What Constrains Indian Manufacturing?

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Economics and REsEaRch dEpaRtmEnt

W cr

i mufurg?

Poonam Gupta, Rana Hasan, and Utsav Kumar

August 2008

RD WoRking PaPER SERiES no. 119


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ERD Wrin Paper N. 119

WhatConstrains indian ManufaCturing?

Poonam GuPta, Rana Hasan, and utsav KumaR

august 2008

Poonam Gupta is Associate Proessor at the Delhi School o Economics; Rana Hasan is Principal Economist in the

Development Indicators and Policy Research Division, Asian Development Bank; and Utsav Kumar is Economist at the

Conerence Board, New York. The views presented here are those o the authors and not necessarily o the institutions

they are afliated with.


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Asian Development Bank6 ADB Avenue, Mandaluyong City1550 Metro Manila, Philippines

www.adb.org/economics

2008 by Asian Development BankAugust 2008

ISSN 1655-5252

The views expressed in this paper

are those o the author(s) and do notnecessarily reect the views or policies

o the Asian Development Bank.


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FoREWoRD

The ERD Working Paper Series is a orum or ongoing and recently completedresearch and policy studies undertaken in the Asian Development Bank or onits behal. The Series is a quick-disseminating, inormal publication meant to

stimulate discussion and elicit eedback. Papers published under this Seriescould subsequently be revised or publication as articles in proessional journalsor chapters in books.


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CoNtENts

I. Introduction 1

II. Stylied acts and Preliminary EvidenceII. Stylied acts and Preliminary Evidence 4

A. Indian Policy rameworkA. Indian Policy ramework 4 B. Perormance o Indian Manuacturing 8

III. Evidence rom Enterprise Surveys 1III. Evidence rom Enterprise Surveys 11

I. Econometric Analysis 1I. Econometric Analysis 14

A. Construction o ariables 1A. Construction o ariables 15 B. Empirical Results rom the ASI Data 16 C. Econometric ramework 17 D. Robustness o Results 24

. Concluding Remarks 2. Concluding Remarks 27

AppendixAppendix 28

Reerences Reerences 4


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AbstRACt

India has undertaken extensive reorms in its manuacturing sector in thelast two decades. However, an acceleration o growth in manuacturing, and aconcomitant increase in employment, has eluded India. What might be holding

the sector back? Using Annual Survey o Industries data at the -digit level,and dierence in dierences estimates, this paper fnds that the postreormperormance o the manuacturing sector is heterogeneous across industries. In

particular, industries that were dependent on inrastructure or external fnance

and were labor-intensive have not been able to reap the maximum benefts oreorms. The results point to the importance o inrastructure development andfnancial sector development or the manuacturing sectors growth to accelerate

urther. They also emphasie the need to clearly identiy and address the actorsinhibiting the growth o labor-intensive industries.


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I. INtRoDuCtIoN

Many emerging countries in recent decades have relied on a development strategy ocused onpromoting the manuacturing sector and the export o manuactured goods. These include many East

Asian countries and most recently, the Peoples Republic o China. India, too, hoped or a dynamicmanuacturing sector when it introduced substantial product market reorms in its manuacturingsector starting in the mid-1980s. But the sector never took o as it did in other countries. India

no doubt has grown impressively in the last 15 years; however, the main contribution to growthhas come rom the services sector rather than rom the manuacturing sector. Moreover, in soar as subsectors within manuacturing that have perormed well, these have been the relativelycapital- or skill-intensive industries, not the labor-intensive ones as would be expected or a labor-

abundant country like India. What could be the reasons behind the rather lackluster perormanceo the manuacturing sector in India?

As igure 1 shows, the manuacturing sectors share in Indias gross domestic product (GDP) hasbeen stagnant since the early 1990s despite several wide-ranging reorms in this sector. Similarly,igure 2 shows that the contribution to GDP growth has primarily been rom the services sector,and this contribution has been increasing over time.

FIGURE 1

SECTORAL SHARES IN GDP, INDIA

60

50

40

30

20

10

0

Manufacturing

Agriculture

Services

1970 1974 1978 1982 1986 1990 1994 1998 2002 2006

Sources: Authors calculations, Central Statistical Organiation (India).


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FIGURE 2

SECTORAL CONTRIBUTION TO INDIAN GDP GROWTH, 19512007

60

50

40

30

20

10

0

Services

C

ontribution

to

GDP

growth

(percent)

Industry Agriculture

19511960 198011990 19902000 20002007

Sources: Authors calculations, Central Statistical Organiation (India).

Several hypotheses have been put orward to explain the lack o dynamism in Indiasmanuacturing sector. Inrastructure-related bottlenecks are widely believed to be a part o theexplanation. In particular, poor quality o power supply, road networks, and ports and airports are

believed to create signifcant disadvantages or Indian manuacturers by pushing up their costs oproduction, and making them uncompetitive in export markets.1

Besides inrastructure, some key areas o policies remain unchanged. In particular, even though

there have been extensive product market reorms, it has been widely observed that the labor marketreorms to complement these have not been undertaken (see Panagariya 2006 and 2008, Kochharet al. 2006). Moreover, credit constraints due to weaknesses in the fnancial sector may be holding

back small- and medium-sie frms rom expanding (see Banerjee and Duo 2004, Nagaraj 2005,McKinsey 2006).2 inally, business regulations might have inuenced key decisions o frms andpotential investors. As the World Banks Doing Business surveys o business regulations across theworld have ound, the procedures and costs or starting and, especially, closing a manuacturing

business in India are among the most cumbersome in the world.

1 As indicated in Gordon and Gupta (2004), the nature o production o services is probably such that it is less aected

by inrastructure bottlenecks.2 Banerjee and Duo (2004) use frm-level data rom the late 1990s and early 2000s and show that medium-sie frms

even those well above the small scale thresholdwere subject to credit constraints and appeared to be operatingwell below their optimal scale.

Another possibility is that hysteresis in the pattern o development in Indian manuacturing implies that the relative

proftability o capital- and skill-intensive activities remains higher than that o labor-intensive activities despite thereorms o the early 1990s (Kochhar et al. 2006). Other actors oten believed to be aecting the perormance o

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WhAtConstrAins indiAn MAnufACturing?

PoonAM guPtA, rAnA hAsAn, And utsAv KuMAr


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It would be useul to empirically test the hypotheses related to the idea that various elements

o the policy and institutional environment acing the manuacturing sectoreither let untouched bythe liberaliations o the 1990s or dealt with only partiallyhave emerged as signifcant bottlenecksto growth and employment generation.

One obvious way to test these hypotheses is to exploit interstate heterogeneity in the policyand institutional environment, including labor market regulations, fnancial sector development,and inrastructure or dierent states o India; and then to test whether industrial perormance

has been better in the states with better policy and institutional ramework. This is precisely whathas been done in the existing literature to show the importance o labor market exibilities inexplaining the gains rom product market liberaliation. Besley and Burgess (2004), or example,exploit state-level amendments to the Industrial Disputes Act (IDA) arguably the most important

set o labor regulations governing Indian industryover 19581992, and code legislative changesacross major states as pro-worker, neutral, or pro-employer. These legislative amendments are thenused in the regression analyses o various outcomes in the manuacturing sector, including output,

employment, investment, and number o actories. Besley and Burgess fnd that pro-worker laborregulations have had a negative impact on output, employment, and investment in organiedmanuacturing.4

A related paper by Aghion et al. (2006) relates various dimensions o industrial perormanceto the extent to which an industry was covered by industrial licensing requirements, and state-levelmeasures o the stance o labor regulations. They fnd that the eects o industrial delicensing wereunequal across Indian states. In particular, delicensed industries located in states with pro-employer

labor regulations grew aster in terms o both output and employment levels than those with pro-worker regulations. Similarly, Mitra and Ural (2007) show that industries experiencing larger tarireductions grew aster in pro-employer states relative to pro-worker states.5

In this paper we relate the pattern o growth in Indias manuacturing sector to cross industryheterogeneity in the reliance on inrastructure and the fnancial sector and in the use o labor

relative to capital. In particular, we calculate the dependence o industries on inrastructure andthe fnancial sector, and the labor intensity o industries. Using Annual Survey o Industries (ASI)data at the -digit level, we employ dierence in dierences estimates to compare the perormanceo industries more dependent on inrastructure, on the fnancial sector, and on labor-intensiveindustries post-delicensing with that o the control group.

