# Explaining Caste-based Digital Divide in India

R Vaidehi Research Scholar, Department of Economics and Finance, Birla
Institute of Technology and Science-Pilani, Hyderabad campus, E-mail:
<EMAIL_ADDRESS>Bheemeshwar Reddy A Assistant Professor,
Department of Economics and Finance, Birla Institute of Technology and
Science-Pilani, Hyderabad campus, E-mail<EMAIL_ADDRESS>Sudatta Banerjee Assistant Professor, Department of Economics and Finance,
Birla Institute of Technology and Science-Pilani, Hyderabad campus, E-mail:
<EMAIL_ADDRESS>
(09-11-2020)

With the increasing importance of information and communication technologies
in access to basic services like education and health, the question of digital
divide based on caste assumes importance in India where large socioeconomic
disparities persist between different caste groups. Studies on caste-based
digital inequality are still scanty in India. Using nationally representative
survey data, this paper analyzes the first-level digital divide (ownership of
computer and access to the Internet) and the second-level digital divide
(individual’s skill to use computer and the Internet) between the
disadvantaged caste groups and others. Further, this paper identifies the
caste group based differences in socioeconomic factors that contribute to the
digital divide between these groups using a non-linear decomposition method.
The results show that there exists large first-level and second-level digital
divide between the disadvantaged caste groups and others in India. The non-
linear decomposition results indicate that caste-based digital divide in India
is rooted in historical socioeconomic deprivation of disadvantaged caste
groups. More than half of the caste- based digital gap is attributable to
differences in educational attainment and income between the disadvantaged
caste groups and others. Findings of this study highlight the urgent need for
addressing educational and income inequality between the different caste
groups in India in order to bridge the digital divide.

Keywords: Digital Divide, Caste, India, Inequality, Education
JEL Codes: J15, I24

##  1 Introduction

In the contemporary world, information and communication technologies (ICTs)
are increasingly becoming crucial for citizens to actively take part in
economic, political and social life (Roztocki ., 2019). Several researchers
and policymakers argue that a wide adoption of ICTs enables economic
development(Aker Mbiti, 2010; Donner, 2015; Heeks, 2010; Roztocki ., 2019;
Saith ., 2008; Swaminathan Swaminathan, 2018). Different international
agencies continue to recommend the use of ICTs for achieving inclusive growth
and for improving governance; for generating new economic opportunities for
underserved populations; and, for the delivery of health, education and other
public services across the world, especially so in developing countries
(World-Bank, 2016; Atchoarena ., 2017; Kelly ., 2018). For example, the goal
9.c of Sustainable Development Goals (SDGs) of the United Nations is to
significantly increase access to ICT and strive to provide universal and
affordable access to the Internet in the least developed countries by
2020(UNDP, 2018). However, given the rising economic inequalities and
persistence of poverty across the world, all individuals or social groups do
not have equal access to ICTs. The inequality in access to ICTs is often
referred to as the Digital Divide. The Organisation for Economic Co-operation
and Development (OECD) defines the digital divide as the gap between
individuals, households, businesses and geographic areas at different
socioeconomic levels with regard both to their opportunities to access
information and communication technologies (ICTs) and to their use of the
Internet for a wide variety of activities (OECD, 2001, p. 5).

Different studies have shown that inadequate access to digital technology can
adversely affect the educational, health and labour market outcomes of the
underprivileged social groups (Warren, 2007; Mossberger ., 2007; Robinson .,
2015; Pagani ., 2016; Gao ., 2018; Haenssgen, 2018). In the wake of the
COVID-19 pandemic, educational institutions across the world had to shift to
online teaching. However, as a result of the digital divide, a large number of
poor students without computers and access to the Internet could not
participate in online education (Van Lancker Parolin, 2020), thus elucidating
the role of digital divide in exacerbating the existing educational inequality
between the digital-haves and digital-have nots. DiMaggio Bonikowski (2008)
have shown that people who use the Internet earned higher wages than those who
did not. Peng (2017) found that digital skills improve employment
opportunities by lessening worker displacement and also enhancing the chances
of reemployment for displaced workers. The digital divide can widen
consumption inequality between households. In the context of increasing use of
ICTs in providing health services in developing countries, studies have found
that lack of access to digital technology among the marginalized groups can
further increase health inequalities(Haenssgen, 2018).

Only a limited number of studies have examined the digital divide in India.
Especially, studies that have examined caste-based digital inequality using
nationally representative data are sparse in India. The only exception to this
is a recent study by (Tewathia ., 2020). They have employed the network
effects approach to examine the relationship between social and digital
inequality in India. This study is based on the data from the Indian Human
Development Survey 2011. They find that there are substantial disparities in
ownership and usage of computer and the access to the Internet among different
socioeconomic groups in India. They show that educational attainment, caste,
and occupation of the household are some of the factors that are associated
with ownership and use of computer and the Internet in India. Another study by
Singh .(2013) based on a sample of 500 individuals across four states
(Haryana, Madhya Pradesh, Punjab and Rajasthan) in the north and central parts
of India found that education and some familiarity with the English language
is a crucial determinant of computer and Internet use. Their results also show
a significant difference between males and females in the use of computer and
the Internet. Venkatesh Sykes (2013,p.239) based on a longitudinal study of
210 families in rural India show that “the social network constructs
contributed significantly to the explanation of technology use”. A recent
study finds that there is a substantial regional divide in digitalization in
India (Tewathia, 2020b).

Caste is a unique marker of social stratification in South Asia, especially so
in India. The caste system is characterized by graded socioeconomic
inequality. Certain caste groups have historically faced social and economic
discrimination, including in access to education in India. These disadvantaged
caste groups include Scheduled Castes (SC), Scheduled Tribes (ST) and Other
Backward Classes (OBC) which together account for around 80% of the Indian
population. To address the caste-based inequalities, the Indian state
introduced reservations in education and employment for the disadvantaged
caste groups as a constitutional guarantee since the early years of
independence. However, several recent studies have shown that the
disadvantaged caste groups continue to experience discrimination in labour
market and caste-based inequalities in education, income and wealth persist in
contemporary India (Borooah, 2005; Borooah Iyer, 2005; Kijima, 2006;
Madheswaran Attewell, 2007; Thorat Attewell, 2007; Deshpande, 2011; Zacharias
Vakulabharanam, 2011; Azam, 2012). Recent studies have also shown that the
disadvantaged caste groups have lower chances of economic and educational
mobility as compared to the non-disadvantaged in India (Motiram Singh, 2012;
Sharma Paramati, 2018; Asher ., 2018; Choudhary ., 2019). 111 Notably, the
economic, educational and employment performance of Muslims, a religious
minority in India, is found to be as worse as one of the most disadvantaged
caste groups such as Scheduled Castes (Sachar ., 2006; Desai Kulkarni, 2008;
Basant ., 2010; Asadullah ., 2014; Kumar ., 2020).Taking cognisance of this
fact, this paper additionally presents religion-based digital divide analysis
in the Appendix, while the paper focuses exclusively on caste-based digital
inequalities. In India, caste and religious categories often overlap. For the
analysis in the Appendix, the Muslim social group includes all people who have
reported their religion as Islam, irrespective of their caste. To ensure that
Muslims are compared to the non-SC, ST and OBC population in other religions,
the paper first divides the population into Muslims and the rest. Next, it
separates the SC, ST and OBC from the non-Muslim population. For the analysis
done in the Appendix, the Muslims are compared to this residual category which
we refer to as the non-disadvantaged (non-SC, ST, OBC and Muslims) (See
Appendix).

Disparities in access to and use of ICTs can further deepen the existing
socioeconomic inequalities between social groups in a multi-group society (Van
Dijk, 2005; Helsper, 2012; Hargittai, 2018; Muschert Ragnedda, 2018; Witte
Mannon, 2010). However, the research on digital inequality based on caste is
scant in India. The existing studies cited above either deal with limited
geographical areas, or having relied on data which is almost a decade old, do
not throw light on the current levels of digital disparities. Further, owing
to the lack of data on second-level digital divide (that is, digital skills)
the existing studies engage only with the first-level digital divide (that is,
the ownership of computers and access to the Internet) and consequently
inequalities in digital skill remain unexplored.The present study uses the
latest nationally representative sample survey data to analyse the caste-based
digital divide. The significance of the present study also lies in explaining
the caste-based digital divide in terms of the socioeconomic differences that
exist between the caste groups. This exercise, to our knowledge, has not been
undertaken in the context of India within the existing literature on digital
divide. The present study examines the first and second-level digital
inequality between Others and each of the disadvantaged caste groups — SC, ST
and OBC.

Rest of the paper is organized into following sections. The next section
discusses the socioeconomic and demographic factors that are associated with
the digital divide. The third and fourth sections discuss the data and
methodology respectively. The fifth section presents results on how different
factors contribute to the digital divide between the Other group and each of
the disadvantaged caste groups in India. The final section presents discussion
and conclusions.

