the demographic dividend: challenges to employment and employability

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Indian Journal of Labour EconomicsJournal of the Indian Society of LabourEconomics ISSN 0971-7927Volume 58Number 1 Ind. J. Labour Econ. (2015) 58:43-65DOI 10.1007/s41027-015-0008-x

The demographic dividend: challenges toemployment and employability

Jayasankar Krishnamurty & AbhayKumar

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ARTICLE

The demographic dividend: challenges to employmentand employability

Jayasankar Krishnamurty • Abhay Kumar

Published online: 17 November 2015

� Indian Society of Labour Economics 2016

Abstract The concept of demographic dividend is a major policy issue in India. In

order to study the employment and employability scenario, we have taken the

existing series forward to 2026 for both the population and labour force by age, sex

and residence for all the states and Union Territories while using the latest data sets.

The Indian discussion on the issue of demographic dividend has concentrated on the

broad age group of 15–59 years. We also look at trends for the disaggregated age

groups of 15–29, 30–44, and 45–59 years, as their paths differ considerably from

those of the aggregate. Three major findings may be highlighted. Firstly, the surge

in the working age population would comprise growing numbers of older workers,

many of whom have missed out on education and training opportunities. A massive

programme of adult education and training for older workers is needed. Secondly,

there are wide regional variations in labour force growth patterns. This calls for the

creation of a national labour market wherein regional shortages and surpluses are

adjusted. Finally, the current low female participation rates conceal a potential

demographic dividend, which can be reaped by adopting measures to expand the

level of female education and labour force participation rates.

This paper is a revised version of a keynote address delivered by J. Krishnamurty at the Annual

Conference of the Indian Society of Labour Economics (ISLE), Ranchi, December 2014.

& J. Krishnamurty

Institute for Human Development, New Delhi, India

e-mail: [email protected]

J. Krishnamurty

Chemin du Champ-d’Anier 7, 1209 Geneva, Switzerland

A. Kumar

Lokashraya Foundation, A-101, 5-Hailey Road, New Delhi 110001, India

e-mail: [email protected]

123

Ind. J. Labour Econ. (2015) 58:43–65

DOI 10.1007/s41027-015-0008-x

Author's personal copy

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Keywords Demographic dividend � Employment � Working age population �Education and training � Regional variation

Introduction

The phrase ‘demographic dividend’ has become a very popular one in India, and is

widely used by politicians, journalists and social scientists. However, many of its

implications need to be examined closely. We need to re-examine the limited data

available and incorporate the specifics of the Indian situation. Literacy rates and

educational attainment levels have been rising in recent years, but the bulk of the

labour force has not been able to avail of this benefit. Again, the proportion of the

labour force with skills and training remains extremely low. In India, we are making

a delayed start from this low base and the progressive ageing of the Indian

population of working age makes the challenge more daunting. The ‘dividend’ over

the next 15 years would be that of older workers with poor literacy and skills; the

growth of younger workers would slow down, but they would, on an average, be

better educated and hopefully better trained, and equipped with the skills required

by the labour market. There would also be significant variations in the patterns in

the different states.

Demographic dividend: What does it mean?

The Population Reference Bureau (Gribble and Bremner 2012) defines the term

‘demographic dividend’ as follows:

The demographic dividend is the accelerated economic growth that may result

from a decline in a country’s mortality and fertility and the subsequent change

in the age structure of the population. With fewer births each year, a country’s

young dependent population grows smaller in relation to the working-age

population. With fewer people to support, a country has a window of

opportunity for rapid economic growth if the right social and economic

policies are developed and investments made.

Dependency burden

The key element in the above definition is the reduction of the dependency burden

by an absolute fall in the number of children and the elderly relative to the labour

force that is growing rapidly. However, the use of the age-based dependency burden

leads to several problems. First, it ignores changes in the specific participation rates

by age, sex and residence, which may happen over time due to a variety of reasons.

Hence, the growth in the working age population may be accompanied by changes

in participation patterns, which may enhance or retard the growth rate of the

workforce. Second, the age composition, educational attainments and capacity to

acquire required skills on the part of persons within the age group of 15–59 years

44 Indian Journal of Labour Economics

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has to be taken into account. Finally, if the Indian fertility rate has been declining

more rapidly than what the official projections indicate, we may be under-estimating

reductions over time in the age-based dependency burden.

There are other definitions of ‘demographic dividend’ that place greater emphasis

on the positive impacts on savings due to the reduced dependency burden, or on

human capital due to greater expenditure per head on children, or the more general

effects on women’s status and roles. However, in discussions of employment, such

as this one, the important aspect that would be focused on is the impact of

acceleration in the labour force growth on employment and growth.

Employment and the demographic dividend

The term ‘demographic dividend’ is now widely used in India to refer to the phase

in time wherein the labour force age group (usually defined as those aged

15–59 years) grows faster than the general population. This is the population

composition effect resulting from declining mortality and fertility. The surge in

labour force growth, along with other impacts of the age structure changes, is

expected to lead to a rapid expansion in output and incomes.

Age structure changes may be accompanied by related or unrelated participation

rate changes, which increase or decrease their impact. Among the most important

changes over time are delayed marriages, and late start and early completion of

child-bearing and rearing. These, ceteris paribus, tend to raise the rates of female

workforce participation. Again, a higher average of the number of years of

schooling or education, whether of boys or girls, tends to lower the labour force

participation among the school-going age groups. Apart from this, changing

attitudes to work and to education, family, retirement and women’s roles may occur,

partly due to improved life expectancy and better health. They can have many

positive general impacts, analytically distinct from the direct impact of increased

labour force participation on economic growth.

Youth bulge

Another term that is widely used is ‘youth bulge’. This refers to a rising proportion

of the population (and labour force) in the youth age groups (typically 15–29 years).

While this might be regarded as a visual description of changes in age pyramids

over time, it tends to have other connotations as well. The common view is that the

younger, the labour force is, on an average, the more dynamic it is likely to be in

terms of education and skills and productivity. This is a very relevant issue in the

Indian context, where, older persons generally tend to be less educated and skilled

than their younger counterparts. Two caveats may be pointed out here. First, within

the age group of 15–29 years, the proportion in the labour force is likely to decline

over time as more and more take up education and training. In the case of women,

this may be partially offset by increases in the age at marriage and duration of child-

bearing, leading to greater labour force participation. Secondly, if indeed, fertility

has been declining faster than projected, then over the period from 2012 onwards

The demographic dividend 45

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(and especially by 2026), the proportion of people aged 15–29 years would prove to

be smaller than what current projections indicate.

Two views of the demographic dividend

The dividend may be viewed in relation to employment creation in two different

ways:

A hypothetical dividend from demographic trends which would be reaped

provided certain policies are followed; and

A dividend from demographic trends which would be reaped even with the

existing policies and programmes.

Some analysts like Bloom, et al. (2003), who hold the first view, argue that the

demographic dividend is contingent upon the adoption of certain labour market

policies, in particular, the removal of laws that are believed to restrict the freedom

of employers to hire and fire workers; adoption of education and training policies

that respond to, or correctly anticipate, demand conditions in the labour market; and,

macro-economic policies that are conducive to growth.

