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JES 42,3
Technical efficiency and its determinants factors in Spanish textiles industry (2002-2009)
346 Received 18 June 2013 Revised 3 December 2013 9 May 2014 Accepted 12 May 2014
Justo De Jorge-Moreno Department of Economics and Business, University of Alcala, Alcala de Henares, Spain, and
Oscar Rojas Carrasco Escuela de Auditoria e Ingeniería en control de gestión, Universidad de Talca, Talca, Chile Abstract Purpose – The purpose of this paper is to provide new evidence about the technical efficiency and its determinants in Spanish textile sector during the period 2002-2009. The empirical results suggest that the effects of trade liberalization have led to higher levels of inefficiency in the Spanish sector, due to the lack of flexibility of firms to adjust to the environment, and perhaps to aggressive competition with fuzzy rules of the game. Controlling for specific factor like age, intensity of capital, salary by worker, regions and market share, the authors have obtained that the interaction between market share and size indicates that as firms have more size are also more inefficient. Design/methodology/approach – In this paper, the stochastic frontier production function is considered, specifically, a panel data version of Battese and Coelli (1995), in which the technical inefficiency is estimated from the stochastic frontier and simultaneously explained by a set of variables. This approach avoids the inconsistency problems of the two-stage approach used in other empirical works when analyzing the inefficiency determinants. Findings – This work provides new evidence about the technical efficiency and its determinants can be due to environmental or firm-specific factors in Spanish textile sector during the period 2002-2009. The authors have estimated the Cobb-Douglass stochastic production frontier following Battese and Coelli (1995) model to analyze an unbalanced panel. Originality/value – The empirical results suggest that the trend of the inefficiency shows a curvilinear behavior in the form of U (turning point third-quarter of 2004). This result is related to the efficiency analysis through Kernel distributions (in static and dynamic form) confirmed a clear process of divergence. In the period 2002-2005 the efficiency of the firms analyzed maintained higher levels than the 2005-2009 period where there is deterioration. This may be related to the increased competition due to the end of the Multi-Fiber Arrangement in January 2005 and the entry of Chinese products in 2004. Keywords Efficiency, Stochastic frontier, Size, Market share, Multi-fibre Agreement Paper type Research paper
Journal of Economic Studies Vol. 42 No. 3, 2015 pp. 346-357 © Emerald Group Publishing Limited 0144-3585 DOI 10.1108/JES-06-2013-0085
1. Introduction On the Uruguay Round in 1995 agreements to eliminate trade restrictions on textiles were reached and garment factories were defined by the Multi-fiber Agreement (AMF), which was replaced by the Agreement on Textiles and Clothing of World Trade Organization. This agreement was expected to gradually both importers as exporters of textiles and clothing, because of the new situation in 2005 the sector was fully integrated into normal General Agreement on Tariffs and Trade (GATT) rules. This is a great change in the international trade scenario for textile manufacturers across the world offering opportunities for penetration into markets, that have been off limits under the previous regime while posing threats of market loss in the face of competition
from other countries Bhandari and Ray (2012). The growing and increasingly aggressive competition experienced by the European textile industry from countries like China, India, Pakistan, Vietnam, etc., mainly based on low cost, has led to a deep crisis (Coll and Blasco, 2009). We are interested in analyze determinants of technical efficiency in Spanish textile firms on the microeconomic level during the period 2002-2009. The objective of this paper is to analyze the effect on efficiency, of some organizational factors related to the managerial ability, to use properly and adjust capital and labor according to environmental conditions, as the change of the regulatory environment conditions. Size and Market share are included in the analysis as two of the most important factors that condition the organization of firms and then the degree of their efficiency. Our paper contributes to the empirical