##  2 Factors associated with the digital divide

The digital divide can be broadly categorized into three levels (Ragnedda
Kreitem, 2018; Scheerder ., 2017). The first-level digital divide refers to
the divide that is measured through access to ICTs (i.e., having a computer
and access to the Internet) and is expressed as a binary outcome. The second-
level digital divide refers to the gap in digital skills required to use
computer and the Internet. The third-level digital divide measures
inequalities in the capacities to get the benefits from the access and use of
the Internet (Ragnedda Kreitem, 2018, p. 6). The first and second-level
digital inequality at individual level, which is the focus of this paper, are
mainly associated with factors such as differences in socioeconomic status as
well as demographic factors including educational attainment, income level,
occupation, social origin (race, caste, ethnicity, religion and class), place
of residence (urban or rural areas) and language (Compaine, 2001; Mossberger
., 2003; Fairlie, 2004; Helsper, 2010; Nishijima ., 2017). In this section, we
discuss some of the factors that explain the digital divide at individual
level.

Differences in educational attainment is the most significant factor that is
associated with the digital divide among individuals (Haight ., 2014; A. Van
Deursen Van Dijk, 2011; Vicente López, 2010; Cruz-Jesus ., 2016). Better
educational attainment facilitates effective use of a computer, and also aids
in absorption and comprehension of information received through the Internet
(Hsieh ., 2008). Hence, inequalities in educational attainment can lead to a
digital divide between individuals with higher and lower educational
attainment. Studies have also found that language can create a barrier for
adopting digital technologies (Ono Zavodny, 2008; Singh ., 2013; Jara .,
2015). Availability of content in the local language is critical to the use of
the Internet.

Different studies have shown that income is a crucial predictor of the digital
divide (Fairlie, 2004; Chaudhuri ., 2005; Vicente López, 2010). Higher
economic inequality with a high level of poverty may hinder the process of
ICTs diffusion among poorer households. Individual’s occupation is also an
essential factor associated with the digital divide. In other words,
individuals employed in fields where application of ICTs are very common, such
as scientific and technical workers, are more likely to use digital
technologies than people employed in other occupations (Lengsfeld, 2011; Novo-
Corti ., 2014). Gender is a key factor that contributes to the digital divide
(Bimber, 2000; Hargittai Shafer, 2006; Martínez-Cantos, 2017). However,
scholars differ on the extent of environmental and attitudinal factors in the
differential use of ICTs by gender. Bimber(2000) shows that even after
accounting for differences in income between males and females, women used the
Internet less frequently than their male counterparts. On the contrary,
Hilbert(2011) emphasises the unfavourable conditions related to employment,
education and income in the digital divide between men and women. Age is also
significantly associated with the digital divide. Age is negatively related to
the access to and use of ICTs. Across the world younger people are more likely
to use computer and the Internet as compared to the older population (Loges
Jung, 2001; Abbey Hyde, 2009). Studies from different countries have shown
that there exists a vast digital divide between rural and urban areas
(Hindman, 2000; Prieger, 2013; Salemink ., 2017). As compared to the
population in rural areas, the urban population has better access to the
Internet owing to better availability of supporting infrastructure such as
electricity and broadband connectivity (Park, 2017; Philip ., 2017; Salemink
., 2017). Geographical location of the individual, both at the global level as
well as within countries has also been important in determining access to the
digital world(Chinn Fairlie, 2007). In India, the state– a geographical and
administrative region– of residence of an individual, is also a major factor
associated with the digital divide(Tewathia, 2020b).

##  3 Data

To study the caste-based digital inequalities in India, this paper uses unit
level data from survey on Household Social Consumption: Education, (GOI,
2017-2018). This survey is a nationally representative multistage stratified
household sample survey carried out by the National Sample Survey, Government
of India between July 2017 and June 2018. The survey collected information on
households’ access to and use of computer and the Internet in India. The
survey defined a computer as any devices like, desktop computer, laptop
computer, notebook, netbook, palmtop, tablet (or similar handheld
devices)(GOI, 2019, p. B-6-7). A household is considered to be having access
to Internet if a household member of age 5 years and above used Internet to
find, evaluate and communicate information from any location during the last
30 days preceding the date of survey, via any device, like, desktop, laptop,
palmtop, notebook, netbook, smartphone, tablets, etc., (GOI, 2019, p. B-7). In
the context of the rising use of budget smartphones in developing countries,
it is important to account for the use of smartphones as an alternative to
computers. While the definition of ’computer’ does not include smartphones,
the use of smartphones to access the Internet is captured in the definition of
household members’ access to the Internet in the survey. This allows for
comprehensive analysis of digital divide, representing both computer and
smartphone usage in India.

The data set contains detailed information on various socioeconomic and
demographic characteristics of each member of the household. The survey
provides data on whether the household has a computer and has access to the
Internet. Further, the survey has recorded information on each member (five
years and above) of the household regarding their ability to operate the
computer and use the Internet. The use of the Internet in the last 30 days was
also ascertained from each individual of the sample households.222The survey
considered a person knowledgeable in the use of computers, if he/she was
capable of carrying out any of the specified tasks using a computer. These
tasks included, copying or moving a file or folder, using copy and paste tools
to duplicate or move information within a document, sending e-mails with
attached files (e.g. document, picture, and video), using basic arithmetic
formulae in a spreadsheet, connecting and installing new devices (e.g. modem,
camera, printer), finding, downloading, installing and configuring software,
creating electronic presentations with presentation software (including text,
images, sound, video or charts), transferring files between a computer and
other devices, writing a computer program using a specialized programming
language. (GOI, 2019, p. B-7). The survey considered a person knew how to use
the Internet, if he/she could perform any of the following activities using
the Internet on any device — use Internet browser for website navigation,
using e-mail and social networking applications, etc., to find, evaluate and
communicate information (GOI, 2019, p. B-8)

The present paper uses this data to analyze the digital divide at individual
level between the Other group and each of the disadvantaged caste groups in
India. As discussed earlier, the disadvantaged caste groups include STs, SCs
and OBCs. Consequently, the Other group consists of the rest of the Indian
population and is a residual category.

To examine the digital divide between Other and each of the disadvantaged
caste groups, the computer ownership rate, the Internet access rate, the
computer literacy rate, the Internet literacy rate and the Internet use rate
are considered as outcome variables. The computer ownership rate for a caste
group is simply the proportion of individuals having computer at home.
Similarly, the Internet access rate for a caste group represents the share of
individuals having Internet facility at home. The computer literacy rate for a
caste group is defined as the proportion of individuals from the caste group
who know how to operate a computer. The share of individuals in a caste group
knowing how to use the Internet is the Internet literacy rate for the caste
group. The Internet use rate for a caste group is defined as the proportion of
individuals in that particular caste group who have used the Internet during
the last 30 days from the date when the survey was conducted.

We restrict our analysis to individuals in the age group between 15 years and
59 years, which has resulted in a sample of 349,914 observations. In the
present study, all outcome variables are binary variables. For analysis of the
digital divide, the covariates include individual’s age, sex, number of years
of education, occupation (type of employment) of the head of household and per
capita income of the household, place of residence (rural or urban) and state
(geographical and administrative unit in India). Monthly per capita
expenditure of households is considered as a proxy for per capita income. The
occupation or type of employment of the head of the household, to which an
individual belongs, is classified into six categories: self-employed in
agriculture, self-employed in non-farm, regular worker, casual works in non-
agriculture, casual worker in agriculture and other workers. Digital
infrastructure in India varies substantially between rural and urban areas as
well as across the states in India. The state variable in the analysis
represents the state in which the individual is residing.

##  4 Methodology

The Oaxaca-Blinder decomposition (Blinder, 1973; Oaxaca, 1973), a linear
regression decomposition approach, is used to explain the contribution of
factors that drive average socioeconomic outcomes such as wages or income gap
between two groups. The Oaxaca-Blinder method allows to decompose the average
gap in an outcome variable, $Y$, between two groups $a$ and $d$ into the
following :

$\bar{Y^{a}}-\bar{Y^{d}}=\left[\left(\bar{X_{a}}-\bar{X_{d}}\right)\hat{\beta_{a}}\right]+\left[\bar{X_{d}}\left(\hat{\beta_{a}}-\hat{\beta_{d}}\right)\right]\hskip
56.9055pt\left(1\right)$

where $X_{i}$ represents the vector of average values of the covariates and
$\hat{\beta_{i}}$ is a vector of coefficients for group $i$. The Oaxaca-
Blinder decomposition separates the mean difference in an outcome between two
groups into two parts explained and unexplained differences (Blinder, 1973;
Oaxaca, 1973). In this study, we focus on the explained part as this can
inform policy that seek to bridge the digital gap between different caste
groups in India.