According to the alternative view, growth performance is shown to be correlated

with the labour force surge due to demographic change, quite irrespective of labour

market and macroeconomic policies, with some attempt to take into account other

factors which might explain growth. The evidence used by Aiyar and Mody in

support of their position related to the Indian states from 1970 to 2001. However,

the demographic and economic situation has changed greatly since then, as has the

data available (Aiyar and Mody 2011). Again, they focus more on the impact of the

reduced dependency ratio and of existing policies to improve social and economic

conditions, rather than on labour force growth per se. Finally, it may be observed

that there is some truth in both views: if the ‘right’ policies are followed, the

dividend would be larger, but even in their partial absence, some dividend would be

reaped.

While there is much debate on what elements of labour market regulation are

necessary, there is broad agreement that once the magnitudes of labour force

expansion are clear, policies must focus on providing the required education and

skills to workers along with creating the market conditions required for enterprise

growth. Growing labour force numbers would help to keep wages competitive,

while growing education and skill levels would enable the country to move up the

skills ladder when wage rates start rising due to growing domestic and/or

international demand. Hence, it is important to track the path of the likely labour

force growth and link it to the need for and availability of the required education and

skills.

Key assumptions underlying the dividend

Most of the discussion on the demographic dividend assumes the prevalence of the

following situations over time:


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• There are growing labour shortages in the economy, which are usually indicated

by rising wage rates.

• This cannot be adequately counteracted through the functioning of the labour

market or by factor substitution.

• Virtually no migration takes place, or the impacts of migration are very limited.

• Changing age structure, particularly the growth of the working age population, is

the driving force behind the demographic dividend.

• Age-sex-residence specific LFPR patterns do not change significantly.

• The growing numbers in the labour force can be equipped with the skills that

would be in demand in the labour market.

These assumptions need to be critically examined for India. As we show, there is

no doubt at all that over the next 15 years or so, the Indian age structure would shift

relatively towards the working age groups (15–59 years). There would be a surge in

labour force growth, but its age composition and its impacts on employment, output

and income warrant closer examination. The great diversity of India should also be

kept in mind as trends at the all-India level may be at variance with those observed

in several states.

Population and labour force projections

The changing structure of the population and labour force, 2012–2026

Let us first look at the options currently available in India for making population and

labour force projections for 15 years, that is, from 2012 to 2026. We have chosen

this period because the population that would comprise the labour force in 2026 has

already been born. Of course, the estimates of age-based dependency would be

affected, but we do not use them. The effects of mortality reduction would not make

a great deal of difference. Rural to urban migration trends can be factored in, based

on some assumptions or using past trends. The trend of international migration is

difficult to incorporate in the projections, but generally it is not large enough to

create serious inaccuracies in the population and labour force estimates.

At present, we have only the official set of population projections, published by

the Planning Commission in 2008, to work with. These were prepared without the

benefit of the 2011 Census results. It is possible that the figures for 2011 used in

making new projections (after smoothing the latter and correcting for undercount)

may differ significantly from the actual figures. A new set of population projections,

taking into account the adjusted 2011 Census results and revised assumptions about

fertility and mortality trends, may give different results. This would, in turn, have an

impact on any fresh labour force projections based on the new official projections.

We already have labour force projections, prepared as part of the Twelfth Plan

exercises, based upon the ‘official’ projections made prior to the 2011 Census.

Another set of labour force projections was published by the National Commission

for Enterprises in the Unorganised Sector (NCEUS) in 2009. This was based on the


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same population projection, but on a slightly different methodology for projecting

the labour force, building up the estimates from the state level.

We can use the existing results of the Planning Commission (up to 2022) and the

NCEUS (up to 2017), but we need to take the series forward to 2026. We also need

to update the participation rates by using new data on LFPRs for 2011–2012 from

the most recent NSS round. This would make it possible to have population and

labour force projections going until 2026. The results would still be provisional and

would have to be replaced when the new population projections become available.

While population projections may be similar to those used as in earlier exercises,

the results for different labour force segments may change for several reasons as

indicated below:

• For age groups with fairly stable participation, such as males aged 30–59 years,

not much change is likely.

• Some difference may arise for females in the age groups of 15–24 or

15–29 years depending on assumptions about the spread of education and, in the

case of girls, also the rising age at marriage and child bearing.

• For those aged 60 years and above, improved survival rates would have several

possible effects: it may raise participation among younger members of this

group enjoying better health, and more people may retire early due to greater

formalisation of work or better earnings; however, participation rates may be

lowered by the rising proportion of the older elderly (for example, those aged

70 years and above) in the total number of persons aged 60 years and above.

• For females, the outcomes would be very different depending on what we

assume to be the trajectory of specific female participation rates, which would be

influenced by longer schooling periods, later marriage and a reduction in the

burdens of child bearing and rearing.

• Assumptions about the pace of urbanisation could make a difference as rural and

urban participation rates differ greatly, especially for women, but also for men in

certain age groups.

Given our population projections, the assumptions we make about the path of

specific participation rates are the important determinants of labour force growth

and composition. The difficulty in the Indian case is that participation rates,

particularly for women and children, fluctuate greatly between the periodic sample

surveys, which makes it difficult to use them for predicting future levels and trends.

There is no unanimity as to whether these fluctuations are genuine or arising due to

problems in the surveys or due to the perception biases of respondents. Given these

problems, any method followed would have some degree of arbitrariness.

We have decided to prepare estimates assuming that the age-sex-residence

specific participation rates would remain unchanged over the period to 2026. In our

view, this has the advantage that female participation rates would not be assumed to

fall as some surveys have suggested. It is closer to our own expectation that some of

these rates would rise over the projection period. At the same time, we probably

over-state the growth of the labour force aged 15–29 years (and hence the youth

bulge) since the participation rates may decline due to the spread of education.


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Despite this, we urgently need to have an idea of the scale and composition of

labour force growth and its impacts on the labour market, output and productivity.

Given that we are taking of a period of about 15 years, there is enough space for

policies to change some of the givens: to make it possible for more young people to

have a sound education and training; to enable more and more young women to

contribute through labour force participation; and to create conditions wherein an

adequate number of good quality jobs become available to those who seek them.

The population projections we have prepared extend from 2012 to 2026. The

methodology adopted for projections is detailed out in ‘‘Appendix’’. They should be

regarded as provisional. Still, they permit us to examine the likely consequences of

demographic trends for India as a whole as also for the major states. We are less

confident of projections of the labour force since participation rates are notoriously

unreliable and could change for any number of reasons. Still, by looking at both sets

of projections, some idea can be formed of the direction in which we are heading.

Population

All-India

While estimates of population have been produced for the years 2012, 2017, 2022

and 2026, for the present purpose, it is enough to work with the figures for

2012–2026, as the addition of the intermediate years do not make much difference

to the revealed trends.

