Effects of Drought and Flood on Farmer Suicide in Indian States: An Empirical Analysis

Abstract

The study examines the effects of drought and flood on farmer suicides using state-level panel data from 17 Indian states for the period 1995-2011.The empirical estimates based on fixed effect Negative Binomial model show that while drought significantly increases farmer suicide, flood has no direct impact on the same. The result also shows that farmer suicides are higher in cotton producing states of India because these states experience frequent drought conditions. Further, our findings reveal that states with high level of rural poverty, experience higher number of farmer suicides as a result of frequent occurrence of drought and moderate flood. In order to obtain robust results, fixed effect Poisson model is used to estimate the disaster impact on farmer suicides in Indian states. Overall, the findings are also consistent with fixed effect Negative Binomial model. Hence, in order to reduce the occurrence of farmer suicides, it is important for the government to devise suitable policies such as loan waiver for poor farmers, compulsory crop insurance scheme, improving farm income through revamping of agricultural marketing policy, creating public awareness among farmers and providing micro-irrigation facilities in drought prone areas.

Full text

ORIGINAL ARTICLE
Effects of Drought and Flood on Farmer Suicides in Indian
States: An Empirical Analysis
Yashobanta Parida
1
&Devi Prasad Dash
2
&
Parul Bhardwaj
3
&Joyita Roy Chowdhury
4
Received: 2 June 2017 /Accepted: 2 January 2018 / Published online: 26 January 2018
#Springer International Publishing AG, part of Springer Nature 2018
Abstract The study examines the effects of drought and flood on farmer suicides using state-
level panel data from 17 Indian states for the period 1995–2011. The empirical estimates based
on unconditional fixed effect Negative Binomial model show that while drought significantly
increases farmer suicides, flood has no direct impact on the same. The results also show that
incidence of farmer suicides is higher in cotton producing states of India because these states
experience frequent drought conditions. Furthermore, our findings reveal that states with high
levels of rural poverty experience a higher number of farmer suicides as a result of frequent
occurrence of droughts and moderate floods. To obtain robust results, fixed effect Poisson
model has been used in the study. Overall, the findings are consistent with unconditional fixed
effect Negative Binomial model. Hence, in light of the results obtained by this study, it is
important for the government to devise suitable policies such as loan waiver for poor farmers,
compulsory crop insurance scheme, improving farm income through revamping of agricultural
marketing policies, creating public awareness among farmers and providing micro-irrigation
EconDisCliCha (2018) 2:159–180
https://doi.org/10.1007/s41885-018-0023-8
*Yashobanta Parida
yash_parida@rediffmail.com; yashparida@gmail.com
Devi Prasad Dash
devi100.dash@gmail.com
Parul Bhardwaj
parulbhardwaj2708@gmail.com
Joyita Roy Chowdhury
joyita.rc2006@gmail.com
1
Centre for International Trade and Development, Jawaharlal Nehru University, New Delhi 110067,
India
2
Department of Humanities and Social Sciences, Indian Institute of Technology Ropar, Rupnagar,
India
3
Noida, India
4
Department of Economics, University of Utah, Salt Lake City, UT, USA

facilities as well as introducing alternative cropping pattern in the drought prone areas in order
to reduce the occurrence of farmer suicides.
Keywords Farmer suicides .Natural disaster .Rural p overty .Count model .Indian states
JEL Classification Q10 .C39 .I32
Introduction
Natural disasters are a recurrent phenomenon in India. Every year different states of India
experience several forms of natural disasters due to the geo-climatic conditions. Around 12% of
land in India is vulnerable to flood, 68% of cultivable land is vulnerable to drought, 60% of the land
mass is prone to earthquakes and 8% of the total area is prone to cyclones (National Disaster
Management Authority). Higher disaster-prone areas as well as a higher degree of socioeconomic
vulnerability due to poverty, low per capita income and rapid urbanization are responsible for
increasing disaster impact in terms of damage to physical and human capital as well as increasing
fiscal pressure on the federal governments in India.
1
India has experienced 192 floods, 69 droughts
2
and 113 cyclones during 1970–2009 (Centre for Research on the Epidemiology of Disasters). In
global ranking, India’srankis14thintermsofGlobalClimateRiskIndex(Kreft2017).
Natural disaster impact depends on the frequency, magnitude and severity of the disasters. India
lost around 0.46% of Gross Domestic Product (GDP) annually, crop losses were estimated at
around 0.18% of GDP and around 6% rural population were affected each year due to floods during
the period 1980–2011 (Parida 2017). In contrast, moderate and low flood events significantly
increase agricultural productivity, agricultural wages and economic growth through enhancing
fertility of agricultural land (Brammer 1990; Banerjee 2007,2010; Loayza et al. 2012;Parida2017).
Natural disaster is indirectly responsible for farmer suicides through increasing crop failure.
Around 17% of farmers committed suicides due to crop failure in 2013 (Ministry of Home
Affairs 2014a). Udmale et al. (2015) showed that frequent droughts in rural Maharashtra
adversely affected the poor farming community. In addition, other studies have also shown that
drought is one of the important factors causing farmer suicides because of the resultant income
loss due to crop failures over the years (Sarma 2004;Sridhar2006; Vaidyanathan 2006; Mishra
2006; Dongre and Deshmukh 2012; Khairnar et al. 2015).
Despite globalization and liberalization of the Indian economy, agriculture and allied
sectors remain a major source of livelihood for the rural population. The contribution of
agriculture to Gross Domestic Product (GDP) has declined relative to other sectors over the
years. The agriculture and allied sectors contributed close to 22.5% to GDP in 2000–01, which
declined to around 13% in 2015–16. Moreover, with an increased emphasis on industrial and
service sectors, the share of agriculture in GDP is expected to decline further. Consequently,
1
India lost around 12% of federal revenue due to natural disasters during the period 1996–2000 (Financing
Rapid Onset Natural Disaster Losses in India: A Risk Management Approach, The World Bank, August 2003,
Page 8).
2
Author’s calculation from (http://farmer.gov.in/Drought/Droughtreport.aspx), between 2000 and 2011, 17 states
experienced 69 droughts. Out of this, Tamil Nadu experienced 9 times, while Andhra Pradesh, Karnataka faced 8
times each. However, Madhya Pradesh and Maharashtra faced 7 times each. Gujarat, Himachal Pradesh, Odisha
and Utter Pradesh faced 4 times each, while West Bengal and Kerala faced 2 times each and rest of states faced
less than 2 times.
160 EconDisCliCha (2018) 2:159–180

there is a sharp decline of rural workforce participation
3
in the agriculture and allied sectors
from 81.1% in 1983 to 64.1% in 2011. Nonetheless, agriculture and allied sectors provide
more employment than any other sector in rural India. As per the government of India’s
estimate, the farming population declined by 7% during the period 2001–2011 because of
shrinking economic prospects of agriculture (Census 2011, GoI).
4
The various reasons attrib-
utable for this phenomenon are low agricultural income, lack of incentives in agriculture and
allied sectors, crop loss due to frequent occurrence of natural disasters, inadequate agricultural
infrastructure and government’s poor agriculture related marketing policies. Among the many
uncertainties facing agriculture and allied sectors, one of the most unpredictable factors is the
irregular occurrence of natural disaster events, mostly the rainfall volatility. Climate change in
last two decades has impacted the Indian agriculture and allied sectors severely. The failure of
rainfall leads to drought and excess rainfall causes flood which adversely affects the
agricultural output and rural employment, thus creating a downward pressure on
agricultural income and agricultural wages. The frequent occurrence of floods and
droughts affect the socioeconomic conditions of rural households and enhances rural
poverty. A set of studies have found increasing evidence of the linkage between
farmer suicides and climate change across various economies (Judd et al. 2006;
Mohanakumar and Sharma 2006; Sridhar 2006; Berry et al. 2011;Guiney2012).
Apart from drought and flood impacts, over the last few decades, increasing debt burden,
especially among the medium and small-scale farmers, has also been found to be a major
reason behind the rising incidence of farmer suicides across the country (Asadi 2000;Shah
2012; Sadanandan 2014). The heavy indebtedness among the farmers also stems from various
external factors such as crop failure, prevalence of irregular monsoons, rising costs of
cultivation and declining access to institutional credit. Additionally, subsequent studies done
in Indian context have indicated various reasons such as excessive economic liberalization,
low import tariffs, growing disparities between agriculture and non-agriculture sectors, with-
drawal of state support system, government’s poor extension services and dumping of
agricultural goods in global markets as the main determinants of increasing farmer suicides
in the recent years. (Mishra 2006; Sridhar 2006; Vaidyanathan 2006; Jeromi 2007).
Apart from the aforesaid factors, in the recent years, the phenomenon of Bt Cotton
cultivation has also resulted in rising number of farmer suicides mainly in western and
southern states of India (Rao and Suri 2006;Herring2008; Smale et al. 2009;Gruereand
Sengupta 2011). The continued adoption of Bt cotton crop leads to the abandonment of the
entire soil in the long-run for further agricultural usage. Thus, the introduction of Bt cotton in
some parts of India coupled with drought conditions and excessive dryness of soil has reduced
the agricultural yield below the normal level. Such agricultural losses have led to an increasing
number of farmer suicides in southern and western parts of India. To put the numbers in
perspective, in 2014, the number of farmer suicides rose to a staggering high of 5650. This
implies a suicide rate of 1.5 on an average for every 0.1 million population in India (Ministry
of Home Affairs 2014b). As per the National Crime Records Bureau (NCRB) report, nearly
296,417 farmers committed suicide over the period 1995 to 2013, with an average of 15,000
farmers committing suicide per year.
In view of the increasing incidence of farmer suicides, the present study empirically
examines the effect of drought and flood, and cropping pattern in states and incidence of rural
3
Employment and Unemployment reports (National Sample Survey, 38th and 68th rounds)
4
www.thehindu.com/opinion/columns/sainath/over-2000-fewer-farmers-every-day/article4674190.ece
EconDisCliCha (2018) 2:159–180 161

