مرکزی صفحہ International Journal of Social Economics Determinants of financial inclusion in rural India: does gender matter?

Determinants of financial inclusion in rural India: does gender matter?

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جلد:
47
رسالہ:
International Journal of Social Economics
DOI:
10.1108/ijse-07-2019-0439
Date:
June, 2020
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Determinants of financial inclusion
in rural India: does gender matter?

Determinants
of financial
inclusion

Simrit Kaur
Shri Ram College of Commerce, University of Delhi,
New Delhi, India, and

Cheshta Kapuria
Faculty of Management Studies, University of Delhi, New Delhi, India

747
Received 8 July 2019
Revised 28 January 2020
Accepted 28 April 2020

Abstract
Purpose – Since finance is an efficacious instrument for economic development, social inclusion and women
empowerment, the present paper examines the determinants of accessing institutional and non-institutional
finance across male- and female-headed households in rural India.
Design/methodology/approach – Multinomial logistic regression is applied for categorizing households’
accessing finance in four categories, namely Only Institutional Finance (IF), Only Non-institutional Finance
(NIF), Both Sources of Finance (BF) and Neither Source of Finance (N). Both household and state-level
determinants have been analysed. Household data set is sourced from the Situation Assessment Survey (NSSO,
70th round) and state-level data sets from Basic Road Statistics 2016, Agricultural Statistics at a Glance 2016,
Rainfall Statistics of India 2014, database on Indian Economy RBI and Census 2011. Econometric regressions
have been evaluated for female-headed households (FHHs), male-headed households (MHHs) and overall
pooled households (HHs).
Findings – Four important findings emerge. First, FHHs have a lower probability of accessing IF and a higher
probability of accessing NIF vis-a-vis MHHs. Second, in general, education levels, monthly household
consumption expenditure, land size holding, irrigated area and penetration of scheduled commercial banks
favourably influence FHHs accessing IF. Third, FHHs belonging to socially disadvantaged castes have a lower
probability of accessing IF. Fourth, a substantial proportion of FHHs acce; sses neither IF nor NIF relative
to MHHs.
Practical implications – The paper thoroughly addresses the issue of accessing finance by FHHs and
MHHs, which will further assist policymakers in formulating holistic financial policies for rural India.
Social implications – The paper recommends increasing women’s access to financial services as an effective
tool for reducing poverty and lowering income inequality in rural India.
Originality/value – This article contributes to the scant empirical literature on finance and gender.
Keywords Gender, Institutional finance, Non-institutional finance, Infrastructure, Multinomial logistic
regression, Rainfall, Rural India, Economic policy and development
Paper type Research paper

1. Introduction
Finance is an efficacious instrument for economic development. Access to finance is often
empowering, especially for women, as it leads to broader economic and social inclusion (Park
and Mercado, 2015). Internationally, a renewed impetus towards financial inclusion (FI)
started majorly after the Global Financial Crisis [1] of 2008. This initiative also had its echo in
India, with the government exploring several ways and means to ensure greater inclusion of
the “financially excluded” segments of the society.
Globally, the indicators of FI have shown improvements. However, the facets related to the
gender gap in FI have a long way to go, particularly in developing economies. Despite
controlling for several aspects such as income, education, employment status, rural residency
and age, gender remains significantly related to access and usage of financial services
(Demirguc-Kunt et al., 2018). Figure 1 shows the gender gaps in developing economies in
terms of financial account ownership during three Findex [2] rounds.
JEL Classification — J16, G21, E60

International Journal of Social
Economics
Vol. 47 No. 6, 2020
pp. 747-767
© Emerald Publishing Limited
0306-8293
DOI 10.1108/IJSE-07-2019-0439

IJSE
47,6

748
Figure 1.
Financial account
ownership in
developing economies
by gender

An important observation that emerges from Figure 1 is the substantial increase in account
ownership [3] across males and females over the period 2011–2017. While the proportion of
males having financial accounts increased from 47% in 2011 to 67% in 2017, the
corresponding increase for females was from 37 to 59%. However, despite the increase, the
gaps between ownership of account across genders did not change much, since the gaps
between gender participation have remained close to 8 percentage points.
Recent years have seen “”feminization” of Indian agriculture due to the widespread
migration of men from rural to urban areas (Economic Survey, 2017–2018). Data reveals that
the number of women in each sub-sector of the rural economy, namely cultivators,
entrepreneurs and labourers, has been increasing, presently constituting 56% (ILO, 2017) of
the total agricultural workforce in the country. Despite this, rural women are financially
constrained in comparison to men of similar socio-economic conditions (Ghosh and
Vinod, 2017).
It has been widely recognized that both male and female farmers who have access to welldesigned credit, savings and insurance services can avail the benefits of structured financing
models [4] to generate income; can invest in uncertain yet profitable enterprises along with
their asset portfolios; can access markets in an effective way; and can adopt efficient
strategies to stabilize their food consumption (Zeller et al.,1997). Despite this significantly
accepted perception, most of the rural financial programmes target the male-headed
households (MHHs) as their intended client. Further, according to the All-India Debt and
Investment Survey (NSSO, 2014) report, interest rates levied, as well as, paid by femaleheaded households (FHHs) are, on an average, higher than those paid by MHHs. Barriers that
restrict women’s access to finance have been explained by factors such as lower financial
literacy (Lusardi and Tufano, 2009), institutional discrimination (Fletschner, 2009), land titles
(Agarwal, 2003), differential conduct under law or customs that may constrain women to
enter contracts under their own name, which includes opening of a bank account (Asli and
Klapper, 2013), and financial product inflexibility (Fletschner and Kenney, 2014).
Though there is a wide array of research on financial inclusion and its relationship with
economic growth, yet its association with gender equality remains mostly unexplored. While
several cross-country studies on this aspect have been carried out earlier, very few studies
have addressed the role of gender in financial inclusion at country- or within-country-level
analysis (particularly for developing economies) in a comprehensive manner. Therefore, this
study aims to measure the facets of gender gaps in rural India, pertaining to the financial
sector. To the best of our knowledge, this is the only study that thoroughly addresses the
issue of access to finance by FHHs for rural India.

The underlined objective of the study is to examine the determinants of access to
institutional and non-institutional [5] sources of finance across MHHs and FHHs in rural
India. Based on household level, Situation Assessment Survey (SAS) data (National Sample
Survey, 2013), we examine the household, as well as, state-level attributes impacting
institutional and non-institutional sources of finance. Select state variables included in the
study are infrastructure (proxied by road density and irrigation), climate change as captured
through rainfall deviation and financial development indicated by penetration of scheduled
commercial bank branches in rural India. This paper is organized as follows. In Section 2, we
present a synopsis of the relevant literature on FI with reference to gender. Section 3
discusses the methodology, assessment of the econometric model and data sources. In section
4, we discuss the distribution of access to various sources of finance (i.e. institutional and noninstitutional finance) by FHHs and MHHs. Section 5 analyses the results related to household
and state-level drivers of FI. We specifically analyse the impact of gender on access to finance.
Section 6 concludes the study from a broad policy perspective.