Our results indicate that the aggregate perormance o the manuacturing sector masksimportant interindustry dierences. Quite interestingly, we fnd that the industries with greaterneed or inrastructure, greater dependence on the fnancial sector, and greater labor intensity

have perormed relatively worse in the post-delicensing period. Quantitatively, the results indicate

Indian manuacturing are public ownership o enterprises, remaining reservations in small-scale industries, and stringentregulations on land use in India as discussed in Panagariya (2008). In recent years the availability o skilled labor has

also emerged as a constraint on the growth o manuacturing and services.4 While, in principle, the approach o Besley and Burgess (2004) has considerable merit, it is not without controversy. Bhattacharjee

(2006), in particular, has argued that deciding whether an individual amendment to the IDA is pro-employer or pro-worker in

an objective manner is quite difcult. Even i individual amendments can be so coded, the actual workings o the regulationscan hinge on judicial interpretations o the amendments. Moreover, i noncompliance with regulations is widespread, then

even an accurate coding o amendments that takes into account the appropriate judicial interpretation loses its meaning.5 There are some dierences between Mitra and Ural (2007) and Aghion et al. (2006) in terms o the states deemed to

have pro-employer or pro-worker labor regulations. See Mitra and Ural (2007) or details.

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that in the post-delicensing period, the above-median, inrastructure-intensive industries grew

10% less than the industries below the median o inrastructure dependence. Similarly, industriesabove-median in the distribution o fnancial dependence grew 18% less than the industries belowthe median o fnancial dependence; and or labor intensity, industries with above-median labor

intensity grew 19% less than the below-median industries post-delicensing.There are two ways to interpret these results. irst, one can use the results to identiy which

industries have not benefted much rom reorms. Second, to the extent that the heterogeneity

across industries on parameters such as inrastructure dependence is exogenous and determinedby actors such as production technology, causal inerences may probably be drawn as well. Thus,or example, we can claim that i industries dependent on inrastructure have not benefted asmuch rom reorms, it is because o the unavailability o adequate inrastructure; the same is true

or fnancial sector-dependent industries. or labor-intensive industries, an interpretation in termso the limited supplies o labor would not be appropriate in the Indian context. A more naturalinterpretation would be to relate the relatively weak perormance o labor-intensive industries to

the quality o labor, skill mismatch, and regulations on employment that make the eective priceo hiring labor too high.

In order to ensure that the results are not driven by outliers; that the standard errors are

corrected or heteroskedasticity and autocorrelation; and that the estimates are not biased dueto omitted variables, we conduct extensive robustness tests, and fnd our results to be robust tothese sensitivity analyses.

There are two conclusions that can be drawn rom these results. irst, product market reormsalone might not be sufcient to spur growth; or gains rom these reorms to be maximied theymay have to be complemented by reorms in other areas. Second, the potential benefts rom productmarket reorms might not be realied unless these are matched by enabling conditions such as high-

quality inrastructure, availability o the right kind o labor, and fnancial markets that are deepenough. There is no room or complacency, and eorts should be made to remove these constraints

i the manuacturing sector is to play the role that it did in the case o East Asian countries.One point that needs to be noted about our analysis is that it is based on data only on the

organied (or registered) manuacturing sector, and not the unorganied sector. This is primarilybecause o the unavailability o detailed data or the latter. A question that comes up then is

whether the lack o quality data on the unregistered manuacturing should preclude any analysiso the registered manuacturing sector. Though there is no denying the act that the unorganiedmanuacturing sector is important when it comes to employment, its output, wages, and productivityare very low. In terms o policy objectives, improving the perormance o registered manuacturing

is a key aspect to making India a powerhouse in manuacturing.

II. stylIzED FACts AND PRElImINARy EvIDENCE

A. Indian Pic Fraewr

Since the early 1950s up until the early 1980s, the evolution o Indias manuacturing sectorwas guided by industrial and trade policies that protected domestic industry and gave the state acentral role in investment decisions. While a strict regime o import and export controls defned

trade policy, industrial policy worked through an elaborate system o industrial licensing. Under the

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stylized fACtsAnd PreliMinAry evidenCe

Industries Development and Regulatory Act o 1951, investors needed to obtain a license beore

establishing an industrial plant, adding a new product line to an existing plant, substantiallyexpanding output, or changing a plants location.

While the state-led import substitution policy ramework had helped create a diversifed

manuacturing sector, industrial stagnation since the mid-1960sincreasingly blamed on thepolicy rameworkled to some tentative steps aimed at liberaliing these regimes in the late1970s and early 1980s (see Ahluwalia 1987 and 1991). Relaxations o the industrial licensing

system were introduced and import licensing requirements were eased. However, by most accountsthese reorms were marginal. Tari rates as high as 400% were not uncommon, nontari barriersremained widespread, and the industrial licensing regime continued to impose binding constraintsto entry and growth or most frms. The so-called small-scale sector reservations (introduced in

1969), which limited the entry and operations o frms above a certain sie threshold in a numbero labor-intensive industries continued in ull orce. (This was largely the case until 2000, andrecent reorms have let only 5 items on the list.)

More serious liberaliation eorts began in 1985 with delicensingthe exemption rom the

requirement o obtaining an industrial licenseo 25 broad categories o industries, which mapsinto 1 industries in our -digit level data. The next major reorm o the licensing regime came in

1991 when industrial licensing was abolished except in the case o a small number o industries(see igure 2a and Appendix Table A.4 or the time path o delicensing). This was also the yearin which a decisive break was made with the trade policies o the past. The liberaliation o 1991included the removal o most licensing and other nontari barriers on the imports o intermediate

and capital goods, the simplifcation o the trade regime, devaluations o the Indian rupee, andthe introduction o an explicit dual exchange market in 1992 (see igure 2b).

FIGURE 3

CUMULATIVE SHARE OF INDUSTRIES DELICENSED

100

90

80

70

60

50

40

30

20

10

0

Percent

of

industries

delicensed

1985 1989 1991 1993 1997

Year of delicensing

2729

86 88

947

Sources: Based on Aghion et al. (2006) and extended by the authors.



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FIGURE 4

AVERAGE NOMINAL RATE OF PROTECTION, 19881998

160

140

120

100

80

60

40

20

0

Average

tariff(percent)

1988 1989 1991 1993 19951990 1992 1994 1996 1997 1998

149 149 149 149

107

50

67

85

4539

44

Source: Hasan, Mitra, and Ramaswamy (2007).

Despite these impressive reorm measures, there are certain areas in which there has beenlittle progress. One area in which there has been no major policy change is in the labor regulations

that apply to Indias industry sector. According to Panagariya (2008), it is rigidities introduced bythese (unchanged) regulations that are holding back the manuacturing sector in general and itslabor-intensive subsectors in particular. Since the issue o Indias labor regulations is one o themost contentious ones in the context o debates on economic reorms, some details are in order.

While Indias labor regulations have been criticied on many accounts including, or example,

the sheer sie and scope o regulations, their complexity, and inconsistencies across individual pieceso regulation, a ew specifc pieces o legislation are the controversial ones. irst, as per ChapterB o the IDA it is necessary or frms employing more than 100 workers to obtain the permissiono state governments in order to retrench or lay o workers.6 While the IDA does not prohibitretrenchments, critics o the act argue that it is difcult to carry them out. Datta-Chaudhuri (1996)

has argued, or example, that states have oten been unwilling to grant permission to retrench.

Second, additional rigidities in using eectively a frms existing workers are believed to stemrom Section 9A o the IDA and the Industrial Employment (Standing Orders) Actwhich pertain to

procedures that must be ollowed by employers beore changing the terms and conditions o work.While the two pieces o legislation seek to make labor contracts complete, air, and legally binding,they can constrain frms rom making quick adjustments to changing conditions. In particular, workerconsent is required in order to modiy job descriptions or move workers rom one plant to another in

6 Until 1976, the provisions o the IDA on retrenchments or layos were airly uncontroversial. The IDA allowed frmsto lay o or retrench workers as per economic circumstances as long as certain requirements such as the provision

o sufcient notice, severance payments, and the order o retrenchment among workers (last in frst out) were met.An amendment in 1976 (the introduction o Chapter B), however, made it compulsory or employers with more than

00 workers to seek the prior approval o the appropriate government beore workers could be dismissed. A urther

amendment in 1982 widened the scope o this regulation by making it applicable to employers with 100 workers ormore.

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response to changing market conditions. In and o itsel, this does not seem to be an unreasonable

objective. The problem, according to some analysts, is that the workings o Indias Trade UnionAct (TUA) make it difcult to obtain worker consent. While the TUA allows any seven workers inan enterprise to orm and register a trade union, it has no provisions or union recognition (or

example, via a secret ballot). The result, according to Anant (2000), has been multiple and rivalunions, making it difcult to arrive at a consensus among workers.

Similarly, hiring contract workers can enable frms to get around many o the regulatory

restrictions on adjusting employment levels, productions tasks, and others; however, it is argued thatSection 10 o the Contract Labour Act, which empowers the government to prohibit the employmento contract labor in any industry, operation, or process, limits this course o action.