poverty on farmer suicides using a state-level panel data from 17 Indian states over the period
1995 to 2011. The study has the following objectives. First, we examine if the states with
higher drought or flood prone areas have experienced a higher incidence of farmer suicides.
Second, we analyze the impact of drought on farmer suicides by classifying the states based on
wheat and cotton cultivation. Finally, the study examines if the states with higher incidence of
rural poverty experience a higher number of farmer suicides as a result of frequent droughts
and floods. We have employed unconditional fixed effect Negative Binomial model
because our outcome variable, Bnumber of farmer suicides in Indian states^is a count
variable. For robust analysis, the study has used fixed effect Poisson model and
Ordinary Least Squares (OLS). This empirical study not only contributes towards
development literature, but also provides suitable policy suggestions to minimize the
occurrence of farmer suicides in Indian states.
The rest of the paper is structured as follows. Second section presents the overview of
farmer suicides in various Indian states. Third section explains the linkage between farmer
suicides and vulnerability of a region towards disaster. Fourth section discusses the data
sources and econometric identification. Empirical results are provided in section five. Finally,
conclusion and discussion of the results are presented in sixth section.
Overview of Farmer Suicides in Indian States
A Farmer suicide is one of the greatest economic adversities that the Indian agriculture
sector has ever faced in the twenty-first century. As noted by Basu et al. (2016), more
than 0.3 million farmers have committed suicides across various states of India. In this
section, we analyze the trend of farmer suicides in India for the period 1995 to 2011. We
have used state-wise farmer suicide data available from NCRB. The focus is primarily on
the 17 major states of India, where the problem of farmer suicides is more acute. Figure 1
shows the gender-wise and total farmer suicides occurring per million rural population in
India for the period 1995–2011. As Fig. 1shows, the total number of farmer suicides per
million rural population is on a decelerating trend over the years. Moreover, the incidence
of female farmer suicides is considerably lower than that of male farmer suicide.
Consequently, the trend seen in male suicides mirrors the trend of total farmer suicides
in India, while female suicides have remained more-or-less stable over the recent years.
However, female suicides trend in certain states like Andhra Pradesh, Maharashtra and
Karnataka has been rising continuously during this period. Considering state-wise data on
farmer suicides as shown in Fig. 2, the average number of farmer suicides per million of
rural population during the period 1995–2011 is the highest in the southern and western
states of Karnataka, followed by Maharashtra and Kerala.
Nagthan et al. (2011) examine farmer suicide trends in Karnataka and explain that
socioeconomic factors like humiliation from private money lenders, indebtedness, family
pressures and crop failures accounted for nearly 58% of the farmer suicides cases across the
state. A report submitted by the Tata Institute to the Mumbai High Court in 2005, cited
government’s lack of interest, the absence of a safety net for farmers and lack of access to
information related to agriculture as the main causes for the poor condition of farmers in the
state of Maharashtra. A report by the Kerala State Farmers’Debt Relief Commission (2007)
finds that the evidence of rising debt burden is the main driving factor behind increasing
farmer suicide rates in the state. In contrast, the northern states have seen relatively lower
162 EconDisCliCha (2018) 2:159–180

incidences of farmer suicide, which is a result of a good network of irrigation facilities present
in those states.
Exploring the Linkage between Farmer Suicides and Vulnerability
of a Region towards Natural Disasters
In this section, the study identifies the linkages between state-wise farmer suicides and
vulnerability of a particular region to drought and flood. We have collected information
regarding disaster-prone areas such as state-wise drought and flood prone areas from various
sources, described in the footnote of Table 1. Later, the disaster prone areas are normalized
using state-wise geographical areas. Further, we have estimated state-wise average number of
farmer suicides per million rural population
5
for the period 1995–2011. Table 1reveals that
Karnataka state has experienced the highest incidence of farmer suicides in India. Karnataka
has the largest proportion of drought prone area, and a relatively smaller flood prone area
compared to the other states. Although, according to Rural Head Count Ratio (RHCR),
Karnataka is relatively a less poor state than other states of India. Maharashtra state, which
ranks next to Karnataka in terms of average number of farmer suicides, has the highest
percentage of drought prone area and a relatively low flood prone area.
Therefore, it is observed that the top 5 states (Karnataka, Maharashtra, Kerala, Andhra
Pradesh and Madhya Pradesh) which have seen the highest incidence of farmer suicides also
feature among the top five states with the highest percentage of drought prone area. However,
as can be seen from Table 1, that none of these five states features among the top five states
which contain the highest percentage of area liable to flood and the states with the highest
RHCR (with the exceptions of Andhra Pradesh and Madhya Pradesh).
5
Here we have calculated farmer suicides per million of rural population for the period 1995 to 2011 in 17 states.
The state-wise and year-wise agrarian population data is not available in India. Therefore, we have normalized
farmer suicide by rural population because 64% of rural workforce depends on rural agriculture and allied sectors
(68th round NSS, 2011).
17
21 21
24 24 24
22
24 23 23
21 21 20 19 20 19 18
25
32 33
38 38 38
35
39 38
40
36 35 34 33 34
32
30
897
999
77766655555
0
5
10
15
20
25
30
35
40
45
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Total Farmer Suicides Male Farmer Suicides Female Farmer Suicides
Fig. 1 Farmer suicides per million rural population during 1995–2011. Note: Author calculation. Total farmer
suicides (male + female) per million rural populations in 17 major states in India. Female farmer suicides per
million rural female population. Male farmer suicides per million rural male population
EconDisCliCha (2018) 2:159–180 163

This has two important implications: (i) the states which have experienced a relatively
larger number of farmer suicides contain a higher percentage of drought prone area and a lesser
percentage of flood prone area and (ii) the less poor states have witnessed a higher number of
farmer suicides. For instance, Karnataka records the highest number of farmer suicides as
shown in Fig. 1. However, the state is less poor than other Indian states according to the
RHCR, as can be seen in Table 1. The northern states such as Himachal Pradesh, Punjab and
Uttar Pradesh are relatively well irrigated states, which consist of a relatively lower percentage
of drought prone area and a concomitant lower number of farmer suicides.
Farmer Suicides and State-Wise Indebtedness
As mentioned earlier, one of the reasons often discussed in literature for the rising incidence of
farmer suicides is the high level of indebtedness of the farmers in different states of India.
Dongre and Deshmukh (2012) found that farmers in the Vidarbha region of Maharashtra
ranked debt as the most important reason for farmer suicides.
In Table 2, we examine the crucial linkage between farmer suicides and level of indebted-
ness of farmer. The data shows that the states with higher number of farmer suicides are the
ones which record the largest difference in percentage of indebted farmers over the period
2003 and 2013. However, there are a few exceptions, the most notable being states like Odisha
and Bihar which have experienced a large difference in percentage of indebted farmers during
this time period, but have witnessed a lower incidence of farmer suicides from 1995 to 2011. In
contrast, states like Madhya Pradesh have experienced a higher number of farmer suicides, but
the percentage of indebted farmers has been on a decline.
Data Sources and Econometric Identification
This section explains data sources, definition of variables and empirical identification
employed in this study. The state-wise total farmer suicide data is obtained from the various
issues of annual report titled BAccidental Deaths and Suicides in India^, NCRB, Ministry of
Home Affairs, Government of India (GoI). NCRB introduced state-wise farmer suicide data
(self-employed in agriculture and farming sectors) from the year 1995. The state-wise area
62
55 54
36 35
27 24
21
17
13 12 99754
1
0
10
20
30
40
50
60
70
Fig. 2 State-wise average farmer suicides per million rural population during 1995–2011. Note: Author’s
calculation. Average number of farmer suicides per million state-wise rural population in 17 major Indian states
164 EconDisCliCha (2018) 2:159–180

