Human Development,Per Capita Income Differentials and English as Source of Demographic Dividends Among the Youth in India

Abstract

India has a large youthful population; this ‘young nation’ is both a national and international resource that facilitates global value added. Such gains would accrue only when the youth are adequately educated, skilled and occupied in gainful employment. India, however, is staring at a missed opportunity that favours harnessing demographic dividends offered by demographic transition.

Keeping the tradition of high levels of diversity of labour force in India, this article examines the youthful demographic parameters according to socio-religious communities (SRCs). The economic momentum needed to reach a US$5 trillion (GDP) economy can only occur when the youth of India is educated, skilled and employed. A rather rare data set is analysed to explore the importance of English language education in augmenting demographic dividends.

Traditionally, disadvantageous groups, especially Muslims and Scheduled Castes/Scheduled Tribes (SCs/STs), are the youngest in India, yet their share in workforce is lowest when compared to other groups. This article reviews few human development dimensions like the rate of poverty change in the recent past and traces the path of educational transition and human development according to SRCs. A further analysis suggests that irrespective of any SRC identity, knowledge and fluency in English are some of the most dominant discriminatory factors in India in terms of the ability to earn higher incomes.

Keywords

Human development demographic dividend socio-religious communities employment poverty English language

Introduction

India, since independence and more so since about the early 1990s, has made many noteworthy gains in human development indicators such as levels of literacy, education and health and has also significantly reduced poverty (UNDP India, 2018). The fruits of such development must reflect in the improvements in quality of living of the masses. One way to assess this is to find out the differentials in outcome indicators of human development according to socio-religious communities (SRCs) across the country, especially the Scheduled Castes/Scheduled Tribes (SCs/STs) and Muslims—the largest minority community of India. Recent assessment suggests these communities lag in several human development indicators. Historically, in India, caste, religion and geographic regions have had varied socio-economic outcomes. They are affected by deep-rooted and complex issues in the country’s societal fabric, and they must be understood through empirical and evidence-based policy research (Sachar Committee Report, 2006).

Since 1970, India has experienced a decline in the level of poverty, which was accelerated after 1991, but it also concurrently led to an increase in inequality. During the period between 2004–2005 and 2011–2012, India experienced high growth in GDP along with a significant decline in estimated poverty. The official overall poverty rate declined from 37.2% to 21.9% during the same period (Planning Commission, 2013). Such decline in poverty rate is often considered an achievement and celebrated widely. While such a decline took place at the macro level, the poverty got concentrated among some specified groups. Despite several targeted action policies, some socio-religious groups remain traditionally trapped in poverty. This deserves special analytical and policy attention.

Demographic Dividend

Accelerated future economic development in India is largely dependent on the value added from the current and future generations of youthful labour force, and this is termed as the ‘demographic dividend (DD)’. The dividend gets generated only if appropriate human development investments are made to enhance the per capita value added of the individual labourer. Given an opportunity, when a country has a larger share of working-age population (15–64 years) in comparison to its non-working population, there is potential for greater economic achievement. Yet, having a favourable demographic age structure does not necessarily guarantee an economic boost for a country. History shows the possibilities to miss the chance to reap demographic dividend (UNFPA, 2018). Achievement of the DD requires strategic investment decision-making in the areas of human development and creation of economic opportunities. A ‘dividend’ is lost if a country’s young population is not educated, healthy or properly skilled. Investing in the youth takes a long-term understanding of sustainability of the labour force and economy. A well-planned investment strategy is a precondition to tapping the potential of the country’s new-technology-aligned youth. The Economic Survey (2016–2017) has forecasted that India is likely to experience a peak in DD in the early 2020s. In a scenario where the world is ageing, the number of young Indians are rising. Yet a critical question arises. Is India ready to reap DDs? Is the young population equipped with the required education, health, skills and employment that are essential to generate it? Reports have emerged about the redundancy of the youth labour in India (Bluedorn, 2019) and increasing unemployment rates to levels hitherto not recorded in independent India.

There are at least two counts that underline the economic importance of empowering deprived communities. The first one is that the above-identified communities together constitute over 40% of the total labour force. Ensuring their basic entitlements also in addition to enhancing them are not only necessary but also essential to boost the national GDP through DDs. Second, since the value-added gain (internal rate of return) is relatively high at the level of mass education and skill formation, it will be prudent for the national and state governments to invest in education, skill formation and creation of quality social infrastructure. Such public policies will also generate an accelerating affect to ensure improvement in economic equity and overall national welfare.

This article empirically reviews select outcome indicators of human development among the youth population of India. The differentials in these indicators according to SRCs are also estimated and analysed. While employment and wages are essential indicators to measure DDs, this article uses a rare data set to explore the importance of English language education in augmenting DDs. One of the objectives of this article is to encourage debate and research in a unique area of understanding differentials according to SRCs.