Given that the outcome variables such as possession of computer or the use of
the Internet by individuals is binary in this study, a direct application of
Oaxaca-Blinder linear decomposition (Blinder, 1973; Oaxaca, 1973) is not
possible. Hence, this paper employs Fairlie (2005) non-linear decomposition
method. The Fairlie (2005) non-linear decomposition is an adaptation of
Oaxaca-Blinder decomposition for binary dependent variables. Earlier studies
have used the Fairlie non-linear decomposition method for analyzing the
digital divide based on race and ethnicity (Fairlie, 2004, 2017; Manlove
Whitacre, 2019). Following Fairlie (2005), the decomposition for a non-linear
model of the type $P(Y=1)=F(X\hat{\beta})$ for the mean digital gap between
Others ($a$) and a disadvantaged caste group ($d$) can be divided into
following components:

$\bar{Y^{a}}-\bar{Y^{d}}=\left[\sum\limits_{\forall{N^{a}}}\frac{F(X_{i}^{a}\hat{\beta}^{a})}{N^{a}}-\sum\limits_{\forall{N^{d}}}\frac{F(X_{i}^{d}\hat{\beta}^{a})}{N^{d}}\right]+\left[\sum\limits_{\forall{N^{d}}}\frac{F(X_{i}^{d}\hat{\beta}^{a})}{N^{d}}-\sum\limits_{\forall{N^{d}}}\frac{F(X_{i}^{d}\hat{\beta}^{d})}{N^{d}}\right]\hskip
56.9055pt\left(2\right)$

The first component of the equation (2) represents the share of the digital
gap that is attributed to differences in the distribution of covariates
between Others and a disadvantaged caste group. It is often referred to as the
explained part or the covariate effect. The other component is called the
unexplained part or the coefficient effect. The unexplained part of the
digital gap arises due to differences in the effect of covariates between two
caste groups.

Alternatively, the non-linear decomposition of the mean digital gap between
two caste groups can also be written as:

$\bar{Y^{a}}-\bar{Y^{d}}=\left[\sum\limits_{\forall{N^{a}}}\frac{F(X_{i}^{a}\hat{\beta}^{d})}{N^{a}}-\sum\limits_{\forall{N^{d}}}\frac{F(X_{i}^{d}\hat{\beta}^{d})}{N^{d}}\right]+\left[\sum\limits_{\forall{N^{d}}}\frac{F(X_{i}^{a}\hat{\beta}^{a})}{N^{d}}-\sum\limits_{\forall{N^{a}}}\frac{F(X_{i}^{a}\hat{\beta}^{d})}{N^{a}}\right]\hskip
56.9055pt\left(3\right)$

The equation (2) and (3) differ in terms of weights used. As against equation
(2), the first component of the equation (3) is weighted by a disadvantaged
caste group’s regression coefficients($\hat{\beta}^{d}$) and the second
component is weighted by the Other’s average covariates ($X^{a}$). Equation
(2) and (3) intuitively have the same meaning, though they may yield different
results. Following the recommendations of Oaxaca Ransom(1994), the study has
carried out decomposition analysis using coefficient estimates from a pooled
sample to weight the explained part of the decomposition.

Equation (4) identifies the individual contribution of each of the covariates
$X_{i}$.

$\frac{1}{N^{d}}\sum\limits_{\forall{N^{d}}}F\left[\hat{\alpha^{*}}+X_{1i}^{a}\hat{\beta}_{1}^{*}+X_{2i}^{a}\hat{\beta}_{2}^{*}\right]-F\left[\hat{\alpha^{*}}+X_{1i}^{d}\hat{\beta}_{1}^{*}+X_{2i}^{a}\hat{\beta}_{2}^{*}\right]\hskip
56.9055pt\left(4\right)$

In equation (4), $\hat{\beta^{*}}$ denotes probit coefficients from the pooled
sample. Here, the sample sizes of two caste groups are not comparable. To deal
with this issue, following (Fairlie, 2005) the paper selects a random sub-
sample from the larger caste group equal to the size of the smaller caste
group and averages the decomposition results of 1000 sub-samples. Finally, as
results are sensitive to the ordering of the independent variables, the order
for each iteration is randomised to address the concern of path-dependency of
results.

##  5 Results

###  5.1 Descriptive statistics

Table 1: Summary statistics

| Others(1) | ST(2) | SC(3) | OBC(4) | (1)-(2) | (1)-(3) | (1)-(4)
---|---|---|---|---|---|---|---
Variables | Mean | Mean | Mean | Mean | t-test | t-test | t-test
COR | 0.207 | 0.055 | 0.066 | 0.095 | 0.153*** | 0.142*** | 0.113***
| [0.001] | [0.001] | [0.001] | [0.001] |  |  |
IAR | 0.411 | 0.141 | 0.182 | 0.243 | 0.270*** | 0.229*** | 0.168***
| [0.002] | [0.002] | [0.002] | [0.001] |  |  |
CLR | 0.312 | 0.112 | 0.135 | 0.189 | 0.200*** | 0.176*** | 0.123***
| [0.001] | [0.001] | [0.001] | [0.001] |  |  |
ILR | 0.380 | 0.145 | 0.176 | 0.238 | 0.235*** | 0.204*** | 0.143***
| [0.001] | [0.002] | [0.002] | [0.001] |  |  |
IUR | 0.347 | 0.123 | 0.150 | 0.206 | 0.224*** | 0.197*** | 0.141***
| [0.001] | [0.001] | [0.001] | [0.001] |  |  |
Age | 34.625 | 33.489 | 33.531 | 34.006 | 1.137*** | 1.095*** | 0.619***
| [0.038] | [0.055] | [0.051] | [0.032] |  |  |
Urban | 0.444 | 0.124 | 0.226 | 0.300 | 0.320*** | 0.218*** | 0.145***
| [0.002] | [0.001] | [0.002] | [0.001] |  |  |
Male | 0.515 | 0.507 | 0.516 | 0.509 | 0.008*** | -0.001 | 0.007***
| [0.002] | [0.002] | [0.002] | [0.001] |  |  |
Log(Income) | 7.793 | 7.256 | 7.396 | 7.508 | 0.537*** | 0.397*** | 0.285***
| [0.002] | [0.002] | [0.002] | [0.001] |  |  |
Education | 9.638 | 6.384 | 7.025 | 7.868 | 3.254*** | 2.612*** | 1.770***
| [0.015] | [0.023] | [0.022] | [0.014] |  |  |
SEA | 0.272 | 0.395 | 0.225 | 0.325 | -0.123*** | 0.047*** | -0.053***
| [0.001] | [0.002] | [0.002] | [0.001] |  |  |
SENA | 0.290 | 0.121 | 0.162 | 0.239 | 0.169*** | 0.128*** | 0.051***
| [0.001] | [0.001] | [0.002] | [0.001] |  |  |
RSW | 0.249 | 0.095 | 0.155 | 0.161 | 0.154*** | 0.094*** | 0.088***
| [0.001] | [0.001] | [0.002] | [0.001] |  |  |
CWA | 0.058 | 0.181 | 0.197 | 0.093 | -0.123*** | -0.140*** | -0.036***
| [0.001] | [0.002] | [0.002] | [0.001] |  |  |
CWNA | 0.076 | 0.165 | 0.220 | 0.137 | -0.089*** | -0.144*** | -0.062***
| [0.001] | [0.002] | [0.002] | [0.001] |  |  |
OW | 0.035 | 0.024 | 0.019 | 0.026 | 0.011*** | 0.016*** | 0.010***
| [0.001] | [0.001] | [0.001] | [0.000] |  |  |
Observations | 106540 | 49420 | 57848 | 142062 |  |  |
Notes: Computer ownership rate (COR); Internet access rate (IAR); Computer
literacy rate (CLR); Internet literacy rate (ILR); Internet use rate (IUR);
Self-employed in agriculture (SEA); Self-employed in non-agriculture (SENA);
Regular salaried Workers (RSW); Casual worker in agriculture (CWA); Casual
worker in non-agriculture (CWNA); Other work (OW). Standard errors are in
parentheses. ***, **, and * indicate significance at the 1, 5, and 10 percent
critical level. Survey sampling weights are used for the estimation.

Table 1 presents mean and corresponding standard errors for each outcome
variable and independent variable by caste group and t-test results for gap in
outcome and independent variables between the Others and each of the
disadvantaged caste groups. All the disadvantaged caste groups fare poorly
with respect to every outcome variable— computer ownership rate, Internet
access rate, computer literacy rate, Internet literacy rate and Internet use
rate. Only 14.1% of the STs have access to the Internet as compared to 41.1%
of individuals from the Other group. Differences in the computer literacy rate
across the caste groups are also pronounced, with only 11.2% of STs and 13.5%
of SCs knowing how to use a computer. The same statistic for OBCs is 18.9% .
However, the corresponding figure for the Others is 31.2%. Compared to STs,
SCs, and OBCs , the Others are more affluent and better educated. For example,
mean years of schooling for Others is 9.3, whereas the same for STs and SCs is
just around 7 years; and, it is 7.8 years for OBCs. A much higher proportion
of Others live in urban areas as compared to SC and ST individuals. For
instance, 45% of Other individuals live in urban areas with better access to
digital infrastructure. The corresponding percentage for ST individuals is
only 12.4%. The lower socioeconomic position of the disadvantaged caste groups
is also reflected in their occupational distribution. For example, 19.7% of SC
individuals belong to the category of ’casual workers in agriculture
households’ which is considered as a last resort in terms of occupation
because of the poor working conditions and low wages. On the contrary, the
corresponding share for the Others is only 5.8%. Clearly, the overall
socioeconomic status of the Otherss is significantly better than the rest of
the caste groups.

