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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.