DEA Performance Measurements in Cotton Production of Harran Plain, Turkey: A Single and Double Bootstrap Truncated Regression Approaches
DEA Performance Measurements in Cotton Production of Harran Plain, Turkey: A Single and Double...
Işgın, Tamer;Özel, Remziye;Bilgiç, Abdulbaki;Florkowski, Wojciech J.;Sevinç, Mehmet Reşit
agriculture Article DEA Performance Measurements in Cotton Production of Harran Plain, Turkey: A Single and Double Bootstrap Truncated Regression Approaches 1 , 1 2 3 Tamer Isgın ¸ *, Remziye Özel , Abdulbaki Bilgiç , Wojciech J. Florkowski and Mehmet Resit ¸ Sevinç Department of Agricultural Economics, Faculty of Agriculture, Harran University, 63050 Sanlıurfa, ¸ Turkey; email@example.com Department of Agricultural Economics, Faculty of Agriculture, Atatürk University, 25240 Erzurum, Turkey; firstname.lastname@example.org Department of Agricultural & Applied Economics, University of Georgia, Athens, GA 30602, USA; email@example.com Department of Bozova Vocational School, Harran University, 63850 Sanlıurfa, ¸ Turkey; firstname.lastname@example.org * Correspondence: email@example.com; Tel.: +90-414-3183718 Received: 9 February 2020; Accepted: 17 March 2020; Published: 3 April 2020 Abstract: A single and a double bootstrap of data envelopment analysis examines Harran Plain cotton farming in Turkey. The single bootstrap technique was employed to derive the bias-corrected eciency values under both constant returns to scale (CRS) and versus variable returns to scale (VRS) technologies while discriminating between the two technologies using a smoothed bootstrap test statistic. Results indicated that the farms operated under VRS technology. Given that VRS technology prevailed across Harran Plain cotton farmers sampled, we then determined factors aecting the bias-corrected technical eciencies using the double bootstrap technique. Another important ﬁnding in the single bootstrap analysis is that cotton farmers in the region have a U-shaped technical eciency based on the input and output scale. Thus, small-scale farmers tend to use their resources more eciently in cotton farming than that of both medium- and large-scale farmers. Interestingly, the medium-scale farmers with resource ineciency are at the forefront of the other two types of farmers (i.e., small-scale and large-scale) on the Harran Plain in Turkey. The results also showed that most of the farm and farmer speciﬁc as well as economic factors play a signiﬁcant role in explaining the technical eciency values. Keywords: cotton; data envelopment analysis; eciency measurement; single and double bootstrap; Turkey 1. Introduction Cotton has strategic importance in Turkish agriculture, industry, and trade. Turkey is one of the top cotton-producing countries and produced 2,450,000 MT in 2017 , while the textile industry represents one of the leading sectors in the Turkish economy and accounted for 16% of total export value in 2017. Exports of ready-to-wear cotton items were worth $17 billion and textiles were valued at $8 billion in 2017 . Rapid economic development and a changing demographic structure in Turkey has led to a fast increase in domestic demand for textile products. Speciﬁcally, due to the speedy increase in the number of textile and clothing stores and shopping centers throughout the country, domestic textile sales have increased signiﬁcantly in recent years. Turkey’s growing young population, migration to urban areas, refugee inﬂux from Syria and other countries, and the increase in tourism have contributed Agriculture 2020, 10, 108; doi:10.3390/agriculture10040108 www.mdpi.com/journal/agriculture Agriculture 2020, 10, 108 2 of 17 to a substantial increase in the domestic consumption of cotton products. In 2017/2018, domestic consumption was expected to reach 1.57 MMT (7.2 million bales), a ﬁvefold increase in the annual cotton consumption since the 1980s [2,3]. Turkish total cotton imports reached 401,000 MTs in 2018, of which nearly 30% (118,000 MTs) were imported from the United States . The growing domestic demand for cotton has turned the previously net exporting country into a net importer since 1992. Southeastern Anatolia, known as the Fertile Crescent or Upper Mesopotamia, covers 20% of all irrigable land in Turkey. The Southeastern Anatolian Project (SAP) has been a massive $32 billion public development project intended to improve farmer welfare in the region [2,4]. When all irrigation schemes under that project are completed, an additional 1.7 million ha will double the country’s irrigated farmland. The Southeastern Anatolia (SEA) region will cement its prominent position as a cotton supplier. The Harran Plain, one of the largest plains in the SEA region covers 225,109 ha, of which 140,000 ha are currently being irrigated . Local farmers view cotton as the most proﬁtable crop. The cotton area increased more