Draft version August 14, 2014 Preprint typeset using L A T E X style emulateapj v. 12/16/11 COSMETIC INDUSTRY IN MEXICO: AN EXPLORATORY ANALYSIS David Gayt´ an *1 , Alejandro Ruiz *2 and Francisco Benita **3 * Instituto de Investigaciones Econmicas y Sociales, Autonomous University of Zacatecas, Preparatoria Avenue, 98068 Zacatecas, Mexico. Email: davidgaytan81@gmail.com 1 and bruiz alx@hotmail.com 2 . ** Department of System & Industrial Engineering, Tecnolgico de Monterrey, Campus Monterrey, Ave. E. Garza Sada 2501 Sur, 64849 Monterrey, Nuevo Le´ on, Mexico. Email: francisco benita@hotmail.com 3 Draft version August 14, 2014 ABSTRACT This study provides an overview of the broad cosmetics industry in Mexico. We implement a data envelopment analysis (DEA) based on Malmquist productivity index, thus we measure the produc- tivity change over the years 1998, 2003 and 2008. The index can be further decomposed into two components: the change in efficiency and the technical change. Keywords: Cosmetic clusters, Mexico, DEA, Malmquist index 1. INTRODUCTION The cosmetic manufacturing sector accounts for 1.2 percent of gross domestic product in Mexico, a 14 bil- lions of dollars market. Moreover, in 2013 exports and imports exceeded 2.5 millions and 1.2 millions of dollars, respectively, resulting in an important trade surplus [1]. Empirical evidence has suggested that trade liberaliza- tion promotes productivity growth, technology transfer and an substantially increase on exports of manufactures and services relative to traditional commodity exports. However, a focal problem today in the dynamics of cosmetic industry clusters is related to the efficiency lev- els. By studyng the efficiency of firms one can deter- mine wether the incentives created by market reforms lead to improvements in the use of resources. This study is one step in this direction. It intends to adopt a DEA-Malmquist index (DEA-MI) to study the efficiency of Mexico’s cosmetic industry by federal states through years 1998, 2003 and 2008. The spatial dimension of the data can reveals marked disparities in inputs across federal states, then one can dentify more accurately the impact of productive public/private expenditure on cos- metic industrial productivity. 2. METHODS Technical efficiency is a form of productive efficiency which is concerned with output maximization, i.e., max- imizing output using given inputs. The DEA was first intriduced in [2] al as a non-parametric performance mea- surement technique. It is an application of linear pro- gramming to measure the relative efficiency of Decision Making Units (DMUs) with same goals and objectives. The technique has been applied to various industrial and non-industrial contexts, such as banking, education, hos- pital, etc. (see e.g. [3] and [5]). On the other hand the concept of the Malmquist pro- ductivity index was originally introduced in [6] as a quan- tity for analyzing the consumption of inputs. Afterwards, in [4] was constructed a Malmquist productivity index directly from input and output data using DEA. Specif- ically, the DEA-MI, relies on firstly constructing an ef- ficiency frontier over the whole sample realized by DEA and then computing the distance of individual observa- tions from the frontier. In practice, this DEA-MI has proven to be a good tool for measuring the productiv- ity change of DMUs over time, and has been successfully applied in many fields. Based on data from the Economic Census 1999, 2004 and 2009, a dynamic DEA-MI model allow us to appreci- ate intertemporal effects in efficiency measuring through federal states in Mexico. 3. RESULTS 4. DISCUSSION REFERENCES [1] CANIPEC (2013). Informe anual 2013, C´amara Nacional de la Industria de Productos Cosm´ eticos, CANIPEC, Mexico. [2] Charnes, A., Cooper, W. and Rhodes, E. (1978). Measuring the efficiency of decision making units, European Journal of Operational Research, 2(6): 429–444. [3] Emrouznejad, A., Parker, B. and Tavares, G. (2008). Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA, Socio-Economic Planning Sciences, 42(3): 151–157. [4] F¨are, R. Grosskopf, S. Lindgren, B. and Roos, P.(1992). Productivity change in Swedish pharmacies 1980-1989: A nonparametric Malmquist approach, Journal of Productivity Analysis, 3: 85–102. [5] Gattouf, S., Oral, M. and Reisman, A. (2004). pistemology of data envelopment analysis and comparison with other fields of OR/MS for relevance to applications, Socio-Economic Planning Sciences, 38(2-3): 123–140. [6] Malmquist, S. (1953). Index numbers and indifference surfaces, Trabajos de Estad´ ıstica, 4(2): 209–242.