RISK PREFERENCES AND PESTICIDE USE BY COTTON FARMERS IN CHINA
JiKun Huang
Center for Chinese Agricultural Policy
Elaine M. Liu*
University of Houston
September, 2009
Abstract
Insect resistant Bt cotton has been lauded for bringing about a reduction in pesticide
use. It is a cost saving measure that comes with substantial health benefits. However,
several studies have shown that Chinese Bt cotton farmers continue to use excessive
amount of pesticide beyond the profit-maximizing optimal level. Using a survey and
field experiment, we elicit the risk preferences of 320 Chinese cotton farmers. We find
that more risk-averse farmers spray more pesticide. Farmers who are more loss averse
use less pesticide. Our findings suggest that when farmers decide on how much
pesticide to spray, profit maximization is not their only goal. They do take into account
of their own health condition and the possibility of pesticide poisoning.
Keywords: Risk Preferences, Prospect Theory, Pesticide Use
* corresponding author
Email: [email protected]
The authors are indebted to Alan Krueger, Orley Ashenfelter, Anne Case, Angus Deaton, JiKun Huang, Chris
Paxson, Carl Pray, Molly Fifer, Analia Scholosser, Tomomi Tanaka, Stephanie Wang and Nate Wilcox for helpful
discussions, and seminar and conference participants at Princeton University, Rutgers University, University of
Houston, National University of Singapore, National Taiwan University, North America ESA for their suggestions.
Special thanks to Raifa Hu, Zijun Wang, Liang Qi, YunWei Cui and other research staff at the CCAP for their help.
Financial support from Princeton University Industrial Relations Section is gratefully acknowledged. All errors are
our own.
1. Introduction
Modern agricultural biotechnology has made much progress over the past two decades, enhancing
its potential to greatly increase productivity and living standards in developing countries. These
advancements include a wide array of genetically modified crops that are insect-resistant, virus-resistant,
drought-resistant, and even nutrient-enriched. For example, prior to the invention of Bt cotton, farmers
were forced to choose between letting cotton bollworms (the main pest for cotton) erode away their cotton
yields or sacrificing their own health by spraying large amounts of pesticide. Bt cotton was devised
specifically to counter bollworm infestations and it has been scientifically proven to be effective at pestresistance. Emboldened by this scientific evidence, many policy makers have encouraged the adoption of
Bt cotton. However, farmers do not always follow the recommendations given by scientists. One study by
Liu (2008) finds that some Chinese cotton farmers waited ten years after the introduction of Bt cotton to
switch to this new technology; and in particular, she finds that farmers who are more risk averse switch to
Bt cotton later. In addition, several studies find that farmers continued to use excessive amounts of
pesticides even after they adopted pest-resistant Bt cotton (Huang et al., 2002b; Pemsl et al., 2005; Wang,
2008; Yang et al., 2005). These findings present a puzzle as to why farmers would deviate from profit
maximization, especially considering the fact that spraying pesticide is detrimental to their
health. Motivated by that question, this paper investigates the determinants of pesticide use.
Several hypotheses seek to explain the overuse of pesticides by Chinese Bt cotton farmers. These
include: misinformation from the agricultural extension services, lack of knowledge about the purpose of
adopting Bt cotton, substantial uncertainty about the quality and effectiveness of production inputs, or the
rise of secondary pests. One important factor that has not come into the discussion so far is the influence
of farmers’ own risk preferences.
There is a vibrant amount of literature quantifying the impact of risk preference on agricultural
production decisions. Most of these studies rely on the assumption of an objective function and use
advanced econometric techniques to impute the coefficient of risk aversion that will fit the model (Saha,
1
Shumway and Talpaz, 1994; Chavas and Holt, 1996; Antle, 1980). As suggested by Just and Lybbert
(2009), the assumption of utility function and much arbitrary heuristics could cause bias when estimating
individual risk aversion.
This paper takes a different approach in estimating farmers’ risk preference. We employ a
technique from experimental economics modeled after Tanaka, Camerer and Nguyen (TCN) (2009) to
elicit farmers’ risk preferences. The experiment design elicits risk preference parameters beyond expected
utility theory. It incorporates prospect theory components, including loss aversion and nonlinear
probability weighting (Kahneman and Tversky, 1979).The major advantage of the TCN design is that it
allows us to empirically test whether expected utility theory or prospect theory fits our data better. Along
with the field experiments, three hundred-twenty Chinese cotton farmers from 16 villages across 8
counties in 4 provinces were also surveyed. We collect household characteristics, individual
characteristics as well as detailed plot information from their harvest and planting in spring and summer
of 2006. We relate farmers’ elicited risk preferences to their pesticide use, controlling for their
background characteristics.
Our main findings are that farmers who are more risk-averse use more pesticides. If the average
farmer from this sample becomes risk neutral, he would spray approximately 13% less pesticide (It is
equivalent to the effect of 6 more years of education).1 We also find that farmers who are more lossaverse use less pesticide. It may seem surprising at first glance. However, this is consistent with farmers
placing more weight on the importance of their health over the importance of money in the loss domain.
