EFFECT OF SELECTED INSECTICIDE ON WHITEFLY (Bemisia tabaci) INFESTING BRINJAL PLANTS 18 Estimating cost efficiency and sources of inefficiency in paddy farming: A study in Vietnam’s Mekong Delta Phuc Trong Hoa Pham Xuan Hungb Lieu Thi Hoangc Nguyen T.M. Phuongd a,b,c,dUniversity of Economics, Hue University, 99 Ho Dac Di Street, Hue City, Vietnam.  pxhung@hueuni.edu.vn (Corresponding author) Article History ABSTRACT Received: 28 December 2023 Revised: 2 February 2024 Accepted: 15 February 2024 Published: 6 March 2024 Keywords Cost inefficiency determinants Farm heterogeneity Mekong river delta Rice production Stochastic frontier analysis Vietnam. The misuse of chemical fertilizers, pesticides and herbicides in rice cultivation is leading to low-quality outputs, high production costs, health issues and environmental problems (e.g., degraded soil quality, water pollution and increasing greenhouse gases). The efficient use of production inputs would be a feasible way to mitigate these issues. This paper employed a true random-effects model to measure cost efficiency and investigate the factors affecting cost inefficiency among Vietnamese rice producers. This study used the surveyed data of 350 rice households collected in the Mekong Delta, Vietnam. The findings of this research show that the mean cost efficiency score is 0.92 with a wide variation (0.26 – 0.99). This study indicates that there is still potential for inefficient rice producers to save production costs by improving their cost inefficiency. The study also reveals a positive relationship between cost inefficiency and farm size, natural disasters and rice diseases. This suggests that as farms grow, natural disasters and rice diseases become more prevalent and rice producers become increasingly incapable of managing input costs. This study suggests that supportive policies should focus on improving rice farmers’ skills to manage production inputs and deal with rice diseases and natural disasters to minimize rice production costs. Contribution/Originality: This study uses an accurate random-effect model technique to determine cost efficiency while taking into consideration farm variation which has usually been ignored in past efficiency studies. The findings suggest policy implications for supporting rice households to save production costs and this is also a reference for other paddy-producing countries. DOI: 10.55493/5005.v14i1.5000 ISSN(P): 2304-1455/ ISSN(E): 2224-4433 How to cite: Ho, P. T., Hung, P. X., Hoang, L. T., & Phuong, N. T. (2024). Estimating cost efficiency and sources of inefficiency in paddy farming: A study in Vietnam’s Mekong Delta. Asian Journal of Agriculture and Rural Development, 14(1), 18–24. 10.55493/5005.v14i1.5000 © 2024 Asian Economic and Social Society. All rights reserved. 1. INTRODUCTION Rice plays an important role in ensuring economic development and food security in developing and underdeveloped countries (Trong, Burton, Ma, & Hailu, 2022). However, the abuse of inputs such as pesticides, herbicides and chemical fertilizers is causing health problems, high product ion costs, low-quality outputs and environmental problems (e.g., soil quality degradation, water pollution and increasing greenhouse gas emission) (Ho, Asian Journal of Agriculture and Rural Development Volume 14, Issue 1 (2024): 18-24. http://www.aessweb.com/journals/5005 https://orcid.org/0000-0001-9449-0263 https://orcid.org/0000-0001-9817-3863 https://orcid.org/0009-0006-9448-3171 https://orcid.org/0009-0002-8428-8699 mailto:pxhung@hueuni.edu.vn http://www.aessweb.com/journals/5005 Asian Journal of Agriculture and Rural Development, 14(1) 2024: 18-24 19 Hung, & Tien, 2023). The efficient use of inputs would be a feasible way to mitigate these issues (Ho, 2021; Ho et al., 2023). There have been several studies conducted worldwide on the estimate of rice production efficiency (Trong et al., 2022). However, researchers seemed to focus primarily on estimating productive efficiency while ignoring the influence of input prices which have a signif icant effect on household decisions about which inputs to use and in what ways to