Livelihood Diversification in Rural India

Abstract

This study aims to ascertain the level of livelihood diversification and examines the socio-economic contexts of livelihood diversification in rural India. Employing data from India Human Development Survey (IHDS-II), 2011-12, an Inverse Herfindhal-Harschman Diversity Index (IHHDI) was calculated incorporating eleven income sources (livelihood strategies). In addition, the contribution of each strategy in households’ total income has been calculated. Furthermore, binary logistic regression was applied to predict the households’ engagement in each livelihood strategy and the likelihood of high IHHDI. Results indicate that the higher livelihood diversifications were found among the households with large size, high dependency, lower social groups, low educated, landless, marginal and small farming, and economically poor. This study also highlights the significance of diversification strategies in raising households’ income. It is suggested that broadened policy support is required to promote diversification for economic development in rural India.

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https://doi.org/10.20896/saci.v9i3.1206
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RESEARCH OPEN ACCESS
Livelihood Diversification in Rural India
Dr Hemant Patidar†*and Dr Satheesh Chothodi¥
Abstract
This study aims to ascertain the level of livelihood diversification and examines the socio-economic
contexts of livelihood diversification in rural India. Employing data from India Human Development
Survey (IHDS-II), 2011-12, an Inverse Herfindhal-Harschman Diversity Index (IHHDI) was calculated
incorporating eleven income sources (livelihood strategies). In addition, the contribution of each
strategy in households’ total income has been calculated. Furthermore, binary logistic regression
was applied to predict the households’ engagement in each livelihood strategy and the likelihood
of high IHHDI. Results indicate that the higher livelihood diversifications were found among the
households with large size, high dependency, lower social groups, low educated, landless, marginal
and small farming and economically poor. This study also highlights the significance of
diversification strategies in raising households’ income. It is suggested that broadened policy
support is required to promote diversification for economic development in rural India.
Keywords: Livelihood Diversification; Livelihood Strategy; Household Income; Rural India
† Assistant Professor, Department of General & Applied Geography, School of Applied Sciences, Doctor Harisingh Gour
Vishwavidyalaya Sagar, Madhya Pradesh, 470003
*Corresponding Author, Email: hpatidargeog@gmail.com
¥Assistant Professor, Department of General & Applied Geography, School of Applied Sciences, Doctor Harisingh Gour
Vishwavidyalaya, Sagar, Madhya Pradesh, 470003, Email: satheeshchothodi@gmail.com

