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Volume 30, No. (6), 2020 (December)
(Impact Factor 0.481; JCR 2019) |
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EVALUATING THE DETERMINANTS OF PESTICIDE RESIDUES IN VEGETABLES: A CASE OF
LEMON MARKET IN PAKISTAN
Abedullah1* and S. Kouser2
1Pakistan Institute
of Development Economics, Islamabad, Pakistan
2COMSATS Institute of Information Technology,
Islamabad, Pakistan
*Corresponding author’s e-mail: abedullah@pide.org.pk
ABSTRACT
In developing
countries, vegetable markets are inefficient in terms of information exchanges
between producers and consumers on food safety attributes. This study attempts
to investigate the determinants of pesticide residues and estimates
information efficiency of vegetable market, using socioeconomic and biophysical
data collected from a representative sample of 360 farmers in Pakistani Punjab.
Chromatography technique is employed to quantify pesticide residues in okra,
brinjal, spinach and cauliflower. A large proportion of
spinach samples (83%), followed by okra (72%), brinjal (60%) and cauliflower
(50%) have surpassed the maximum residue limits of pesticides, implying that
they are lemons. Results of pesticide residue model show that magnitudes of pesticide
residues in vegetables vary with pesticide quantity and spray interval at the
farm level. Results of information efficiency model reveal that vegetable
prices are negatively but insignificantly correlated with pesticides residues,
implying that vegetable market is a lemon market in Pakistan. Proper
implementation of food safety standards and product labeling may help to
provide safe vegetables to consumers.
Key
words: Vegetables, information
asymmetry, lemons market, gas chromatography, pesticide residues, food safety,
Pakistan.
https://doi.org/10.36899/JAPS.2020.6.0173
Published online August 03,2020
INTRODUCTION
Food
markets can play a crucial role in ensuring food safety in developing
countries (Clark and Hobbs, 2018). Food safety1 is a main public
health risk. Ingestion of contaminated food causes food-borne illnesses among
600 million people and deaths of 420,000 individuals worldwide every year (WHO
2015). Food safety is an unobservable quality attribute to consumers at the time
of purchase (Reiler et al. 2015). Product quality can be reflected by its
price. Nevertheless, price
can act as a signal for food safety only if markets are efficient in term of
information exchanges between producers and consumers (Fama 1970). In case of vegetables,
producers have complete information on hazardous chemicals used during
production process but for consumers, it is difficult to identify
safe/uncontaminated vegetables prior to purchase, resulting in market failures
for food safety. Akerlof
(1970) has called this information asymmetry between producers and consumers as
a lemon dilemma, which likely results in adverse selection of poor-quality
products. As safe vegetables are costly to produce; only high prices can enable
such producers to survive in the market. Otherwise, low quality products, known
as lemons by Akerlof, will occupy the market. In developing countries, the
vegetable
market, extensively sprayed with toxic pesticides, could be one of these lemon
markets. Pesticides are chemical compounds that are commonly used to eradicate
pests such as insects, weeds, fungi and diseases in modern agriculture (Guler
et al. 2010; Duong et al. 2019). Since green revolution, there is a
substantial increase in pesticide consumption in Pakistan. The total amount of
pesticides
used in the country has increased from 38 thousand metric tons in 1997 to 206
thousand metric tons in 2017 (GoP 2019). Moreover, most of the pesticides used
in the country are either highly hazardous (34.2%) or moderately hazardous (35.0%)
(Damalas and Khan 2017). Currently, 108 types of hazardous insecticides are
being used in Pakistan (Mehmood et al. 2017). Indiscriminate, excessive,
and unintended use of toxic pesticides results in numerous negative
externalities including development of pest resistance, annihilation of natural
enemies, loss of biodiversity, poisoning of humans and animals, pollution of
soil and water resources and contamination of food with pesticide residues
(Desneuxet al. 2007; Damalas and Eleftherohorinos 2011; Kouser and Qaim 2014;
Syed et al. 2014; Abedullah et al. 2016; Wentz 2017). One of the important food safety risks
for consumers is
the prevalence
of pesticide residues in food commodities (Henson
and Traill 1993). Zhou and Jin (2009) have observed that food safety
risk increases with pesticide residues in vegetables. Therefore, it is
imperative to important food safety risks for consumers is the prevalence of pesticide
residues in
food commodities (Henson and Traill 1993). Zhou
and Jin (2009) have observed that food safety risk increases with pesticide
residues in vegetables. Therefore, it is imperative to monitor pesticide
residues
to
ensure safe vegetables in Pakistan. Vegetables are integral parts of
the daily diet of all Pakistanis. They are rich source of carbohydrate,
protein, vitamins, minerals, fiber and antioxidants and contribute to preserve
a healthy life.
