ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE

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 ConferenceAddis 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.

Important Findings

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• Inefficiency is rather high – the average farmer is 60 percent less than the ‘best’ farmer (in this sample).• Implies increases in output without increased use of inputs

is possible in the short run• This result is robust across different (translog, agro-ecology)

specifications• Most of the increase in output is attained by increased use of

‘traditional’ inputs.• Extended use of modern inputs is indispensable.

• Evidence that inefficiency varies across agro-ecological zone.• Evidence that average farming efficiency improved during the

1995 – 2004 period.

Organization of presentation

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• Background information • Description of Data• Stochastic Production Frontier: Relative Advantage• Results• Conclusion• Caveats

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• About 45 percent of rural residents live below the poverty line,

• Agricultural value added per worker was less than one-half the average in SSA in 2003 ($154, 2000 prices).

• Per hectare fertilizer application in Ethiopia was only 6 percent of application rates in four neighboring countries (Kenya, Sudan, Uganda, and Egypt).

• 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.

Ethiopian Agriculture

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• 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.

• 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.

• 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.

Ethiopian Agriculture…contd.

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• In the long-run, such yield increases can be achieved through (technological) shifts to modern farming systems.

• Efficiency increases are also possible in the short-run through improving the efficiency of existing farming systems.

• Understanding existing relative efficiency levels across farm households is essential.

• This study aims to answer the following questions (Ethiopian context): 1. Given the prevailing input use intensity, how efficient are

farm households relative to ‘best’ performing (or, otherwise called ‘model’) farmers?

2. What determines such (in) efficiencies, and what can be done about these determinants?

Ethiopian Agriculture…contd.

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• Data from 6 of the 7 rounds Ethiopian Rural Household Survey (ERHS) are used.

• The ERHS is a longitudinal household data set that comprises 15 of the 319 woredas located in 6 of the 9 regions in Ethiopia.

• In addition to the agriculture sections of surveys, agro-ecologic data is used in the analysis.

• As each survey was conducted on different number of households, the panel data formed is unbalanced. A total of 7,947 cases used.

Data Description

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ERHS Sample Villages

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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

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Mean values of household, peasant association, and agroecologic specific variables used in the inefficiency equation.

Variable UnitsAverage across survey

YearsSex of head of household 0 if female, 1 if male 0.79Age of head of household Years 50Education level of head 0 if illiterate, 1 if literate 0.18Household size Count 6Number of plots cultivated Count 4.3Livestock units per household Index 3Number of extension officers in PA Count 0.8Was crop damaged by drought 0 if no, 1 if yes 0.15Mean elevation (Meters) Meters 2086Distance to nearest health center Kilometers 18Distance to closest market Kilometers 22

Distance to nearest PA center Kilometers 20

Data Description…contd.

 

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• Competing approaches to SPF analysis are the classical linear regression model and data envelopment analysis.

• SPF is distinct because it uses composed error terms, which take in to account both idiosyncratic and efficiency differences.

• SPF acknowledges ‘not all farmers are equally (technically) efficient’ and explicitly accounts for efficiency differences in the analysis

Empirical Model: Stochastic Production Frontier (SPF)

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• The theoretical model used in this study is:

Where , represents farm household h, represents time period t, is output of farmer h at time period t, is a (1Xk) vector of inputs.

is a (kX1) vector of unknown parameters, and are the idiosyncratic and inefficiency components of

the composed error term of farmer h at time period t.

)exp(*),( hthththt UVXfY

)728,1,...,1(h

htyhtx

)16,...,1(t

htV htU

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The inefficiency equation is specified as:

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.

• 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 .

• The data support the hypothesis that SPF is the appropriate approach to follow

• The hypothesis of ‘constant returns to scale’ is not rejected.

hththt hZU

SPF … contd.

htU

htZ

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ML parameter estimates associated with agricultural inputs used in SPF analysis.

VariableEstimated Coefficient

Calculated elasticity

Constant 4.799 Area of cultivated land 0.225 0.225Household members 16 years and older 0.129 0.129Level of education 0.154 0.166Amount of rainfall 0.350 0.350Amount of Fertilizer used 0.002 0.082Number of ploughing oxen 0.111 0.143Average land quality -0.098 -0.219Number of hoes used 0.053 0.071Number of ploughs used 0.030 0.050Participation in extension program 0.111 0.117

Results: Production Frontier.

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• Most of the increase in output was attained by increased use of traditional inputs.