evidence of efficiency in Spain, adding to the previous papers the relevance of changes affecting the factors of production and the way these factors are used and combined. Second, our paper differs from previous literature in Spain by the way that we use an improved frontier model that not only allows us to estimate the firm’s technical inefficiency but, simultaneously, it identifies the variables that are statistically related to inefficiency, i.e. the determinants of the reached inefficiency. For this purpose, we want to explain firm differences in efficiency, following the methodology proposed by Lieberman and Dhawan (2005), which tries to connect the resource-based view of the firm and the frontier analysis, specifically; we apply Battese and Coelli’s (1995) model. This frontier model not only allows us to estimate the firm’s technical inefficiency but, simultaneously, to identify the variables that are statistically related to inefficiency, to avoid the econometric problem of the two-step procedure. By using frontier techniques, several studies have analyzed both external and internal factors to explain the sources of technical inefficiency and with different models but normally with Battese and Coelli (1992, 1993) models. Some authors who have used the stochastic production frontier approach (SFA), in textile sector at international level are as follows: Pitt and Lee (1981) using data from establishments in Indonesia found a relationship between efficiency and ownership, age and firm size. Jaforullah (1999) investigates the production technology, the possibilities of substitution between, factors of production and technical efficiency of the textile industry (handlooms) in Bangladesh. Mini and Rodríguez (2000) study the relationship between size, measured by total employees and technical efficiency in the Philippine textile industry. Goaïed and Ayed-Mouelhi (2000) investigate technical efficiency adjusted for the age of capital and types of labor in Tunisian textile, clothing and leather. Battese and Prasada Rao (2001) conducted an empirical study to evaluate the efficiency technique in midsize and large companies, in the Indonesian garment industry. Kim (2003) investigates in Korean textile sector, whether technical efficiency is related with firm size, dependency on external funds, research and development investments and exports. Kouliavtsev et al. (2007) estimate a variable elasticity of substitution (VES) production function using the NBER database. Bhandari and Maiti (2007) examine the relation between both size and age with technical efficiency to firm-level cross-sectional data on India’s textile firms. El-Atroush and Montes-Rojas (2011) examine factors that affect textile and apparel supply chain operations for firms operating under different technologies and ownership structures. Bhandari and Ray (2012) investigate how location, proprietary and organizational characteristics of a firm affect its efficiency. Bhandari and Maiti (2012) observe a significant positive association between a firm’s size and its technical efficiency, but no such clear relation between a firm’s age and technical efficiency of the Indian leather firms.
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On a national level in textile sector in Spain, we only record of the works of Gumbau (1988) and Coll and Blasco (2009) who analyze the evolution of efficiency in the period 1980-1986 and 1995-2005, respectively. The structure of this paper is as follows. Section 2 presents a descriptive analysis of the data. Section 3 describes the frontier methods used to measure for firms in Spanish textile. Section 4 discusses the frontier results. Section 5 provides some concluding remarks. 2. Data The statistical information comes from the SABI database, produced jointly by Bureau van Dijk and Informa, from the financial information that firms must present to the firms Registration Office (BORME). This database covers all sectors of Spanish business activity. It is highly representative of firms from 17 Spanish autonomous “communities” (i.e. regions). The study samples taken from SABI include all the firms belonging to the Retail sale of clothing in specialized stores (CNAE 4771) (download February 2012). Our sample includes 789 firms from the SABI database and refers to an unbalanced panel where we have eliminated those firms, which we do not have two consecutive years of data for. Summary statistics of the data are presented in Table I. 3. Methodology and model specification On this paper, the stochastic frontier production function is considered, specifically, a panel data version of Battese and Coelli (1995), in which the technical inefficiency is estimated from the stochastic frontier and simultaneously explained by a set of variables. This approach avoids the inconsistency problems of the two-stage approach used in other empirical works when analyzing the inefficiency determinants[1]. The Battese and Coelli (1995) model can be expressed as: Y it ¼ f UðX it ; bÞexpUðV it U it Þ
