Assessing the Total Factor Productivity of Cotton Production in Egypt

RESEARCH ARTICLE Assessing the Total Factor Productivity of Cotton Production in Egypt Xosé A. Rodríguez 1 *, Yahia H. Elasraag 2 1Department of Quantitative Economics, University of Santiago de Compostela, Santiago de Compostela, Spain, 2Department of Agricultural Economics, Cairo University, Gammaa St., Giza, 12613, Egypt *[email protected] Abstract The main objective of this paper is to decompose the productivity growth of Egyptian cotton production. We employ the stochastic frontier approach and decompose the changes in total factor productivity (CTFP) growth into four components: technical progress (TP), changes in scale component (CSC), changes in allocative efficiency (CAE), and changes in technical efficiency (CTE). Considering a situation of scarce statistical information, we propose four alternative empirical models, with the purpose of looking for convergence in the results. The results provide evidence that in this production system total productivity does not increase, which is mainly due to the negative average contributions of CAE and TP. Policy implications are offered in light of the results. Introduction The agricultural sector is considered as one of the major sectors in Egypt’s economy, where its contribution to the country’s GDP is currently around 14% [1]. The Egyptian agriculture has at least six features that make it unique among other agricultural systems [2]. First, the limitedness of the arable lands, given Egypt’s location in a dry desert region. Second, the dependence on the Nile water for irrigation; in fact the agricultural sector is the main consumer of the Nile water, accounting for around 84% of its consumption. Third, the intensive cropping systems, which are based on the scarcity of land resources and the availability of irrigation water supply. Fourth, the predominance of the small holding pattern. Fifth, an intensive use of inputs, particularly chemical fertilizers and labor, and finally a cropping pattern based on many basic groups, such as cereals, legumes, fibers, sugar crops, oil crops, fodder, fruits, vegetables and horticultural crops. The production of cotton has great importance in the Egyptian agricultural sector. First, because cotton production is a very intensive activity in the use of labor and it is carried out principally by small family exploitations. Additionally, it generates new opportunities of employment in the subsequent processes (e.g. ginning, transportation, commercialization and the local textile industry). Second, cotton production is important in Egypt because it helps to guarantee the food security for population [1]. This contribution to the food security takes several forms. To begin with, cotton production is a source of income for rural families, who can PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 1/14 OPEN ACCESS Citation: Rodríguez XA, Elasraag YH (2015) Assessing the Total Factor Productivity of Cotton Production in Egypt. PLoS ONE 10(1): e0116085. doi:10.1371/journal.pone.0116085 Academic Editor: Alejandro Raul Hernandez Montoya, Universidad Veracruzana, MEXICO Received: November 5, 2014 Accepted: December 1, 2014 Published: January 27, 2015 Copyright: © 2015 Rodríguez, Elasraag. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data were obtained from the Ministry of Agriculture and Land Reclamation (Egypt). To access data, please send requests to: Xosé A. Rodríguez, University of Santiago de Compostela, E-mail address: [email protected]. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. then afford the basic products (milk, fruits, vegetables, clothes, etc). Also the exports of cotton enable the import of food. In addition, the oil extracted from the cotton seeds is used for culinary purposes, and the chaff (oilcake) is used to feed animals, since it is rich in proteins. Finally, the production of cotton is important as a raw material to impel the textile industry in Egypt. Considering the importance of cotton production in Egypt, two issues are surprising. First, there are not empirical studies on this sector that analyze the evolution of total factor productivity (TFP) from its determinants. Second, a substantial decline was observed in cotton production (period 2004–2008), while the production of other agricultural products, such as sugarbeet, fruits, vegetables, rice and wheat, increased in Egypt in a remarkable way [1]. In addition, the decline of cotton production in Egypt took place in a context in which the Egyptian economy was growing and so was the Egyptian cotton textile industry, while the production, consumption, exports, and imports of cotton did not decrease at the global level but remained stable [3]. Some studies on cotton in Egypt, such as [4], enumerate multiple