It is important to note that not all analysts agree that Indias labor laws have made or a rigidlabor market. In particular, a counter argument to the views above is that the rigidity-inducingregulations have been either ignored (see Nagaraj 2002) or circumvented through the increasedusage o temporary or contract labor (see Dutta 200 and Ramaswamy 200).7 Ultimately, whether

Indias labor laws have created signifcant rigidities in labor markets or not is an empirical issue,

as is the broader question o whether and to what extent various policies have been the mainconstraints on the growth o Indian manuacturing.

Another well-known constraint on growth is Indias crumbling inrastructure. According to theDeputy Chairman o the Planning Commission, Montek Singh Ahluwalia, India needs to increase itsinvestment in inrastructure rom 5% o GDP to 8% o GDP by the end o the Eleventh ive-Year

Plan, yielding an investment o $400 billion in its inrastructure to sustain the current growthrates. One does not need any scientifc evidence to show that inrastructure in India needs to beimproved, as the erratic and costly electricity supply, congested roads, ports, and airports are orall to witness. A recent survey o the Indian economy compares Indian inrastructure with that o

other countries and fnds India to be badly lagging in most o the areas (OECD 2007).

An interesting comparison in this regard is with the inrastructure in the Peoples Republic o

China (PRC). Total investment anticipated in inrastructure in the Tenth ive-Year Plan (20022006)in India was 5% compared to the PRCs 12.6% in 2005 (igure ). Not only is the PRCs investmentas a share o GDP almost 2.5% times greater than that o India, the PRCs GDP base is larger aswell. In almost all sectors, investment as a share o GDP in the PRC is ar greater than those in

India (igure ).

7 or a detailed review o Indian labor regulations and the debate surrounding the issue o rigidity, see Anant et al.(2006).

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FIGURE 5

INFRASTRUCTURE INVESTMENT, THE PRC AND INDIA

India, 10th Five Year Plan PRC, 2005

Share in GDP (percent)

0 2 4 6 8 10 12 14

Total

Power and gas

Transport

Irrigation

Telecommunications

Drinking water0.370.3

0.80.64

0.713.3

4.61.60

1.723.6

5.0412.6

Sources: Authors calculation, Planning Commission (2007), Lall et al. (2007).

Another area in which there has been rather slow progress on reorms is the fnancial sector (orthe banking sector, more narrowly). Reorm eorts in this area have been directed at deregulating

interest rates, some dilution o public ownership o banks, and limited opening up o the sector toprivate domestic and oreign banks. However as pointed out oten, and most recently in the OECDEconomic Survey on India (OECD 2007), some major challenges still remain. These include a veryhigh share o public ownership in banks and a low level o bank intermediation partly because o

regulations on the allocation o credit that require banks to allocate a substantial percentage o

their total advances into government securities and other priority sectors.8

b. Perfrance f Indian manfacrin

We look at a airly long time series o data on Indian registered manuacturing rom 197 to

200.9 Below we summarie some o the empirical regularities that we observe in the data on thevarious indicators o industrial perormance and on employment related variables.10 arious panelso igure 4 show that the growth o value-added, employment, capital ormation, and actories has

8 In addition, since the perormance o the bank managers is not linked as tightly with the proftability o the banks,and is probably inuenced more by the incidence o nonperorming loans, they have little incentive to provide credit

to the private sector. Hence they are extremely cautious, and rather than lending to the private sector, would ratherinvest in sae government securities (see Banerjee and Duo 2004).

9 Reerence period or ASI is the accounting year o the industrial unit ending on any day during the fscal year. Thereore,

in ASI 2002004, the data collected rom the respective industrial units relates to their accounting year ending onany day between 1 April 200 and 1 March 2004. In this paper, 200 reers to fscal year 2002004.

10 The only comprehensive database available on Indian manuacturing is the ASI data, which include data on registeredmanuacturing, i.e., actories with more than 20 workers i not using power, and actories employing more than 10

workers i using power. One caveat o using this data is that we are only looking at a raction o total manuacturing.

Registered manuacturing comprises 70% percent o the total output being produced in the manuacturing sector butonly 20% o the total manuacturing employment.

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been stable throughout the last three decades and has not necessarily accelerated in the postreorm

period. I anything, there is probably a stagnation starting sometime in the mid-1980s to the late1990s.

FIGURE 6

PERFORMANCE OF INDIAN MANUFACTURING

Panel A: Real Gross Value-Added Panel B: Employment

Panel F: Labor Productivity

Panel D: Number of FactoriesPanel C: Capital Stock

Panel E: Contractual Labor

Year

total_empl_log empl_log

1973 1977 1981 1985 1989 1993 1997 2001

Year

1973 1977 1981 1985 1989 1993 1997 2001

Year

1973 1977 1981 1985 1989 1993 1997 2001

Year

1973 1977 1981 1985 1989 1993 1997 2001

Year

1973 1977 1981 1985 1989 1993 1997 2001

Year

1998 1999 2000 2001 20032002

16.0

15.8

15.6

15.4

15.2

Employment

11.8

11.6

11.4

11.2

11.0

Logofnumberoffactories

7.5

7.0

6.5

6.0

Logoflabo

rproductivity

23.5

23.0

22.5

22.0

21.5

Logofrealgrossvalue-added

24.5

24.0

23.5

23.0

22.5

22.0

Log

ofrealcapitalstock

20

18

16

14

12

Shareofcontractualla

borin

totalemployment(percent)

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Panel B o igure 4 shows the employment o blue collar workers and total employment.

The trends seem to be broadly similar or both the variables. The data on contractual labor isavailable only since 1998, but the trends show an increase in the share o contractual labor intotal employment. The pace o growth o capital stock seems to be aster than that o employment.

These dierent trends in employment and investment are probably reected in the growth o laborproductivity over time. inally, the number o actories does not seem to have kept pace with thegrowth o value-added.

The trends in these charts are also picked up, as seen in Table 1 below. The table estimatesthe trend growth rates or the aggregate values o various perormance indicators pertaining tothe manuacturing sector. The regression equations include the dependent variables in logs, andregress them on a linear trend and a dummy that takes the value 1 or post-1992 period, and ero

otherwise. Thus the coefcient measures the percentage change in the dependent variable post-1992 ater accounting or its trend growth rate.

The results indicate a marginal pickup in the growth rate o value-added post-1992; and in

the rate o investment, but no signifcant improvement in the number o actories operating or in

employment.11

table 1Pre-reforMand Post-reforM PerforManCeof indian ManufaCturing

value-added

CaPitalstoCk

nuMberoffaCtories

totaleMPloyMent

Trend 5.997*** 7.18*** 2.70*** 1.292***

[21.82] [27.16] [7.75] [5.26]

Trend*Post1992 0.447** 0.88*** -0.75* -0.01

[2.18] [4.55] [2.04] [0.08]

Observations 1 1 1 1

R-squared 0.98 0.99 0.87 0.77

* indicates signifcance at 10%; ** signifcance at 5%; *** signifcance at 1%.

Note: Three-digit ASI data rom 197 to 200 has been used in the analysis. All variables are measured in log. Robustt statistics are given in brackets.

Thus the data show that the aggregate value-added has increased at about 6% a year in thesample period, and there has been a modest annual growth acceleration o about hal a percentagepoint between 199 and 200. This modest pickup in value-added is not accompanied by anadditional growth in employment or in the number o actories. Employment has grown at the rate

o 1.% a year, with no change in the rate o growth post-delicensing. New actories have comeup at the rate o 2.7% a year; with the rate decelerating post-1992. Investment rate, however,

has been commensurate with the growth o value-added. Investment accelerated by about 8.5%post-1992. Poor perormance o employment is a very important question in itsel and we cannot

do ull justice to this issue here, hence propose to take it up in another paper.

Is this growth pick-up impressive and does it imply that the reorms have paid o? Whenwe compare this perormance with the pace o growth in the manuacturing sector o many East11 The ASI data is available until Y 2002004; hence we do not include data or 20042006 in the analysis when there

has been growth acceleration in the industrial sector.

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Asian countries including the PRC, we realie that not only in terms o employment, but also in

terms o value-added, the perormance o Indian manuacturing has not been close to that o EastAsian countries. or example, manuacturing value-added in Republic o Korea grew at an averageannual real growth rate o approximately 17% between 19601980; the PRCs manuacturing sector

witnessed an average growth rate o 12% per year between 19902005.

