Price expectation formation of smallholder
farmers in Ethiopia: The role of information
Mekbib Haile and Matthias Kalkuhl
The Center for Development Research (ZEF), Bonn
University
ZEF-IFPRI Meeting on Food Price Volatility and Food Security
Bonn, July 7-9, 2014
Introduction
• Agricultural production: time lag between production decision
and output realization
– However, output later depends on decisions today
– Producers need to make their own price expectations during production
decision time (e.g. planting, weeding,…)
• Therefore,
– Producers gather various information to form a price expectation, which is as
close to the (upcoming) harvest period actual prices as possible
• However, information is costly
2
Objectives
1.
Identifying the relevant variables that constitute the information set of a
typical subsistent farmer in his/her price expectation formation
• What information shapes smallholders’ price expectations?
2.
Identifying the role of information in the accuracy of smallholder farmers
in their price expectations
– ‘Who are the good predictors?’ (why do some predict better than others?) Does
information play a role?
3
Conceptual framework
•
•
Ideal information
set
•
•
•
Weather (forecasts)
All past and current input
and output prices
International prices
Public and private stocks
Non-agricultural prices, etc
Public institutions
Some relevant information
Ability to use information
•
•
•
•
Access to information
Distance to market
Education level
Cooperative
membership, etc
Risk aversion and time preference
Smallholder farmer
Household characteristics
Price expectation
Production decision
•
•
•
Production
Figure 1. Own illustrations
•
•
Crop harvest
Market surplus
Fertilizer, seed, labor, land
allocation
Crop choice
Land management, etc
Realized price
4
Theoretical Model (1)
5
Theoretical Model (2)
6
Theoretical Model (3)
7
Optimality Conditions
8
Implications
9
Key Issues
• Empirically test if information improves the price signal
– Do households with better access to information have more
accurate price expectations?
• Measuring the quality of the price signal:
– The deviation of smallholders’ price expectations from realized
(harvest period) prices
• Measuring access to information :
– Ownership of information assets (radio, TV, (mobile) phones)
10
Empirical Model
11
Measuring prediction error
12
Alternative measures
13
Data
• Primary data:
– Household survey from 7 communities out of 4 districts of rural Ethiopia
– 415 households that were randomly selected from each village (Survey started by
IFPRI in late 1980s, Webb, von Braun, & Yohannes, 1992)
• The respondents:
– Trade grains with nearby town markets
– Information sharing among neighbors is common
– Liquidity constrained, limited storage capacity
• Secondary data:
– CSA community level price survey
14
Descriptive statistics
Source of information
Ethiopian commodity exchange
Private traders
Farmer cooperatives
Relatives
Close-by market
Radio or Television or phone
Neighbors (other farmers)
0
10
20
30
40
50
60
Proportion of households (%)
15
Self-reported prices
1600
600
590
1400
580
1200
570
560
1000
ETB/100kg
550
800
Teff
540
600
Wheat
530
Maize
520
400
Sorghum
200
Barley
0
Horse
beans
Prev. harv.
price
•
Sowing price
New harv.
New harv.
realized prices expected price
510
500
490
Prev harvest
Sowing time
Expected price
New Harvest
Realized price
A natural experiment where farmers report their price expectations and we later
observe the prices that they were making expectations of.
16
Descriptive results (I)
Variable
Mean
SD
Min.
Max.
N
Relative mean price prediction error (RMPE)
0.18
0.10
0
0.54
401
Relative index price prediction error (RIPE)
0.17
0.12
0
0.62
401
Access to information (dummy)
0.79
0.40
0
1
415
Years of schooling
1.90
3.18
0
15
415
Family size
6.07
2.69
1
18
415
Age of head
54
16.05
16
100
415
Distance to nearby grain market (km)
9.46
3.47
0.01
29
415
Distance to nearby grain market (hr)
1.94
0.76
0.10
5
415
Distance to extension agents (km)
2.83
3.32
0.01
45
415
Discount rate
0.69
14.06
0.01
9.90
415
•
•
The relative mean price prediction error ranges from 0 (accurate prediction) to close to 55% off
Similarly, the relative index-price prediction error ranges from 0 (accurate prediction) to close to 62% off
17
Descriptive results (II)
Proportion of households
Prediction Error
1%
4%
0-10%
9%
11-20%
33%
21-30%
31-40%
25%
41-50%
≥51%
28%
18
Econometric results
Dependent variable: Relative Mean/Index price Prediction Error
Variables
Sex of head (1 if male)
Age of head
Family size
Head’s years of schooling
Access to information
Share of farm income
Share of market surplus
RMPE
RIPE
0.0086***
0.0210***
(0.0026)
(0.0044)
-0.0002
-0.0006***
(0.0002)
(0.0002)
-0.0018
0.0001
(0.0011)
(0.0008)
0.0008
-0.0013**
(0.0005)
(0.0005)
-0.0104**
-0.0205***
(0.0041)
(0.0043)
-0.0552**
-0.1252***
(0.0262)
(0.0105)
-0.0287**
(0.0089)
19
Dependent variable: Relative Mean/Index price Prediction Error
Variables
RMPE
RIPE
Distance to grain market (km)
-0.0004
0.0063***
Distance to extension agents’ office (km)
(0.0012)
0.0002
(0.0019)
(0.001)
-0.0042*
(0.0019)
Discount rate
0.0004**
0.0002**
(0.0002)
(0.0001)
0.2694***
0.3228***
(0.0255)
(0.0173)
District dummies
Yes
Yes
No. of crops
Yes
NA
N
400
400
Wald chi2 test (p-value)
0.00
0.00
Root MSE
0.096
0.115
Adjusted R-square
0.15
0.20
Constant
Note: Standard Errors are bootstrapped and clustered in seven kebeles (villages).
***, **,* denote statistically significance a 1%, 5% and 10% levels respectively.
20
Key factors
• Access to information
─ defined by ownership of either a radio, television or mobile phone
•
•
•
•
Years of schooling
Distance to nearby grain markets
Share of farm income
Behavioral variables (time preference)
─ Farmers with high discount rates do worse
• The model, although statistically reliable, explains only a small proportion
of the variation in prediction error across smallholder farmers
─ High crop price volatility in the country (Rashid, 2011; Tadesse & Guttormsen,
2011).
─ Large information sharing among farmers
─ Cognitive or psychological bias might play a role (Rabin, 1998)??
21
Conclusions
• Farmers use a set of information in their price expectation
formations
– Planting period farm-gate prices and rainfall expectations are key
• Information plays a key role
– Farmers with better access to information have better price signal
– (Semi-) public institutions need to provide market information as public good
through organized market information systems
• EGTE, ECX, ATA, Extension agents
– disseminate accurate and timely output and input prices (wholesale, retail)
22
Limitations
• Do smallholders who predict well for this year necessarily
do so for other periods?
– Follow up the household?
• Does the forecasting efficiency translate to optimal
decision and better profit?
– Supply response model
– Work-in-progress
23
Thank you!
mekhaile@uni-bonn.de
Back up slides
25
Implications of the Theoretical Model
Alternative measures of PA
Name
Absolute
mean
deviation (AMD)
Measurement
Description
n
the absolute deviations of farmers’ price
e
pc ,t 1  pc ,t n expectations from realized prices in the