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 33
Introduction
The world has witnessed rapid economic
development in the last few decades.
Nonetheless, the subsistence agriculturists and
farm wage labourers in the rural areas of low-
income countries (LICs) constitute more than
two-thirds of the global poor and food insecure
populace (FAO et al., 2014; IFAD, 2010).
Moreover, due to several environmental risks
and structural and institutional stresses, the
rural people are highly vulnerable to be
livelihood insecure (Eakin, 2005; Harvey et al.,
2014; Morton, 2007; Tschakert, 2007). However,
rural people are often engaged in diverse
livelihoods, mainly in developing countries, to
meet consumption needs, accumulate wealth,
and reduce risk exposure during the crisis
(Cavendish, 2000; Ellis, 1998; Reardon, 1997).
Empirical studies show that the non-farm
livelihood diversification strategies enable rural
households to enhance food security, obtain
improved incomes, increase agricultural
production and cope with environmental
stresses (Babatunde & Qaim, 2010; Barrett et al.,
2001; Bezu et al., 2012; Hoang et al., 2014; Liu
et al., 2008).
Rural India is home to 833 million people (68.84
per cent of the country’s total population) (GOI,
2011). Agriculture is the mainstay of livelihood
and rural economy in India as it employs more
than 50 per cent of the total workforce in India
(GOI, 2018). The climatic extremities, soil
degradation, water scarcity, diminishing
resources, rising cost of agriculture, and
increasing population have triggered livelihood
challenges in rural India in recent decades. A
considerable share of rural people in various
states is livelihood insecure (Patidar, 2019).
Rural livelihood in India is undergoing a rapid
environmental and social-economic
transformation (Chand et al., 2017; Sujithkumar,
2007). Studies demonstrate that farming
activities are gradually developing in India
(Bhandari, 2013; Israr et al., 2014; Khatun & Roy,
2012; Ramchandani & Karmarkar, 2014), and
diversification helps the households in raising
income (Israr et al., 2014; Saleth, 1997; Sharma,
2018; Sharma & Singh, 2019; Sujithkumar, 2007).
Studies on rural livelihood in India have figured
out the determinants of livelihood
diversification. Nonetheless, there is a lack of
empirical studies that have analysed livelihood
diversification in how various livelihood
strategies (income sources) contribute to the
total income of rural households in India. This
study aims to ascertain the level of livelihood
diversification and examines the socio-economic
determinants of livelihood diversification in rural
India. This study begins with a description of the
methodology deployed for this study. Following
this, it discusses the results and discussion of the
study—livelihood diversification and its
contribution to the households’ total income
and socio-economic determinants of livelihood
diversification.
Methodology
Data
In this study, we have used data from IHDS II
having the samples of 27,579 rural households,
collected from 1,503 villages from 35 states and
union territories (UTs). The IHDS is a nationally
representative, multi-topic collaborative
panel survey conducted by the National Council
of Applied Economic Research (NCAER), New
Delhi and the University of Maryland. The data
have been procured online from IHDS website,
https://ihds.umd.edu/ (Desai et al., 2015).
Household Characteristics (Covariates)
In this study, we have selected some household
characteristics as determinants of livelihood
diversification, that include: size of household,
dependency ratio, caste and religion, the highest
adult education in the household, (a proxy of
household educational condition), agricultural
landholding, livestock owned and valued, and
household poverty status as per the estimation
of Tendulkar Committee (GOI, 2009).
Livelihood Strategies and Calculation of
Livelihood Diversity Index
Livelihood diversifications, including a
combination of agriculture and non-agricultural
activities, have been termed livelihood

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 34
strategies. The select livelihood strategies are
crop income (LS1), income from livestock rearing
(LS2), income from agricultural property (LS3),
income from agricultural labour wages (LS4),
income from businesses (LS5), income from
salary (LS6), income from property and pension
(LS7), income from remittances (LS8), income
from Mahatma Gandhi National Rural
Employment Guarantee Act (MGNREGA)
1
wage
labour (LS9), income from Non- MGNREGA wage
labour (LS10), income from cash benefits from
governments through various schemes (LS11).
The livelihood diversity index has been
calculated by following the method of the
Inverse Herfindhal-Harschman Diversity Index
(IHHDI) as suggested by Anderson & Deshingkar
(2015).














n
ij
sa
IHHDI
1
2
1
In the model, each ‘aj’ represents the
proportional contribution of each of the
livelihood activities ‘j’ to the households ‘i’
income. The maximum possible value of this
index is the total number of different income
sources, which is attained if total income is
distributed equally between each source. The
minimum possible value is one when all income
is obtained from one source only.
Statistical Analysis
In order to predict the likelihood of livelihood
diversity and likelihood of engagement in
various livelihood strategies, two kinds of
dependent variables have been recognised.
First, the engagement of a household in each of
the livelihood strategies (LS1 to LS11) have been
made dichotomous (household engaged= 1;
household not engaged= 0), and second, IHHDI
has been made dichotomous (Below 1.338 as
low diversification= 0; above 1.338 as high
diversification= 1). Further, the likelihoods of
household engagement in various livelihood
strategies (LS1 to LS11) and of high IHHDI have
been predicted across various household
characteristics using the logistic regression
model.
 
