This article examines the effects of R&D on cotton yield and relationship between R&D and commodity support programs. The results indicate that yield elasticities with respect to cotton R&D is around 0.2-0.5 based on different regions. It further indicates that R&D increases government expenditures when both commodity programs and R&D funding exist. However, if the future WTO Doha negotiations rules out the possibility of price support programs, increasing R&D funding may provide one of the solutions for farmers to recover their income with 5-6 years lag.cotton, R&D, commodity support programs, Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Hudson, Darren

Pan, Suwen

Wang, Changgang

English

Research Papers in Economics

2010 AAEA Selected Paper # 10810 
  
Is Investment in Agricultural Research a Good Substitute for Price 
Support in U.S. Cotton? 
 
 
 
Suwen Pan*, Chenggang Wang, and Darren Hudson 
Department of Agricultural and Applied Economics 
Texas Tech University 
P.O.  Box 42132 
Lubbock, TX 79409-2132 
Phone: (806)742-0277 ext 226  
FAX: (806)742-1099 
Email: s.pan@ttu.edu 
 
*Corresponding Author 
 
Selected Paper Prepared for Presentation at the American Agricultural Economics 
Association Annual Meeting, Denver, July 26 – July 28, 2010 
 
 
 
 
May, 2010 
 
 
 
 
 
Copyright 2010 by S. Pan, C. Wang, and D. Hudson. All rights reserved. Readers may 
make verbatim copies of this document for non-commercial purposes by any means, 
provided that this copyright notice appears on all such copies.   2
 
Abstract 
This article examines the effects of R&D on cotton yield and relationship between R&D 
and commodity support programs.  The results indicate that yield elasticities with respect 
to cotton R&D is around 0.2-0.5 based on different regions. It further indicates that R&D 
increases government expenditures when both commodity programs and R&D funding 
exist. However, if the future WTO Doha negotiations rules out the possibility of price 
support programs, increasing R&D funding may provide one of the solutions for farmers 
to recover their income with 5-6 years lag.  
Keywords: cotton, R&D, commodity support programs 
 
     3
Introduction 
The nature of agricultural policy has been changing over time. However, the 
purposes of the agricultural policies are the same, which include supporting farmers’ 
income and increasing domestic agricultural production. As a result, most developed 
countries set up several programs to support these two objectives. For example, the US 
has a loan rate, target prices, direct payments and other programs to support farmers’ 
income. At the same time, the US also provides funds to support agricultural research as 
well as agricultural technology extension. Based on a report from OECD, the US 
government spent $42.6 billion in producer supports, $2,144 million on research and 
development, and $423 million in extension in 2005. 
Agricultural research in the United States has been shown to be a main 
determinant of agricultural production. The agricultural sector had, on average, a 1.61% 
annual growth in real output and 1.63% growth in productivity during the 20
th century 
(Huffman and Evenson 1993). Consequently, agricultural research has boosted yields and 
reduced costs of production.  Examples of these efficiency gains can be seen in more 
productive varieties, the development of tools to combat pests and disease, and the 
promotion of agricultural practices that protect and preserve environmental resources.  
Research programs serve as supply shifters, increasing the level of output for a given 
price.  The real question is how much of a shift in supply do these programs provide. 
While policy makers have come to agree that both income support and technology 
support are important in agriculture, few have attended to the influence of R&D investment 
on income support programs.  Increased productivity due to R&D and extension 
programs leads producers to produce more, other things equal.  Higher yields, in turn,   4
dampen price and, therefore, increase the commodity program payment to producers 
from the government.  
During the last couple years, allegations were levied against the U.S. and other 
developed countries that their domestic and export subsidies caused significant impacts 
on world markets by encouraging excess production and trade and depressing world 
prices.  Following these arguments, Brazil, with the support of Australia and the Western 
and Central African (WCA) countries, filed a petition challenging the U.S. cotton 
programs at the September, 2002 meeting of the World Trade Organization (WTO) 
Settlement Body. Brazil alleged that U.S. cotton subsidies were depressing world prices 
and were injurious to their farmers and  the WCA countries [Benin, Burkina Faso, Chad 
and Mali] also claimed to be losing export earnings of US$ 1 billion a year, including 
both direct and indirect costs, as a result of the subsidies paid by the US and the EU 
(BBMC, 2003).  
Therefore, the purpose of the paper is to investigate the effects of R&D on cotton 
yields as well as farmer income. In particular, we are interested whether R&D can 
provide another income source for farmers if further WTO negotiation rules out the 
commodity price support programs. To analyze the income support and technology 
support in the US industry, a time series (VAR) model is used to estimate yield elasticites 
with respect to the R&D, which then is incorporated into a dynamic simultaneous model 
to account for the endogeneity between the income support and technology support.  
 