(1)
where Yit denotes the production of the observation in t (t ¼ 1, 2, …, T) for firm i (i ¼ 1, 2, …, N), Xit is a (1 × k) vector of known values, function of the production inputs and other explanatory variables associated with firm i in observation t, β is a (k × 1) vector of unknown parameters to be estimated, Vit is distributed as independent and identically distributed N(0, σ2v), and distributed independently of the Uit which are non-negative random variables which are assumed to account for technical inefficiency Variable
Table I. Summary statistic
Mean
Lnsales 6.561 LnFixes Assets 4.453 Lmaterials 6.063 Lemployment 2.002 Capital by worked 31.02 20.14 Salary by worked 22.36 Age Market share 0.0012 Source: SABI and own elaboration
SD
Minimum
Maximum
1.414 1.945 1.436 1.299 99.39 11.64 9.723 0.010
2.302 0.691 0.682 0.000 0.004 0.489 9 0.00
14.312 13.022 13.745 9.547 3,232.38 273.73 98 0.2362
in production and which are assumed to be independently distributed as truncations at zero of the N(mit, σ2u) distribution. The mean of this distribution is: (2) mit ¼ zit d where zit it is a p × 1 vector of variables, which may influence the efficiency, of a firm; and δ it is a (1 × p) vector of parameters, to be estimated. The production function coefficients (β) and the inefficiency model parameters (δ) are estimated by maximum likelihood together with the parameterization from Battese and Corra (1977), replacing σ2V and σ2u with σ2 ¼ σ2v + σ2u and γ ¼ σ2u/(σ2v + σ2u). Given that technical efficiency is the ratio of observed production over the maximum technical output obtainable for a firm (when there is no inefficiency), the efficiency (TE) of firm i in year t could be written as: TE ¼
f ðX it ; bÞ expðV it U it Þ ¼ expðU it Þ f ðX it ; bÞ expðV it Þ
(3)
The efficiency scores obtained from expression (3) have a value of one when the firm is efficient and less than one otherwise. This work assumes the Cobb-Douglas production function, with non-neutral technological progress[2]. In this way it is possible to observe the frontier shifting after controlling for the other factors considered. In particular, the function to estimate has the following form: LnðsalesÞ ¼ bo þ b1 Ln K it þ b2 Ln C it þ b3 Ln E it þ b4 t þ b5 Ln K it t þ b6 Ln C it t þ b7 Ln E it t þ V it –U it
(4)
where the technical inefficiency effects are assumed to be defined by: U it ¼ d0 þ d1 t þ d2 t 2 þ d3 A þ d4 A2 þ d5 K_W it þ d6 S_W it þ d7 MS þ d8 MS Size_1 þ d9 MS Size_2 þ
17 X
di10 Reg þ W it
(5)
i¼1
where, considering the variables in logarithms. In particular, to measure the output, the production of goods and services, we consider the sum of the sales and the variation in sales inventory for each of the firms analyzed. K is the fixed asset as proxy capital variable (Coll and Blasco, 2009; Bhandari and Ray, 2012), C is materials (El-Atroush and Montes-Rojas, 2011), E is employment (Kouliavtsev et al., 2007; Bhandari and Ray, 2012). These variables are converted into constant Euros using deflators from the Spanish National Statistics Institute (INE). Finally, this stochastic frontier model includes years of observation (t) in such a way that non-neutral technical change is specified (see e.g. Battese and Broca, 1997). However, neutral technical change is present if the coefficients of the interactions between year of observation and the input variables are zero. Regarding the inefficiency term, t, t2 and A, A2 represents the temporal evolution of inefficiency and age of the firm, respectively in a quadratic form. K_W and S_W represents capital per worker (the ratio of the fixes assets over employment) and salary per worker (the ratio of personnel cost over employment). MS represents market share (the ratio of the sales of an individual firm over sector sales by year). MS × Size represents the iterations between MS and size (size_1 ¼ 1-10 workers; size_2 ¼ 11-50 workers; size_3 ¼ firms with a number of workers higher than 50).