nn
Logit 











...
1
ln 2211
Where π indicates the probability of the engagement of households in each of the livelihood
strategies (LS1 to LS11) and the likelihood of high IHHDI, α is the Y intercept, βn is the regression
coefficients associated with the reference groups, and the Xn are a set of predictors.
Results and Discussion
Livelihood Diversification and its Contribution
to the Households’ Total Income
Rural households are often engaged in multiple
portfolios to meet their basic needs. The
motivation for livelihood diversification for the
households lies in the attempt to support the
1
Mahatma Gandhi National Rural Employment Guarantee
Act (MGNREGA) is an Indian labour law and livelihood
security programme, passed in 2005 with an aim to
guarantee the ‘right to work’ and enhance livelihood
livelihood and well-being (Gautam & Anderson,
2016). In this study, among all the livelihood
strategies, crop income (LS1) contributed nearly
21 per cent of the total rural income, while this
strategy was performed by about 56 per cent of
rural households at varying scale with an average
income of INR 48041 per annum, followed by
security in rural areas by providing at least 100 days of
wage employment in a financial year to every household
whose adult members volunteer to do unskilled manual
work (Government of India, 2016).

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 35
Non-farm Non-NREGA labour (LS10) (19.7 per
cent), agricultural labour (LS4) (17 per cent).
Income from Salary (LS6), a regular and high
return livelihood strategy, contributed 12.5 per
cent. However, nearly 20 per cent of households
received income from this strategy. Businesses
(LS5) and Remittances (LS8) contributed 8.6 and
7.2 per cent respectively. Income from non-farm
activities in the form of liquid cash may be crucial
to intensify agricultural and purchase the farm
inputs, improve farm practices and hire wage
labour and enhance farm productivity (Ellis et al.,
2003; Ellis & Mdoe, 2003). The combination of
farm and non-farm livelihoods may be highly
complementary that provide positive
strengthening (Martin & Lorenzen, 2016).
MGNREGA, a rural employment programme also
contributed 2.6 per cent to the total income
which made a significant contribution to
household income during the seasonal
unemployment in rural India. Rural households
(39.80 per cent) were also directly assisted by
governments with several cash benefits under
various programs (LS11), which contributed 3.4
per cent to the household income with an
average cash incentive of INR 3471.88.
Studies have evidenced that relative socio-
economic distinctions are important
determinants in livelihood diversification and
their contribution to income and well-being
(Gautam & Anderson, 2016; Jiao et al., 2017;
Martin & Lorenzen, 2016). The results show that
large households were found to be more diverse
(1.405) than medium (1.335) and small size
(1.296). However, crop income (LS1) was the
major contributors (27 per cent) in the total
income of large households which was higher
than medium (19.1 per cent) and small
households (20.6 per cent). The reason might be
the small fragmented agricultural land and
higher number of livestock which were survived
by relatively larger households. While, non-
MNREGA wage labour (LS 10) was the largest
income contributor (21.7 per cent) for medium
size households. The small households got their
income through agricultural labour (LS4) and
remittances (LS8) which was comparatively
higher than a large household. The contribution
from governments through various programmes
and schemes (LS11) decreased with increasing
household size (Table 1). The households with a
medium dependency ratio recorded higher
diversification than others (Table 1). The
contributions from farm strategies (LS1, LS2 and
LS3), farm labour (LS4) and salary (LS6) were
found decreasing with increasing dependency
ratios, while, the contributions from other
strategies (LS8, LS9, LS10 and LS11) were
increasing with increasing households size (Table
1).
Livelihood diversification was recorded to be
varied across social and religious groups. The
results of this study reveal that Scheduled Castes
(SCs) (1.478) had the highest livelihood
diversification, followed by Scheduled Tribes
(STs) (1.386), Other Backward Classes (OBCs)
(1.315), Muslims (1.314), Brahmins (1.305) and
other upper castes (1.199). SCs, due to the poor
socio-economic conditions and being largely
unskilled, were engaged in agricultural labour
(LS4) and non-farm labour (LS10) which
contributed together more than 52 per cent to
their total income. Likewise, STs too made nearly
45 per cent of total income from LS4 and LS10,
although a substantial share of income of STs
came from crop income (LS1). Patidar and Singh
(2010) found that tribal in South-East Rajasthan
were highly engaged in primitive economic
activities. However, these social groups, being
largely unskilled, took part in non-farm
economic pursuits, despite low wages, in order
to reduce vulnerability to poverty as these
strategies were available round the year and
were the mean of regular income (Rigg, 2006).
On the other hand, forward castes (26.8), other
religious groups (29.6), OBCs (23.5) and
Brahmins (20.2) earned the major share of their
income from crop income (LS1), salary (LS6),
businesses (LS5) and remittances (LS8) with the
smaller shares of contribution from farm and
non-farm labourers (LS4 & LS10).