We find that more educated farmers seem to better understand the advantages of using Bt cotton since
they use less pesticide. For every additional year of education, farmer would reduce pesticide use by 0.56
Kg/Ha (~2%). Attendance of a training session is associated with a dramatic reduction of pesticides used,
but over time this effect wanes. Lastly, in terms of the pest-resistant quality of Bt cotton seeds, we find
that the pricier seeds are not significantly more pest resistant than the cheaper seeds, and the source of
seeds do not make any difference.
1
The average farmer in the sample is risk averse with coefficient of risk aversion equals to 0.48.
2
This paper proceeds as follows: Section 2 provides background on Bt cotton. Section 3
describes the data set and provides some summary statistics on farmers’ characteristics. Section 4
describes the game. Section 5 presents our findings on the determinants of pesticide use. Section 6
concludes.
2. Background
China has been one of the largest cotton producers in the world. Unlike commercial cotton
farmers in the US, Chinese cotton farmers are generally subsistence farmers, who are more risk-averse,
less tolerant of pest infestation, and place the highest priority on solving severe pest problems (Bentley
and Thiele, 1999; Pray et al., 2002). During the early 1990s, many Chinese cotton farmers experienced
failures in controlling bollworm due to continual outbreaks of increasingly pesticide-resistant bollworm
infestations. In an attempt to ameliorate the bollworm problem, the provincial government in some parts
of China began commercializing Bt cotton seeds in 1997.2 Bt cotton seeds are planted in a similar fashion
to traditional cotton seeds, but Bt cotton seeds carry the Bacillus thuringiensis (Bt) toxin that targets
cotton bollworm. Using data collected in 2001, Huang et al. (2002a) find that Bt cotton adoption leads to
a significant decrease in the use of pesticides. Bt cotton farmers reduce their total pesticide expenditure by
82 percent. In 2006, Chinese scientists tested bollworm pests with Bt cotton and conclude that bollworms
found in China’s cotton fields have not yet become resistant to Bt cotton (Wu, 2007).
While the picture may seem rosy, some problems still exist. First, Chinese cotton farmers are well
known for using excessive amounts of highly hazardous pesticides, and this practice has continued even
after the adoption of Bt cotton (Huang et al., 2002b; Pemsl et al., 2005; Yang et al., 2005). Huang et al.
(2002b) find that Bt cotton farmers applied 11.8 kg per hectare when the optimal pesticide use ranges
from 0.4 – 4.2 kg per hectare.3 Pemsl et al (2005) find that the optimal input pesticide level was about 5
kg per hectare in 2004, but Chinese farmers applied, on average, 14 kg per hectare. The problem of
2
It was a rolling decision. In some provinces, Bt cotton was approved in 1998.
0.4 kg/ha is based on the estimation with Cobb-Douglas production function. 4.2 kg/ha is based on the estimation
with a Weibull damage control function.
3
3
pesticide overuse is further exacerbated by the fact that nearly 40 percent of the pesticides used by
Chinese cotton farmers contain active ingredients that are classified as extremely or highly hazardous
(Classes 1a or 1b) by the World Health Organization (WHO).4 There are estimates that some 400 to 500
Chinese cotton farmers die every year from pesticide poisoning (Conko and Parkash, 2004).
The discussion above raises the question of why farmers would spray excessive amounts of
pesticide if Bt cotton has proven its resistance to the cotton bollworm and if farmers know that spraying
pesticide is detrimental to their health? Their uncertainty about the quality of Bt cotton seeds could be a
major reason. Existing studies have found that the quality of Bt cotton seeds vary dramatically. Pemsl
(2006) collects leaves from cotton farmers in Shandong and found that some of the so-called “Bt” leaves
do not contain the Bt trait. Due to the high demand for Bt cotton seeds, it is not surprising that the
unscrupulous are trying to exploit this situation for profit through various nefarious means. Therefore,
some lower-quality seeds have permeated the market via different channels. For example, some firms and
local research institutions release Bt cotton seeds into the market before obtaining government approval
(Yang et al., 2005). Farmers also reproduce the trademarked Bt cotton seeds via on-farm propagation and
these self-propagated seeds are of lower quality (Pray et al., 2002). Some seed companies simply
repackage their conventional cotton seeds to sell as authentic Bt cotton seeds (LouYang Agricultural
News, 2003). To sum up, there were an estimated 140 genetically modified cotton varieties available in
2004, so it could be difficult for farmers to know which seeds are effective Bt cotton seeds a priori (Pray
et al., 2006). According to a conversation with a Monsanto representative, seeds produced by Delta &
Pineland, Monsanto’s joint venture in China, are guaranteed to be 99 percent pure, meaning that 99 out of
100 seeds contain the Bt trait, while seeds from other brands vary greatly in terms of quality (Zhu, 2007).
One other reason why farmers could be using excessive amounts of pesticide is proposed by
Wang, Just, and Pinstrup-Anderson (2006). They suggest that the population of secondary pests, mainly
mirids, has been slowly rising. They examine pesticide use by Chinese cotton farmers using survey data
4
WHO classifies insecticides into 4 classes: Class 1a is extremely hazardous (highest toxicity). Class 1b is highly
hazardous. Class 2 is moderately hazardous. Class 3 is slightly hazardous.