causes (at the national and international level) that can explain the evolution of cotton production. One of the suggested causes is the low productivity growth, given that productivity is an important determinant of the evolution of product competitiveness. And competitiveness is one of the principal factors that determine increases in the demand of products and, as a consequence, increases in the possibilities of production. In view of the above considerations, the main objective of this study is to carry out a disaggregate analysis (by provinces) of TFP in Egyptian cotton production. In addition, it aims to analyze the evolution of TFP from its determinants (including the efficiency). On the basis of these results, this paper aims to provide evidence on the possible problems affecting the cotton production system in Egypt. One of the peculiarities of this study is the use of scarce data. To overcome this limitation, we propose four alternative empirical models to compare the results obtained. If the results obtained by the different models are similar, they can be considered to be more consistent and meaningful. To meet our objectives the rest of the paper is organized as follows. In the subsequent section we present a brief review of our methodology. Section three deals with the relationship between the statistical information available and the empirical models proposed. Section four presents the results. The final section presents the main conclusions and policy implications. Methodology Solow [5] proves that, under constant returns to scale, in a long run competitive equilibrium technical progress may be the only source of total productivity growth. Following this proposal, many studies of productivity growth in agriculture tended to compute productivity as a residual after accounting for input growth; i.e., productivity growth was identified with technical progress. Currently, some authors continue to analyze productivity growth in agriculture computing productivity as a residual, using index numbers such as Törnqvist (e. g., [6,7,8,9,10]). In these studies firms are assumed to maximize profits, and thus they are assumed to be efficient. In reality, however, even if firms have as an objective to obtain the maximum benefit, not all of them are able to achieve it, and as a consequence inefficiencies may arise. If there is inefficiency, it is necessary to take into account its effect over productivity growth. It is likewise crucial to consider the impact on productivity of non-constant returns to scale and of the violation of the various conditions necessary for long run competitive equilibrium. There exist two main types of approaches that can be applied to estimate efficiency and decomposing productivity growth: data envelopment analysis (DEA) and stochastic frontier analysis (SFA) [11]. In general, the SFA approach has been preferred in the agricultural economics Total Factor Productivity of Cotton Production in Egypt PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 2/14 literature [12,13,14]. The most important potential advantage of SFA is that it can separate noise in data from variations in efficiency. Given the inherent variability of agricultural production, the assumption that all deviations from the frontier are associated with inefficiency (as assumed in DEA approach) is difficult to accept in this sector. Following previous literature in the agricultural field (e. g., [13,15,16,17,18,19,20]), the starting point for a measure of productivity can be a stochastic frontier production function: Qit ¼fðXit;t;aÞevituit ð1Þ where Q it is the production of the i-th firm (i=1,2...N) in the t-th time period (t=1,2... T); f() is the production technology; X it is a vector of J input quantities of the i-th firm in the tth time period; t is the time trend variable; αis a vector of unknown parameters to be estimated; v it is a vector of random variables which are assumed to be iid. Nð0;s2 vÞ, and independent of the u it , which is a vector of non-negative random variables assumed to account for technical inefficiency in production. There are several specifications that make the technical inefficiency term u it time-varying. In this paper we use the Error Components Model (ECM) specification proposed by [21], and the Technical Efficiency Effects Model (TEEM) specification proposed by [22]. In ECM, the inefficiency term u it is defined as: uit ¼uiexpðZ½tTÞ ð2Þ where the distribution of u i is taken to be the non-negative truncation of the normal distributionNðm;s2 uÞ, and ηis a parameter that represents the rate of change in technical inefficiency. The positive (negative) value of ηis associated with improvements (deterioration) in cotton production technical efficiency over time. In TEEM, the non-negative random variables (u it ) are assumed to be independently distributed as truncations at zero of the Nðmit;s2 uÞdistribution; where: mit ¼zitdð3Þ where z it is a vector of variables which may influence the efficiency of a firm and δis a vector of parameters to be estimated. Differentiating the production technology in Eq. (1) with respect to time and rearranging terms we have that: dln fð:Þ dt ¼TP þX j ej_ xjð4Þ where TP =@lnƒ(.)