reduce production costs (Ho, 2021). Some studies have attempted to take price effects into account in measuring efficiency in rice farming using the cost frontier function to estimate cost efficiency (CE) (Coelli, Rahman, & Thirtle, 2002; Huang, Huang, & Fu, 2002; Nguyen, Hoang, & Seo, 2012; Siagian & Soetjipto, 2020; Tu & Trang, 2016). However, farm heterogeneity has not been studied which might result in biassed estimations and cost- efficiency evaluations. Therefore, the goal of this study is to determine how successfully rice farmers control production costs by measuring and analysing cost efficiency and its influencing elements. We take farm heterogeneity into account using a translog stochastic cost front ier function with the true random-effects (TRE) model approach (Greene, 2005; Greene, 2005). We estimate the pooled and TRE models and use the likelihood ratio test to examine the existence of farm heterogeneity in the current dataset. This paper makes two contributions to the literature: (i) This study first attempts to employ the true random- effects model to take farm heterogeneity which was always ignored in previous studies related to efficiency measurement into account to obtain robust estimates for measuring cost efficiency and examine the factors affecting cost inefficiency in rice production among Vietnamese rice farmers. (ii) The f indings of this paper provide useful policy implications for Vietnamese policymakers to design proper policies for the rice sector to help rice farmers minimize production costs. This study is also a reference for other rice farming countries. 2. LITERATURE REVIEW The work of Farrell (1957) served as the foundation for empirical efficiency measurement . Since his work, the efficiency measurement method has been widely developed in several approaches. The parametric approach also known as the stochastic frontier analysis (SFA) method (Aigner, Lovell, & Schmidt, 1977; Battese & Corra, 1977) and the non-parametric approach (mathematic programming) also known as the data envelopment analysis (DEA) method are the two methods that are frequently used in empirical studies to estimate efficiency (Charnes, Cooper, & Rhodes, 1978). A comprehensive review of concepts and estimation methods of efficiency measurement is prov ided by Batiese (1992); Bauer (1990); Førsund, Lovell, and Schmidt (1980) and Greene (2008). The SFA and DEA methods are two commonly used approaches for measuring efficiency in rice farming (Bravo- Ureta et al., 2007; Trong et al., 2022). The SFA method has the advantage of being able to separate statistical noise from the inefficiency term but its d isadvantage is that it requires prior assumptions about the production funct ional form and the distribution of the error term (Bauer, 1990). On the other hand, the DEA method does not require these assumptions but is l imited in its ability to d istinguish between statistical noise and inefficiency estimates (Bauer, 1990). In this study, the parametric SFA approach is used for its advantage of separating random noise and heterogeneity from the inefficiency term. In empirical research, the translog (TL) and Cobb-Douglas (CD) product ion functions are frequently used when addressing functional form selection. The log-likelihood ratio (LR) test can be used to determine the dataset's optimal functional form. The truncated normal distribution has been proven to be more acceptable in previous studies. The exponential, half-normal, gamma and truncated normal distributions are the most often used in the literature for the inefficiency term distribution (Baccouche & Kouki, 2003; Ho, 2021). We have examined all three forms of inefficiency distributions in this study and found that the models performed better when the normal distribution was truncated. Variations in rice farming inefficiency can be affected by farmer and farm characteristics and geographical factors. The present study is based on l iterature reviews suggests that the cost-inefficiency performance of Vietnamese rice farmers is influenced by various factors including the educational attainment and gender of the household head, the number of household members, the area under rice cultivation, the distance between the house and the rice f ield, the number of rice-farming training attendances, natural disasters and rice diseases (George Edward Battese & Coelli, 1995; Dhungana, Nuthall, & Nartea, 2004; Ho, 2021; Rahman, 2003; Sherlund, Barrett, & Adesina, 2002; Siagian & Soetjipto, 2020; Trong & Napasintuwong, 2015; Tu & Trang, 2016; Wadud & White, 2000). 