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Industrial Sickness: Trends and Patterns

This paper attempts to trace the trends and pattern in industrial sickness during the pre- and post-reform periods, especially in the large and medium sector. The study shows that while the first period (the 1980s) was dominated by an ailing small-scale sector, the post-reform period (the 1990s) was one of sickness in the large and medium sector. The onset of recession in 1997 had an apparent immediate effect on non-SSI sickness, but its effects on the small sector were visible only after a two-year lag, indicating perhaps the greater resilience of SSIs.

Industrial Sickness: Trends and Patterns

This paper attempts to trace the trends and pattern in industrial sickness during the pre- and post-reform periods, especially in the large and medium sector. The study shows that while the first period (the 1980s) was dominated by an ailing small-scale sector, the post-reform period (the 1990s) was one of sickness in the large and medium sector. The onset of recession in 1997 had an apparent immediate effect on non-SSI sickness, but its effects on the small sector were visible only after a two-year lag, indicating perhaps the greater resilience of SSIs.

ROSEMARY K ABRAHAM, G OMKARNATH

I Introduction

T
he liberalisation policy initiated in 1991 was in part a response to the growing industrial sickness during the 1980s. During the early years of reform, the policy shift lived up to the expectations of policy-makers and the incidence of sickness registered a drastic decline, particularly in small-scale industry (SSI). However, this declining trend was reversed in 1997, when recession hit the economy. More than the SSI sector, the recession battered the large- and medium-scale industry1 (non-SSI sector). In the non-SSI sector, the average annual growth rate of sick units did not show any significant reduction in the 1990s. The real amount blocked in sick units with 100 per cent erosion of net worth showed only a marginal fall, proving that the economic burden of non-SSI sickness continued unabated. The government then prepared for a major overhaul of the rules and regulations regarding industrial sickness. The Sick Industrial Companies Act of 1985 (SICA), which provided the legal framework for the rehabilitation/ winding up of sick non-SSI units, is going to be repealed. The Board for Industrial and Financial Reconstruction (BIFR) – the body set up in 1987 to expedite the revival of sick units – has been wound up, and the redefined burden of revival/winding up of sick units has been put on the national company law tribunal. In this context, it would be useful to look at the trends and patterns of sickness over the past decade and to compare it with those of the1980s so as to examine whether the earlier strategies of the government have yielded results and whether the present policy reforms can be justified.

This paper is an attempt to trace the trends and patterns in sickness during the 1980s and 1990s with special reference to the large and medium sector. Further, sickness in the large and medium-scale sector is positioned against that in the small-scale sector. Studies conducted so far have not made any further revelations than that it is the non-SSI sector that constitutes more to the economic burden. However, what is of greater concern is the extreme vulnerability of large and medium-scale units to external imbalances and sudden policy shifts. Section II deals with the magnitude and growth of industrial sickness in India over the past two decades. Section III analyses the composition of sickness. The state-wise and industry-wise distribution of sickness is also studied. The performance of BIFR over the years is mentioned briefly in Section IV. Section V presents the summary and conclusions.

II Magnitude and Growth of Sickness/Failure

Problems in the data set: Ascertaining the magnitude and growth of sickness in both the pre- and post-reform periods is not an easy task. One major government source identified for obtaining data was the Economic Survey,2 which publishes data collected by RBI from commercial banks. However, the data was ridden with definitional and accounting problems, making comparisons almost impossible. For instance, till the end of 1988, the reference point was the end of December, which marks the end of a calendar year. After that, the calendar year was replaced with the financial year by shifting the reference period to the end of March, resulting in loss of data for one year, (1989), and thus affecting the continuity of the series. While studying non-SSI sickness, the data for 1990 can be taken as a proxy for 1989, though this value in the series refers to the level of sickness three months later. However, even this kind of approximation is not possible for SSI data due to the changes in the accounting of sickness. The data, for example, show a sudden upsurge in SSI sickness in 1982, as given in Figure 1. This happened because the State Bank of India – the major provider of working capital to sick units – started including these SSI units in the protested bills/recalled accounts in its list of sick units from that year onwards, making comparison with previous years impossible. Thus, one will have to avoid the use of data for 1980 and 1981. Again, in 1990 (in Figure1, this value is shown against 1989 to facilitate easier comparisons), there is a sudden fall in sickness because of the exclusion of nontraceable and non-existent units from the sick SSI units list. Since the data on non-traceable and non-existent units were not given separately from 1990 onwards as well as for the period 1982-88, both pre- and post-reform comparison of sickness for the small-scale sector was difficult to make.3 What can be done at the most is to take two periods in complete isolation and look for the trends within that period.4 The unreliability of on small-scale sector sickness makes the comparison of overall sickness for the two decades also liable to wide fluctuations. The trends and patterns in non-SSI sector sickness, on the other hand, show conformity and are amenable to comparisons.5 The data on non-SSI sickness is classified into “weak units” and “sick units”, as defined by the SICA of 1985. The latter will be called SICA units to avoid confusion.

The next issue to be tackled was the definitional problem. The periodic revision of definition of sickness6 was found less likely to affect the trend of sickness. There was a shift in the definition

Figure 1: Number of Sick SSI Units Figure 2: Number of Sick Units in Non-SSI Sector

3500

350000

3000

300000

2500

250000

2000

200000

Number of Units

Number of Units

500

50000

0

0 150000

1500

100000

1000

19801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002

19801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002

Year Number of Sick SSI Units

of sickness from complete “net worth erosion” to “repeated default on loan payment”. The advantage of the latter definition is that sickness is detected at an earlier stage. From practical experience, on average, the year-wise distance between repeated default on loan payment and net worth erosion is not more than one or two years. In addition, the dual classification of non-SSI sickness – as sick and weak units – is supposed to take care of this issue. However, the discomfort of “weak” status must have forced firms to cover their sickness and go for creative accounting, where losses and net worth erosion appear only when it becomes impossible to hide the facts. This can be one reason why the percentage of weak units showed a decreasing trend over the years. For instance, in 1980, 71 per cent of the total non-SSI sick units belonged to the “weak” category. In 2002, the corresponding figure is 12 per cent. Given that failure has a history, this shows the extent of creative accounting by firms. This is substantiated by the fact that many of the annual reports do not show losses even when the firms are defaulters.