affected by flood data is compiled from Central Water Commission (CWC), GoI. Furthermore,
the country-wise flood data is collected from Dartmouth Flood Observatory (DFO;
http://floodobservatory.colorado.edu). The state-wise drought data is taken from Department
of Land Resources, Ministry of Rural Development, GoI (http://farmer.gov.
in/drought/droughtreport.aspx).
In addition, we have compiled state-wise drought and flood related information from EM-
DAT database. The state government expenditure on flood control and drainage, expenditure
on agriculture and allied activities, and total expenditure is obtained from various volumes of
State Finance Reports published by Reserve Bank of India. The state-wise agriculture Gross
State Domestic Product (GSDP) at constant prices is taken from the National Account Survey
(NAS), available in Ministry of Statistics and Program Implementation, GoI. The state-wise
data on rural male, rural female, rural total population and literate population are available
Tab le 1 Impact of drought prone area and flood prone area on farmer suicides in Indian states
States Average farmer
suicides per million
rural population from
1995 to 2011
Rank Drought prone
area over State
geographical
area (%)
Rank Liable to flood
prone area over
State
geographical area
(%)
Rank Rural
HCR,
2011
(%)
Rank
Karnataka 62 1 44.0 2 0.1 17 19.8 13
Maharashtra 55 2 63.2 1 0.7 15 22.5 10
Kerala 54 3 25.2 5 22.4 8 7.3 17
Andhra
Pradesh
36 4 36.1 3 5.1 12 39.3 5
Madhya
Pradesh
35 5 28.9 4 0.6 16 45.2 2
Tripura 27 6 3.0 16 31.4 4 22.5 11
Tamil Nadu 24 7 22.6 6 3.5 14 24.3 9
Wes t
Bengal
21 8 13.1 11 29.8 5 30.1 8
Gujarat 17 9 22.4 7 7.1 11 31.4 7
Rajasthan 13 10 9.3 13 9.5 9 21.4 12
Haryana 12 11 18.9 8 53.2 2 11 15
Assam 9 12 6.1 14 40.2 3 42 3
Odisha 9 13 16.8 9 9.0 10 47.8 1
Himachal
Pradesh
7 14 6.0 15 4.1 13 11.1 14
Punjab 5 15 0.0 17 73.4 1 7.4 16
Utter
Pradesh
4 16 14.8 10 24.9 6 38.1 6
Bihar 1 17 10.1 12 24.5 7 40.1 4
State-wise liable to flood prone area estimated by Rashtriya Barh Ayog (National Flood Commission), Report of
Working Group on Flood Management and Region-Specific Issues for XII Plan, 2011, Planning Commission,
Government of India. The state and year wise data on drought affected area is available from Department of Land
Resources, Ministry of Rural development, Government of India. For few states such as Haryana, data on
drought prone is available from Agriculture Research Data Book, 2002. For Assam, the data for drought prone
area is available from Agriculture and Irrigation in Assam, Government of Assam. For Tripura, drought prone
area is available from National Disaster Risk Reduction Portal, Tripura. For Kerala, drought prone area is
available from K. K. Nathan (2000)BCharacteris tics of Drought in Kerala, India^Water Technology Centre,
Indian Agricultural Research Institute, New Delhi 110,012. The rural poverty Head Count Ratio (HCR) data for
each state is taken from the estimations conducted by Prof. Tendulkar, and available in Planning Commission
(2014)BReport of the Expert Group to Review the Methodology for Measurement of Poverty^, Government of
India
EconDisCliCha (2018) 2:159–180 165

from the Census of India. In India, the population census is conducted with an every ten-year
interval (for examples 1991, 2001 and 2011). Thus, we have applied linear interpolation
method to generate population figures for the intervening years.
The state-wise monthly data on rural agricultural wages for male workers is compiled from
the various volumes of Agriculture Wages in India. Real agricultural wage is constructed by
deflating nominal agricultural wages for male workers by state-wise consumer price index for
agricultural laborers (base year 1986–87= 100). The state-wise consumer price index for
agricultural laborers (base year 1986–87 = 100) is obtained from Labour Bureau, GoI. The
state-wise gross irrigated area is extracted from the various volumes of Land Use Statistics.
Similarly, state-wise cotton and wheat production information is obtained from the state-wise
area production and yield statistics, Directorate of Economics and Statistics, Ministry of
Agriculture and Farmer Welfare, GoI. The data on RHCR was compiled from the BReport
of the Expert Group to Review the Methodology for Measurement of Poverty^, Planning
Commission, GoI.
In this empirical analysis, we could not use ‘state-wise total area affected by flood’as one of
the explanatory variables because it combines low, moderate and even high flood magnitudes
based on total area affected. Moreover, it does not provide a clear information regarding
impact of area affected by flood on farmer suicides. Our objective is to examine the relative
impact of flood magnitudes on farmer suicides in Indian states. To account for that, we have
categorized the state-wise flood affected area in three categories for 17 states for the period
1995 to 2011. First, high flood magnitude dummy equals 1, if state-wise area affected by flood
lie above 75 percentile, otherwise it is zero. Second, moderate flood magnitude dummy equals
Tab le 2 State-wise average farmer suicides per million rural population and indebtedness in Indian states
State Average farmer suicides
per million rural
population from
1995 to 2011
Indebted farmer
households
in 2003 (%)
Indebted farmer
households
in 2013 (%)
Difference in percentage
between 2003 and 2013
Karnataka 62 61.6 77.3 15.7
Maharashtra 55 54.8 57.3 2.5
Kerala 54 64.4 77.7 13.3
Andhra Pradesh 36 82.0 92.9 10.9
Madhya Pradesh 35 50.8 45.7 −5.1
Tripura 27 49.2 22.9 −26.3
Tamil Nadu 24 74.5 82.5 8.0
West Bengal 21 50.1 51.5 1.4
Gujarat 17 51.9 42.6 −9.3
Rajasthan 13 52.4 61.8 9.4
Haryana 12 53.1 42.3 −10.8
Assam 9 18.1 17.5 −0.6
Odisha 9 47.8 57.7 9.9
Himachal Pradesh 7 33.4 27.9 −5.5
Punjab 5 65.4 53.2 −12.2
Utter Pradesh 4 40.3 43.8 3.5
Bihar 1 33.0 42.5 9.5
Percentage of indebted farmer households = (Indebted farmer households / All farmer households)*100. Data on
percentage of indebted farmer households in the year 2003 is obtained from Situation Assessment Survey of
Farmers, Indebtedness of Farmer Households, 59th round of National Sample Survey (NSS), 2003. Furthermore,
data on the percentage of indebted farmer households in year 2013 is obtained from Income, Expenditure,
Productive Assets and Indebtedness of Agricultural Households in India, 70th round NSS
166 EconDisCliCha (2018) 2:159–180