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 36
Education is a critical explanatory cause of
cumulative processes of rural differentiation
(Francis & Hoddinott, 1993). Occupational
opportunities vary as per the skills and education
(Ellis, 1998). The educational and skill constraints
leads to poverty (Carcillo et al, 2017), and
economically poor undergoes exclusion from the
highly remunerated occupations (Dercon &
Krishnan, 1996). The results indicate the
uneducated (1.387), followed by primary (1.369)
and secondary educated (1.351), recorded
higher livelihood diversification than others. The
households having the highest adult education
above graduation got 31.3 per cent income from
salary (LS6) and the share of income from this
livelihood strategy decreased with decreasing
level of education (Table 1). The uneducated and
below primary education household having the
highest adult education as zero and low earned
the larger share of their income from agricultural
labour (LS4), non-farm labour (LS10) and crop
income (LS1). The educated households also
catered good income from Businesses (LS5) and
Property and pensions (LS7) that steadily
decreased with declining level of education
(Table 1).
It is well accepted that despite the increase in
non-farm economic portfolios, livelihoods and
well-being are very much dependent on
agriculture and linked to land ownership. Land
poverty is another constraint in non-farm
employment by limiting cash revenues from
farming needed to start non-farm businesses or
support migration (Reardon et al., 2000). Nearly
Table 1: Contribution of Various Livelihood Strategies (LS) in the Households’ Total Income
Background Characteristics
Percentage of contribution from various livelihood Strategies in households’ total income
Mean
IHHDI
LS1
LS2
LS3
LS4
LS5
LS6
LS7
LS8
LS9
LS10
LS11
Household size (No of members)
Small (3 & below)
20.6
2.1
03.7
17.6
6.5
10.2
3.9
11.7
2.0
15.6
5.5
1.296
Medium (4 to 6)
19.1
3.4
01.1
18.7
9.3
13.4
2.1
5.3
3.3
21.7
2.4
1.335
Large (7 & more)
27.0
3.7
01.8
11.4
9.5
13.2
2.6
6.1
1.7
19.8
3.2
1.405
Dependency ratio
Low (Below 50%)
20.8
3.9
1.9
18.0
9.3
15.6
2.4
5.6
2.6
17.5
2.0
1.340
Medium (50% to 79%)
23.0
3.6
2.1
15.8
8.5
11.8
3.1
5.9
2.4
20.3
3.4
1.365
High (80% & above)
20.0
2.0
1.9
16.7
7.9
09.7
2.7
9.8
2.8
21.4
4.8
1.318
Caste and Religion
Brahmin
20.2
6.8
4.2
2.8
11.2
18.9
8.3
11.2
1.5
11.4
3.3
1.305
Forward castes other than Brahmins
26.8
9.7
3.1
10.4
8.6
14.8
5.5
7.3
1.9
9.4
2.1
1.199
Other Backward Classes (OBCs)
23.5
4.0
2.1
15.9
10.2
10.7
2.0
8.1
2.8
16.9
3.6
1.315
Scheduled Castes (SCs)
13.9
-0.8
0.8
23.4
5.6
12.7
1.8
5.7
2.7
29.1
5.0
1.478
Scheduled Tribes (STs)
21.6
1.3
1.3
25.6
4.4
12.1
1.4
3.9
5.4
19.5
3.4
1.386
Muslim
17.8
-0.9
1.6
14.7
13.1
11.4
1.3
9.7
0.8
28.8
1.3
1.314
Christian, Sikh, Jain and others
29.6
4.7
4.4
3.5
9.6
17.7
6.4
9.4
0.4
11.8
1.8
1.110
Highest Adult Education
No education
21.2
0.1
2.4
22.1
4.9
4.6
0.7
11.6
2.8
22.8
6.5
1.387
Below primary
16.6
-3.2
3.3
31.0
6.0
6.2
0.8
7.1
5.0
23.0
4.0
1.369
Primary but below secondary
21.4
3.3
1.2
18.4
8.4
9.2
1.6
6.5
3.3
24.0
2.7
1.351
Secondary but below high secondary
22.3
5.3
1.9
12.7
10.6
15.8
4.3
5.4
2.1
17.6
1.7
1.271
High secondary but below graduation
23.1
5.9
2.1
10.3
11.6
20.5
4.5
5.1
1.5
13.1
2.1
1.301
Graduation & above
18.5
7.7
2.6
5.0
12.5
31.3
7.8
5.5
0.7
6.0
2.3
1.300
Agriculture landholding
No landholding
0.0
0.1
0.0
24.9
11.4
16.4
2.3
6.8
2.7
29.9
4.8
1.406
Marginal (Below 1 hectare)
39.2
5.7
3.7
14.4
6.0
7.8
2.0
5.1
2.8
11.1
2.2
1.236
Small (1 to 2 hectares)
30.7
7.8
2.3
8.7
7.6
10.1
1.9
9.5
2.4
17.0
2.0
1.290
Semi-medium (2-4 hectares)
38.1
1.1
4.2
2.2
7.8
14.2
5.3
9.0
2.2
13.3
2.7
1.330
Medium (4 to10 hectares)
18.2
4.4
1.8
5.3
6.5
14.8
7.0
19.0
4.1
15.7
3.1
1.463
Large (Above 10 hectares)
25.3
3.3
2.2
19.3
7.7
11.2
3.0
6.8
2.2
15.5
3.5
1.373
Livestock owned /value
No livestock
11.4
0.6
2.2
20.9
10.5
15.3
3.0
6.7
2.2
22.7
4.0
1.369
Law value (below INR 20000)
20.7
-0.8
1.9
18.1
7.8
10.3
2.0
8.9
4.4
23.2
3.5
1.442
High value (INR 20000 & above)
34.9
9.8
1.7
10.5
6.5
10.2
2.9
6.8
1.9
12.5
2.4
1.211
Household by poverty line
Non-poor
21.4
3.9
2.1
15.6
9.3
13.7
3.2
7.8
2.2
17.7
2.9
1.310
Poor
19.4
0.0
1.5
22.6
5.5
7.5
0.6
5.0
4.4
27.7
5.5
1.453
Total
21.0
3.1
2.0
17.0
8.6
12.5
2.7
7.2
2.6
19.7
3.4
1.338
Source: Computed from IHDS II, 2011-12 (Desai et al., 2015)

