Agricultural and Food Policy, International Relations/Trade, Production Economics,

Haque, Samiul

Kenney, Roman

English

Research Papers in Economics

Data
Bias and Scale Effects of Decoupled Farm 
Payments
*Samiul Haque
Department of Agricultural Economics , Purdue University.
Contact via phone: (765) 307-0109, or email: shaque@purdue.ed
*Dr. Roman Keeney
Department of Agricultural Economics, Purdue University.
Contact via phone: (765) 494-4273 or email: rkeeney@purdue.edu
Poster prepared for presentation at the Agricultural & Applied Economics Association’s
2011 AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 24-26,
2011
Copyright 2011 [Haque & Keeney]. All rights reserved. Readers may make verbatim copies of this 
document for non-commercial purposes by any means, provided that this copy right notice appears 
on all such copies. 
Results
Method
Author Information
Farm support programs such as direct payments were initially considered a
“green box” policy at the GATT/WTO negotiation since it was thought to be
least trade distorting. These payments are based on historical yields and
acreage and thus considered to be decoupled from production decisions. The
extent to which these payments are truly decoupled from production decision
has come under intense scrutiny during recent years. The large size of US
agriculture implies that the absolute amount of these support payments can be
substantial. As these payments are delivered to farmers they have the potential
to significantly impact the income and wealth of the recipients. Thus these
payments have the potential to create distortions in trade and the efficient
allocation of resources and production decision. A market allocation system
that is truly decoupled should not affect farm activities. It is of general interest
to quantify the impact associated with such payments.
These government payments may potentially induce factor bias, altering the
proportional input usage, alter the scale of production, and or cause
adjustment in optimal out mix (output bias). Recognizing and estimating
these effects would play crucial role in the formulation of future policy. If, for
example, direct payments are dis-favorable for fertilizer usage, then it might
impact the input supply chain. On the other hand if such policy is dis-
favorable to labor usage, then it might cause migration of labor out of farming
and impact urban-rural wage gap. Understanding the primary impacts of
direct payments in production (scale and bias effects) would help us gauge the
secondary effects of such policy.
This research will explore the impact of “green box” payments on the use of
labor, capital, material, and land in the aggregate production of crop and
livestock in US agriculture for the years 1953-2009. Depending on the sign
and magnitude of these bias impacts it will help us present the tendency of
such effects as relevant consideration in the formulation of future policy. In
order to address this goal we will use the standard translog cost function
approach and adapt measures of input and output bias first proposed by
Binswanger (1974, 1978) and Antle (1984) and later further developed by
Antle and Capalbo (1988) for categorizing the impacts of technical change.
) ,
~
,
~
( DP W Q f LogTC 
Figure 2
Where Q is a vector of output quantities, W is a vector if input prices,
and DP is the farm payment variable. We can use Shepherd’s Lemma
to derive the relevant input cost share and revenue share of cost
equations.
LogW S
i
i
LogTC



(1)
(2)
DP
LogTC
j
DP W Q
j
j
i c
i
ce
i
Q Log
LogTC
Q
S MB MB 













 



 





   









1
log
) ,
~
,
~
( log
Our preliminary results suggested serial correlation. In order to
address this issue we re-estimated the first differenced form of the
model. Also, in order to address simultaneity bias of output, we use
one period lagged output as expected future output. Following Antle
and Capalbo (1988), we estimate input bias as follows:
Our results suggest that there is substantial farm payment induced
hicksian bias in the US agriculture during the period 1953-2009. Our
hicksian bias indicates that technological change was biased to
towards labor, capital, and materials. Though capital using bias is not
statistically significant and labor using bias is significant only at the
10% level. There appears to be substantial land saving bias.
Motivation
0
20000
40000
60000
80000
100000
120000
140000
160000
1
9
5
3
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1
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1
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2
0
0
1
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2
0
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2
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7
2
0
0
9
rlv
rcr
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
sl
sk
slnd
sm
Figure 1
, is the input factor share of total 
cost , and 
LogQ R
i
i
LogTC