/@t represents the technical progress or technical change; ε j =@lnƒ(.)/@lnx j is the elasticity of output with respect to the jth input, and a dot over xindicates its rate of change. Differentiating the log of Q in Eq. (1) with respect to time, the change in production can be represented as: _ Q¼TP þX j ej_ xjdu dt ð5Þ In Eq. (3), the overall productivity change is affected not only by TP and changes in input use, but also by the change in technical efficiency (−du/dt); thus if du / dt is positive (negative), technical efficiency deteriorates (improves) over time. Total Factor Productivity of Cotton Production in Egypt PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 3/14 To examine the effects of technical progress and changes in efficiency on TFP growth, the traditional definition for productivity total growth is used; that is, TFP is taken as the residual resulting from the output growth unexplained by input growth: T_ FP¼_ QX j sj_ xjð6Þ where S j is the share of input jin production costs. By substituting Eq. (5) in Eq. (6),Eq. (6) is rewritten as: T_ FP¼TP du dt þðRS 1ÞX j lj_ xjþX j ðljSjÞ_ xjð7Þ where RS ¼X j ejdenotes the measurement of returns to scale and λ j =ε j /RS. In Eq. (7) the total factor productivity growth is decomposed into four components: technical progress (TP), changes in technical efficiency (CTE = -du/dt), changes in the scale component [CSC = (RS-1)Sλ j ẋ j ] and changes in allocative efficiency [CAE ¼X j ðljSjÞ_ xj]. The interpretation of the four components in equation (7) is the usual. TP measures the change in frontier output: TP is positive (negative) if exogenous technical change shifts the production frontier upward (downward) for a given level of inputs. CTE can be interpreted as the rate at which an inefficient firm catches up to the production frontier. Depending on whether RS>1, RS <1orRS = 1, positive scale effects, negative effects or non-scale effects, respectively, will exist. Finally, CAE measures inefficiency in resource allocation resulting from deviations of input prices from their marginal value. Data and empirical models In this study we use panel data at the province level covering the period 1990–2008. The dataset was obtained from “Agricultural Statistics”, a publication by the Ministry of Agriculture and Land Reclamation [23] which contains information on the main nine cotton-producing provinces. One of the limitations of this publication is that it offers too scarce data for a detailed study of total productivity. The most important problem from the empirical point of view is that, in this publication, the only real and direct data provided are the production (Q) and the cotton area (X A ). The other inputs—labor input (X L ), capital input (X K ) and materials input (X M )—can be calculated according to the input rate per hectare for every year (also published by the same source): XLit ¼ltXAit XKit ¼ktXAit XMit ¼mtXAit ð8Þ where l t ,k t and m t are the input rates per hectare for labor, capital and materials, respectively, in year t(t = 1, 2, ...T). This method of estimation of the quantities of the productive factors (labor, capital and materials) may increase the degree of correlation between them. This might be problematic when considering the four inputs jointly in a production function. Additionally, there is a lack of province-level statistical information on specific variables such as education, credit use, infrastructures, land quality, average size of plantations, composition of labor or characteristics of machinery, which may have a significant impact on the behavior of efficiency and productivity. There is also no data at the farm level, such as age, farm Total Factor Productivity of Cotton Production in Egypt PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 4/14 income, off-farm income, womens’participation, experience or farm size. Table 1 presents the summary statistics for the variables used in the analysis for the total sample (171 observations). All of these variables show high variability across the sample, since all of them have a very high standard deviation in relation to its average (the Pearson’s coefficient of variation is found to be superior to 62% for all of them). Given that the quantities of the productive factors (labor, capital and materials) are estimated from the data on the cotton area, one possibility to verify that this procedure of