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!