The Indian population (see Table 1) is projected to rise from 1210.3 million in

2012 to 1399.8 million in 2026, signifying an increase of 189.5 million or 15.7 %

over a period of 14 years. The male population would rise from 626.6 million to

752.2 million and the female population from 583.7 to 674.7 million.

Table 2 shows that the share of persons aged 0–14 years would decline by about

7 points, while the share of persons aged 60 years and above would rise by 3.6

points. As mentioned earlier, projections of the population aged 0–14 years may

prove to have been over-estimates.

A major weakness in the general discussion on the demographic changes in India

is that it concentrates on the broad age group of 15–59 years. We should also look at

trends for finer age groups such as 15–29, 30–44 and 45–59 years, respectively. In

the discussion below, we focus more on the ages 15–29 years and 30–59 years. Our

estimates indicate that the proportion of the population of working age would grow

from 62.6 to 66.3 % between 2012 and 2026; however, the proportion of the

Table 1 Projected population,

all-India (rural and urban)

2012–2026 (millions)

Source: Calculated by authors

from Census data

Year Males Females Persons

2012 626.6 583.7 1210.3

2017 665.5 619.4 1284.9

2022 700.8 651.9 1352.7

2026 725.2 674.7 1399.8


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population aged 15–29 years would decline from 28.7 to 27.4 % and that of the

population aged 30–59 years would increase from 34 to 38.8 %, respectively.

We can also look at this in terms of compound annual growth rates of the

different segments over the period 2012–2026. While the total population of all ages

is projected to grow by 1 % annually, the population aged 15–59 years would grow

by 2 % annually, reflecting the boom in the growth of population of working age.

Equally important, while the population aged 15–29 years would grow at the rate of

0.7 %, the population aged 30–59 years would grow at the rate of 2 % per annum.

So in relative terms, there is no youth bulge coming. Of course, the absolute

number of persons aged 15–29 years would increase by 37 million between 2012

and 2026. However, the absolute numbers of persons aged 30–44 years would

increase by 58 million and that of those aged 45–59 years would increase by much

more, that is, 75 million.

We have checked these results against those of the official projections. They tell

a similar story (Planning Commission 2008). Between 2012 and 2022 (the last year

of the official projection), the share of the population aged 15–29 years in the total

population would decline from 28.5 to 25.7 %, while the corresponding share of the

population segment aged 30–59 years would increase from 34.4 to 38.5 %.

Astonishingly, the absolute size of the population aged 15–29 years would increase

by just 2.6 million or less than 1 %.

The bulk of the increase in India’s working age population would be among older

persons. In fact, according to our estimates, 44 % of the increase in the working age

population would be in the age group of 45–59 years. These are persons who were

aged 30–44 years in 2012 and many of them would have had little education or skill

training.

The States

The pattern for the states reveals significant differences. We look at patterns among

the 20 larger states of shares of the population in different age groups (Table 9 of

‘‘Appendix’’).

Table 2 Population size

(millions) and shares (%) by

broad age groups, all-India

persons all areas, 2012 and 2026


from Census data

Age group (years) 2012 2026

Millions % Millions %

0–14 352.7 29.1 307.0 21.9

15–59 757.8 62.6 927.7 66.3

60 and over 99.8 8.2 165.2 11.8

15–29 347.0 28.7 384.0 27.4

30–44 246.7 20.4 305.0 21.8

45–59 164.1 13.6 238.7 17.0

All Ages 1210.3 100 1399.8 100


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• Ages 0–14 years There are declines in all the states though the decline is

sharpest in the states of Bihar, Rajasthan and Jharkhand.

• Ages 15–59 years Increases are projected in all states, except Tamil Nadu and

Kerala, where the population in this age group would decline slightly. The states

of Bihar, Rajasthan and Haryana are expected to see fairly large relative

increases.

• Ages 60 years and above Proportions of this population are expected to increase

generally, with the sharpest increases to be witnessed in Kerala, Tamil Nadu,

Bihar, West Bengal and Karnataka.

Within the working ages:

• Ages 15–29 years The proportions of the population in this age group would

increase sharply in Bihar and Jharkhand, and less sharply in Rajasthan but many

other states would witness declines. The sharpest relative declines would be seen

in Tamil Nadu, Kerala, Haryana, Punjab, Jammu and Kashmir, and Himachal

Pradesh.

• Ages 30–44 years There would generally be increases; the sharpest increases

would be seen in Himachal Pradesh, Haryana, Punjab, Jammu and Kashmir, and

Uttarakhand.

• Ages 45–59 years There would generally be increases, with the sharpest

increases observed in Assam, Haryana, Gujarat, Maharashtra, Andhra Pradesh,

Tamil Nadu, and Kerala.

Table 3 presents the same information in a more dramatic form by comparing the

compound annual growth rates for the population aged 15–29 and 30–59 years,

respectively, for the period 2012–2026. The following points may be stressed:

• The states of Andhra Pradesh, Himachal Pradesh, Jammu and Kashmir,

Karnataka, Kerala, Punjab, and Tamil Nadu are projected to have negative

growth rates among the population aged 15–29 years.

• In all states, with the exception of Bihar, the growth rates for the population

aged 30–59 years would exceed those of the population aged 15–29 years.

• The states of Gujarat, Haryana, Jammu and Kashmir, Madhya Pradesh,

Maharashtra, Punjab, Rajasthan, Uttar Pradesh, and Uttarakhand would see

faster growth in the population aged 30–59 years than the Indian average of 2 %

per annum.

Labour force projections

Building labour force projections by using the population projections requires

making additional assumptions about trends in age-sex-residence specific patterns

of labour force participation. How valid these assumptions prove would depend

upon the impact of development policies and programmes, and on ongoing social


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and economic processes of change and their effects on individual and household

decisions about labour force participation.

All-India

The official labour force projections published in 2008 for the Eleventh Plan extend

to 2022, and may be examined for the period 2012–2022. They are based on trends

in LFPRs based on earlier NSS Rounds. They naturally do not take into account the

LFPRs revealed by NSS surveys after 2008. As is well known, the extrapolation of

past trends in participation rates is fraught with problems as the rates reported by

different surveys may reflect problems of measurement rather than real changes.

Table 4 highlights some features of the labour force projections, considering

rural and urban areas, and males and females together. More detailed breakdowns

are provided in the Planning Commission Report as Tables A6a and A6b. It should

be noted that these estimates use the concept of current daily status (CDS) rather

than usual status principal and secondary activity (UPSS).