2. Review of literature
In this section, literature is reviewed from two perspectives, namely FI and thereafter its
relationship with gender.
2.1 Financial inclusion
The study contributes to the existing literature in few distinct ways. Several studies have
suggested that economies with higher financial intermediation tend to develop faster (Beck
et al., 2007). It can favour disadvantaged and poor people allowing them to increase their
income and the probability of being employed (Bruhn and Love, 2014). In the absence of
inclusive financial systems, poverty traps could emerge and hamper economic development
(Allen et al., 2016; Tarsem, 2018). Specifically, FI influences economic growth and
development in the following ways. First, individuals’ access to the formal financial
system is more likely to increase aggregate savings. With a rise in savings, the potential to
raise investible resources improves which positively affects long-run economic growth
(Aghion et al., 2009; Ghosh and Vinod, 2017). Second, with an increment in the investible
surplus, FI expands the dispersion of credit, further facilitating financial institutions to
modify their loan portfolios. Consequently, lending to firms that were earlier financially
excluded might also lessen the average credit risk of the loan portfolio, which favourably
impacts the recycling of funds. The net effect is an overall increase in aggregate economic
activity (Gwalani and Parkhi, 2014; Ghosh and Vinod, 2017). Third, higher levels of FI enable
greater institutionalization of the economy, allowing use of interest rate as a primary policy
tool, with implications for economic growth (Cecchetti and Kharroubi, 2012). FI also favours
women empowerment (Swamy, 2014) and contributes to financial stability.
FI varies greatly in India between states and households. India has a coexistence of dual
(institutional and non-institutional) financial systems in the rural credit market. The presence
of a non-institutional credit market alongside strong institutional credit markets continues to
draw the attention of several development economists (Kumar et al., 2010, 2017; Kumar, 2013;
Pal and Naha, 2015; Tripathi, 2017). Notably, only a few studies such as by Kumar et al. (2017)
and Tripathi (2017) have probed inequity towards accessing institutional credit based on
household characteristics such as gender and social groups.
Most studies in India have been historically conducted with bank-level data (Burgess and
Pande, 2005; Kumar, 2013; Gunther, 2017), as well as survey data that provides interesting
understandings of the unbanked. For instance, Basu and Srivastava (2005), using the Rural
Finance Access Survey, show that rural banks mainly cater to the rich, while Ghosh and

Determinants
of financial
inclusion

749

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47,6

Vinod (2017) analyse the All-India Debt and Investment Survey, to highlight that women are
disadvantaged in accessing institutional credit. Gender too is a crucial factor of a broader
macroeconomic outcome, including economic development (Duflo, 2012; Ghosh and Vinod,
2017). The present study attempts to analyse household characteristics in accessing
institutional and non-institutional credit sources.

750

2.2 Financial inclusion and the gender gap
For women, equal access to financing initiates a “virtuous spiral of social, economic, and
political empowerment” (Cheston and Kuhn, 2002; Rastogi and Ragabiruntha, 2018).
A variation has been observed in the extent to which institutions reach out to women and the
conditions under which they tend to do so. However, women are at a disadvantage when an
institution does not fund specific activities that are typically run by them (FAO, 2011).
Women also have limited control over resources accepted as collateral. Further, loans to
women are smaller than those granted to men for similar activities (Fletschner, 2009; Duflo,
2012; Asli and Klapper, 2013; Ghosh and Vinod, 2017; Gunther, 2017). In support of this
argument, quantitative studies in rural Paraguay, Caribbean, Sub-Saharan Africa and South
Asia find that rural women are more likely to be credit constrained than men (Beck et al., 2009;
Fletschner, 2009; Fletschner and Mesbah, 2011; Presbitero et al., 2014).
In the Indian context, women account for two-fifths of the workforce, yet the access to
finance among women is low (Ghosh and Vinod, 2017; Singh and Pattanaik, 2019). Estimates
indicate that while close to 63% of Indian men had an account in 2014, the comparative figure
for Indian women was 43% (Demirguc Kunt et al., 2015). According to the IFC Annual
Report (2014), for women-owned enterprises, out of the total credit requirement of $158bn,
only $42bn was provided by formal sources. Thus, a significant gap of $116bn remained
unfinanced from formal services which represent 73% of the total demand. Studies show that
higher access to formal credit leads to greater access to farming resources that improve
agricultural production on women farms in developing countries by as much as 4%.
Additional income accrued to women could help in empowering them and assist in having
access to the formal financial system (Food and Agricultural Organization, 2011).
Based on the existing literature, we have selected household and state-level indicators that
determine FI for women in rural India. Table 1 presents the explanatory variables of FI used
in our model estimation.

3. Data and methodology
In Section 3, we provide the data utilized and methodology adopted in the present study.
3.1 Methodology and model specification
In order to evaluate the determinants of FI across different credit sources in rural India, we
adopt the multinomial logistic regression. For this purpose, households’ participation in
financial credit has been categorized under the following four categories:
(1) Access only the Institutional Finance (IF).
(2) Access only the Non-Institutional Finance (NIF).
(3) Access Both Institutional and Non-Institutional Finance (BF).
(4) Access Neither Institutional nor Non-Institutional Finance (N). These households
have been considered to be financially excluded and also represent the reference
category for our econometric analysis.

Category
1
2

Indicator

Literature review

Dependent variable
Financial
Access to institutional
inclusion
credit in a household
Access to institutional
credit for females

Expected
sign

Kumar et al. (2017)
Demirguc-Kunt et al. (2018), Ghosh and
Vinod (2017), Bruhn and Love (2014), Duflo
(2012)

Independent variable
Household
Female-headed household Kumar et al. (2017), Fletschner and Kenney
indicators
(2014), Swamy (2014), Fletschner (2009)
4
Age of the household head Rastogi and Ragabiruntha (2018), Kumar
et al. (2017), Pal and Naha (2015), Kumar
5
Level of education
(2013), Kumar et al. (2010), Khandekar and
6
Social group
Faruqee (2003) and Deshpande (2000)
7
Religion group
8
Land size holding
9
Household size
10
Consumption patterns
11 State
Physical infrastructure
Binswanger and Khandeker (1995)
indicators
(road and irrigation)
12
Rainfall (exogenous
Chuange (2019), Binswanger and
climate shock)
Khandeker (1995)
13
Financial infrastructure
Kumar (2013), Burgess and Pande (2005),
(commercial bank
Basu and Srivastava (2005), Binswanger
penetration)
and Khandeker (1995)
Source(s): Authors’ representation based on the previous literature
3