Table 2: Marginal effects from probit regressions on the probability of having
computer at home, internet access at home, computer literacy, Internet
literacy and Internet use.

Variables | CO | AIF | CL | IL | IU
---|---|---|---|---|---
Age | 0.001*** | 0.002*** | -0.003*** | -0.006*** | -0.005***
Gender: reference = Male |  |  |  |  |
Female | 0.012*** | 0.027*** | -0.022*** | -0.069*** | -0.067***
| (0.001) | (0.003) | (0.001) | (0.002) | (0.002)
Place of residence: reference = Rural |  |  |  |  |
Urban | 0.022*** | 0.055*** | 0.016*** | 0.034*** | 0.031***
| (0.002) | (0.004) | (0.001) | (0.003) | (0.002)
Caste group: reference = Other |  |  |  |  |
ST | -0.029*** | -0.100*** | -0.019*** | -0.046*** | -0.039***
| (0.003) | (0.005) | (0.002) | (0.003) | (0.003)
SC | -0.035*** | -0.071*** | -0.018*** | -0.042*** | -0.038***
| (0.002) | (0.005) | (0.002) | (0.003) | (0.003)
OBC | -0.035*** | -0.055*** | -0.013*** | -0.028*** | -0.025***
| (0.002) | (0.004) | (0.001) | (0.002) | (0.002)
Education | 0.008*** | 0.020*** | 0.017*** | 0.032*** | 0.026***
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
Log(Income) | 0.072*** | 0.127*** | 0.032*** | 0.069*** | 0.065***
| (0.002) | (0.003) | (0.002) | (0.002) | (0.002)
Occupation : reference = SEA |  |  |  |  |
SENA | 0.039*** | 0.065*** | 0.009*** | 0.028*** | 0.031***
| (0.003) | (0.005) | (0.001) | (0.003) | (0.003)
CWA | -0.026*** | -0.073*** | -0.006*** | -0.018*** | -0.017***
| (0.002) | (0.005) | (0.001) | (0.003) | (0.002)
CWNA | -0.026*** | -0.060*** | -0.009*** | -0.009*** | -0.007**
| (0.002) | (0.005) | (0.001) | (0.003) | (0.002)
RSW | 0.051*** | 0.093*** | 0.027*** | 0.055*** | 0.057***
| (0.003) | (0.005) | (0.002) | (0.003) | (0.003)
OW | 0.024*** | 0.025** | 0.034*** | 0.057*** | 0.046***
| (0.005) | (0.009) | (0.004) | (0.007) | (0.006)
Observations | 349914 | 349914 | 349913 | 349913 | 349913
Notes: Computer ownership (CO); Access to Internet facility (AIF); Computer
literacy (CL); Internet literacy (IL); Internet use (IU); Self-employed in
agriculture (SEA); Self-employed in non-agriculture (SENA);Regular salaried
workers (RSW); Casual worker in agriculture (CWA); Casual worker in non-
agriculture (CWNA); Other work (OW). Robust standard errors are in
parentheses. ***, **, and * indicate significance at the 1, 5, and 10 percent
critical level. Survey sampling weights are used for the estimation.

Table 2 reports marginal effects from probit regressions on the probability of
individuals having a computer at home, Internet access, computer literacy,
Internet literacy and Internet use. The estimates indicate that there are
substantial gaps in outcome variables between our reference category, Others,
and each of the disadvantaged caste groups (SCs, STs, and OBCs). In other
words, our probit analysis confirms that caste-based digital divide, measured
using different indicators, persists despite controlling for other
socioeconomic and demographic variables in our model. A recent study also
finds that caste is an important predictor of access to ICTs in India
(Tewathia ., 2020). As shown in the earlier studies (Fairlie, 2004),
socioeconomic status of individuals, such as income, positively correlates
with having a computer and use of Internet. Educational attainment of
individuals is one of the important factors that is associated with the
individual’s access to Internet and computer literacy. This finding is in
tandem with the existing studies (Haight ., 2014; Cruz-Jesus ., 2016).
Further, concurring with various studies from different countries(Bimber,
2000; Hargittai Shafer, 2006; Singh ., 2013; Martínez-Cantos, 2017) the
estimates indicate that there is a significant gender based digital gap
between males and females. Females are less likely to use the Internet and be
computer literate as compared to men. Living in urban areas is associated with
better digital outcomes. In other words, there is a considerable rural-urban
digital gap in India.

Table 3: Nonlinear decompositions of caste group disparities in computer
ownership rate

Variables | Others-ST | % explained | Others-SC | % explained | Others-OBC | % explained
---|---|---|---|---|---|---
Gap in COR | 15.3 |  | 14.2 |  | 11.3 |
Age | 0.003*** | 2.0 | 0.003*** | 21.0 | 0.002*** | 1.8
| (0.000) |  | (0.000) |  | (0.000) |
Female | 0.000* | 0.0 | 0.000*** | 0.0 | 0.000 | 0 .0
| (0.000) |  | (0.000) |  | (0.000) |
Education | 0.035*** | 22.9 | 0.032*** | 22.5 | 0.022*** | 19.5
| (0.001) |  | (0.001) |  | (0.001) |
Log(income) | 0.070*** | 45.8 | 0.063*** | 44.4 | 0.044*** | 39.0
| (0.002) |  | (0.002) |  | (0.001) |
Place of residence | 0.027*** | 17.7 | 0.017*** | 12.0 | 0.010*** | 8.9
| (0.002) |  | (0.001) |  | (0.001) |
Occupation | 0.009*** | 5.9 | 0.010*** | 7.0 | 0.008*** | 7.1
| (0.001) |  | (0.001) |  | (0.000) |
State | -0.005** | -3.3 | -0.019*** | -13.4 | -0.016*** | -14.2
| (0.002) |  | (0.001) |  | (0.001) |
Total explained | 155958 | 91.0 | 164383 | 74.7 | 248598 | 62.1
Notes: Computer ownership rate(COR). Bootstrap standard errors are in
parentheses(1000 replications). ***, **, and *indicate significance at the 1,
5, and 10 percent critical level. Survey sampling weights are used for the
estimation.

The nonlinear decomposition approach has been employed to examine the
contribution of caste-based differences in socioeconomic variables to the
digital gap between Others and each of the disadvantaged caste groups. Table 3
reports decomposition estimates of the gap in computer ownership rate. The
first row reports the gap in computer ownership rate between Others and each
of the disadvantaged caste groups. The second row from the bottom presents the
digital gap explained by all covariates in the model between Others and STs,
SCs and OBCs separately.

The gap in computer ownership rate between the Others and STs is 15.3
percentage points. The differences in covariates explain 91%
((0.139/0.153)*100) of the gap in household computer ownership rate between
Others and STs (see Column 2, Table 3). All the estimates of decomposition of
gap between the Others and STs are positive except state. The positive
coefficients imply that if STs had the same characteristics as the Others,
their computer ownership rate would be higher. Differences in per capita
income between Others and STs has contributed to 45.8% of the total gap (15.3)
in computer ownership rate between them. Similarly, differences in the
educational attainment between Others and STs accounted for 22.9% of the total
gap in computer ownership rate between them. Other characteristics such as
occupation, place of residence and age together explained around one-fourth of
the total gap in computer ownership rate between the Others and STs.

