Chapter 07. What to Make in India?
Manufacturing or Services?
7.1 INTRODUCTION
Echoing
the Sage of Singapore, Prime Minister Narendra Modi has elevated the revival of
Indian manufacturing to a key policy objective of the new government,
identifying this sector as the engine of long-run growth. “Make in India” is
now a flagship initiative not to mention a catchy campaign. But the question
arises “What should India make?” Early development thinking, exemplified most famously
(though not exclusively) in the two-sector model of Lewis (1954) was fixated on
the idea of sectoral transformation: moving resources from the agricultural/traditional
sector to the manufacturing/ non-traditional sector. There was never any doubt about
the hierarchy (the latter was unquestionably superior) and hence no doubt about
the desirability of the structural transformation.
Although
development thinking over the last two decades has moved away from discussions
about sectoral transformation and towards a more explicit growth perspective,
the importance of structural transformation is starting to be rehabilitated –
but without abandoning the growth perspective. Rodrik (2013 and 2014) provides the
clearest exposition of this marriage of the two perspectives.
7.2 DESIRABLE FEATURES OF SECTORS THAT CAN SERVE AS ENGINES OF STRUCTURAL TRANSFORMATION
India
is taken up as a case study for addressing this question due to the poor
performance of manufacturing in India and the relatively strong performance of
services – which in some ways mirrors the performance of many Sub-Saharan African
countries (Ghani and O’Connell, 2014).
Lee
Kuan Yew was clearly on to something when he challenged the Indian model of
development. Historically, there have been three modes of escape from
under-development: geology, geography, and “jeans” (code for low-skilled
manufacturing). In recent years West Asia, Botswana and Chile, and further back
in time Australia and Canada, exploited their natural resources endowed by geology
to improve their standards of living. Some of the island successes (Barbados,
Mauritius, and others in the Caribbean) have exploited their geography by
developing tourism to achieve high rates of growth. In the early stages of
their success, East Asian countries (China, Thailand, Indonesia, Malaysia etc)
relied on relatively low-skilled manufacturing, typically textiles and
clothing, to motor economic growth. Later on they diversified into more sophisticated
manufacturing but “jeans” offered the vehicle for prosperity early on. No
country has escaped from underdevelopment using relatively skill-intensive
activities as the launching pad for sustained growth as India seems to be
attempting.
Put
differently, India seems to have defied its “natural” comparative advantage,
which probably lay in the “jeans” mode of escape because of its abundant
unskilled and low-skilled labor. Instead, it found or created—thanks to
historical policy choices and technological accidents—such advantage in
relatively skilled activities such as information technologies and business
process outsourcing (Kochhar et. al., 2007). The Indian experience, still a
work-in-progress, raises the question of whether structural transformation
necessarily requires manufacturing to be the engine of growth. But before we
compare manufacturing with alternative sectors in terms of their potential for
structural transformation, it is worth elaborating on the desirable attributes
of such sectors.
In
fact, building upon the Rodrik (2013) framework, it is argued that there are
five attributes that allow a sector to serve as an engine of structural transformation and thereby
lead an economy to rapid, sustained and inclusive growth:
1.
High level of productivity: As described above, economic development is about moving from
low productivity to high productivity activities.
2.
Unconditional Convergence (i.e. faster productivity growth in lower productivity
areas):
This
too has been discussed earlier. Recall that convergence ensures that the
relevant sector acts as an “escalator” which automatically leads to higher
levels of sectoral and economy-wide productivity. In fact one can distinguish
between two types of unconditional convergence:
A.
Domestic convergence: In large countries such as India, China, Brazil, and
Indonesia, one would ideally like to see convergence within a country.
That
is, productivity growth should be faster in richer than poorer parts.
Otherwise
severe within-country regional inequality may arise.
B.
International convergence: whereby less-productive economic units (firms, sectors
or entire economies) in all countries catch-up with units at the international
frontier (i.e. those in the most productive countries).