III. EvIDENCE FRom ENtERPRIsE suRvEys

What lies behind this relatively lackluster perormance? Beore turning to the main econometricanalysis o this paper, it is useul to examine the views o managers based on two recent surveyso manuacturing frms: the Investment Climate Survey conducted by the World Bank and a survey

o about 250 frms rom some o the most labor intensive sectors, conducted by ICRIER. 12

The World Banks investment climate survey (ICS) data consists o the responses o managers toa wide range o questions including those pertaining to their perception on how various regulatory

and other actors inuence their frms. A key question asks about the extent to which variousactors are considered a problem or the operation and growth o the surveyed frms business.or each actor listed, respondents can reply in terms o a fve-point scale: 0=no obstacle; 1=minor

obstacle; 2=moderate obstacle; =major obstacle; 4=very severe obstacle.1 The scale enables oneto compare frms responses about various actors (ranging rom regulatory and governance issuesto inrastructure-related concerns) in terms o how they inuence frms operations or growthprospects.

igure 5 depicts the raction o frms describing a given actor as a major or very severe obstaclein the 2005 ICS survey (similar patterns are observed in the 2002 ICS survey). Tax-related issues,incorporating difculties with either the tax administration system or complaints about tax rates,

are considered to be a major or severe constraint by more than 40% o respondents. O course, it isnot easy to interpret this fnding given what is probably a natural penchant among frms to want topay as little as possible in taxes. Ignoring tax-related issues then, the situation with inrastructure

can be seen to be a critical obstacle or operations and growth rom the perspective o the frms.14Almost 40% o respondents cite it as a major or severe obstacle. In addition to inrastructure, onefth or more o the respondents cite governance issues (which include concerns with corruption)and the cost and access to fnance as the major obstacles. Surprisingly, an almost equal percent o

respondents cite skills and labor regulations as major obstacles (around 15%).

12 The survey, How to Enhance Employment Generation and Exports o Labour Intensive irms, was conducted by a team

led by Deb Kusum Das. We thank him or sharing the data with us.1 We are aware that the phrasing o this question may not be ideal since it lumps together operations and growth. It

is quite possible, or example, that some aspect o industrial regulations may not be a problem or the operations oa frm, unless the frm tried to expand its operations.

14 Inrastructure includes electricity, telecommunications, and transportation. Disaggregating this variable shows that

the concern with inrastructure is overwhelmingly driven by concerns with electricity. Telecommunications are hardlyconsidered a problem.

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FIGURE 7OBSTACLES FOR OPERATIONS AND GROWTH

(ALL FIRMS, 2005)

Tax issuesInfrastructure

Governance

Finance

Regulations

Skills

Labor regulations

Macro policy and uncertainty

Land

Anticompetitive practice

0 0.1 0.2 0.3 0.4

Mean of obstacle

Note: raction reporting issue as major or very severe obstacle.

Source: Authors estimates based on World Bank-CII survey data.

A useul ollow-up question entailed asking frms what constituted the single most importantobstacle or their operation and growth. By ar the biggest problem relates to inrastructure andwithin this, electricity was cited as the key issue. Indeed, 1% o frms listed electricity as the

source o their single most important obstacle to operations and growth.

table 2single Most iMPortantobstaClefor oPerationand groWthofthe firM

faCtornuMber (PerCent)

ofresPonses

Inrastructure 821 (6)Tax issues 510 (22)

Governance 21 (10)

inance 10 (6)

Skills 91 (4)

Labor regulations 82 (4)

Another source o the views o manuacturing frm managers comes rom the ICRIER survey o250 enterprises engaged in manuacturing activities in fve dierent sectors (apparel, bicycles, gemsand jewelry, leather, and sports goods). It is useul to examine the results o this survey because

it covers frms rom some o the most labor-intensive manuacturing activities. Thus to the extent

that labor regulations create serious constraints and growth prospects o frms, the sample frmsshould be among those most aected.15

Broadly speaking, the respondents fnd electricity and inrastructure in general; fnancing;and skilled labor availability to be the most serious constraints on growth. Just like in the ICS

15 Not all the frms covered responded to all the questions in the survey. or the purposes o the present study, we ocuson the responses relating to the questions on the hurdles to the growth o the frms.

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survey, they also point to specifc regulations especially those related to taxes (and fscal benefts,

in general) among things that can be improved.16 The chart below summaries the responses othe frms.17

FIGURE 8

AREAS OF IMPROVEMENT (PERCENT)

Fiscal benefits

52Infrastructure

33

Credit

5

Training facility

10

Source: Authors calculation using data rom (ICRIER 2007).

In response to the questions on hurdles to increasing employment, a majority o the respondentsreported shortage o labor (o mostly skilled and semi-skilled labor) as the key hurdle to hiring

more labor.18 urther, most o the frms (approximately 90%) responded in the negative to beingaected by any labor disputes or to having labor unions in their organiation and/or any impacto the unions on their activities.

Taken together, there are some striking similarities in the results o these two very dierentsurveys. irst, inrastructure-related issues are very high on the list o constraints aced by frms.

Indeed, ignoring tax-related issues, concerns with electricity seem to be paramount. Second,fnance-related issues also seem be a problem, especially or the smaller frms. Third, surprisingly,

labor regulations do not show up as a signifcant concern or frms. Indeed, both surveys suggestthat concerns with skills-related issues are more important than those having to do with laborregulations.

16 A look at the specifc responses makes it clear that the concern with fscal issues is very narrowly defned and is more

in the nature o a personal issue to the frms, to the extent that taxes directly aect their bottom lines. In responseto the question, what would you like to see changed to help you?, majority answered that they would like taxes to

be lowered or subsidies received rom the government.17 The survey also tried to fnd whether the technological changes are such that they are inhibiting employment growth.

About two thirds o the respondents acknowledged technological changes (either a lot or modest) taking place globallyin their industry. O those answering afrmatively to worldwide changes in technology in their respective industries,

70% o the respondents adopted new technology during the 5 years prior to the year o the survey; however, majority

o them still fnd a gap between the technologies they used and those used globally. In general, however, there isno clear evidence on the impact o adoption o new technologies on labor. One potential explanation or the lack o

a clear pattern could be that while adoption o new technology, on one hand, might be labor-saving (substitutioneect), on the other hand, growth resulting rom adopting new technology might be expansionary and could lead to

hiring o more labor (growth eect).18 Interestingly, approximately 10% o the frms rue the lack o training acilities. This is consistent with shortage o

labor or, more precisely, the shortage o the right kind o labor or the right kind o skills.

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While the concerns with electricity are not surprising or anyone with some amiliarity with

the Indian industrial scene, the low ranking o labor regulations as obstacles or operations andgrowth is surprising. One interpretation o these results could be that labor regulations may notmatter much to frms in practice. This could happen, or example, i noncompliance with labor

regulations is not costly. Alternatively, frms may be able to get around restrictions on layos byhiring contract workers. A second interpretation o these results, however, is that labor regulationsmay not matter that much to incumbentfrms. But it may matter to a nonincumbent investorcontemplating entry into the manuacturing sector.

More generally, an investors choice on which specifc sectors (or example, services versusmanuacturing) and subsectors (or example, a more labor-intensive manuacturing industry versusa less labor-intensive one) to enter, as well as the production technologies, scale, and desired

levels o employment to adopt, can be expected to be inuenced by the regulatory ramework.In this way, there may be an ex-ante eect o the law that would be very difcult to capturethrough the surveys o incumbent manuactures. In other words, deterred by specifc elements o

labor regulations such as Chapter B o the IDA, potential investors, especially those contemplatinglarge investments, choose to avoid investing in manuacturing altogether, or i they do invest inmanuacturing, they avoid subsectors, product lines, or scales o production or which the regulationshave most bite.

In what ollows, we turn to an approach that has the potential or getting around theselection problem inherent in surveys o incumbent frms. In particular, we use industry-leveldata rom Indias organied manuacturing sector to examine the relative perormance o industries

with various characteristics.

Iv. ECoNomEtRIC ANAlysIs

We are interested in testing the variants o the ollowing hypotheses: did industries that aremore labor-intensive; or industries that rely more on inrastructure; or industries that rely more on

the fnancial sector or their fnancing needs, grow less than the control group o industries in thepost-delicensing period? The econometric methodology is derived rom Rajan and Zingales (1998),who analye the eect o fnancial development on growth by comparing the growth o industriesdepending more on the fnancial sector that are in countries with greater fnancial depth, with

the growth o these industries in countries with shallower fnancial markets. Thus i the fnancialsector indeed matters or growth, then one would see higher growth in industries that rely on thefnancial sector in countries with a deeper fnancial sector, and vice versa. This technique getsto the causality issue much more cleverly than the alternative econometric ways to measure this

relationship. The methodology has subsequently been used in several dierent contexts (see, e.g.,DellAriccia et al. 2005, Rajan and Subramanian 2005).