Methods and Material: Data and Analysis

Several relevant socio-economic and developmental parameters are estimated using the national sample survey (NSS) 68th round (2011–2012) data on the youth and working-age population of India. Some socio-religious groups remain traditionally trapped under low value-added sectors of the economy and are disadvantaged. This is essentially due to the youth being unable to participate in the nation’s developmental opportunities. This article examines demographic parameters according to socio-religious groups so that targeted policies can be initiated to harness the untapped potential of India’s large youth population, which, when provided with appropriate skills and employment opportunities, can boost the economy and GDP growth.

Socio-religious Communities of India

Research and analysis based on caste and religion are not very popular in India, excepting focus on the estimates for the SCs and STs. However, the analysis in this article is based on the SRC categorisation as used by the Prime Minister (PM’s) High-Level Committee also known as Sachar Committee (Sachar Committee Report, 2006).

Census data are essential for national- and state-level planning. In India, census is conducted a decade apart, during the first year of the decade. The latest census was conducted during 2011, and population counts for the SCs and STs became available quickly. However, usable data according to religion became available only recently. Bureaucratic decisions often appear discriminatory and politically motivated. It would be fair to say that it is not a technical issue, that census has not been able to count the population according to religion but only that the data were withheld from public scrutiny for long.

According to Census of India (2011), just about 235 million or 20% of the population is categorised as religious minority. The respective share of population according to religion are—Hindus (79.8%), Muslims (14.2%), Christians (2.3%), Sikhs (1.7%), Buddhists (0.7%), Jains (0.4%) and other religions (0.7%). Thus, Muslims are the largest socio-religious minority accounting for 70% of all identified religious minorities, and they reside in substantial proportions in most parts of India. The next largest group is Christians with 2.3% of the country’s population, and they are numerically and proportionately small. The exceptions to this are Kerala, Tamil Nadu, Goa and all north-eastern states other than Assam.

Although Census of India is a credible source of demographic data, the unit-record-level or individual- and household-level data are not available for academic purposes and are not useful for technical analysis, especially in creating meaningful SRC categorisations. Alternatively, India is also well known for undertaking annual (also quinquennial large) surveys known as the ‘national sample surveys’, and analysing variables at the level of the individual and households are feasible. In fact, the National Sample Survey Office (NSSO) databases are the foundation of poverty research in India. They are also useful in generating population distribution according to religion and the SRC categories. The following analysis is based almost entirely on the NSSO survey databases. The SRC distribution estimated from the NSSO 68th round f.or reference year 2011–2012 for the whole of India is presented in Table 1.

However, the adjusted share of Muslims in the population estimation both from the census and from the NSSO is 14%, and since the latter data are amenable for detailed analysis, this article uses it for all the following analysis.

Table 1.

Distributions of the Socio-religious Communities in India.

States	Hindu—SCs/STs	Hindu—OBC	Hindu—Gen.	Muslims—All	Christians	Sikhs	Other Minorities	OM—Other than Muslim
All India	25.4	36.2	19.9	13.7	2.2	1.6	1.0	4.8

Source: Employment and unemployment survey, National Sample Survey 68th Round (2011–2012), Government of India.

Human Development Issues

The Demographic Distributions

The second section of this article will explore the Indian youth and associated human and economic development parameters in a comparative perspective. Table 2 presents distribution of total population (all ages) by sex and according to SRCs, and the distributions appear as normal shares of the identified communities.

Table 2.

Distribution of the Indian Population as a whole by Sex and Social Groups.


Social Group	Male (%)	Female (%)	Total (%)
SCs/STs	27.3	27.4	27.3
OBCs	36.9	37.1	37.0
Muslims	13.8	13.9	13.8
All Others	22.0	21.6	21.8
Total	100.0	100.0	100.0

Source: Authors’ calculations using NSS 68th round (2011–2012) Employment and Unemployment data.

In this analysis, the focus is on measures of the shares of youth according to SRCs who will anchor the future Indian economic growth (Peterson, 2017). India is blessed with relatively higher young and youth population. This ‘young India’ is both a national and international resource if the youth are adequately educated, skilled and occupied in gainful employment.

The mean age of the population reflects the youthfulness of specified SRC. A total of 66% of India’s population fall under the age bracket of 15–64 years, which is unique compared to the developed world. The mean age for India has averaged at 28.1 years, and a little lower for men (Table 3). Males comprise a larger proportion of the labour force. Muslims are the youngest with 24.8 year average. In comparison, the ‘all others’ category averages 30.7 years. SCs/STs are the second youngest group followed by other backward castes (OBCs). This difference suggests Muslims in India have the maximum young population, and their future contribution to the Indian economy will not only be large but essential.

Table 3.

Mean Age Among Different Socio -Religious Groups.

Social Group	Male	Female	Total
SCs/STs	26.5	27.1	26.8
OBCs	27.9	28.6	28.2
Muslim	24.8	25.5	25.1
All Others	30.7	32.0	31.3
Total	27.7	28.5	28.1

Source: Authors’ calculations using NSS 68th round (2011–2012) Employment and Unemployment data.