c 1
respective grain markets for n crops that the
farmer grows.
Relative
mean n p c ,t 1  pce,t
the relative deviations of farmers’ price
n expectations from realized prices in the
deviation (RMD)

p
c 1
c ,t 1
respective grain markets
e the absolute deviation of indices of farmers’
Absolute
index NPI

NPI
v ,t 1
i ,t
deviation (AID)
expected prices from realized price indices in
the respective markets/villages
Relative
index NPI v,t 1  NPIie,t
the relative deviation of indices of farmers’
deviation (RID)
NPI v,t 1 expected prices from realized price indices in
the respective markets/villages




Notes: Subscripts t and t+1 refer to current sowing and next harvesting periods; c, i, v
denote crop, farmer and village specific prices respectively. n is the number of crops that
a farmer reports his expectations for.
27
“Average out”?
Table 1. Consistency of farmers’ forecasting errors between crops
Crop-to-crop errors
Reg. coef.
Corr. coef.
Barley and wheat
0.49***
0.38
Maize and sorghum
(0.10)
0.82***
0.47
(0.16)
Notes: Standard errors are in parentheses. *** denote statistically significance at 1% level.
28
Seasonal prices in Ethiopia
Source: EGTE wholesale prices
• Large price variations between two consecutive harvest seasons
•
Farmers who make their forecasts based on current planting time prices seem to do
better than those who rely on previous-harvest period prices
29
Dependent variable: Expected price
Prev. harvest price
Sowing price
Sowing rainfall
(Expected) growing rainfall
Prev. harvest wholesale price
Sowing wholesale price
Input expenditure
OLS
0.42***
(0.06)
0.58***
(0.05)
0.36*
(0.15)
-0.02
(0.06)
0.12**
(0.05
-0.13
(0.07)
-0.003
(0)
Prediction accuracy
Intercept
R2
Adjusted R2
N
1.36
(18.13)
0.80
0.80
1187
OLS-Proxy
0.44***
(0.06)
0.56***
(0.06)
0.33
(0.2)
-0.03
(0.08)
0.14**
(0.05)
-0.15*
(0.07)
-0.001
(0)
2.72***
(0.78)
-47.54*
(21.66)
0.81
0.80
1187
FE-like
0.46***
(0.07)
0.49***
(0.06)
-0.29
(0.89)
-0.62**
(0.28)
0.09*
(0.05
-0.05
(0.06)
-0.001
(0)
84.10
(51.64)
0.93
0.89
1187
Notes: Robust standard errors adjusted for household clusters are in parentheses. ***,
**,* denote statistically significance a 1%, 5% and 10% levels respectively.