751

þ
þ


þ

þ
þ

þ
Table 1.
Explanatory variables
for financial inclusion

Multinomial logistic regression evaluates maximum likelihood estimation similar to binary
response regression models. Log-likelihood and pseudo- R2 for measuring the strength of our
models have been evaluated (Table 7). We apply the maximum likelihood method to estimate
a multinomial logit model (MLM) along with cluster-robust standard errors (robust White
estimator). The content of the model draws significantly from Greene (2003).
0

P½Yi ¼ j ¼

Determinants
of financial
inclusion

eβ jxi
; j ¼ 1; 2; 3; 4 . . .
4
P
0
eβ kxi

(1)

k¼1

Where j 5 1,2,3,4 represents a household’s categorization to the four types of finance. The
estimated equations provide a set of probabilities for j þ 1 choices to the household and the
explanatory variables are defined as xi. Out of the four credit categories, only one is taken as
the reference category Greene (2003). Thereby, only three-parameter vectors are required to
predict all the four probabilities of credit choices. The probabilities are stated as follows:


0
j
eβ jxi
¼
; for j ¼ 1; . . . J ; βo ¼ 0
(2)
P Yi ¼
J
P
xi
0
1 þ eβ kxi
k¼1

For our study, we use j 5 4 (access neither institutional nor non-institutional finance) as the
reference category which means it is the omitted group. β coefficients are difficult to interpret
in a non-linear model, particularly for a multinomial logistic model where there is the

IJSE
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uncertainty of one-to-one correspondence between probability and the sign of the coefficient
(Greene, 2003). A positive coefficient means that when the explanatory variable increases, the
probability of falling in one of the categories increases:
 

dpij
pij 1  pij βr j ¼ k
¼
pij pik βr j ≠ k
dxrik

752

Hence, we must resort to the transformation of these coefficients in odds ratio (Cameron and
Trivedi, 2010). One of the prerequisites of the MLM is meeting the assumption of
independence of irrelevant alternatives (IIA), that is the odds ratio for one category in MLM
model is independent of the odds ratios for other categories (Greene, 2003). In order to check
the IIA assumption, we have applied the IIA Suest–Hausman test and Small–Hsiao IIA test.
The results of both tests suggest that the IIA assumption is met and we can apply the MLM
(Tables 2 and 3).
Due to complexity in the interpretation of odds ratio, marginal effects (Greene, 2003)
were used for better analysis of the results. It is the effect caused by a change in one unit of
the dependent variable on the probability of falling into any of the possible outcomes
(Cameron and Trivedi, 2010). We have calculated the marginal effects corresponding to
j 5 1, 2, 3 as:


vP½Yi ¼ j
¼ P½Yi ¼ j βj  β ; j ¼ 1; 2; 3; 4 . . .
(3)
δj ¼
vxi
Hence, all the sub-vectors of β have marginal effects, both through the probabilities and
through the weighted average that appears in δj. Parameter estimates can be used to compute
these probability values. Delta method is used to compute the standard errors.
The estimated multinomial logit equations will further provide a set of probabilities for a
decision-maker with a given set of characteristics. Also, Greene (2003) suggests that only the
marginal effects (and not the coefficients) are essential indicators to analyse the impact of the
given set of characteristics on the probabilities of interest.
Qij ¼ α þ HHi β þ Sti γ þ θij
Where:
Qij is the financial inclusion variable that is the probability of HH’s access to a source of
finance j (where j 5 1 to 4).
HHi is a vector of household characteristics, such as gender, age, education level, social
group, religion of the household head, landholding, number of members in a household
(household size), monthly consumption expenditure of a household
Sti is a vector of state characteristics, such as road density, irrigated area, rainfall
deviations and the number of scheduled commercial banks.
β and γ represent marginal effects for the explanatory variables, namely HHi and Sti.
θi is the random error term assumed to be independently and identically (i.i.d.) distributed
with constant variance.
3.2 Data
Household-level characteristics: This paper uses farm-level household data from a
nationally representative decennial survey conducted by the National Sample Survey
Office (NS,SO), in 2013. The survey focusses on the status of farmers and farming in India
(70th, NSSO, 2013). For our study, we have worked on the SAS database. The unit of
measurement is the sample “household”. The survey covers 4,529 villages across the

1
1.03Eþ04
1.02Eþ04
115.883
56
2
1.08Eþ04
1.08Eþ04
111.96
56
3
1.21Eþ04
1.20Eþ04
183.3
56
4
8102.3
8058.03
88.542
56
Note(s): A significant test is evidence against Ho
0.197
0.26
0.21
0.38

6094.4
6723.6
7443.6
5071.1

6053.9
6688.6
7409.3
5032.6

Ho: Odds (Outcome-J vs Outcome-K) are independent of other alternatives
FHHs
MHHs
χ2
df
P>χ 2
lnL(full)
lnL(omit)
lnL(full)
lnL(omit)
81.142
70.025
68.728
77.056

χ2
56
56
56
56

df
0.16
0.38
0.22
0.33

P>χ 2
9278.48
9735.49
1.13Eþ04
7338.55

Overall
lnL(full)

9241.8
9696.97
1.13Eþ04
7289.17

lnL(omit)

df
58
58
58
58

χ2
73.35
77.03
69.71
98.753

0.42
0.41
0.14
0.58

P>χ 2

Determinants
of financial
inclusion

753

Table 2.
Small–Hsiao test of IIA
assumption

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country and has gathered information from 35,200 farming households for the agricultural
year 2012–2013.
State-level characteristics: The study uses state-level variables categorized as follows:
physical, environmental and financial development indicators. These are explained as
follows:
(1) Physical variables