Table 4: Nonlinear decompositions of caste group disparities in Internet
access rate

Variables | Others-ST | % explained | Others-SC | % explained | Others-OBC | % explained
---|---|---|---|---|---|---
Gap in IAR | 27.0 |  | 22.9 |  | 16.8 |
Age | 0.003*** | 1.1 | 0.003*** | 0.1 | 0.002*** | 0.1
| (0.000) |  | (0.000) |  | (0.000) |
Gender | -0.000 | 0.0 | 0.000** | 0.0 | -0.000 | 0 .0
| (0.000) |  | (0.000) |  | (0.000) |
Education | 0.063*** | 23.4 | 0.052*** | 22.7 | 0.036*** | 21.4
| (0.002) |  | (0.001) |  | (0.001) |
Log(income) | 0.083*** | 30.8 | 0.059*** | 25.8 | 0.045*** | 26.8
| (0.003) |  | (0.002) |  | (0.001) |
Place of residence | 0.039*** | 14.5 | 0.027*** | 11.8 | 0.014*** | 8.3
| (0.002) |  | (0.001) |  | (0.001) |
Occupation | 0.008*** | 3.0 | 0.015*** | 6.6 | 0.008*** | 4.8
| (0.001) |  | (0.001) |  | (0.000) |
State | 0.008*** | 3.0 | 0.010*** | 4.4 | 0.010*** | 6.0
| (0.002) |  | (0.002) |  | (0.002) |
Total explained |  | 75.7 |  | 72.6 |  | 68.4
Notes: Internet access rate(IAR). ***, **, and *indicate significance at the
1, 5, and 10 percent critical level. Survey sampling weights are used for the
estimation

Similar to the STs, in the case of SCs and OBCs too, income and education
emerge as the most significant contributors in explaining the gap in computer
ownership rate between them and Others. The negative coefficients of the state
indicate that differences in state characteristics between them and the Others
are in favor of STs, SCs and OBCs . For instance, if SC individuals had the
same distribution across Indian states as the Other individuals, the computer
ownership gap rate between them would have been 10.5% higher. Our
decomposition results are similar to Fairlie (2004) who found that income and
education are the most important factors that explain digital gap between
white and black racial groups in the USA.

Table 5: Nonlinear decompositions of caste group disparities in computer
literacy rate

Variables | CLR | % explained | CLR | % explained | Gap in CLR | % explained
---|---|---|---|---|---|---
CLR | 20.0 |  | 17.7 |  | 12.3 |
Age | -0.014*** | -7.0 | -0.014*** | -7.9 | -0.009*** | -7.3
| (0.000) |  | (0.000) |  | (0.000) |
Gender | -0.000 | 0.0 | -0.001*** | -0.6 | -0.000*** | 0.0
| (0.000) |  | (0.000) |  | (0.000) |
Education | 0.093*** | 46.5 | 0.074*** | 41.9 | 0.053*** | 43.1
| (0.002) |  | (0.001) |  | (0.001) |
Log(income) | 0.063*** | 31.5 | 0.050*** | 28.3 | 0.036*** | 29.3
| (0.002) |  | (0.002) |  | (0.001) |
Place of residence | 0.024*** | 12.0 | 0.018*** | 10.2 | 0.011*** | 8.9
| (0.002) |  | (0.001) |  | (0.001) |
Occupation | 0.011*** | 5.5 | 0.011*** | 6.2 | 0.008*** | 6.5
| (0.001) |  | (0.001) |  | (0.000) |
State | -0.002 | -1.0 | -0.001 | -0.6 | -0.005*** | -4.1
| (0.002) |  | (0.001) |  | (0.001) |
Total explained (%) |  | 87.5 |  | 77.6 |  | 76.5
Notes: Computer literacy rate(CLR). ***, **, and *indicate significance at the
1, 5, and 10 percent critical level. Survey sampling weights are used for the
estimation.

Table 4 reports estimates of decomposition of the gap in the Internet access
rate between the Others and each of the disadvantaged caste groups. The gap in
the Internet access rate between the Others and ST individuals was the highest
amongst all disadvantaged caste groups, i.e., ST individuals have 27
percentage points lower access to the Internet as compared to the Other
individuals. The covariate effect, that is, the explained part of the total
gap in the Internet access rate between the Other and ST individuals is 75.7%.
Among all the explanatory variables, the difference in individual’s education
and per capita income are the most crucial factor that explain the gap in the
Internet access rate between the Others and each of the disadvantaged caste
groups. The individual’s educational attainment alone contributes around one-
fourth of the total gap in the Internet access rate between the Others and ST,
SC and OBC individuals. After education and per capita income, disparities in
place of residence contributes the most to the gap in the Internet access rate
between the Others and each of the disadvantaged caste groups. The differences
in the distribution of population in urban areas contributes 14.5% of the gap
between the Others and STs. The corresponding figure for OBCs is only 8.3%. As
shown in Table 1, unlike STs a much higher proportion of OBCs live in the
urban areas in India.

Table 5 presents the results of the decomposition of the computer literacy
rate gap between Others and each of the disadvantaged caste groups. The gap in
computer literacy rate between the Others and each of the disadvantaged caste
groups is positive. The decomposition estimates of Others and STs and Others
and OBCs show that differences in covariates explain around three fourth of
the total gap in the computer literacy rate. The differences in covariates
between Others and STs account for 87.5% of the total gap between their
computer literacy rate.

The differences in the educational attainments of individuals from Others and
each of the disadvantaged caste groups is the most critical factor that
explains gaps in computer literacy rate between them. For example, nearly half
of the total gap in the computer literacy rate between Others and ST
individuals is explained by differences in their educational attainment. The
contribution of income is important in explaining the gap in the computer
literacy rate between Others and each of the disadvantaged caste groups. Per
capita income explains more than one-fourth of the total gap between Other and
SC individuals. If OBCs had the same income as the Others, the gap in computer
literacy rate between the Others and OBC individuals would decrease by 29.3%.
Other covariates, like the occupation of the head of household and
individual’s place of residence, also contribute to the gap between the Others
and the rest of the caste groups. The state to which an individual belongs
explains a tiny proportion of the gap. The negative sign of age shows that the
gap between Others and STs in computer literacy rate would be 7 percentage
points higher if STs had a similar age distribution as the Others.

Table 6: Nonlinear decompositions of caste group disparities in Internet
literacy rate

Variables | Others-ST | % explained | Others-SC | % explained | Others-OBC | % explained
---|---|---|---|---|---|---
CLR | 0.235 |  | 0.204 |  | 0.143 |
Age | -0.015*** | -6.4 | -0.014*** | -6.9 | -0.008*** | -5.6
| (0.000) |  | (0.000) |  | (0.000) |
Gender | 0.000 | 0.0 | -0.000*** | 0.0 | -0.000 | 0.0
| (0.000) |  | (0.000) |  | (0.000) |
Education | 0.105*** | 44.6 | 0.085*** | 41.7 | 0.059*** | 41.4
| (0.002) |  | (0.001) |  | (0.001) |
Log(Income) | 0.070*** | 29.7 | 0.052*** | 25.5 | 0.036*** | 25.3
| (0.002) |  | (0.002) |  | (0.001) |
Place of residence | 0.026*** | 11.0 | 0.019*** | 9.3 | 0.011*** | 7.7
| (0.002) |  | (0.001) |  | (0.001) |
Occupation | 0.010*** | 4.2 | 0.009*** | 4.4 | 0.006*** | 4.2
| (0.001) |  | (0.001) |  | (0.000) |
State | -0.001 | -0.4 | 0.006*** | 2.9 | 0.002 | 1.4
| (0.002) |  | (0.001) |  | (0.001) |
Total explained (%) | 155957 | 82.9 | 164383 | 77.1 | 248598 | 74.4
Notes: Internet literacy rate (ILR). ***, **, and * indicate significance at
the 1, 5, and 10 percent critical level. Survey sampling weights are for used
the estimation.

Table 7: Nonlinear decompositions of caste group disparities in Internet use
rate

Variables | Others-ST | % explained | Others-SC | % explained | Others-OBC | % explained
---|---|---|---|---|---|---
CLR | 22.4 |  | 19.7 | -7.6 | 14.1 |
Age | -0.014*** | -6.2 | -0.015*** | -7.6 | -0.009*** | -6.4
Gender | (0.000) |  | (0.000) |  | (0.000) |
Education | 0.000* | 0.0 | -0.001*** | -0.5 | 0.000** | 0.0
| (0.000) |  | (0.000) |  | (0.000) |
Log(Income) | 0.089*** | 39.7 | 0.074*** | 37.5 | 0.051*** | 36.1
| (0.002) |  | (0.001) |  | (0.001) |
Place of residence | 0.074*** | 33.0 | 0.057*** | 28.9 | 0.039*** | 27.6
| (0.002) |  | (0.002) |  | (0.001) |
| 0.027*** | 12.0 | 0.020*** | 10.1 | 0.012*** | 8.5
| (0.002) |  | (0.001) |  | (0.001) |
Occupation | 0.009*** | 4.0 | 0.008*** | 4.1 | 0.007*** | 5.0
| (0.001) |  | (0.001) |  | (0.000) |
State | 0.001 | 0.4 | 0.008*** | 4.1 | 0.005*** | 3.5
| (0.002) |  | (0.001) |  | (0.001) |
Total explained |  | 83.0 |  | 76.5 |  | 74.4
Notes: Internet use rate (IUR). ***, **, and * indicate significance at the 1,
5, and 10 percent critical level. Survey sampling weights are for used the
estimation.

