1. 1 The Comparative Analysis of Technical Efficiency of Jasmine Rice Production in Thailand Using Survey and Measurement Data The Comparative Analysis of Technical Efficiency of Jasmine Rice Production in Thailand Using Survey and Measurement Data Aree Wiboonpongse Aree Wiboonpongse Songsak Sriboonchitta Chiang Mai University, Thailand At Fourth International Conference on Agriculture Statistics, October 22-24, 2007, Beijing, China

2. 2 Outline 1. Significance of the Study 2. Key questions and objectives 3. Data and Model Specifications 4. Empirical Results 5. Policy Implications and Conclusions

3. 3 Jasmine rice of Thailand is of premium quality and its export share has increased substantially due to its high price. However, its production yield can be regarded as low compared to those of other varieties. Questions are naturally asked whether Thai Jasmine rice productivity can be improved. Most studies on agricultural production models have used survey rather than measurement data from cropcut samples and record keeping. Survey data obtained from interview using questionnaire are subject to errors in recalling of farmers. Errors in the yield data could be caused by incorrect recall as well as the loss during harvesting. 1. Significance of the Study

4. 4 1.“Analysis of Production Efficiency and the Impact of Blast Disease on Hom Mali rice Output Using Stochastic Frontier Model”“Analysis of Production Efficiency and the Impact of Blast Disease on Hom Mali rice Output Using Stochastic Frontier Model” Data crop cut sampling (263 ob.; 168, 25, and 70 from CM, PIT TGR) Results: Factors with positive impact chemicals, irrigation and labor Factors with negative effect neck blast and severer drought The related researches 2.“On The Estimation of Stochastic Production Frontiers with self-selectivity: Jasmine and Non-Jasmine Rice in Thailand”“On The Estimation of Stochastic Production Frontiers with self-selectivity: Jasmine and Non-Jasmine Rice in Thailand” Data survey sampling (489 ob., 112(CM), 176(PIT), 201(TGR) 207(Jasmine rice) Methodology The probit maximum Likelihood Model with endogenous switching frontier. Results: Factor affecting Jasmine rice chemical fertilizer, labour, other chemicals, irrigation server drought and the self-selectivity.

5. 5 rice field in the north

6. 6 blastneck blast

7. 7 without neck blast

8. 8 threshing in NE carry to threshing in N

9. 9 The present research attempts to compare the efficiency levels of Jasmine rice production given the identical set of environmental factors and production inputs but estimated by two different sets of output data, cropcut measurement and farmers survey. Aim to answer the major question: How has the different type of yield data affected estimates of stochastic production frontier? 2. Key questions and objectives

10. 10 Data on rice yield of crop year 1999/2000 were collected from two different methods: 1. Interviewing farmers (called survey data) 2. Measuring the rice weight from sample plots in the field (called measurement data) ***The same 130 farmers provided two types of yield data*** 3. Data and Model Specifications 3.1 Data on Rice Farmer Samples

11. 11 Sampling procedure 1. Three selected major Jasmine rice production areas in the upper north, lower north and north east regions (Chiang Mai Province, Pitsanuloke Province and TungGula Rongha rice Plane) 2. Districts of most intensive Jasmine rice were selected For the survey 3. For the survey:- Simple random sampling of farm unit of observations For the measurement For the measurement:- three-one square meter plots along the diagonal of a rice field were randomly selected

13. 13 Variable Sample Mean Sample Standard Deviation MinimumMaximum Yield (kgs/m 2 ): Survey 1 0.240.110.020.53 Yield (kgs/m 2 ): Measurement 2 0.150.140.010.33 Seed (kgs/m 2 )0.0080.0060.0010.027 Fertiliser (kgs/m 2 )0.0080.0050.0010.027 Labour(man-hour/m 2 )0.0150.0100.0010.049 Education (years)4.951.870.0014.00 Age (years)44.729.2725.5071.00 Labour (adult persons)2.531.041.008.00 Labour ratio0.54 0.18 0.001.00 Sources: 1. Wiboonpongse, A. and Sriboonchitta, S., 2001. 2. Gypmanyasiri, P., Kramol, P. and Limniranknl, B.,2001 2. Gypmanyasiri, P., Kramol, P. and Limniranknl, B.,2001. Table 1: Summary statistics of key variables for the sample Jasmine rice farmers

14. 14 3.2 Model specifications

15. 15 3.2 Model specifications (cont.)