The relative differential in mean age reflects a relatively higher fertility than the comparable groups as is the case of Muslims of India. While the all-India mean age for males is 27.7 years and 28.5 years for women, the respective means are 24.8 years and 25.5 years for Muslims. When these means are compared with the ‘all others’ category (which is mostly dominated by caste Hindus), the differential is much larger, higher than about 6 years for both men and women.

It is observed that fertility among the Muslims has declined faster during the past several decades, and the contraceptive practice has increased among the Muslim population (Shariff, 1995). The trend suggests that the mean-age differentials are narrowing, and they will be much less in a couple of decades. Further the low mean age advantage for India will evaporate in about 2–3 decades.

The Youth in Workforce

In this subsection, data on youth according to SRCs are presented. The population shares are according to ages—less than 15 years (Child), 15–29 years (Youth) and 15–65 years (the working ages) are presented in Table 4a.

It can be clearly observed that the child population among the Muslims are considerably high when compared to the OBCs as well as all-others category. Their share among the youth is also high at 28.1% on an average about 2 percentage points higher than other SRCs (Table 4a. A 35% share of child population among Muslims points to the higher youth population in near future, implying their importance in Indian labour market in the coming decades as compared to other socio-religious groups that are turning older. Table 4b presents these data according to gender and the shares are meaningful when compared to the respective SRC shares in the total population (consult data in Table 1). The relative youthful advantage for Muslims is seen among both men and women.

Table 4a.

Composition of Child, Youth and Total Working Group Among Socio-religious Groups—All India.

Social Groups	Child (<15 years)	Youth (15–29 years)	All Working Ages (15–64 years)
SCs/STs	32.1	27.3	64.0
OBCs	29.8	26.3	65.2
Muslim	35.3	28.1	61.2
All others	23.7	26.5	69.4
Total	29.9	26.9	65.2

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Table 4b.

Gender Composition of Child, Youth and Total Working Group Among Socio-Religious Groups—All India.

	Up To 14 Years		Youth (15–29 years)		Working Age (15–64 years)
Social Group	Male	Female	Male	Female	Male	Female
SCs/STs	29.1	29.6	27.7	27.9	26.8	26.9
OBCs	36.7	37.3	36.2	36.2	37.0	36.9
Muslim	16.3	16.4	14.4	14.5	12.9	13.1
All others	17.9	16.7	21.7	21.3	23.3	23.1
Total	100	100	100	100	100	100

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Primary Workers

Primary worker is a concept, which is closely aligned to gainfully employed population in the economy. All those who report themselves as primary workers are assumed to be fully employed. A low or high share of the primary workers in a country indicates the engagement of the working age population in the market related activities in the economy.

Only 38% of the youth and 52% of the population aged 15–64 years have reported their status as primary workers. One can observe a relatively higher reporting of primary workers among the youth belonging to the SCs and STs. On the other hand, the proportions are substantially lower than the national average among the ‘all others’ category, which largely comprises dominant caste Hindus. Note that these are basic proportions not disaggregated by sex and education, which is not the focus of this article.

Suffice it to say that although the Muslim community has recorded the largest share in the age-specific population when compared to other SRCs, its share in employment or workers does not correspond to its youth population. Their representation is lowest in the workforce as compared to other SRCs belonging to the 15–64 years age group (Table 5) and significantly lower than the national average—47.2% when compared to 52.0%. However, most of the lower labour participation among the Muslims has emerged from the abysmally lower participation rates among Muslim women (Table 6).

Table 5.

Composition of Primary Workers Among Different Age Categories by SRCs—All India.

Social Groups	Youth (15–29 years)	Working Age (15–64 years)
SCs/STs	42.4	56.5
OBCs	37.0	52.8
Muslim	37.8	47.2
All others	31.7	47.9
Total	37.5	52.0

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Table 6.

Composition of Workers Among Different Age Categories by Socio-religious Groups and Gender in India.

	Youth (15–29 years)		Working Age (15–64 years)
Social Group	Male	Female	Male	Female
SCs/STs	30.3	36.1	27.5	35.1
OBCs	35.4	37.0	37.1	39.3
Muslim	15.7	10.0	13.0	7.6
All others	18.5	16.8	22.5	17.9
Total	100	100	100	100

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Table 7.

Education Profile of Youth (15–29 years) Population by Socio-religious Groups.