3.
Expansion: To ensure that the dynamic productivity gains from convergence
spread through the economy, it is necessary that the sector experiencing
convergence absorbs resources.
Convergence
accompanied by contraction will fail to ensure economy-wide benefits, because
the country’s resources that are outside the sector in question will not
experience higher, convergent productivity growth. Convergence, in the case of the
industrial sector, should be accompanied by natural industrialisation and not
premature deindustrialisation, if it is to lead to truly inclusive growth.
4.
Alignment with comparative
advantage: To ensure that expansion occurs and the
benefits of fast-growing sectors are widely shared across the labor force,
there should be a match between the skill requirements of the expanding sector
and the skill endowment of the country. For example, in a labour abundant
country such as India, the converging sector should be a relatively low-skilled
activity so that more individuals can benefit from convergence.
5.
Tradability: Historically, countries that had growth spurts enjoyed rapid
growth in exports, typically manufacturing exports (Johnson, Ostry and
Subramanian (2010)). Rapid growth has seldom been based on the domestic market.
Part of the reason for this might be that trade serves as a mechanism for technology
transfer and learning, which may have spillovers on related industries
(Hausmann, Hwang, and Rodrik (2007)). Perhaps a more important part is that trade
and exports in particular provide a source of unconstrained demand for the
expanding sector. This is particularly important for a country of India’s size
because of the possibility that its expansion can run up against the limited political
and economic ability of trading countries to absorb Indian exports and/or to
turn the terms of trade against itself.
The
two sectors—manufacturing and services (including services disaggregated by
subsector)— are now evaluated, in succession, along these five dimensions in
the Indian context.3
7.3 THE MANUFACTURING SCORECARD
7.3.1 Productivity Level
Table
7.1 compares productivity (measured simply as value added per worker) levels in
the various Indian sectors – including manufacturing – for two time periods:
1984 and 2010. Several features stand out. First, in India it is highly
misleading to speak generally of manufacturing because of the clear difference
between unregistered manufacturing – which is a very low productivity activity –
and registered manufacturing – which is an order of magnitude (7.2 times)
more productive.
It
is registered manufacturing, not manufacturing in general, which has the
potential for structural transformation.
Second,
the level of productivity in registered manufacturing is not only high relative
to unregistered manufacturing, it is high compared to most other sectors of the
economy and it is even high in an absolute sense, at US$ 7800 at market exchange
rates and nearly three times as much at PPP exchange rates. If the entire
Indian economy were employed in registered manufacturing, India would be as
rich as say Korea.
Third,
these differentials between registered manufacturing and the rest of the
economy were alreadly prevalent (if not to the same extent) in 1984 – fast
productivity growth over the period (about 5 percent per year) has only
exacerbated the differences.
Thus,
on the first criterion of high levels of productivity, registered manufacturing
scores spectacularly well.
7.3.2 Domestic convergence
Figure
7.1 provides evidence that registered manufacturing is characterised by
unconditional domestic convergence. Here the unit of observation is the
State-Industry level, but almost identical results are derived when looking at
more aggregated levels (across major states in India) and less aggregated
levels (across factories). Broadly a regression coefficient on log of initial productivity
of about (-) 2.5 percent suggests that a state that is twice as rich as another
has an average growth rate of productivity that is 2.5 percent slower – a
considerable amount given that the average growth rate of productivity over the
period 1984-2010 was about 4.4 percent.
7.3.3 International Convergence
With
respect to registered manufacturing, it seems that states and firms within
India are converging to the Indian frontier but that could mean little unless they
are also converging to the international manufacturing frontier. Are they? Rodrik
(2013) shows that there is unconditional convergence across countries and
sectors in manufacturing. But India is a negative outlier in the relationship
in two senses: first, on average, manufacturing sectors in India exhibit labour
productivity growth that is 14 percent less than the average country’s
manufacturing sector. Second, Indian industries converge at a much slower rate than
average (0.005 percent)—almost not at all. In contrast, China is a positive
outlier, posting faster labour productivity growth than average and converging
faster to the global frontier. Registered manufacturing in India has thus not been a strong
performer.
