Fantu Nisrane, Guush Berhane, Sinafikeh Asrat, Gerawork Getachew, Alemayehu Seyoum Taffesse International Food Policy Rese...
Important Findings  <ul><li>Inefficiency is rather high – the average farmer is 60 percent less than the ‘best’ farmer (in...
Organization of presentation <ul><li>Background information  </li></ul><ul><li>Description of Data </li></ul><ul><li>Stoch...
Ethiopian Agriculture <ul><li>About 45 percent of rural residents live below the poverty line, </li></ul><ul><li>Agricultu...
Ethiopian Agriculture…contd. <ul><li>Ethiopians received 2.5 and 7.5 times more cereal aid per head than Developing Countr...
<ul><li>In the  long-run , such yield increases can be achieved through (technological) shifts to modern farming systems. ...
Data Description   <ul><li>Data from 6 of the 7 rounds Ethiopian Rural Household Survey (ERHS) are used. </li></ul><ul><li...
ERHS Sample Villages
Data Description…contd . Mean Value of Output and Inputs used in Production Year Variable 1994 1995 1997 1999 2004 2009 Re...
Data Description…contd. Mean values of household, peasant association, and agroecologic specific variables used in the ine...
Empirical Model: Stochastic Production Frontier (SPF)   <ul><li>Competing approaches to SPF analysis are the classical lin...
<ul><li>The theoretical model used in this study is:  </li></ul><ul><li>Where ,    represents farm household h, </li></ul>...
SPF … contd . <ul><li>The inefficiency equation is specified as: </li></ul><ul><li>Where  are assumed to be a function of ...
Results: Production Frontier.   ML parameter estimates associated with agricultural inputs used in SPF analysis. Variable ...
<ul><li>Most of the increase in output was attained by increased use of traditional inputs.  </li></ul><ul><li>Most increa...
Results: Production Frontier … contd. ML Estimates of time and AEZ dummy variables Variable Estimated Coefficient Calculat...
Results: Production Frontier … contd. <ul><li>The Northern Highlands region has inferior production frontier,  </li></ul><...
Results: Other specifications of Production Frontier <ul><li>Baseline estimates robust under other specifications, </li></...
Results: Inefficiency Equation…contd. ML estimates of the inefficiency function parameters. Variables  Coefficient Constan...
Results: Inefficiency Equation…contd. ML estimates of the inefficiency function parameters… Contd. Number of agricultural ...
Results: Inefficiency Equation <ul><li>Efficiency of agricultural production increases with:  </li></ul><ul><ul><li>Family...
Average efficiency estimates of farmers by AEZs and PAs. AEZ/ PA  1994 1995 1997 1999 2004 2009 Average across Years North...
Average efficiency estimates of farmers by AEZs and Pas…contd. AEZ/ PA  1994 1995 1997 1999 2004 2009 Average across Years...
Results: Inefficiency Equation…contd. <ul><li>Overall average agricultural efficiency (across zones and periods) is about ...
Results: Inefficiency Equation…contd. <ul><li>Average efficiency grew at annual rate of 2.5 percent </li></ul><ul><ul><li>...
Conclusion and Policy Recommendation <ul><li>During 1994-2009 period, most of the increase in agricultural output among su...
Caveats <ul><li>Three major categories: basic SFA approach, variables used and sample </li></ul><ul><li>SFA tries to expla...