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Finally Reg denotes a vector of dummies capturing the 17 Spanish autonomous “communities” (i.e. regions). The coefficients t and t2 in Equation (5) measure how inefficiency changes over time[3]. Consequently, if δ1 and δ2 is negative and positive and statistically significant (inefficiency in the U-shaped), technical change (improving efficiency, depending on the area where the observations were found) is observed, and δ1, δ2 can be indicated as the coefficient of technological change in Uit. If δ1 and δ2 it is positive and negative and statistically significant (inefficiency in the inverted U-shaped). 4. Estimation of model and results Table II shows the results of the model estimated simultaneously according to the maximum likelihood (Equations (4) and (5)). The data used, as mentioned above, are an unbalanced panel in the period 2002-2009 from the SABI database. As it was mentioned in the previous section, the results shown in Table II assume a Cobb-Douglas stochastic production function, which has found ample acceptance in the literature[4]. The χ2 is statistically significant at the 1 percent level (w234 ¼ 3.8E+05). The positive and statistically significant coefficient of the time variable t shows the existence of technological progress, indicating that there has been the incorporation of production technologies that can contribute to improvements in the productive system, the coefficient of the variable indicates that the rate of output growth of firms in the sample is 8.9 percent annually. The elasticity of mean output with respect to the kth input variable, for example, employment, in Equation (4) has two components: β3 + β6 × t. The first component is the traditional elasticity of the output with respect to the input, this is referred to the elasticity of frontier output, and the second component of the elasticity is the non-neutral factor which is referred to the elasticity of the technical efficient (this component is zero for neutral stochastic frontier models). The elasticities are estimated in Table III. The elasticity of mean output are all positive and statistically significant at the 1 percent level and the elasticity of technical efficiency are only substantial components of the elasticity of the mean output for employment, materials and fixes assets these last inputs with a negative sign. The non-neutral technical change across all inputs is an adequate hypothesis for this model because the hypothesis of neutral technical change: H0: β7 ¼ β8 ¼ β9 ¼ 0 is χ2(3) ¼ 345.11 is rejected and, also, H0: β5 ¼ β7 ¼ β8 ¼ β9 ¼ 0 is χ2(4) ¼ 289.75 is rejected. Model 1 is preferred to model 2. Also, we accept the hypothesis of constant returns to scale (w2 (1) ¼ 0.31). One of the most important stylized facts refers to the results obtained in the part of the error term where the explanatory variable of inefficiency, shows interesting relation and curvilinear behavior. We now look more closely at the distribution of the firms and its relation with the technical inefficiency, by examining the curvilinear model from the coefficients estimated in the model. The function is as follows: U it ¼ 1:450:21 t þ 0:04 t 2 þ 0:03 A0:00003 A2 þ 0:00009 K_W 0:08 S_W 3035 MS22897 MS Size_15763 MS Size_2 þ
17 X
b7 Regi
i¼1
The trend of the inefficiency t and t2, shows a curvilinear behavior (in U shape), since its coefficients δ1 (−0.21) and δ2(0.04) are negative and positive, respectively, and statistically significant at the 1 percent level. The turning point is in the middle of 2004,
Model 1* Est. Coef.
SD
Model 2 Est. Coef.
SD
Spanish textiles industry (2002-2009)