1, if state-wise area affected by flood lie between 50 and 74 percentile, otherwise it is zero.
Finally, low flood magnitude dummy equals 1, if state-wise area affected by flood is less than
equal to 49 percentile, otherwise it is zero. As the information regarding state-wise area
affected by drought was not available, we have created state-wise dummy variables for drought
affected years. For state-wise cotton production and wheat production, we have created
dummies for ten cotton producing states and for nine wheat producing states respectively.
Further, we have normalized all other variables for empirical purpose. The details of summary
statistics are shown in Table 12.
The study examines the effect of drought and flood on farmer suicides in major 17 Indian
states
6
using state-level panel data from 1995 to 2011. The study also examines whether the
states with a higher incidence of rural poverty experience a greater number of farmer suicides
as a result of drought and flood. The following equations have been formulated for examining
the various determinants of farmer suicides in Indian states.
SFSit ¼β0þβ1DDit þβ2HFMDit þβ3MFMDit þβ4LEFIit þβ5Zit þθrþγtþμ1it ð1Þ
SFSit ¼α1þα2CPSDit þα3CPSD*DDðÞ
it þα4WPSD*DDðÞ
it þα5Zit þθrþγtþμ2it ð2Þ
SFSit ¼ϑ1þϑ2RHCRit þϑ3RHCR*DDðÞ
it þϑ4RHCR*HFMDðÞ
it þϑ5Zit þθrþγtþμ3it ð3Þ
Where SFS
it
is the state-wise number of farmer suicides, DD
it
is the state-wise drought
dummy variable, HFMD
it
is the high flood magnitude dummy variable, MFMD
it
is the
moderate flood magnitude dummy variable, LEFI
it
is the natural logarithm of government
expenditure on flood control and drainage, CPSD
it
is the cotton producing states dummy,
CPSD ∗DD is the interaction term between cotton producing states dummy and drought
dummy, WPSD ∗DD is the interaction term between wheat producing states dummy and
drought dummy, RHCR
it
is the rural head count ratio, RHCR ∗DD is the interaction term
between rural head count ratio dummy and drought dummy, RHCR ∗HFMD is the interaction
term between rural head count ratio dummy and high flood magnitude dummy, Z
it
denote the
control variables, θ
r
controls for the unobserved region effects, γ
t
indicate year specific effects
and μ
it
is the error term.
In Eqs. (1)to(3), the dependent variable is the number of farmer suicides. This is a non-
negative count variable. The variance of the dependent variable exceeds the mean implying
that the ‘number of farmer suicides’variable is over-dispersed as shown in Table 12.The
summary statistics clearly show that the outcome variable has violated the assumptions of
normal distribution. In this case, Ordinary Least Squares (OLS) estimation produces biased,
inefficient and inconsistent results. It is appropriate to apply Negative Binomial or Poisson
model to estimate Eqs. 1to 3.Kahn(2005) estimates Zero-Inflated Negative Binomial model
and controls for unobserved continent fixed effects using a cross country disaster data set. In
our study, we could not use the Zero-Inflated Negative Binomial model because the share of
observations indicating zero farmer suicides is not extremely large. However, Kellenberg and
Mobarak (2008) have estimated conditional Fixed Effect (FE) Negative Binomial model
6
At present, India has 29 states (including Telangana). Telangana state is recently formed from Andhra Pradesh
in 2014. Total farmer suicides stand at 94% in the major 17 states and the remaining 6% of farmer suicides occur
in rest of the states.
EconDisCliCha (2018) 2:159–180 167

because it controls for unobserved country effects in a cross-country disaster data set. But this
models ‘is not a true fixed-effects model’(Hilbe 2012: 474) as it does not control for all time
invariant covariates efficiently (Allison and Waterman 2002). Only the conditional FE Neg-
ative Binomial model controls for time invariant unobserved effects under a very specific set of
assumptions (Guimarães 2008). Therefore, we use an unconditional FE Negative Binomial
model to analyze the effects of flood and drought on farmer suicides. This model provides
consistent parameter estimates and controls for the unobserved time invariant effects efficiently
when the cross-sectional units are less than 20 (Hilbe 2012: 473). In our study, we have
controlled for the unobserved time invariant region effects as the cross-section units (number
of states) are equal to 17. There are a few empirical studies which have used unconditional FE
Negative Binomial model and controlled for the unobserved region (or continent) fixed effects
using disaster fatality dataset (Anbarci et al. 2005;Kahn2005; Escaleras et al. 2007; Parida
2016). The unconditional FE Negative Binomial produces inconsistent estimates due to the
incidental parameter problem in case of short panel data (Cameron and Trivedi 1998:282).
Therefore, to obtain robust results, we use fixed effect Poisson model. Unlike the uncondi-
tional FE Negative Binomial model, the FE Poisson model does not suffer from the incidental
parameter problem and it completely controls for the unobserved time invariant effects
(Wooldridge 2002:674–676).
Empirical Results; Effects of Drought and Flood on Farmer Suicides
The FE Negative Binomial estimates of Eq. (1) are shown in Table 3. In Model-1, the
coefficient of drought dummy is positive and statistically significant, implying that the
frequent occurrence of drought significantly increases farmer suicide in Indian states due to
crop failure.
Agricultural crop failure due to natural disasters leads to lower agricultural income. As a
result, farmers are unable to repay their loans, which eventually lead to higher incidence of
farmer suicides. In Model-4, the coefficient of drought dummy is positive and statistically
significant after adding all control variables. Our estimates are robust throughout the models.
Further, the coefficient of dummy variables indicating high and moderate flood magnitudes
are statistically insignificant from Model-2 to Model-4. The results show that occurrence of
flood does not significantly affect the incidence of farmer suicides in Indian states. There are a
few reasons which help to explain the fact that flood has no direct impact on farmer suicides.
First, frequent occurrence of flood in different states helps in groundwater recharging and
increases the storage capacities of dams. This in turn significantly helps in irrigation. Second,
although flood affects kharif crops, but it also increases the productivity of the forthcoming
rabi crops by increasing soil quality through increasing moisture in the agricultural land
(Brammer 1990;Banerjee2010; Singh et al. 2011). Finally, flood not only increases agricul-
tural productivity, but also increases rural agricultural wages and economic growth (Banerjee
2007;Loayzaetal.2012; Parida 2017). However, drought not only has an adverse impact on
rice yields in India, but also results in farmer suicides due to the resultant income loss from
crop failures over the years (Sarma 2004;Sridhar2006; Vaidyanathan 2006;Mishra2006;
Dongre and Deshmukh 2012;Khairnaretal.2015; Birthal et al. 2015).
In Model-3 and Model-4, we have introduced a new variable, namely real agricultural
wages for male workers. The coefficient of this variable is negative and statistically significant
in both models which imply that the states with higher agriculture wages for male workers
168 EconDisCliCha (2018) 2:159–180

witness lower incidence of farmer suicides. Agricultural wages act like insurance for the rural
farmers, which reduces their stress levels and causes a decline in the number of farmer
suicides. In Model-4, the coefficients of public expenditure on flood control and drainage,
and public expenditure agriculture and allied activities are negative and statistically significant,
which shows that there exists a negative correlation between government interventions and
farmer suicides. This result appears logical as government plays an important role in mini-
mizing the occurrence of farmer suicides through increasing expenditures on agriculture and
allied sectors and flood control measures. To control for endogeneity issue, we have introduced
one year lag of the variables indicating public expenditure on flood control and drainage and
public expenditure on agriculture and allied activities in Model-4.We also estimate Eq. (1)
using OLS model and the estimates are shown in Table 9. In Model-3, the coefficient of
drought dummy is positive and significant, which shows that drought has significantly
increased the occurrence of farmer suicides by 32%.
7
The OLS estimates are consistent with
the estimates of unconditional FE Negative Binomial model.
The results of FE Poisson model for Eq. (1) are shown in Table 6. The coefficient of
drought dummy is positive and significant through all the models, which implies that our
7
We estimate log linear OLS model using Eq. (1) with drought dummy. The estimates are shown in Model-3 in
Tab le 9. The impact of drought on farmer suicides is calculated in percentage terms. The coefficient of drought
dummy is 0.278 (shown in Model 3 of Table 9) and we take [exp (0.278)-1]*100 = 32.04%.
Tab le 3 Impact of drought and flood on farmer suicides: FE Negative Binomial model
Variables Model-1 Model-2 Model-3 Model-4
Drought dummy 0.658***
(0.124)
0.654***
(0.124)
0.560***
(0.127)
0.299**
(0.127)
High flood magnitude dummy −0.004 (0.116) −0.014 (0.114) 0.082 (0.106)
Moderate flood magnitude dummy 0.065 (0.128) 0.068 (0.127) 0.076 (0.108)
Ln(Real agricultural wages for male
workers)
−0.514***
(0.123)
−0.269**
(0.121)
Ln(Expenditure of flood control and
drainage
/Revenue development expenditure)
−0.041 (0.040)
Ln(Expenditure of flood control and
drainage
/Revenue development
expenditure)(−1)
−0.067*
(0.040)
Ln(Expenditure of agriculture and allied
activities/Total expenditure)
−0.142 (0.124)
Ln(Expenditure of agriculture and allied
activities/Total expenditure)(−1)
−0.277**
(0.138)
Region FE Yes Yes Yes Yes
Time FE Yes Yes Yes Yes
χ2 (d.f.) 576.14 (22) 579.95 (24) 620.72 (25) 1202.17 (28)
Log-likelihood −2120 −2120 −2116 −1961
No.ofstates 171717 17
Observations 289 289 289 272
Robust standard errors are reported in parentheses. Dependent variable is the total number of farmer suicides.
Low flood magnitude is the base category dummy variable
*p < 0.1 denote 1%, 5% and 10% levels of significance respectively
**p < 0.05
***p < 0.01
EconDisCliCha (2018) 2:159–180 169