Table 3 Compound annual growth rates (CAGR) of population by broad age groups for states,

2012–2026

State 15–29 Years 30–59 Years 15–59 Years Total

Andhra Pradesh -0.4 1.7 0.9 0.7

Assam 0.8 2.0 1.5 1.0

Bihar 2.2 1.5 1.8 1.0

Chhattisgarh 1.2 1.7 1.5 1.1

Gujarat 0.1 2.1 1.3 1.0

Haryana 0.6 2.9 1.9 1.3

Himachal Pradesh -0.8 2.0 0.9 0.6

J&K -0.3 2.3 1.2 0.9

Jharkhand 1.6 1.7 1.7 1.1

Karnataka -0.4 1.9 0.9 0.8

Kerala -1.2 1.2 0.4 0.5

Madhya Pradesh 1.3 2.2 1.8 1.3

Maharashtra 0.3 2.3 1.5 1.1

Orissa 0.2 1.8 1.1 0.7

Punjab -0.6 2.2 1.1 0.8

Rajasthan 1.5 2.3 1.9 1.2

Tamil Nadu -1.5 1.3 0.3 0.4

Uttar Pradesh 1.6 2.1 1.8 1.4

Uttarakhand 0.3 2.4 1.5 1.1

West Bengal 0.2 1.8 1.1 0.8

All India 0.7 2.0 1.5 1.0

Source: Calculated by authors from Census data


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The Planning Commission projections indicate an absolute decline in the number

of persons in the age group of 15–59 years between 2012 and 2022. The sharpest

increases are seen in the age groups of 60 years and above, and 30–44 years,

respectively. The former reflects the small base on which the increase is measured.

Even after accepting the many limitations of this exercise, it is interesting to note

that it shows that the most rapid expansion in the labour force between 2012 and

2022 would take place in the older ages.

On the basis of our population projection for 2026, some projections of the labour

force have been attempted, as shown in Table 5. Apart from differences in the

participation rates assumed and the period covered, these are based on usual

principal or secondary status (UPSS) and are not strictly comparable with the results

provided by the Planning Commission based on current daily status (CDS). Our

projection is based on the assumption of keeping the labour force participation rates

of 2011–2012 by age, sex and residence constant through 2026.

We assume that the projection results as reported in Table 5 probably over-state

the growth of the labour force in the age group of 15–59 years, as they ignore the

likely effects of education, which we have not considered while projecting the

labour force. Nevertheless, rapid growth in the labour force may be expected at

around 1.86 % annually, as compared to a 1 % per annum growth in population.

Notice, however, that while the older labour force (aged 30–59 years) would grow

quite rapidly at the rate of 2.04 % annually, the younger labour force (aged

15–29 years) would only grow at the rate of 1.16 % annually. This would have

serious consequences not merely with regard to the ageing of the labour force, but

more importantly in terms of the problems of educating and training the increased

numbers, many of whom are already in the labour force.

Table 4 Planning commission

labour force projections

2012–2022, persons, all areas

(millions/percentages)

Source: Compiled from the

Planning Commission, 2008

Age (years) 2012 2022 Increase Percentage increase

0–14 6.36 3.93 (-) 2.43 (-) 3.82

15–29 190.88 187.43 (-) 3.45 (-) 1.81

30–44 186.69 229.48 42.97 23.02

45–59 119.09 147.21 28.12 19.83

60 and over 38.83 55.40 16.57 42.67

15–59 496.66 564.12 67.46 13.58

Total 541.85 623.45 81.60 15.06

Table 5 Labour force

projections 2012–2026, UPSS,

persons, all areas all India

(millions/percentages)


from NSSO data

Age

(Years)

2012 2026 Absolute

increase

Compound average growth

rate (% p.a.)

0–14 3.4 2.8 -0.6 -1.38

15–29 159.6 187.6 28.0 1.16

30–59 284.4 377.5 93.1 2.04

60 ? 36.7 58.9 22.2 3.43

15–59 444.1 565.1 121.0 1.74

Total 484.2 626.8 142.6 1.86


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The states

Tables 6 and Table 7 examine variations among the states in labour force shares and

growth over the period 2012–2026. The following key features may be observed:

• The relative share in the labour force of persons aged 15–29 years generally

declines, and significant absolute declines occur in Kerala and Tamil Nadu. The

important exception is Bihar wherein the share increases from 34.8 to 37.5 %.

The states of Bihar, Jharkhand and Rajasthan would exhibit rapid growth of

more than 2 % per annum in their youth labour force.

• The share in the labour force of persons aged 30–59 years generally increases,

but Bihar, and to a lesser extent, Chhattisgarh and Jharkhand, are projected to

have relative declines in the share of population aged 30–59 years. In the age

group of 30–59 years, the labour force growth would be above the national level

of 2 % per annum compound in Gujarat, Haryana, Jammu and Kashmir, Madhya

Pradesh, Maharashtra, Punjab, Rajasthan, Uttar Pradesh, and Uttarakhand.

• In the age group of 15–59 years, as a whole, the states of Bihar, Haryana,

Madhya Pradesh, Rajasthan, Uttar Pradesh and Uttarakhand show labour force

growth rates of more than 2 % per annum whereas states like Kerala and Tamil

Nadu would record less than 1 % growth in their labour force from 2012 to

2026.

The poorly educated: a brief digression

In 2011–2012, about a third of the persons in the population aged 15–29 years were

poorly educated (hereafter PE), that is, illiterate or with education levels up to or

below the primary level (see Table 8). Among those aged 30–44 years, over half

were in the PE category, and among those aged 45–59 years, nearly three-quarters

were in this category.

A rough exercise provides a way of looking at the problems ahead. Taking the

2011–2012 figures as approximating the situation in 2012, consider the 95 million

persons aged 15–29 years who were PE. They would almost certainly remain PE in

2026 as well. There were another 129 million PE in the age group of 30–44 years in

2011–2012, who would be in the age group of 45–59 years by 2026. Together, we

estimate that there would be some 224 million PE persons of working age in 2026,

assuming (not very realistically) that there remain no PE persons in the age group of

15–29 years in 2026. This implies that in 2026, about a quarter of the population of

working age would be in the PE category. Given our assumptions, the PE would be

aged above 30 years; and, they would find it difficult to access or benefit from

training and skill development programmes.

Of course, with the ongoing improvements in the access to and utilisation of

educational facilities by school- and college-going populations, there is still an

opportunity to promote skills among the younger segments of the population who

would be in the working age group in 2026. Even if the population aged


123


Table

6P

roje

cted

age-

wis

ela

bo

ur

forc

efo

rIn

dia

and

stat

esfo

r2

01

2an

d2

02

6,

and

thei

rp

rop

ort

ion

sin

the

tota

lla

bou

rfo

rce

Sta

tein

mil

lion

sin

%

0–

14

15

–29

30

–59

15

–59

60?

To

tal

0–

14

15

–29

30

–59

15

–59

60?