7.3.4 Expansion or Pre-mature non-Industrialisation?
It
is a stylised fact that the process of development includes stages of
industrialisation followed by deindustrialisation: a country first experiences
a rising share of resources – especially labour – devoted to the industrial
sector, after which the services sector becomes more important, so that the
share
of
employment in the industrial sector declines from
its
peak. In recent years, however, “deindustrialisation”
seems
to be taking place
prematurely.
That is, poor countries seem to be
reaching
their peak levels of industrialisation at
lower
levels of industrialisation and income
(Rodrik,
2014; Amirapu and Subramanian, 2015).
What
about India? The phenomenon of deindustrialisation
is
particularly salient for India for
three
reasons. Looming ahead is the demographic
bulge,
which will disgorge a million youth every
month
into the economy in search of employment
opportunities.
Rising labour costs in China create
opportunities
for low-skilled countries such as
India
as replacement destinations for investment
that
is leaving China. And a new government that
has
assumed power offers the prospect of
refashioning
India in the image of Gujarat—one of
the
few manufacturing successes.
But
the sobering fact is that India seems to be deindustrialising
too.
In fact, to call the Indian
phenomenon
de-industrialisation is to dignify the
Indian
experience, which is more aptly referred to
as
premature non-industrialisation because India
never
industrialised sufficiently in the first place.
To
make the point first consider Figure 7.2, which
plots
the share of manufacturing in total
employment
over time for South Korea, a poster
child
for manufacturing-led growth. South Korea’s
GDP
per capita in 2005 PPP dollars is also shown
alongside
the series for several years. The figure
displays
the typical shape: share of employment in
manufacturing
starts very low at around 5 percent
and
rises over time to almost 30 percent before
starting
to decline after a fairly high level of GDP
has
been reached.
In
contrast, Figure 7.3 illustrates the Indian
experience.
The Figure shows India’s share of
registered
manufacturing in total output and
employment
over time (on the same axes as the
graph
for Korea). The general trend is constant
with
a downward trend over the last few years for
which
data are available. In other words, the
pronounced
inverted U shape that characterises
the
cross-section and Korea is notably absent in
India.
But
what has been the counterpart development
among
Indian states? Tables 7.2A and 7.2B show
the
year in which the share of registered
manufacturing
peaked (in first value added and
then
employment terms), the peak share of
registered
manufacturing (in value added or
employment),
and the per capita GDP associated
with
peak registered manufacturing levels.
From
the tables, a few points are striking. Gujarat
has
been the only state in which registered
manufacturing
as a share of GDP surpassed 20
percent
and came anywhere close to levels
achieved
by the major manufacturing successes in
East
Asia. Even in Maharashtra and Tamil Nadu,
manufacturing
at its peak accounted for only about
18-19
percent of state GDP. The peak shares in
employment
terms are even less significant: no
major
Indian state has achieved more than 6.2
percent
of employment from registered
manufacturing
in the last 30 years, and many major
states
peaked at less than half that. Even in Gujarat,
employment
in registered manufacturing has only
been
about 5 percent of total employment, while
annual
growth in registered manufacturing
employment
has been 1.8 percent between 1984
and
2010 (slower than the growth rate of total
employment
over the period: 2.4 percent).