3. MATERIALS AND METHODS 3.1. Stochastic Cost Frontier Function This paper uses the true random-effects model technique to estimate cost efficiency and identify inefficiencies across Vietnamese rice fields as previously indicated. The stochast ic true random-effects model of the cost frontier function is expressed as 𝐶𝑖𝑡 = 𝑓(𝑊𝑖𝑡 , 𝑌𝑖𝑡 ,𝛽𝑖 ) ∗ 𝑒𝑥𝑝(𝑤𝑖 + 𝜀𝑖𝑡 ) (1) Where C denotes the variable cost, i = 1, 2, …, n represents the i-th household, t = 1, 2, …, Ti is the time period. f (·) represents the function, Y represents output quantity, W represents a vector of input prices and β denotes a vector of parameters that need to be estimated. w is to capture unobserved farm heterogeneity (Greene, 2005; Greene, 2005), εit is a composed error term, εit = vit + uit, while vit captures a random noise term and uit is to capture the cost inefficiency. Asian Journal of Agriculture and Rural Development, 14(1) 2024: 18-24 20 To ensure that the estimates of Equation 1 follow economic theory, the stochastic cost frontier function in Equation 1 has to meet the following properties of a cost function: (i) the cost function, C(Y, W) is non-negative, continuous, concave and homogeneous of degree one in input prices (W) for given output (Y); (ii) the cost function, C(Y, W) is non-decreasing in input prices (W) for given output (Y) and (iii) the cost function, C(Y, W) is non- decreasing, concave and homogeneous of degree one in output (Y) for given input prices (W) (Kumbhakar & Lovell, 2003). The cost inefficiency (𝑢𝑖𝑡) among farms is assumed due to the differences in farms and farmers’ characteristics. This relationship is written as a linear function. 𝑢𝑖𝑡 = 𝜑0 + 𝜑𝑍𝑖𝑡 (2) Where 𝜑0 and φ are parameters that need to be estimated, Zit is a vector of explanatory factors that include farm and farmer characteristics to capture the cost inefficiency variations among rice farms. 3.2. Data Source and Variable Definition We used the primary data that was collected in the Mekong River Delta using a face-to-face interview method. We chose to study in the Mekong River Delta because it is the main r ice-cultivation area of Vietnam which contributes more than 50% of paddy quantity and harvested rice area (GSO, 2023). After cleaning the data, the final data set used in this study consists of 350 rice farmers with 918 observations as each rice farmer can grow rice in two or three seasons. The variables used in the stochastic cost frontier function include variable cost (C), output quantity (Y), the prices of seed, fertilizers and labor inputs (Wseed, Wfert and Wlab) and dummy variables to measure rice variety (DHQV) and copping season (DS-A and DA-W) effects. We use variables related to farmer and farm characteristics including gender, educational levels, rice farming experience, extension, household size, land ownership, farm size, paddy diseases, natural disasters and distance to examine the sources of cost inefficiency among rice farmers. The definition and statistical summary of these variables are given in Table 1. Table 1. Definition and descriptive statistics of variables. Variable Definition Mean Std. dev. Variable cost, input prices and paddy quantity VC Variable cost (USD). 859.01 735.48 Y Total quantity of harvested paddy (kg). 15,305.26 14,144.24 Wfert The price of fertilizer (USD/kg). 0.40 0.06 Wlab Labor wage (USD/working day). 5.77 1.58 Wseed The price of seed rice (USD/kg). 0.43 0.13 DS-A Equal to 1 if the summer-autumn season, 0 otherwise. 0.36 0.48 DA-W Equal to 1 if the autumn-winter season, 0 otherwise. 0.27 0.45 DHQV Equal to 1 if high-quality rice varieties, 0 otherwise. 0.42 0.49 Factors affecting cost inefficiency Gender Gender of household head (Equal to 1 if male, 0 otherwise). 