To

tal

An

dh

raP

rad

esh

20

12

0.2

12

.22

5.2

37

.43

.34

0.9

0.5

29

.86

1.6

91

.38

.11

00

.0

20

26

0.2

12

.73

1.9

44

.65

.65

0.3

0.3

25

.26

3.4

88

.61

1.1

10

0.0

Ass

am2

01

20

.03

.76

.41

0.1

0.5

10

.70

.33

5.0

59

.79

4.6

5.1

10

0.0

20

26

0.0

4.6

8.2

12

.81

.01

3.8

0.2

33

.05

9.6

92

.67

.21

00

.0

Bih

ar2

01

20

.29

.91

5.8

25

.72

.52

8.5

0.8

34

.85

5.6

90

.48

.91

00

.0

20

26

0.1

14

.71

9.8

34

.64

.63

9.3

0.4

37

.55

0.5

88

.01

1.6

10

0.0

Ch

hat

tisg

arh

20

12

0.0

3.8

6.7

10

.50

.81

1.3

0.4

33

.45

9.4

92

.76

.91

00

.0

20

26

0.0

4.8

8.5

13

.31

.41

4.7

0.3

32

.65

7.9

90

.59

.21

00

.0

Gu

jara

t2

01

20

.28

.51

4.8

23

.31

.62

5.1

0.8

33

.95

9.0

92

.96

.31

00

.0

20

26

0.2

8.9

19

.92

8.9

2.9

31

.90

.52

8.0

62

.49

0.4

9.1

10

0.0

Har

yan

a2

01

20

.03

.05

.68

.60

.59

.20

.13

2.7

61

.19

3.9

6.0

10

0.0

20

26

0.0

3.6

8.4

12

.00

.91

2.9

0.1

27

.66

5.4

92

.97

.01

00

.0

Him

ach

alP

rad

esh

20

12

0.0

1.1

2.3

3.3

0.4

3.7

0.1

28

.46

1.1

89

.51

0.4

10

0.0

20

26

0.0

1.0

3.0

4.0

0.6

4.6

0.1

22

.16

5.5

87

.61

2.4

10

0.0

J&K

20

12

0.0

1.7

2.8

4.4

0.4

4.8

0.3

34

.55

7.6

92

.17

.51

00

.0

20

26

0.0

1.8

3.8

5.5

0.6

6.1

0.2

28

.86

1.2

90

.09

.81

00

.0

Jhar

kh

and

20

12

0.2

4.1

6.8

10

.90

.81

1.9

1.8

34

.35

6.9

91

.27

.01

00

.0

20

26

0.1

5.5

8.6

14

.11

.71

5.9

0.9

34

.55

4.0

88

.41

0.7

10

0.0

Kar

nat

aka

20

12

0.1

7.9

15

.72

3.5

1.9

25

.60

.53

0.7

61

.29

1.9

7.6

10

0.0

20

26

0.1

7.8

20

.12

8.0

3.3

31

.40

.32

4.9

64

.18

9.0

10

.71

00

.0

Ker

ala

20

12

0.0

3.7

9.4

13

.21

.31

4.5

0.0

25

.66

5.0

90

.79

.31

00

.0

20

26

0.0

3.2

11

.11

4.3

2.1

16

.40

.01

9.4

68

.08

7.4

12

.61

00

.0


123


Table

6co

nti

nu

ed

Sta

tein

mil

lion

sin

%

0–

14

15

–29

30

–59

15

–59

60?

To

tal

0–

14

15

–29

30

–59

15

–59

60?

To

tal

Mad

hy

aP

rad

esh

20

12

0.1

10

.01

5.9

25

.92

.02

8.0

0.3

35

.65

6.8

92

.47

.31

00

.0

20

26

0.1

12

.72

1.8

34

.53

.53

8.1

0.2

33

.55

7.2

90

.69

.11

00

.0

Mah

aras

htr

a2

01

20

.21

5.2

31

.04

6.2

3.4

49

.80

.43

0.5

62

.29

2.7

6.8

10

0.0

20

26

0.2

16

.54

2.5

59

.05

.46

4.6

0.3

25

.56

5.9

91

.38

.41

00

.0

Ori

ssa

20

12

0.1

6.0

10

.31

6.3

1.3

17

.80

.73

4.0

57

.89

1.8

7.6

10

0.0

20

26

0.1

6.6

12

.91

9.5

2.1

21

.70

.43

0.3

59

.79

0.0

9.6

10

0.0

Pu

nja

b2

01

20

.13

.96

.81

0.7

0.9

11

.60

.43

3.4

58

.19

1.5

8.1

10

0.0

20

26

0.0

3.8

9.2

13

.01

.51

4.5

0.3

26

.56

3.2

89

.71

0.0

10

0.0

Raj

asth

an2

01

20

.21

0.0

16

.02

6.0

2.1

28

.40

.83

5.3

56

.49

1.7

7.5

10

0.0

20

26

0.2

13

.32

1.9

35

.23

.83

9.2

0.4

34

.05

5.8

89

.89

.71

00

.0

Tam

ilN

adu

20

12

0.0

8.3

20

.22

8.6

2.9

31

.50

.12

6.5

64

.39

0.8

9.1

10

0.0

20

26

0.0

7.0

24

.03

1.0

4.4

35

.40

.11

9.9

67

.78

7.6

12

.31

00

.0

Utt

arP

rad

esh

20

12

1.0

26

.73

9.0

65

.76

.47

3.2

1.4

36

.55

3.3

89

.88

.81

00

.0

20

26

0.9

35

.25

2.6

87

.81

1.3

10

0.1

0.9

35

.25

2.5

87

.81

1.3

10

0.0

Utt

arak

han

d2

01

20

.01

.32

.33

.60

.23

.90

.63

4.3

59

.29

3.5

5.9

10

0.0

20

26

0.0

1.5

3.2

4.8

0.3

5.1

0.4

29

.96

3.0

92

.96

.71

00

.0

Wes

tB

engal

20

12

0.6

12

.82

2.2

35

.02

.23

7.8

1.5

33

.95

8.7

92

.65

.91

00

.0

20

26

0.4

13

.82

8.0

41

.84

.04

6.1

0.8

29

.96

0.7

90

.68

.61

00

.0

All

Ind

ia2

01

23

.41

59

.62

84

.44

44

.13

6.7

48

4.2

0.7

33

.05

8.7

91

.77

.61

00

.0

20

26

2.8

18

7.6

37

7.5

56

5.1

58

.96

26

.80

.52

9.9

60

.29

0.2

9.4

10

0.0

Source:

Cal

cula

ted

by

auth

ors

from

NS

SO

dat

a


123


15–29 years in 2026 is relatively smaller than it was in 2011–2012, if it is better

educated and well-trained, it could still contribute greatly to development.

Some implications of our findings

Even if the size of the population and labour force aged 15–29 years is not going to

increase much over the next couple of decades, the absolute numbers are large, and

rapid action would be required to improve their education and skill levels. At the

Table 7 CAGR of labour force for states, 2012–2026

State 15–29 (years) 30–59 (years) 15–59 (years) Total

Andhra Pradesh 0.28 1.7 1.27 1.49

Assam 1.44 1.86 1.71 1.86

Bihar 2.88 1.64 2.14 2.33

Chhattisgarh 1.71 1.72 1.72 1.89

Gujarat 0.35 2.14 1.54 1.73

Haryana 1.22 2.96 2.39 2.47

Himachal Pradesh -0.26 2.04 1.38 1.54

J&K 0.5 2.23 1.62 1.79

Jharkhand 2.1 1.7 1.85 2.08

Karnataka -0.01 1.81 1.25 1.47

Kerala -1.15 1.18 0.59 0.86

Madhya Pradesh 1.77 2.27 2.08 2.22

Maharashtra 0.58 2.29 1.77 1.88

Orissa 0.62 1.66 1.29 1.43

Punjab -0.07 2.19 1.44 1.58

Rajasthan 2.06 2.25 2.18 2.33

Tamil Nadu -1.21 1.23 0.59 0.86

Uttar Pradesh 1.99 2.16 2.09 2.26

Uttarakhand 1.04 2.48 1.98 2.03

West Bengal 0.53 1.68 1.28 1.44

All India 1.16 2.04 1.74 1.86

Source: Calculated by authors from NSSO data

Table 8 Total population (males and females all areas) and population with primary education or below,

by broad age group, NSS 68th round, 2011–2012

Age group (years) Primary and below (millions) Total population (millions) % Primary and below

15–29 95.0 292.2 32.5

30–44 128.5 236.5 54.9

45–59 97.6 132.7 73.5

15–59 321.0 661.4 48.5

Source: Computed from the NSS 68th Round, 2011–12


123


same time, it is just as important to address the needs of older persons who would be

in the workforce for many more years.