Second,
in nearly all states (with the exception of
Himachal
Pradesh and Gujarat), registered
manufacturing
as a share of value added is now
declining
and, for most states, has been doing so
for
a long time. The peak share of manufacturing
in
output for many states was reached in the 1990s
(Andhra
Pradesh and Tamil Nadu) or even in the
1980s
(Maharashtra). Interestingly, peak
employment shares seem to be following a slightly
different
story, with less marked declines
observable
for most states. Nevertheless, most
states
have not been experiencing secular growth
in
employment shares over time (the only
exceptions
are Himachal Pradesh, Tamil Nadu,
Haryana
and – possibly – Karnataka). Many of
the
states that do exhibit peak years in 2010 (such
as
Andhra Pradesh, Rajasthan and Orissa) seem
to
have employment shares that have been mostly
flat,
reflecting neither relative growth nor decline.
Third,
and this is perhaps the most sobering of
facts,
manufacturing has even been declining in the
poorer
states: states that never effectively
industrialised
(West Bengal and Bihar) have started
de-industrialising.
Some
comparisons are illuminating. Take India’s
largest
state Uttar Pradesh. It reached its peak
share
of manufacturing in output at 10 percent of
GDP
in 1996 at a per capita state domestic
product
of about $1200 (measured in 2005
purchasing
power parity dollars). A country like
Indonesia
attained a manufacturing peak share
of
29 percent at a per capita GDP of $5800.
Brazil
attained its peak share of 31 percent at a
per
capita GDP of $7100. So, Uttar Pradesh’s
maximum
level of industrialization was about onethird
that
in Brazil and Indonesia; and the decline
began
at 15-20 percent of the income levels of
these
countries.
Thus
far, we have shown that, for all but a few states, Indian manufacturing is
certainly not growing and is probably shrinking. One possible consequence of
manufacturing failing to satisfy requirements 2b and 3 is that, in contrast to
China, there is no evidence of convergence between states in India in overall
per capita GDP. For Chinese provinces, the poorer the initial level of per
capita GDP, the faster the subsequent growth, so that poorer provinces start
catching up with richer ones. In India, there is no convergence, because poorer
states are not likely to grow faster than richer ones on average (Amirapu and
Subramanian 2015).
Regional
disparities have thus persisted within India.
Had
manufacturing attracted resources while exhibiting domestic convergence in
productivity, the sector would have expanded in poorer states increasing overall
levels of income in these states and contributing to a narrowing of the income distribution
across India. Instead it seems that manufacturing has failed to be such an
escalatorof progress.
Several
explanations are possible for why manufacturing has not been this escalator in
India.
They
fall under four broad categories: distortions in labour markets; distortions in
capital markets; distortions in land markets; and inappropriate specialisation
away from India’s natural comparative advantage and toward skill intensive activities.
Amirapu and Subramanian (2015) provides some evidence in support of the last explanation.
7.3.5 Alignment with Comparative
Advantage
As
argued earlier, in order for a sector to offer transformational possibilities,
it must not only be characterised by high levels and growth rates of productivity,
it must also absorb resources from the rest of the economy. But in order to do
so, the sector’s use of inputs must be aligned with the country’s comparative
advantage. That will allow the abundant factor of production (usually unskilled
labour) to benefit from productivity growth and convergence, and in so doing
make growth not only rapid and sustainable but also inclusive. In other words,
the dynamic sector must at least initially be relatively unskilled labour
intensive. Is this true of India manufacturing? Kochhar et. al. (2006) found
that Indian manufacturing was
unusually
skill labour intensive. Another simple
metric
for assessing the alignment of dynamism with
comparative
advantage is the relative skill intensity
of
manufacturing relative to other sectors. Table
7.3
presents some numbers. From the 2004/5
NSSO
Employment and Unemployment Survey,
the
share of employees with at least primary and
secondary
education for major sectors (and
subsectors)
of the Indian economy is computed.
It
turns out that registered manufacturing is a sector
that
is relatively skilled labor intensive. As table
7.3
shows, the share of workers with at least
secondary
education is substantially higher in
registered
manufacturing than in agriculture, mining
or
unregistered manufacturing and also greater than
in
several of the service subsectors. In some ways,
this
should not be surprising. High labour
productivity
in this sector (Table 7.1) is at least in
part
a consequence of higher skills in the work
force.