Social Group	Illiterate	Below Secondary	Secondary	Higher Secondary	Above Higher Secondary	Total
SCs/STs	40.1	32.6	22.4	19.7	14.2	27.8
OBCs	33.6	34.9	39.9	37.6	36.1	36.2
Muslim	19.8	16.9	12.6	9.8	7.5	14.5
All others	6.5	15.6	25.1	32.9	42.3	21.5
Total	100.0	100.0	100.0	100.0	100.0	100.0

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Education Profile of the Youth

The importance of mass education and higher levels of technical literacy cannot be overemphasised, especially when India is amid profound economic reforms, which have redefined the way Indians live, think, behave and excel within the comity of globalising nations. Literacy, vocational and technical training, and higher education are drivers of social and economic development, and they form the very foundation of livelihood and lifestyle in the growing and developing India. A large body of research establishes the wide-ranging social and economic benefits of literacy and education that accrue to individuals, households and the larger society (Birdsall, 2001; Caldwell, 1976; Schultz, 1988). The benefits of expanding literacy and educational opportunities for girls and women through increase in both market (money income) and non-market (gains in human development) returns are also well established (Psacharopoulos, 1994).

Social group differences in enrolment are striking (Sachar Committee Report, 2006). SCs, STs and Muslim children are far less likely to enrol in schools and are slightly more likely to drop out than others. It is particularly worrisome to note that the Muslim children are as disadvantaged as Dalit and Adivasi children, although little attention is paid to religious background as a source of educational disadvantage. There is wide divergence in the three Rs (reading, writing and arithmetic) by social and religious background—children from all above-cited communities falling behind. Not surprisingly, this educational deficiency is reflected in lower access to salaried jobs among these communities (Desai et al., 2010).

It is startling to find (Table 7) that compared to the respective share in the 15–29-year population, the SCs/STs and Muslims have a proportionately higher illiteracy. A total of 40% of SC/ST labourers are illiterate when compared to their share in population (28%). Similarly, almost one-fifth of all illiterate labour force is Muslims, while their share in the workforce is 14.5%.

Let us compare their respective share in the educational category ‘above higher secondary’. Only 14% of SC/ST and 7.5% of Muslims labour force are educated above the higher secondary level when compared to 42% of ‘all others’, which is disproportionately larger than their share in labour force of 22%.

It is such a large disproportionate representation in higher-level education and associated skill formation, which has hurt exclusive SRCs, namely the SC/STs and the Muslims. Such a puzzling disparity also affects the ability of the Indian economy to reap the benefits of DDs and cause huge social inequity and disparity in income levels.

Youth and Sectors of Economy

The strongest evidence of inequity and deprivation emerges from distribution of workers according to various sectors of the economy. In this analysis, the Indian economy is divided into three classical groups, namely agricultural or primary sector, industry and manufacturing (secondary), and services or tertiary sectors. However, given our expertise in analysing primary data, it was possible to further subdivide the services sector into traditional (small business, retail, etc.) and modern, which encompass employment in information technology, transport and tourism, medical and financial services. The modern services sector produced relatively high per capita value added, thus generating better DDs.

Sectoral distribution of youth workers according to sectors of the economy (in Table 8) suggests that while the OBCs are at parity participation in modern services employment, SCs/STs and Muslims are highly underrepresented. The representation of the ‘all others’ is substantial—31% when compared to only 18% in the youth workforce. The relative advantage of ‘all others’ category appears to be linked with the underrepresentation of the SC/STs and Muslims.

The case of Muslim employment and income vulnerability in the context of DDs emerges from the fact that their representation in traditional service and industry is disproportionally very high. Most of the industrial employment involving the Muslim population is in terms of unskilled manual labour and low-paid temporary daily wage. Historically, the Muslims are not landed, and, therefore, their share in agriculture is rather low, and, often, it will be of daily manual wage labour type.

Table 8.

Sector-Wise Distribution of Youth Workers According to SRCs.

Social Group	Agriculture and Allied	Industry	Traditional Services	Modern Services	Total
SCs/STs	37.5	31.7	20.7	23.7	31.7
OBCs	40.2	30.7	33.9	36.4	36.0
Muslim	7.9	21.6	21.6	8.7	14.3
All others	14.3	15.9	23.8	31.2	18.0
Total	100.0	100.0	100.0	100.0	100.0

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Regardless of the sector, forward castes and other minority religions have higher salaries than OBCs, Dalits, Adivasis and Muslims (Desai et al., 2010). While, historically, Muslims are less landed, their share in modern services is the reflection of low levels of education and quota-reservation-based employment at higher levels of education.

Figure 1.

Source: Reproduced from Shariff (2016): Figure 4.12, p. 141.

In continuation of the relationship of education and income levels in the following is an analysis of relative per capita value added at different levels of education by SRCs.

The analysis suggests that, even after attaining a certain level of education, the disadvantaged communities show a lower level of efficiency and, hence, lower level of development. Figure 1 presents levels of educational achievements for up to middle and above middle levels and Figure 2 presents associated efficiency quotients. The efficiency quotients estimated for different communities at different levels of education suggest high variation. For example, the other minorities and Hindu general sections of the population are at the top of the list at each level of education. Efficiency of the two top communities is followed by Muslims, Hindu OBCs and Hindu SCs/STs at each level of education. This in a way opens doors to further research in answering as to why the educational attainment also does not aid equally in the development of each community. Why do the Hindu general sections and other minorities show a higher level of efficiency, leading to higher levels of economic and social development (Figure 2) when compared to the same level of the educated from the other deprived communities?