754

These include two variables, namely physical infrastructure proxied by road density and
irrigation. Road density has been measured by road length (km) per 1,000 population, which
is total surfaced roads per 1,000 population. The data for total surfaced road is from Ministry
of Road Transportation and Highways (2016) and for the population is Census, 2011.
Irrigation is the proportion of irrigated area and is sourced from Ministry of Agriculture and
Farmers Welfare (2016).
(2) Environmental variable
Environmental variable is captured by the percentage deviation of the rainfall from the
normal [6] rainfall. It is based on the rainfall records for the period from 1951 to 2000. Data for
the states is sourced from the Purohit and Kaur (2014).
(3) Financial development
Financial progress is measured by the number of commercial bank branches in a state in 2013
per 1,000 population. Data is sourced from Reserve Bank of India (2015) and Census, 2011.
4. Access to finance in India: household-level characteristics
One of the strategies to combat inequality amongst rural women is through FI. However,
there are disparities in access to institutional credit. Several households and state-level
factors need to be considered for achieving this goal. Often in emerging economies, access to
credit is dependent on household head characteristics such as gender, education, social group
and monthly household consumption expenditure (MHCE). Literature highlights that often
FHHs, disadvantaged social groups (SCs and STs), poor and uneducated people remain most
vulnerable. This section highlights important household-level characteristics [7] that relate to
access to finance for FHHs and MHHs.
India’s rural credit market has the coexistence of twin systems, namely formal and
informal agencies with the dominance of the latter over former. The reason for the dominance
of the informal credit market is primarily on account of two factors, namely (1) information
asymmetry between borrowers and lenders and (2) direct contact (personal) of traders and
commission agents with the cultivator to finance the advance payment for the purchase of
products (Pradhan, 2013). Figure 2 shows the distribution of rural households in accessing IF
and NIF.
Figure 2 reveals that MHHs are found to be almost equally inclined towards formal and
informal sources of finance, both being close to 20%. However, FHHs have a higher
inclination towards the informal source of finance (22.80%) vis-a-vis accessing the formal
source of finance (10.9%). It is worth mentioning that almost half of the agricultural
households do not access financial markets (Figure 2). FI remains high across both MHHs and
FHHs. Though, FHHs are 10% more likely to be financially excluded vis-a-vis MHHs (47 visa-vis 57%).
One of the major reasons behind gender exclusion from rural financial markets is the
questionable creditworthiness of FHHs. Several constraints including women belonging to a
disadvantageous social groups, lower financial literacy rates, weak property rights and
cultural barriers further restrict women’s access to institutional finance (Fletschner and

Determinants
of financial
inclusion

755

Figure 2.
MHHs and FHHs
accessing IF and NIF

Kenner, 2014). Based on the NSS micro-level data, we further analyse household head socioeconomic characteristics in accessing institutional and non-institutional finance. More details
(descriptive statistics) on the FI of the rural households on the basis of household and statelevel characteristics are given in Annexure Table 1.
Social group: In rural India, the historically entrenched caste system exerts a significant
influence on economic outcomes, an indicator of which is the vast difference in consumption
expenditures between caste groups (Deshpande, 2000). Social groups continue to play a
pivotal role in the rural Indian economy. Table 4 illustrates the distribution of MHHs and
FHHs in accessing IF and NIF across social groups.
Table 4 shows that for accessing IF, FHHs have lower participation, vis-a-vis MHHs
across all the social groups. For instance, while over 16% of SCs in MHHs access IF, the
corresponding figure for FHHs is less than 10%. A contrasting scenario is found for accessing
NIF, as FHHs access NIF more than MHHs (except amongst SCs wherein participation in NIF
remains almost the same for both FHHs and MHHs). FI across social groups is higher for
FHHs as compared to MHHs. On an average, non-participation in the finance market for

1
2
3
4

χ2

df

P>χ 2

χ2

df

P>χ 2

χ2

df

P>χ 2

15.56
14.38
18.25
21.584

56
56
56
56

0.278
0.224
0.326
0.521

16.88
17.3
58.665
42

56
56
56
56

0.25
0.33
0.472
0.26

19.514
53.086
66.22
75.656

58
58
58
58

0.31
0.45
Table 3.
0.634 Suest-based Hausman
0.34 test of IIA assumption

Social group

Institutional

Non-institutional

Both sources

Reference category social group (Row-wise 5 100)
Female
Male
Female
Male
Female
Scheduled caste
8.14
16.53
22.58
23.08
10.95
Scheduled tribe
6.18
10.71
22.17
18.05
3.53
Other backward class
10.92
19.73
24.80
21.67
9.77
Others
15.42
24.14
19.34
16.69
9.20
All
10.90
19.09
22.78
20.17
9.06
Source(s): Based on SAS, NSSO, 2013 (collected for Jan–Dec 2013)

Male
13.40
5.19
16.13
13.34
13.51

Neither source
Female
58.34
68.11
54.51
56.04
57.25

Male
46.99
66.05
Table 4.
42.47 Distribution of MHHs
45.83 and FHHs in accessing
47.23
IF and NIF across
social groups (%)

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756

FHHs is 10 percentage points more than MHHs for each social group, except STs, where not
much difference between MHHs and FHHs is observed.
Level of education: Education is considered as one of the key enablers of FI amongst
women. Generally, educated households are aware of the benefits as well as the limitations of
various sources of credit (Kumar et al., 2017). Education not only increases awareness but also
reduces inequalities by improving women’s position within the family. In Table 5, we show
the distribution of MHHs and FHHs in accessing finance based on their education level.
Table 5 reveals some pertinent observations such as for illiterates, access to IF is lower
than 10% for FHHs as compared to around 25% for NIF. Overall, while close to 57% of
illiterate FHHs are financially excluded, the corresponding figure is close to 47% for MHHs.
Interestingly, 40% of FHHs with education “Higher secondary and above” access IF. Our
data shows that less than 4% of such FHHs rely on NIF to meet their borrowing
requirements as compared to 12.6% for MHHs. This reflects that better educated FHHs
access NIF less vis-a-vis MHHs. For both MHHs and FHHs, while education levels do not
necessarily impact participation in IF, nevertheless, it certainly acts as a disincentive for
participation in NIF.
Monthly household consumption expenditure: Our study uses MHCE as a proxy for
income levels. Several studies have highlighted that higher income intrinsically leads to
greater autonomy in decision-making for the FHHs (Pal and Naha, 2015; Ghosh and
Vinod, 2017; Rastogi and Ragabiruntha, 2018) Table 6 provides the descriptive statistics
to study the distribution of MHHs and FHHs for accessing IF and NIF that varies across
by MHCE.
Table 6 shows that in general, both MHHs and FHHs that access IF have higher MHCE.
For instance, FHHs that access IF have an average MHCE of about INR 6,700, whereas FHHs
that access NIF have an average MHCE of about INR 5,500. However, it is rather interesting to
note the stark difference between MHCE levels for FHHs and MHHs. While the maximum

Education

Table 5.
IF and NIF across
education levels (%)

Noninstitutional

Both sources

Reference category education levels (Row-wise 5 100)
Female Male Female Male
Illiterate
8.41
14.4
24.89
23.91
Literate but up to primary
14.77
19.63
20.63
19.49
education
Middle
16.01
20.72
16.23
19.24
Secondary
20.2
24.97
9.09
15.82
Higher secondary and above
39.4
28.26
3.69
12.6
All
10.9
19.09
22.78
20.17
Source(s): Based on SAS, NSSO, 2013 (collected for Jan–Dec 2013)