A number of measures are required which are briefly sketched in the rest of this

paper. Several are already well known but must be applied; some need more

thinking and analysis. The general comments and suggestions below are for further

consideration by experts in the fields of education and skill development.

Matching education and training with emerging labour market needs

The most basic problem is matching education and training systems with the

emerging labour market needs. The requisite education and training systems,

especially when high levels of skills are needed, may be missing and labour market

demands are difficult (in some skills, impossible) to anticipate.

We need to think in terms of education providing the foundation upon which

skills can be built and rebuilt. A great expansion and strengthening of vocational

training is obviously required if those coming into the labour force from schools

have some skills, but many more who are already in the labour force would need

training of one kind or another. The process of skill training can be undertaken by

training providers of many kinds, including training institutes, non-governmental

organisations (NGOs), skilled workers and others. But the agent who signals, or

decides, on what the training system must provide has necessarily to be the

employer. In the Indian case, this is likely to be private employers, not the

Government. Education, especially vocational education, and some basic training

may lead to employment, but we need to think more and more of employment being

followed by training and retraining.

How do we train the poorly educated?

We believe that this is the really important issue which we cannot afford to ignore.

There would be large numbers of poorly educated workers for a long time to come.

We cannot be sure that there would be adequate work opportunities for such people.

Many may continue to secure work but on poor terms in the formal and informal

sectors. Inequality in wages and earnings may increase and an underclass of older

poorly educated and poorly paid workers may emerge. At the same time, it is

possible that at least some of the poorly educated may acquire skills and progress in

the labour market.

One study shows that a quarter of the production workers surveyed in the auto

components industry were poorly educated semi-skilled workers, indicating that

some skill acquisition is possible even for the poorly educated (Unni and Rani 2008,

p. 135). But would this continue to be true; is it true of other industries, and what

proportion of the poorly educated would be able to acquire better skills.

Policies to promote women’s participation in the labour force

The Indian female LFPRs are low by international standards and this provides a

potential dividend which needs to be tapped. If the proportion of women aged


123


15–29 years in the labour force with education and undergoing training were to rise

over the next decade or so, this would greatly contribute to productivity, growth and

employment creation. Since labour shortages manifest themselves as the economy

grows, a surge in the number of educated and trained young women entering the

labour force would contribute to the demographic dividend.

Policies to facilitate labour mobility and create a national labour market

Our projections at the state level, as well as the recent experience in India, suggest

that there must be a national labour market in which regional surpluses and

shortages get sorted out. In some states, the population in the working age groups

would increase rapidly, while in others, a slowing down is apparent. We need

policies which favour mobility, allow workers to work in different parts of the

country, and ensure that their skills are recognized and that they are not exploited.

Policies to promote investment in states with rapid labour force growth

States like Bihar, Jharkhand and Rajasthan, wherein there is a rapid growth of the

labour force, especially among the younger age groups, could also attract

investment, leading to job creation within the state itself. This would depend on

the nature of industry, the source of raw materials, and the location of infrastructure

and markets. Policy interventions on education and skill development in these states

are needed.

Policies to promote education

Education would help to improve employment prospects, provided the market for

skilled work is growing. This would become important if the new jobs require a

greater level of skills, which can only be acquired by an education-cum-skills-

training process. A massive national campaign on workers’ education should be

launched as there would be a large number of workers, especially older workers,

with poor educational attainments for many years to come. Unless this issue is

addressed, older workers would face difficulties in acquiring new skills. The

experience of some East Asian countries is relevant here. As Ray (2010) notes,

‘‘China’s anti-illiteracy efforts stand out as the greatest experience in mass

education in history, of over a billion people made literate in a space of about

30 years.’’ Something similar is needed in India.

Policies to promote skills

The set of measures needed to promote skills depends on the kind of growth path we

envisage for the Indian economy over the next 20 odd years. Much would depend

upon our expectations about changes over the period in the sectoral composition of

output, the size structure of enterprises, the relative roles of domestic and foreign

demand, and the forms of employment that would be created. An expansion in

domestic or international sub-contracting may directly result in particular forms of


123


skill transfer; a persistence of the informal sector and family-based enterprises

would again affect training strategies and possibilities; and rapid urbanisation would

alter the setting for training and possibly the mechanisms whereby it can be done.

There are limits to the scale on which skills can be expanded through vocational

education or formal institutional training, especially of the kind that government

systems have been providing. They are too slow to respond to rapidly changing

demands. However, institution-based training can provide good quality training in

certain skills that are generally in demand and not specific to particular enterprises

(Aggarwal et al. 2003).

The apprenticeship system, again, has not worked well. A recent ILO–World

Bank Report (2013) outlines possible futures for the system. Less than 0.01 % of the

workforce is covered by the apprenticeship system, as compared to Germany and

Australia, which both have around 3.7 % of their workforce participating in

apprenticeships. Among the other serious problems inherent in the existing system

is poor coverage of the organised and unorganised sectors.

We must re-fashion the training system to increase employer responsibility and

sharing in cost. Instead of depending upon learning on the job without recognised

certification, workers must be provided recognised and portable skills. We need to

move increasingly from institutional training in Government facilities to a wide

range of training by providers. This may require a system of training vouchers. An

urban training guarantee scheme providing training vouchers to the urban

unemployed is another possibility. Also, the cost of such training should be met

or recovered at least partly by employers who are the major beneficiaries of training

of their workers.

Labour market information

A great weakness has been the lack of a good labour market information system.

The Employment Market Information Programme (EMI) and the employment

exchanges have failed in this task and there is no easy or cheap solution. It is

essential that the EMI is revamped so that valid information on job creation is made

available on a quarterly basis before the end of the next quarter. This would help all

labour market actors. However, it is the employer, who is best positioned to know

his/her firm’s labour demand, and should thus become important in deciding on the

skills to be imparted to his/her workers.

Concluding remarks

By way of conclusion we stress the following points made earlier:

• The expansion of the Indian labour force during the next decade or so would

largely be made up of persons aged 30 years or above, many of whom would

have had little education, little training or capacity to benefit from training.


123


• While this would be the case in much of India, as a whole, in the states of Bihar

and Jharkhand, and to a lesser extent, in Rajasthan, rapid growth in the labour

force may be expected, particularly among the younger ages.

