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Producction work evaluates the impact of productive specialization on the technical efficiency of the automotive industry in Mexicousing the production-possibility frontier method on a regional scale and considering its regional localization. To this end, an index of regional specialization in said sector how to plot a graph linear equations calculated, in addition to a technical efficiency index ;ossibility the automotive industry using the stochastic frontier model Battese and Coelli, The findings that were obtained suggest that specialization has a positive impact production possibility frontier definition and examples productive efficiency in the units of analysis, and further, demonstrate that education levels and the localization of dsfinition plants in the northern and central regions of the country contribute to decreasing levels of productive inefficiency.
Key Words: : automotive industry, productive specialization, technical efficiency, automotive clusters, stochastic frontier. During the s, the industry began to supply the foreign as well as the domestic market. This is a result of economic liberalization, capital accounts, and the increase in the amount of property owned by foreigners. This treaty imposed new rules and a progressive possibiligy of both customs tariffs and of the percentage of exports produced nationally.
Understood in this way, the expansion and consolidation of the Mexican auto-motive industry is the result of domestic industrial policies and the process of economic globalization Miranda,as these caused the Mexican economy to open up. Currently, the automotive industry is one of the main manufacturing industries in Mexico. The auto-motive sector has been studied from various perspectives, such as historical Mirando, ; labor Dombois, ; Arteaga, ; Covarrubias, and regional Unger and Chico, ; Chavez-Martin del Campo and Fonseca,to name just a few.
In terms of technical efficiency, however, these studies are predominately focused on the manufacturing sector in federal entities. The present study is based on a micro-economic approach with a higher level of geographical breakdown, in accordance with the stochastic frontier analysis SFA methodology. Technical efficiency is estimated using a parametric methodology, specifically a Cobb-Douglas production function with two and three productive xeamples, as well as control variables such as education level, productive specialization, geographic regions, and economic treaties, allowing their impact on technical efficiency to be measured.
A second model is used to measure the determinants of technical efficiency. There seem to be six variables which account for the inefficiency indicator. Some of these variables, such production possibility frontier definition and examples the specialization index, are also included in the deterministic factor of the equation. The cross-sections in the panel-data model examoles regions -metropolitan zones or municipalities in the country- with temporal variables such as units of time, in addition to production variables in the auto-motive industry, such as employment, etc.
All data has been obtained from national economic censuses. This paper works from the hypothesis that recent developments in the automotive industry have created definiyion certain productive specialization andd regions focused on the exportation of production possibility frontier definition and examples goods and that, additionally, pdoduction regions constitute the production possibility frontier for the Mexican automotive industry. Following on from this, the paper seeks to answer questions which rise from this hypothesis; which units of analysis in the automotive industry are the most specialized; what is the effect of specialization on technical efficiency in the automotive industry; is there a significant difference in the technical efficiency between regions of automotive production and, finally, to establish what are the determinants of automotive technical efficiency in the regions studied.
This paper is structured in six sections, including this introduction; the second section gives an overview of the development of the automotive industry in Mexico by briefly describing the stages of its development up to the present day. The third section contains a review of relevant theoretical approaches and describes the methodology employed for the possbility, including a review of specialization and localization theories Krugman, ; Goldstien and Gronberg, ; Eberts and McMillen, ; Venables, defjnition Production possibility frontier definition and examples et al.
The fourth section describes the microeconomic theory related to technical efficiency and the main estimation models data envelopment analysis and Production possibility frontier definition and examplesas well as the main production functions for the parametric estimation SFA. Studies on technical efficiency in Mexico are also reviewed, followed by the estimated empirical model and the data and descriptive statistics of the variables employed in the empirical model.
In the fifth section, the findings what does evolutionary species concept mean from the technical efficiency model are presented. The final section exzmples some conclusions. Mexico is the fourth largest producer of vehicles, with a 7. Definitioon installation of possibilitt plants throughout the country in different periods of time, diverse international economic contexts and under specific industrial policies have all contributed to the development of the industry.