MHCE
Table 6.
Monthly household
consumption
expenditure by MHHs
and FHHs (in INR [8])

Institutional

Mean
Female
Male

Std. dev.
Female
Male

Institutional Sources
6,709
7,480
750
1,100
Non-Institutional Sources
5,497
5,768
800
380
Both Sources
5,430
6,824
1,050
600
Neither Sources
5,117
5,631
405
256
Source(s): Based on SAS, NSSO, 2013 (collected for Jan–Dec 2013)

Neither source

Female
9.21
6.94

Male
13.81
12.98

Female
57.49
57.66

Male
47.89
47.9

7.41
28.93
2.63
9.06

14.63
13.95
11.45
13.51

60.35
41.79
54.29
57.25

45.41
45.25
47.68
47.23

Min
Female
Male
4,745
3,985
2,862
3,567

12,730
5,201
9,250
3,966

Max
Female
Male
45,500
53,900
43,718
40,300

461,500
113,900
122,800
357,500

MHCE for FHHs that access IF is INR 45,500, the corresponding figure for MHHs is almost ten
times higher at INR 4,61,500. Households that participate in neither form of finance, in
general, have the lowest MHCE. This indicates that relatively poorer households remain
financially excluded.

Determinants
of financial
inclusion

5. Econometric results
We have analysed FHHs, MHHs and overall rural households. The marginal effects [9] of
rural households are summarized in Table 7. The association between household and statelevel variables with participation in different sources of finance are examined.

757

5.1 Household level
The results reveal that access to institutional credit is significantly influenced by education,
landholding size, household size and MHCE for rural households. With respect to the
relationship between gender and access to institutional credit, the results show that FHHs
have a lower probability of participation in IF vis-a-vis MHHs. Our findings also indicate that
the marginal effect for participation in “both IF and NIF simultaneously” remains negative
and significant at 1% for FHHs vis-a-vis MHHs. These results corroborate with the findings
of Demirguc-Kunt et al. (2018), wherein gender gap persists in the ownership of bank accounts
and savings and borrowing behaviour.
The level of education positively influences access to formal finance. For instance, FHHs
with primary school education are 5% more likely to access formal credit as compared to
uneducated (illiterate) FHHs. Similarly, with an increase in the level of education of the FHH,
from the middle to higher secondary, though the probability of access to IF is positive, it
remains non-significant. However, for MHHs, higher levels of education have been estimated
to significantly increase the probability of access to IF, while decreasing the probability of
participation in NIF. The results for the overall households remain similar to those of MHHs.
These results are in line with Kumar et al. (2017), who also found that access to formal finance
increases with the level of education, as educated households are more aware of credit
opportunities and formalities required to obtain formal finance.
The findings with landholding size are also noteworthy. For FHHs, the coefficient for
the small and medium land holdings is positive and is statistically significant at the 1%
level. The results suggest that an additional hectare of land increases access towards
institutional credit by about 14% for small landholdings and 23% for medium
landholding. Similarly, for MHHs, landholding sizes favour participation in IF, across
all landholding sizes. For instance, while the probability of participation increases to
33.1% for the households having the highest land size holdings, the same is at 21.4% for
households having landholding of up to 2 ha in relation to the reference category of being
landless. These results are similar with the findings of Zins and Weill (2016). In their study,
they find that in the case of Africa, relatively better-off households are more likely to
access formal finance. Our results are also consistent with the findings in the literature by
Pal and Laha (2015); Kumar et al. (2017) and Ghosh and Vinod (2017).
The results presented in Table 6 indicate that the probability of participation in IF is lower
amongst all the social groups irrespective of the gender of the household head, with “General”
as a reference category. For FHHs, access to IF is significantly lower for all marginalized
groups, though the impacts are stronger for SCs. It can be observed that access to noninstitutional finance for a marginalized social group that is SCs (11.2%) and OBCs (5.1%) is
almost double for FHHs as compared to MHHs SCs (5.2%) and OBCs (2.7%). This clearly
shows the significant dependence of females over informal finances. Our findings are
compatible with Kumar (2013) and Deshpande (2000), who find that in the case of India, banks
discriminate between borrowers because of their caste.

Table 7.
Correlates of MHHs
and FHH, access to IF
and NIF (multinomial
logit marginal effects)

0.016 (0.049)
0.338 (19.926)
0.212** (0.085)
0.157 (0.116)

Religion
(Rc 5 Hindu)
Muslim
Christian
Sikh
Others

Household size
(Rc 5 Household 1)
Household size 2#
Household size 3

0.0634* (0.00)
0.161* (0.091)

0.027 (0.037)
0.140*** (0.044)
0.233*** (0.057)
1.414 (82.143)

0.093** (0.044)
0.098** (0.04)
0.054** (0.027)

Social group
(Rc 5 General)
ST
SC
OBC

Landholding
(Rc 5 landless)
Marginal
Small
Medium
Large

0.047* (0.028)
0.031 (0.043)
0.051 (0.063)
0.047 (0.082)

0.014*** (0.03)
0.000*** (0)

Education level
(Rc 5 illiterate)
Primary
Middle
Secondary
H_secondary

Female household
head
Age
Age*Age

ME (z value)

Only institutional

0.032 (0.027)
0.188 (0.161)

0.007 (0.024)
0.0066 (0.0240)
0.098 (0.061)
1.936 (215.71)

0.067** (0.033)
0.306 (16.264)
0.061 (0.083)
0.297* (0.161)

0.025 (40.033)
0.112*** (0.029)
0.051** (0.023)

0.054** (0.023)
0.035 (0.036)
0.078 (0.059)
0.162* (0.00)

0.007*** (0.02)
0.00*** (0)

ME (z value)
FHH

Only noninstitutional

0.058** (0.026)
0.000 (0.093)

0.076** (0.031)
0.150*** (0.036)
0.176*** (0.046)
1.178 (216.173)

0.0784* (0.0449)
1.553 (73.918)
0.348*** (0.08)
0.021 (0.124)

0.110*** (0.04)
0.03 (0.03)
0.033 (0.022)

0.028 (0.023)
0.044 (0.036)
0.075 (0.058)
0.064 (0.074)

0.010*** (0.02)
0.000*** (0)

ME (z value)

Both sources

0.002 (0.008)
0.002 (0.019)

0.214*** (0.022)
0.310*** (0.023)
0.324*** (0.024)
0.331*** (0.038)

0.02 (0.013)
0.071 (0.06)
0.129*** (0.043)
0.004 (0.04)

0.068*** (0.01)
0.050*** (0.011)
0.01 (0.007)

0.052*** (0.008)
0.086*** (0.009)
0.076*** (0.011)
0.095*** (0.01)

0.008*** (0.002)
0.00*** (0)