However,
vegetables are highly vulnerable to insect infestation because of their
tenderness and better nutritive status. This motivates farmers to intensively
use hazardous pesticide to protect their vegetable crops. As a result, vegetables have higher probability of
traces of pesticides, termed as pesticide
residues, than any other food crop (Syed et al. 2014).
Ingestion of unprocessed vegetables contaminated with pesticide residues may
cause short term (acute) health symptoms such as vomiting, stomachache,
diarrhea etc (Jardim et al. 2014; Reiler et al. 2015) and long term
(chronic) health effects such as birth defects, endocrine
disruption and
carcinogenic effects (Schreinemachers
2003; Miligiet al. 2006; Barret al. 2010; Waller et al. 2010; Bal et al. 2012).
Hence, contaminated food creates vicious cycle of diseases and impedes
socio-economic development. To avoid
consumers’ potential health hazards, the Codex Alimentarius
Commission of the Food and Agriculture
Organization (FAO) of the United Nations and the World Health Organization (WHO) has set the
maximum residue
limits (MRLs) for each pesticide in all food items (FAO and WHO
2004). MRL
is the highest permissible level of pesticide residues that is legally
tolerated when pesticides are applied under good agricultural practices. To
protect consumers’ health,
pesticide residues should be below the permissible limits in vegetables. A
growing body of literature confirms that more than 50% of fresh vegetables have
residues of pyrethroid, organophosphate and organochlorines exceeding MRLs in
Pakistan (Parveen et al. 2005; Tahir et al. 2009; Ahmed et al. 2011; Latif et al. 2011; Saqib et al. 2011; Syed et al.
2014; Amir et al. 2017). Using pesticide residue analysis and descriptive
statistics, these studies have concluded that different agronomic and
socio-economic factors like amount and toxicity of pesticides, spray duration,
farmer’s pesticide literacy, his farming experience, food processing technique
etc could influence the level of pesticide residues in vegetables. Although
without controlling for other confounding factors, it is difficult to conclude
about the net impacts of these factors on the prevalence of pesticide residues.
Previous studies on food safety have used indirect approach of farmer’s/ consumer’s
perceptions to quantify food safety risk (Zhou and Jin 2009; Mercado et al.
2018). The earlier studies have also detected the existence of lemons (bad
product) in the market for used vehicles (Bond,
1982 1984; Genesove 1993), thoroughbred yearlings (Chezum and Wimmer 1997),
cherries (Rosenman and Wilson 1991) and child care (Mocan 2007). These studies have measured the extent
of market efficiency by investigating the relationship between product price
and indirect measure of product quality (seller characteristics). Only Hoffmann
et al. (2013) and Ma et al. (2014) have used the direct measure of product
quality. Ma et al. (2014) have quantified the extent to which seed prices
reflect Bt toxin concentration in genetically modified cotton crop to resist
insects. Hoffmann et al. (2013) have related aflatoxin concentration in maize
flour with its price. Nevertheless, to our best knowledge, no study has
estimated vegetable market efficiency to investigate reflection of food safety
attributes in its price. This study aims to detect lemons in the vegetable
markets, to evaluate the determinants of pesticide residues in four common
vegetables (okra, brinjal, cauliflower, and spinach) of Punjab province and to
test the extent of information efficiency in the market using a precise measure
of food safety. For this purpose, this study conducts regression analysis by
implementing two unique surveys: farm survey to compile information on
vegetable farm and farmer characteristics and biophysical survey to collect
vegetable samples for pesticide residue analysis. The paper contributes to
previous empirical literature on pesticide residue analysis, lemons market and
information efficiency in three ways. Firstly, this study employs a unique and
direct measure of food safety attribute (pesticide residues). By regressing
pesticide residues on farm and farmer specific characteristics, this study allows determining the true factors influencing
pesticide residue in vegetables. Secondly, the study also measures the extent
of market efficiency by analyzing the association between pesticide residues
and vegetable prices. This helps to investigate whether vegetable prices
reflect signals of food safety. Dominance of contaminated vegetables could be
the primary vehicle for lemons market and could lead to adverse selection by
consumers. Lastly, the study estimates unbiased impacts of pesticide residues
on vegetable prices by controlling for observed and unobserved heterogeneity.