4
from 2004, and they find that Bt cotton farmers use less pesticide on bollworm, but Bt cotton farmers
spray more pesticides in order to target mirids. While it seems reasonable, it is worth noting that their
findings primarily come from tabulation of Bt cotton versus non-Bt cotton plots without controlling for
individual or farm characteristics.
3. Data
The Bt cotton survey was designed and collected by the Center for Chinese Agricultural Policy
(CCAP), a government-affiliated research agency, in the winter of 2006. Four provinces (Shandong,
Hebei, Henan and Anhui) with high Bt cotton adoption rates and similar cotton growing seasons (AprilOctober) were selected. CCAP selected 2 counties per province, 2 villages per county, and interviewed
twenty households in each village. The CCAP team compensated each participating household 10 Yuan
for completing the survey (equivalent of 1/3 of daily wage). We interviewed the head of household or
whoever is most responsible for farming. In addition, we obtained detailed information on inputs and
outputs used in each cotton plot, perceived pest infestation, incidence of pesticide poisoning, and past
training experience. Most farmers own are responsible for multiple plots, as arable land for farming is
assigned by the government. Out of the 945 cotton plots belonging to 320 farmers, 930 of them are used
to grow Bt cotton, and the remaining 15 are used to grow conventional cotton.5
In Table 1, the summary statistics at the household level are presented. The average interviewee
is about 50 years old and has completed 7 years of education. In China the land is government owned, and
it is assigned to every household in the village. The average household in the sample is assigned 0.59
hectares of farmland. In this region, cotton is the major cash crop, which is planted on 0.54 hectares of
farmland per household and farmers usually practice rotational cropping with wheat as the primary grain
crop (0.33 hectares). Our sample spent most of their time on the farm, and when production on farm
stops, they do limited amounts of off-farm work. Ownership of a set of durable goods is used as a proxy
5
In winter 2007, the CCAP collected a subset of cotton seeds from a 2006 sample. When researchers tested the
seeds in the lab, they found that farmers often misreported the Bt versus non-Bt status of the seeds. Some so-called
“non-Bt” seeds in fact contained the Bt gene, and vice versa.
5
for wealth in 2006.
Table 2 presents the summary statistics at the plot level breaking down by Bt versus non Bt cotton.
Bt cotton is more expensive than traditional cotton, but farmers who grow Bt cotton spray less pesticide
and have higher yield. In this paper, wealth is proxied by the price of durable goods owned per capita. Bt
cotton farmers are statistically wealthier than traditional cotton farmers. However, without a baseline
survey prior to adoption, we cannot conclude any causality on whether the differential wealth
accumulation is related to their decisions to plant Bt cotton.
Since not all pesticides are the same, we ideally wish to have a better measure of pesticide use on
its level of effectiveness and we need the severity of pest infestations in the region. Then we can use a
production function to estimate whether farmers overuse specific pesticides. To do so, we would need
information on the active ingredients of pesticides. However, China’s pesticide market is extremely
fragmented (PWC, 2004), in our survey alone, we find more than 50 brands/formulations. Many farmers
purchase the pesticides which are blends of various brands of pesticides. It is also difficult to classify
these pesticides by the WHO’s measures, since most pesticide bottle labels are missing the active
ingredients. Therefore, in our analysis, we will only use the total amount and total cost of pesticides.
We also collect information on pesticide poisoning. Farmers were asked if anyone in their
household had experienced any health impairments after mixing and spraying pesticides in the past 10
years. Thirty percent (129) of farmers reported that they experienced at least one symptom since 1996,
where the most frequently reported symptoms were headaches or dizziness (53 percent), skin irritation
(26 percent), restlessness (16 percent) and vomiting (59 percent). We also asked the farmers to report any
costs associated with this health impairment. Forty-one farmers reported that they were hospitalized due
to pesticide poisoning, and the average medical cost and imputed labor cost was 139 Yuan (approximately
five days of wages).
4. Experimental Design
A key objective for this paper is to elicit individual risk preference. We conducted a field
6
experiment modeled after the one designed by Tanaka, Camerer, and Nguyen (2009).6 TCN’s experiment
is similar to Holt and Laury’s design economics offering subjects a series of pair-wise lotteries of both
risky and safe options, but it elicits three parameters concerning risk preferences—risk aversion, loss
aversion, and nonlinear probability weighting. Following TCN, we assume a utility function of the
following form:
 ( y )   ( p )( ( x )  ( y ))
U ( x , p; y , q )  
 ( p ) ( x )   (q ) ( y )
x  y  0 or x  y  0
x0 y
 x1
for x  0
and  ( p )  exp[ (  ln p ) ]
where  (x)  
1
  (  x ) for x  0
σ is the standard measure of risk aversion. A higher sigma indicates a higher degree of risk
aversion. λ measures the sensitivity to loss versus gain. ω(p) is the probability weighting function
adapting from Prelec (1998). If α< 1, ω(p) has an inverted S-shape. Individuals overweight small
probability and underweight large probability. This complicated utility function form gives us flexibility
in allowing for the rejection of prospect theory. If α=1 and λ=1, then utility function reform would reduce
to the standard expected utility function.