ME (z value)

Only institutional

0.015** (0.007)
0.037 (0.025)

0.109*** (0.01)
0.169*** (0.012)
0.174*** (0.017)
0.174*** (0.044)

0.024** (0.01)
0.019 (0.044)
0.098 (0.064)
0.087** (0.036)

0.013 (0.01)
0.052*** (0.009)
0.027*** (0.008)

0.045*** (0.007)
0.058*** (0.008)
0.097*** (0.011)
0.135*** (0.012)

0.002 (0.001)
0.00 (0.000)

ME (z value)
MHH

Only noninstitutional

0.013** (0.006)
0.004 (0.016)

0.098*** (0.016)
0.137*** (0.017)
0.165*** (0.018)
0.180*** (0.028)

0.033*** (0.01)
0.092 (0.06)
0.028 (0.037)
0.048* (0.028)

0.09*** (0.01)
0.015* (0.008)
0.016*** (0.006)

0.014** (0.006)
0.013* (0.008)
0.017* (0.009)
0.051*** (0.009)

0.003*** (0.01)
0.000*** (0)

ME (z value)

Both sources

0.010 (0.006)
0.014 (0.015)

0.154*** (0.015)
0.235*** (0.015)
0.272*** (0.016)
0.283*** (0.029)

0.038*** (0.010)
0.028 (0.031)
0.119*** (0.018)
0.016 (0.030)

0.049*** (0.010)
0.030*** (0.009)
0.005 (0.006)

0.053*** (0.007)
0.085*** (0.008)
0.089*** (0.008)
0.098*** (0.009)

0.008*** (0.001)
0.000*** (0.000)

0.037*** (0.010)

ME (z value)

Only institutional

0.020*** (0.006)
0.015 (0.018)

0.089*** (0.008)
0.144*** (0.010)
0.157*** (0.014)
0.155*** (0.040)

0.029*** (0.008)
0.020 (0.027)
0.066*** (0.020)
0.103*** (0.028)

0.028*** (0.009)
0.052*** (0.008)
0.019*** (0.006)

0.043*** (0.006)
0.064*** (0.007)
0.104*** (0.009)
0.138*** (0.010)

0.002* (0.001)
0.000 (0.000)

0.007 (0.008)

ME (z value)
Overall

Only noninstitutional

758

Dependent
variable
Explanatory
variables

(continued )

0.009* (0.005)
0.007 (0.014)

0.112*** (0.012)
0.151*** (0.012)
0.179*** (0.013)
0.193*** (0.022)

0.027*** (0.009)
0.050* (0.030)
0.191*** (0.014)
0.002 (0.024)

0.090*** (0.009)
0.000 (0.007)
0.002 (0.005)

0.011** (0.005)
0.006 (0.006)
0.021*** (0.007)
0.035*** (0.007)

0.004*** (0.001)
0.000*** (0.000)

0.032*** (0.007)

ME (z value)

Both sources

IJSE
47,6

ME (z value)

Only institutional
ME (z value)

Both sources

0.015 (0.025)
0.007 (0.029)
0.03 (0.028)
0.022 (0.034)
0.01 (0.008)
0.002 (0.052)
0.059 (0.055)
2.974*** (0.372)

27,541
35054.323
543.67
0.0000
0.0776

0.014 (0.023)
0.011 (0.026)
0.033 (0.029)
0.072 (0.0375)
0.005 (0.007)
0.047 (0.049)
0.027 (0.053)
0.413 (0.392)

ME (z value)
FHH

Only noninstitutional

0.032*** (0.01)
0.048*** (0.01)
0.070*** (0.011)
0.084*** (0.012)
0.023*** (0.005)
0.136*** (0.031)
0.063** (0.029)
1.094*** (0.208)

ME (z value)

Only institutional

18,292
21844.387
3964.45
0.0000
0.0832

0.016** (0.008)
0.009 (0.009)
0.016 (0.01)
0.020* (0.012)
0.020*** (0.004)
0.224*** (0.027)
0.088*** (0.026)
0.248 (0.201)

ME (z value)
MHH

Only noninstitutional

0.026*** (0.008)
0.052*** (0.008)
0.061*** (0.009)
0.058*** (0.01)
0.017*** (0.004)
0.194*** (0.026)
0.034 (0.023)
3.790*** (0.135)

ME (z value)

Both sources

0.026*** (0.008)
0.038*** (0.008)
0.051*** (0.008)
0.064*** (0.009)
0.001 (0.002)
0.069*** (0.017)
0.111*** (0.015)
1.724*** (0.102)

ME (z value)

Only institutional

0.021*** (0.006)
0.035*** (0.006)
0.041*** (0.007)
0.025*** (0.007)
0.001 (0.002)
0.014 (0.014)
0.043*** (0.012)
2.616*** (0.081)

0.015** (0.007)
0.000 (0.007)
0.014* (0.008)
0.022** (0.009)
0.013*** (0.002)
0.122*** (0.014)
0.043*** (0.014)
0.422*** (0.105)

27,541
32267.805
6116.71
0.0000
0.0866

ME (z value)

Both sources

ME (z value)
Overall

Only noninstitutional

Note(s): Standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1. ME 5 Marginal Effect (dy/dx). The explanation of the variable is given in Table A1

Observations
Log likelihood
LR chi2(81)
Prob > chi2
Pseudo R2

MHCE (Rc 5 MHCE 1)
MHCE 2
0.04 (0.033)
MHCE 3
0.064* (0.035)
MHCE 4
0.065* (0.037)
MHCE 5
0.092** (0.043)
Road density
0.009 (0.009)
Irrigated area
0.086 (0.066)
Rainfall
0.013 (0.068)
Sch commercial
3.125*** (0.479)
bank

Dependent
variable
Explanatory
variables

Determinants
of financial
inclusion

759

Table 7.