(3)
, is the revenue  share of total 
cost . 
Variable Coefficient 
Estimates  & 
Standard Errors
dlogq1 -0.187731451 
(0.147248158)
dlogq1w3 -0.015698452 
(0.013850412)
dlogq2 -0.440278962*** 
(0.120286023)
dlogq1w4 0.006913751 
(0.019401796)
dlogq1q1 -0.205627117 
(0.142902970 )
dlogq2w1 0.015427998*** 
(0.007308681)
dlogq1q2 0.024934778 
(0.057246438)
dlogq2w2 0.005564036 
(0.003993922)
dlogq2q2 0.225376266***
(0.081891228)
dlogq2w3 -0.053322687*** 
(0.014256129)
dlogw1 0.211997560*** 
(0.041346441)
dlogq2w4 0.032330654*** 
(0.011204648)
dlogw2 0.189332913*** 
(0.034590626)
dlogcons 0.315827050*** 
(0.065079255)
dlogw3 0.290193842*** 
(0.031178520)
dlogconssq -0.026740137*** 
(0.005207597)
dlogw4 0.396300989*** 
(0.048099889)
dlogconslogq1 -0.018817786 
(0.012396119)
dlogw1w1 0.125158820*** 
(0.005225336)
dlogconslogq2 0.055509182*** 
(0.016202536)
dlogw1w2 -0.026061651*** 
(0.002519410)
dlogconslogw1 0.004869005*** 
(0.001548517)
dlogw1w3 -0.017341674*** 
(0.001991382)
dlogconslogw2 0.002362468*** 
(0.000877431)
dlogw1w4 -0.081755495*** 
(0.005668863)
dlogconslogw3 -0.025605442*** 
(0.004191326)
dlogw2w2 0.108067374*** 
(0.002703598)
dlogconslogw4 0.018373968*** 
(0.002945229)
dlogw2w3 -0.015507278*** 
(0.001119638)
dlogw2w4 -0.066498444*** 
(0.003408863)
dlogw3w3 0.100236578*** 
(0.005397655)
dlogw3w4 -0.067387625*** 
(0.003693111)
dlogw4w4 0.215641564*** 
(0.007639273)
dlogq1w1 0.012439233 
(0.015108598)
dlogq1w2 -0.003654532 
(0.008400753)
Equations (1), (2), and (3) is our system of equation. Just like any
well behaved cost function we impose the homogeneity, cross
equation symmetry, and adding up restriction. We estimate the
system using iterative SUR. Since the cost shares add up to one,
we drop one share equation to avoid singularity problem. Our
results are invariant to the share equation dropped.
DP
DP W Q LogS MB
i c
i 


) ,
~
,
~
(
(4)
where . The first term of (4) measures the
total bias caused by technological
change and the second term measures the scale effect. Thus after
accounting for the scale effect we are left with pure factor bias due to
farm payments.
BIAS Estimate & SE at Average
Total Bias-Labor 0.03226975*
( 0.01628012)
Total Bias-Capital 0.01905790
( 0.01124057)
Total Bias-Land -0.1498382***
( 0.04074535)
Total Bias-Materials 0.03315012***
( 0.008920924)
Scale Effect-Labor 5.611047e-05
( 0.0003171795)
Scale Effect-Capital 1.376131e-05
(0.0004464773)
Scale Effect-Land -0.0001447942 
(0.0007979811)
Scale Effect-
Materials
2.628956e-05 
(0.0002671071)
Pure Bias-Labor 0.03221364*
( 0.01627715)
Pure Bias-Capital 0.01904414
( 0.01124896)
Pure Bias-Land -0.1496934***
( 0.04072912)
Pure Bias-Materials 0.03312383***
( 0.00892428)
Our data was obtained from ERS. We have two output indexes, 
livestock (q1) and crop (q2), and four input price indexes, labor 
(w1), capital (w2), land (w3), and material (w4). We also have 
information on total cost and value of direct payment (DP).  

Bias and Scale Effects of Decoupled Farm Payments

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Research Papers in Economics