• Most increase in value of output attributed to changes in:• the amount of rain received in the region, • size and quality of cultivated land, • the numbers of oxen used for cultivation, • changes in quality and quantity of labor use, and

• Among modern inputs: participation in the extension program has moderate effect.

• Fertilizer application has one of the lowest elasticities for an average farmer and even lower among those that actually apply fertilizer.

Results: Production Frontier

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ML Estimates of time and AEZ dummy variables

Variable Estimated Coefficient Calculated elasticity

1995 dummy 0.314* 0.3691997dummy 0.097*** 0.1021999 dummy 0.040 0.0422004 dummy 0.108** 0.1152009 dummy -0.060 -0.056Central Highlands 0.401* 0.493Arussi/Bale 0.572* 0.772Hararghe 0.785* 1.193Enset 0.897* 1.451Note: Estimates with *, **, and *** are significant at 1, 5, and 10 percent.

Results: Production Frontier … contd.

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• The Northern Highlands region has inferior production frontier,

• Farmers benefited from productivity (frontier or time dummies) improvements across the 1994 -2004 period, but productivity declined between 2004 and 2009.

• However, (average) farm efficiency improved during the 1995 – 2004 period.

Results: Production Frontier … contd.

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Results: Other specifications of Production Frontier

• Baseline estimates robust under other specifications,

• Large output increase resulted from even larger use of almost all inputs

• Contribution of cultivated area and rainfall more pronounced in later years of the survey

• Different specifications imply productivity declined during later period, specially between 2004 and 2009.

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ML estimates of the inefficiency function parameters.Variables CoefficientConstant -11.716Sex (Male = 1) -1.849Age 0.030Level of education -0.604a

Female dummy 4.524Household size -0.222Number of plots* log of cultivated area -0.190Cultivated area/ number of members 16 years and older -0.004Oxen dummy -1.787Livestock units -0.305Note: All coefficient estimates are significant at 1 percent except a.

Results: Inefficiency Equation…contd.

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Results: Inefficiency Equation…contd.

ML estimates of the inefficiency function parameters… Contd.Number of agricultural extension agents in peasant association -0.760b

Crop affected by drought 4.104Survey month (=1 if surveyed during harvest months) 8.797Elevation -0.001Distance to health center 0.111Distance to closest market -0.071Distance to nearest PA center 0.098Sigma-squared (total variation in V and U) 43.095Gamma (percentage of variance explained by u) 0.994Log likelihood -21026

Note: All estimates are significant at 1 percent except b, which is significant at 2 percent.

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Efficiency of agricultural production increases with: – Family size– If household head is male or if labor force has male members– Ownership of more cattle and ploughing oxen– Increase in area of cultivated land per given number of plots– Geographic dispersion of plots – Availability of more extension agents and agricultural services.– Reduced drought.

Results: Inefficiency Equation

Average efficiency estimates of farmers by AEZs and PAs.

AEZ/ PA 1994 1995 1997 1999 2004 2009Average

across YearsNorthern 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

Average efficiency estimates of farmers by AEZs and Pas…contd.

AEZ/ PA 1994 1995 1997 1999 2004 2009Average

across YearsArussi/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

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Results: Inefficiency Equation…contd.

• 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. Measured average efficiency scores robust under other

specifications• trans-log (0.402), • Per-hectare (0.39), • Average from agroecologic zone level estimates (0.48 and 0.54)• Average of crop specific estimates (about 0.5)

• Average efficiency after removing lowest and highest deciles is 0.55, while considering middle 6 deciles it increases to 0.68.

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Results: Inefficiency Equation…contd.

• Average efficiency grew at annual rate of 2.5 percent Average efficiency levels markedly higher during last three rounds,

0.5, relative to first three, 0.32. Average efficiency among least and most efficient narrowed

between 1995 and 2004.

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• During 1994-2009 period, most of the increase in agricultural output among subsistence households was attained by increased use of traditional inputs.

• Ample room to increase output by helping farm households improve their efficiency in the short-run

• In the long run, increased output levels can be realized only by increased application of modern inputs.

Conclusion and Policy Recommendation

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Caveats

Three major categories: basic SFA approach, variables used and sample1. SFA tries to explain unobservable levels of farmers’ inefficiency

with observable factors2. Variables used/not used may explain the observed large divergence

in efficiency. Among other things: Some farmers may have used inputs not included in analysis The use of value as opposed to quantity of output

• Considered as a flaw as inaccurate pricing and type of output may create measured efficiency differences.

• However, preliminary crop-level estimates are not different.3. The sample villages are not national representatives