IJSE
47,6

760

Our estimates indicate that the probability of participation in IF is lower for Sikh FHHs,
with “Hindus” as a reference category. Participation in IF for religion category “others” and
Christians remains non-significant irrespective of the gender. Our findings agree with Kumar
et al. (2017) that religion does not make a difference in access to IF. However, for NIF, the
probability of participation has been estimated to be positive and significant for both Muslim
FHHs and Muslim MHHs. This clearly implies that there is a higher probability of
participation in non-IF for Muslims.
In the case of household size, rather surprisingly, for FHHs, the probability of participation
in IF is higher for household category 3 as compared to household category 2 and reference
category 1. This signifies that access to institutional credit increases with the size of
household members. However, as the age of the female household head increases, the
probability of participation in IF significantly decreases. Whereas for MHH, the age of a
household is positive and statistically significant at 1% level.
According to the results presented in Table 7, a 1% increase in per capita monthly
expenditures increases access to IF by about 9% for both FHHs and MHHs. Further, the
impacts are stronger for FHHs. We can gather from the results that farmers are using credit
for consumption purposes and bridging the gap between income and consumption. Kumar
et al. (2017) and Ghosh and Vinod (2017), in their respective studies, have emphasized that
access to IF helps lower credit constraints of the households.
5.2 State level
The state-level findings reveal that an increase in commercial bank branches increases the
probability of participation in IF and lowers the probability of participation in NIF for rural
households. Further, with an increase in commercial bank branches, the impacts for FHHs are
three times higher than for the MHHs. Several studies have shown that availability of better
banking facilities assists in overcoming the obstacle of low PIC for rural households
(Binswanger and Khandker, 1995; Burgess and Pande, 2005; Basu and Srivastava, 2005).
Our findings corroborate the positive influence of infrastructure (roads and irrigation) on
institutional credit for rural households, particularly MHHs. According to Binswanger and
Khandeker (1995), better roads lower the transaction costs of credit services, resulting in
increased lending to farmers, rising demand for agricultural inputs and thereby, increasing
the crop yields. Similarly, the development of irrigation systems is not only essential for
raising productivity levels and achieving food security but also to address climate-induced
agricultural uncertainties. However, the results have been contrasting for FHHs. The impacts
remain non-significant across different sources of credit for females.
In the case of rainfall deviations (climate vulnerability), MHHs resort to formal credit
agencies. With 1% increase in rainfall deviations, the probability of access to IF increases by
6.3% for MHHs. Our results coincide with Chuang (2019), that access to institutional finance
builds resilience to these exogenous climatic shocks through consumption smoothening.
6. Conclusions and policy implications
This paper examines the relationship between access to IF and NIF by MHHs and FHHs for
rural India. Using primary NSS household-level data (2013), multinomial logistic regression is
applied to test the relationship between access to various sources of finance across gender,
controlling for household and state-specific variables, such as age, social group, religion,
education, consumption expenditure [10], land holdings, infrastructure, environment and
financial penetration. Our findings show that FHHs have a lower probability of accessing IF
and a higher probability of accessing NIF vis-a-vis MHHs. Moreover, a substantial proportion
of HHs access neither IF nor NIF. For instance, while close to 58% of the FHHs [11] do not
access credit, the corresponding figure for MHHs is 47%. Further, in general, education levels,
MHCE, land size holding, irrigated area and penetration of scheduled commercial banks

influence access to IF favourably, while belonging to socially disadvantaged castes lowers
the probability of accessing IF.
Indian policymakers have continually adopted several measures to institutionalize the
financial framework. This is evident from the nationalization drive in 1969, 1980 [12] and the
compulsory allocation of capital to the priority sector. Ideally, the influence of such measures
should have manifested equally for men and women. However, the empirical findings from
our study suggest that IF is still not widely available to FHHs. These results are robust in
multinomial regressions that analyse several household and state-level characteristics.
Both demand-side and supply-side factors constrain women from participation in IF. On
the demand side, attitudinal factors such as lack of skills or confidence to manage finances,
the deep-rooted patriarchal structure, differential treatment under the law or by custom,
cultural constraints such as meta-preference for sons, unfavourable inheritance laws restrict
access of women to financial services. On the supply side, product services are often not
attuned to serve FHHs. Lack of institutional policies directed towards women, as also
information gaps, limits their accessibility to IF. Based on our findings, select policy
interventions that can help address the gaps are (1) cultural, (2) educational, (3) institutional
and (4) product related. These are briefly described further:
(1) Cultural: Discriminative socialization remains an essential aspect of inequality towards
women. Our findings suggest that FHHs have a lower probability to access formal
finance. Socially accepted norms of behaviour have profound effects on access to
finance. Strong preference for male child continues, which leads to detrimental
customary practices. Government schemes such as Beti Bachao Beti Padhao, Sukanya
Samridhi Yojana (empowerment of girls) and Janani Suraksha Yojana are encouraging
steps towards gender equality. The amendments in the Hindu Succession Act (2005) for
overcoming weak property rights and issuance of Woman Farmer Certificate towards
recognizing the female farmers are also steps in the right direction.
(2) Educational: Education is critical for women’s empowerment. Our study provides
evidence to the same that educated FHHs are more inclined towards IF. In order to
increase financial awareness, it is critical to educate females. In this context, policies
such as Sarv Siksha Abhiyan, Rashtriya Madhyamik Shiksha Abhiyan, Balika
Samridhi Yojna, Mukhyamantri Rajshri Yojna, Mid-day meal by rewards, incentives,
scholarships, drinking water facilities and separate girls’ toilets shall undoubtedly
help improve education and thereby impact participation in IF.
(3) Institutional: More women now have accounts in formal financing institutions.
Several initiatives such as Jan Dhan Yojana [13], Basic Saving Bank Deposit (BSBD),
simplified Know Your Customer norms, compulsory opening branches in unbanked
villages, business correspondents, Kisan Credit Card have helped in achieving FI
goals. Interestingly, empirical estimations reveal that for FHHs, the outreach of
formal institutions is three times more impactful as compared to MHHs. One of the
notable innovations related to financial product design has come from micro-finance
institutions. The reasons could be that women’s groups and co-operatives have a
better understanding of products as well as the type of service delivery that might
appeal to this segment (Fletschner and Kenney, 2014; Tarsem, 2018). There is a
further need in India towards new and dedicated institutions particularly addressing
gender inclusion such as Well Fargo Bank in the United States [14], Banco Santender
Santiago in Chile [15] and Sero lease and Finance (Selfina) [16] in Tanzania.
(4) Product-related: There is a need to link financial products with the needs of women.
There are several noteworthy initiatives adopted by the government such as
Bandhan Bank, Micro Finance Institutions and self-help groups that facilitate finance

