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Technical efficiency of producers’ in the dryland areas of west africa a multiioutput stochaistic metafrontier approach

  1. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency of Producers’ in the Dryland Areas of West Africa: a Multi-Output Stochastic Metafrontier Approach Alphonse Singbo & Pierre Sibiry Bamako, Mali May 29, 2015 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  2. Objective Framework Modelling Data Summary results Conclusion Contents 1 Motivation A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  3. Objective Framework Modelling Data Summary results Conclusion Contents 1 Motivation 2 Objective of the study A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  4. Objective Framework Modelling Data Summary results Conclusion Contents 1 Motivation 2 Objective of the study 3 Model Specification A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  5. Objective Framework Modelling Data Summary results Conclusion Contents 1 Motivation 2 Objective of the study 3 Model Specification 4 Data A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  6. Objective Framework Modelling Data Summary results Conclusion Contents 1 Motivation 2 Objective of the study 3 Model Specification 4 Data 5 Results A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  7. Objective Framework Modelling Data Summary results Conclusion Contents 1 Motivation 2 Objective of the study 3 Model Specification 4 Data 5 Results 6 Conclusion A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  8. Objective Framework Modelling Data Summary results Conclusion Agriculture value added Agriculture value added and Population growth A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  9. Objective Framework Modelling Data Summary results Conclusion Agriculture value added Agriculture value added and Population growth Country Agriculture Rate of pop. (% of GDP) (1998 − 2015) US 1.4 0.7 Brazil 5.3 0.9 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  10. Objective Framework Modelling Data Summary results Conclusion Agriculture value added Agriculture value added and Population growth Country Agriculture Rate of pop. (% of GDP) (1998 − 2015) US 1.4 0.7 Brazil 5.3 0.9 Ghana 25.2 2.22 Niger 38.3 3.3 Nigeria 22.3 2.9 Mali 39.4 2.7 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  11. Objective Framework Modelling Data Summary results Conclusion Motivation Increase economic productivity is a way to help smallholder farmers to become more resilient to the greater climate risks. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  12. Objective Framework Modelling Data Summary results Conclusion Motivation Increase economic productivity is a way to help smallholder farmers to become more resilient to the greater climate risks. In order to make smallholder farming profitable through an IMOD, it is important to examine the economy performance of these farmers. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  13. Objective Framework Modelling Data Summary results Conclusion Motivation Increase economic productivity is a way to help smallholder farmers to become more resilient to the greater climate risks. In order to make smallholder farming profitable through an IMOD, it is important to examine the economy performance of these farmers. However, the study on inter-regional efficiency in Dryland systems is rare. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  14. Objective Framework Modelling Data Summary results Conclusion Motivation Increase economic productivity is a way to help smallholder farmers to become more resilient to the greater climate risks. In order to make smallholder farming profitable through an IMOD, it is important to examine the economy performance of these farmers. However, the study on inter-regional efficiency in Dryland systems is rare. Additionally, no study in WCA attempts to compare the production possibilty in the region using on household level data. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  15. Objective Framework Modelling Data Summary results Conclusion Motivation Increase economic productivity is a way to help smallholder farmers to become more resilient to the greater climate risks. In order to make smallholder farming profitable through an IMOD, it is important to examine the economy performance of these farmers. However, the study on inter-regional efficiency in Dryland systems is rare. Additionally, no study in WCA attempts to compare the production possibilty in the region using on household level data. Previous studies related to Africa have used aggregate country level data. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  16. Objective Framework Modelling Data Summary results Conclusion Objective A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  17. Objective Framework Modelling Data Summary results Conclusion Objective 1 Measure and compare technical efficiency of producers in the dryland systems. From the modelling : Estimate Technical Inefficiency in each country : Ghana, Niger, Nigeria and Mali Estimate the metatechnology ratio (technology gap) of each country relative to the whole region A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  18. Objective Framework Modelling Data Summary results Conclusion Objective 1 Measure and compare technical efficiency of producers in the dryland systems. From the modelling : Estimate Technical Inefficiency in each country : Ghana, Niger, Nigeria and Mali Estimate the metatechnology ratio (technology gap) of each country relative to the whole region 2 Identify the position of each country regarding the metafrontier. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  19. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency, Metafrontier Efficiency and Metatechnology Ratio (MTR) A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  20. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency, Metafrontier Efficiency and Metatechnology Ratio (MTR) Definition Technical Efficiency (TE) : the ability of a producer to obtain the maximum output from a given input vector. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  21. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency, Metafrontier Efficiency and Metatechnology Ratio (MTR) Definition Technical Efficiency (TE) : the ability of a producer to obtain the maximum output from a given input vector. Definition Metafrontier Technical Efficiency (MTE) : the ability to obtain the maximum output from a given input vector with respect to the production frontier of the region (metafrontier). A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  22. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency, Metafrontier Efficiency and Metatechnology Ratio (MTR) Definition Technical Efficiency (TE) : the ability of a producer to obtain the maximum output from a given input vector. Definition Metafrontier Technical Efficiency (MTE) : the ability to obtain the maximum output from a given input vector with respect to the production frontier of the region (metafrontier). Definition Metatechnology Ratio (MTR) : the distance between the frontier of a country relative to the metafrontier. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  23. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  24. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework Input x A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  25. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework Input x Output y A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  26. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework Input x Output y a b c d e f g i h j O A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  27. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework Input x Output y a b c d e f g i h j Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽 O A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  28. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework Input x Output y a b c d e f g i h j Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽 A B O A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  29. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework Input x Output y a b c d e f g i h j Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽 A B O A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  30. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework Input x Output y a b c d e f g i h j Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽 A B O 𝑇𝐸 = 𝑂𝐴 𝑂𝐵 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  31. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency framework Input x Output y a b c d e f g i h j Production Frontier ≡ 𝑃𝐹 𝑥; 𝛽 A B O 𝑇𝐸 = 𝑂𝐴 𝑂𝐵 For example, 𝑇𝐸 = 0.6 indicates that the output vector, y, is 60% of the maximum output that could be produced by a farm using the input vector, x. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  32. Objective Framework Modelling Data Summary results Conclusion Metafrontier framework A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  33. Objective Framework Modelling Data Summary results Conclusion Metafrontier framework Input x Output y A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  34. Objective Framework Modelling Data Summary results Conclusion Metafrontier framework Input x Output y 1 1 1 1 1 1 Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  35. Objective Framework Modelling Data Summary results Conclusion Metafrontier framework Input x Output y 1 1 1 1 1 1 Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  36. Objective Framework Modelling Data Summary results Conclusion Metafrontier framework Input x Output y 1 1 1 1 1 1 Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1 2 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  37. Objective Framework Modelling Data Summary results Conclusion Metafrontier framework Input x Output y 1 1 1 1 1 1 Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  38. Objective Framework Modelling Data Summary results Conclusion Metatechnology ratio (MTR) A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  39. Objective Framework Modelling Data Summary results Conclusion Metatechnology ratio (MTR) Input x Output y Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  40. Objective Framework Modelling Data Summary results Conclusion Metatechnology ratio (MTR) Input x Output y Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  41. Objective Framework Modelling Data Summary results Conclusion Metatechnology ratio (MTR) Input x Output y Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  42. Objective Framework Modelling Data Summary results Conclusion Metatechnology ratio (MTR) Input x Output y Country 1 frontier ≡ 𝐶𝐹1 𝑥; 𝛽 1 0 C D E  TE of A according to CF2: 𝑻𝑬 𝑨 𝑪𝑭 𝟐 = 𝟎𝑪 𝟎𝑫  TE of A according to the Metafrontier: 𝑻𝑬 𝑨 = 𝟎𝑪 𝟎𝑬  𝑴𝑻𝑹 𝑨 𝟐 = 𝑻𝑬 𝑨 𝑻𝑬 𝑨 𝑪𝑭 𝟐 = 𝟎𝑪 𝟎𝑬 𝟎𝑪 𝟎𝑫 = 𝟎𝑫 𝟎𝑬 𝑴𝑻𝑹 𝑨 𝟐 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  43. Objective Framework Modelling Data Summary results Conclusion Empirical Modelling First step : Stochastic distance function. For each country run the Modified Translog function to estimate parameters and the TE − ln yi m = β0 + βdDi k + βlFi l + k βk ln(xi k) + l βl ln( yi l yi m ) +1 2 k j βkj ln(xi k) ln(xi j) + 1 2 l h βlh ln( yi l yi m ) ln( yi h yi m ) +1 2 k l βkl ln(xi k) ln( yi l yi m ) + vi − ln(TEi ) where : vi ∼ iid N(0, σ2 v ) and vi and TEi are distributed independently and TEi ∈ (0, 1[ A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  44. Objective Framework Modelling Data Summary results Conclusion Empirical Modelling Second step : Metafrontier programming. From the parameters estimated in Step 1, we solve tle LP problems to ensure that the metafrontier envelop the country frontiers (convexity) A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  45. Objective Framework Modelling Data Summary results Conclusion Empirical Modelling Second step : Metafrontier programming. From the parameters estimated in Step 1, we solve tle LP problems to ensure that the metafrontier envelop the country frontiers (convexity) The LP problem is : minβ x · β st : xi · β ≥ xi · βk where : βk is the estimated coefficient vector associated with the country group stochactic frontier obtained in step 1. x is the arithmetic average of the xi vectors over all farms. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  46. Objective Framework Modelling Data Summary results Conclusion Empirical Modelling Third step : Compute the Metatechnology ratio (MTR). MTRi k = exiβk exiβ Final step : Compute TE relative to the metafrontier. TE i = TEk i × MTRk i A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  47. Objective Framework Modelling Data Summary results Conclusion Data The baseline survey data collected within the CRP dryland systems in 2012 were used in four countries : Ghana, Niger, Nigeria and Mali A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  48. Objective Framework Modelling Data Summary results Conclusion Data The baseline survey data collected within the CRP dryland systems in 2012 were used in four countries : Ghana, Niger, Nigeria and Mali Outputs are aggregated into two groups : Cereals and Other crops (legumes and cotton) A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  49. Objective Framework Modelling Data Summary results Conclusion Data The baseline survey data collected within the CRP dryland systems in 2012 were used in four countries : Ghana, Niger, Nigeria and Mali Outputs are aggregated into two groups : Cereals and Other crops (legumes and cotton) Inputs are aggregated into five categories : capital, materials, livestock, labor and land A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  50. Objective Framework Modelling Data Summary results Conclusion Data The baseline survey data collected within the CRP dryland systems in 2012 were used in four countries : Ghana, Niger, Nigeria and Mali Outputs are aggregated into two groups : Cereals and Other crops (legumes and cotton) Inputs are aggregated into five categories : capital, materials, livestock, labor and land The multilateral Tornqvist price index is used to construct an implicit quantity index for each output and input. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  51. Objective Framework Modelling Data Summary results Conclusion Data Data are in Purchasing Power parity ($) A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  52. Objective Framework Modelling Data Summary results Conclusion Data Data are in Purchasing Power parity ($) Two Outputs Ghana Niger Nigeria Mali Cereal 53, 358.34 578.42 3, 151.67 2, 66.77 Other crops 52, 626.20 214.35 5, 996.91 323.10 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  53. Objective Framework Modelling Data Summary results Conclusion Data Data are in Purchasing Power parity ($) Two Outputs Ghana Niger Nigeria Mali Cereal 53, 358.34 578.42 3, 151.67 2, 66.77 Other crops 52, 626.20 214.35 5, 996.91 323.10 Five Inputs Capital 52.86 36.00 99.33 85.93 Labor (man − hours) 5.42 7.35 7.34 11.75 Land (ha) 6.24 9.46 9.21 14.38 Materials 371.68 346.92 1, 551.89 930.30 Livestock (livestock units) 1.64 9.46 2.63 9.08 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  54. Objective Framework Modelling Data Summary results Conclusion Estimated parameters A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  55. Objective Framework Modelling Data Summary results Conclusion Estimated parameters Estimated of the Distance Function Elasticities Variables ln OtherCrops ln Capital ln Labor ln Land ln Materials ln Livestock A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  56. Objective Framework Modelling Data Summary results Conclusion Estimated parameters Estimated of the Distance Function Elasticities Variables Ghana ln OtherCrops −0.619∗∗∗ ln Capital 0.167 ln Labor 0.396 ln Land 0.063 ln Materials 0.121∗∗ ln Livestock −0.693∗∗∗ A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  57. Objective Framework Modelling Data Summary results Conclusion Estimated parameters Estimated of the Distance Function Elasticities Variables Ghana Niger ln OtherCrops −0.619∗∗∗ −1.056∗∗∗ ln Capital 0.167 1.649∗∗ ln Labor 0.396 0.779 ln Land 0.063 4.798 ln Materials 0.121∗∗ −0.098 ln Livestock −0.693∗∗∗ −15.923∗∗∗ A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  58. Objective Framework Modelling Data Summary results Conclusion Estimated parameters Estimated of the Distance Function Elasticities Variables Ghana Niger Nigeria ln OtherCrops −0.619∗∗∗ −1.056∗∗∗ −0.615∗∗∗ ln Capital 0.167 1.649∗∗ 0.088∗ ln Labor 0.396 0.779 0.148 ln Land 0.063 4.798 −0.020 ln Materials 0.121∗∗ −0.098 0.048∗∗ ln Livestock −0.693∗∗∗ −15.923∗∗∗ −0.107 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  59. Objective Framework Modelling Data Summary results Conclusion Estimated parameters Estimated of the Distance Function Elasticities Variables Ghana Niger Nigeria Mali ln OtherCrops −0.619∗∗∗ −1.056∗∗∗ −0.615∗∗∗ −0.065∗∗∗ ln Capital 0.167 1.649∗∗ 0.088∗ −0.160∗∗∗ ln Labor 0.396 0.779 0.148 1.227∗∗∗ ln Land 0.063 4.798 −0.020 −0.836∗∗∗ ln Materials 0.121∗∗ −0.098 0.048∗∗ 0.090 ln Livestock −0.693∗∗∗ −15.923∗∗∗ −0.107 0.296∗∗∗ A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  60. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency and Metatechnology ratio A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  61. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency and Metatechnology ratio Estimates of technical efficiency (TE) and Metatechnology ratio (MTR) A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  62. Objective Framework Modelling Data Summary results Conclusion Technical Efficiency and Metatechnology ratio Estimates of technical efficiency (TE) and Metatechnology ratio (MTR) Country TE country MTR frontier Ghana 0.996 0.164 Niger 0.996 0.063 Nigeria 0.452 0.053 Mali 0.989 0.109 A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  63. Objective Framework Modelling Data Summary results Conclusion Implications A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  64. Objective Framework Modelling Data Summary results Conclusion Implications Smallholder producers are operating at the high level of their country frontier indicating that there are homogeneous A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  65. Objective Framework Modelling Data Summary results Conclusion Implications Smallholder producers are operating at the high level of their country frontier indicating that there are homogeneous Comparing to the metafrontier, Nigeria producers are operating at the lowest level and Ghana producers are at the highest level A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  66. Objective Framework Modelling Data Summary results Conclusion Implications Smallholder producers are operating at the high level of their country frontier indicating that there are homogeneous Comparing to the metafrontier, Nigeria producers are operating at the lowest level and Ghana producers are at the highest level Along with the GPS mapping it is possible to map the position of producers performance in the region. A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
  67. Objective Framework Modelling Data Summary results Conclusion Thanks Comments and suggestions are most welcome ! A. Singbo & P. Sibiry Metatechnology frontier in the Dryland Areas of WA
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