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Sources of Inefficiency and Growth in Agricultual Output in Subsistence Agriculture: A Stochastic Frontier Analysis

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Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI, Seventh International Conference on Ethiopian Economy, EEA Conference, June 26, 2010

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Sources of Inefficiency and Growth in Agricultual Output in Subsistence Agriculture: A Stochastic Frontier Analysis

  1. 1. Fantu Nisrane, Guush Berhane, Sinafikeh Asrat, Gerawork Getachew, Alemayehu Seyoum Taffesse International Food Policy Research Institute (IFPRI) (Ethiopia Strategy Support Program, ESSP-II) ESSP-II Conference Addis Ababa 22-24 October, 2009 Sources of Inefficiency and Growth in Agricultural Output in Subsistence Agriculture: A Stochastic Frontier Analysis The views expressed in this paper are those of the authors and do not represent the official position of their institution. ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE
  2. 2. Important Findings <ul><li>Inefficiency is rather high – the average farmer is 60 percent less than the ‘best’ farmer (in this sample). </li></ul><ul><ul><li>Implies increases in output without increased use of inputs is possible in the short run </li></ul></ul><ul><ul><li>This result is robust across different (translog, agro-ecology) specifications </li></ul></ul><ul><li>Most of the increase in output is attained by increased use of ‘traditional’ inputs. </li></ul><ul><ul><li>Extended use of modern inputs is indispensable. </li></ul></ul><ul><li>Evidence that inefficiency varies across agro-ecological zone. </li></ul><ul><li>Evidence that average farming efficiency improved during the 1995 – 2004 period. </li></ul>
  3. 3. Organization of presentation <ul><li>Background information </li></ul><ul><li>Description of Data </li></ul><ul><li>Stochastic Production Frontier: Relative Advantage </li></ul><ul><li>Results </li></ul><ul><li>Conclusion </li></ul><ul><li>Caveats </li></ul>
  4. 4. Ethiopian Agriculture <ul><li>About 45 percent of rural residents live below the poverty line, </li></ul><ul><li>Agricultural value added per worker was less than one-half the average in SSA in 2003 ($154, 2000 prices). </li></ul><ul><li>Per hectare fertilizer application in Ethiopia was only 6 percent of application rates in four neighboring countries (Kenya, Sudan, Uganda, and Egypt). </li></ul><ul><li>Developing Countries and the four neighboring countries irrigated 10 and 11 times more land than in Ethiopia. The level of irrigation in Egypt was 45 times higher. </li></ul>
  5. 5. Ethiopian Agriculture…contd. <ul><li>Ethiopians received 2.5 and 7.5 times more cereal aid per head than Developing Countries and the poorest five countries during 1993 to 2003. </li></ul><ul><li>In 2006/7 Ethiopian cereal yield was 1520 KG/ha, only half the 2000-2002 world average, which was 3087 KG/ha, and only one-fifth of the Egyptian yield level of 7271 KG/ha. </li></ul><ul><li>If farmers were to achieve the world or Egyptian average yield levels the difference between actual and counterfactual output levels would be about 26 or 97 folds of the cereal donations to Ethiopia in 2006/7. </li></ul>
  6. 6. <ul><li>In the long-run , such yield increases can be achieved through (technological) shifts to modern farming systems. </li></ul><ul><li>Efficiency increases are also possible in the short-run through improving the efficiency of existing farming systems. </li></ul><ul><li>Understanding existing relative efficiency levels across farm households is essential. </li></ul><ul><li>This study aims to answer the following questions (Ethiopian context): </li></ul><ul><ul><li>Given the prevailing input use intensity, how efficient are farm households relative to ‘best’ performing (or, otherwise called ‘model’) farmers? </li></ul></ul><ul><ul><li>What determines such (in) efficiencies, and what can be done about these determinants? </li></ul></ul>Ethiopian Agriculture…contd.