The investigation of these issues not only provides insights into food safety risk
caused by information asymmetry-based market failure in this particular market,
but it can also help exploring other similar markets. The remainder of this
research article is organized as follows: the next section describes the data,
measure of food safety and econometric model. Section 3 presents and discusses
descriptive and econometric results. The last section concluded the study.
MATERIALS AND METHODS
A
multistage sampling technique was employed for data collection. In the first
stage, three major vegetable growing districts (Gujranwala, Faisalabad and
Multan2) representing east, central, and south parts of the Punjab
province,
were purposively selected. These districts cover all vegetable producing
agro-ecological zones of the province. In the second stage, four most pesticide
ridden vegetables (Okra, Brinjal, Spinach and Cauliflower) were identified.
Okra and cauliflower are kharif (summer) vegetables, while brinjal and
spinach are rabi (winter) vegetables. Thirty farmers
of each vegetable were randomly selected from the peri-urban farming system of
each district. This generates a sample of 360 (=3*4*30) farmers. Before the
last harvest of the vegetables, farm survey
was conducted with the help of trained enumerators and using a pretested
and well-structured questionnaire. The questionnaire was used to collect
information on farmers’ socio-economic characteristics and input-output details
particularly about frequency, type and quantities of pesticide use, duration
between consecutive sprays etc. In addition to the farm survey, vegetable
samples from each selected farmer were procured at three maturity levels in
triplicate form during the biophysical survey. Thus, 3240 (=3*4*30*3*3) samples
were analyzed. Samples were stored at -40o C to avoid degradation of
pesticide residues. Residues of pesticides were extracted from the homogenized
vegetable sample using the method proposed by Kadenezki et al. (1992),
with some modifications illustrated by Khan et al. (2009). Further, pesticide
residue analysis was conducted to measure pesticide residues of seven commonly
sprayed pesticides (Alpha
HCH, Chlorpyrifos, Deltamethrin, Dimethoate, Gama HCH, Monocrotophos,
Profenophos)
using Gas Chromatography equipped with Electron
Capture Detector (GC-ECD), as illustrated by Chandra et
al. (2010). Pesticide residues of three samples of same vegetable
procured at one maturity level from the same farm were averaged to obtain its
single value, implying that there are three mean values of pesticide residues
for each pesticide for one vegetable for each farmer. Thus, we have total 1080
vegetable samples from 360 farmers.
Econometric Model:
This
study models the lemon dilemma in the vegetable market of Pakistan. To
investigate the extent to which vegetable prices reflect vegetable safety
information and how consumers use different price signals to infer vegetable
safety, linear regression model was used to show price-quality relationship
(lemon market model) proposed by Akerlof (1970) as follows:
(1)
Where is the vegetable
specific farm gate price received by the ith farmer. is a vector of
covariates included to incorporate variations in the . Based on extant
lemon literature, this study uses pesticide residues measured in mg/kg by GC-ECD technique, vegetable dummies, district dummies,
source of irrigation (freshwater or wastewater), farmer’s education and
vegetable experience (Bond 1982, 1984; Rosenman and Wilson 1991; Genesove 1993;
Chezum and Wimmer 1997; Mocan 2007; Ma et al. 2014; Hoffmann et al. 2013). is vector of
parameters to be estimated, while is random error
term. This lemon model is estimated using ordinary least square (OLS)
regression technique.
As
mentioned above, this study quantifies food safety in terms of vegetable
contamination with injurious pesticides, which leave pesticide residues in
harvested vegetables. These pesticide residues were measured by GC-ECD. We hypothesize negative relationship
between vegetable prices and pesticide residues, which refers to an efficient
vegetable market. Efficient market helps allocating appropriate prices to
products by exchanging information on its desirable and undesirable quality
attributes between producers and consumers. Alternative to this hypothesis is a
lemons market, which offers either same or higher prices for poor quality
product (lemons) because of information failures in markets resisting consumers
in differentiating product food safety attribute. Pesticide residues may be an
endogenous covariate because it varies with observed and unobserved farm and
farmer characteristics. However, observed characteristics can be incorporated
as control variables but if unobserved characteristics are also playing a
significant role, the estimated coefficients for pesticide residues may still be
biased. Instrumental variable (IV) regression is used to test and control for
observed and unobserved heterogeneity (Smith and Blundell 1986; Rivers and
Vuong 1988; Wooldridge 2002). IV approach comprises two steps: the first step
estimates residuals () from pesticide
residue equation using appropriate instruments and the second step tests the
significance of predicted residuals in the lemon market model. A significant
coefficient of the residuals confirms the endogenity and controls for resulting
bias in the estimates (Wooldridge 2002). In the first step of IV, pesticide
residues ( is treated as
dependent variable and proposed linear model is estimated by OLS as:
(2)
Where
is a vector of
covariates. For proper model identification, should have at
least one instrumental variable besides variables in vector. is a vector of
parameters to be estimated and is an error term.