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 37
38 per cent rural households reported not to
own landholding and therefore, did not obtain
any crop income (LS1), and depended largely
upon agricultural labour (LS4) and non-farm
labour (LS10). Nevertheless, the highest
livelihood diversity (1.463) was found among
households with medium scale farming,
followed by the landless households (1.406),
depicting an inverse convex line if arranged as
per landholding and livelihood diversity.
Agricultural land is crucially important for
households including poor as well as better off
households (Martin & Lorenzen, 2016). The
households with marginal farms produced a
major share of income from crop income (LS1),
and recorded the lowest diversification index
(1.236), while for households with small and
medium farming land, salary (LS6) constituted a
major share of total income (Table 1). It is worth
mentioning that several households, which did
not own agricultural landholding, drew income
from salary (LS6) and therefore, 16.4 per cent of
income for households without landholding was
obtained from LS6.
The synergies between agriculture and livestock
rearing are quite common as the crop
residues are used as livestock feeds and the
animal manures are widely used as natural
fertilisers. The interactive relation between
these livelihood strategies reduces the cost and
enhances livelihood insurance among rural
households. Livestock rearing (LS2) contributed
slightly higher than 3 per cent to total rural
income. Nonetheless, about 58 per cent earned
an average of INR 6689.10 per annum from this
strategy. Many households (about 25 per cent)
had livestock of value below INR 20000, yet that
provided them a smaller share to the income.
The livelihood diversification was found the
highest (1.442) among households having
livestock value below INR 20000 households
than others. The Households with livestock of
value above INR 20000 recorded the lowest
diversification index (1.211), and obtained
nearly 45 per cent of total income from these
synergic livelihood strategies (LS1 and LS2). On
the other hand, the households without and low
value livestock, Non-farm labour (LS10),
agricultural labour (LS4) and Salary (LS6) made
the major contributions along with other
livelihood strategies.
The poorer households recorded high livelihood
diversification (1.453) than the non-poor
(1.310). Over 50 per cent of total income of
poorer households was acquired from only Non-
farm labour (LS10) and farm labour (LS4), while
obtaining small shares of income from other
strategies. However, the non-poor households
still strived to diversify their livelihood for the
additional insurance from natural and economic
risks. The distress causing low and irregular
income is the primary reason for higher
diversification among poor households.
Socio-Economic Determinants of Livelihood
Diversification
Table 2 presents the results from binary logistic
regression. The results reveal that in reference
to small households, medium (OR=1.136,
p<0.001) and large households (OR=1.460,
p<0.001) are more likely to have high livelihood
diversification. Likewise, the likelihood of the
households’ involvement in each of the
livelihood strategies, except LS3, is higher among
medium and large households than the smaller
households (Table 2). The households having a
high dependency ratio are likely to be less
diversified (OR=0.784, p<0.001), while
households with medium dependency ratio
(OR=1.010, ns) is likely to be slightly higher than
households with low dependency ratio. The
households with high dependency ratio are likely
to be less involved in all the livelihood strategies
except the LS3, LS7, LS8 and LS11 than
households with low dependency (Table 2).
Caste and religious background is also the
significant predictor. In reference to Brahmins,
SCs (OR=1.304, p<0.001), STs (OR=1.095, ns) and
OBCs (OR=1.069, ns) are likely to have high
livelihood diversification, while Christians, Sikhs
and Jains (OR=0.658, p<0.001), Muslims
(OR=0.807, p<0.01) and Forward castes
(OR=0.853, p<0.05) are likely to be less
diversified than Brahmins. The lower social
groups are more likely to involve in crop income
(LS1), farm labour (LS4) and non-farm labour
(LS10), whereas the higher social strata are likely
to involve in more remunerative livelihood
strategies such as businesses (LS5), salaried job