  7. 7. Data Description <ul><li>Data from 6 of the 7 rounds Ethiopian Rural Household Survey (ERHS) are used. </li></ul><ul><li>The ERHS is a longitudinal household data set that comprises 15 of the 319 woredas located in 6 of the 9 regions in Ethiopia. </li></ul><ul><li>In addition to the agriculture sections of surveys, agro-ecologic data is used in the analysis. </li></ul><ul><li>As each survey was conducted on different number of households, the panel data formed is unbalanced. A total of 7,947 cases used. </li></ul>
  8. 8. ERHS Sample Villages
  9. 9. Data Description…contd . Mean Value of Output and Inputs used in Production Year Variable 1994 1995 1997 1999 2004 2009 Real value of output per household (birr) 1,144 1,214 1,836 1,687 1,811 2,559 Cultivated area (hectares) 1.2 1.2 1.9 0.9 1 1.6 Household members 16 years or older 3.3 3.3 3.9 3 2.7 3 Annual rain 12 months before survey (MLs.) 909 1008 1070 961 942 1049 Fertilizer used (KGs) 38 41.7 48.6 49.6 27.8 82.7 Number of oxen used for ploughing (count) 1.3 1.3 1.7 1.4 0.9 1.1 Average land quality (index) 2.5 2.4 2.1 2.2 2.2 2 Number of hoes owned (count) 0.8 0.9 0.9 1.3 1.1 2.8 Number of ploughs owned (count) 0.8 1.1 1.1 1.4 1.1 4.3 Participated in the extension package 0.06 0.03 0.05 0.12 0.08 0.28
  10. 10. Data Description…contd. Mean values of household, peasant association, and agroecologic specific variables used in the inefficiency equation. Variable Units Average across survey Years Sex of head of household 0 if female, 1 if male 0.79 Age of head of household Years 50 Education level of head 0 if illiterate, 1 if literate 0.18 Household size Count 6 Number of plots cultivated Count 4.3 Livestock units per household Index 3 Number of extension officers in PA Count 0.8 Was crop damaged by drought 0 if no, 1 if yes 0.15 Mean elevation (Meters) Meters 2086 Distance to nearest health center Kilometers 18 Distance to closest market Kilometers 22 Distance to nearest PA center Kilometers 20
  11. 11. Empirical Model: Stochastic Production Frontier (SPF)   <ul><li>Competing approaches to SPF analysis are the classical linear regression model and data envelopment analysis. </li></ul><ul><li>SPF is distinct because it uses composed error terms, which take in to account both idiosyncratic and efficiency differences. </li></ul><ul><li>SPF acknowledges ‘not all farmers are equally (technically) efficient’ and explicitly accounts for efficiency differences in the analysis </li></ul>
  12. 12. <ul><li>The theoretical model used in this study is: </li></ul><ul><li>Where , represents farm household h, </li></ul><ul><li>represents time period t, </li></ul><ul><li>is output of farmer h at time period t, </li></ul><ul><li>is a (1Xk) vector of inputs. </li></ul><ul><li> is a (kX1 ) vector of unknown parameters, </li></ul><ul><li>and are the idiosyncratic and inefficiency components of the composed error term of farmer h at time period t. </li></ul>
  13. 13. SPF … contd . <ul><li>The inefficiency equation is specified as: </li></ul><ul><li>Where are assumed to be a function of household and region specific variables, , and a set of parameter values, , to be estimated simultaneously with the production function parameters. </li></ul><ul><li>Various specifications were used to test for robustness of Cobb-Douglas specification, agro-ecologic diversity, and to test for structural change during the study period . </li></ul><ul><li>The data support the hypothesis that SPF is the appropriate approach to follow </li></ul><ul><li>The hypothesis of ‘constant returns to scale’ is not rejected. </li></ul>
  14. 14. Results: Production Frontier. ML parameter estimates associated with agricultural inputs used in SPF analysis. Variable Estimated Coefficient Calculated elasticity Constant 4.799   Area of cultivated land 0.225 0.225 Household members 16 years and older 0.129 0.129 Level of education 0.154 0.166 Amount of rainfall 0.350 0.350 Amount of Fertilizer used 0.002 0.082 Number of ploughing oxen 0.111 0.143 Average land quality -0.098 -0.219 Number of hoes used 0.053 0.071 Number of ploughs used 0.030 0.050 Participation in extension program 0.111 0.117
  15. 15. <ul><li>Most of the increase in output was attained by increased use of traditional inputs. </li></ul><ul><li>Most increase in value of output attributed to changes in: </li></ul><ul><ul><li>the amount of rain received in the region, </li></ul></ul><ul><ul><li>size and quality of cultivated land, </li></ul></ul><ul><ul><li>the numbers of oxen used for cultivation, </li></ul></ul><ul><ul><li>changes in quality and quantity of labor use, and </li></ul></ul><ul><li>Among modern inputs: participation in the extension program has moderate effect. </li></ul><ul><li>Fertilizer application has one of the lowest elasticities for an average farmer and even lower among those that actually apply fertilizer. </li></ul>Results: Production Frontier
  16. 16. Results: Production Frontier … contd. ML Estimates of time and AEZ dummy variables Variable Estimated Coefficient Calculated elasticity 1995 dummy 0.314* 0.369 1997dummy 0.097*** 0.102 1999 dummy 0.040 0.042 2004 dummy 0.108** 0.115 2009 dummy -0.060 -0.056 Central Highlands 0.401* 0.493 Arussi/Bale 0.572* 0.772 Hararghe 0.785* 1.193 Enset 0.897* 1.451 Note : Estimates with *, **, and *** are significant at 1, 5, and 10 percent.