In the second step, predicted values of is used in Eq.
(1) if endogeneity proves. Following the past literature, we used pesticide
quantity and interval between sprays as instruments for pesticide residues
(Parveenet al. 2005; Latif et al. 2011; Saqib et al. 2011; Syed et al.
2014).
RESULTS AND DISCUSSION
Descriptive
Analysis: Table
1 presents descriptive comparison of the farm and farmer characteristics across
three districts: Faisalabad, Gujranwala and Multan. Vegetable farmers living in
peri-urban areas of these districts had almost same age (42 years), education
(6 schooling years) and vegetable growing experience (19 years). The highest proportions
of farmers in Faisalabad (75%) were using freshwater, followed by Multan (41%)
and Gujranwala (29%). Likewise, pesticide quantity applied by these farmers was
approximately same (6 litter/acre). However, farmers in Multan were applying
pesticides at the shortest interval (5 days) compared to their counterparts in
Gujranwala (7 days) and Faisalabad (8 days). Food safety attribute in this
study was measured by residues of pesticides in vegetables using GC-ECD.
Pesticide residues were observed highest in Faisalabad (0.270 mg/kg), followed
by Gujranwala (0.157 mg/kg) and Multan (0.125 mg/kg). Findings of pesticide
residues were also consistent with the earlier studies (Syed et al.
2014; Amir et al. 2015, 2017; Randhawa et al. 2015).
Table 1.
Descriptive statistics of sample farms and farmers across districts.
Variables
|
Faisalabad
|
Gujranwala
|
Multan
|
Farmers’
age (years)
|
42.8±10.8
|
42.7± 11.92
|
40.9±10.8
|
Farmers’
education (years)
|
6.0±4.3
|
5.5±4.2
|
5.9±3.9
|
Farmers’
vegetable experience (years)
|
17.9± 9.9
|
19.3±11.5
|
19.8± 8.8
|
Source
of irrigation (%)
|
75
|
29
|
41
|
Pesticide
quantity (liter/acre)
|
5.8± 3.5
|
5.9±4.6
|
6.1±4.2
|
Spray
interval (days)
|
7.5±4.1
|
7.4±4.9
|
4.8±5.9
|
Pesticide
residues (mg/kg)
|
0.3±0.3
|
0.2±0.2
|
0.1±0.2
|
Price
of brinjal (Rs/kg)
|
13.8±2.6
|
14.3±2.0
|
16.1±2.1
|
Price
of cauliflower (Rs/kg)
|
7.7±0.6
|
6.6±0.8
|
7.1±0.7
|
Price
of spinach (Rs/kg)
|
9.4±0.9
|
9.1±0.8
|
9.2±1.0
|
Price
of okra (Rs/kg)
|
15.8±4.5
|
20.2±1.7
|
20.9±1.8
|
Observations
|
120
|
120
|
120
|
Note:
Standard deviations have been reported in parentheses.
Figure 1: Comparison of pesticide residues
across vegetables and districts
Note: B stands for brinjal, C for cauliflower, O for
okra and s for spinach.
Source: Authors’ own survey data
Figure 1 showed
further comparison of pesticide residues across vegetables and districts. In
Faisalabad, highly contaminated vegetables were spinach and brinjal, while in
Gujranwala, okra and brinjal were highly contaminated. Multan had almost
same level of contamination in all four vegetables. Besides pesticide quantity,
other factors like farmers’ education, vegetable growing experience, duration
between sprays etc. could influence the contamination of vegetables with
pesticide. But it was difficult to
conclude the possible sources of contamination based on this simple comparison.
This study explores the possible determinants in the next section. In Table 1,
okra was the costlier vegetable compared to other vegetables at the farm
gate and the same trend continued at the retail level.