We use this technique to look at the constraints that Indian industry might have experienced

post-delicensing. Hence we analye the perormance o the industries that rely more heavily oninrastructure, industries that depend on the fnancial sector, and the labor-intensive industriespost-delicensing. An evidence o lackluster growth in these industries is attributed then to the

unavailability o inputs or actors that the respective industries rely more heavily on. Thus i theinrastructure-dependent industries have not perormed well post-delicensing, then it is likely tobe due to the act that the inrastructure availability has not been adequate or these industriesto tap the maximum beneft rom the reorms.

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A. Cnrcin f variae

1. Refr variae

As discussed in Section II, industrial and trade policy has seen wide-ranging reorms over theperiod under study. While limited reorms were started rom the mid-1980s onward, major policychanges were undertaken ollowing the crisis in 1991. Some o the reorms introduced were moregeneric and aimed at macroeconomic management; others were more specifc to the industries. The

reorms spanned several areas, including delicensing o industries, trade reorms, and exchange ratereorms. In subsequent years these were complemented by the liberaliation o oreign investmentboth oreign direct investment (DI) and portolio; dereservation o industrial sectors under small

scale; fnancial sector liberaliation; and privatiation o public sector units.

In our econometric exercise we look at the eect o delicensing on Indian manuacturingindustries, which is one o the most comprehensive programs that covered almost all the industries.

The act that these reorms were undertaken at dierent points in time allows us to include time

fxed eects to account or unobservable but common macroeconomic shocks in the regressions. Wedo not have complete data or trade reorms, but we control or it in the robustness exercises. 19 Inrobustness tests we also estimate a specifcation in which we include the interaction o industry

characteristics with a post-1992 dummy in the benchmark specifcation to account or the reormsthat were more generic in nature, besides the delicensing. Results remain broadly unchanged andare presented selectively here.

. Indria Characeriic

Next we defne three industrial characteristics o various industries: labor intensity, dependence

o industries on external fnance, and inrastructure dependence. Rajan and Zingales (1998) assumethat there are probably technological reasons why some industries depend more on external fnance

than others. We extend this reasoning to labor intensity and inrastructure intensity. To the extentthat these two characteristics defne input usage, the technological requirement assumption isperhaps as valid as or defning external fnancing dependence. We briey describe the variousindustrial characteristics below, and urther details are provided in the appendices.

. lar Ineni

Labor intensity is the ratio o total employment to capital stock. Since there are no comprehensive

databases o employment at the frm level, we use the ASI industry-level data to calculate thisratio.

. Dependence n Eerna FinanceWe calculate the external fnancial dependence o frms in two dierent ways using two dierent

databases: the frst one uses frm-level data rom the Prowess database published by the Center or

Monitoring Indian Economy and employs the same defnition as used by Rajan and Zingales (1998).

19 See Mitra and Ural (2007), Kumar and Mishra (2008), and Topalova (2005) or analyses on eects o tradeliberaliation.

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The second measure is calculated using the ASI data as the ratio o outstanding loans to invested

capital. The index o external fnance dependence does not correlate well across two databases andacross dierent defnitions. Neither o these correlates too well with the index calculated by Rajanand Zingales (1998), which was calculated or industrial data at 2-digit level or US industries. To

the extent that our frm-level data (rom Prowess) is only or listed frms whose access to fnancialmarkets is likely to be dierent rom that o small and medium enterprises, possibly aecting thecross industry ranking, we use the fnancial dependence indicator calculated using ASI data.

. Infrarcre Dependence f Indrie

This is the ratio o expenses on distribution (i.e., storage and transportation) and power and

uel to gross value-added using frm-level data. To the extent that we have data on expenses on uelconsumption in both CMIE and ASI, we calculate an indicator just as the ratio o uel expenditureto gross value-added. These are highly correlated across the two databases, and with the indicatorthat includes distribution expenses as well. Appendix B1 indicates which industries are below or

above the median or each o these characteristics.

In order to get around the concern that these characteristics would reect the equilibrium

conditions between the demand and supply o the respective inputs, we use the data rom anearlier year (in general we use averages over the early 1990s, but where data are available weconfrmed that the industry characteristics are correlated highly with the ones calculated usingdata or earlier periods) rather than contemporaneous data. urthermore, to smooth out the noise

in the data, we use 5-year averages o the relevant variables to calculate the industry indicators.We also confrmed, where possible, that the relative industry rankings across various characteristicsdo not change over time. This robustness check gives credence to the belie that there are perhapsexternal technological reasons or why an industry uses more external fnance; or uses more labor

than capital; or depends more on inrastructure; and to the act that using data rom the early1990s is legitimate.

The questions that come to mind about these industry eatures are, are these capturing someother eatures o the industries; and, how are the three eatures correlated with each other? AppendixTable B2 reports correlations among these characteristics and some o the other eatures o theindustries that we could calculate. As we can see rom this appendix table, the various industry

characteristics are not correlated signifcantly with each other. The exceptions include labor-intensiveindustries being negatively correlated with imports and fnancial dependence; and inrastructuredependence being negatively correlated with import and DI intensity. Labor-intensive industriesare also somewhat more export-intensive.

b. Epirica Re fr he AsI Daa

We begin by exploring the possibility that the overall perormance o the manuacturing sectormasks signifcant interindustry heterogeneity. Are there certain industries that have not beneftedas much rom the reorms?

In Table below we fnd that perormance varies across dierent sectors. In particular, weidentiy industries that depend more on inrastructure, industries that depend more on the fnancialsector or their fnancing needs, and labor-intensive industries (see Appendix B1). We divide the

industries into those belonging to above or at median values or each industry characteristic, and

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estimate separate regressions or industries below and above median values. We use log o value-

added as the dependent variable and control or industry and year fxed eects, and a dummy ordelicensing that varies across industries and years in the regressions.

table 3

groWthof gross value-added Post-deliCensingaCross industries

dePendentoninfrastruCture

dePendentonexternalfinanCe labor-intensive

aboveMedian

beloWMedian

aboveMedian

beloWMedian

aboveMedian

beloWMedian

Delicensing -0.15*** 0.*** 0.08 0.18*** -0.01 0.24***

[.12] [4.46] [1.1] [2.64] [0.22] [.19]

Observations 682 679 682 679 682 679

Number o industries 22 22 22 22 22 22

Time fxed eects Yes Yes Yes Yes Yes YesIndustry fxed eects Yes Yes Yes Yes Yes Yes

R-squared 0.77 0.66 0.71 0.70 0.69 0.72

* indicates signifcance at 10%, ** signifcance at 5%, *** signifcance at 1%.

Note: We have used -digit ASI data rom 197 to 200 in the analysis. The industry characteristics are defned in Section

III and Appendix B. The dependent variable used is value-added in log. Robust t statistics are given in brackets.

Results in Table show that the industries that are more inrastructure-intensive, on average,experience 15% lower growth in value-added post-delicensing (i.e., in the delicensed period relativeto the earlier period), as compared to % higher output growth in value-added o industries thatare less reliant on inrastructure. Similarly the industries more dependent on the fnancial sector,

i.e., the labor-intensive industries, have ared much worse than the industries that do not relyas much on the fnancial sector and are less labor-intensive. Thus, there seems to be signifcantheterogeneity in the perormance o Indian manuacturing across industry groups.

C. Ecneric Fraewr

We use the ollowing econometric specifcation to analye the impact o delicensing on variousperormance indicators:

Yit = i di + t dt+ (delicensing dummyit ) +

(characteristic o industry i * delicensing dummyit) + it (1)

where Yit is the outcome variable measured in log. As beore, we consider gross value-added at

constant prices, employment, capital stock, and number o actories as the outcome variables.

In equation 1, dis is industry-specifc dummy and is is the respective coefcient; Its is year-specifc dummy, and t is the respective coefcient. The fxed eects account or the industry-specifc

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omitted variables; and the year-fxed eects control or year-specifc shocks that are common to all

industries. Since we are using industry fxed eects and year-fxed eects in the regression equation,the only additional variables we can include are the ones that vary with both industry and year.The next term in equation 1 is the delicensing dummy, which varies over time and industry. The

dummy takes a value 1 rom the year when the delicensing requirement or a particular industrywas removed, and remains 1 or the rest o the sample period.20

We are interested in testing the variants o the ollowing hypotheses: did industries that are

more labor-intensive (or industries that rely more on inrastructure or the fnancial sector or theirfnancing needs) grow less than the control group o industries in the post-delicensing period?Testing or these hypotheses requires us to set up the regression equation or dierence in dierencesestimates. Continuing with the specifc hypothesis involving labor-intensive industries, consider the

ollowing possible cases or any given outcome variable:

outCoMevariableinPre-reforMPeriod

outCoMevariableinPostreforMPeriod

More labor-intensive (treatment group) L,Pre L,Post

Less labor-intensive (control group) C,Pre C,Post

Essentially, we would like to test the hypothesis that (L,Post- L,Pre)-(C,Post-C,Pre) is

signifcantly dierent rom ero. The coefcient in equation 1 allows us to do this. In theregression equations, we use the interaction o each o the industry characteristics with delicensingseparately and together.