  17. 17. Results: Production Frontier … contd. <ul><li>The Northern Highlands region has inferior production frontier, </li></ul><ul><li>Farmers benefited from productivity (frontier or time dummies) improvements across the 1994 -2004 period, but productivity declined between 2004 and 2009. </li></ul><ul><li>However, (average) farm efficiency improved during the 1995 – 2004 period. </li></ul>
  18. 18. Results: Other specifications of Production Frontier <ul><li>Baseline estimates robust under other specifications, </li></ul><ul><li>Large output increase resulted from even larger use of almost all inputs </li></ul><ul><li>Contribution of cultivated area and rainfall more pronounced in later years of the survey </li></ul><ul><li>Different specifications imply productivity declined during later period, specially between 2004 and 2009. </li></ul>
  19. 19. Results: Inefficiency Equation…contd. ML estimates of the inefficiency function parameters. Variables Coefficient Constant -11.716 Sex (Male = 1) -1.849 Age 0.030 Level of education -0.604 a Female dummy 4.524 Household size -0.222 Number of plots* log of cultivated area -0.190 Cultivated area/ number of members 16 years and older -0.004 Oxen dummy -1.787 Livestock units -0.305 Note : All coefficient estimates are significant at 1 percent except a.
  20. 20. Results: Inefficiency Equation…contd. ML estimates of the inefficiency function parameters… Contd. Number of agricultural extension agents in peasant association -0.760 b Crop affected by drought 4.104 Survey month (=1 if surveyed during harvest months) 8.797 Elevation -0.001 Distance to health center 0.111 Distance to closest market -0.071 Distance to nearest PA center 0.098 Sigma-squared (total variation in V and U) 43.095 Gamma (percentage of variance explained by u) 0.994 Log likelihood -21026 Note: All estimates are significant at 1 percent except b, which is significant at 2 percent.
  21. 21. Results: Inefficiency Equation <ul><li>Efficiency of agricultural production increases with: </li></ul><ul><ul><li>Family size </li></ul></ul><ul><ul><li>If household head is male or if labor force has male members </li></ul></ul><ul><ul><li>Ownership of more cattle and ploughing oxen </li></ul></ul><ul><ul><li>Increase in area of cultivated land per given number of plots </li></ul></ul><ul><ul><li>Geographic dispersion of plots </li></ul></ul><ul><ul><li>Availability of more extension agents and agricultural services. </li></ul></ul><ul><ul><li>Reduced drought. </li></ul></ul>
  22. 22. Average efficiency estimates of farmers by AEZs and PAs. AEZ/ PA 1994 1995 1997 1999 2004 2009 Average across Years Northern Highlands 0.203 0.037 0.395 0.545 0.517 0.276 0.322 Haresaw 0.003 0.086 0.472 0.569 0.433 0.074 0.258 Geblen 0.150 0.002 0.216 0.399 0.490 0.087 0.222 Shumsheha 0.361 0.024 0.452 0.625 0.592 0.550 0.424 Central Highlands 0.415 0.265 0.393 0.568 0.605 0.549 0.47 Dinki 0.211 0.016 0.209 0.43 0.535 0.471 0.32 Debre Berhan Milki 0.434 0.255 0.341 0.65 0.654 0.521 0.473 Debre Berhan Kormargefia 0.559 0.219 0.412 0.624 0.526 0.538 0.481 Debre Berhan Karafino 0.363 0.223 0.360 0.582 0.660 0.537 0.454 Debre Berhan Bokafia 0.532 0.343 0.402 0.645 0.579 0.526 0.505 Yetemen 0.378 0.358 0.516 0.58 0.665 0.536 0.511 Turufe ketchema 0.467 0.398 0.488 0.553 0.627 0.576 0.515 Bako Tibe 0.529 0.529 Somodo 0.684 0.684