The
detailed summary of residues of seven common pesticides quantified at three
maturity levels of four vegetables have been reported in Table 2. The highest
proportion of spinach samples (83%) had surpassed maximum residue limits (MRLs)
set by FAO and WHO (2004), followed by okra (72%), brinjal (60%) and
cauliflower (50%). The in-depth analysis explained that profenophos,
deltamethrin, dimethoate, and chlorpyrifos are the main culprits of unsafe
vegetables. These toxic pesticides are known to be carcinogen and banned in the
country of origin. The contaminated vegetable samples exceeding MRLs were
lemons (bad quality products) in the vegetable market of Pakistan, implying
that most of the vegetable market was occupied by lemons as mentioned by
Akerlof (1970). This could be due to the absence of price incentive for
producing expensive but safe vegetables.
Table 2. Summary
of vegetable samples in percentage contaminated with pesticide residues across
pesticides.
Pesticides
|
Brinjal
|
Cauliflower
|
Spinach
|
Okra
|
Deltamethrin
|
43.70
(8.52)
|
49.26
(15.19)
|
38.15
(15.19)
|
27.04
(16.67)
|
Profenophos
|
37.78
(25.56)
|
52.22
33.33
|
32.96
(20.37)
|
30.37
(15.19)
|
Dimethoate
|
29.63
(23.33)
|
27.78
(20.37)
|
10.37
(3.73)
|
18.89
(13.33)
|
Chlorpyrifos
|
19.63
(1.85)
|
6.67
(0.00)
|
10.74
(6.30)
|
31.48
(25.93)
|
Monocrotophos
|
0.00
(0.00)
|
0.00
(0.00)
|
14.82
(0.37)
|
8.15
(4.81)
|
Alpha
HCH
|
10.00
(0.00)
|
9.63
(3.33)
|
7.41
(2.22)
|
22.22
(5.19)
|
Gama
HCH
|
5.56
(1.11)
|
0.00
(0.00)
|
4.82
(1.48)
|
0.74
(0.00)
|
Lemons
(PR> MRLs)
|
(60.37)
|
(49.63)
|
(82.59)
|
(72.22)
|
Total
samples
|
270
|
270
|
270
|
270
|
Note:
Percentage of vegetable samples surpass maximum residue limits (MRLs) are
reported in parentheses
Econometric Analysis: Results for determinants
of pesticide residues in vegetables were presented in column 1 of Table
3. The coefficient of pesticide residues had expected negative sign but it
was insignificant, refuting our hypothesis of efficient vegetable market. This
indicated that pesticide residues in vegetables were not reflecting into price
signals in our vegetable market. However, as discussed in section 2, pesticide
residue variable was endogenous and to correct its endogeneity, we had
estimated instrumental variable (IV) regression. The results of the first stage
(pesticide residue model) of IV as given in Eq. 2 were reported in
column 2 of Table 3. Since pesticide quantity was expected to be highly
correlated with the pesticide residues, we start investigation with this
variable. Pesticide quantity was found to be positively and significantly
contributing to determine their residues in vegetables. All other things equal,
the coefficient indicated that 1 litter/acre increase in pesticide quantity
increased vegetable contamination with 0.03 mg/kg of pesticide residues.
Similar findings had been reported by earlier studies using simple descriptive
analysis (Syed et al. 2014; Amir et al. 2015, 2017; Randhawa et al.
2015).
Nonetheless, pesticide residues decreased significantly with increased in spray
interval. Similarly, farmer’s education and vegetable experience were
negatively affecting prevalence of pesticide residues. As reported in previous
studies (Zhou and Jin 2009), farmer’s education contributes to enhance
pesticide literacy, which could help to ensure the supply of safe vegetables.
To
control for potential endogeneity of pesticide residues in Equation 1, we used
two instruments i.e., pesticide quantity and spray interval. We tested the
validity of these instruments. Empirical tests indicated that pesticide
quantity and spray intervals were highly correlated with pesticide residues but
they did not affect the vegetable prices. Pesticide quantity and spray interval
had strong impacts on the expression of pesticide residues in vegetables. The
predicted residual from first stage was significant in the second stage of IV
regression, implying that it rejected the null hypothesis regarding the
exogeneity of pesticide residues. Endogeneity tests also confirmed that
selected instruments had helped to address the endogeneity problem of pesticide
residues.