However, a unique feature of the Muslim community, which is consistent across different periods, is the fact that, even at the lower levels of education and even if they are illiterate, their relative contribution is comparable or even better than all other communities in India. There is a strong suggestion to the fact that access to higher education and skills for the Muslims, in fact, consistently contributes to GDP with the greatest levels of efficiency. They are the most productive, although, at very high levels of education, they sustain high levels next only to the H-General and the other minority categories.

Figure 2.

Source: Reproduced from Shariff (2016): Figure 4.12, p. 141.

Poverty, Dependency and Work Contracts as Evidence of Vulnerability

As stated earlier, to reap the benefit of DD, it requires that country’s youth is equipped with appropriate education, skills and health, which are lacking among those living below poverty line.

Table 9.

Poverty Among Child, Youth and Working Age Groups by Socioreligious Groups.

Social Group	Child (<15 years)	Youth (15–29 years)	15–64 years
SCs/STs	41.2	30.3	30.4
OBCs	25.6	17.4	17.0
Muslim	31.0	22.7	22.5
All others	13.1	8.4	7.9
Total	28.9	19.8	19.2

Source: Authors’ calculations using NSS 61st and 68th round (2004–2005 and 2011–2012), Consumption and Expenditure Survey data.

Table 10.

Dependency Ratio and MPCE by Social Groups.

Social Group	Dependency Ratio 1 (young and old)	Dependency Ratio 2 (non-workers/workers)	MPCE*
SCs/STs	56.4	1.4	1,145
OBCs	53.5	1.5	1,441
Muslim	63.5	2.0	1,328
All others	44.1	1.7	2,219
Total	53.4	1.6	1,514

Source: Authors’ calculations using NSS 68th round (2011–2012) Employment and Unemployment data.

Note: Dependency Ratio 1 is calculated by adding together the children (aged under 15 years), and the older population (aged 65+), dividing that by the working-age population (aged 15–64 years) and multiplying that by 100, so the ratio is expressed as the number of ‘dependents’ per 100 people aged 15–64 years.

Dependency Ratio 2 is calculated by dividing total workers (both primary and secondary) by non-workers, which is expressed as the number of ‘dependents’ per working person.

*Monthly Per Capita Expenditure.

In Table 9, it is not difficult to predict that if a considerable section of people from disadvantage groups (especially its youth and children) are living in poverty, then how can one expect to reap the DDs. The SCs/STs, followed by the Muslims, are the most disadvantage group, suffering from high incidence of poverty. The incidence of poverty is highest at 41.2% and 30.3%, respectively, for child and youth population belonging to the SCs/STs category, and the respective incidence for Muslim community is 31% and 22.7%, while their share in population is just over 14%. It appears, however, that a relatively higher proportion of child poverty across the SRCs is an evidence of a positive relationship between higher number of children and low incomes.

Dependency Ratio and Mean Per capita Expenditure

Low participation in workforce results in high dependency ratio. A shift in the dependency ratio indicates towards the potential social support requirements and a burden on working population. There are two types of dependency ratios—one is age based and the other is work/gainfully employed based. The first type of dependency ratio reflects the share of children and the old (above 65 years of age) who are dependent on the 15–64-year age group; the second type is estimated by dividing the actual non-working population by the working population.

Both the definitions show high dependency ratio of Muslim community followed by SCs/STs. However, due to the low participation of the Muslim community in the workforce, the strength of dependency ratios in case of second method was found to be larger when compared to other groups. The second method implies that one working person has the burden of two non-working people.

Table 11.

Percentage Distribution of Work Status by Social Groups—All Youth.

Social Group	Own Account Worker	Employer	Worked as Helper in Household Enterprise (unpaid family worker)	Worked as Regular Salaried/Wage Employee	Worked as Casual Wage Labour: In Public Works	Worked as Casual Wage Labour: in Other Types of Work	Total
SCs/STs	13.4	0.1	21.7	15.3	1.3	48.3	100
OBCs	16.3	0.3	29.2	21.6	0.6	32.0	100
Muslim	21.7	0.9	24.2	20.9	1.3	31.0	100
All others	17.9	1.0	27.0	39.4	0.2	14.6	100
Total	16.5	0.5	25.7	22.7	0.8	33.8	100

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Work Status and Social Groups

The type of work has a close relationship with the SRCs. Table 11 present distribution of workforce according to the type of work status. Overall, 17% are ‘own account workers’, another 34% are ‘casual wage labourers’. Only 23% ‘worked on regular salaried employment’. Once these work status distributions are analysed according to SRCs, one finds that Muslims and the SCs/STs are least employed in regular salaried jobs. Muslims are most likely to work on own account or self-employed vocations and more so the ‘Muslim female worker’.