Tables 6 presents decomposition estimates of gap in the Internet literacy rate
between Others and each of the disadvantaged caste groups. For example, the
gap in the Internet literacy rate between Others and OBCs would have been
reduced by 74.4% if OBCs had the same characteristics as Others. Among all the
covariates in the model, caste-based differences in the educational attainment
of individuals are the most important drivers of the gap in the Internet
literacy rate. Results from regression decomposition in Table 7, show the
specific contributions of difference in different observed characteristics to
the gap in the Internet use rate between Others and each of the disadvantaged
caste groups. The educational attainment of individuals is the most
significant contributor to the gap in the Internet use rate between Others and
each of the disadvantaged caste groups. For example, the difference in the
educational attainment of the non-disadvantaged and STs explains almost 38.97%
of the total gap in the Internet use rate between these two groups. Per-capita
income also contributes a good deal to the gap in the Internet use rate by
individuals belonging to Other group and each of the disadvantaged caste
groups. For instance, 25.5% of the Internet use rate gap between the non-
disadvantaged and OBCs can be explained through this factor. The differences
in the age distribution of the disadvantaged caste groups are favorable to
them.

##  6 Discussion and conclusion

Using nationally representative data from Household Social Consumption:
Education survey 2017-18, this paper has examined the relative contribution of
caste group based disparities in different socioeconomic variables to the
digital gap between Others and each of the disadvantaged caste groups (SCs,
STs and OBCs). The descriptive results from the study show that there are
large gaps between Others and each of the disadvantaged caste groups in
computer ownership rate, Internet access rate, computer literacy rate,
Internet literacy rate and Internet use rate. Especially, the digital gap
between the Others and STs and SCs is very large. For example, only around 6%
of SC and ST individuals had a computer at home as compared to 20% of Other
individuals. Similarly, the gap in computer literacy rate between Other
individuals and STs is 20 percentage points, which is the highest gap between
the Others and each of the disadvantaged caste groups.

The decomposition analysis presented in the above section shows that more than
two-thirds of the first and second-level digital gap between Others and each
of the disadvantaged caste groups are attributable to differences in their
socioeconomic factors. The results from the decomposition analysis reveal that
caste based digital divide can be mostly explained by the large and persistent
inequality between Others and disadvantaged caste groups in education and
income. In other words, the sizeable digital divide based on caste in
contemporary India reflects the historical, socio-economic backwardness of the
disadvantaged caste groups (SCs, STs and OBCs). Despite several measures
including affirmative action in education and employment, educational
backwardness still persists among disadvantaged caste groups in India (Borooah
Iyer, 2005; Deshpande, 2011; Basant ., 2010). Further, there is ample evidence
of higher level of poverty among the SCs, STs and OBCs as compared to Others
(Kijima, 2006; Sachar ., 2006). It is this educational and income disparity
between the Others and the disadvantaged caste groups that emerge as the major
contributor to the digital gap. In the wake of increased use of digital
technologies, caste-based digital divide in India and educational and income
inequality will reinforce each other. Hence, there is an urgent need to
improve educational attainments and incomes of disadvantaged caste groups to
address caste-based digital divide in India.333Notably, the Appendix shows
similar results for religion-based digital divide. We see that education
followed by income explains more than 50% of the total digital gap between
Muslims and the non-disadvantaged group (See Appendix).

The analysis of the digital divide in this paper has some limitations. First,
this study only deals with the first and second-level of the digital divide.
We measure the second-level digital divide based on computer and Internet
skills as a binary variable. A composite index of different computer and
Internet skills will better capture the second-level digital divide. Further,
our analysis does not take into consideration the quality of Internet
connections. Our data does not permit us to analyse gaps in the effective use
of the Internet by household members in terms of gaining tangible benefits.
The second limitation of our study is that our decomposition analysis cannot
be interpreted as a causal analysis. Constrained by data set, the study uses
limited covariates. The model does not include variables such as unobserved
behavioural characteristics of individuals in using computer and the Internet.
Despite these limitations, this paper has provided policy relevant empirical
evidence on the factors contributing to the digital divide between Others and
disadvantaged caste groups in India.

From a policy perspective, understanding the factors that contribute to the
digital divide between Others and disadvantaged caste groups is vital to take
appropriate measures toward bridging the digital divide. The results indicate
some possible strategies that could aid policymakers to address caste-based
digital inequalities in India. Education is a crucial factor in explaining the
gap in digital skills such as knowing how to use computer and the Internet.
Therefore, it is essential to improve the educational attainment of
disadvantaged caste groups in India. This could be achieved by primarily
focusing on younger cohorts of individuals from the disadvantaged caste group
by decreasing their school dropout rate and providing more incentives to them
for continuing their education. Typically, a disadvantaged caste group is more
likely to be poor and less educated, and hence are less likely to have a
computer at home. The economically weaker sections in developing countries may
not be able to afford a computer, given the high cost of computers. One
possible way to mitigate this problem is to provide subsidy to bring down the
cost of computers and the Internet connection for the poor households. Some of
the state governments in India have distributed free laptops to students who
have graduated higher secondary schooling.444In the year 2020-21 the state
government of Tamil Nadu plans to distribute 500,000 laptops to school and
college going students. See https://www.thehindubusinessline.com/news/tamil-
nadu-government-to-distribute-free-laptops-to-5-lakh-students-
in-2020-21/article31106316.ece  Such subsidization programmes are a step in
the right direction as they not only help the students, but also provide
access to computer to other household members. Another way to make computer
affordable to the poor households is to develop low-cost computers for the
poor in developing countries. Alternatively, providing access to free computer
and the Internet through community centers will help to bridge the caste-based
digital divide in India . The second level digital divide can be bridged
through digital literacy programmes AJ. Van Deursen Van Dijk (2014).555 To
address the digital skill gap in the context of developing countries a recent
paper suggests two kinds of intervention – one, directly providing digital
skills to those who lack them through digital literacy programmes or, in the
short run, adopting digital technologies that minimise requirement of digital
skills James (2019). The new policy of the government of Kerala to provide
free high speed Internet to below poverty line households will work towards
shrinking the digital gap.666In Kerala the state government initiated a
programme to provide free high speed Internet connection to over 20 lakh Below
Poverty Line households see
https://www.thehindu.com/news/national/kerala/govt-clearance-for-k-fon-
project/article29901484.ece Digital inequalities in India are rooted in the
educational and income inequalities. Government policies that are cognizant of
this fact will be closer to achieving digital equality in India

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Appendix

For the analysis in the Appendix, the Muslim social group includes all people
who have reported their religion as Islam, irrespective of their caste. To
ensure that Muslims are compared to the non-SC, ST and OBC population in other
religions, the paper first divides the population into Muslims and the rest.
Next, it separates the SC, ST and OBC from the non-Muslim population. For the
analysis done in the Appendix, the Muslims are compared to this residual
category which we refer to as the non-disadvantaged (non-SC, ST, OBC and
Muslims)