Column
3 of Table 3 revealed the results of lemon model (second stage of the IV approach)
after controlling for the endogeneity problem. The coefficient of predicted
pesticide residue was still negative and insignificant, confirming the
existence of lemon market, implying vegetable prices do not reflect vegetable
quality trait like pesticide residues. Hence, prevailing information asymmetry
in the vegetable market did not help consumers to differentiate between
contaminated and uncontaminated vegetables. Therefore, consumers were paying
same prices for contaminated vegetables as for safe vegetables. This concludes
that vegetable market in Pakistan was a lemon market. Other control variables
significantly affecting vegetable prices were types of vegetables, source of
irrigation and district dummies.
Table
3. Determinants of pesticide residues.
Variables
|
OLS regression
|
IV regression
|
Vegetable price
|
Stage I
Pesticide residues
|
Stage II
Vegetable price
|
Pesticide
quantity (liter/acre)
|
-
|
0.026***
(0.003)
|
-
|
Spray
interval (days)
|
-
|
-0.013***
(0.003)
|
-
|
Pesticide
residues (mg/kg)
|
-0.249
(0.578)
|
-
|
-1.953
(1.540)
|
Okra(dummy)
a
|
3.732***
(0.337)
|
-0.022
(0.038)
|
3.700***
(0.454)
|
Cauliflower(dummy)
a
|
-7.150***
(0.338)
|
-0.034
(0.045)
|
-7.288***
(0.276)
|
Spinach(dummy)
a
|
-4.517***
(0.390)
|
-0.019
(0.043)
|
-4.617***
(0.330)
|
Irrigation
(dummy)b
|
1.578***
(0.360)
|
-0.012
(0.028)
|
1.504***
(0.347)
|
Education
(years)
|
0.008
(0.029)
|
-0.007***
(0.002)
|
-0.009
(0.035)
|
Vegetable
experience (years)
|
0.015
(0.011)
|
-0.004***
(0.001)
|
-0.008
(0.011)
|
Faisalabad(dummy)c
|
-1.580***
(0.330)
|
0.124***
(0.030)
|
-1.345***
(0.425)
|
Multan(dummy)c
|
0.550**
(0.280)
|
-0.000
(0.020)
|
0.511**
(0.207)
|
Constant
|
13.800
(0.465)
|
0.236***
(0.063)
|
14.375***
(0.593)
|
R2
|
0.835
|
0.31
|
0.83
|
Wald
chi2(9)
|
-
|
-
|
2699.63
|
Observations
|
360
|
360
|
360
|
***, **,*
Significant at the 1%, 5%, and 10% level of significance, respectively.
Note:
Standard errors are reported in parentheses. aThe base vegetable
crop is brinjal. bSource of irrigation is 1 for freshwater and 0 for
wastewater. cThe base district is Gujranwala.
Conclusion:
The
findings of this study revealed that large proportion of samples of spanich
(83%) followed by okra (72%), brinjal (60%) and cauliflower (50%) have
surpassed the maximum residue limits (MRLs) of safe vegetables. Our econometric
model indicated that 1 litter/acre increase in pesticide quantity increased
pesticide residues by 0.03 mg/kg. However, pesticide residues decreased with
increase in spray interval, farmer’s education and vegetable growing
experience. The results of the lemon model demonstrated that pesticide residue
had negative but insignificant relation with vegetable price, rejecting our
null hypothesis of efficient vegetable market. This suggests that pesticide
residues are not reflecting into market prices of vegetables due to information
asymmetry between producers and consumers for vegetables’ safety attributes.
This concludes that vegetable market in Pakistan is a lemon market.
Acknowledgements: This work was
supported by Punjab Agricultural Research Board (PARB) of Pakistan. The authors thank
Faqir Muhammad Anjum for sharing pesticide residues data and PARB for providing
funding to collect socioeconomic data and to analyze pesticide residues. Any and all errors
are the sole responsibility of the authors.
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1Food safety means handling, preparing and storing food in such a way to minimize risk of food-borne illnesses (AIFS 2017). Unsafe food contains harmful bacteria, viruses, parasites or chemical substances including pesticide residues, heavy metals, food additives etc (WHO 2015). 2Each district is producing the highest amount of vegetables in its region except Multan, which is the second largest supplier of vegetable after Khanewal (GoP 2010). Multan was selected because of high pesticide consumption and large negative health externalities reported in the literature (Masud and Baig 1991; Syed et al. 2014). |
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