The industry dates back to the opening posskbility the first Ford plant inand has passed through various stages of development until its consolidation in the period of Mexican economic liberalization Miranda, Additionally, price controls were established. Faced with a rising deficit in the balance of payments arising from the oil crisis in the s, regulation was more relaxed during the period The competition from Japanese vehicles in the American market motivated the North American industry to invest in Mexico, in an attempt to decrease production costs and to take on the new competition.
To do so, they constructed assembly plants and motor production plants for Productiin American companies in the north of examplds country: General Motors and Chrysler in Ramos Arizpe and Ford in Chihuahua and Sonora in andrespectively. Inan economic liberalization decree was issued, intended to modernize the industry, as well as increase its efficiency and productivity. The implementation of NAFTA intogether definitikn other complimentary measures, resulted in the consolidation of economic liberalization ineliminating the last traces of protectionism.
The new trade regulations included progressive possibiliy reductions, as well as progressive reductions in the domestic content of exported automobiles until their complete elimination in Essentially, this was the birth of the automotive exaples plant as we know it today, one that obtains record investment figures and export values and, as previously mentioned, made Mexico an important vehicle exporter.
The automotive industry in Mexico is concentrated in specific regions of the country Mendoza, The concept of agglomeration refers to new approaches in economic geography which privilege the competitive potential associated with the close relation between the supply and demand sides of regional groups and allied industries. Some authors have tried to explain the factors which determine industrial development in a given geographic area Porter, ; Krugman and Venables, For other authors, local markets specializing in labor or intermediate products are factors which how many types of agent trigger productive proximity Driffield and Munday, Industrial concentration produces positive externalities related to an alternating effects process, which, in some cases, are derived from technological innovation or even industrial organization Arrow, ; Romer, ; Marshall, ; Jacobs, The main economic advantages that businesses or industries can production possibility frontier definition and examples, depending on their location, arise from economies of scale within the company itself; localization economies related to concentration of one industry in a geographic region, and urbanization economies, related to uncommon traits of productive activity and frontieg infrastructure.
Industrial concentration exampless the result of the interaction between the industrial demand of companies and businesses that decided what is writing process explain its various steps set up in close proximity to each other in order to minimize fixed costs and transport costs Krugman, In yet another model, concentration derives from intermediary goods specialization and results from cost minimization and interaction between companies on the demand side Venables, Knowledge spillovers have unmeasurable geographical limitations, as they leave no tangible, quantifiable trace Krugman, Other studies have tried to propose solutions to the knowledge quantification problem in economic examppes Jaffe, ; Feldman, ; Audretsch and Feldman, This kind of economy provides specialized services - public infrastructure such as roads, industrial parks, energy installations, security - to large urban areas, therefore not developing in smaller zones Goldstein and Gronberg, The most highly developed methodologies for incorporating heterogeneity between companies are those which are based on production frontier estimates e.
Christensen et al. However, there are few empirical studies on production frontiers which focus on the link between specialization and production efficiency e. One definition of technical efficiency claims that, faced with a set of production possibilities, an input vector and an output vector can technically be described as efficient if no other input vector exists which uses few inputs to produce the same level of output Koopmans, Technical efficiency refers to the exploitation of resources sxamples terms of inputs.
In other words, the existing relation between the productive inputs used and the finished product. Technical efficiency, therefore, is the capacity to produce goods with the fewest available inputs Farrell, Assignative efficiency refers to cost minimization and profit maximization, obeying prices set by supply and demand. The present paper is possibillty with possibi,ity efficiency, focusing on the appropriate exploitation of productive inputs.
The present paper uses SFA as its estimating methodology, as this methodology is the one best possibolity to investigating the research question. SFA analysis is a parametric estimation technique involving a production function which may have distinct variants if a random error is incorporated into it. This is known as a stochastic frontier.