estimate is robust and consistent. This finding is also in line with our earlier findings (results
obtained using unconditional FE Negative Binomial model, as shown in Table 3). Moreover,
the coefficients of agricultural wages for male workers and government expenditure on flood
control and drainage are negatively correlated with the incidence of farmer suicides (see
Table 6). This finding is also consistent with our earlier results as shown in Table 3.Another
interesting finding is that the coefficient of moderate flood magnitude dummy is positive and
significant in Model-3 and Model-4 which implies that the states with frequent occurrence of
moderate flood witness higher occurrence of farmer suicides. However, this result is not
consistent with our earlier findings which showed that the coefficient of moderate flood
magnitude dummy is not statistically significant (see Table 3).
Effects of Cotton and Wheat Production on Farmer Suicides
The above analysis shows that while drought has a direct impact on farmer suicides, floods
have no direct impact on the same. In this section, we examine the effects of cotton and wheat
production in various states on the incidence of farmer suicides using Eq. (2) over the period
1995–2011. The FE Negative Binomial model is applied to estimate Eq. (2) and the estimates
are presented in Table 4. In our empirical analysis, we consider total ten cotton producing
states and nine wheat producing states.
8
Since cotton is a cash crop; farmers invest heavily in
its cultivation, which forces them to borrow money from various formal and informal sources.
In the event of a failure of the standing cotton crop because of natural disasters like droughts,
floods, farmers are unable to repay loans which increase the occurrence of farmer suicides in
cotton producing states. This argument is supported by our empirical results. In Model-1, the
coefficient of cotton producing state dummy is positive and significant, which implies that the
cotton producing states experience a higher number of farmer suicides than other states
producing different crops. In Model-4, the coefficient of cotton producing state dummy
remains positive and significant after adding other control variables. This shows that our
estimates are robust throughout the models.
Further, in Model-2 to Model-5, the interaction variable (Cotton producing state
dummy*drought dummy) is positive and significant, which implies that the probability of
farmer suicide is higher in cotton producing states because those states experience frequent
occurrence of droughts.
9
A host of factors are responsible for the rise in farmer suicides in
cotton producing states. First, the study by Gruere and Sengupta (2011)showthat‘Bt cotton
production has indirectly contributed to farmer indebtedness, leading to suicides, but its failure
is mainly the result of the environment in which it was planted’. In the southern state of
Andhra Pradesh, suicides among the cotton growing farmers are prevalent due to adverse
rainfall, low yields, unremunerative prices and increase in cost of cultivation (Parthasarathy
and Shameem 1998).
8
In this analysis, we have used dummy variables to indicate states producing wheat and cotton because 10 out of
17 states use 99.9% land for cotton production and 9 out of 17 states use 99.23% land for wheat production. For
example, the percentage of cotton area estimated = cotton cultivated area for 10 states over total cotton cultivated
area for 17 states in the year 2011 (Land Use Statistics at a Glance, 2014). Therefore, we have used the dummy
variables for cotton and wheat production states instead of the area of production. The percentage figure shows
that some states produce wheat and not cotton. Similarly, other states produce cotton and not wheat.
9
In year 2011, 83% of total cotton in India was produced by five major states namely Andhra Pradesh,
Karnataka, Madhya Pradesh, Maharashtra and Gujarat. The drought prone area combining all these five states
stands at 75%, while farmer suicide stands at 67% in the period 1995–2011 in these states.
170 EconDisCliCha (2018) 2:159–180

Furthermore, introduction of new Bt cotton seeds, increasing usage of insecticides and
pesticides have led to the declining yield of cotton cultivation, thus adversely affecting
profitability conditions of farmers (Shetty 2004; Stone 2011; Thomas and De Tavernier
2017). The agricultural output losses have put cotton farmers under huge debt trap due to
the increasing dependence on non-institutional credit sources (Behere and Behere 2008;
Chhikara and Kodan 2013; Manoranjitham et al. 2010;Merriott2016; Dandekar and
Bhattacharya 2017). The average percentage of indebtedness among the rural communities
is quite high in the cotton producing southern and peninsular states with Andhra Pradesh
topping the list (Rajeev et al. 2011). Nevertheless, cotton producing states constitute nearly
88% of total farmer suicides in India, accounting for 11,026 suicides out of total 12,602
suicides in 2015 (The Hindu Business Line, 5th January, 2017).
Furthermore, farmer suicides are higher in wheat producing states as shown in Model-3 to
Model-5 of Table 4. In Model-4 and Model-5, the interaction term (Wheat producing state
dummy*drought dummy) is positive but not statistically significant. This implies that higher
frequency of droughts do not increase the incidence of farmer suicides in wheat producing
states. Other factors can help explain the rising farmer suicides in wheat producing states.
Wheat is a rabi crop which depends more on irrigation than adequate rainfall. In Model-4, we
see that the coefficient of gross irrigated area is negative and statistically insignificant, which
implies that the provision of irrigation facilities is not sufficient enough to minimize the
occurrence of farmer suicides. Finally, the coefficient of literacy rate is found to be negative
and significant, which shows that higher literacy among farmers reduces the incidence of
suicides. We estimate Eq. (2) using OLS model and results are shown in Table 10. The OLS
estimates are consistent with the results obtained from using unconditional FE Negative
Binomial model (see Table 4). To obtain robust results, we have estimated Eq. (2)usingFE
Tab le 4 Farmer suicides in cotton and wheat production states: FE Negative Binomial model
Variables Model-1 Model-2 Model-3 Model-4 Model-5
Cotton producing state dummy 0.602***
(0.131)
0.353***
(0.131)
0.377***
(0.090)
0.403***
(0.091)
0.446***
(0.098)
Cotton producing state
dummy*drought dummy
0.722***
(0.126)
0.633***
(0.110)
0.493***
(0.139)
0.524***
(0.138)
Wheat producing state dummy 1.332***
(0.144)
1.286***
(0.145)
1.355***
(0.182)
Wheat producing state
dummy*drought dummy
0.240
(0.170)
0.236
(0.167)
Ln(Gross irrigated area in million
hectares)
−0.024
(0.054)
Literacy rate −0.015*
(0.007)
Region FE Yes Yes Yes Yes Yes
Ti me F E Ye s Yes Yes Yes Ye s
χ2 (d.f.) 487.09 (22) 594.27 (23) 1059.03 (24) 1122.66 (25) 1075.96 (27)
Log-likelihood −2123 −2113 −2088 −2087 −2085
No.ofstates 1717171717
Observations 289 289 289 289 289
Robust standard errors are reported in the parentheses. Dependent variable is the total number of farmer suicides.
*p< 0.1denote 1%, 5% and 10% levels of significance respectively
**p<0.05
***p< 0.01
EconDisCliCha (2018) 2:159–180 171

Poisson model, the estimates of which are shown in Table 7. We find that the estimates are
robust throughout the models and consistent with our earlier findings as shown in Table 4.
Effects of Rural Poverty and Drought on Farmer Suicides
As discussed in the earlier sections, there are many reasons responsible for the higher
incidence of farmer suicides in Indian states, and rural poverty is one of the main determinants.
In this section, we analyze the effects of rural poverty on farmer suicides in 17 Indian states
using Eq. (3) for the time period 1995–2011. Around 26% of rural population live below the
poverty line and 64% of rural workforce is dependent directly and indirectly on agriculture and
allied sectors for their livelihood in the year 2011 (NSS 68th round, 2011). Any extreme
climate event such as drought and flood not only affect agricultural output, but also affects the
households’economic condition through damage to crops, damage to house and private
properties, and deterioration of human health. The study uses RHCR
10
estimated by
Lakdawala
11
and Tendulkar
12
using four rounds of household consumer expenditure
13
data.
We estimate Eq. (3) using four rounds of RHCR
14
for years 1999, 2004, 2009 and 2011 along
with drought dummy matched for these respective years. The unconditional FE Negative
Binomial estimates are shown in Table 5. The coefficient of RHCR is positive but not
statistically significant in Model-1 to Model-3, implying that rural poverty is not the sole
cause for the occurrence of farmer suicides in Indian states.
Furthermore, the coefficients of the interaction terms (RHCR*drought dummy) and
(RHCR*moderate flood magnitude dummy) are positive and significant from Model-2 to
Model-3ofTable5. This result implies that relatively poor states witness higher incidence of
farmer suicides coupled with the negative impacts of frequent occurrence of droughts and
moderate floods. Moreover, in Model-3 the coefficient of Mahatma Gandhi National Rural
Employment Guarantee (MGNREGS)
15
dummy is insignificant, implying that MGNREGS is
not an effective social protection measure for the farmers. In Model-4, the coefficient of
agricultural productivity is not significant, which shows that a small increase in agricultural
productivity is not sufficient enough to minimize the incidence of farmer suicides. For
robustness analysis, we estimate Eq. (3) using FE Poisson model, the estimates of which are
presented in Table 8. The coefficients of interaction terms (RHCR*drought dummy) and
(RHCR*moderate flood magnitude dummy) are positive and significant from Model-2 to
Model-3. Our estimates are robust and these are consistent with our earlier findings shown in
Table 5. Next, we estimate Eq. (3) using OLS model and estimates are shown in Table 11.The
coefficients of (RHCR*drought dummy) and (RHCR*moderate flood magnitude dummy) are
10
See Planning Commission (2014)BReport of the Expert Group to Review the Methodology for Measurement
of Poverty^, Government of India.
11
We have used Rural Head Count Ratio for the year 1999–2000 estimated by Lakdawala.
12
We have used Rural Head Count Ratio for three years such as 2004–05, 2009–10 and 2011–12 estimated by
Ten dulka r.
13
Thick rounds of Household Consumer Expenditure surveys are conducted by National Sample Survey Office
(NSSO), Ministry of Statistics and Programme Implementation, GoI, within five years intervals, such as 1999,
2004, 2009 and 2011.
14
We have used only four rounds of Rural Head Count Ratio (RHCR) data along with drought data compiled for
those years only. The data rounds used are as follows: 55th round (1999–2000) of RHCR estimated by
Lakdawala and rest of the three rounds of RHCR (61st round, 2004–05; 66th round, 2009–10; 68th round,
2011–12) are estimated by Tendulkar. Total number of observations are (17 States × 4 years = 68 observations).
We have matched the drought data with RHCR data available for these four years.
15
MGNREGS is the largest public funded program in India.
172 EconDisCliCha (2018) 2:159–180