Table 8: Summary statistics

| (1)ND | (2)ST | (3)SC | (4)OBC | (5)Muslim | (1)-(2) | (1)-(3) | (1)-(4) | (1)-(5)
---|---|---|---|---|---|---|---|---|---
Variables | Mean | Mean | Mean | Mean | Mean | t-test | t-test | t-test | t-test
COR | 0.241 | 0.054 | 0.066 | 0.097 | 0.082 | 0.187*** | 0.175*** | 0.144*** | 0.159***
| [0.001] | [0.001] | [0.001] | [0.001] | [0.001] |  |  |  |
IAR | 0.458 | 0.139 | 0.181 | 0.245 | 0.23 | 0.319*** | 0.277*** | 0.213*** | 0.228***
| [0.002] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
CLR | 0.357 | 0.11 | 0.135 | 0.196 | 0.143 | 0.247*** | 0.222*** | 0.161*** | 0.215***
| [0.002] | [0.001] | [0.001] | [0.001] | [0.002] |  |  |  |
ILR | 0.428 | 0.143 | 0.176 | 0.244 | 0.202 | 0.285*** | 0.252*** | 0.184*** | 0.226***
| [0.002] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
IUR | 0.391 | 0.121 | 0.15 | 0.211 | 0.179 | 0.270*** | 0.242*** | 0.180*** | 0.212***
| [0.002] | [0.002] | [0.001] | [0.001] | [0.002] |  |  |  |
Age | 35.13 | 33.501 | 33.542 | 34.323 | 32.501 | 1.629*** | 1.588*** | 0.807*** | 2.629***
| [0.042] | [0.056] | [0.051] | [0.036] | [0.055] |  |  |  |
Urban | 0.46 | 0.123 | 0.225 | 0.281 | 0.387 | 0.337*** | 0.236*** | 0.180*** | 0.073***
| [0.002] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
Male | 0.516 | 0.507 | 0.516 | 0.509 | 0.509 | 0.009*** | 0.000 | 0.006*** | 0.007**
| [0.002] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
MPCE | 7.860 | 7.250 | 7.396 | 7.516 | 7.496 | 0.609*** | 0.464*** | 0.344*** | 0.364***
| [0.002] | [0.002] | [0.002] | [0.002] | [0.002] |  |  |  |
Education | 10.268 | 6.363 | 7.028 | 8.087 | 6.934 | 3.905*** | 3.240*** | 2.181*** | 3.334***
| [0.016] | [0.023] | [0.022] | [0.015] | [0.023] |  |  |  |
SEA | 0.294 | 0.395 | 0.225 | 0.357 | 0.174 | -0.101*** | 0.069*** | -0.063*** | 0.120***
| [0.002] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
SENA | 0.276 | 0.12 | 0.161 | 0.214 | 0.354 | 0.156*** | 0.115*** | 0.062*** | -0.079***
| [0.002] | [0.001] | [0.002] | [0.001] | [0.002] |  |  |  |
RSW | 0.275 | 0.094 | 0.155 | 0.164 | 0.148 | 0.181*** | 0.120*** | 0.111*** | 0.127***
| [0.002] | [0.001] | [0.002] | [0.001] | [0.002] |  |  |  |
CWA | 0.042 | 0.185 | 0.199 | 0.097 | 0.091 | -0.142*** | -0.157*** | -0.055*** | -0.049***
| [0.001] | [0.002] | [0.002] | [0.001] | [0.001] |  |  |  |
CWNA | 0.055 | 0.163 | 0.22 | 0.126 | 0.18 | -0.108*** | -0.165*** | -0.071*** | -0.125***
| [0.001] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
OW | 0.039 | 0.024 | 0.019 | 0.024 | 0.027 | 0.015*** | 0.020*** | 0.015*** | 0.012***
| [0.001] | [0.001] | [0.001] | [0.000] | [0.001] |  |  |  |
SB1 | 0.253 | 0.254 | 0.25 | 0.246 | 0.258 | -0.001 | 0.002 | 0.007*** | -0.005*
| [0.001] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
SB2 | 0.252 | 0.24 | 0.242 | 0.252 | 0.248 | 0.012*** | 0.010*** | 0.000 | 0.004*
| [0.001] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
SB3 | 0.252 | 0.268 | 0.253 | 0.248 | 0.255 | -0.016*** | -0.001 | 0.004* | -0.003
| [0.001] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
SB4 | 0.243 | 0.239 | 0.255 | 0.254 | 0.239 | 0.005* | -0.011*** | -0.011*** | 0.004*
| [0.001] | [0.002] | [0.002] | [0.001] | [0.002] |  |  |  |
N | 85258 | 47340 | 57256 | 117600 | 48416 |  |  |  |
Notes: Computer ownership rate (COR); Internet access rate (IAR); Computer
literacy rate (CLR); Internet literacy rate (ILR); Internet use rate (IUR);
Log per-capita income(MPCE); Self-employed in agriculture (SEA); Self-employed
in non-agriculture (SENA);Regular salaried Workers (RSW); Casual worker in
agriculture (CWA); Casual worker in non-agriculture (CWNA); Other work(OW);
Observation(N); Non-disadvantaged(ND); Sub-round(SB). Standard errors are in
parentheses. ***, **, and * indicate significance at the 1, 5, and 10 percent
critical level. Sampling weights of the survey used for the calculations.

Table 9: Marginal effects from probit regressions on the probability of having
computer at home, internet access at home, computer literacy, Internet
literacy and Internet use.

Variables | CO | AIF | CL | IL | IU
---|---|---|---|---|---
Age | 0.001*** | 0.002*** | -0.003*** | -0.006*** | -0.005***
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
Gender: reference = Male |  |  |  |  |
Female | 0.011*** | 0.026*** | -0.022*** | -0.069*** | -0.067***
| (0.001) | (0.003) | (0.001) | (0.002) | (0.002)
Place of residence: reference = Rural |  |  |  |  |
Urban | 0.022*** | 0.061*** | 0.016*** | 0.036*** | 0.033***
| (0.002) | (0.004) | (0.001) | (0.003) | (0.002)
Social group: reference = ND |  |  |  |  |
ST | -0.037*** | -0.117*** | -0.025*** | -0.058*** | -0.049***
| (0.003) | (0.006) | (0.002) | (0.003) | (0.003)
SC | -0.043*** | -0.088*** | -0.024*** | -0.054*** | -0.047***
| (0.003) | (0.005) | (0.002) | (0.003) | (0.003)
OBC | -0.042*** | -0.070*** | -0.017*** | -0.038*** | -0.034***
| (0.002) | (0.004) | (0.002) | (0.003) | (0.002)
Muslim | -0.046*** | -0.078*** | -0.025*** | -0.046*** | -0.038***
| (0.003) | (0.005) | (0.002) | (0.003) | (0.003)
Education | 0.008*** | 0.020*** | 0.017*** | 0.032*** | 0.026***
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000)
Log per-capita income | 0.072*** | 0.121*** | 0.031*** | 0.067*** | 0.062***
| (0.002) | (0.003) | (0.002) | (0.002) | (0.002)
Occupation : reference = SEA |  |  |  |  |
SENA | 0.041*** | 0.068*** | 0.011*** | 0.030*** | 0.032***
| (0.003) | (0.005) | (0.001) | (0.003) | (0.003)
CWA | -0.025*** | -0.070*** | -0.006*** | -0.017*** | -0.015***
| (0.002) | (0.005) | (0.001) | (0.003) | (0.002)
CWNA | -0.025*** | -0.058*** | -0.009*** | -0.008** | -0.006**
| (0.002) | (0.005) | (0.001) | (0.003) | (0.002)
RSW | 0.051*** | 0.096*** | 0.027*** | 0.056*** | 0.058***
| (0.003) | (0.005) | (0.002) | (0.003) | (0.003)
OW | 0.024*** | 0.027** | 0.034*** | 0.058*** | 0.047***
| (0.005) | (0.009) | (0.004) | (0.007) | (0.006)
Month of interview: reference = SB1 |  |  |  |  |
SB2 | -0.011*** | 0.004 | -0.007*** | -0.004 | 0.002
| (0.002) | (0.004) | (0.001) | (0.002) | (0.002)
SB3 | -0.016*** | 0.032*** | -0.006*** | 0.005* | 0.010***
| (0.002) | (0.004) | (0.001) | (0.002) | (0.002)
SB4 | -0.016*** | 0.081*** | -0.007*** | 0.015*** | 0.026***
| (0.002) | (0.004) | (0.001) | (0.002) | (0.002)
State controls | Yes | Yes | Yes | Yes | Yes
Notes: Computer ownership (CO); Access to Internet facility(AIF); Computer
literacy (CL); Internet literacy (IL); Internet use (IU); Self-employed in
agriculture (SEA); Self-employed in non-agriculture (SENA);Regular salaried
workers (RSW); Casual worker in agriculture (CWA); Casual worker in non-
agriculture (CWNA); Other work(OW); Sub-round(SB); Non-disadvantaged(ND).
Robust standard errors are in parentheses. ***, **, and * indicate
significance at the 1, 5, and 10 percent critical level. Sampling weights of
the survey used for the estimation.

Table 10: Nonlinear decompositions of social group disparities in computer
ownership rate

Variables | ND-ST | % explained | ND-SC | % explained | ND-OBC | % explained | ND-Muslims | % explained
---|---|---|---|---|---|---|---|---
Gap in COR | 0.187 |  | 0.175 |  | 0.144 |  | 0.159 |
Age | 0.003*** | 1.6 | 0.003*** | 1.7 | 0.002*** | 1.4 | 0.004*** | 2.5
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
Gender | 0.000 | 0.0 | 0.000** | 0.0 | 0.000 | 0.0 | -0.000* | 0.0
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
Education | 0.042*** | 23 | 0.039*** | 22.9 | 0.027*** | 19.4 | 0.046*** | 28.3
| (0.001) |  | (0.001) |  | (0.001) |  | (0.001) |
Log per-capita income | 0.082*** | 43.4 | 0.074*** | 42.9 | 0.055*** | 37.5 | 0.063*** | 39.6
| (0.003) |  | (0.002) |  | (0.001) |  | (0.002) |
Place of residence | 0.033*** | 17.7 | 0.021*** | 12 | 0.015*** | 10.4 | 0.007*** | 3.8
| (0.002) |  | (0.002) |  | (0.001) |  | (0.001) |
Occupation | 0.011*** | 5.9 | 0.012*** | 7.4 | 0.010*** | 6.9 | 0.005*** | 3.1
| (0.001) |  | (0.001) |  | (0.001) |  | (0.001) |
Sub-round | -0.000 | 0.0 | 0.000 | 0.0 | 0.000*** | 0.0 | -0.000 | 0.0
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
State | -0.002 | -1.6 | -0.017*** | -10.3 | -0.015*** | -9.7 | -0.009*** | -5
| (0.002) |  | (0.001) |  | (0.002) |  | (0.001) |
Total explained |  | 90 |  | 76.6 |  | 65.9 |  | 72.4
Notes: Computer ownership rate(COR); Non-disadvantaged(ND). ***, **, and
*indicate significance at the 1, 5, and 10 percent critical level. Survey
sampling weights are used for the estimation

Table 11: Nonlinear decompositions of social group disparities in Internet
access rate