• Given the wide differences in labour force growth rates in different states, a

national labour market is essential in order to balance regional surpluses and

shortages.

• Increased labour force participation and employment of women, particularly

among the younger and better educated women, represents a hidden demo-

graphic dividend waiting to be reaped.

• Finally, growing segments of the labour force would be relatively older and this

would require a rethinking of adult education as well as training strategies and

approaches.

Appendix 1: Methodology for population and labour force projections

The National Commission on Population (NCP) constituted a technical group for

projecting the population of India as a whole and the individual States for the period

2001–2026. The technical group presented its report in 2006. Their projections,

however, did not provide breakdowns of the projected population by age and sex for

rural and urban areas. This is required for preparing labour force projections. Two

attempts were made to project the labour force—one by the Planning Commission

Working Group (PCWG) and the other by the National Commission for Enterprises

in the Unorganised Sector (NCEUS). Both faced a paucity of the projected

population data and, therefore, they set up separate sub-groups to first project the

population by age and sex for rural and urban areas and then project the labour

force.

The limitations of the projections made by the Planning Commission and the

NCEUS are that they are based on older data sets. The PCWG projection, which

extends up to 2022, is made for rural and for urban India; the NCEUS projections,

on the other hand, were prepared for the rural and urban segments of each State and

Union Territory but extend only up to 2017. It was, therefore, necessary to extend

the projections to at least 2026 and incorporate fresh data wherever possible.

We undertook a separate exercise to project the population and labour force for

all States and Union Territories by age, sex and residence up to 2026, using the

recently available data sets. The ‘Ratio Method’ was used for projecting population

for 2012–2026 at 5-year intervals for all the States and Union Territories (Table 9).

Population projection

The ratio method for projecting population involves the following steps:

• The projected population for rural and urban areas by age and sex for each of the

States and Union Territories available from NCEUS were collected. This data is


123


Table

9P

roje

cted

age-

wis

ep

op

ula

tio

nfo

rIn

dia

and

stat

esfo

r2

01

2an

d2

02

6an

dth

eir

pro

port

ion

inth

eto

tal

po

pu

lati

on

Sta

teY

ear

inm

illi

on

in%

0–

14

15

–2

93

0–

44

45

–59

30

–59

60?

15

–59

To

tal

0–

14

15

–2

93

0–

44

45

–59

30

–59

60?

15

–59

To

tal

Him

ach

alP

rad

esh

20

12

1.7

1.9

1.5

1.0

2.5

0.7

4.5

6.9

24

.82

8.1

22

.31

4.5

36

.81

0.3

64

.91

00

.0

20

26

1.4

1.7

2.0

1.4

3.4

1.0

5.1

7.6

18

.42

3.1

26

.31

8.4

44

.71

3.8

67

.81

00

.0

Har

yan

a2

01

27

.47

.95

.43

.38

.72

.01

6.5

25

.92

8.5

30

.52

0.8

12

.63

3.4

7.6

63

.91

00

.0

20

26

6.4

8.6

7.7

5.3

13

.03

.22

1.5

31

.12

0.5

27

.52

4.6

17

.14

1.7

10

.36

9.2

10

0.0

Raj

asth

an2

01

22

2.6

20

.41

2.8

8.2

21

.05

.04

1.4

69

.03

2.7

29

.61

8.5

11

.93

0.4

7.3

60

.01

00

.0

20

26

18

.52

5.3

16

.61

2.1

28

.79

.05

4.0

81

.52

2.7

31

.02

0.3

14

.93

5.2

11

.16

6.2

10

0.0

Utt

arP

rad

esh

20

12

71

.65

9.9

35

.32

3.2

58

.51

4.5

11

8.4

20

4.5

35

.02

9.3

17

.21

1.4

28

.67

.15

7.9

10

0.0

20

26

69

.67

4.4

45

.83

2.4

78

.22

6.5

15

2.6

24

8.8

28

.02

9.9

18

.41

3.0

31

.41

0.7

61

.31

00

.0

Bih

ar2

01

23

3.7

29

.21

7.2

12

.02

9.2

7.1

58

.49

9.1

34

.02

9.5

17

.31

2.1

29

.47

.25

8.9

10

0.0

20

26

24

.73

9.5

19

.71

6.2

35

.91

3.7

75

.41

13

.82

1.7

34

.71

7.3

14

.23

1.6

12

.16

6.3

10

0.0

Ass

am2

01

29

.39

.16

.54

.11

0.5

2.0

19

.73

1.0

30

.02

9.5

20

.91

3.1

34

.06

.56

3.5

10

0.0

20

26

7.7

10

.37

.56

.41

3.9

3.7

24

.23

5.6

21

.62

8.9

21

.01

8.1

39

.11

0.4

68

.01

00

.0

Wes

tB

engal

20

12

22

.82

5.9

20

.21

3.8

34

.07

.75

9.9

90

.42

5.3

28

.62

2.4

15

.23

7.6

8.5

66

.21

00

.0

20

26

16

.92

6.5

22

.82

0.9

43

.71

3.4

70

.21

00

.51

6.8

26

.32

2.7

20

.84

3.5

13

.36

9.8

10

0.0

Ori

ssa

20

12

10

.91

1.6

8.9

6.0

14

.93

.72

6.5

41

.12

6.5

28

.32

1.6

14

.63

6.2

9.0

64

.51

00

.0

20

26

8.6

12

.01

0.2

8.8

19

.05

.73

1.0

45

.31

9.0

26

.42

2.5

19

.44

2.0

12

.66

8.4

10

0.0

Mad

hy

aP

rad

esh

20

12

24

.12

1.2

14

.08

.92

2.9

5.2

44

.27

3.4

32

.82

8.9

19

.11

2.1

31

.27

.16

0.1

10

0.0

20

26

22

.02

5.6

17

.41

3.8

31

.28

.95

6.8

87

.72

5.0

29

.21

9.8

15

.83

5.6

10

.26

4.8

10

0.0

Gu

jara

t2

01

21

6.2

16

.81

3.1

8.7

21

.85

.03

8.6

59

.92

7.0

28

.12

1.9

14

.63

6.5

8.4

64

.51

00

.0

20

26

13

.91

7.0

16

.31

3.2

29

.48

.94

6.4

69

.32

0.1

24

.62

3.5

19

.04

2.5

12

.96

7.0

10

0.0

Mah

aras

htr

a2

01

23

0.4

32

.32

4.9

16

.34

1.3

10

.37

3.6

11

4.3

26

.62

8.3

21

.81

4.3

36

.19

.06

4.4

10

0.0

20

26

27

.23

3.5

31

.72

5.0

56

.71

5.9

90

.21

33

.32

0.4

25

.22

3.8

18

.74

2.5

11

.96

7.7

10

0.0


123


Table

9co

nti

nu

ed

Sta

teY

ear

inm

illi

on

in%

0–

14

15

–2

93

0–

44

45

–59

30

–59

60?

15

–59

To

tal

0–

14

15

–2

93

0–

44

45

–59

30

–59

60?