than ﬁvefold between 1995 and 2017, reaching 116,391 ha with a harvest of 546,917 MTs . The Harran Plain alone accounts for about 22% of the country’s cotton production. However, both yield and quality are low due to ineciencies. The main reasons for the ineciency is excessive or inadequate use of inputs such as fertilizer, improved seeds, irrigation, and extension services, which helps explain the less than expected productivity of the cotton sector, as well as the internal ineciency in the use of available farm resources such as land, labor, and capital in the SEA region. For example, the use of excessive water has been cited as one of the main causes of soil salinity, which leads to reduced yields in cotton production in the region [4,5]. The Ministry of Agriculture and Forestry (MAF) has been providing technical and ﬁnancial assistance to farmers to build modern drip irrigation systems and prevent ecological problems by avoiding water wastage. The MAF eorts have focused on moving from open canal irrigation systems to closed systems to reduce water loss during transportation . While cotton yield in the country (1683 kg/ha) ranks third after China (1751 kg/ha) and Brazil (1686 kg/ha) in general, in some years the yield in Turkey has been more than the other two countries . The increased use of improved technologies and/or the eciency of farmer input use in cotton production on the Harran Plain oers potential to close the yield gap permanently. As stated in one of earlier studies , improvements in cotton productivity in the region may result from more ecient input use. In the near future, growth in the region’s cotton production, especially in the plain, can result from more ecient use of land, capital, labor, and other inputs, especially irrigation. Unfortunately, there are other external factors hindering the rational use of inputs by cotton producers in the country. For example, most of the inputs used in cotton farming are import-based, preventing the use of the desired inputs at full capacity as a result of swings in exchange rates. For example, when the January and year-end December periods are taken as a reference, the exchange rates of the USD ($) against the Turkish Lira (TL) in 2011, 2012, and 2013 were 19.63%, 3.25%, and 16.32%, respectively, exhibiting high volatility with non-stationary structure. Of course, fuel and fertilizer are among the most vulnerable inputs exposed to the exchange rate uncertainty. As such, at a time when the appreciated Turkish Lira lowers the production costs, it would be possible to increase the amount of energy inputs such as fuel, pesticides, and fertilizer, as well as overhead inputs such as labor and capital, including land, which appears to be quasi-ﬁxed in cotton production. Although cotton is the most frequently cultivated crop in the Harran plain, little is known concerning farmers’ eciency in the use of production inputs. In addition, all of the above-mentioned problems show that the input-oriented approach to solving problems in cotton production would be more rational and advantageous than alternative techniques. Additionally, as in other production sectors (industry and service sectors), the decision-making units (DMU) in the agricultural sector have more control over inputs than they have over outputs. An input-oriented approach was therefore used in our analysis of technical eciency. Meanwhile, the research on the technical eciency of cotton production and the determinants of the variability of eciency levels among farmers with advanced analysis techniques is almost non-existent in the region or in the country. Therefore, this study attempts Agriculture 2020, 10, 108 3 of 17 to analyze the technical eciency of cotton production on the Harran Plain using an up-to-date data enveloping analysis, DEA, (including the application of the double bootstrap technique) and seeks to close the current knowledge gap by providing empirical evidence on resource utilization eciency. In this context, the available double bootstrap DEA techniques developed by Simar and Wilson , hereafter SW, were applied to the data. SW have empirically shown that the traditional two-stage DEA method (TTS-DEA) involves severe limitations. First, the TTS-DEA method is incompatible with the underlying data generation process (DGP) to produce meaningful statistical properties (e.g., unbiasedness) to describe technical eciency scores. Additionally, SW have shown the correlation of environmental factors with error term since input and output variables interact with environmental factors. Secondly, the DEA eciency scores are serially correlated invalidating statistical inferences. To overcome the constraints, SW used a double bootstrap procedure with consistent inferences to explain and predict eciency scores with valid standard errors and conﬁdence intervals . Another feature that