Draft version August 14, 2014Preprint typeset using LATEX style emulateapj v. 12/16/11

COSMETIC INDUSTRY IN MEXICO: AN EXPLORATORY ANALYSIS

David Gaytan*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.com1 and bruiz alx@hotmail.com2.

** Department of System & Industrial Engineering, Tecnolgico de Monterrey, Campus Monterrey, Ave. E. Garza Sada 2501 Sur, 64849Monterrey, Nuevo Leon, Mexico. Email: francisco benita@hotmail.com3

Draft version August 14, 2014

ABSTRACT

This study provides an overview of the broad cosmetics industry in Mexico. We implement a dataenvelopment 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 twocomponents: the change in efficiency and the technical change.Keywords: Cosmetic clusters, Mexico, DEA, Malmquist index

1. INTRODUCTION

The cosmetic manufacturing sector accounts for 1.2percent of gross domestic product in Mexico, a 14 bil-lions of dollars market. Moreover, in 2013 exports andimports 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 transferand an substantially increase on exports of manufacturesand services relative to traditional commodity exports.

However, a focal problem today in the dynamics ofcosmetic 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 reformslead to improvements in the use of resources. This studyis one step in this direction. It intends to adopt aDEA-Malmquist index (DEA-MI) to study the efficiencyof Mexicos cosmetic industry by federal states throughyears 1998, 2003 and 2008. The spatial dimension ofthe data can reveals marked disparities in inputs acrossfederal states, then one can dentify more accurately theimpact of productive public/private expenditure on cos-metic industrial productivity.

2. METHODS

Technical efficiency is a form of productive efficiencywhich is concerned with output maximization, i.e., max-imizing output using given inputs. The DEA was firstintriduced 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 DecisionMaking Units (DMUs) with same goals and objectives.The technique has been applied to various industrial andnon-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 indexdirectly 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 DEAand then computing the distance of individual observa-tions from the frontier. In practice, this DEA-MI hasproven to be a good tool for measuring the productiv-ity change of DMUs over time, and has been successfullyapplied in many fields.

Based on data from the Economic Census 1999, 2004and 2009, a dynamic DEA-MI model allow us to appreci-ate intertemporal effects in efficiency measuring throughfederal states in Mexico.

3. RESULTS

4. DISCUSSION

REFERENCES[1] CANIPEC (2013). Informe anual 2013, Camara Nacional de

la Industria de Productos Cosmeticos, CANIPEC, Mexico.[2] Charnes, A., Cooper, W. and Rhodes, E. (1978). Measuring

the efficiency of decision making units, European Journal ofOperational Research, 2(6): 429444.

[3] Emrouznejad, A., Parker, B. and Tavares, G. (2008).Evaluation of research in efficiency and productivity: a surveyand analysis of the first 30 years of scholarly literature in DEA,Socio-Economic Planning Sciences, 42(3): 151157.

[4] Fare, R. Grosskopf, S. Lindgren, B. and Roos, P.(1992).Productivity change in Swedish pharmacies 1980-1989: Anonparametric Malmquist approach, Journal of ProductivityAnalysis, 3: 85102.

[5] Gattouf, S., Oral, M. and Reisman, A. (2004). pistemology ofdata envelopment analysis and comparison with other fields ofOR/MS for relevance to applications, Socio-EconomicPlanning Sciences, 38(2-3): 123140.

[6] Malmquist, S. (1953). Index numbers and indifference surfaces,Trabajos de Estadstica, 4(2): 209242.