estimation does not affect the final results significantly consists in using two alternative stochastic frontier production functions (instead of only one with four inputs). One of the stochastic frontier production functions includes only the input which is measured directly, X A : Frontier A: Qit ¼fðXAit;t;aÞevit uit ð9Þ The other alternative stochastic frontier production function includes the three inputs which are estimated from X A : Frontier B: Qit ¼fðXLit;XKit;XMit ;t;aÞevit uit ð10Þ From the two alternative stochastic frontier production functions (9 and 10), we estimate four alternative models: Model A1: It uses the frontier production function (A) and the ECM specification (Eq. 2). Model A2: It uses the frontier production function (A) and the TEEM specification (Eq. 3). Model B1: It uses the frontier production function (B) and the ECM specification (Equation 2). Model B2: It uses the frontier production function (B) and the TEEM specification (Eq. 3). In order to estimate the four models and decompose TFP growth (Eq. 7) it is necessary to choose a functional form for the stochastic frontier production functions (Eq. 9 and Eq. 10). Taking into account the related literature (e. g., [13,15,16,18]), and that the translog form is a flexible functional form, in this paper we propose a translog stochastic frontier production function: lnQit ¼a0þX j ajlnxijt þattþ1 2X j X l ajllnxijtlnxilt þ1 2attt2þX j ajtðlnxijtÞðtÞþvit uit ð11Þ where jand kindicate the input quantities used in the production process. Since in this study the statistical information on province-specific variables is not available, we create dummy variables (D i ) for the different provinces to identify the possible heterogeneous behavior across them, with D i being equal to 1 if the province is iand zero otherwise. Additionally, we incorporate a time variable to verify if inefficiency has increased or decreased Table 1. Summary statistics. Variables Units Minimum Maximum Mean Std. Dev. Production (Q) Tons (thousands) 3.01 221.14 68.30 45.00 Cotton Area (X A ) Hectares (thousands) 1.54 71.56 28.40 17.78 Labor (X L ) Workers (thousands) 25.91 1176.35 386.55 253.24 Capital (X K ) Hours (thousands) 100.14 5195.77 1778.59 1209.67 Materials (X M ) Tons (thousands) 0.34 15.67 6.22 3.89 doi:10.1371/journal.pone.0116085.t001 Total Factor Productivity of Cotton Production in Egypt PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 5/14 in the analyzed period. Consequently, we specify the technical inefficiency effects (Eq. 3) as: mit ¼d0þX N i¼2 diDit þbtð12Þ From the estimation of the translog stochastic frontier production function (Eq. 11), the components of productivity change can be calculated. The technical efficiency levels (TE) and changes in technical efficiency (CTE), changes in allocative efficiency (CAE), technical progress (TP), and returns to scale (RS) are obtained as TE ¼Qit fð:Þ¼expðuitÞ;CTE ¼expðuitÞexpðuit1Þð13Þ CAE ¼X j ðljSjÞ_ xjð14Þ TP ¼@lnQit @t¼atþatttþX j atjlnxijt ð15Þ RS ¼X j ej¼X j @lnQit @lnxit ¼X j ½ajþajjlnxijt þX j6¼l ajllnxilt þajtðtÞ;½CSC ¼ðRS 1ÞXlj_ xjð16Þ Results The Maximum Likelihood estimates for the parameters of the four alternatives models can be obtained by using the FRONTIER 4.1 program [24], in which variance parameters are expressed in terms of s2 s¼s2 uþs2 vand g¼s2 u s2s, where γis an unknown parameter to be estimated. The adequacy of the models proposed and the different variants with regard to the type of distribution for inefficiency and the possible mu (μ) and eta (η) values have been tested. From the various alternatives, we selected (following the criterion of the likelihood ratio test) the results shown in table 2. The results of the four models are similar. Taking into account the values obtained for the log likelihood function, models A2 and B2 offer a better specification than models A1 and B1. On balance, model B2 presents the best specification. All variables appearing in natural logarithms were divided by their geometric mean prior to estimation. The time trend was at zero in 1999. As a result, the coefficients of the first-order terms of the variables in natural logarithms can be interpreted as production elasticities in that year evaluated at the geometric mean of the explanatory variables. The first-order coefficients, α j , have the anticipated positive sign and they are statistically significant for the cotton area and materials inputs in models A1, A2 and B2, but they are not statistically significant for the capital and labor inputs in models B1 and B2. One possible explanation for the latter may be that these inputs are not used appropriately in the productive process. The technical progress coefficient, α t , is statistically significant in the four models, but with negative sign; i. e., technical progress