Methods 
Basic model structure   5
A partial equilibrium world fiber model was used to estimate the effects of U.S. cotton 
subsidy programs on the world market. This model incorporates the regional supply 
response of cotton, different competing goods in different producing regions, 
substitutability between cotton and competing fibers, and the linkage between raw fiber 
and textile sectors (Pan et al., 2004).  The China and U. S. textile models include supply, 
demand, ending stocks, and market equilibrium for cotton and man-made fibers.  Cotton 
A-index, Chinese domestic cotton price, U.S. cotton textile price index, U.S. non-cotton 
price index, U.S. farm price, and polyester prices are endogenously solved in the models 
by respectively equalizing world exports and imports, Chinese domestic cotton supply 
and demand, U.S. cotton and non-cotton textile supply and demand, U.S. domestic cotton 
supply and demand, and man-made fiber supply and demand.   
  Chinese cotton mill use was estimated following a two-step process in which total 
textile fiber mill use is first estimated as a residual of textile fiber consumption and the 
net trade of textile fiber, followed by allocations among various fibers such as cotton, 
wool, and man-made fibers (represented by polyester) based on their relative prices.  The 
U. S. cotton and non-cotton textile mill use was solved endogenously with the domestic 
textile demand and textile net trade (net imports).  All these equations were estimated 
based on the cotton textile price index, non-cotton textile price index, cotton domestic 
price, and non-cotton domestic price. 
  U.S. cotton production was modeled using separate acreage and yield equations.  
Cotton production is a function of last year’s cotton net returns and the relative net 
return(s) of competing crops.  As part of the total U.S. cotton supply, imports and exports   6
are functions of domestic price, international price (A-index)
1, exchange rates, tariff rates, 
and quota restrictions.  Similarly, the U.S. man-made fiber model is modeled using 
capacity and utilization.  The capacity and utilization equations are estimated by the man-
made fiber price and petroleum spot price.   
  Western and Central African countries and other countries were assumed to be 
price takers in the cotton market. The elasticties used in the study are presented in Table 
1 (Pan et al., 2004). The short run elasticities of cotton acreage response range from 0.10 
to 0.54, with Mexico having the highest value. The long-run acreage response elasticities 
range from 0.21 to 1.15, with the highest in Australia. These elasticties have been used in 
several studies such as Chinese currency valuation (Pan et al., 2007b) and cotton in a free 
trade scenario (Pan et al., 2007a).  
Scenarios 
  To analyze the scenarios, we adjusted the model to include the R&D elasticites 
based on a VAR model in the US cotton yield equation.  A 10 year baseline was created 
following the assumption that current R&D cotton funding and income support programs 
continue.  Three scenarios were examined under different assumption: first, under the 
assumption that cotton commodity programs such as target price, counter cycle payments, 
and loan rate are removed and R&D funding is kept at the current level (Scenario 1); 
second, R&D funding increases by 100% and commodity programs still exist (Scenario 
2); third, R&D funding increases by 100% and there are no commodity support programs 
(Scenario 3).  The results based on the three scenarios are used to compare with baseline 
number.  
                                                 
1 The A-Index is a measure of world cotton fiber prices.   7
 
Results 
Effects of R&D on cotton yields 
Table 1 presents the stationary tests for regional yields and R&D funding and Table 2 
presents the lag selection for VAR model. The results indicate that all the yield and R&D 
funding are stationary in the level. The lag tests indicate that the effects of R&D on 
cotton yields are between 5-8 year lags based on different regions.  Table 3 presents the 
VAR results based on lags found in Table 2.  Long term regional cotton yield elasticites 
with respect to cotton R&D are presented in Table 4. These results indicate that 
elasticities are between 0.2 and 0.5, which are consistent with the R&D literature 
(Huffman and Evenson 1993).      
Simulation Results 
 