What it does suggest, however, is that
registered
manufacturing does not really satisfy
requirement
number four. The skill intensity of the
sector
is not quite aligned with India’s comparative
advantage.
7.4 THE SERVICES SCORECARD
The
scorecard analysis can be repeated for the
services
sector in India. But before that is done, it
is
important to recognise that services in the
aggregate
is not a useful category of analysis
because
it is an amalgam of different and disparate
species
of economic activity, from government
services
and construction that are non-tradable to
finance
and business services that largely are
tradable;
from certain activities that are labour
intensive
and others such as telecommunications
that
are highly capital and skill labor intensive. Any
meaningful
analysis of services must distinguish
between
different service subsectors—although
the
degree of disaggregation will of course be
determined
by data availability.
We
work with the six different subsectors shown
in
Table 7.4 and repeat the analysis undertaken
above
for registered manufacturing.
7.4.1 Productivity Level
Table
7.4 provides comparative data on the level
of
productivity for these service subsectors as well
as
for manufacturing (both registered and
unregistered).
The first point to note is the
astounding
variation within services, reinforcing the
case
for disaggregation. In 1984 for example, the
level
of productivity in the real estate and business
services
sectors was 25 times as much as in public
administration
(essentially government) and close
to
20 times as much as in retail. The productivity
levels
in two—financial services and business
services—out
of six service subsectors exceed that
of
registered manufacturing.
7.4.2 Domestic convergence
The
issue of whether there was unconditional
convergence
within India for service subsectors
over
the last 3 decades is now examined. Notably,
unconditional
domestic convergence is found in
nearly
all the service subsectors, and across many
time
horizons (not reported here). In fact, the speed
of
domestic convergence for most service
subsectors
is found to be similar to that in registered
manufacturing
(about 2 percent) and, in some
cases,
substantially higher. For example, real estate
and
business services seem to converge at double
the
rate at which registered manufacturing
converges.
7.4.3 International Convergence
Rodrik
(2013) provides evidence using UNIDO
data
that industries in the (organized) manufacturing
sector
consistently exhibit global convergence in
labour
productivities, although Indian
manufacturing
industries converge to the global
frontier
much more slowly than the average, if at
all.
What about the service subsectors?
Using
data on sectoral productivities from the
World
Bank’s World Development Indicators
(WDIs),
Ghani and O’Connell (2014) argue that
services
in the aggregate have also exhibited
convergence
to a similar or even greater degree
than
manufacturing – at least for recent time periods
(approximately
1990 to 2005). This is an
interesting
finding, but for this analysis in particular
services
should be disaggregated as we might well
expect
convergence behaviour to vary by subsector
due
to significant differences in sectoral
characteristics
such as tradability.
Table
7.5 reports international convergence results
by
service subsectors over the period 1990 to
2005
using data from the Groningen Growth and
Development
Centre (GGDC). Although the set
of
countries in the analysis is severely limited due
to
data availability,7 the
results are still interesting.
We
see that some service subsectors (Finance,
Insurance,
and Real Estate; Community, Social and
Personal
Services; and Construction) do seem to
exhibit
strong international convergence, while
others
(Trade, Hotels and Restaurants; Transport,
Storage
and Communication) do not. Surprisingly,
the
set of sectors exhibiting convergence seems to
include
even some apparently non-tradable
sectors,
such as construction.
The
conclusion thus far seems to be that many–
but
not all – service subsectors satisfy the
requirements
of high productivity growth, domestic
convergence,
and international convergence.
7.4.4 Expansion of Services?
Evidence
that the share of output and employment
from
manufacturing in India had hardly changed in
30
years has already been presented. In the Tables
below
analogous evidence for services in India –
both
in aggregate and for particular service
subsectors
is presented.