shifts the production frontier downward for a given level of inputs. This result probably suggests that there are other factors which are not considered in the production function and whose negative effects on output outweigh the positive effects of the possible technical progress. Total Factor Productivity of Cotton Production in Egypt PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 6/14 Table 2. Maximum Likelihood estimates of the stochastic frontier production models. Model A1 Model B1 Model A2 Model B2 Variables Coefficients Standard error Coefficients Standard error Coefficients Standard error Coefficients Standard error Frontier Production Function Constant 0.249289 (0.114530)** 0.318254 (0.1267098)** 0.289005 (0.068000)*** 0.339955 (0.074938)*** ln(XA) 0.563986 (0.072296)*** 0.699767 (0.037490)*** ln(XL) -0.015121 (0.191079) -0.035219 (0.160326) ln(XK) 0.090003 (0.262512) -0.267552 (0.228417) ln(XM) 0.444902 (0.426944) 1.042037 (0.367086)*** T -0.051286 (0.014198)*** -0.065920 (0.017786)*** -0.056645 (0.009873)*** -0.032985 (0.013500)** ½[ln(XA)]² 0.056628 (0.065249) 0.107206 (0.050583)** ½[ln(XL)]² -1.326062 (1.328960) -2.713082 (1.109566)** ½[ln(XK)]² -0.188999 (1.253958) -1.498155 (1.117122) ½[ln(XM)]² -2.452777 (2.239771) -5.212243 (2.014632)*** ½[t]² 0.009530 (0.002870)*** 0.017803 (0.004239)*** 0.010833 (0.002081)*** 0.014033 (0.003458)*** ln(XL)ln(XK) -0.058979 (0.609509) -0.398795 (0.579186) ln(XL)ln(XM) 1.348156 (1.448512) 3.092126 (1.210854)** ln(XK)ln(XM) 0.683495 (1.567905) 2.067456 (1.380572) ln(XA)(t) 0.062670 (0.008544)*** 0.054666 (0.005415)*** ln(XL)(t) -0.118374 (0.059259)** -0.157438 (0.056790)*** ln(XK)(t) -0.025466 (0.076149) -0.005993 (0.061207) ln(XM)(t) 0.195844 (0.122160) 0.208574 (0.107433)* Inefficiency Effects Constant -0.370452 (0.375941) -1.063039 (0.601952)* D2 -0.298510 (0.342997) -1.005989 (0.564093)* D3 -0.386331 (0.365466) -1.055583 (0.579813)* D4 -0.285047 (0.350853) -0.981394 (0.572738)* D5 0.310457 (0.240579) -0.147981 (0.362236) D6 -0.338407 (0.298452) -0.858379 (0.477231)* D7 0.149254 (0.271432) -0.339536 (0.399204) D8 0.421313 (0.225923)*0.005871 (0.332894) D9 -0.077183 (0.321359) -0.634279 (0.464208) T -0.120472 (0.026703)*** -0.154287 (0.045678)*** Sigma-squared 0.0705182 (0.009734)*** 0.055808 (0.006899)*** 0.154840 (0.040440)*** 0.212099 (0.067683)*** Gamma 0.062285 (0.088807) 0.052890 (0.067910) 0.888835 (0.037669)*** 0.926244 (0.029062)*** Mu 0.038352 (0.093079) 0.047633 (0.055249) Eta 0.134596 (0.030282)*** 0.151787 (0.030046)*** Log likelihood function -23.082432 -6.236776 -0.725175 15.178791 LR test of the onesided error 24.227818 32.570919 68.942331 75.402053 Total number of observations 171 171 171 171 Note: ***,** and *indicate significance at 1, 5 and 10% level, respectively doi:10.1371/journal.pone.0116085.t002 Total Factor Productivity of Cotton Production in Egypt PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 7/14 The dummy variable coefficients of some provinces are statistically significant at the 10% level for model B2, which confirms that there are some province-specific effects. The negative and statistically significant coefficient for the time variable (β) in models A2 and B2 suggests that technical inefficiency in cotton production in Egypt tended to decrease during the studied period. As the eta (η) value is statistically different from zero (it takes a positive value in models A1 and B1), this implies that technical inefficiency in these provinces is not time-invariant; the level of technical efficiency improves in the analyzed period (as seen in table 3). The positive and statistically significant coefficient of ηis compatible with the negative and statistically significant coefficient β(as we explained before). The variance parameter, gamma (γ), is statistically significant in models A2 and B2, and close to one, which suggests the relevance of technical inefficiency in explaining output variability. This estimate is consistent with the results of previous works (e.g. [25,16]). Table 3 shows the annual levels of the estimated technical efficiency, using the four proposed models in this study (for the total sample). By comparing the results obtained for the four models, it is possible to emphasize the following: For the four models, the estimated annual levels of technical efficiency are similar. With respect to the average level of efficiency of the period 1990–2008, the estimates vary from a minimum level of 0.724 (model A2) to a maximum level of 0.781 (model B2), and the average of the four models is 0.756. The four models consistently show that technical efficiency improves during the considered period. The average annual percentage growth rate is also similar for the four models, varying from a minimum rate of 3.318% (model A1) to a maximum rate of 