1.379** β0 0.033 1.731** 0.017 0.023** 0.003 0.022** 0.002 β1 (Fixes Assets) 0.775** β2 (Materials) 0.007 0.695** 0.003 0.204** β3 (Employment) 0.007 0.282** 0.004 351 0.089** β5 (trend) 0.006 0.013** 0.001 −2.0E-04 β7 (trend×Fixes Assets) 0.006 – – −0.017** β8 (trend×Materials) 0.006 – – 0.017** β9 (trend×Employment) 0.006 – – Equation uit δuo −1.459** 0.27 −1.032** 0.257 −0.210** δ1 (t) 0.082 −0.317** 0.078 0.044** δ2 (t2) 0.008 0.049** 0.008 0.039** δ3 (Age) 0.012 0.039** 0.012 −3.0E-04** δ4 (Age2) −1.0E-04 −1.5E-04** −3.3E-04 δ5 (Capital per worker) 9.0E-04** 2.0E-04 0.001** 3.7E-04 δ6 (Salary per worker) −0.086** 0.005 −0.083** 0.005 δ7 (Market Share) −3,035** 525.3 3,031** 523.6 δ8 (Market Share×size1) −22,897** 2,271.4 −21,960** 2,120.3 δ9 (Market Share×size2) −5,763** 846.4 −5,511.2** 815.6 δ10.2 Aragon −0.978** 0.201 −0.987** 0.199 δ10.3 C.Valenciana −0.509 0.300 −0.571** 0.283 δ10.4 Murcia −0.800** 0.244 −0.670** 0.283 δ10.5 Andalusie −0.847** 0.265 −0.831** 0.261 δ10.6 Castilla La Mancha −0.250 1.626 −0.631 2.147 δ10.7 Extremadura −0.539** 0.179 −0.505** 0.176 δ10.8 Castilla La Mancha −0.339 0.182 −0.255 0.176 δ10.9 Castilla Leon −0.574** 0.130 −0.553** 0.126 δ10.10 Galicia −0.555** 0.145 −0.599** 0.144 δ10.11 Asturias −0.434 0.311 −0.404 0.301 δ10.12 Cantabria 0.543** 0.147 0.559** 0.145 δ10.13 País Vasco 0.135 0.481 −0.119 0.488 δ10.14 Navarra −0.469** 0.142 −0.486** 0.140 δ10.15 La Rioja −0.275 0.212 −0.236 0.202 δ10.16 Baleares 0.268 0.242 −0.164** 0.241 δ10.17 Canarias −0.405 0.213 −0.398 0.210 Equation vit δvo −3.389** 0.023 −3.792** 0.023 σv 0.146** 0.001 0.148** 0.001 Log-Likelihood 2,271.8 2,268 No. of observations 6,310 6,310 Notes: Omitted variables: Andalusie and market share×size_3. *,**Significant at 5 and 1 percent Table II. levels, respectively Results of estimation
when the inefficiency began to grow. The trajectory of age also shows a curvilinear behavior (in inverted U shape), since its coefficients δ3 and δ4 are positive and negative, respectively, and statistically significant at the 1 percent level. The positive relationship between age and efficiency is expected. Older firms have greater market knowledge “Know how,” reputation and economies of scale. However, older companies may have a harder and less flexible adaptation to environmental conditions, as in the case of textiles sector.
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Unexpectedly, the effect of the intensity of capital (capital by worker, K_W) is positive and significantly different from zero, which means the higher the intensity of capital is, the lower the level of firm’s efficiency is. This variable picks the effect on efficiency of the combination of inputs. One possible explanation is that changes in efficiency generated by a technical innovation depend on their nature and diffusion. If it is easy for firms to adopt it, then this change affects efficiency positively, while if it requires an important investment as well as organizational modification, then it could cause a shift in the frontier, thus the relative distance augment. This means that even if an increase in the stock of capital improves efficiency to do it in a different timing than the rest of the firms, this could cause losses of productivity derived from the capital adjustment in the short-term (Diaz and Sanchez, 2008, p. 321). The negative and statistically significant ratio of personnel costs over employment (S_W), indicates that considering the theory of efficiency wages, better qualified staff increases efficiency. The market share variable (MS), is significant and shows a negative sign, which means that the higher the market share is, the lower the inefficiency of the firm is. This variable captures the relevance of the market power of the firm inside its sector. The evidence related to the effect of this determinant on efficiency is not conclusive. Authors such as Hay and Liu (1997), Nickell et al. (1997), Díaz and Sénchez (2008) find a positive relationship between a firm’s efficiency and its market share. The market share is related to firm size. empirical studies that have investigated the relation between firm size and technical efficiency seem to provide more evidence for positive relation between these two variables. Some empirical studies have reported either an ambiguous or negative correlation or curvilinear behavior (inverted U shape) (see Kim, 2003). The variable that captures the interaction of market share and firm size shows that, as the market share on the size increases, so does inefficiency. That is, firms adjust their capacity in the market in which they operate. Larger sizes and greater market share could be related with both diseconomies of scale and scope. Finally, the