distinguishes our study is that earlier studies have implemented a double bootstrap technique by choosing the technical eciency of the farmers (e.g., constant returns to scale, CRS, versus variable returns to scale, VRS) based on verbal assumption without empirically testing, whereas this study empirically presents the existence of the farm technical eciency by using a single bootstrap technique. After the return to scale parameters for the sampled farms, assuming either CRS or VRS are determined by conducting a statistical test based on a single bootstrap technique, a double bootstrap method was then applied to determine both the eciency amount and non-discretionary factors playing roles in the eciency of cotton production on the Harran Plain. Therefore, the current study is novel in this respect. Included in the subsequent sections is the outline of methods used in the analysis and data description. Model comparison together with a speciﬁcation test and discussion of the eects of variables on the DEA eciency scores are given in Section 3. The ﬁnal part presents conclusions along with some policy implications. 2. Materials and Methods 2.1. Survey Design and Variable Selection for the Empirical Speciﬁcation First, 51 villages inhabited by 1029 cotton farmers were purposely selected, thought to be representative of the study area. Next, a total of 126 cotton farmers were selected using a stratiﬁed random sampling design introduced by Yamane  with an allowable percentage error margin of 5%. To assure the representativeness of the cotton farmer population, the selected farmers were divided into four size segments leading to a stratiﬁed distribution yielding 49, 49, 21, and 7 cotton farmers in each stratum. Farmer participation was voluntary. Farmers were supplied with diaries at the beginning of the 2012 production season. For the production season of 2012, we made a comparison between the variations in cotton yields obtained from the participant and non-participant farmers and no signiﬁcant dierence existed, indicating the Hawthorne eect was not present in our data set. In addition, we ascertain that our data set would not run into the problem of self-selection bias reasoning that our participatory farmers were randomly selected and even if the eects of both the self-selection bias and the Hawthorn are possible these eects would be oset thanks to the bootstrap technique used in this analysis. Meanwhile, in a standard DEA context, homogeneity in DMU’s refers to the condition where all DMUs under investigation are subject to similar conditions in terms of the topography, climate, and commonly applied farming techniques. When we apply this perspective, the homogeneity requirement in DMU’s in our data set is attained because all our farmers operate in the same region (the Harran Plains), face the same climatic conditions, and, therefore, apply similar farming techniques. Additionally, our output and input sets are constructed by taking representative (identical) measures into consideration. For example, while the farmers had a choice of using dierent fertilizer types, we converted those usages into the net nitrogen and phosphorous amounts. Similarly, in the calculation of family and hired labor, we converted the total number of hours and used the net Agriculture 2020, 10, 108 4 of 17 man-hour equivalent instead. Thus, we met the homogeneity requirement across all DMUs for every input used and output produced by the cotton sector of the Harran Plain region. The recording of the information was controlled by 10–20 visits to farmers throughout the season depending on the village location. Such an approach built a trusted relationship between the survey workers and the farmers, which better motivated farmers, minimizing the risk of recording false entries. Farmers received payments for recording ﬁnancial and production information after harvest. Each farmer was also interviewed on matters related to (1) production characteristics, including measures such as size of the operation, ownership type, yields, and land characteristics; and (2) farmer characteristics such as gender, age, and education (Table 1). After deleting an outlier value in one of our inputs in DEA, the remaining 125 farms formed our working sample. Table 1 lists deﬁnitions of variables used in the empirical analyses including units of measurement. Five variables (e.g., seed (SEED), the amounts of nitrogen and phosphorus (FRTLZR), family and hired labor (LABOR), herbicide and insecticide value (PESTICIDE), and value of working capital (OTHRCAP) capture the inputs used in cotton production per decare (YIELD). Table 1. Descriptive statistics for variables used in econometric analysis. Variable Name Variable Description Mean Std. Dev Min. Max. VIF First Stage Variables (DEA Variables) YIELD Total cotton yield (kg/da) 461.498 111.555 120.000 