to observe its positive results in the identification of the taxpayers with low efficiency in the tax collection and eligibility for the inspection action. Currently the methodology is in use in the Federal District Revenue (Brazil) as an instrument for selecting companies for auditing.
Sergio Augusto Para Bittencourt Neto,
Simone Borges Simao Monteiro,
Joao Carlos Felix Souza,
Ricardo Matos Chaim,
Using Data Envelopment Analysis to Ranking ICMS’s Taxpayers, Mathematics and Computer Science.
Vol. 3, No. 2,
2018, pp. 54-66.
Brasil, Constituicao da Republica Federativa do Brasil. 1988.
Charnes, A.; Cooper, W. W.; Rhodes, E., Measuring the efficiency of decision making units, European Journal of Operational Research, no. 2(6), pp. 429444, 1978.
W. W. Cooper, L. M. Seiford, and J. Zhu, Handbook on Data Envelopment Analysis. Springer Science & Business Media, 2011.
J. Zhu, Data Envelopment Analysis: A Handbook of Empirical Studies and Applications. Springer, 2016.
P. Wanke, C. P. Barros, and A. Emrouznejad, Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks, European Journal of Operational Research, vol. 249, no. 1, pp. 378389, Feb. 2016.
R. Gulati and S. Kumar, Analysing banks intermediation and operating efficiencies using the two-stage network DEA model: The case of India, International Journal of Productivity and Performance Management, vol. 66, no. 4, pp. 500516, Apr. 2017.
S. K. Jauhar, M. Pant, and A. K. Nagar, Sustainable educational supply chain performance measurement through DEA and differential evolution: A case on Indian HEI, Journal of Computational Science, vol. 19, pp. 138152, Mar. 2017.
V. Gimnez, C. Thieme, D. Prior, and E. Tortosa-Ausina, An international comparison of educational systems: a temporal analysis in presence of bad outputs, J Prod Anal, vol. 47, no. 1, pp. 83101, Feb. 2017.
K. A. Safdar, A. Emrouznejad, and P. K. Dey, Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres, Journal of Medical Systems, vol. 40, no. 1, Jan. 2016.
R. Gholami, D. An Hign, and A. Emrouznejad, Hospital performance: Efficiency or quality? Can we have both with IT?, Expert Systems with Applications, vol. 42, no. 12, pp. 53905400, Jul. 2015.
Bahari A., A. Emrouznejad, Influential DMUs and outlier detection in Data Envelopment Analysis with an Application to Health Care, Annals of Operations Research, vol. 223 (1), pp. 95108, 2014.
M. A. Zare, M. T. T. Fard, and P. Hanafizadeh, The Assessment of Outsourcing IT Services using DEA Technique: A Study of Application Outsourcing in Research Centers, IJORIS, vol. 7, no. 1, pp. 4557, Jan. 2016.
P. W. Jorgensen, D. C. Trotter, and T. R. Hill, Ecosystem services assessments in local municipal decision making in South Africa: justification for the use of a business-based approach, Journal of Environmental Planning and Management, vol. 59, no. 2, pp. 263279, Feb. 2016.
Z. Yang, Y. Shi, and H. Yan, Scale, congestion, efficiency and effectiveness in e-commerce firms, Electronic Commerce Research and Applications, vol. 20, pp. 171182, Nov. 2016.
S. Wang, E. Cheng, J. Zhu, C. Fu, and W. Wang, Using DEA Models to Measure the Performance of Public Culture Services in China, in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016, pp. 447452.
H. Chowdhury and V. Zelenyuk, Performance of hospital services in Ontario: DEA with truncated regression approach, Omega, vol. 63, pp. 111122, Sep. 2016.
A. Emrouznejad, Advances in data envelopment analysis, Ann Oper Res, vol. 214, no. 1, pp. 14, Mar. 2014.
W. D. Cook and L. M. Seiford, Data envelopment analysis (DEA) Thirty years on, European Journal of Operational Research, vol. 192, no. 1, pp. 117, Jan. 2009.
A. Charnes, W. W. Cooper, A. Y. Lewin, and L. M. Seiford, Data Envelopment Analysis: Theory, Methodology, and Applications. Springer Science & Business Media, 2013.
R. D. Banker, A. Charnes, and W. W. Cooper, Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, vol. 30, no. 9, pp. 10781092, Sep. 1984.
L. M. Seiford and R. M. Thrall, Recent developments in DEA, Journal of Econometrics, vol. 46, no. 12, pp. 738, Oct. 1990.
K. Tone, Advances in DEA Theory and Applications: With Extensions to Forecasting Models. John Wiley & Sons, 2017.
J. Zhu, Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Springer, 2014.
J. Zhu, Data envelopment analysis: let the data speak for themselves. Leipzig: Amazon Distribution, 2014.
KASSAI, Slvia, Utilizacao da Analise por Envoltoria de Dados (DEA) na Analise de Demonstracoes Contabeis, Departamento de Contabilidade e Atuaria. Faculdade de Economia, Administração e Contabilidade. Universidade de Sao Paulo, Sao Paulo: USP, 2002.
C. M. D. C. F. FERREIRA, Introducao a analise envoltoria de dados: teoria, modelos e aplicacoes. UFV, 2009.
B. Casu, C. Girardone, and P. Molyneux, Productivity change in European banking: A comparison of parametric and non-parametric approaches, Journal of Banking & Finance, vol. 28, no. 10, pp. 25212540, Oct. 2004.
O. H Ibrahim, Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis. IGI Global, 2013.
T. Coelli, A multi-stage methodology for the solution of orientated DEA models, Operations Research Letters, vol. 23, no. 35, pp. 143149, Oct. 1998.
A. Emrouznejad and M. Tavana, Eds., Performance Measurement with Fuzzy Data Envelopment Analysis, vol. 309. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014.
M.-R. Ghasemi, J. Ignatius, S. Lozano, A. Emrouznejad, and A. Hatami-Marbini, A fuzzy expected value approach under generalized data envelopment analysis, Knowledge-Based Systems, vol. 89, pp. 148159, Nov. 2015.
Brasil, Codigo Tributario Nacional. Diario Oficial da Uniao. 1966.