Farmers were asked to participate in an experiment after the conclusion of the interview.7 They
were told that they will earn additional real payoff depending on the outcome of the game. The
mathematical version of the experiment is presented in Table 3. In reality, we do not use the word
“probability” with our subjects. More specifically, each line in Table 3 is presented to the subject in the
following format:
6
The more detailed description of the experiment, analysis and imputation of risk preference parameter can be found
in Liu (2008).
7
More details about the game can be found in Liu (2008).
7
They understand that there is a bag of 10 numbered balls and depending on whether they have
chosen lottery A or lottery B in each line, the numbered ball they draw randomly will determine the
payoff. They were presented 35 questions separated in 3 series (see Appendix for answer sheets used in
the game). They were asked at which line, from Line 1 to Line 14, they would switch from lottery A to
lottery B for each series.
We choose Lottery A for Line 1 to _____.
We choose Lottery B for Line _____ to 14.
Given that the safe options do not change and the risk option has increasing expected payoff as
we move down the line within in Series 1 and Series 2, the more risk tolerant farmer would choose to
switch to lottery B later. With the 3 switching points of farmers and the utility function form assumption,
we can impute the risk preference parameters. The method of imputation can be found in TCN (2007) and
Liu (2008). We reject the null hypothesis that λ = 1 and α =1 at the 1% level. The summary statistics of
individual risk preference measures are provided in Table 18.
5 Econometric Framework and Regression Results for Pesticide Use
5.1 Basic Framework
To begin, we first replicate Huang et al.’s (2002a) results without the risk preference parameters,
so we estimate the following equation by ordinary least square (OLS):
y ijv   o  1 Bt ijv   2 ( Bt exp) ijv  X ijv '  v   ijv (2)
where i denotes individual, j denotes plot, and v denotes village. yijv is the amount of pesticides in kg per
hectare sprayed for individual i, on plot j, in village v; Btijv equals 1 if Bt cotton is planted and zero
otherwise. (Btexp)ijv is the number of years that farmer i has planted Bt cotton interacted with Bt cotton
term; X’ijv is a vector of individual or plot characteristics, such as plot size, age, years of education; μv is a
village fixed effect. The main coefficients of interest are δ1 and δ2. δ1 represents the effectiveness of Bt
8
For more detailed distribution and analysis of the risk preference parameters used in this paper see Liu (2008).
8
cotton in reducing pesticide use 10 years after its commercialization. The meaning of δ2 is more
complicated. There are two opposing factors that can affect this coefficient. First, if cotton bollworm
builds up a resistance to Bt toxin, more pesticides would need to be used over time, and δ2 should be
positive. In contrast, if farmers become more aware of the benefits of using Bt cotton as they have more
experience with planting Bt cotton, δ2 should be negative.
Column 1 of Table 4 shows the results from the estimation of Equation 2. We find that more
educated farmers use significantly less pesticides. For every additional year of education, a farmer
reduces pesticide use by 0.73 kg/ha, 2.7 percent of total use (of Bt cotton farmers). In other words,
farmers who finish elementary school use 16 percent less pesticides compared to farmers with no
education. The coefficient on plot size is negative and significant, which could be a sign of economies of
scale. In addition, farmers use less pesticide when the price of pesticides is high. The main coefficient of
interest (δ1 ) indicates that the cultivation of Bt cotton reduces pesticide use dramatically. All else equal,
the average Bt cotton farmer reduces the use of pesticides by 19.5 kg/ha, on his Bt plot compared with his
non-Bt plot. The coefficient on the interaction term (δ2) is positive, but it is not statistically significant
from zero, which may simply mean that the two factors mentioned above cancel each other out. Overall,
we conclude that even a decade after the commercialization of Bt cotton, cotton farmers still use
significantly less pesticides on their Bt plots than on their non-Bt plots. In all regressions that follow, the
standard errors are corrected for heteroskedasticity at the individual level.
One very important covariate that is missing from the above estimation is pest severity. Since
both pesticide use and the use of Bt cotton seeds are related to pest severity, excluding a measure of pest
severity from the model could produce omitted variable bias. Unfortunately, in the survey, we do not have
an objective measure of pest severity. However, we asked some questions about the perceived yield loss.
The questions were phrased as follows:
What do you think your potential yield loss will be if you do not spray any
pesticide for controlling bollworm? ___ (0-100 percent)
What do you think your potential yield loss will be if you do not spray any
9
pesticide for controlling mirids9? ___ (0-100 percent)
We use the answers to these questions as proxies for pest severity, where higher values indicate a more
severe pest problem.10 The regression results, including the pest severity proxies, are reported in Column
2 of Table 4. The positive coefficient on bollworm severity reflects the fact that the higher the perceived
yield loss (more severe pest problem), the more pesticides farmers spray. The coefficients on mirids are
insignificant and it is probably due to a multicollinearity problem.11 One other result worth noting is that
the magnitude of the coefficient on education is smaller, but it remains positive and significant at the 10
percent level. It implies that education is correlated with farmers’ perception of pest severity.12
5.2 Risk Preference
As mentioned before, the above specification, as estimated in Column 2 of Table 4, could be
problematic since the pest severity variable is subjective. In particular, since agricultural production is full
of uncertainty and risk, it is likely that individual risk preferences play an important role in pesticide use
decisions.
