The Impact of Extension Services on Farm Level
Outcomes: An Instrumental Variable Approach
Anthony Cawley, Walsh Fellow REDP, Teagasc & NUI Galway
Supervisors
Dr. Kevin Heanue, REDP, Teagasc
Prof. Cathal O’Donoghue, REDP, Teagasc
Prof. Maura Sheehan, Edinburgh Napier University & NUI Galway
Dr. Rachel Hilliard, Dept. of Management, NUI Galway
Outline
 Introduction
• Contribution
 Context
• Literature Review and Research Hypotheses
 Methodology
• Issues, Approach, Instruments and Model
 Data
• Dependent Variables, Explanatory Variables, Summary Statistics
 Preliminary Results
• Relevance and Validity of Instruments, Impact of Extension
 Conclusion
• Policy Implications
 Future Work
• Next steps of PhD
Introduction
 Agricultural extension is the process of transferring specialist knowledge
and technology transfer from a policy/academic level to farm level
 Used to build capabilities of clients, through improved problem solving,
decision making and management
 Many studies have shown that interaction with extension influences
farmers’ technology adoption decisions, productivity and profitability levels
 However, specifically quantifying the causal economic return
and controlling for the inevitable endogeneity is less common
 Thus, this paper applies an Instrumental Variable
approach to combat the endogeneity bias
Context
Extension as policy instrument:
• Driver of performance
• Risk management tool
• Mitigation
• Market failure
Teagasc Advisory Programme:
1. Business and Technology
2. Environmental/Good Farm Practice
3. Rural Development
4. Adult Training and Life Learning
Literature Review
Impact of Extension
• Positive impact on productivity (Davis et al., 2012)
• High benefit cost ratio (Wang, 2041)
• Positive effects on technology adoption (Garforth et al., 2013)
• Positive impact on gross margin (Lӓpple et al., 2013)
• Results can be variable (Feder and Anderson, 2004)
• Krishna and Patnam (2014) Diminishing Returns
Impact using IV estimation
• Increased impact of education on earnings using IV approach
given measurement error (Card, 1995)
• OLS estimates of education on earnings not significantly
downward biased (Callan and Harmon, 1999)
• Increased effects of obesity on medical expenditure using IV
(Cawley and Meyerhoefer, 2012)
• Increased impact of agri education on family farm income
(Heanue & O’Donoghue, 2014)
Research Objectives
 Most studies do not account for endogeneity as central focus
 Therefore estimated results do not account for differences between
participants due to self selection, measurement error and omitted
variable bias, leading to inconsistent and biased estimations
 We expect that extension engagement will positively impact farm
level outcomes
 Therefore 2 hypotheses are tested:
1. Extension services positively impact farm level outcomes (family
farm income per hectare)
2. The impact of extension on farm income is robust whilst addressing
the issue of endogeneity
Methodological Challenge: Endogeneity
Definition:
• Correlation between regressors and error term
Causes:
• Omitted Variable Bias
• Self Selection Bias
• Measurement Error
Solution:
• Instrumental Variable Regression (dependent on validity of instruments)
Process:
• 2 Stage Least Squares Regression:
1. Regress Instruments on endogenous variable
2. Insert predicted values of variable into structural equation & regress
Instruments
Requirements:
1. Must be correlated
with the endogenous
regressor (Relevant)
2. Not necessary to
explain dependent
variable in original
regression (Valid)
3. Must be exogenous
to the error term
Distance to
Local Office
Policy
Change
• Larger
distance to
local office
expected to
reduce
likelihood of
engagement
• Expected to
negatively
affect
decision to
participate
• Does not
belong in
original
regression
• Introduction
of the SFP in
2005
increased
advisory
clients by
20%
• Expected to
positively
affect
decision to
participate
• Does not
belong in
original
regression
Interactive
Term
• Shows the
effect of
distance
conditional
on the policy
change
• Expected to
negatively
affect
decision to
participate
• Does not
belong in
original
regression
Functional Form of Model
^
Data
Requirements
•
Participants vs Non Participants, farm outcomes
Data Source
•
•
•
Teagasc National Farm Survey (NFS) which is collected for the Farm
Accountancy Data Network (FADN) required by the European Union
It collects an annual panel data set of approximately 1,100 farms nationwide in
Ireland weighted to represent the total farming population
Collects data on farm profile, output, revenue, costs, and production
Variables
•
•
•
•
Dependent Variable: Family Farm Income / ha
Main Explanatory Variables: Advisory Contact
Controls: Land, System, Size, Labour, Soil, Region, Stock, farm characteristics
Instruments: Distance to advisory office, policy change, interaction term
Descriptive Statistics – Policy change
20% overall
increase in client
numbers (40,700)
42,623
cont.
Selected Summary Statistics NFS – Weighted
Years 2000-2013
Variable
Description
Mean
SD
FFI/ha
Family farm income per ha
456.4
432.9
Ln FFI/ha
Log of family farm income per ha
5.919
0.938
Advisory Contact = 1 if Teagasc Client
0.532
0.499
Dist. Adv Office
Distance to advisory office (km)
10.69
8.600
SFP Year
= 1 if year is after 2005
0.655
0.475
Stocking Rate
Stocking density per hectare
1.359
0.633
Farm Size
Farm size in utilisable hectares
88.59
82.94
Age
Age of farmer
54.12
17.33
Off farm Job
= 1 if employed off farm
0.352
0.478
Sub sample chosen:
1.Must be in the NFS sample before 2005
2.But not a Teagasc Client before 2005
Results – NFS weighted (n = 9,086)
Model
OLS
1 Instrument
1st stage
Advisory
Contact
.1903***
(.0196)
Policy 
2 Instruments
2nd stage 1st stage
2nd stage 1st stage
2nd stage
.3511***
(.0414)
.3501***
(.0413)
.3462***
(.0412)
.5248***
Distance
.5252***
.5573***
-.0021***
.0005
Interact
-.0033***
CD Wald
F Statistic
R2
Cent. R2
Sargan
3 Instruments
2638.9
1330.0
889.8
.2200
.3634
.2142
.0000
.3646
.2143
.7504
.3651
.2146
.2017
Conclusions
• Clear positive causal relationship between extension
participation and farm income
• Instruments were proven valid therefore endogeneity is
addressed and estimations are more consistent
• OLS underestimates benefits of advisory contact
• Policy implications:
 Participation in Teagasc beneficial to clients
 Effectiveness of extension services should be promoted
Future Work
• Submit for publication
• Disaggregate the analysis into more intensive specific forms of
extension and measure impact with greater precision
• Examine the role of the adviser and facilities on knowledge
transfer
• Bolster the management theoretical contribution, particularly in
relation to the process of knowledge transfer