0.96 0.20 Education The educational levels of household head (Years). 6.17 3.27 Experience Paddy production experience of household head (Years ). 26.95 12.08 Extension The number of paddy production extension attendances (Number ). 2.45 4.88 House-size The number of household members (People). 3.75 1.49 Land-own Percentage of rice land owned by households (%). 75.11 37.41 Farm-size Farm size (Hectare). 2.38 2.09 Disaster Paddy loss due to natural disasters (%). 11.00 12.98 Disease Paddy loss due to paddy diseases (%). 2.86 5.45 Distance The distance from the house to the rice field (km). 1.47 4.64 4. RESULTS AND DISCUSSION 4.1. Estimates of the Stochastic Cost Frontier Function The calculated parameters for both models are shown in Table 2 and they were implemented with STATA software (version 17). Table 2. Estimates of the translog stochastic frontier cost function. Variable Pooled TRE Coefficient Std. err. Coefficient Std. err. Cost frontier model Constant –0.304*** 0.023 –0.320*** 0.022 lnWseed 0.252*** 0.035 0.250*** 0.040 lnWfert 0.587*** 0.042 0.555*** 0.055 lnY 0.914*** 0.013 0.872*** 0.015 ½lnWseed_sq 0.176 0.107 0.196* 0.109 Asian Journal of Agriculture and Rural Development, 14(1) 2024: 18-24 21 Variable Pooled TRE Coefficient Std. err. Coefficient Std. err. ½lnWfert_sq 0.424 0.274 0.686** 0.328 ½lnY_sq 0.091*** 0.015 0.113*** 0.015 lnWseed_Wfert –0.118 0.134 –0.201 0.133 lnWseed_Y 0.002 0.027 0.000 0.026 lnWfert_Y 0.104*** 0.040 0.074 0.045 DS-A 0.184*** 0.017 0.178*** 0.010 DA-W 0.205*** 0.019 0.180*** 0.012 DHQV –0.047*** 0.017 –0.028* 0.016 Cost inefficiency model Constant –3.541*** 0.640 –4.500*** 0.821 Gender –0.586 0.506 0.032 0.690 Education –0.088 0.120 –0.082 0.140 Experience 0.044 0.119 –0.071 0.142 Extension –0.078 0.109 –0.104 0.136 House-size –0.197* 0.111 –0.156 0.120 Land-own –0.178 0.110 0.081 0.136 Farm-size 0.188 0.120 0.299** 0.124 Disaster 1.121*** 0.156 1.211*** 0.147 Disease 0.231** 0.103 0.219** 0.096 Distance 0.164* 0.091 0.101 0.120 Model properties Σwi – – 0.174*** 0.009 E(σuit ) 0.163 – 0.139 – σvit 0.192*** 0.006 0.106*** 0.005 Log-likelihood 130.05 – 286.65 – Note: *, **, and *** denote the statistically significant levels at 10%, 5%, and 1%, respectively. We use the LR test to identify the preferred functional form and model. The LR ratio to test the Cobb-Dough functional form against the translog form (H0: αjk = βh = γj = 0) for pooled and TRE models are 288.68 and 63.44, respectively which are much greater than the critical values at a significant level of 99%, 𝜒0.99 2 (6) = 16.074 (Kodde & Palm, 1986) for all models. This suggests that the translog functional form represents rice production technology better than the Cobb-Dough functional form. Subsequently, we test the absence of farm heterogeneity (𝐻0: 𝜎𝑤𝑖 = 0) using the LR test of the pooled model against the TRE model with the translog form. The LR statistic is 313.2 much greater than the critical values at a significant level of 99%, (𝜒0.99 2 (1) = 5.412) confirming that there is strong evidence of the existence of farm heterogeneity among rice farmers. The existence of farm heterogeneity in the present data is also confirmed by the significant estimate of the parameter 𝜎𝑤𝑖 . The estimated parameters of the cost front ier functions for pooled and TRE models are provided in Table 2. The first-order coefficients of seed price, fertilizer price and output are as expected, positive and statistically signif icant at 1%. Thus, the parameter estimates of the pooled and TRE models satisfy the properties of the cost frontier function that the optimal variable cost has a positive relationship with output quantity and input prices. The estimate of the DHQV coefficient is negative and statistically significant in both pooled and TRE models suggesting that the variable production cost of the high-quality rice variety is lower than that of the conventional variety group. The estimates of both models also show that there is strong evidence of the impact of cropping seasons on rice farmers’ variable cost frontier. The variable costs for rice production in the summer-autumn (DS-A) and autumn-winter (DA-W) seasons are significantly higher than those in the winter-spring (DW-S) season. The est imate of theta (𝜎𝑤𝑖 ) in the TRE model that measures farm heterogeneity is statistically signif icant implying that unobserved farm heterogeneity exists among rice farmers that needs to be taken into account. Thus, the use of the TRE model to control the heterogeneity effect is appropriate. Table 3. Summary of partial cost elasticities with respect to output quantity and input prices. Variable Pooled TRE Mean Std. dev. Mean Std. dev. Seed price 0.239 0.065 0.239 0.067 Fertilizer price 0.543 0.137 0.521 0.165 Labor price 0.218 0.159 0.240 0.162 Output quantity 0.876 0.093 0.827 0.107 We predicted the partial variable cost elasticities with respect to paddy quantity and input prices to understand how the variable cost varies due to the changes in output quantity and input prices. The results will provide v ital information to support pol icymakers in designing appropriate policies to manage the rice sector based on the input Asian Journal of Agriculture and Rural Development, 14(1) 2024: 18-24 22 price and quantity instruments. The estimated results for each model are consistent as shown in Table 3. The partial elasticities of variable cost in rice farming with respect to paddy quantity and input prices satisfy the properties of the cost function as mentioned above that they are positive for input prices and output quantity. The results also show that variable cost is more elastic with respect to paddy quantity than input prices. The partial variable cost elasticity with output quantity is 0.827 implying that if output quantity increases by 10%, the variable cost will increase by 8.27% given that other factors remain unchanged. The mean elasticity of variable costs with respect to fertilizer is 0.521, double that of seed and labor prices, 0.239 and 0.24, respectively. Thus, a 10% increase in the input prices of fertilizer, seed and labor will increase the variable cost of rice production on average by 5.21%, 2.39% and 2.4% respectively keeping other factors constant. 4.2. Cost Efficiency Analysis Table 4 presents a summary of the cost efficiency estimates categorised by rice variety groups and cropping seasons. The average cost efficiency estimates for the pooled model are lower than those of the TRE model, implying that inefficiency levels were overestimated because the pooled model is not able to consider farm heterogeneity. The average cost efficiency over sampled farms is 0.919 with a wide variation in cost efficiency among rice farmers (0.26– 0.99) implying that there is room for inefficient rice farms to save production costs if they reduce their inefficiency. Our estimated mean cost efficiency is consistent with the results of Tu and Trang (2016) who showed that the mean cost efficiency in paddy production in An Giang prov ince, Vietnam’s Mekong Delta is 0.9 (0.72 –0.97). In addition, our result is close to the finding of Siagian and Soetjipto (2020) who found a result of 0.86 for rice households in Indonesia. Table 4. Statistical summary of cost efficiency scores by cropping seasons and rice varieties. Variable Observation Pooled TRE Mean Std. dev. Mean Std. dev. By cropping season Winter– spring 339 0.920 0.089 0.936 0.080 Summer–autumn 329 0.905 0.073 0.924 0.062 Autumn–winter 250 0.875 0.115 0.890 0.114 By variety High quality rice varieties 384 0.893 0.114 0.909 0.106 Conventional rice varieties 534 0.910 0.074 0.926 0.070 Overall cost efficiency 918 0.903 0.093 0.919 0.087 Table 4 shows that there are differences in the mean cost efficiency among cropping seasons and rice variety groups. The traditional rice variety adopters had an average cost efficiency of 0.926 which is greater than that of the high-quality rice variaty adopters (0.909). This indicates that the high-quality rice variety group performed more inefficiently than the conventional variety group. The mean cost efficiency of the winter-spring cropping season is 0.936, higher than those of the summer-autumn and autumn-winter 0.924 and 0.89, respectively. Thus, we find that seasons considerably affect variable cost frontiers and cost efficiency in rice farming among Vietnamese rice farms. 