How do we interpret a negative and signifcant coefcient or the interaction term o a particularindustry characteristic, say, inrastructure-dependent industries? The coefcient indicates that theindustries that use inrastructure more intensively have grown less post-delicensing, compared

to the industries that use inrastructure less intensively. Can this be interpreted as a causalrelationship between the lack o inrastructure and perormance? As mentioned in the introduction,

to the extent that an industry characteristic is exogenous o perormance, e.g., it is some sort oa technical requirement; or i we can control or potential omitted variables, then we can probably

claim causality in this result.

or exogeneity in our industry characteristics we use the data rom the earliest possible periodor which we have the data (in our case, the early 1990s). We control or omitted variables varying

only over industries and over years by including the respective fxed eects. To rule out otherpotential omitted variables we conduct extensive robustness tests to be described later.

In Table 4 we present our results or the benchmark case as given by equation (1). Coefcients

on both the industry and year-fxed eects have been suppressed rom the table. In the results incolumn 1, the coefcient or delicensing shows a 12% increase in value-added per industry post-delicensing. Given that the average delicensing period is about 15 years, it amounts to a less than

a 1% increase in value-added per year in the post-delicensing period. However as shown in Table, certain industries did not are as well during the post-delicensing period. Thus when we controlor the dierent eects on these industries separately, the post-delicensing impact on growth othe control group improves substantially.

20 The delicensing dummy is based on the inormation provided in Aghion et al. (2006), updated until 200. As o 200,all but three industries had been delicensed; see Appendix Table A4.

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In Table 4 columns 24 we include, one at a time, these characteristics with the interaction o

delicensing. As expected, the perormance o the control group goes up considerably. The industriesmore dependent on inrastructure, labor, and external fnance, respectively, have witnessed slowergrowth as opposed to their respective control group. In column 5 we include them together, and we

fnd that industries ranking higher on each o our three industry characteristics have aired poorlyin the post-delicensing period. inally, the last column is the same as column 5 except that in thiscolumn, instead o including the index o industry characteristics, we divide them into above medianand below median groups. We also include the interaction o the dummy variables, which takes the

value 1 when an industry is above the median o the respective characteristic with delicensing.Once again we fnd that the results hold and industries above the median in each o the threecharacteristics have not done as well as the control group in the post-delicensing period.

table 4value-added Post-deliCensing

(1) () () () () ()

Delicensing 0.12** 0.18*** 0.26*** 0.5*** 0.9*** 0.6***[2.50] [.10] [.1] [4.65] [7.5] [5.61]

Inrastructure dep* delicensing -0.17** -0.18***

[2.42] [2.59]

Labor intensity*delicensing -0.0** -0.51***

[2.02] [.55]

External fnance dep*delicensing -

0.9***

-

1.22***

[4.01] [5.49]

Inrastructure dummy*delicensing -0.10*

[1.88]Labor intensity dummy*delicensing -0.19***

[4.07]

External fnance dummy*Delicensing

-0.18***[.40]

Industry fxed eects Yes Yes Yes Yes Yes Yes

Year fxed eects Yes Yes Yes Yes Yes Yes

Observations 161 161 161 161 161 161

Number o industries 44 44 44 44 44 44

R-squared 0.70 0.70 0.70 0.70 0.71 0.71

* signifcant at 10%; ** signifcant at 5%; *** signifcant at 1%.Note: Dependent variable is log value-added. Robust t statistics in brackets.

The results rom Table 4 column 6 indicate that in the post-delicensing period the above-median inrastructure-intensive industries grew 10% less than the industries below the median

o inrastructure dependence. Similarly industries above median in the distribution o fnancial

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dependence grew 18% less than the industries below the median o fnancial dependence; or labor

intensity, industries with above-median labor intensity grew 19% less than the below-medianindustries post-delicensing.

In Table 5 results are presented or the dependent variable, number o actories (in log). The

overall perormance o Indian manuacturing seems to be even less impressive when we look atthe number o actories. Overall there is no acceleration in the rate o expansion o actories post-delicensing. These results are on account o the act that the perormance has been particularly

worse or the labor-intensive industries and industries dependent on the fnancial sector. Oncewe control or these as in the previous set o regressions, the perormance o the control group(industries less dependent on inrastructure or fnancial sector or labor) is seen to be much better.The point remains that industries more dependent on external fnance and labor-intensive industries

have ared much worse post-delicensing in terms o new actories opening.

table 5nuMberof faCtories

(1) () () () ()Delicensing 0.04 0.0 0.11** 0.15** 0.1***

[1.09] [0.86] [2.20] [2.41] [.42]

Inrastructure dep* delicensing 0.02 0.01

[0.9] [0.2]

Labor intensity*delicensing -0.16** -0.22***

[2.15] [2.86]

External fnance dep*delicensing -0.27** -0.9***

[2.24] [.05]

Industry fxed eects Yes Yes Yes Yes Yes

Year fxed eects Yes Yes Yes Yes Yes

Observations 161 161 161 161 161Number o industries 44 44 44 44 44

R-squared 0.58 0.58 0.58 0.58 0.58

* signifcant at 10%; ** signifcant at 5%; *** signifcant at 1%.

Note: Dependent variable is log number o actories. Robust t statistics in brackets.

Next we look at employment and capital stock. The issues related to employment are manioldand much more complex, and all o these probably cannot be addressed in this paper. Some o the

issues that merit attention include: why has growth not been employment-intensive; is technologydisplacing labor; how has the employment o unskilled versus skilled workers evolved over time;how is the skill premium changing over time etc. or brevity we look only at total employment

here, which includes manual workers, supervisors, and regular as well as contract employees.

We look at two econometric specifcations here. The frst is the estimates rom equation 1,results o which are reported in Table 6a and 7a, respectively, or employment and capital stock.

rom column 1 o Table 6a, employment has increased by a mere 7% over the entire delicensingperiod. With the average delicensing period about 15 years, this translates into a less than 0.5%increase in employment per year post-product market reorms.

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As expected, once we introduce heterogeneity based on industry characteristics, the perormance

o the control group improves rather substantially (columns 25). The maximum increase is in thecase o the industries less dependent on external fnance. Notably, inrastructure-dependent andexternal fnance-dependent industries, as well as labor-intensive industries, are the weakest perormers

in so ar as employment generation in the post-delicensing period is concerned.In addition, we estimate the ollowing regression equation:

Eit = i di + t dt+ Yit + Yit x delicensing dummyit

(Yit x *characteristic o industry i* delicensing dummyit) + it (2)

In equation 2, Eit reers to log o employment (or log o invested capital in real terms), di isindustry-specifc dummy, and dt is year-specifc dummies. We also include log o gross value-addedin the equation; the coefcient can be interpreted as the employment elasticity o output. It

measures the percentage change in employment or a 1% increase in output. The next term is theinteraction o delicensing dummy with Yit. Its coefcient gives the employment elasticity o outputpost-delicensing. inally, we include the interaction oYit, delicensing, and industry characteristics.The coefcient measures the employment elasticity o output or the industry characteristic usedin the interaction with post-delicensing. Thus i we are including labor intensity in the interaction

term in equation (2), then it measures the change in the elasticity o employment post-delicensingin labor-intensive industries. I it is positive, it implies that the employment elasticity in labor-intensive industries has increased post-delicensing and so on.

Results on employment rom specifcation 2 are in Table 6b. The results indicate that theemployment elasticity o output is about 50% on average, though there are dierences acrossindustries. The elasticity is higher or labor-intensive industries than or inrastructure-dependent

or or fnancially dependent industries. Results also indicate that there has been no change in theelasticity o employment post-delicensing, this is true on average or all industries, including orthe industry characteristics that we control or explicitly in our regressions.

These results have two implications: frst, i growth were to accelerate in Indian manuacturingit would probably generate employment at the same rate as beore; second, in order to generatemore employment in Indian manuacturing it is imperative that the labor-intensive sectors growaster. As we mentioned earlier, aggregate employment masks several nuances related to dierent

kinds o employment, but we do not have space to discuss them all here.