Typically, in decision making under uncertainty, we would use the neo-classical utility theory,
where risk preference is solely characterized by the coefficient of risk aversion. As suggested in the other
early agricultural economic research, if farmers follow safety-first principals by setting a target income
and minimizing the probability of severe yield loss below that income (Moscardi and de Janvry, 1977;
Young, 1979), then it is likely that farmers’ risk preferences will be best captured by prospect theory
instead of neoclassical utility theory.13 In particular, we have rejected the null hypotheses that λ=1 and
α=1 from the experiment result. Therefore, we include the elicited measure of risk preferences in the rest
9
Reported by Wang et al. (2006) as the most serious secondary pest to Chinese Bt cotton farmers.
Same methodology is also used by Huang et al. (2002a).
11
Correlation between bollworms and mirids severity is 0.5.
12
In a separate regression not reported in this paper, when we regress the yield loss on levels of education,
controlling for village fixed effects. The coefficient on education is negative and significant at the 1 percent level.
Thus, higher levels of education are associated with lower perceptions of yield loss.
13
The applications of these two concepts, “safety-first rule” and “loss aversion,” can be found in many behavioral
finance studies (Camerer and Kunreuther, 1989; Polkovnichenko, 2005; Campbell and Kräussl, 2007).
10
10
of the estimation. We can rewrite Equation 2 as following:
y ijv   o  1 Bt ijv   2 ( Bt exp) ijv   3 i   4 i   5 i  X ijv '  v   ijv (3)
where  is the coefficient of risk aversion, λ is a measure of loss aversion, and α is a measure of nonlinear
probability weighting. A higher  or λ implies greater risk or loss aversion, respectively. α<1 (α>1)
implies overweighing (underweighting) of small probability events. The results of estimating Equation 3
are shown in Column 3 of Table 4. The coefficient on  of 7.32 indicates that if a farmer is more riskaverse than the average farmer by one standard deviation, he uses 4 kg/ha, or 9 percent, more pesticides
than the average farmer. There could be several reasons why we find a positive coefficient on the risk
aversion parameter. One is that farmers worry about severe bollworm pest infestations, and this concern
is exacerbated by the fact that lower quality seeds are rampant in the seed market. Not knowing whether
the seed is effective and not knowing how severe the bollworm problem would be, the more risk averse
farmers would likely spray more pesticide.
The coefficient on λ of -0.499 implies that if a farmer is one standard deviation more loss-averse
than the average farmer, he uses 1.95 kg/ha, or 7 percent, less pesticides than the average farmer. The
negative sign on this coefficient may seem surprising at first glance. However, it is not clear what the sign
of this coefficient should be. Let us walk through an exercise to illustrate this point. If we suppose that
the farmers’ reference point is the income they would have earned from planting traditional cotton, then it
follows that farmers are particularly sensitive to any loss below that income level. In such a case, it is
very unlikely that any Bt cotton farmer (which describes most farmers in our sample) would fall below
that target income level, given that the low-quality Bt cotton seeds are no worse than traditional seeds.
Therefore, the loss aversion parameter would not dictate their pesticide use. On the other hand, if the loss
aversion parameter captures more than loss over incomes; specifically, if farmers are particularly lossaverse with respect to their health, they might spray less pesticide.
The coefficient on nonlinear probability weighting parameter is negative but statistically
insignificant. It is difficult to predict how the nonlinear probability weighting parameter would impact
11
pesticide use without making an assumption on probability distribution of various events. For instance, if
severe pest infestation is a low probability event, the farmers who overweight low probabilities should be
spraying more than those who underweighting small probability. On the other hand, if the quality of Bt
seeds are of main concern and supposing that it is probable the Bt cotton seeds are of low-quality, then
those who underweight high probabilities (and who also overweight low probabilities) should spray less
than those who overweight high probability (underweighting low probability). Given these two opposite
effects, the sign of this coefficient is ambiguous.
5.3 Continued Education
Negative and significant coefficients on education in all of the above estimations suggest that
less educated farmers did not benefit as much from Bt cotton. In fact, what this education variable
captures is the knowledge related to the use of Bt cotton or pest management that farmers possess. While
it may be too late to provide formal schooling to adult farmers, one policy intervention to help educate
farmers is continuing education, or training sessions. The Chinese government has already provided such
services, but the utilization rate of these services is far below 100 percent. In the sample, merely 35
percent of farmers have ever attended a Bt cotton training session. These training sessions are usually
provided by agricultural extension services or seed companies in each village. There is at least one farmer
in each village who has reported attending a training session, so the low participation rate is not due to the
unavailability of training at the village level. Figure 1 shows the distribution of the most recent training
session they last attended. Out of all the farmers who have ever attended a Bt cotton session, nearly half
of them attended one last year.