4.3. Determinants of Cost Inefficiency An important thing in efficiency measurement is to investigate the factors affecting farmers’ inefficiency performance by estimating the inefficiency model. Table 2 presents the estimated outcomes of the cost inefficiency model (described in Equation 1) in the present study. These findings were calculated concurrently with the stochastic cost frontier models (defined in Equation 2). The results of the inefficiency model indicate that the estimates of educational levels, experience, household size, and extension are negative. However, they are not statistically significant implying that there is no clear evidence of the effects of these factors on farmers’ cost inefficiency performance. There is also no statistical evidence on the relationship between gender, rice land ownership, distance and cost inefficiency. We find that farm size has a positive impact on farmers’ cost inefficiency indicating that the larger the farm size, the more cost-inefficient farmers are. This finding is consistent with the findings of Tu and Trang (2016) in An Giang, Vietnam and Siagian and Soetjipto (2020) in Indonesia. Our research f inds that natural disasters and paddy diseases have a positive impact on cost inefficiency. 5. CONCLUSION AND POLICY IMPLICATIONS This research measures cost efficiency and investigates the factors that influence the cost inefficiency among paddy households by applying the stochastic frontier analysis approach with a true random effects model. This paper used primary data gathered from 350 paddy households in the Mekong Delta, southern Vietnam. The estimated results confirm that the absence of farm heterogeneity was rejected and is necessary to be controlled. This means that the estimated results of the true random effects model are more plausible than those of the pooled one. The first- order estimates of variable input prices are positive and statistically significant as expected. We also find that variable costs have a positive relationship with paddy quantity. The partial cost elasticities with respect to paddy quantity and the prices of inputs indicate that variable costs in rice production are inelastic for output quantity and input prices. The partial cost elasticity estimate with respect to Asian Journal of Agriculture and Rural Development, 14(1) 2024: 18-24 23 fertilizer price is 0.521, double that of seed and labor prices, 0.24 implying that fertilizer is the major input cost of rice farming. The partial cost elasticity estimate with respect to output quantity is 0.827 implying that if output quantity increases by 10%, the variable cost will increase by 8.27% keeping other factors constant. The estimation of cost efficiency reveals that although rice farmers exhibit a high cost efficiency (0.92) on average, cost efficiency scores among rice farmers have a wide range of variation (0.26 – 0.99). The research results suggest that inefficient rice farmers still have the potential to reduce production costs if they improve their efficiency. The estimates of the cost inefficiency model report that the variation in cost inefficiency among rice farms is due to rice area, natural disasters and rice d iseases. The results show that rice areas, natural disasters and rice d iseases positively and signif icantly impact farmers’ cost inefficiency. However, there is no evidence on the effects of education, rice farming experience, gender, household size, rice land ownership, extension and distance on farmers’ cost inefficiency performance. This study suggests that training programs should focus on improving the production skills of rice farmers so that they can manage input costs and deal with rice diseases and natural disasters better to reduce paddy losses. Funding: This research is supported by the University of Economics, Hue University (Grant number: NNC.ĐHKT.2023-06). Institutional Review Board Statement: Not applicable. Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. 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