Table 5 presents the percentage changes on the baseline in  the world US farm price, 
yield, production, exports, net farm income, and government commodity programs 
(Target price, direct payments, and loan rate)  under the three different scenarios 
mentioned above. The numbers under baseline are derived from the assumption that US 
keeps its own policies and R&D investment in the next 10 years. The percentage changes 
under the various scenarios are derived from a comparison with the baseline. 
It indicates that, if cotton R&D increases 100% and farm programs are eliminated 
(scenario 2 in the table), farm price would decrease (around 20% over 10 years) due to an 
average yield increase of 31% average over the 10 years (averaged across all U.S. growth 
regions). However, net farm income would increase 46% over the 10 years at the cost of 
an 86% increase in government commodity program expenditures.    8
Although net farm income would decrease over the next 10 years in scenario 3 
(double R&D and without commodity programs), the decline happens in the first 5-6 
years and the lagged effects are felt, production increases would begin to cover lost 
revenue.  Net farm income would be higher than base number under scenario 3 beginning 
with 2016/17.  At the same time, government commodity program expenditure would be 
reduced by 31% over the next 10 years.  
 
Conclusion 
The effects of cotton commodity programs have been a topic during the last 
several years. Those programs have provided a great income safety net for cotton 
producers. However, farmers would be lost if future WTO negotiations eliminate price 
support programs. This study indicates that R&D funding increases may provide one way 
for cotton farmers to alleviate income losses from lost price supports should those arise.        
   9
Reference 
 
Benin, Burkina Faso, Mali, and Chad (BBMC). “Poverty Reduction: Sectoral Initiative in 
Favor of Cotton.” World Trade Organization, May 2003.  
Pan, S., M. Fadiga, S. Mohanty, and D. Ethridge. “Structural Models of the U.S. and the Rest-of the-
World Natural Fiber Market.” CER # 04-03, Cotton Economics Research Institute, 
Texas Tech Univ., Mar. 2004. 
Pan, S., S. Mohanty, D. Ethridge, and M. Fadiga (2006). “The Impacts of U.S. Cotton Programs 
on the World Market: An Analysis of Brazilian WTO Petitions.” Journal of Cotton Science  
2006(10): 180-192. 
 
Pan, S., M. Fadiga, S. Mohanty, and M. Welch (2007a), “U.S. Cotton in the Free World.” 
Economic Inquiry 2007(45): 188-197. 
 
Pan, S., S. Mohanty, M. Welch, D. Ethridge, and M. Fadiga (2007b), “The Effects of Chinese 
Currency Appreciation on World Cotton Market.” Contemporary Economic Policy 2007 (25): 
185-205.    10
Table 1. Dickey Fuller Unit Root Test 
 
 Delta 
Yield 
Southeast 
Yield 
Southwest 
irrigated 
yield  
Southwest 
dryland 
yield 
West 
yield 
R&D  
 
Test 
statistics 
-3.59*  -5.47* -6.76* -2.85* -3.63*  -2.07* 
 
* indicate the data set is stationary.   11
 
Table 2. VAR lag test 
 
 AIC  BIC  Lag 
Delta (Difference) 
R&D 
-89.90* -88.49* 7 
Southeast (Difference) 
R&D 
-92.72* -91.30* 6 
Southwest Irrigated 
R&D 
-93.61* -92.06* 8 
Southwest Dryland 
R&D 
-96.58* -95.04* 8 
West (Difference) 
R&D 
-91.24* -89.83* 6 
   12
Table 3. VAR Results  (only yield equations are reported here) 
 