965.500 N/A SEED Cotton seed quantity (kg/da) 2.502 0.963 1.030 6.667 N/A Net nitrogen and net phosphorus used in cotton FRTLZR 28.263 10.662 9.884 86.575 N/A production (kg/da) LABOR Working hours depleted (family as well as hired labor/da) 51.466 41.434 2.091 71.867 N/A PESTICIDE Herbicide and insecticide value (Turkish Liras/da) 27.264 14.543 2.091 71.867 N/A Value of working capital other than seeds, fertilizers, and OTHRCAP pesticides and ﬁxed capital, including depreciation, repair, 293.643 109.589 62.200 611.022 N/A and maintenance (Turkish Liras/da) LAND Cotton area (da) 107.905 111.397 6.500 800.000 N/A Second Stage Variables 1 if farming experience less than 10 years, 0 otherwise EXPERN1 0.192 0.395 0.000 1.000 N/A (reference group) EXPERN2 1 if farming experience between 10 and 20 years, 0 otherwise 0.424 0.496 0.000 1.000 2.303 EXPERN3 1 if farming experience between 20 and 30 years, 0 otherwise 0.232 0.424 0.000 1.000 2.027 EXPERN4 1 if farming experience greater than 30 years, 0 otherwise 0.152 0.360 0.000 1.000 2.240 1 if farmer attended an elementary school, 0 otherwise ESCHOOL 0.496 0.502 0.000 1.000 N/A (reference group) SSCHOOL 1 if farmer attended a secondary school, 0 otherwise 0.128 0.335 0.000 1.000 1.386 HSCHOOL 1 if farmer attended a high school, 0 otherwise 0.376 0.486 0.000 1.000 1.884 HSIZE1 1 if household size less than 6 members, 0 otherwise 0.224 0.419 0.000 1.000 N/A HSIZE2 1 if household size between 6 and 10 members, 0 otherwise 0.400 0.492 0.000 1.000 2.195 1 if household size greater than 10 members, 0 otherwise HSIZE3 0.376 0.486 0.000 1.000 2.605 (reference group) OFF-FARM 1 if farmer has an o-farm job, 0 otherwise 0.304 0.462 0.000 1.000 1.429 FSIZE1 1 if farm size under cotton less than 5 ha, 0 otherwise 0.328 0.471 0.000 1.000 N/A FSIZE2 1 if farm size under cotton between 5 and 10 ha, 0 otherwise 0.272 0.447 0.000 1.000 1.936 FSIZE3 1 if farm size under cotton between 10 and 20 ha, 0 otherwise 0.216 0.413 0.000 1.000 2.532 1 if farm size under cotton greater than 20 ha, 0 otherwise FSIZE4 0.184 0.389 0.000 1.000 5.499 (Reference group) LNDOWNR 1 if farmer owns the land he farms, 0 otherwise 0.824 0.382 0.000 1.000 1.218 1 if farm is located in the central district of Sanliurfa LOCNCNTR 0.496 0.502 0.000 1.000 N/A (reference group) LOCNACKL 1 if land is located in the Akcakale district of Sanliurfa 0.232 0.424 0.000 1.000 1.720 LOCNHRRN 1 if farm is located in the Harran district of Sanliurfa 0.272 0.447 0.000 1.000 1.579 HRDLBOR 1 if farm uses only family labor, 0 otherwise 0.112 0.317 0.000 1.000 1.713 FMLYLBRT Share of family labor in total labor (%) 0.317 0.315 0.000 1.000 1.561 TRACTDMY 1 if owns a tractor, 0 otherwise 0.720 0.451 0.000 1.000 2.228 TMACHNRY Number of total machines on the farm except tractors 6.760 5.583 0.000 22.000 3.372 PRCLNMBR Number owned or rented parcels 1.976 1.329 1.000 7.000 1.468 IRRGNMBR Number of times irrigation is applied to the land in operation 6.688 1.568 3.000 12.000 1.805 CAPLABRT Natural log of capital to labor ratio 2.484 0.988 0.711 4.301 2.029 LNDLABRT Natural log of land to labor ratio 5.821 1.024 7.532 3.847 1.039 Cotton support amount given based on production (Turkish SUBSIDY 2.572 2.658 0.158 18.480 4.470 Liras/10,000) Note: VIF stands for variance inﬂation factor while da refers to decare or hectare/10. The number of observations (N) is 125. LAND variable was not used in the DEA analysis. Agriculture 2020, 10, 108 5 of 17 Farm and farmer characteristics used as explanatory variables aecting DEA eciency scores include mutually exclusive multiple dummy variables representing farmer experience (Table 1), household size, education level, location, and farm size along with single dummy variables indicating o-farm work and land ownership. Other performance-related determinants that are discrete in nature include farmer age in years, the number of parcels owned or rented, and the irrigation frequency (the time irrigation recurs on the ﬁeld). The only performance-related determinant measured in continuous fashion is the percentage share of family labor input in the total labor force, ranging from zero to one. Location dummies indicate the municipal division of the Sanliurfa province and are used to identify the impact of location on the farm performance. It is hypothesized that farmers located in the central district are more ecient than those located in the Harran and Akcakale districts because farmers in the central district may have easier access to information. To avoid the dummy identiﬁcation problem, one of the location dummies is used as a reference variable. Similarly, land ownership could have an ambiguous impact on eciency. For example, land ownership could create an incentive to use soil-improving techniques in favor of eciency, while tenancy might encourage the tenant farmer to use inputs more