firm’s location shows statistically significant differences in the efficiency differences. As we mentioned before, one of the most important environmental factors refers to the process of market deregulation in the textile industry since early 2005. We discuss the distribution of the efficiency of the companies analyzed in different periods of time. A variety of techniques to estimate density functions non-parametrically exists. Kernel smoothing is used here, (Silverman, 1986; Wand and Jones, 1995). It is a technique that provides a way of uncovering the data structure without imposing any parametric structure. Figure 1 shows the Kernel density distributions for initial year (2002), intermediate year (2005) and final year (2009). In our case, to confirm the divergence analysis of Kernel functions, we have tested the hypothesis of equal density functions. In order to do this, we applied the test chart Elasticity with respect to*
Table III. Elasticities of mean output with respect to inputs
Elasticity of frontier output
β1 (fixes assets) β2 (materials) β3 (employment) Note: *,**significance
Elasticity of the technical efficiency
0.023 (0.003)** −2.0E−04 (0.006) 0.775 (0.007)** −0.017 (0.006)** 0.204 (0.007)** 0.017 (0.001)** at 5 and 1 percent levels, respectively
Elasticity of mean output 0.022 (0.003)** 0.758 (0.001)** 0.221 (0.001)**
10
Spanish textiles industry (2002-2009)
Efficiency_2002 Efficiency_2005 Efficiency_2009
Density
8
6
353 4
2
0 0.2
0.4
0.6 Efficiency
0.8
1.0
and through bootstrap techniques to obtain the p-value (see Bowman and Azzalini, 1997). The tests shows rejects the null hypothesis of equal distributions Kernels (2002 vs 2005, p-value ¼ 0.000, 2005 vs 2009, p-value ¼ 0.000, 2002 vs 2009, p-value ¼ 0.000) The results in Figures 1 and 2 shows the changes in the external form of the distribution of efficiency on the initial year 2002, intermediate (2005) and final (2009) of the companies in the Spanish textile sector. As can be seen, these changes confirmed the process of divergence occurred. 5. Conclusions This work provides new evidence about the technical efficiency and its determinants can be due to environmental or firm-specific factors in Spanish textile sector during the period 2002-2009. We have estimated the Cobb-Douglass stochastic production frontier following Battese and Coelli (1995) model to analyze an unbalanced panel. The theory suggests that cases involving technical inefficiency may be due to time lags in firms’ acquisition of new technology and commensurate skills upgrades among staff; differential incentive systems; organizational factors associated with X-efficiency (Leibenstein, 1966) or associated with human capital, such as lack of incentives to the improvement of efficiency (Mirrelees and Miller, 1996) or dimensional factors associates with scale and scope economies (Johnston et al., 2000) among others factors. The empirical results achieved in this work suggest that the trend of the inefficiency shows a curvilinear behavior in the form of U (turning point third-quarter of 2004). This result is related to the efficiency analysis through Kernel distributions which confirmed a clear process of divergence. In the period 2002-2005 the efficiency of the firms analyzed maintained higher levels than the 2005-2009 periods where there is deterioration. This may be related to the increased competition due to the end of the Multi-fibre Arrangement in January 2005 and the entry of Chinese products in 2004. In relation to specific factors, a positive relationship between efficiency and market share exist. The firm size has significant effects on technical efficiency. However, the interaction between market share and size indicates that as firms are bigger are also more inefficient. This result could be explained by the complexity of larger firms in organization and managerial control. The smaller companies can adjust their resources and capabilities
Figure 1. Distribution Kernel by years
Figure 2. Graph contrasts of equal distributions Kernels by years
0
2
4
6
8
0.0
0.2
0.4 0.6 0.8 Efficiency
1.0
Efficiency 2002 over 2005
TE_2002 TE_2005
0
2
4
6
8
0.0
0.2
TE_2005 TE_2009
0.4 0.6 0.8 Efficiency
1.0