16. 16 3.2 Model specifications (cont.)

17. 17 4. Empirical Results 4.1 Hypotheses testing 4.1 Hypotheses testing Table 2: Generalised likelihood-ratio tests of null hypotheses for parameters in the stochastic frontier production function models for survey data and measurement data models

18. 18 Production function For the measurement data model (Model 1): * The major production variables: Seed(+) * The dummy variables: dummy variables for neck blast(-), severe drought(-) TGR(-), irrigation(+) and chemicals(+) For the survey data model (Models 2 and 3): * Model 2:- * Model 2:- There is no single variable affect the management efficiency of farmers * Model 3:- Generally the significant variables have the expected sign except fertiliser and labour variables fertiliser and labour variables The dummy variables: The dummy variables: dummy variables for neck blast(-), severe drought(-), Phitsanuloke Province(-), and chemicals(+) 4.2 Production frontier estimates

19. 19 Table 3: Maximum-likelihood estimates for parameters of the preferred stochastic frontier production models for measurement data and survey data models

20. 20 Inefficiency equation For the measurement data model (Model 1): * Education(-), Age(-)  the higher formal education and older farmers tended to reduce the TI in Jasmine rice production For the survey data model (Models 2 and 3): Model 3:- * Model 3:- Human resource variables became highly significant Labour Ratio(-)  Higher labour force ratio in Jasmine rice tended to have smaller TI

21. 21 Table 3: Maximum-likelihood estimates for parameters of the preferred stochastic frontier production models for measurement data and survey data models (cont.) production models for measurement data and survey data models (cont.)

22. 22 The predicted technical efficiencies for the sample farmers in the three major areas of Jasmine rice production of Thailand range between 0.11 and 0.99 for the measurement data model and range between 0.10 and 0.99 for the survey data model. The mean technical efficiency of farms over all prefectures is estimated to be 0.62 and 0.65 for the measurement data and survey data models, respectively. A frequency distribution of the predicted technical efficiencies in the decile ranges from less than 0.5 to 1.0 indicates that the majority of the areas have average technical efficiency of Jasmine rice farms between 0.7-1.0. 4.3. Technical efficiency indexes

23. 23 Interval Measurement data Model 1 Survey data Model 2Model 3 < 0.5037 (28.5) 33(25.4) 0.50-0.6020 (15.4)17 (13.1)15(11.5) 0.60-0.7026 (20.0)33 (25.4)23(17.7) 0.70-0.8016 (12.3)15 (11.5)18(13.8) 0.80-0.9012 (9.2) 17(13.1) 0.90-1.0019 (14.6)16 (12.3)24(18.5) Mean technical efficiency0.620.630.65 Table 4: Percentages of technical efficiencies of Jasmine rice farmers in Chiang Mai and Pitsanuloke provinces and TungGula Ronghai area within decile ranges Pitsanuloke provinces and TungGula Ronghai area within decile ranges Source: From estimation. Note: Number in the parentheses are the percentage.

24. 24 Rice productivity in the study areas was determined primarily by the amount of seeds sown and the responsiveness of the rice variety to chemical fertilizer input. The availability of irrigation would definitely enhance yield. Rice productivity can be seriously impaired at the presence of severe drought and neck blast. Development of human resource to possess good management practice ability is seen to be crucial for the increase in production efficiency, which can be materialized by learning and experience accumulation processes as well as by increasing the proportion of male labour input. 5. Policy implications and conclusions

25. 25 * The comparative study between the use of survey data and measurement data to estimate the technical efficiency provided the evidence of extremely different conclusions from the use of different dependent variables despite the identical sets of independent parameters. * Use of measurement data appeared to provide results which are not sensitive to the model specification at the given severity of multicollinearity. 5. Policy implications and conclusions (cont.)

26. 26 * Apart from the robust variables, the remaining weak determinants would generate different values of estimate and which are highly sensitive to model specification. * Agricultural economists and users of survey data, therefore, should be cautioned to pay special attention to survey design so as to minimize error. * The test of model specification might be an effective means to detect the errors in estimation due to the choice of variables. 5. Policy implications and conclusions (cont.)

27. 27 Thank you very much