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table 6aeMPloyMentPost-deliCensingresultsfroM equation 1

(1) () () () ()Delicensing 0.07** 0.15*** 0.11* 0.6*** 0.59***

[2.47] [4.7] [1.92] [.95] [7.0]Inrastructure dep* delicensing -0.25*** -0.25***

[5.69] [6.12]Labor intensity*delicensing -0.075 -0.22**

[0.64] [2.05]External fnance dep*delicensing -0.67*** -0.78***

[.48] [4.62]Industry fxed eects Yes Yes Yes Yes Yes

Year fxed eects Yes Yes Yes Yes YesObservations 161 161 161 161 161

Number o industries 44 44 44 44 44R-squared 0.5 0.6 0.5 0.6 0.7


Note: Dependent variable is log Employment. Robust t statistics in brackets.

table 6beMPloyMentPost-deliCensingresultsfroM equation 2

(1) () () () ()

Log GA 0.52*** 0.57*** 0.47*** 0.62*** 0.55***

[20.76] [19.49] [17.0] [9.14] [7.0]

Delicensing*GA -0.000 -0.001 0.001 0.001 0.004

[0.27] [0.5] [0.48] [0.28] [0.86]

Inrastructure*GA -0.20*** -0.16***

[.98] [.19]

Inrastructure*delicensing*GA 0.00 0.001[0.90] [0.28]

Labor intensity*GA 0.09*** 0.08***

[.6] [2.70]

Labor intensity*delicensing*GA -0.000 -0.002

[0.1] [0.66]

inancial dep*GA -0.26* -0.107

[1.92] [0.74]

inancial dep*delicensing*GA -0.001 -0.004

[0.10] [0.4]



Observations 161 161 161 161 161

Number o industries 44 44 44 44 44

R-squared 0.69 0.70 0.70 0.70 0.70


GA = log value-added.Note: Dependent variable is log Employment. Robust t statistics in brackets.

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or analying the patterns in investment we use both the specifcations used or employment

(Tables 7a and 7b). Thus we look at the capital elasticity (i.e., how investment changes) o value-added and compare it with the behavior o investment post-delicensing. We also compare investmentbehavior across industries and see whether there are any patterns in the investment changes across

industries post-delicensing. Here we fnd that capital elasticity (Table 7b) is higher than that oremployment elasticity. Across industries, inrastructure and fnancially dependent industries seehigher investment than the labor-intensive industries as value-added increases. Investment has alsoincreased somewhat post-delicensing; quite interestingly this is on account o a higher investment

in the labor-intensive industries. Thus over time and especially post-delicensing, the labor-intensiveindustries seem to be substituting away rom labor and adopting relatively more capital-intensivetechnology! In addition, we fnd that industries that are more dependent on external fnance seea decline in investment in the post-delicensing period.

table 7ainvestMentPost-deliCensingresultsfroM equation 1

(1) () () () ()Delicensing 0.26*** 0.24*** 0.6*** 0.66*** 0.94***

[4.97] [.94] [4.62] [5.] [7.]

Inrastructure dep* delicensing 0.06 0.06

[0.77] [0.70]

Labor intensity*delicensing -0.224 -0.41***

[1.55] [2.96]

External fnance dep*delicensing -0.94*** -1.18***

[.59] [4.91]



Observations 161 161 161 161 161


R-squared 0.71 0.71 0.71 0.72 0.72


Note: Dependent variable is log real invested capital. Robust t statistics in brackets.

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table 7binvestMent Post-deliCensingresultsfroM equation 2

(1) () () () ()

Gross value-added (log) 0.85*** 0.81*** 0.88*** 0.71*** 0.7***[6.17] [25.27] [26.5] [10.28] [8.19]

Delicensing*GA 0.01*** 0.01*** 0.004 0.02*** 0.01

[4.29] [2.69] [1.51] [.47] [1.58]

Inrastructure*GA 0.16** 0.12*

[2.2] [1.69]

Inrastructure* delicensing*GA 0.001 0.004

[0.21] [0.7]

Labor intensity*GA -0.06* -0.08

[1.76] [1.05]

Labor intensity*delicensing*GA 0.01* 0.007

[1.74] [1.57]

inancial dep*GA 0.6** 0.28

[2.4] [1.64]

inancial dep*delicensing*GA -0.02** -0.02*

[2.22] [1.76]



Observations 161 161 161 161 161


R-squared 0.90 0.90 0.90 0.90 0.90

* signifcant at 10%; ** signifcant at 5%; *** signifcant at 1%.Note: Dependent variable is log real invested capital. Robust t statistics in brackets.

D. Rne f Re

We do extensive tests or the robustness o our results. These include checking the robustnessto dierent time periods, omitted variables, and potential outliers. We account or the lags between

policy and implementation; we also account or the possibility that the outcomes might be correlatedby the industries or by the year o delicensing. While we do obtain small variations in coefcientsand in the standard errors across these dierent specifcations, overall, the results are quite robustto various sensitivity tests. One result that does seem a bit sensitive to some o the corrections

or autocorrelation is the result on inrastructure dependence. In some o the corrections orautocorrelations, the coefcients o the interaction between inrastructure and delicensing become

less signifcant, but even here its eect holds at about the 20% level o signifcance. Details oneach robustness test ollow.

Though in the methodology used here the omitted variables that vary only by industry or byyear have been accounted or through the respective fxed eects, the estimates remain susceptible

to the omission o variables that vary over industryyear dimensions o the data. In particular,there might have been the ollowing two types o omissions: frst, the interaction o delicensing

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with industry characteristics other than the ones included; and second, the interaction o policy

variables other than delicensing and their interactions with the industry characteristics included.

We explicitly control or only one o the major policy changes pertaining to Indian industries

delicensing. What about the other policy changes? In order to address these concerns we carry out

two robustness tests. irst, to control or the reorms that were more generic rather than specifcto industries, we include in our regressions interaction o industrial characteristics with a post-1992dummy. Second, we construct a trade policy measure that is industry-specifc and interact it with

industrial characteristics. Results that inrastructure-dependent, external fnance-dependent, andlabor-intensive industries have not benefted as much rom reorms are airly robust across thesevarious specifcations.

While we are unable to conduct these tests or some o the other reorms, the results areunlikely to change. The reason is that the reorms are highly correlated over time and across sectors.Thus even i we get a somewhat dierent coefcient when we include interaction o industrycharacteristics with dierent reorms instead o delicensing, the basic message we want to bring

homethat without sufcient inrastructure development, fnancial depth, and progress on actors

inhibiting labor-intensive industries, Indian industry is unlikely to realie its potentialwould hold.or this argument it is really immaterial what kind o reorms we are talking about. Second, i we

include the interactions o industry characteristics with dierent reorm measures, e.g., delicensingand trade reorms, in the same specifcation, then the coefcient or a particular policy measurewould become weaker and probably even lose its statistical signifcance. Such a specifcation will beo little use since again the interest is in a composite reorm measure rather than specifc reorm

measures. Thus, even i the coefcients might be biased in the benchmark specifcation, to theextent that we do not really care about attributing it to delicensing per se, we are fne. or omittedindustry characteristics we include other industrial characteristics in the regressions, such as exportintensity or DI intensity interacted with delicensing, and fnd the results to be robust.

We report results or some o the robustness tests in Table 7. The reported results are or the

dependent variable log value-added. In order to address the concerns related to autocorrelationwe reduce the sample length to the period rom 1980 onward (see column 1). We can restrict theperiod urther but then we would start losing our control period. In the results reported in column2 we calculate the standard errors corrected or Newey West adjustment.

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table 7robustness tests

(1) () () () () () ()

1980s

andbeyond neWey2

Without

tobaCCo,PetroleuM

tradereforM

tradeand

deliCens-ing

deliCens-

inglaggedtWoyears

Clusterses

Delicensing 0.71*** 0.9*** 1.1*** 0.62*** 0.95*** 0.9**

[4.69] [5.12] [8.41] [.18] [7.80] [2.86]

Trade openness 1.04*** 0.72***

[6.] [.46]

Inrastructure*Delicensing

-0.22** -0.18* -0.18*** 0.0 -0.2*** -0.18

[2.57] [1.8] [2.62] [0.29] [.4] [1.7]

Labor

intensity*Delicensing

-0.45*** -0.51** -0.60*** -0.0* -0.50*** -0.51***

[2.97] [2.2] [4.08] [1.94] [.8] [4.26]

External fnance*

Delicensing

-0.94*** -1.2*** -1.7*** -1.09*** -1.11*** -1.22

[.52] [.80] [6.21] [2.88] [4.97] [1.57]

Inrastructure*Trade openness

-0.41*** -0.45***

[5.26] [5.01]

Labor intensity*

Trade openness

-0.05 0.0

[0.97] [0.55]

inancial dep*Trade openness

-0.52** 0.1

[2.01] [0.6]

Industry fxed eects Yes Yes Yes Yes Yes Yes YesTime fxed eects Yes Yes Yes Yes Yes Yes Yes

Observations 1056 161 1299 1056 1056 161 161

Number o industries 44 44 42 44 44 44 44

R-squared 0.67 0.71 0.68 0.69 0.71 0.71


Note: Dependent variable is log value-added. Robust t statistics in brackets.