Efficiency 2005 over 2009
0
2
4
6
8
0.0
0.2
TE_2002 TE_2009
0.4 0.6 0.8 Efficiency
1.0
Efficiency 2002 over 2009
354
Density
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Density
to the needs of the market segment they serve. Also the small firms which are less efficient will exit the market under economic difficulties, more easily than large firms. The relationship between age and inefficiency shows a curvilinear behavior in the form of inverted U shape. Older firms have greater market knowledge “Know how,” reputation and economies of scale. Finally, better trained employees and the ratio capital over employed has a positive and negative effect over efficiency, respectively. In this sense, the theory suggests that cases involving technical inefficiency may be due to time lags in firms’ acquisition of new technology and commensurate skills upgrades among staff; differential incentive systems; organizational factors associated with X-efficiency (Leibenstein, 1966) or associated with human capital, such as lack of incentives for the improvement of efficiency (Mirrelees and Miller, 1996) or dimensional factors associates with scale and scope economies (Johnston et al., 2000) among others factors. To sum up, after controlling for specific factor like age, intensity of capital, salary by worker, regions and market share, we have obtained that the interaction between market share and size indicates that as firms are bigger are also more inefficient. Environmental factors in deregulation terms affect at the levels of efficiency. The effects of trade liberalization, with the exemption of tariffs on imports from Asian countries with low labor costs such as China, India, etc., implies the loss of protection and the creation of a highly competitive market. This could lead to higher levels of inefficiency in the Spanish textile sector due to the lack of flexibility of firms to adapt to the environment, and perhaps with fuzzy rules of the game. The lack of studies analyzing the textile sector in Spain in the time period chosen in this work and the methodology chosen for the time prevented any comparison at national level. The generalizability of these results, perhaps it is only possible by working at a micro-level data and a four-digit disaggregation. This could lead to future research. Notes 1. In a two-stage procedure, first, a stochastic frontier production function is estimated and the inefficiency is obtained under the assumption of independently and identically distributed inefficiency effects. But in the second step inefficiency effects are assumed to be a function of some variables, which contradicts the assumption of identically distributed inefficiency effects. 2. The Cobb-Douglas production function was chosen because of its simplicity and validity in different works (Zellner et al., 1966). Nevertheless, we also tried to use the trans-log function, but the likelihood function had problems of convergence. 3. We try to introduce a non-neutral measure of change in inefficiency over time but it has not significant impact on the inefficiency model. 4. Authors such as Gumbau (1998), Martín and Suárez (2000), De Jorge and Suarez (2011), El-Atroush and Montes-Rojas (2011) used this type of specification among others.
References Battese, G.E. and Broca, S.S. (1997), “Functional forms of stochastic frontier production functions and models for technical inefficiency effects: a comparative study for wheat farmers in Pakistan”, Journal Productivity Analysis, Vol. 8 No. 4, pp. 395-414. Battese, G.E. and Coelli, T. (1992), “Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India”, Journal of Productivity Analysis, Vol. 3 Nos 1-2, pp. 153-169.
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Battese, G.E. and Coelli, T. (1993), “A stochastic frontier production function incorporating a model for technical inefficiency effects”, Working Paper in Econometrics and Applied Statistics 69/83, Department of Econometrics, University of New England, Armidale. Battese, G.E. and Coelli, T. (1995), “A model for technical inefficiency effects in a stochastic frontier production function for panel data”, Empirical Economics, Vol. 20 No. 2, pp. 325-332.