positive and significant in Model-3 implying that one unit change in rural poverty increase
farmer suicides by 2.89% and 3.52% respectively as a result of frequent occurrence of
droughts and moderate floods respectively. The OLS estimates are consistent with the results
of unconditional FE Negative Binomial model (see Table 5).
Conclusion and Discussion
The study examines the effect of extreme weather events (mainly floods and droughts) on
farmer suicides in Indian states over the period 1995 to 2011. The estimates based on
unconditional FE Negative Binomial model reveal that drought has significantly increased
the incidence of farmer suicides across Indian states. However, flood has almost no direct
impact on the occurrence of farmer suicides. The estimates further show that public expendi-
ture on flood control and irrigation related activities significantly reduces the occurrence of
farmer suicides. The study also shows that agricultural wage earnings of male workers
significantly reduce farmer suicides due to increasing income securities of rural households.
The result also shows that the incidence of farmer suicides is higher in cotton producing states
because these states experience frequent drought conditions. Further, our findings reveal that
states with high level of rural poverty experience a higher number of farmer suicides as a result
of frequent occurrence of droughts and floods. Another interesting finding of the study is that
higher literacy rate has significantly reduced farmer suicides. For robustness analysis, we have
employed FE Poisson model and OLS model, which confirms that drought causes a higher
incidence of farmer suicides.
Further, our empirical findings also suggest that the negative impact of natural disasters and
other factors of economic deprivation also influence the incidence of farmer suicides to a
Tab le 5 Impact of poverty and drought on farmer suicides: FE Negative Binomial model
Variables Model-1 Model-2 Model-3
Rural Head Count Ratio (RHCR) 0.010 (0.019) 0.007 (0.017) 0.024 (0.021)
Rural HCR *drought dummy 0.012 (0.008) 0.016* (0.008) 0.017** (0.007)
Rural HCR *high flood magnitude dummy 0.003 (0.009) 0.007 (0.007)
Rural HCR *moderate flood magnitude dummy 0.024**
(0.010)
0.020** (0.010)
MGNREGS dummy 0.549 (0.402)
Ln(Agricultural productivity per hectare gross irrigated
land)
−0.168 (0.234)
Coastal state dummy 2.677***
(0.523)
Region FE Yes Yes Yes
Ti me F E Yes Yes Yes
χ2 (d.f.) 130.72 (10) 145.95 (12) 272.13 (14)
Log-likelihood −500 −498 −488
No. of states 17 17 17
Observations 68 68 68
Robust standard errors are reported in the parentheses. Dependent variable is the total number of farmer suicides.
Low flood magnitude is the base category dummy variable
*p< 0.1denote 1%, 5% and 10% levels of significance respectively
**p<0.05
***p< 0.01
EconDisCliCha (2018) 2:159–180 173

significant extent. To end such humanitarian crisis, both central and state governments should
initiate strict monitoring and expedite the following agricultural policies. First, state govern-
ments should initiate compulsory crop insurance schemes and insure all farmers including
sharecroppers and landless farmers. Second, our results show that drought creates major
adverse impacts on the agriculture and allied sectors. Therefore, it is important to reduce the
dependence of Indian agriculture on the vagaries of monsoon by improving minor and major
irrigation facilities and introducing alternative cropping pattern in various states. This
should be coupled with increasing agrarian investment in the areas of controlling
flood, irrigation management and agricultural research and development. Finally, our
results show that higher agricultural wage income of male workers and availability of
alternative employment opportunities in agriculture and allied sectors can potentially
arrest the rising incidence of farmer suicides.
Acknowledgements We would like to thank Prof. B. N. Goldar, Mr. Jagadish Prasad Sahu, Ms. Piyali
Majumder, two anonymous reviewers and Editors-in-Chief Prof. Ilan Noy for giving their valuable suggestions
and comments. We thank participants at 10th Doctoral Thesis Conference, IBS Hyderabad. Any remaining errors
are ours.
Appendix
Tab le 6 Impact of drought and flood on farmer suicides: FE Poisson model
Variables Model-1 Model-2 Model-3 Model-4
Drought dummy 0.541***
(0.104)
0.559***
(0.103)
0.434***
(0.105)
0.396***
(0.101)
High flood magnitude dummy 0.141 (0.103) 0.119 (0.095) 0.142 (0.093)
Moderate flood magnitude dummy 0.157 (0.104) 0.176* (0.103) 0.191* (0.099)
Ln (Real agricultural wages for
male workers)
−0.572***
(0.120)
−0.406***
(0.119)
Ln(Expenditure of flood control and
drainage/Revenue development
expenditure)
−0.029 (0.028)
Ln(Expenditure of flood control and
drainage/Revenue development
expenditure) (−1)
−0.048*
(0.029)
Region FE Yes Yes Yes Yes
Ti me F E Yes Ye s Ye s Yes
χ2 (d.f.) 608.31 (22) 625.87 (24) 749.45 (25) 1020.66 (26)
Log-likelihood −53,109 −52,520 −48,686 −39,526
No.ofstates 171717 17
Observations 289 289 289 272
Robust standard errors are reported in parentheses. Dependent variable is the total number of farmer suicides.
Low flood magnitude is the base category dummy variable
*p< 0.1denote 1%, 5% and 10% levels of significance respectively
**p<0.05
***p< 0.01
174 EconDisCliCha (2018) 2:159–180

Tab le 7 Farmer suicides in cotton and wheat production states: FE Poisson model
Variables Model-1 Model-2 Model-3 Model-4 Model-5
Cotton producing state dummy 0.404***
(0.077)
0.186**
(0.093)
0.368***
(0.089)
0.385***
(0.087)
0.478***
(0.094)
Cotton producing state
dummy*drought dummy
0.565***
(0.117)
0.545***
(0.114)
0.477***
(0.116)
0.511***
(0.117)
Wheat producing state dummy 1.372***
(0.173)
1.334***
(0.175)
1.553***
(0.197)
Wheat producing state
dummy*drought dummy
0.147 (0.144) 0.089 (0.150)
Ln(Gross irrigated area in million
hectare)
−0.070
(0.044)
Literacy rate −0.018***
(0.005)
Region FE Yes Yes Yes Yes Yes
Time FE Yes Yes Yes Yes Yes
χ2 (d.f.) 495.90 (22) 671.67 (23) 970.32 (24) 1021.58 (25) 948.71 (27)
Log-likelihood −55,160 −50,531 −45,474 −45,290 −43,543
No.ofstates 1717171717
Observations 289 289 289 289 289
Robust standard errors are reported in parentheses. Dependent variable is the total number of farmer suicides
*p < 0.1 denote 1%, 5% and 10% levels of significance respectively
**p<0.05
***p< 0.01
Tab le 8 Impact of poverty and drought on farmer suicides: FE Poisson Model
Variables Model-1 Model-2 Model-3
Rural Head Count Ratio (RHCR) 0.018 (0.017) 0.010 (0.014) 0.011 (0.014)
Rural HCR *drought dummy 0.014** (0.005) 0.020*** (0.005) 0.026*** (0.004)
Rural HCR *high flood magnitude Dummy 0.005 (0.007) 0.013*** (0.003)
Rural HCR *moderate flood magnitude dummy 0.023*** (0.006) 0.023*** (0.006)
MGNREGS dummy 0.425 (0.307)
Ln (Agricultural productivity per hectare
gross irrigated land)
−0.080 (0.109)
Coastal state dummy 2.911*** (0.628)
Region FE Yes Yes Yes
Ti me F E Yes Yes Yes
χ2 (d.f.) 140.57 (10) 196.40 (12) 279.39 (14)
Log-likelihood −10,724 −9393 −6946
No. of states 17 17 17
Observations 68 68 68
Robust standard errors are reported in parentheses. Dependent variable is the total number of farmer suicides.
Low flood magnitude is the base category dummy variable
*p< 0.1 denote 1%, 5% and 10% levels of significance respectively
**p<0.05
***p < 0.01
EconDisCliCha (2018) 2:159–180 175