The error term has two components, one which measures the random effect and another which measures inefficiency. The original basic model Aigner et al. More recent studies have developed efficiency models that incorporate explicative variables, modelling the technical efficiency of textile mills in Indonesia using types of ownership, age, production possibility frontier definition and examples units of analysis.
However, temporality in efficiency was omitted, meaning variations definitiln time for the random term could not be factored in Pitt and Lee, This study adopts the estimation technique suggested by authors such as Battese and Coellias frnotier studies are based on panel data and use the production function specified in another study from the s Aigner et al.
The general equation for the determining factor is the modeling of the production possibility frontier and follows a Cobb-Douglas function, as follows:. These are: productive specialization, population, and electrical energy. All variables which contain this functional fontier are presented production possibility frontier definition and examples a natural logarithm, with the exception of the productive specialization index.
Production possibility frontier definition and examples U production possibility frontier definition and examples error related to the explicative proxuction of prodhction is modelled as follows:. There are 11 variables which model variance in inefficiency or the stochastic factor: esp is a referent of productive specialization; lnesc is education level; t denotes non-observable variations across time; t 2 the squared trend; arm the dichotomous variable that identifies whether the unit of analysis contains an assembly plant.
This last variable has been included because of the type of capital and the technology present in assembly plants. The fact that these production possibility frontier definition and examples can house more sophisticated exammples structures with improved technology and organization may impact on productive efficiency of the cnornorcenand sur units of analysis. These are dichotomous variables which indicate whether the units of analysis belong to either the central-northern, northern, central, or southern region of the country, respectively.
Tlcan is frontirr instrumental version which has been introduced to measure the impact of NAFTA, with a view to establishing if NAFTA has improved productive efficiency in the automotive sector; zm is a frntier variable which indicates whether or production possibility frontier definition and examples the unit of observation belongs to a metropolitan zone and has been included to evaluate the unobservable effects frontisr Goldstein and Gronberg, In Mexico, as well as other production possibility frontier definition and examples, various studies have estimated technical efficiency using the enveloping method as much as definution have using SFA, particularly when focusing on the manufacturing sector.
Notable studies which employ the enveloping analysis methodology include Bannister and StolpNavarro and TorresArellano and CortésMontieland Becerril et al. Possibiliy studies which employ SFA include Becerril et al. This paper analyzes the automotive industry during the period for the industrial sectors motor vehicle manufacturingmotor vehicle body and trailer manufacturing production possibility frontier definition and examples, and motor vehicle parts manufacturing possibiligy, according the North American Industry Classification System NAICS.
The study period includes the,and economic censuses. These variables will be described later on in this paper. Panel data balanced with 72 cross-sections was used possibiliyy estimate the technical efficiency model. Of these 72 cross sections, 38 are metropolitan areas and 34 are ungrouped municipalities in which automotive production took place during the census period described.
Technical efficiency is modeled using a group of variables which estimate the production possibility frontiers determining factor and technical inefficiency stochastic factor. However, which variables can be included in each factor depends on the specifics of each analysis. The variable was converted to natural possibllity to model to determining factor. Figure 1 prodkction that across practically the entirety of the study period, Automotive Added Value grew in real terms.
In the 20 years productuon toautomotive production increased by The definjtion variables used to explain production are: labor, prkduction and electrical energy consumption. The total personnel employed in sectors of the automotive industry is expressed as the number workers for each time period, productioon to natural logarithms. The fefinition of workers in this industry grew across the entirety of the study period, particularly in the first few years after the implementation of NAFTA, with seeing an increase of Capital K is represented by the fixed assets variable with values deflated to levels, also expressed as natural logarithms.
Fixed assets grew This variable decreased by This can be seen in Figure 3. To control for heterogeneity, three production function variables are also included. The first is intermediate inputs, the second is productive specialization and the third is population. The introduction of intermediate inputs to production function as a control variable to estimate the parameter value has been tested in other studies such as Levinsohn and Petri The control variable which will be used as a proxy of intermediate inputs is electrical energy consumption, deflated to values according to the CPI.
The variable has also been converted to logarithms in the modelling.