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Battese, G.E. and Corra, G. (1977), “Estimation of a production frontier model: with application to the pastoral zone of eastern Australia”, Australian Journal of Agricultural Economics, Vol. 21 No. 3, pp. 169-179. Battese, G.E. and Prasada Rao, D.S. (2001), “Productivity potential and technical efficiency levels of firms in different regions using a stochastic metaproduction Frontier model”, unpublished paper, School of Economics, University of New England CEPA. Bhandari, A.K. and Maiti, P. (2007), “Efficiency of Indian manufacturing firms: textile industry as a case study”, International Journal of Business and Economics, Vol. 6 No. 1, pp. 71-88. Bhandari, A.K. and Maiti, P. (2012), “Efficiency of the Indian leather firm: some results obtained using the two conventional methods”, Journal Productivity Analysis, Vol. 37 No. 1, pp. 73-93. Bhandari, A.K. and Ray, S. (2012), “Technical efficiency in the Indian textiles industry: a non parametric analysis of firm-level data”, Bulletin of Economic Research, Vol. 64 No. 1, pp. 109-124. Bowman, A.W. and Azzalini, A. (1997), Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations, Oxford University Press, Oxford. Coll, V. and Blasco, O. (2009), “Evolución de la eficiencia técnica de la industria textil española en el periodo 1995-2005’. análisis mediante un modelo frontera estocástica”, Revista de Estudios de Economía Aplicada, Vol. 27 No. 3, pp. 1-32. De Jorge, J. and Suarez, C. (2011), “Influence of R&D subsidies on efficiency: the case of Spanish manufacturing firms”, Cuadernos de Economía y Dirección de la Empresa, Vol. 14 No. 3, pp. 185-193. Diaz, A. and Sanchez, R. (2008), “Firm size and productivity in Spain: a stochastic frontier analysis”, Small Business Economic, Vol. 30 No. 3, pp. 315-323. El-Atroush, I.M. and Montes-Rojas, G. (2011), “Technical efficiency estimation via metafrontier technique with factors that affect supply chain operations”, International Journal of Business and Economics, Vol. 20 No. 29, pp. 117-138. Goaïed, M. and Ayed-Mouelhi, R.B. (2000), “Efficiency measurement with unbalanced panel data: evidence on tunisian textile, clothing and leather industries”, Journal of Productivity Analysis, Vol. 13 No. 3, pp. 249-262. Gumbau, M. (1998), “La eficiencia técnica de la industria española”, Revista Española de Economía, Vol. 15 No. 1, pp. 67-84. Hay, D. and Liu, G. (1997), “The efficiency of fims: what difference does competition make?”, The Economic Journal, Vol. 107 No. 442, pp. 597-617. Jaforullah, M. (1999), “Production technology, elasticity of substitution and technical efficiency of the handloom textile industry of Bangladesh”, Applied Economics, Vol. 31 No. 4, pp. 437-442. Johnston, A., Porter, D., Cobbold, T. and Dolamore, R. (2000), Productivity in Australia Wholesale and Retail Trade, Productivity Commission, Canberra. Kim, S. (2003), “Identifying and estimating sources of technical inefficiency in Korean manufacturing industries”, Contemporary Economic Policy, Vol. 21 No. 1, pp. 132-144. Kouliavtsev, M., Christoffersen, S. and Russel, P. (2007), “Productivity, scale and efficiency in the U.S. Textile industry”, Empirical Economics, Vol. 33, pp. 1-18.
Leibenstein, H. (1966), “Allocative efficiency vs ‘X-efficiency”, American Economic Review, Vol. 56 No. 3, pp. 392-414. Lieberman, M. and Dhawan, R. (2005), “Assessing the resource base of us and japanese automakers: a stochastic frontier production function approach”, Management Science, Vol. 51 No. 7, pp. 1060-1075. Martín, A. and Suárez, C. (2000), “Technical efficiency of Spanish manufacturing firms: a panel data approach”, Applied Economics, Vol. 32 No. 10, pp. 1249-1258. Mini, F. and Rodríguez, E. (2000), “Technical efficiency indicators in a philippine manufacturing sector”, International Review of Applied Economics, Vol. 14 No. 4, pp. 461-473. Mirrelees, B. and Miller, D. (1996), Retailing Management: a Best Practice Approach, RMIT Press, Victoria. Nickell, S., Nicolitsas, D. and Dryden, N. (1997), “What makes firms perform well?”, European Economic Review, Vol. 41 Nos 3-5, pp. 783-796. Pitt, M. and Lee, L. (1981), “Measurement and sources of technical inefficiency in the Indonesian weaving industry”, Journal of Development Economics, Vol. 9 No. 1, pp. 43-64. Silverman, B.W. (1986), Density Estimation For Statistics And Data Analysis, Chapman and Hall, Londres. Wand, M.P. and Jones, M.C. (1995), Kernel Smoothing, Chapman & Hall, London. Zellner, A., Kmenta, J. and Dreze, J. (1966), “Specification and estimation of cobb – douglas production functions”, Econometrica, Vol. 34 No. 4, pp. 784-795.
Corresponding author Dr Justo De Jorge-Moreno can be contacted at:
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