Job Contract and Employment Security

In a fast-growing economy, the type of job contracts highlights stability of paid employment and associated economic benefits. These data suggest Muslims work under highly vulnerable situations, and over 90% undertake paid employment with no written contracts. The ‘All-Other’-category youths have the most secure jobs. Over one-third of them have written contracts, suggesting organised sector jobs (Tables 12a and 12b). The job vulnerability of the SCs/STs and OBCs is also high.

Table 12a.

Type of Job Contract by Socio-religious Groups.

Social Group	No Written Job Contract	Written Job Contract: For 1 Year or Less	More than 1 Year to 3 Years	More than 3 Years
SCs/STs	84.0	2.4	1.2	12.5
OBCs	81.3	2.3	1.6	14.8
Muslim	90.2	1.9	0.5	7.4
All others	63.8	3.8	2.8	29.6
Total	78.9	2.6	1.6	16.8

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Table 12b.

Type of Job Contract of Working Youth Population by Socio-religious Groups.

Social Group	No Written Job Contract	Written Job Contract: For 1 Year or Less	More than 1 Year to 3 Years	More than 3 Years
SCs/STs	90.7	2.8	1.3	5.2
OBCs	87.2	3.2	1.9	7.7
Muslim	95.4	1.3	0.6	2.6
All others	73.6	6.0	4.9	15.6
Total	86.6	3.4	2.1	7.8

Source: Authors’ calculations using NSS 68th round (2011–2012), Employment and Unemployment data.

Role of English Education in Reaping Demographic Dividends

One of the crucial issues with respect to reaping DDs is type and quality of higher-level education and overall education policy in general. Since providing education is the responsibility of states in India, there is a wide diversity in the medium of instruction policy in India. There are issues relating to mother tongue, regional languages, Hindi as the language of the nation and English as a foreign language without which Indians cannot make it to the higher levels of learning and higher levels of earning. The complication has emerged since India is a nation of multiple languages, and dialects and the very foundation for the formulation of the states was based on language. There is no country on this planet, which has such a large diversity of languages and associated culture, literature and social value systems. However, it may not entirely be the legacy of the British Raj that now education of children in English is a mechanism to overcome serious socially motivated differentials, and also that contemporary globalising economic system is anchored in English language. Today’s Internet revolution, supported by technological innovation, is largely anchored upon the English language. To capture the prevalence of English-medium higher education in India, the authors have estimated income-earning differentials associated with knowledge and fluency in the English language and according to SRCs and other dimensions. These data are generated from the National Council of Applied Economic Research (NCAER's) Human Development Survey—2011–2012.

In India, knowledge and education in English language generate better incomes over the lifetime. First, let us review the ‘English language ability’ according to the SRCs (Tables 13a and 13b). Overall, only 4.8% in the sample households know English fluently, and another 20% know it a little. In the fluency category, the proportion is double that of the ‘all others’ category, and least for SCs/STs and Muslims. By looking at the same data another way, it can be inferred that out of all those who know English fluently, 43% are from the ‘all others’ group, largely comprising dominant caste Hindus. This is double their share in population.

Table 13a.

Distribution of English Ability According to SRCs.

Social Group	None	Little	Fluent	Total
SCs/STs	81.8	15.5	2.7	100.0
OBCs	75.6	20.2	4.2	100.0
Muslim	81.1	15.9	3.1	100.0
All others	61.4	28.8	9.8	100.0
Total	75.2	20.1	4.8	100.0

Source: Authors’ calculations using Human Development Survey (IHDS II) data (2011–2012).

Table 13b.

Distribution of English Ability According to SRCs.

Social Group	None	Little	Fluent	Total
SCs/STs	32.2	22.9	17.0	29.6
OBCs	36.2	36.2	31.2	35.9
Muslim	14.3	10.5	8.4	13.2
All others	17.3	30.4	43.4	21.2
Total	100.0	100.0	100.0	100.0

Source: Authors’ calculations using Human Development Survey (IHDS II) data (2011–2012).

Table 14a.

Distribution of English Ability and Mean Per capita Income (MPCI).

Social Group	None	Little	Fluent	Total
SCs/STs	17,399	27,621	61,685	20,235
OBCs	19,894	32,004	67,394	24,675
Muslim	17,333	30,143	57,582	21,028
All others	28,650	44,571	91,117	41,602
Total	20,030	34,648	76,471	26,250

Source: Authors’ calculations using Human Development Survey (IHDS II) data (2011–2012).

Note: In the absence of individual-level income, author has used average household per capita income in the analysis. Fluency estimates are generated for employed/working household members only.

Table 14b.

Distribution of English Ability and MPCI Compared with Income.

Social Group	None	Little	Fluent	Total
SCs/STs	28.2	44.8	100.0	32.8
OBCs	29.5	47.5	100.0	36.6
Muslim	30.1	52.3	100.0	36.5
All others	31.4	48.9	100.0	45.7
Total	26.2	45.3	100.0	34.3

Source: Authors’ calculations using Human Development Survey (IHDS II) data (2011–2012).