  23. 23. Average efficiency estimates of farmers by AEZs and Pas…contd. AEZ/ PA 1994 1995 1997 1999 2004 2009 Average across Years Arussi/Bale 0.388 0.317 0.526 0.439 0.538 0.603 0.473 Sirbana Godeti 0.66 0.541 0.585 0.572 0.516 0.564 0.575 Korodegaga 0.177 0.14 0.479 0.33 0.555 0.652 0.385 Oda Dawata 0.566 0.566 Hararghe 0.478 0.367 0.43 0.568 0.608 0.631 0.512 Adele Keke 0.478 0.367 0.430 0.568 0.608 0.631 0.512 Enset 0.249 0.249 0.300 0.331 0.397 0.275 0.298 Imdibir 0.312 0.143 0.269 0.163 0.393 0.291 0.262 Aze-Deboa 0.221 0.4 0.363 0.404 0.482 0.262 0.355 Adado 0.436 0.36 0.464 0.533 0.327 0.417 0.423 Gara-Godo 0.064 0.187 0.21 0.26 0.401 0.128 0.206 Do'oma 0.121 0.067 0.067 0.099 0.421 0.209 0.154 Average across Zones 0.321 0.230 0.385 0.473 0.517 0.443 0.394
  24. 24. Results: Inefficiency Equation…contd. <ul><li>Overall average agricultural efficiency (across zones and periods) is about 0.4, even allowing for data errors and in agro-ecological differences it is a substantial divergence. </li></ul><ul><ul><li>Measured average efficiency scores robust under other specifications </li></ul></ul><ul><ul><ul><li>trans-log (0.402), </li></ul></ul></ul><ul><ul><ul><li>Per-hectare (0.39), </li></ul></ul></ul><ul><ul><ul><li>Average from agroecologic zone level estimates (0.48 and 0.54) </li></ul></ul></ul><ul><ul><ul><li>Average of crop specific estimates (about 0.5) </li></ul></ul></ul><ul><li>Average efficiency after removing lowest and highest deciles is 0.55, while considering middle 6 deciles it increases to 0.68. </li></ul>
  25. 25. Results: Inefficiency Equation…contd. <ul><li>Average efficiency grew at annual rate of 2.5 percent </li></ul><ul><ul><li>Average efficiency levels markedly higher during last three rounds, 0.5, relative to first three, 0.32. </li></ul></ul><ul><ul><li>Average efficiency among least and most efficient narrowed between 1995 and 2004. </li></ul></ul>
  26. 26. Conclusion and Policy Recommendation <ul><li>During 1994-2009 period, most of the increase in agricultural output among subsistence households was attained by increased use of traditional inputs. </li></ul><ul><li>Ample room to increase output by helping farm households improve their efficiency in the short-run </li></ul><ul><li>In the long run, increased output levels can be realized only by increased application of modern inputs. </li></ul>
  27. 27. Caveats <ul><li>Three major categories: basic SFA approach, variables used and sample </li></ul><ul><li>SFA tries to explain unobservable levels of farmers’ inefficiency with observable factors </li></ul><ul><li>Variables used/not used may explain the observed large divergence in efficiency. Among other things: </li></ul><ul><ul><li>Some farmers may have used inputs not included in analysis </li></ul></ul><ul><ul><li>The use of value as opposed to quantity of output </li></ul></ul><ul><ul><ul><li>Considered as a flaw as inaccurate pricing and type of output may create measured efficiency differences . </li></ul></ul></ul><ul><ul><ul><li>However, preliminary crop-level estimates are not different. </li></ul></ul></ul><ul><li>3. The sample villages are not national representatives </li></ul>

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