In
contrast to registered manufacturing – the share
of
output from aggregate services rose dramatically
over
the last 30 years, from about 35 percent to
more
than 50 percent of GDP. The share of
aggregate
services in employment, in contrast,
increased
in a far more modest fashion (see Table
7.6).
But there is nevertheless a distinct contrast
with
registered manufacturing. Aggregate services
employment
grew faster than that in registered
manufacturing
and a number of service
subsectors—transport,
real estate and
construction—registered
substantially faster
employment
growth. In other words, services are
becoming
an ever more important source of wealth,
and
while they have not delivered rapid employment
growth,
a number of service sub-sectors have
generated
more rapid employment growth than
manufacturing.
7.4.5 Alignment with comparative
advantage?
We
argued above that, in a low-skilled labour
abundant
country like India, a sector must make
use
of this dominant resource in order to offer the
greatest
possibilities for expansion and structural
transformation.
We also saw that registered
manufacturing
was a fairly skill-intensive sector
with
high average educational attainment.
The
same table also shows that services in
aggregate
are no less skill-intensive: on average,
78
percent of workers in the service sector have
at
least a primary education (77 percent in
registered
manufacturing), and 48 percent have at
least
a secondary education (43 percent in
registered
manufacturing). Furthermore, a large
number
of service subsectors – including 1)
Banking
and Insurance, 2) Real Estate and
Business
Services, 3) Public Administration, 4)
Education,
and 5) Health and Social Services –
have
significantly higher educational attainment
(90
percent or more of workers have at least
primary
education) than registered manufacturing.
What
this implies is that most service subsectors
(precisely
the high productivity, high growth
subsectors,
for the most part), have a limited
capacity
to make use of India’s most abundant
resource,
unskilled labor. This may explain why
the
share of employment from services has risen
so
modestly, even while the share of output from
services
has grown so spectacularly.
7.5 SUMMARY SCORECARD AND
CONCLUSIONS
Table
7.6 below provides a summary scorecard
comparing
registered manufacturing and selected
service
subsectors. Before proceeding further, let
us
make clear a few important points. First, we
compare
service sectors with only the registered
(i.e.:
formal) manufacturing sector, because
unregistered
manufacturing is one of the lowest
productivity
sectors in the Indian economy– apart
from
agriculture – and so offers little promise for
transformation.
So, when there is talk on the
transformational
potential of manufacturing in India
the
focus must be exclusively on registered
manufacturing.
Second,
another contribution of this chapter is to
offer
an alternative way of thinking about
transformational
sectors beyond the traditional
distinction
based on manufacturing versus services.
We
have taken the position of comparing sectors
based
on their easily observable underlying
properties.
To be sure, there may be less tangible
differences
between manufacturing and services
that
are left out in our analysis.
For
example, our present analysis does not
consider
the extent to which certain sectors (such
as
registered manufacturing) may be more likely
to
induce learning spillovers to other sectors of
the
economy, which may be important. Other
missing
dimensions include the political one: Dani
Rodrik
has suggested that manufacturing may play
an
indirect role in the political development of young
nations
by providing a forum in which citizens learn
to
practice compromise in a democratic context
through
the struggle between labour and capital
“on
the manufacturing shop floor” (Rodrik,
2013b).
Though our analysis leaves out such
channels,
we believe they are second-order in
comparison
with the 5 desirable features laid out
earlier.
Proceeding
to the comparison, there does not seem
to
be anything distinctive or superior about
registered
manufacturing when compared with
certain
other service subsectors. Like
manufacturing,
several of the service subsectors
also
exhibit high productivity and convergence –
both
domestic and international. However, they
also
share the shortcoming that these sectors are
highly
skill intensive in their resource requirements,
which
is out of kilter with the skill profile of the
Indian
labor force. Their potential to generate
widely
shared or inclusive growth is thus likely to
be
limited – and indeed seems to have been so
given
the lack of expansion observed earlier (and
which
is recorded in the scorecard).