Knowledge, Fluency in English and Income: Given the aforementioned type of lopsided distribution of knowledge and fluency in English language, let us understand the association this language with mean per capita income (MPCI).

Table 15.

Distribution of English Ability and MPCI in Comparison to ‘All Others’ Category.

Social Group	None	Little	Fluent	Total
SCs/STs	60.7	62.0	67.7	48.6
OBCs	69.4	71.8	74.0	59.3
Muslim	60.5	67.6	63.2	50.5
All others	100.0	100.0	100.0	100.0
Total	69.9	77.7	83.9	63.1

Source: Authors’ calculations using Human Development Survey (IHDS II) data (2011–2012).

Table 16.

Distribution of English Ability, MPCI and Compared to National MPCI Average.

Social Group	None	Little	Fluent	Total
SCs/STs	86.9	79.7	80.7	77.1
OBCs	99.3	92.4	88.1	94.0
Muslim	86.5	87.0	75.3	80.1
All others	143.0	128.6	119.2	158.5
Total	100.0	100.0	100.0	100.0

Source: Authors’ calculations using Human Development Survey (IHDS II) data (2011–2012).

It is clear from the distributions from Tables 14a and 14b that the highest income per capita is earned by those having fluent English (₹76,471) when compared to little knowledge (₹34,648) and with no English knowledge (a meagre ₹20,030). These earnings amount to a 42% jump in income with little English and a 55% increase when fluency is achieved. When compared to no knowledge of English, the fluency increases incomes by a whopping 74%.

The data show that, on an average, mean per capita income (MPCI) of an individual having fluency in English is higher than those having no or little fluency in English. In other word, if working individuals in households have fluency in English, generally, household income of those households is high as compared to households having none of the members fluent in English.

A further analysis by the SRCs suggests that irrespective of any identity, those who have little English education earn, on an average, about one-half of those fluent within the same community. Similarly, those who do not have any knowledge or education of English earn, on an average, about 19% lower income than those who at least know some English, but this income is 74% lower than those who are fluent in English within the same community.

Knowledge, Fluency in English, Income Comparisons Among the SRCs: It has become clear that knowledge and fluency in English is one of the most dominant discriminatory factors in India in terms of the ability to earn higher incomes. Yet such discriminatory incomes get compounded when distinctions are made about this impact according to various SRCs in India. At any level of English education, ‘all others–HCH’ category earns considerably more than any other category. For example, the OBCs earn 31% less when compared to this group even when English is not a factor. Even fluency in English is achieved, the OBCs continue to have lower income by 26% when compared to the ‘all others–(HCH. These differences in the case of Muslims work out to be 40% and 37%, respectively.

These relationships for the SCs/STs are of similar levels and scale, which suggest it is only the ‘all others–HCH’ who gain substantially from the relative advantage of English language. First, they are the ones who have better access to English education, and then they also have better access to English-favoured labour market that prevails in India.

However, for the SC/STs and Muslims, the income growth from ‘none’ to ‘little’ and ‘little’ to fluent is a secular increase, suggesting the fact that providing English education among these communities will bring considerable income growth, which will even help them to come out of poverty.

A dominant finding of this study is that English language has emerged as the most secular factor that benefits all irrespective of SRCs. Note that the fluency in English provides an opportunity to earn incomes within a narrowband of an income for all communities. Surprising enough, English language in India has a huge role to play to both augment and effect income equity in India.

Conclusion

India has seen a significant fall in poverty since its independence, but there is always a voice against its uneven nature. Traditionally, disadvantaged groups remained in that category even after 60 years of independence. These disadvantaged groups include Muslims, SCs/STs and, to some extent, OBCs in the country. These groups perform poorly in most of the human development indicators like education and employment.

In this article, we investigate the dynamics of poverty in India between 2004–2005 and 2011–2012 and an assessment of DD according to socio-religious groups. This article uses NSS data at two points of time for this purpose. We found that traditionally disadvantageous groups have also gained from the fruits of development, but the extent of this gain is considerably low as compared to other groups. The Muslim community and SCs/STs in India remain traditionally trapped in low literacy, employment opportunities and are mainly engaged in informal sector and have high dependency ratios as compared to other socio-religious groups.

Although the mean age of the Muslim community in the country is the youngest, their share in the workforce is lowest as compared to other groups. The SCs/STs are the second youngest group, followed by OBCs. Further, a 35% share of the child population among Muslims indicates towards the higher youth population in the future, which shows an important role of the Muslim community in reaping DD. However, the youth condition in 2011 as per NSS cast a doubt in reaping a DD in the future also.

In education attainment also, Muslims and SCs and STs in India are too far from other social groups or communities. The share of SCs/STs and Muslims in the earlier secondary education level is disproportionately low. A low level of education translates into low-income unskilled jobs, which can be seen among Muslims and SCs/STs. Both Muslims and the SCs/STs are least employed in regular salaried jobs. About 90% of the Muslim community works with no written contract, showing a job insecurity and vulnerability.