15

–59

To

tal

An

dh

raP

rad

esh

20

12

21

.42

4.4

19

.11

2.9

32

.07

.85

6.4

85

.62

5.0

28

.52

2.4

15

.13

7.4

9.1

65

.91

00

.0

20

26

17

.22

3.2

22

.51

8.3

40

.81

2.9

64

.09

4.1

18

.32

4.6

24

.01

9.4

43

.41

3.7

68

.01

00

.0

Kar

nat

aka

20

12

15

.21

7.0

13

.39

.02

2.3

5.5

39

.36

0.1

25

.42

8.3

22

.11

5.1

37

.19

.26

5.4

10

0.0

20

26

12

.71

6.0

16

.31

2.5

28

.89

.44

4.9

66

.91

9.0

23

.92

4.4

18

.74

3.1

14

.06

7.0

10

0.0

Tam

ilN

adu

20

12

15

.31

7.3

16

.11

1.5

27

.67

.64

4.9

67

.92

2.6

25

.52

3.7

16

.94

0.7

11

.26

6.2

10

0.0

20

26

13

.11

4.0

17

.81

5.2

33

.01

1.7

47

.07

1.9

18

.31

9.5

24

.82

1.2

45

.91

6.3

65

.41

00

.0

Ker

ala

20

12

7.9

8.5

8.2

6.0

14

.24

.32

2.6

34

.82

2.7

24

.32

3.5

17

.24

0.7

12

.36

5.0

10

0.0

20

26

6.9

7.1

8.8

8.0

16

.86

.52

3.9

37

.31

8.4

19

.12

3.7

21

.44

5.1

17

.36

4.3

10

0.0

Pu

nja

b2

01

27

.08

.16

.14

.11

0.2

2.7

18

.32

8.0

25

.02

8.8

21

.81

4.7

36

.59

.76

5.3

10

0.0

20

26

6.0

7.3

8.0

5.8

13

.94

.22

1.2

31

.31

9.1

23

.42

5.6

18

.64

4.2

13

.36

7.6

10

0.0

J&K

20

12

3.4

3.6

2.4

1.6

4.0

0.9

7.6

11

.92

8.3

30

.62

0.2

13

.23

3.4

7.7

64

.01

00

.0

20

26

3.0

3.5

3.3

2.2

5.5

1.5

8.9

13

.42

2.0

25

.82

4.3

16

.44

0.7

11

.56

6.5

10

0.0

Jhar

kh

and

20

12

10

.09

.66

.04

.21

0.2

2.3

19

.73

1.9

31

.23

0.0

18

.81

3.0

31

.87

.16

1.8

10

0.0

20

26

7.8

12

.07

.25

.71

2.9

4.7

24

.93

7.4

20

.83

2.0

19

.41

5.2

34

.61

2.6

66

.61

00

.0

Ch

hat

tisg

arh

20

12

7.7

6.9

4.8

3.3

8.0

1.9

15

.02

4.6

31

.32

8.2

19

.41

3.2

32

.67

.96

0.8

10

0.0

20

26

6.8

8.2

5.5

4.7

10

.23

.41

8.5

28

.62

3.7

28

.81

9.3

16

.53

5.7

11

.86

4.6

10

0.0

Utt

arak

han

d2

01

23

.13

.01

.91

.33

.20

.96

.21

0.1

30

.32

9.6

19

.11

2.5

31

.68

.56

1.2

10

0.0

20

26

2.9

3.1

2.8

1.7

4.5

1.3

7.6

11

.72

4.4

26

.32

3.6

14

.43

8.0

11

.26

4.4

10

0.0

All

Ind

ia2

01

23

52

.73

47

.02

46

.71

64

.14

10

.89

9.8

75

7.8

12

10.3

29

.12

8.7

20

.41

3.6

33

.98

.26

2.6

10

0.0

20

26

30

7.0

38

4.0

30

5.0

23

8.7

54

3.7

16

5.2

92

7.7

13

99.8

21

.92

7.4

21

.81

7.0

38

.81

1.8

66

.31

00

.0

Source:

Cal

cula

ted

by

auth

ors

from

Cen

sus

dat

a


123


available for 1 April 2002, 2007, 2012, and 2017. This forms the base data for

projecting population for the further period of 2022 and 2026.

• The ratio of population of each age group by sex and residence to their total

population was calculated for different periods. This was done for all States and

Union Territories.

• Growth rates of the above ratios for each age group by sex and residence were

then calculated.

• Average growth rates over the two previous periods were used to project the

ratio of each age group to the total population for future years. In some

instances, there were minor variations in the total of these ratios as they did not

add to unity. In such situations, they were adjusted to unity by multiplying these

ratios by (1/P

xi).

• In the last step, the projected ratio for each age group was multiplied with the

total projected population for the corresponding year (provided by NCP) to get

the age group-wise projected population. This exercise was repeated by sex and

residence for each State and Union Territory.

• The reference date used by the NCEUS is 1 April whereas the NCP reference

date is 1 March. In order to adjust the reference date, the total projected

population of the NCP is projected for 1 April by using a log linear model. Also,

the NCP projection provides data only for the urban and total population. The

rural population has been derived by subtracting the urban projected population

from its total population.

• Rural–urban migration has not been explicitly adjusted in this exercise.

Labour force projection

The UPSS LFPRs from the 68th Round of the NSS (2011–2012) were obtained for

each State and Union Territory by age group, sex and rural–urban residence.

Keeping these specific LFPRs constant, the labour force for each of the years 2017,

2022 and 2026 was estimated from the projected population.

References

Aiyer, S. and A. Mody (2011), ‘‘The Demographic Dividend: Evidence for the Indian States’’, IMF

Working Paper 11/38, Washington.

Aggarwal Ashwani, Anil Grover, Aswani Kumar, Q. L Juneja (2003), Industrial Training Institutes of

India: The Efficiency Study Report, Sub-regional Office for South Asia, ILO, New Delhi.

Bloom, David E., David Canning, and Jaypee Sevilla (2003), The Demographic Dividend A New

Perspective on the Economic Consequences of Population Change, Monograph Report, Rand

MR1274, Santa Monica.

Gribble, James and Jason Bremner (2012), ‘‘The Challenge of Attaining the Demographic Dividend’’,

Policy Brief, Population Reference Bureau, September.

ILO and the World Bank (2013), Possible Futures for the Indian Apprenticeship System: Options Paper

for India, ILO DWT for South Asia and ILO Country Office for India; The World Bank, New Delhi.

Planning Commission (2008), Report of the Working Group on Labour Force & Employment Projections

constituted for the Eleventh Five Year Plan (2007–2012), New Delhi.


123


Ray, Shovan (2010), ‘‘Education for Development: India and East Asia’’, Working Paper, Korea Institute

for International Economic Policy (KIEP), Seoul, 4 June.

Unni, Jeemol and Uma Rani (2008), Flexibility of Labour in Globalizing India: The Challenge of Skills

and Technology, Tulika Books, New Delhi.


123


the demographic dividend: challenges to employment and employability

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