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 38
(LS6), property and pensions (LS7) and
remittances (LS8) (Table 2). Education is
negatively associated with livelihood diversity. In
reference to households having no education,
households with the higher education— up to
primary (OR=0.896, p<0.01), secondary
(OR=0.791, p<0.001), high secondary (0.905,
p<0.05) and graduation and above (OR=0.857,
p<0.01) are likely to be less diversified.
Nonetheless, the high remunerative livelihood
strategies that require less physical works (LS5,
LS6 and LS7) and crop income (LS1) are more
preferred strategies with an increasing level of
education. Contrary to this, the labour-intensive
and low remunerative strategies (LS4, LS8, LS9
and LS10) are left to be involved by the
households without education and low
education.
Rural households which have agricultural
landholding have better livelihood
diversification options than the households
without any landholding. In reference to
households with no landholding, marginal
(OR=1.759, p<0.001), small (OR=1.904, p<0.001),
semi-medium (OR=2.275, p<0.001), medium
(OR=2.785, p<0.001), and large farmers
(OR=1.935, p<0.001), are likely to be more
diversified. Nonetheless, many of the salaried
households do not have landholding and
therefore, salaried job (LS6) is more preferred by
households without any landholding. At the
same time, the likelihood of involvement in farm
labour (LS4) and non-farm labour (LS10) also
decrease with increasing possession of
agricultural landholding (Table 2). The
households having livestock of value below INR
20000 (OR=1.310, p<0.001) are likely to be highly
diversified than households with no livestock.
But the likelihood of high diversification remains
insignificantly low for the households with high-
value livestock (OR=0.985, ns). The households
without livestock are well involved in salary (LS6)
and businesses (LS5) which provide them
sustained income, and therefore, despite the
absence of livestock, they are less vulnerable. On
the other hand, households with low-value
livestock are more vulnerable to livelihood
security and, therefore, more involved in
diversified strategies. Households with highly
valued livestock go for less diversification as they
receive better remunerative outcomes from
crop income (LS1) and remittances (LS8). The
poor households (OR=1.193, p<0.001) are likely
to be more diversified than the non-poor. This
indicates how the poor households, being more
vulnerable to diversity more than non-poor.
Poor households are likely to involve more in less
remunerative strategies (LS1, LS4, LS9, LS10)
than non-poor households, while high
remunerative strategies (LS5 and LS6) are well
preferred by the non-poor. The results of this
study accord to the theory that poverty causes
distress diversification.
Conclusion
The results reveal that diversification strategies
are commonly a combination of agriculture and
non-agriculture activities, including remittances
and governments’ financial supports. This study
highlights that diversification increases
household income. The wage labours, both farm
labour and non-farm labour, constitute the
major part of the income of the lower social
castes, low educated, landless, marginal and
small farming households and economically
poor. The households with better social and
economic advantages have also been found
striving for diversification in order to accumulate
wealth to mitigate against the future natural and
economic risks. Despite rapid economic
development and several livelihoods raising
efforts, the livelihood conditions in rural India is
still much disgraceful due to the growing
population and climatic extremities. This study
highlights the positive aspects of the
diversification strategies for rural households for
raising their income. Therefore, it is suggested
that the poverty alleviation programme must be
extended to support and encourage livelihood
diversification and inclusive mobility across
livelihood to increase the rural income.