To investigate the impact of Bt cotton training on pesticide use, we estimate the following
regression:
y ijv   o  1 Bt ijv   2 ( Bt exp) ijv  X ijv 'Z ij'   3 training i   4 (training * t ) i   v   ijv
(4)
where all the terms are the same as in Equation 3 except that Zij is a vector of individual risk preferences,
which consist of coefficient of risk aversion (), loss aversion (λ), and nonlinear probability weighting (α).
12
Trainingi =1 if farmer i has attended a Bt cotton training in the past and zero otherwise. ∆t equals time
elapsed since the last training session, which ranges from zero to 10. δ3 represents the impact of ever
attending a Bt cotton training session. Here, δ4 represents the difference in pesticide use of each year
elapsed since attending a training session. Column 4 presents the results for the estimation of Equation 4.
The coefficient on training, δ3, is negative, which indicates that, all else equal, farmers who have attended
a training session use less pesticides. Interestingly, the positive sign on δ4 indicates that the more time
that has elapsed since the training took place, the more pesticides a farmer uses. The average farmer who
attended a training session 3 years ago uses 2.2 kg/ha, or 8 percent, less pesticides. Now, the coefficient
on education is no longer statistically significant once the training variable is included. In some way, both
education and training are proxies for farmers’ knowledge, and these two variables are likely to be highly
correlated. 14
The findings here suggest that training does help farmers reduce their pesticide use. However,
slowly over time, farmers seem to forget the knowledge they acquired from the training session. The
coefficients on training presented in this section could suffer from upward bias due to an omitted variable.
For example, it is possible that the more motivated farmers are more likely to attend training sessions, and
they could have more knowledge about Bt cotton even in the absence of training sessions. Unfortunately,
we cannot further investigate with the existing data set. However, the findings in this section highlight the
importance of knowledge and the channel through which the government can spread this knowledge to
farmers.
5.4 Seed Quality
As mentioned in Section 2, there is wide variation in the quality of Bt cotton seeds. Figure 2
shows the distribution of the cost of Bt cotton seeds from 2 to 200 Yuan/kg. Figure 3 is a histogram of the
14
In a separate regression and controlling only for village fixed effects, we found that one year of education
increases the probability of attending a training session by five percent.
13
distribution of 930 Bt cotton plots by the source of the seed, with the average price from each source.15
The cheapest seeds are those produced from on-farm propagation, or saved seeds. We asked farmers if
they know whether the saved seeds are of lower, the same, or better quality than the first generation of Bt
cotton seeds. Thirty percent of farmers reported that saved seeds are of the same quality, seven percent
reported that they do not know, and 63 percent correctly answered that the saved seeds are of inferior
quality. The misperception of the quality of Bt cotton seeds may explain why a full 25 percent of seeds of
Bt cotton plots come from on-farm propagation.
To investigate whether the source or the price of Bt cotton seeds are determinants of pesticide
use, we restricted the sample to only Bt cotton plots. While pesticide use could be an indicator of Bt
cotton seed quality, it is not a good indicator for non-Bt cotton. The quality of conventional cotton is
probably determined by its yield performance, which will not be captured in our estimation.
The results from the estimation of Equation 3, where the regressor of interest is the seed price, are
presented in Column 1 of Table 5. The coefficient on the price of the seed is not statistically different
from zero. The finding here complements Pemsl’s (2006) finding, that the more expensive Bt cotton
seeds are not significantly more resistant than the cheaper Bt cotton seeds. In Column 2, we include a
series of dummy variables for the source of the seeds, where the default source is “others.” None of the
source dummies are statistically different from zero. In an alternative regression specification for which
the results are not presented here, we include an interaction term between source indicators and the price
of the seeds, and again, none of the coefficients are statistically different from zero. This suggests that the
quality of Bt seeds may not be consistent within a type of source.
5.5. Robustness Check
So far we have imposed a strong function form on the utility function when we impute the risk
preference parameters. As a robustness check, we will relax the utility function form, and simply divide
15
For the saved seeds and the seeds exchanged by neighbors, we asked farmers for the estimate of the market value
of seeds.
14
farmers into 18 groups depending on their 3 switching points. For instance, group 1 includes the farmers
who switch from A to B before line 6 both Series 1 and Series 2 and switch before line 4 in Series 3 (thus
group 1 should contain the most risk seeking and least loss averse individuals). Group 18 includes those
farmers who switch from A to B after line 11 in both Series 1 and Series 2 and switch after line 4 in Series
3 (thus group 18 should be the most risk averse and loss averse group) (see Figure 4). In a regression,
instead of using the 3 risk preference parameters, we include the group dummies.16 An F-test rejects the
null hypotheses that these group dummies jointly equal to zero at the 5% level. Therefore, even without
imposing the utility functional form, we know the field experiment can predict pesticide use to some
extent. We are inclined to believe that the field experiment design captures individual heterogeneity in
risk preferences and the functional form helps to ease the interpretation.
