Th4_Does adoption of improved rice varieties impact farmers’ economic efficiency?
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Transcript of Th4_Does adoption of improved rice varieties impact farmers’ economic efficiency?
Does adoption of improved rice varieties impact farmers’ economic
efficiency? A case study of NERICA in West Africa
Aminou Arouna and Aliou Diagne
Africa Rice Center (AfricaRice)
Outline1. Introduction
2. Objectives
3. Data analysis methods
4. Data collection
5. Results
6. Conclusion and policy recommendation
Introduction• Rice is the most important food crop of developing
world and staple food of 3.5 billion people
• In Africa, rice consumption is increasing at a rate of 5.7% (1980 to 2009)
• Local production of the continent is largely insufficient to meet the demand
• The gap has increased over time, from 1.2 MT in 1960 to 8.3 MT in 2012
Introduction
Introduction• New technologies (e.g. NERICA) have received
particular attention as a means to close the gap
• Recent estimates showed that land productivity of rice has increased by 30% in Sub-Saharan Africa
• Closing gap between demand and local production cannot be reach only by increasing the land productivity but by increasing overall productivity
Introduction• Does new technologies varieties improve the
efficiency efficiencies of rice farmers?
• To what extent rice production can be improved under existing new technologies?
Objectives• This study seeks to assess the impact of NERICA
adoption on technical, allocative and economic efficiencies of rice farmers in West Africa.
• Specifically, the study aims to:
1. Estimate technical, allocative and economic efficiencies of adopters and non-adopters
2. Analyze the determinants of technical, allocative and economic efficiencies
3. Quantify the impact of NERICA adoption on technical, allocative and economic efficiencies
Data analysis methods Efficiency analysis• Use of stochastic
frontier approachWeather disturbances and
heterogeneous environmental factors like soil quality and irrigation access
• Double-log production frontier is estimated for both production and cost functions
Data analysis methods Impact assessment
• Impact = outcome with the intervention compared to what it would have been in the absence of the intervention
10
Data analysis methods
True Impact = Y1 – Y0 under participation
Naïve impact estimate = Y1 – Y0 (Difference of observed outcomes)
Selection bias = Y0 – Y0 > 0 Overestimation of benefits
Time
Outcome
Y1
Y0
Y1
Y0
Adopters
Non-adopters
Selection bias
True impact
Naïve impact estimateBet
ter
off
Worse off
Impact assessment
Data analysis methods
Total actual impact at time t= N
Total unrealized potential impact at time t =
Total potential impact = total actual impact + total unrealized potential impact =
N = total population size; = actual population adoption among potential adopters rate at time t;
= potential population adoption rate
Time
Tota
l Im
pact
t1
Total actual impact at time t1 Total actual impact at time t2
t2
Total unrealized potential impact at time t1 Total unrealized potential impact at time t2
Total potential impact (assumed constant)
Data analysis methods
This study is grounded within the “counterfactual” outcomes
Under this framework, each farm household has ex-ante two potential outcomes: an outcome when adopting NERICA () and an outcome when not adopting NERICA ()
If we let the binary outcome variable d stand for NERICA adoption status: d=1 adoption and d=0 non-adoption and:
Data analysis methods
The general impact model equation that will be estimated is:
The impact is estimated using:
Data collection• Ex-post survey impact assessment
survey of the multinational NERICA rice dissemination project in 2010
• Stratified random sampling• Data collected from 3,096 rice-
producing households in 250 villages in 7 west african countries (Benin, Gambia, Ghana, Guinea, Mali, Nigeria and Sierra Leone)
• Data collected concerned: socioeconomic characteristic, rice input and output, farm and nonfarm activities, etc.
Results• Gap efficiencies
Results• Efficiencies for adopters and nonadopters
Results• Determinants of farmers’ efficiencies
Technical efficiency
Allocative efficiency
Economic efficiency
Coeff Std. Err Coeff Std. Err Coeff Std. Err
Agriculture -0.7988*** 0.1923 -0.0062 0.0285 -0.3403** 0.1702
Education 0.0734 0.0453 -0.0064 0.0076 0.1115** 0.0429
Extension -0.1259** 0.0585 0.0151 0.0093 -0.1296** 0.0508
Age -0.0010 0.0017 -0.0005* 0.0002 0.0001 0.0015
Results• Impact of adoption of NERICA varieties efficiencies
Technical Efficiency
Allocative Efficiency
Economic Efficiency
LATE
LATE 0.3568*** 0.0048 0.2344***
LATE1 0.4306** 0.0031 0.2674***
LATE0 0.0549 0.0125 0.1112
PSB 0.0738* -0.0017 0.0329
Observed
Diffmo -0.0272** -0.0026** -0.0511***
Wald test F(5, 1066)=8.07*** F(5, 694)=8.93*** F(5, 565)=18.37***
Conclusion and implications• Adoption of NERICA increases technical efficiency
for potential and actual adopters
• The impact on economic efficient is smaller but also significant on potential and actual adopters
• High inefficiency among both adopters and non-adopters of NERICA varieties
• Providing farmers with improved varieties should be accompanied by training on both good agricultural practices and farm manage tools
Thank you!Merci!