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 39
Table 2: Logistic Regression Model Predicting the Engagement of Households in Various Livelihood Strategies and the Likelihood of High Livelihood
Diversification (IHHDI) Across Various Household Characteristics
Household Characteristics
Likelihood of engagement of households in various livelihood strategies (Odds Ratio)
IHHDI
(Odds
Ratio)
LS1
LS2
LS3
LS4
LS5
LS6
LS7
LS8
LS9
LS10
LS11
Household size (no of members)
Small®
Medium
1.934*
**
1.775
***
0.614*
**
1.384*
**
1.766*
**
1.883*
**
1.013 ns
0.560*
**
1.155
***
1.758***
1.558***
1.136
***
Large
2.435*
**
2.944
***
0.647*
**
1.348*
**
2.942*
**
3.039*
**
1.278**
0.706*
**
1.044
2.401***
2.513***
1.460
***
Dependency ratio
Below 50% ®
50 to 79%
0.954n
s
0.969
ns
1.080
ns
0.871*
**
0.922
ns
0.771*
**
1.348**
*
1.001
ns
0.933
ns
1.011 ns
1.421***
1.010
80% & above
0.770*
**
0.779
***
1.141*
*
0.705*
**
0.674*
**
0.501*
**
1.015 ns
1.154*
**
0.808
***
0.688***
1.352***
0.784
***
Caste and Religion
Brahmins ®
Forward caste other than
Brahmins
1.360*
0.923
ns
0.732*
**
1.749*
**
0.842
ns
1.022
ns
1.208 ns
0.709*
**
1.103
ns
0.806*
0.711***
0.853
*
OBCs
1.767*
**
0.864
ns
0.668*
**
3.194*
**
1.063
ns
0.985
ns
0.735**
0.815*
1.425
***
1.127 ns
1.227**
1.069
ns
SCs
1.684*
**
0.691
*
0.604*
**
4.134*
**
0.627*
**
1.158
ns
0.635**
*
0.672*
**
2.254
***
1.687***
1.572***
1.304
***
STs
2.271*
**
0.815
ns
0.746*
*
3.857*
**
0.733*
1.084
ns
0.686*
0.500*
**
2.353
***
1.301**
1.117 ns
1.095
ns
Muslims
1.774*
**
0.483
***
0.718*
*
1.871*
**
0.954
ns
0.889
ns
0.469**
*
0.826*
1.085
ns
1.582***
0.627***
0.807
**
Christians, Sikhs, Jains and
others
1.424
ns
0.828
ns
0.834
ns
0.643*
0.650*
1.003
ns
0.918 ns
0.876
ns
0.561
**
0.922 ns
0.683***
0.658
***