6 Conclusion
In this study, we investigate the determinants of pesticide use. Our main findings are that more
risk-averse farmers use more pesticide, while more loss-averse farmers use less pesticide. Our finding
suggests that when farmers decide on how much pesticide to spray, profit maximizing is not their only
goal. They do take into account of the potential risk as well as their own health condition.
The findings of this study have important policy implications. They suggest that farmers may not
benefit as much from new technology as policy makers and scientists would hope. Simply achieving an
adoption rate of 100 percent does not guarantee that farmers know how to fully capitalize on the new
technology. In order to ensure that farmers reap all the benefits of modern science, continuing education,
such as training sessions provided by the government, is essential. Local governments also need to
encourage farmers’ participation in training sessions. Given that most farmers are risk-averse, offering
crop insurance could potentially be desirable.
While the government cannot change one’s risk preference, it can intervene to mitigate the
potential agricultural production risk. In particular, the government can reduce this risk by imposing
16
Regression results are presented in Appendix Table.
15
stricter regulations on the seed market to reduce the possibility of low quality seeds or provide some type
of crop insurance.
Another main finding from this study and the existing study by Liu (2008) reinforce the
importance of education. Liu (2008) finds that the more educated farmers are more likely to adopt Bt
cotton. In this study, we find that the more educated farmers are also more likely to spray less pesticide
and attend training sessions, which is also related to the reduction in pesticide use. While it is possible
that there could be unobserved heterogeneity that is correlated with education and their pesticide use and
timing of adoption, another interpretation is that education provides an informed basis on which farmers
can make better decisions, leading to far reaching benefits.
16
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20
Figure 1 Distribution of the Most Recent Training Session Attended
(Year of Attendance)
60
50
30
20
10
0
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Year
100
Figiure 2 Cumulative Distribution Function of Seed Price
90
80
70
60
Percent
Frequency
40
50
40
30
20
10
0
0
2.2
6.6
18
32
46
70
100
123
134.2
144
158
175
Seed Unit Price (Yuan/Kg)
21
140
Figure 3
Frequency and Average Price of Bt Cotton by Source
300
120
250
Frequency
200
Average Price
80
150
60
Frequency
Average Price (Yuan)
100
100
40
50
20
0
0
Seed
Village
Companies Office
Exchange On Farm Research
with Propagation Institute
Neighbors
Seed Agricutural
Vendors Extension
Others
Source
22
Figure 4: Division of Switching Points
Note: Group 1 consists of individuals who switch from A to B between Line 1 to 5 in Series 1 and Series
2 and switch from A to B between Line 1 to Line 4 in Series 3.
23
Table 1
Summary Characteristics
Age
Education
Female
Size of Household
Time Spent Doing On Farm Work (months)
Time Spent Doing Off-Farm Work (months)
Self-Rated Risk Attitude
(1=most adventurous, 5= least adventurous)
σ (Risk Aversion)
λ (Loss Aversion)
α (Probability Weighting)
Total Cotton Sown Area (Ha)
Secondary Cash Crop Sown Area (Ha)
Primary Grain Crop Sown Area (Ha)
Total Crop Sown Area (Ha)
Total Land Owned (Ha)
Cotton Yield (Kg/Ha)
Average Year of Bt Cotton Adoption
Total Value of DGs Per Capita in 2006 (Yuan)
Observations
49.52
(8.89)
7.10
(2.96)
0.14
(0.35)
4.49
(1.45)
7.63
(1.76)
0.13
(0.69)
2.78
(0.92)
0.48
(0.33)
3.47
(3.92)
0.69
(0.23)
0.54
(0.33)
0.12
(0.19)
0.33
(0.33)
1.07
(0.58)
0.59
(0.29)
3356
(889.8)
1998
(1.90)
588.40
(9.37)
320
Note : Standard deviation are in parentheses.
24
Table 2
Summary Characteristics By Seed Type
Size of Plot (Ha)
Amount of Pesticide Sprayed (Kg/Ha)
Total Pesticide Cost (Yuan/Ha)
Cotton Yield (Kg/Ha)
Total Cost on Seeds (Yuan/Ha)
Wealtha (100 Yuan)
Total Number of Plots
Bt Cotton
Non Bt
0.18
(0.15)
26.37*
(19.44)
784.46
(528.55)
3356*
(889.8)
552.51*
(424.76)
0.22
(0.09)
37.84*
(27.34)
936.60
(877.49)
2092.5*
(408.8)
254.95*
(145.54)
1.03*
(0.92)
930
0.53*
(0.58)
15
Note : Standard deviation are in parentheses. *statistically difference
at 5% level.