Tab le 9 Impact of drought and flood on farmer suicides: OLS model
Variables Model-1 Model-2 Model-3
Drought dummy 0.6204*** (0.171) 0.5028*** (0.181) 0.2781* (0.162)
High flood magnitude dummy −0.1328 (0.189) 0.0053 (0.154)
Moderate flood magnitude dummy 0.0837 (0.179) 0.1718 (0.152)
Ln (Real agricultural wages for male workers) −0.6665** (0.259) −0.5845** (0.244)
Ln(Expenditure of flood control and drainage/
Revenue development expenditure)
−0.0454 (0.037)
Ln(Expenditure of flood control and drainage/
Revenue development expenditure) (−1)
−0.0628* (0.036)
Ln(Expenditure of agriculture and allied
activities/Total expenditure)
−0.0888 (0.175)
Ln(Expenditure of agriculture and allied
activities/Total expenditure) (−1)
−0.2894 (0.179)
Constant 3.7411*** (0.731) 5.8491*** (1.260) 4.2616*** (1.022)
Observations 289 289 272
R-squared 0.499 0.514 0.662
Robust standard errors are reported in parentheses. Dependent variable is Ln (total number of farmer sui-
cides+0.1). All regressions include region and year fixed effects.. Low flood magnitude is the base category
dummy variable
*p < 0.1 denote 1%, 5% and 10% levels of significance respectively
**p < 0.05
***p < 0.01
Table 10 Farmer suicides in cotton and wheat production states: OLS model
Variables Model-1 Model-2 Model-3
Cotton producing state dummy 0.6612*** (0.136) 0.4128*** (0.149) 0.3072** (0.155)
Cotton producing state dummy*drought dummy 0.7019*** (0.224) 0.7327*** (0.222)
Wheat producing state dummy 0.8505*** (0.198) 0.7431*** (0.279)
Wheat producing state dummy*drought dummy 0.1338 (0.279) 0.1446 (0.274)
Ln(Gross irrigated area in million hectares) 0.0852 (0.088)
Literacy rate −0.0246** (0.010)
Constant 3.2388*** (0.707) 2.7817*** (0.670) 3.5562*** (0.840)
Observations 289 289 289
R-squared 0.506 0.549 0.557
Robust standard errors are reported in parentheses. Dependent variable is Ln (total number of farmer sui-
cides+0.1). All regressions include region and year fixed effects
*p < 0.1 denote 1%, 5% and 10% levels of significance respectively
**p < 0.05
***p < 0.01
176 EconDisCliCha (2018) 2:159–180

Table 11 Impact of poverty and drought on farmer suicides: OLS model
Variables Model-1 Model-2 Model-3
Rural Head Count Ratio (RHCR) −0.0090 (0.016) −0.0142 (0.015) −0.0101 (0.012)
Rural HCR *drought dummy 0.0269* (0.015) 0.0303**
(0.014)
0.0289***
(0.010)
Rural HCR *high flood magnitude dummy −0.0176 (0.018) −0.0093 (0.014)
Rural HCR *moderate flood magnitude dummy 0.0402***
(0.015)
0.0352* (0.020)
MGNREGS dummy −0.3561 (0.328)
Ln(Agriculture productivity per hectare gross irrigated
land)
−0.2586 (0.166)
Coastal state dummy 1.7518***
(0.252)
Constant 6.3616***
(0.547)
5.9090***
(0.488)
8.9444***
(2.313)
Observations 68 68 68
R-squared 0.068 0.161 0.537
Robust standard errors are reported in parentheses. Dependent variable is Ln (total number of farmer sui-
cides+0.1). All regressions include year fixed effects. Low flood magnitude is the base category dummy variable
*p < 0.1 denote 1%, 5% and 10% levels of significance respectively
**p < 0.05
***p < 0.01
Table 12 Summary statistics of variables
Variables Definition of variables Obs Mean Std.
Dev.
Min Max
Dependent variable
Total number of farmer
suicides
Number of farmer suicides in 17 Indian
states in respective years
289 871.4 936.4 0 4453
Explanatory variables
Disaster variables
Drought dummy Drought dummy equals to 1 if the state
suffered drought situation in respective
years, otherwise 0.
289 0.2 0.4 0 1
Low flood magnitude
dummy
Low flood magnitude dummy is equal to 1,
if state wise area affected by flood lies
less than equal to 49 percentile,
otherwise 0.
289 0.5 0.5 0 1
Moderate flood
magnitude dummy
Moderate flood magnitude dummy is equal
to 1, if state wise area affected by flood
lie between 50 and 74 percentile,
otherwise 0.
289 0.2 0.4 0 1
High flood magnitude
dummy
High flood magnitude dummy is equal to
1, if state wise area affected by flood lie
above 75 percentile, otherwise 0.
289 0.3 0.5 0 1
Major crop production dummy variables
Major cotton producing
state dummy
Major 10 cotton producing states dummy is
equal to 1, otherwise 0.
289 0.6 0.5 0 1
Major wheat producing state
dummy
Major 9 wheat producing states dummy is
equal to 1, otherwise 0.
289 0.5 0.5 0 1
Interaction variables
289 0.2 0.4 0 1
EconDisCliCha (2018) 2:159–180 177

References
Allison PD, Waterman R (2002) Fixed effects negative binomial regression models. In: Stolzenberg RM (ed)
Sociological methodology 2002. Basil Blackwell, Oxford, pp 247–265
Anbarci N, Escaleras M, Register CA (2005) Earthquake fatalities: the interaction of nature and political
economy. J Public Econ 89(9):1907–1933
Asadi M (2000) Seed tribunal: interrogating farmers’suicides. Econ Polit Wkly 35(43/44):3808–3810
Table 12 (continued)
Variables Definition of variables Obs Mean Std.
Dev.
Min Max
Major cotton producing
state dummy*drought
dummy
Major 10 cotton producing states dummy
interacted with drought dummy of
particular states in specific years.
Major wheat producing state
dummy*drought dummy
Major 9 wheat producing states dummy
interacted with drought dummy of
particular states in specific years.
289 0.1 0.3 0 1
Rural Head Count Ratio
(RHCR)*drought dummy
Percentage of rural population living below
the poverty line interacted with drought
dummy for a particular state in different
years.
68 10.4 17.4 0 55.7
Rural Head Count Ratio
(RHCR)*high flood
magnitude dummy
Percentage of rural population living below
the poverty line interacted with high
flood dummy for a particular state in
different years.
68 6.2 13.6 0 55.7
Rural Head Count Ratio
(RHCR)*moderate flood
magnitude dummy
Percentage of rural population living below
the poverty line interacted with
moderate flood dummy for a particular
state in different years.
68 3.4 9.4 0 37.1
Income, government expenditure and other variables
Ln(Real agriculture wages
for male workers)
Nominal agricultural wages for male
workers deflated by state-wise consumer
price index of agricultural laborer
(1987 = 100).
289 3.0 0.4 2.2 4.3
MGNREGS dummy Mahatma Gandhi National Rural
Employme nt Guarantee Scheme
(MGNREGS) takes the value 1 for the
years 2005 and above, and otherwise 0.
289 0.4 0.5 0 1
Coastal state dummy Coastal state dummy takes the value 1 if
the particular state has coastal area,
otherwise 0.
289 0.5 0.5 0 1
Rural Head Count Ratio
(RHCR)
Percentage of rural population living below
the poverty line for each state.
68 27.8 13.6 6.4 60.8
Literacy rate (%) State-wise literate population over adult
population, excluding child population.
289 59.4 10.8 22.7 94
Ln(Expenditure of flood
control and drainage/Rev.
development expenditure)
State government expenditure on flood
control a nd drainage over revenue
development expenditure
289 −8.6 4.8 −19.7 −3.9
Ln(Expenditure of
agriculture and allied
activities/Total expenditure)
State government expenditure on
agriculture and allied activities over total
government expenditure
289 −3.7 0.9 −5.8 −2.2
Ln(Gross irrigated area in
million hectares)
State-wise gross irrigated area in million
hectares.
289 7.8 1.5 3.9 9.9
Ln (Agriculture productivity
per gross irrigated land in
hectare)
State-wise agriculture Gross State
Domestic Product (GSDP) to Gross ir-
rigated area in hectare.
289 13.6 0.8 12.6 16.0
178 EconDisCliCha (2018) 2:159–180