Variables | ND-ST | % explained | ND-SC | % explained | ND-OBC | % explained | ND-Muslims | % explained
---|---|---|---|---|---|---|---|---
Gap IAR | 0.319 |  | 0.277 |  | 0.213 |  | 0.228 |
Age | 0.003*** | 0.9 | 0.003*** | 1.4 | 0.002*** | 0.8 | 0.005*** | 2.2
| (0.000) |  | (0.000) |  | (0.000) |  | (0.001) |
Gender | -0.000 | 0.0 | 0.000 | 0.0 | -0.000 | 0.0 | -0.000** | 0.0
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
Education | 0.074*** | 23.5 | 0.065*** | 23.1 | 0.044*** | 18.0 | 0.068*** | 30.0
| (0.002) |  | (0.002) |  | (0.001) |  | (0.002) |
Log per-capita income | 0.094*** | 29.5 | 0.066*** | 23.8 | 0.053*** | 21.6 | 0.059*** | 25.7
| (0.004) |  | (0.002) |  | (0.002) |  | (0.002) |
Place of residence | 0.042*** | 13.2 | 0.029*** | 10.5 | 0.020*** | 8.2 | 0.009*** | 3.9
| (0.003) |  | (0.002) |  | (0.001) |  | (0.001) |
Occupation | 0.011*** | 3.4 | 0.017*** | 6.1 | 0.010*** | 4.1 | 0.010*** | 4.3
| (0.001) |  | (0.001) |  | (0.001) |  | (0.001) |
Sub-round | -0.000*** | 0.0 | -0.001*** | -0.4 | -0.001*** | -0.4 | 0.000*** | 0.0
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
State | 0.017*** | 5.0 | 0.018*** | 6.5 | 0.020*** | 8.6 | 0.017*** | 7.0
| (0.003) |  | (0.002) |  | (0.002) |  | (0.002) |
Total explained |  | 75.5 |  | 71.1 |  | 60.8 |  | 73.1
Observations | 132596 |  | 142509 |  | 202856 |  | 133671 |
Notes: Internet access rate(IAR); Non-disadvantaged(ND). ***, **, and *
indicate significance at the 1, 5, and 10 percent critical level.Survey
sampling weights are used for the estimation

Table 12: Nonlinear decompositions of social group disparities in computer
literacy rate

Variables | ND-ST | % explained | ND-SC | % explained | ND-OBC | % explained | ND-Muslims | % explained
---|---|---|---|---|---|---|---|---
Gap in CLR | 0.247 |  | 0.222 |  | 0.161 |  | 0.215 |
Age | -0.016*** | -6.5 | -0.015*** | -7.7 | -0.009*** | -5.0 | -0.023*** | -10.2
| (0.000) |  | (0.000) |  | (0.000) |  | (0.001) |
Gender | 0.000 | 0.0 | -0.000*** | 0.0 | -0.000 | 0.0 | 0.001*** | 0.5
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
Education | 0.116*** | 47.0 | 0.097*** | 44.1 | 0.068*** | 41.6 | 0.112*** | 52.2
| (0.002) |  | (0.002) |  | (0.001) |  | (0.002) |
Log per-capita income | 0.070*** | 28.3 | 0.056*** | 25.2 | 0.043*** | 26.1 | 0.047*** | 21.9
| (0.003) |  | (0.002) |  | (0.001) |  | (0.002) |
Place of residence | 0.027*** | 10.9 | 0.020*** | 9.5 | 0.014*** | 8.7 | 0.008*** | 3.7
| (0.002) |  | (0.001) |  | (0.001) |  | (0.001) |
Occupation | 0.014*** | 5.7 | 0.012*** | 5.4 | 0.009*** | 5.6 | 0.009*** | 4.2
| (0.001) |  | (0.001) |  | (0.001) |  | (0.001) |
Sub-round | -0.000* | 0.0 | 0.000 | 0.0 | 0.000*** | 0.0 | -0.000 | 0.0
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
State | 0.000 | 0.0 | 0.003* | 1.4 | -0.001 | 0.0 | 0.005*** | 2.3
| (0.002) |  | (0.001) |  | (0.001) |  | (0.001) |
Total explained |  | 85.4 |  | 77.9 |  | 77.1 |  | 74.5
Observations | 132595 |  | 142509 |  | 202856 |  | 133671 |
Notes: Computer literacy rate(CLR); Non-disadvantaged(ND). ***, **, and *
indicate significance at the 1, 5, and 10 percent critical level. Survey
sampling weights are used for the estimation

Table 13: Nonlinear decompositions of social group disparities in Internet
literacy rate

Variables | ND-ST | % explained | ND-SC | % explained | ND-OBC | % explained | ND-Muslims | % explained
---|---|---|---|---|---|---|---|---
Gap in ILR | 0.285 |  | 0.252 |  | 0.184 |  | 0.226 |
Age | -0.017*** | -5.6 | -0.016*** | -6.8 | -0.008*** | -4.4 | -0.023*** | -9.3
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
Gender | 0.001*** | 0.4 | -0.000 | 0.0 | 0.000* | 0.0 | 0.002*** | 0.9
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
Education | 0.132*** | 46.7 | 0.110*** | 44.5 | 0.075*** | 40.8 | 0.119*** | 52.2
| (0.002) |  | (0.002) |  | (0.001) |  | (0.002) |
Log per-capita income | 0.075*** | 26.4 | 0.057*** | 22.3 | 0.044*** | 23.9 | 0.048*** | 20.8
| (0.003) |  | (0.002) |  | (0.001) |  | (0.002) |
Place of residence | 0.028*** | 9.8 | 0.021*** | 8.3 | 0.014*** | 7.6 | 0.009*** | 4.0
| (0.002) |  | (0.001) |  | (0.001) |  | (0.001) |
Occupation | 0.012*** | 4.2 | 0.010*** | 4.0 | 0.008*** | 4.4 | 0.005*** | 2.2
| (0.001) |  | (0.001) |  | (0.001) |  | (0.001) |
Sub-round | -0.000*** | 0.0 | -0.000* | 0.0 | -0.000*** | 0.0 | 0.000** | 0.0
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
State | -0.000 | 0.0 | 0.010*** | 4.5 | 0.005*** | 3.1 | 0.009*** | 4.2
| (0.002) |  | (0.001) |  | (0.001) |  | (0.001) |
Total explained |  | 81.9 |  | 76.8 |  | 75.5 |  | 75.0
Observations | 132595 |  | 142509 |  | 202856 |  | 133671 |
Notes. Internet literacy rate (ILR); Non-disadvantaged(ND). ***, **, and *
indicate significance at the 1, 5, and 10 percent critical level. Survey
sampling weights are for used the estimation.

Table 14: Nonlinear decompositions of social group disparities in Internet use
rate

Variables | ND-ST | % explained | ND-SC | % explained | ND-OBC | % explained | ND-Muslims | % explained
---|---|---|---|---|---|---|---|---
Gap in IUR | 0.27 |  | 0.242 |  | 0.18 |  | 0.212 |
Age | -0.016*** | -5.9 | -0.016*** | -6.6 | -0.009*** | -5.0 | -0.021*** | -11.7
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
Gender | 0.001*** | 0.4 | -0.000** | 0.0 | 0.000*** | 0.6 | 0.002*** | 1.1
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
Education | 0.112*** | 41.5 | 0.095*** | 39.3 | 0.065*** | 36.2 | 0.104*** | 58.0
| (0.002) |  | (0.002) |  | (0.001) |  | (0.002) |
Log per-capita income | 0.081*** | 30.0 | 0.062*** | 25.2 | 0.046*** | 25.6 | 0.052*** | 29.0
| (0.003) |  | (0.002) |  | (0.001) |  | (0.002) |
Place of residence | 0.031*** | 11.5 | 0.023*** | 9.5 | 0.015*** | 8.3 | 0.009*** | 5.0
| (0.002) |  | (0.002) |  | (0.001) |  | (0.001) |
Occupation | 0.011*** | 4.1 | 0.010*** | 4.1 | 0.010*** | 5.6 | 0.005*** | 2.8
| (0.001) |  | (0.001) |  | (0.001) |  | (0.001) |
Sub-round | -0.000* | 0.0 | -0.000* | 0.0 | -0.000*** | 0.0 | 0.000*** | 0.0
| (0.000) |  | (0.000) |  | (0.000) |  | (0.000) |
State | 0.002 | 0.7 | 0.011*** | 4.6 | 0.008*** | 4.5 | 0.009*** | 5.0
| (0.002) |  | (0.001) |  | (0.002) |  | (0.001) |
Total explained | 132595 |  | 142509 |  | 202856 |  | 133671 |
Notes. Internet use rate (IUR); Non-disadvantaged(ND). ***, **, and * indicate
significance at the 1, 5, and 10 percent critical level.Bootstrap standard
errors in parentheses. Survey sampling weights are for used the estimation.