a. Total Value of Durable Goods owned Per Capita in 2006 (100
Yuan)
25
Series 1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Series 2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Series 3
1
2
3
4
5
6
7
Table 3: Payoff Matrix from the Experiment
Lottery A
Lottery B
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 34 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 37.5 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 41.5 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 46.5 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 53 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 62.5 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 75 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 92.5 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 110 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 150 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 200 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 300 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 500 Yuan and 90% winning 2.5 Yuan
30% winning 20 Yuan and 70% winning 5 Yuan
10% winning 850 Yuan and 90% winning 2.5 Yuan
Lottery A
Lottery B
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 27 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 28 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 29 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 30 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 31 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 32.5 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 34 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 36 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 38.5 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 41.5 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 45 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 50 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 55 Yuan and 30% winning 2.5 Yuan
90% winning 20 Yuan and 10% winning 15 Yuan
70% winning 65 Yuan and 30% winning 2.5 Yuan
Lottery A
Lottery B
50% winning 12.5 Yuan and 50% losing 2 Yuan
50% winning 15 Yuan and 50% losing 10 Yuan
50% winning 2 Yuan and 50% losing 2 Yuan
50% winning 15 Yuan and 50% losing 10 Yuan
50% winning 0.5 Yuan and 50% losing 2 Yuan
50% winning 15 Yuan and 50% losing 10 Yuan
50% winning 0.5 Yuan and 50% losing 2 Yuan
50% winning 15 Yuan and 50% losing 8 Yuan
50% winning 0.5 Yuan and 50% losing 4 Yuan
50% winning 15 Yuan and 50% losing 8 Yuan
50% winning 0.5 Yuan and 50% losing 4 Yuan
50% winning 15 Yuan and 50% losing 7 Yuan
50% winning 0.5 Yuan and 50% losing 4 Yuan
50% winning 15 Yuan and 50% losing 5.5 Yuan
26
Table 4
OLS Regression of Pesticide Use (Kg/Hectare)
(1)
(2)
(3)
(4)
-0.118
(0.138)
-0.726
(0.355)**
-9.441
(3.562)***
-0.377
(0.060)***
-19.515
-0.113
(0.141)
-0.613
(0.340)*
-10.393
(3.764)***
-0.356
(0.063)***
-24.179
7.324
(2.467)***
-0.499
(0.180)***
6.325
(4.006)
-0.099
(0.134)
-0.556
(0.331)*
-8.39
(3.207)***
-0.37
(0.063)***
-25.641
7.286
(2.465)***
-0.495
(0.180)***
5.824
(3.966)
-0.117
(0.133)
-0.5
(0.327)
-8.773
(3.218)***
-0.383
(0.064)***
1.018
(8.070)**
0.990
(0.643)
Bollworms Severityc
(9.456)**
0.942
(0.643)
0.095
(9.521)***
1.041
(0.678)
0.088
(0.682)
-24.768
(9.629)**
0.077
Mirids Severityd
(0.050)*
0.047
(0.049)*
0.043
(0.050)
0.043
(0.035)
(0.036)
(0.035)
-5.694
σ
(value function curvature)
λ
(loss aversion)
α
(probability weighting)
Age
Education (Years)
Field size (Ha)
Price of Pesticide
Btb
Exp with Bt (Years)
Trainingd
e
Training*(∆t)
(2.245)**
1.164
(0.468)**
62.844
55.946
56.61
56.61
(11.181)***
(11.257)***
(11.472)***
(11.472)***
Observations
941
935
925
925
0.33
0.35
0.38
0.38
R-squared
Note: Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant
at 1%. All regressions include village fixed effects. Standard errors are clustered at the individual level.
The unit of observation for the regression is the plot.
a. It represents the highest level of education (in years) completed among all family members
b. Bt Cotton equals 1 if Bt cotton was planted in the plot, 0 if non-Bt cotton was planted in the plot.
c. It is proxied by an answer to a yield loss perception question. What do you think your potential yield
loss will be if you do not control for bollworm? The answer can range from 0 to 100. The higher value
indicates the worse bollworm severity
d. Same as footnote c, but replace bollworm for mirid.
Constant
27
Table 5
OLS Regression of Pesticide Use (Kg/Hectare)
(1)
(2)
σ
(value function curvature)
λ
(loss aversion)
α
(probability weighting)
Age
Education (years)
Field size (Ha)
Price of Pesticide
Exp with Bt (years)
Bollworm Severity
Mirids Severity
Price of Seeds
7.254
(2.472)***
-0.491
(0.182)***
5.929
(4.029)
-0.106
(0.136)
-0.486
(0.319)
-9.098
(3.324)***
-0.374
(0.062)***
1.005
-0.675
0.081
(0.046)*
0.036
(0.036)
0.014
(0.025)
Source
Seed Companies
Village Office
Exchange w/ Neighbors
Saved Seeds
Research Inst
Seed Vendors
Agri. Extension
Constant
Observations
R-squared
25.995
(8.913)***
916
0.37
7.500
(2.528)***
-0.527
(0.181)***
5.523
(3.908)
-0.102
(0.137)
-0.515
(0.321)
-8.769
(3.196)***
-0.368
(0.062)***
1.278
(0.703)*
0.089
(0.047)*
0.044
(0.035)
6.120
(4.966)
1.293
(5.387)
4.118
(5.288)
4.635
(4.882)
-5.821
(7.191)
1.664
(4.145)
1.129
(4.571)
22.215
(9.258)**
916
0.39
Note: Robust standard errors in parentheses. * significant at 10%; **
significant at 5%; *** significant at 1%. All regressions include village
fixed effects. Standard errors are clustered at the individual level.
28