  Delta   Southeast   Southwest 
Irrigated 
Southwest 
Dryland 
West  
 Par  t  Par  t  Par  t  Par  t  Par  t 
Constant  -282.54* -6.11 -64.38 -1.81 1774.79* 21.13 -47670.41*  -5.45  107.42  1.71 
Yield                  
Lag1  -0.92* -6.43  -0.97*  -9.40 -1.09* -15.23 32.45* 5.47  -1.31*  -6.20 
Lag2  -0.53* -4.71  -0.48*  -4.03 -1.08* -18.97 47.34* 8.73  -0.70  0.24 
Lag3  0.55* 3.12  -0.24*  -2.16  -0.93*  -11.45 36.96* 5.54  -0.688  -2.70 
Lag4  0.79* 3.43  -0.72*  -5.56  -0.92*  -18.92 15.40* 5.52  -0.33  -1.37 
Lag5  0.92* 3.52  -0.81*  -6.84  -0.56*  -7.61 12.46* 5.61  0.40*  2.16 
Lag6  0.52* 2.15  -0.66*  8.38 0.61* 7.87  7.15* 5.15  -0.18  1.00 
Lag7  0.66* 4.89      1.38* 12.71 -5.45* -5.16    
Lag8         0.31*  3.90  -17.83*  -5.49     
R&D 
fund 
               
Lag1  0.004 0.14  0.01*  6.81  0.009* 7.78  -0.23* -5.46  -0.002  -0.43 
Lag2  0.0016 0.33  -0.01*  -4.21 -0.02*  -11.17  0.54*  5.39  -0.24*  -3.22 
Lag3  0.01* 2.42  0.007*  2.43 0.02* 0.002 -0.18* -5.18  0.02*  3.71 
Lag4  -0.01* -4.19  -0.01*  -3.83 -0.01* -10.25  0.02*  4.89  0.004  0.56 
Lag5  0.002 0.67  0.005  1.81  0.006* 5.74  0.03* 5.39  -0.01*  -2.30 
Lag6  -0.006 1.79  0.0005  0.311  -0.002*  -2.19  -0.24* -5.50  0.02*  3.81 
Lag7  0.01* 4.51      -0.04*  -23.87 0.60* 5.38     
Lag8         0.04*  30.05  -0.52*  -5.34     
   13
Table 4. Long Term Yield Elasticties with Respect to R&D  
 
Delta Southeast  Southwest 
Irrigated 
Southwest 
Dryland 
West 
0.23 0.30 0.45 0.16 0.37 
   14
Table 5. Effects of R&D on U.S. cotton market 
    2010/11 2011/12 2012/13 2013/14  2014/15 2015/16 2016/17 2017/18 2019/20 average 
       Cents  per  Pound       
Farm price  base  60.53 61.26 61.44 61.42 62.29 61.78 63.09 63.34 63.99 62.13 
 scenario  1  3.79% 2.28% 1.56% 1.31% 1.35% 0.89% 1.02% 0.40% 0.64% 1.47% 
 scenario  2  -36.83% -27.37% -27.00% -21.87% -22.16% -20.24% -19.74% -18.72% -18.65% -23.62% 
 scenario  3  -30.00% -17.23% -20.37% -16.56% -17.74% -16.22% -16.23% -15.72% -15.79% -18.43% 
Yield         Bales  per  Acre      
Delta base  1.88 1.89 1.90 1.92 1.93 1.95 1.97 1.97 1.97 1.93 
 scenario  1  0.00% -1.48% -0.73% -0.64% -0.63% -0.63% -0.48% -0.50% -0.31% -0.60% 
 scenario  2  21.86% 22.99% 22.03% 21.88% 21.45% 21.29% 21.02% 21.03% 20.97% 21.61% 
 scenario  3  21.86% 20.29% 18.32% 19.33% 19.01% 19.16% 19.06% 19.20% 19.36% 19.51% 
               
Southeast base  1.64 1.64 1.65 1.66 1.67 1.68 1.69 1.68 1.68 1.66 
 scenario  1  0.00% -2.12% -1.04% -0.92% -0.91% -0.91% -0.69% -0.73% -0.46% -0.86% 
 scenario  2  33.26% 35.14% 33.96% 33.80% 33.23% 33.04% 32.73% 32.75% 32.67% 33.40% 
 scenario  3  33.26% 31.27% 28.50% 30.01% 29.59% 29.84% 29.75% 29.97% 30.21% 30.26% 
               