We also drop two industries, tobacco and petroleum (these industries typically show extrememeasures on various accounts such as labor productivity and sie) in column and the results

are unchanged. In columns (4) and (5) o Table 7 we include the trade reorm variable. Here asexpected we fnd that the trade reorms have had a growth-enhancing eect on Indian industries,

and again the eect has varied across industries along the same dimension as we have seen inthe earlier tables.

To control or the act that there may be a lag between reorm and actual implementation orthe eect o the reorm is elt with a delay, in column 6 we lag the delicensing dummy by 2 years.

In other words, i an industry was delicensed in 1991, we assume that the eect was elt 2 yearslater, i.e., 199 onward. Our benchmark results hold qualitatively.

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v. CoNCluDINg REmARks

In this paper, we have analyed the perormance o the registered Indian manuacturingsector in India using data rom the ASI. In line with some recent studies, we fnd that industrialperormance has improved with industrial delicensing. However, our analysis also indicates that

there is considerable heterogeneity in the response o industries to policy reorms. In particular,the industries more dependent on inrastructure; industries with greater dependence on sources ofnance external to the frms; as well as those with high labor intensity have not perormed well.

rom a policy perspective, the important question then is what eatures o Indias policy andinstitutional landscape explain this pattern? The ongoing policy debates in India suggest severalleading candidates. In the case o inrastructure-dependent industries, the inadequacy o public

provision o inrastructure is probably the main culprit. Similarly, the ailure to improve the Indianfnancial sectors ability to identiy and fnance creditworthy frms and investors may well lie behindthe relatively weak perormance o industries especially dependent on external fnance.

A complementary analysis o two frm-level surveys o managers in the manuacturing sector

lends urther support to these arguments, especially in the case o inrastructure and fnance. Takentogether, the results o the World Banks investment climate survey and ICRIER survey o labor-intensive manuactures support the notion that weak provision o inrastructure and fnance has

constrained the growth o the manuacturing sector.

As regards the weak perormance o labor-intensive industries, certain elements o Indianlabor regulation may well be an important causal actor as argued by a number o observers o

the Indian economy. In particular, certain elements o the IDA may have raised signifcantly theeective cost o hiring workers, thereby hitting the relative proftability o labor-intensive industriesdisproportionately. Since this is more likely to be the case or larger frms (due to the nature o

the regulations), labor regulations may have led to relatively weaker perormance o labor-intensiveindustries in two ways: frst, by discouraging entry by large frms; and second, by reducing incentives

among small frms to expand.However, other actors cannot be ruled outsuch as the policy o reserving a whole range o

labor-intensive products or production by small-scale frms as recently as 2001. One way to makeheadway on this issue, i.e., establishing whether certain elements o the policy or institutionalramework are causal drivers o the pattern o industrial perormanceis to extend our analysis

to the state level. To the extent that Indias states present sufcient variability in the provisiono inrastructure and fnance, and given the stance o labor regulations (as they actually apply tofrms and not just on paper), carrying out this analysis at the state level should be very useul.

We take up this issue in our orthcoming work.

In the meantime, our econometric analysis has served to highlight rom where the relativelyweaker perormance in Indias manuacturing sector is coming. Unlike previous work that has highlighted

mainly the role o labor regulations in inuencing industrial perormance, our econometric resultsinterpreted in conjunction with perception o managers suggest that steps to improve inrastructureand the fnancial system should go a long way in improving manuacturing perormance. Additionally,our results also point to the urgency o identiying the constraints on labor-intensive manuacturing

in India and relaxing these.

seCtion v

ConCluding reMArKs



38/46

APPENDIx ADAtA souRCEs AND CoNstRuCtIoN oF vARIAblEs

We have primarily used ASI data at the -digit level. Ater the concordance rom NIC87 and NIC70into NIC 98 classifcation, we have data on 49 industries. Data are available or 197200. Data in general

seems good and comparable pre- and post-1998, when there was a change in the sampling ramework. Theollowing industries were excluded rom the analysis. The frst three (dressing and dyeing o ur, saw milling,and publishing) were excluded because o lack o data on inrastructure dependence rom CMIE. The others

that were dropped included processing o nuclear uels and reproduction o recorded media. As noted byAghion et al. (2006), processing o nuclear uels is likely to be aected by noneconomic actors, and hence

we drop them rom our sample. inally, reproduction o recorded media was introduced as a new category in1998. There is no data or this industry or the period 1971998 and is thereore excluded rom the sample.As the table below shows, we exclude less than 1% o the registered manuacturing sector, whether we look

in terms o employment or gross value-added.

aPPendix table a.1industriesnot inCludedinthe saMPle

industry

PerCentageshareinvalue-addedin 2004

PerCentageshareineMPloyMentin 2004

Dressing and dyeing o ur, articles 0.001 0.01

Saw milling 0.02 0.1Publishing 0.8 0.6Reproduction o recorded media 0.02 0.0

Processing o nuclear uels NA NA

Analysis rom here onward, when it reers to total manuacturing output, employment etc., reers tothe registered manuacturing excluding the above fve industries. The real values have been calculated using

respective WPI deators (unless otherwise noted, e.g., or the capital ormation or capital stock variables).

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aPPendix table a.2ConstruCtionof variables

variable datasourCe desCriPtion/ConstruCtion

alue-added ASI Increment to the value o goods and services that is contributed bythe actory obtained by deducting the value o total input

Workers

(blue-collar)

ASI Persons employed directly or through any agency whether or wages

or not, and engaged in any manuacturing process, or in cleaning anypart o the machinery or premises used in the manuacturing process,

or in any other kind o work incidental to or connected with themanuacturing process or subject o the manuacturing process

Total

employment

ASI Blue collared workers as defned above; persons receiving wages

and holding clerical or supervisory or managerial positions; personsengaged in administrative ofce, store keeping and welare section,

sales department; persons engaged in purchase o raw materials etc.,or production o fxed assets or the actory; and watch and ward

sta

Capital stock ASI Sum o fxed capital and physical working capital. ixed capital represents the depreciated value o fxed assets owned by the actory

and covers all types o assets, new, used, or own constructed;or deployed or production, transportation, living or recreational

acilities, hospitals, schools, etc. or actory personnel. Physicalworking capital includes all physical inventories owned, held, or

controlled by the actory as on the closing day o the accountingyear such as the materials, uels and lubricants, stores etc.

Capitalormation

ASI Excess o fxed capital at the end o accounting year over that at thebeginning o the year

Number o

actories

ASI A actory or the purposes o ASI is one that is registered under

sections 2m ( i ) and 2m ( ii ) o the actories Act, 1948; also

premises with 10 or more workers with the aid o power, or 20 ormore workers without the aid o power

Labor

productivity

ASI Ratio o value-added to total employment

Labor intensity ASI Employment/real invested capital)*1000, where deator used is the

wholesale price index or the NIC classifcation 19 (other electricalequipment, to proxy or the capital goods)1

Inrastructuredependence

CMIE Ratio o distribution and power and uel expenses to gross value-added. It is the average o the ratio over the period 19941998.

Dependence on

external fnance

ASI Ratio o outstanding loans to invested capital, averaged over

19911995

Export intensity CMIE Ratio o total oreign exchange earnings to gross value-added.

Trade reorms Hasan,Mitra, and

Ramaswamy(2007)

Nominal rate o protection

1 Results do not depend on the deator used or whether we use only fxed capital, rather than invested capital, whichincludes working capital as well to defne labor intensity. It is not surprising since the correlation o the WPI series

is o the order o .94 with the WPI or electrical goods; and the correlation o fxed capital with invested capital is o

the same order o magnitude.

APPendix



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aPPendix table a.3suMMary statistiCsof asi data

variable observations Mean

standard

deviation MiniMuM MaxiMuMLog (number o actories) 161 6.86 1.42 1.9 21.74Log (total employment) 161 11.11 1.1 6.996 14.1Log (blue collared workers) 161 10.81 1.6 6.8 14.18

Log (white collared workers) 161 9.68 1.2 5.84 12.92Log (real gross value-added) 161 17.88 1.42 1.94 21.74

Log(real invested capital) 161 18.76 1.51 14.6 22.65Log(productivity) 161 6.77 0.75 4.62 9.95

Sie-log(labor per actory

Documents

What Constrains Indian Manufacturing?