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 40
Highest Adult Education
No education ®
Below primary
1.092
ns
1.023
ns
1.033
ns
1.084
ns
1.303*
0.981
ns
2.133**
*
0.688*
**
1.035
ns
1.205***
0.764***
0.988
ns
Primary but below
secondary
1.526*
**
1.157
ns
0.932
ns
0.732*
**
1.454*
**
1.450*
**
3.808**
*
0.577*
**
0.930
ns
1.354***
0.705***
0.896
**
Secondary but below high
secondary
1.381*
*
0.904
ns
1.082
ns
0.492*
**
1.542*
**
1.995*
**
6.804**
*
0.513*
**
0.699
***
0.974 ns
0.573***
0.791
***
High secondary but below
graduation
1.267*
0.882
ns
1.036
ns
0.406*
**
1.835*
**
2.875*
**
8.462**
*
0.538*
**
0.618
***
0.797***
0.553***
0.905
*
Graduation & above
1.344*
*
0.818
ns
1.338*
**
0.243*
**
2.218*
**
4.636*
**
13.515*
**
0.455*
**
0.531
***
0.483***
0.447***
0.857
**
Agriculture landholding
No landholding ®
Marginal (Below 1
hectare)
-
2.313
***
-
1.111*
0.860*
0.625*
**
1.146 ns
1.131*
1.367
***
0.756***
1.136*
**
1.759**
*
Small (1 to 2 hectares)
-
2.464
***
-
0.511*
**
0.940
ns
0.754*
**
0.980 ns
1.606*
**
2.206
***
0.929 ns
1.061
ns
1.904**
*
Semi-medium (2-4
hectares)
-
3.087
***
-
0.410*
**
1.006
ns
0.851*
1.444**
*
1.504*
**
1.604
***
0.958 ns
1.323*
**
2.275**
*
Medium (4 to10 hectares)
-
2.515
***
-
0.265*
**
0.733*
0.728*
*
1.566**
*
2.543*
**
3.363
***
1.017 ns
0.923
ns
2.785**
*
Large (Above 10 hectares)
-
1.926
***
-
1.235*
**
1.187*
0.742*
**
1.173 ns
1.601*
**
1.107
0.827***
1.179*
**
1.935**
*
Livestock owned /value
No livestock ®
Low value (below INR
20000)
2.678*
**
-
0.763*
**
1.282*
**
1.003
ns
0.851*
*
0.742**
*
1.593*
**
1.420***
1.288*
**
1.214*
**
1.310**
*
High value (INR 20000 &
above)
4.572*
**l
-
0.734*
**
0.945
ns
0.864*
0.798*
**
0.943 ns
1.254*
**
1.119**
0.768*
**
1.080*
0.985 ns
Household by poverty
Non-poor ®

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 41
Poor
1.249*
*
0.819
**
0.85
9*
1.491*
**
0.744*
**
0.828*
**
0.449**
*
0.735*
**
1.202***
1.206*
**
1.106*
*
1.193***
Constant
0.000
0.091
0.00
0
0.209
0.071
0.079
0.011
0.323
0.126
0.158
0.361
0.259
Significance level- *** P<0.001, **P<0.01, * P<0.05; ®- reference category; ns - not significant

Patidar & Chothodi. Space and Culture, India 2021, 9:3 Page | 42
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Conflict of Interest
We confirm that we do not have any potential
conflict of interest at the personal level, nor with
any financial and other relationship with our
own institute or any other organisation that may
influence our or their work.
Acknowledgements
The authors are grateful to the National Council
of Applied Economic Research (NCAER), New
Delhi, and the University of Maryland for the
India Human Development Survey (IHDS-II),
2011-12, which we have procured from the
concerned website and the same have been
cited in the study. Besides, we are grateful to the
two anonymous reviewers for the careful
reading of our manuscript and their insightful
comments and suggestions, which have
significantly improved the manuscript.
Author Contribution Statement
While the first author did conceptualisation,
designing the study, and data curation and
drafting of original work, both the authors
reviewed and edited the final draft.