4.905% (model A2), and with the average annual growth rate across the four models being 4.082%. Results of TFP change decomposition for the four models are reported in Table 4. Changes for the total sample and the mean for every period are shown. From the results, we can highlight the following: The four models offer similar results in the sense that the four models indicate the negative contribution of the technical progress component and the positive contributions of technical efficiency and scale components. In addition, models B1 and B2 coincide in the negative contribution of the allocative efficiency component. The four models have similar results for the average magnitude of the contribution of the different components. The average negative contribution of TP varies from a minimum of -2.67% (model B2) to a maximum of -6.17% (model B1). The average positive contribution of CSC varies from a minimum of 2.48% (model A2) to a maximum of 3.46% (model A1). The average positive contribution of CTE is practically identical in the four models and around 2.5%. The average negative contribution of CAE is also similar for models B1 and B2, and it is higher for the model that considered the specific effects of inefficiency (-3.57% for model B2 compared to -1.14% for model B1). The main difference in the results is due to the fact that models A1 and A2 are more restricted. These models only include one productive factor and therefore they do not allow to quantify the behavior of allocative efficiency. In these models the allocative efficiency is assumed not to change, and in this case a slight average productivity growth is estimated (1% for model A1 and 0.13% for model A2). On the other hand, taking into account the possible changes in allocative efficiency (models B1 and B2) the estimated average change in TFP is negative (-1.63% for model B1 and -1.13% for model B2). The negative contribution of the technical progress component must be considered in light of its definition as a residual. A negative value of this component must be interpreted as a downward movement of the production frontier. But this does not mean that in average there was no technical progress in cotton production; what this result probably indicates is that its Total Factor Productivity of Cotton Production in Egypt PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 8/14 contribution was small, and that there are possibly other factors (which have not been explicitly considered in the model) whose negative effect on production outweighs the small positive effect of the possible technical advances (e.g. land quality, climate changes, plant diseases, plant viruses, etc). The positive value for the changes in the scale component means that the cotton production sector took advantage of the economies of scale. With the statistical information available it is not possible to identify the specific factors responsible for this improvement in scale economies. The changes in allocative efficiency exerted a negative effect on TFP growth (models B1 and B2). The presence of allocative inefficiency shows that, during the period of analysis, input Table 3. Technical efficiency by year (total sample). Years Model A1 Model B1 Model A2 Model B2 Minimum Maximum Mean 1990 0.519 0.438 0.378 0.468 0.378 0.519 0.451 1991 0.557 0.482 0.420 0.565 0.420 0.565 0.506 1992 0.594 0.526 0.575 0.547 0.526 0.594 0.561 1993 0.629 0.569 0.710 0.745 0.569 0.745 0.663 1994 0.663 0.611 0.478 0.730 0.478 0.730 0.621 1995 0.695 0.650 0.491 0.517 0.491 0.695 0.588 1996 0.725 0.687 0.721 0.825 0.687 0.825 0.740 1997 0.752 0.722 0.767 0.808 0.722 0.808 0.762 1998 0.778 0.754 0.634 0.786 0.634 0.786 0.738 1999 0.802 0.783 0.749 0.853 0.749 0.853 0.797 2000 0.823 0.809 0.788 0.848 0.788 0.848 0.817 2001 0.843 0.832 0.801 0.830 0.801 0.843 0.827 2002 0.860 0.854 0.879 0.898 0.854 0.898 0.873 2003 0.876 0.872 0.903 0.909 0.872 0.909 0.890 2004 0.891 0.889 0.882 0.882 0.882 0.891 0.886 2005 0.903 0.903 0.905 0.899 0.899 0.905 0.903 2006 0.915 0.916 0.900 0.920 0.900 0.920 0.913 2007 0.925 0.927 0.879 0.901 0.879 0.927 0.908 2008 0.934 0.937 0.895 0.914 0.895 0.937 0.920 Mean(1990–2008) 0.773 0.745 0.724 0.781 0.724 0.781 0.756 Rate a 3.318 4.315 4.905 3.789 3.318 4.905 4.082 (a) Annual average percentage growth rate (1990–2008) doi:10.1371/journal.pone.0116085.t003 Table 4. TFP change decomposition a . Components Model A1 Model B1 Model A2 Model B2 TP -0.0477 -0.0617 -0.0522 -0.0267 CSC 0.0346 0.0330 0.0248 0.0263 CAE 0.0000 -0.0114 0.0000 -0.0357 CTE 0.0230 0.0239 0.0287 0.0248 CTFP 0.0100 -0.0163 0.0013 -0.0113 (a) Mean changes for the total sample (1990–2008) doi:10.1371/journal.pone.0116085.t004 Total Factor Productivity of Cotton Production in Egypt PLOS ONE | DOI:10.1371/journal.pone.0116085 January 27, 2015 9/14