One
sector that markedly stands out from the
others
in the table below is construction: it appears
to
exhibit both types of convergence, does not
require
high education levels and has grown
significantly
in its resource use over the last three
decades.
However, the sector is not tradable and
in
any case is low productivity, so that moving labor
resources
to the sector does not considerably
improve
overall welfare.
So,
in some ways, the choice for India is not
manufacturing
versus services but comparative
advantage
deifying (unskilled-intensive) sectors
versus
comparative advantage defying (skillintensive)
sector
development. This is both a
positive
and a policy question.
While
India’s skill-intensive pattern of development
has
no doubt been costly, there has been a
significant
upside. Myron Weiner, among others,
has
drawn attention to the disappointing post-
Independence
performance of the Indian state in
delivering
education, reflected in very slow
improvements
in literacy rates, especially amongst
women.
While the supply of educational services
by
the state was inadequate, the puzzle arose as
to
why there was not greater demand for education
and
hence greater pressure on the state to meet
this
demand.
One
answer to this puzzle is that the private returns
to
literacy and basic education must have been
low.
There is now evidence that the increasing
opportunities
that are spurring economic growth
also
contribute to raising these returns, leading to
a
greater demand for educational services—public
and
private—and hence improvements in
educational
outcomes (Munshi and Rosenzweig,
2003).
This has put pressure on the supply of
education.
The government’s failures to provide
good
schools are well-known, but growth has
changed
the picture dramatically, largely because
it
has increased the returns from education—and
hence
the demand for it.
Evidence
is provided by the work of economists
Kartik
Muralidharan and Michael Kremer who
show
that private schools are mushrooming in rural
India
(many prominently advertising “English
Medium”)
because of teacher absenteeism in
public
schools. One also hears of companies
creating
training centers to build skills in the cities
(such
as the Infosys institute in Mysore) because
institutions
of higher education are notoriously
inadequate.
This endogenous increase in human
capital
could be one of the offsetting benefits of
the
comparative advantage-defying, skill-intensive
growth
model.
The
policy question is the following. Insofar
as
the government retains influence
over shaping
the pattern of development,
should it try to
rehabilitate unskilled
manufacturing or should
it accept that that is difficult
to achieve, and
create the groundwork for
sustaining the skill
intensive pattern of growth? Attempting the
former
would be a history-defying achievement
because
there are not many examples of significant
reversals
of de-industrialisation. A lot would have
to
change in India—from building the infrastructure
and
logistics/connectivity that supports unskillintensive
manufacturing
to reforming the panoply
of
laws and regulations—or perhaps addressing
corruption
in the manner of their enforcement—
that
may discourage hiring unskilled labor and
achieving
scale in the formal sector.
Sustaining
a skill-intensive pattern on the other hand
would
require a greater focus on education (and
skills
development) so that the pattern of
development
that has been evolving over time does
not
run into shortages. The cost of this skill
intensive
model is that one or two generations of
those
who are currently unskilled will be left behind
without
the opportunities to advance. But
emphasising
skills will at least ensure that future
generations
can take advantage of lost
opportunities.
In
some ways, the choice confronting India is really
about
how to make it a Lewisian economy that
has
unlimited supplies of labor. India can either
create
the conditions to ensure that its existing
unlimited
supplies of unskilled labor are utilisable.
Or,
it can make sure that the currently inelastic
supply
of skilled labor is made more elastic. Both
are
major challenges.
What
the analysis suggests is that while Make in
India,
which has occupied all the prominence, is
an
important goal, the Prime Minister’s other goal
of
“Skilling India” is no less important and perhaps
deserves
as much attention. Make in India, if
successful,
would make India a Lewisian economy
in
relation to unskilled labor. But “Skilling India”
has
the potential to make India a Lewisian
economy
with respect to more skilled labor. The
future
trajectory of Indian economic development
could
depend on both.