Banerjee L (2007) Effect of flood on agricultural wages in Bangladesh: an empirical analysis. World Dev 35(11):
1989–2009
Banerjee L (2010) Effects of flood on agricultural productivity in Bangladesh. Oxf Dev Stud 38(3):339–356
Basu D, Das D, Misra K (2016) Farmer suicides in India. Econ Polit Wkly 51(21):61–65
Behere PB, Behere AP (2008) Farmers’suicide in Vidarbha region of Maharashtra state: a myth or reality? Indian
J Psychiatry 50(2):124
Berry HL, Hogan A, Owen J, Rickwood D, Fragar L (2011) Climate change and farmers’mental health: risks
and responses. Asia Pac J Public Health 23(2_suppl):119S–132S
Birthal PS, Negi DS, Khan MT, Agarwal S (2015) Is Indian agriculture becoming resilient to droughts? Evidence
from rice production systems. Food Policy 56:1–12
Brammer H (1990) Floods in Bangladesh: geographical background to the 1987 and 1988 floods. Geogr J
156(1):12–22
Cameron AC, Trivedi PK (1998) Regression analysis of count data. Cambridge University Press, New York, pp
282–283
Chhikara KS, Kodan AS (2013) Micro, small and medium enterprises in India: trends, composition, issues and
challenges. Productivity 54(1):63–76
Dandekar A, Bhattacharya S (2017) Lives in Debt. Econ Polit Wkly 52(21):77–84
Dongre AR, Deshmukh PR (2012) Farmers’suicides in the Vidarbha region of Maharashtra, India: a qualitative
exploration of their causes. Journal of Injury and Violence Research 4(1):2–6
Escaleras M, Anbarci N, Register CA (2007) Public sector corruption and major earthquakes: a potentially
deadly interaction. Public Choice 132(1):209–230
Gruere G, Sengupta D (2011) Bt cotton and farmer suicides in India: an evidence-based assessment. J Dev Stud
47(2):316–337
Guimarães P (2008) The fixed effects negative binomial model revisited. Econ Lett 99(1):63–66
Guiney R (2012) Farming suicides during the Victorian drought: 2001–2007. Aust J Rural Health 20(1):11–15
Herring RJ (2008) Whose numbers count? Probing discrepant evidence on transgenic cotton in the Warangal
district of India. International Journal of Multiple Research Approaches 2(2):145–159
Hilbe JM (2012) Negative Binomial Regression, 2nd edn. Cambridge University Press, Cambridge
Jeromi PD (2007) Farmers' indebtedness and suicides: impact of agricultural trade liberalization in Kerala. Econ
Polit Wkly 42(31):3241–3247
Judd F, Jackson H, Fraser C, Murray G, Robins G, Komiti A (2006) Understanding suicide in Australian farmers.
Soc Psychiatry Psychiatr Epidemiol 41(1):1–10
Kahn ME (2005) The death toll from natural disasters: the role of income, geography, and institutions. Rev Econ
Stat 87(2):271–284
Kellenberg DK, Mobarak AM (2008) Does rising income increase or decrease damage risk from natural
disasters? J Urban Econ 63(3):788–802
Khairnar DR, Bhosale MJ, Jadhav MA (2015) Lack of irrigation facilities, drought conditions and farmers
suicides in Marathwada region, India. American Journal of Rural Development 3(3):74–78
Kreft S, Eckstein D, Melchior I. Global Climate Risk Index (2017) Who suffers most from extreme weather
events? Weather-related loss events in 2015 and 1996 to 2015. Germanwatch Nord-Süd Initiative eV
Loayza NV, Olaberria E, Rigolini J, Christiaensen L (2012) Natural disasters and growth: going beyond the
averages. World Dev 40(7):1317–1336
Manoranjitham SD, Rajkumar AP, Thangadurai P, Prasad J, Jayakaran R, Jacob KS (2010) Risk factors for
suicide in rural south India. Br J Psychiatry 196(1):26–30
Merriott D (2016) Factors associated with the farmer suicide crisis in India. Journal of Epidemiology and Global
Health 6(4):217–227
Ministry of Home Affairs (2014a) Accidental deaths & suicides in India. Government of India, New Delhi
Ministry of Home Affairs (2014b) Crime In India. National crime records bureau. Government of India, New
Delhi
Mishra S (2006) Farmers’suicides in Maharashtra. Econ Polit Wkly 41(16):1538–1545
Mohanakumar S, Sharma RK (2006) Analysis of farmer suicides in Kerala. Econ Polit Wkly 41(16):1553–1558
Nagthan S, Poddar R, Kunnal IB, Basvaraja H, Banakar B (2011) A probe into socio-economic and psycholog-
ical profile of farmers' suicide in Karnataka. Karnataka Journal of Agricultural Sciences 24(2):157–160
Nathan KK (2000) Characteristics of drought in Kerala, India. Water Technology Centre, http://digitalcommons.
unl.edu/droughtnetnews/61/
Parida Y (2016) Economic impact of floods in Indian States. PhD Thesis. Centre for International Trade and
Development, Jawaharlal Nehru University. http://www.isid.ac.in/~epu/acegd2016/papers/YashobantaParida.pdf
Parida Y (2017) Effect of flood on rural agricultural wages in indian states: an empirical analysis. Centre for
International Trade and Development, Jawaharlal Nehru University, https://www.isid.ac.in/~epu/acegd2017
/papers/YashobantaParida.pdf
EconDisCliCha (2018) 2:159–180 179

Parthasarathy G, Shameem (1998) Suicides of cotton farmers in Andhra Pradesh: an exploratory study. Econ
Polit Wkly 33(13):720–726
Planning Commission (2014) Report of the expert group to review the methodology for measurement of poverty.
Government of India, New Delhi
Rajeev M, Vani BP, Bhattacharya M (2011) Nature and dimensions of farmers’indebtedness in India and
Karnataka. ISEC working paper no. 267
Rao PN, Suri KC (2006) Dimensions of agrarian distress in Andhra Pradesh. Econ Polit Wkly 41(16):1546–1552
Sadanandan A (2014) Political economy of suicide: financial reforms, credit crunches and farmer suicides in
India. Journal of Developing Areas 48(4):287–307
Sarma EAS (2004) Is rural economy breaking down? Farmers' suicides in Andhra Pradesh. Econ Polit Wkly
51(21):3087–3089
Shah E (2012) A life wasted making dust: affective histories of dearth, death, debt and farmers' suicides in India.
J Peasant Stud 39(5):1159–1179
Shetty PK (2004) Socio-ecological implications of pesticide use in India. Econ Polit Wkly 39(4):5261–5267
Singh A, Phadke VS, Patwardhan A (2011) Impact of drought and flood on Indian food grain production. In:
Challenges and opportunities in agrometeorology. Springer Berlin, Heidelberg, pp 421–433
Smale M, Zambrano P, Gruère G, Falck-Zepeda J, Matuschke I, Horna D, Nagarajan L, Yerramareddy I, Jones H
(2009) Measuring the impacts of transgenic crops in developing agriculture during the first decade:
approaches, findings and future directions. Food Policy Review 10. International Food Policy Research
Institute, Washington, DC
Sridhar V (2006) Why do farmers commit suicide? The case of Andhra Pradesh. Econ Polit Wkly 41(16):1559–
1565
Stone GD (2011) Field versus farm in Warangal: Bt cotton, higher yields, and larger questions. World Dev 39(3):
387–398
Thomas G, De Tavernier J (2017) Farmer-suicide in India: debating the role of biotechnology. Life Sciences,
Society and Policy 13(8):1–21
Udmale PD, Ichikawa Y, Manandhar S, Ishidaira H, Kiem AS, Shaowei N, Panda SN (2015) How did the 2012
drought affect rural livelihoods in vulnerable areas? Empirical evidence from India. Int J Disaster Risk
Reduct 13:454–469
Vaidyanathan A (2006) Farmers’suicides and the agrarian crisis. Econ Polit Wkly 41(38):4009–4013
Wooldridge JM (2002) Econometric analysis of cross-section and panel data. MIT Press, Cambridge
180 EconDisCliCha (2018) 2:159–180