About 41.2% and 30.3% of child and youth population, respectively, belonging to SCs/STs lives below poverty line. Among the Muslim community, this percentage is 31% and 22.7%, while their share in population is just over 14%.

Interestingly, English language has emerged as the most secular factor, which benefits all communities. It was found that knowledge and fluency in English is one the most dominant discriminatory factors in India in terms of the ability to earn higher incomes. A further analysis by the SRCs suggests that irrespective of any identity, those who have little English education earn, on an average, about one-half of those fluent within the same community.

However, this article signifies the importance of English language skills, which is highly rewarded in the labour market, but access to quality education remains a far cry for many in the country. Government schemes like Sarva Shiksha Abhiyan have done wonders in improving the enrolment ratio in primary education in India, but Annual Status of Education Report (2019) indicates unsatisfactory learning outcomes in schools. According to United Nations Development Programme (UNDP), children from the poorest households are at least four times more likely to be out of school as compared to their rich counterparts. Even if a poor child gets enrolled, achieving quality education comes with a cost, which remains far from the reach of the poor, leading to an illiteracy–unemployment vicious cycle. Recognising the importance of quality education, the United Nations’ Sustainable Development Goals 4 (2015) focuses on the issue and recommends it as a foundation for sustainable development.

In India, acknowledging the importance of the education sector, the major chunk of the corporate social responsibility (CSR) activities is dedicated towards the sector since 2014–2015. These contributions by CSR towards the education sector are commendable, but the country needs a paradigm shift, which requires large investments. As a policy intervention, it is worthwhile for the Government of India to organise such investments.

Poor human development indicators are the alarm bells for the policymakers of a country to harness its young brigade. There is a possibility of missing an opportunity of favourable demographic transition. In the absence of targeted policies to uplift disadvantageous groups, the country will not be able to gain from this opportunity as was expected.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Birdsall

(2001). Education, growth and inequality. In Dutt

A. K.

(Ed.), The political economy of development (pp. 316–327). Edward Elgar Publishers.

Bluedorn

(2019). Youth inactivity highest in India among emerging markets: IMF economist. Economic Times . https://economictimes.indiatimes.com/news/economy/indicators/youth-inactivity-highest-in-india-among-emerging-mkts-imf-economist/articleshow/68292174.cms?from=mdr

Caldwell

J. C.

(1976). Toward a restatement of demographic transition theory. Population and Development Review , 2(3/4), 321–366.

Census of India. (2011). Religion. Office of the registrar general & census commissioner, India, ministry of home affairs . Government of India.

Desai

Dubey

Joshi

B. L.

Sen

Shariff

Vanneman

(2010). Human development in India: Challenges for a society in transition . Oxford University Press. https://doi.org/10.13140/2.1.3474.0162

Economic Survey. (2017). Economic division, department of economic affairs, ministry of finance . Government of India. https://www.indiabudget.gov.in/es2016-17/echapter.pdf

Peterson

E. W. F.

(2017). The role of population in economic growth (Vol. 7). SAGE Open. https://journals-sagepub-com-s.web.bisu.edu.cn/doi/full/10.1177/2158244017736094

Planning Commission. (2013). Press note on poverty estimates, 2011–12 . Planning Commission, Government of India. https://niti.gov.in/sites/default/files/2020-05/press-note-poverty-2011-12-23-08-16.pdf

Psacharopoulos

(1994). Returns to investment in education: A global update. World Development , 22(9), 1325–1343.

10.

Sachar Committee Report. (2006). Social, economic and educational status of the Muslim community of India . Prime Minister’s High Level Committee, Cabinet Secretariat, Government of India. https://mhrd.gov.in/sites/upload_files/mhrd/files/sachar_comm.pdf

11.

Schultz

T. P.

(1988). Education investments and returns handbook of development economics. In Chenery

Srinivasan

T. N.

(Eds.), Handbook of development economics (1st ed., Vol. 1, Chapier 13, pp. 543–630). Elsevier.

12.

Shariff

(1995). Socio-economic and demographic differentials between hindus and muslims in India. Economic and Political Weekly , 30(46), 2947–2953. https://www.epw.in/journal/1995/46/special-articles/socio-economic-and-demographic-differentials-between-hindus-and

13.

Thorat

Vanneman

Desai

Dubey

(2017). Escaping and falling into poverty in India today. World Development, 93 . https://doi.org/10.1016/j.worlddev.2017.01.004

14.

UNDP India. (2018). India ranks 130 on 2018 . Human Development Index. https://www.in.undp.org/content/india/en/home/sustainable-development/successstories/india-ranks-130-on-2018-human-development-index.html

15.

United Nations Population Fund (UNFPA). (2018). Demographic dividend . https://www.unfpa.org/demographic-dividend