Determinants
of financial
inclusion

761

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762

for the women. However, we still need to work towards introducing more “semiformal [17]” products such as overdraft facilities, trade credit, microcredit, new credit
bureau, no formalities account [18] and microloan services that could help greater FI
for the women.
To conclude, our results suggest that for FHHs, access to IF remains lower than the MHHs.
Also, in rural India, more than half of the FHHs do not access either IF or NIF. Though
substantial improvements have been achieved, yet there is a long way to go before women get
financially empowered. India has been implementing a broad range of forward-looking
policies and practices to circumvent the gender gap towards FI. With gender issues in FI
beginning to be explored more thoroughly, there is scope for greater emphasis on this subject.
An effective policy for the FI of women can be achieved by further intensifying this focus.
Notes
1. The global economy went through a period of unprecedented financial instability in 2008–2009,
accompanied by the worst global economic downturn and collapse in trade in many decades. During
the years of high growth and low interest rates that bred excessive optimism and risk taking and
spawned a broad range of failures – in market discipline, financial regulation, macroeconomic
policies and global oversight (https://globalfindex.worldbank.org/).
2. Launched with the support of the Bill and Melinda Gates Foundation, the Global Findex database is
the world’s most comprehensive data set on how adults save, borrow, make payments and manage
risk (https://globalfindex.worldbank.org/).
3. The 2017 Global Findex database defines account ownership as having an individual or jointly
owned account either at a financial institution or through a mobile money provider.
4. Structural finance for agriculture is an advanced financial strategy that involves usage of collateral,
agricultural value chains, contract farming, Futures, Trade finance.
5. According to NSSO (2014), non-institutional sources include professional money lender (25.31%),
landlord (1.21%), shopkeeper/trader (6.62%), relatives/friends (15.24%) and others (3.72%).
6. Long period average (LPA) popularly known as normal rainfall. This is the benchmark against
which the rainfall is calculated. It is the average rainfall received by India during the south-west
monsoon, for a 50-year period (1951–2000). The current LPA is 89 cm.
7. We have reported four household characteristics which have significant differences in accessing
institutional credit. Cross-tabulation analysis of all the household characteristics can be provided on
request.
8. 1 USD 5 ∼ 70 INR
9. Results of coefficient estimates representing relative risk ratio and model statistics can be provided
on request.
10. An interaction term with respect to female-headed household with quintiles of MHCE levels has
been introduced in the model. It has been found that poorest quintiles levels, that is, MHCE at 20th
and 40th levels have strong negative relationship with access to institutional credit. There has been
a non-significant relationship with 60th onwards quintile with access to IF.
11. We have also introduced the interaction variable in the model, female-headed households with
education levels, at higher education levels having a strong negative and significant relationship
with non-institutional sources of credit. However, with respect to lower levels of education (primary
and middle level), there is negative significant relationship with accessing IF.
12. 14 major commercial banks were nationalized in 1969 and six in 1980 in India.
13. Pradhan Mantri Jan Dhan Yojana (PMJDY) necessitates only an Aadhar card, indirectly lowering
social hurdles towards access. It was not particularly targeted towards women’s financial inclusion.
14. It supports women-owned businesses with lines of credit up to US$100,000.

15. It has considered gender in its loan extension strategy. It operates a micro-finance affiliate (Banefe)
for whom women make up to 54% of borrowers and 60% of loan officers.
16. It provides small-ticket business loans (up to US$ 500), enabling women to acquire equipment for
immediate use with a down payment.

Determinants
of financial
inclusion

17. Semi-formal product is popularly known as micro-finance, which helps financially excluded people
to be a part of formal financial system.
18. One example of no formalities account is Diamond Bank in Nigeria, and Women’s World Banking
developed a savings product called a BETA account that could be opened over the phone with no
minimum balance and no fees.
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India, New Delhi, December.

Determinants
of financial
inclusion

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Appendix

Variable

766

Table A1.
Descriptive statistics of
the rural households

Description

Household characteristics
Age
Age of the household head (years)
Female household
Gender of household-head (Male 1, female 0)
head
Reference category:
Below primary level of education
Illiterate
Primary
Literate without formal schooling: through EGS/NFEC/
AEC, through TLC, others; literate with formal schooling:
below primary, primary education. (Primary-1, otherwise-0)
Middle
Middle education level (Middle-1, otherwise-0)
Secondary
Secondary education level (Secondary-1, otherwise-0)
H_secondary
Higher Secondary, diploma/certificate course, graduate,
postgraduate and above. (Higher secondary -1, otherwise-0)
Reference category:
General (Unreserved caste) (General-1, otherwise-0)
Others
ST
Scheduled Tribe (Disadvantaged) (ST-1, otherwise-0)
SC
Scheduled Caste (Disadvantaged) (SC-1, otherwise-0)
OBC
Other Backward Classes, collective term used by the
Government of India to classify castes which are socially/
educationally/economically disadvantaged (OBC-1,
otherwise-0)
Reference category:
Household head follows Hinduism
Hindu
Muslim
Islam (Muslims-1, otherwise-0)
Christian
Christianity (Christians-1, otherwise-0)
Sikh
Sikhism (Sikhs-1, otherwise-0)
Others
It includes other religions like Jainism, Buddhism,
Zoroastrianism. (Others-1, otherwise-0)
Reference category:
Landless households (<0.02 ha)
Landless
Marginal
Size of the land between 0.02 ha and 2 ha (Marginal-1,
otherwise-0)
Small
Size of the land between 2.01 ha and 4 ha (Small-1,
otherwise-0)
Medium
Size of the land between 4.01 ha and 10 ha (Medium-1,
otherwise-0)
Large
Size of the land more than 10 ha (Large-1, otherwise-0)
Household size 1 ,2, 3 1 5 The size of the household is 6 or less (Reference
category)
2 5 The size of the household is >4≤8 (Hhz2- 1, otherwise-0)
3 5 The size of the household is 12 or more (Hhz3-1,
otherwise-0)
MHCE 1, 2,3,4,5
1 5 lowest quintile class (20 percentile) (Reference category)
2 5 20 quintile class (40 percentile) (MHCE2-1, others-0)
3 5 30 quintile class (60 percentile) (MHCE3-1, others-0)
4 5 40 quintile class (80 percentile) (MHCE4-1, others-0)
5 5 highest quintile class (100 percentile) (MHCE5-1,
others-0)

Mean

Standard
deviation

51
0.08

13.48
0.28

0.34

0.48

0.27

0.44

0.16
0.11
0.11

0.37
0.32
0.32

0.19

0.39

0.13
0.40
0.27

0.34
0.49
0.45

0.80

0.40

0.09
0.07
0.02
0.02

0.29
0.25
0.15
0.14

0.07

0.25

0.66

0.47

0.21

0.41

0.06

0.23

0.01
0.42

0.08
0.49

0.48
0.08

0.50
0.28

0.20
0.21
0.19
0.20
0.20

0.40
0.40
0.40
0.40
0.40

(continued )

Variable

Description

State level characteristics
Road density
Total surfaced road density per 1,000 population
Irrigated area
Area under irrigation (million hectares)
Rainfall
Percentage rainfall from average rainfall based on
1951–2000. (Deviations)
Sch commercial
A commercial bank accepts deposits, provides business
bank
loans and offers necessary investment products per 1,000
people

Corresponding author
Simrit Kaur can be contacted at: kaur.simrit@gmail.com

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Mean

Standard
deviation

4.39
0.50
3.77

3.13
0.24
19.76

0.09

0.04

Determinants
of financial
inclusion

767
Table A1.