southwest irrigated  base  1.82 1.84 1.86 1.89 1.91 1.94 1.96 1.96 1.96 1.90 
 scenario  1  0.00% -1.26% -2.56% -3.81% -5.04% -6.24% -7.42% -7.46% -7.50% -4.59% 
 scenario  2  43.22% 42.86% 42.19% 41.65% 41.08% 40.56% 40.04% 40.02% 40.00% 41.29% 
 scenario  3  43.22% 42.45% 41.62% 41.26% 40.71% 40.24% 39.74% 39.74% 39.75% 40.97% 
               
southwest dryland  base  0.70 0.70 0.70 0.71 0.71 0.72 0.72 0.72 0.73 0.71 
 scenario  1  0.00% -0.43% -1.10% -1.77% -2.48% -3.21% -3.93% -4.02% -4.13% -2.34% 
 scenario  2  23.27% 23.89% 23.32% 23.16% 22.85% 22.69% 22.47% 22.45% 22.38% 22.94% 
 scenario  3  23.27% 22.30% 21.11% 21.64% 21.39% 21.41% 21.28% 21.33% 21.40% 21.68% 
               
West base  3.08 3.09 3.12 3.15 3.17 3.20 3.23 3.24 3.25 3.17 
 scenario  1  0.00% -0.52% -1.33% -2.16% -3.03% -3.92% -4.78% -5.03% -5.30% -2.90% 
 scenario  2  25.50% 26.22% 24.77% 24.72% 24.36% 24.40% 24.16% 24.15% 24.15% 24.72% 
 scenario  3  25.50% 24.33% 22.96% 23.54% 23.22% 23.21% 23.03% 23.06% 23.10% 23.55%   15
Table 5. (Continued) 
    2010/11  2011/12 2012/13 2013/14  2014/15 2015/16 2016/17 2017/18 2019/20 average 
         000  Bales        
Production  base  14468.80  14751.76 15056.88  15381.47  15700.64 15946.40 16117.44 16176.84 16219.06 15535.48 
 scenario  1  -3.10%  -4.95%  -6.88%  -8.85%  -10.70% -12.07% -13.01% -13.33% -13.55%  -9.60% 
 scenario  2  35.71%  34.01%  33.24%  32.28%  31.93% 31.42% 31.07% 30.90% 30.84% 32.38% 
 scenario  3  28.07%  21.55%  23.45%  23.52%  24.08% 24.15% 24.28% 24.72% 25.02% 24.31% 
                 
Export  base  11686.09  11832.23 12173.63  12519.62  12942.42 13684.98 13676.91 13901.50 14042.14 12939.95 
 scenario  1  -3.2%  -4.4%  -7.1%  -9.7%  -12.6% -17.4% -17.3% -18.7% -19.5%  -12.21% 
 scenario  2  38.41%  42.62%  40.64%  39.80%  38.23% 36.23% 35.95% 35.35% 34.91% 38.02% 
 scenario  3  30.03%  27.82%  28.26%  28.96%  28.69% 27.82% 28.03% 28.23% 28.30% 28.46% 
         000000  $        
Farm  net  income base  2737.24  2755.13 2762.10  2826.89  2893.63 2756.48 2813.81 2798.31 2813.47 2795.23 
  scenario  1  -24.22% -24.32%  -21.47% -20.83% -17.54%  -13.12%  -8.77% -8.09% -5.63%  -16.00% 
 scenario  2  44.40%  49.60%  50.07%  52.02%  53.43% 40.03% 41.52% 42.87% 43.71% 46.41% 
 scenario  3  -42.30%  -15.53%  -19.69%  -10.13%  -9.53% -0.54% 3.25%  6.56% 8.49% -8.83% 
                 
government 
expenditure  base  1740.54  1659.66 1582.83  1579.55  1511.80 1349.47 1256.23 1211.21 1167.12 1450.93 
  scenario  1  -46.16% -43.09%  -39.83% -39.11% -35.56% -27.65% -21.30% -18.21% -14.59% -31.72% 
 scenario  2  109.77%  91.83%  97.62%  84.82%  95.39% 66.00% 74.00% 73.94% 78.74% 85.79% 
  scenario  3  -48.20% -42.91%  -40.13% -38.32% -34.96% -26.41% -19.94% -16.40% -12.75% -31.11% 
 
 
 

Is Investment in Agricultural Research a Good Substitute for Price Support in U.S. Cotton?

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