eciently. Additionally, the impact of the share of family labor has an ambiguous eect (positive or negative): a larger share of hired labor may imply a more specialized, and thus more productive labor, but it might also be a source of moral hazard . Farmer age could be expected to have a positive impact on eciency as older farmers are more experienced, but some authors discuss reasons for the opposite relationship , perhaps due to physical deﬁciencies as the farmer gets older (e.g., age impairment non-linearity). Similarly, higher eciency scores are expected for farms where full-time experienced farmers are more educated, household size is smaller, farmer operates on the land parcels close to each other, and irrigates the land. In small-scale families, there may be an inter-individual division of labor in which each member specializes in his/her task, and this attitude can be seen as a factor that increases technical eciency. On the other hand, technical eciency in scattered parcels can, of course, be disadvantageous compared to peers in parcels that are close to each other. 2.2. The Modeling Approach The DEA production frontier is constructed using linear programming techniques, which render a piece-wise linear frontier that envelops the observed input and output data. Technologies produced in this way possess the standard properties of convexity and strong disposability . The DEA technique measures relative eciencies of a collection of farms in transforming inputs into outputs. Its origins date back to Charnes et al. , who introduced the CCR model based on the works of Farrell  and others. Later, Banker et al.  introduced the BCC model and accounted for variable returns to scale by adding a convexity constraint. The original DEA speciﬁcation has led to the multi-stage model development to cope with slacks and to meet criteria identifying the nearest ecient points  and making the model invariant to units of measurement. An input-oriented DEA model is given below for n decision-making units (farms), each producing Y outputs by using m dierent inputs. In this formulation X is the ith farm’s (mx1) input vector. For the whole sample, Y represents the (1xn) cotton yield vector and X denotes the (mxn) input per decare matrix. Focusing on the unit area limits variability by minimizing the eects on input and output values. Under the assumption of constant returns to scale (CRS), the eciency score is the set of solutions to the following linear programming problem: min sub ject to y + Y 0 (1) , x X 0 i i 0 Agriculture 2020, 10, 108 6 of 17 where is the technical eciency score for the ith farm; is an Nx1 vector of constants, where N is the number of farms in the sample; y shows the ith farm output per decare, while x denotes a vector of i i inputs per decare used in the production of y by the ith farm; Y and X denote a projected point due to radial contraction of the input vector x . The objective of the above linear programming is to ﬁnd the minimum so that the input vector x reduces to X , while holding the output level y i i constant. In this context, the value of will range between zero and one, with a score of near-zero implying ineciency, while a score of one implying a point on the frontier where the farm in the region is technically ecient. For a speciﬁcation under variable returns to scale (VRS), the additional convexity constraint 1 = 1 is added to the above linear programming, where 1 is a vector of ones . The constraint guarantees that an inecient farm in the region is only benchmarked against units of a similar size. The scale eciency (SE) of an ith farm is obtained by dividing the technical eciency scores under CRS to the technical eciency scores under VRS and is at a (0, 1) interval. The approach allows the comparison of the technical eciency under the CRS and VRS technologies by using a smoothed single bootstrap technique . The bias-corrected technical eciency values under both technologies were then derived. When the SE is 1, the farm has an ecient economy of scale, otherwise, inputs used in production are not ecient in scale . To determine under what scale these technical eciencies are derived, the test is applied whether the scale eciency is 1 (CRS) or against the alternative hypothesis that the SE < 1 (VRS). The test statistic is: X X N N CRS VRS ˆ ˆ S = ( )/( ) (2) i i i=1 i=1 and the H is rejected if S is signiﬁcantly smaller than one. As such, a critical threshold value (C ) for statistic S is searched and if this critical value C is smaller than S, the H hypothesis is rejected. Unfortunately, the true distribution of S under H is unknown (the hypothesis of CRS) so C cannot be directly calculated, but Simar and Wilson [8,18] showed that one can bootstrap the distribution of S under H in their FEAR R package (FEAR R package, obtains bootstrapped CRS and VRS, respectively, as follows: Bc <