The definition of small industry (and hence of large and medium-scale industry) has also undergone a series of revisions over the years. However, a SIDBI report has stated that the change in real terms was insignificant7 for all revisions except that of 1997 (when the investment limit was raised to Rs 300 lakh from the earlier Rs 60 lakh), where the revised definition allowed for increased scope in investments. The government then lowered the investment criterion to Rs 100 lakh in 1999. When the investment criterion is raised in real terms, more firms will come under the purview of “small-scale industries”. Hence, the number of sick units in the small-scale sector may also be large for that year. In other words, the number of non-SSI units may register a decline (and vice versa). However, such correlations are not visible in the trends presented by the data. For instance, when the revision of definition entailed a decrease in the number of non-SSI sick units after 1997, the figures actually rose. This rise can thus be purely attributed to the recession that hit the economy.

As a result of these definitional and accounting problems attached to the Economic Survey data, different classification periods should be adopted for analysing various aspects of sickness. The trends and patterns in sickness can be inferred from Figures 1-4. In order to compare the degree of sickness for both sectors in both decades, an equal number of years before and after liberalisation are taken. The pre-reform period is taken as 1982-88 and the post-reform period of 1996-2002 is considered, a classification that overcomes most of the problems associated

Year Number of SICA units

Number of total non-SSI sick units including weak units

with the data.8 This is given in Table1. The Economic Survey provides room for a comparison between SSI and non-SSI sector sickness and provides data for comparing sickness records in both pre- and post-reform periods. However, as mentioned above, the comparison of SSI sickness and hence the overall level of sickness in the two periods is still problematic. Other data sources used in the present study are the BIFR and the IDBI report on development banking,9 especially while analysing the composition of sickness. A detailed account of non-SSI sickness is given in BIFR data, which is shown in Table 2. BIFR data give information not only about the number and accumulated losses of sick units, but also about their net worth and the number of workers displaced due to them. Since both IDBI and BIFR data sources do not trace sickness of the early 1980s, while analysing the composition of sickness, previous studies are taken as standards. All three sources exhibit a comfortable degree of conformity with each other in the trends and patterns portrayed.

The Indian experience with planning created an army of inefficient entrepreneurs and firms. The MRTP and import control regime created a set of high cost firms, which emerged as monopolies through a pre-emption of capacity, becoming highly diversified conglomerates with a lack of synergy in their operations and remaining as mini-projects without scale economies. The policy of reservation provided sufficient incentives for the firms to remain “small for ever” without bothering about scale and cost economies. The inherently underdeveloped, controlridden capital market ultimately led to the emergence of highly

Table 1: Sector-wise Sickness in Pre- and Post-Reform Period, Expressed as Average Annual Growth Rates

N=7 Number Per Cent Number Per Cent Nominal Amount Nominal Amount Real Amount Real Amount
Per Cent Per Cent Per Cent Per Cent
Sector Pre- Post- Pre- Post- Pre- Post-
Reform Reform Reform Reform Reform Reform
1982-88 1996-2002 1982-88 1996-2002 1982-88 1996-2002
SSI sick units 41.72 -3.98 29.67 4.59 21.52 0.9
Non-SSI
SICA units 17.63 6.22 15.92 11.33 8.6 7.42
Total non-SSI
units including
weak units 5.34 4.68 20.52 11.64 12.99 7.76
Grand total 40.49 -3.89 22.24 9.98 14.6 6.14

Source:Economic Survey, CMIE, RBI report on Currency and Finance, RBI Report on Trend and Progress of Banking in India, and Handbook of Industrial Policy and Statistics.

leveraged firms, which would be thrown out if protection was Figure 3: Nominal Amount Blocked in Sick Units removed. Apart from there being many hurdles for entry, exit

30000

was also prohibited in order to protect labour interests, with a phobia towards closing even the most inefficient units. This made 25000 India the unique abode of industrial sickness, where firms that must otherwise have been thrown out of the market, continued 20000

15000 10000(Rs crore) 19801982198419861988199019921994199619982000

to operate with government support. Thus, the average annual

growth rate of sickness recorded by the Economic Survey was 40 per cent during 1982-88 in terms of number of units and 22 per cent in terms of the amount blocked in these units. This

was mainly due to the sheer size of sick SSIs – the most protected 5000 sector – which was growing at an unbelievable 42 per cent per

0

annum. During this period, the policy aspect of sickness masks its other dimensions such as entrepreneurial incapability.10 In

2002 2002

most situations, entrepreneurial incapability was considered to be a result of the specific policy regime, which failed to internalise the incentives. Thus, when small doses of liberalisation were attempted as a part of the firefighting strategies, these firms were shattered, leading to their exit from the market.

The 1980s was, in fact, a decade of small industry sickness.

10000

The pre-reform period witnessed a massive shakeout in the SSI sector, which dominated the scenario with its high growth rate 9000 both in terms of number of units and amount blocked (Table 1). 8000 Thus, the share of sick small units in the total amount blocked 7000

Year Amount blocked in SSI units Amount blocked in SICA units Amount blocked in all non-SSI units including weak unitsAmount blocked in all sick units

Figure 4: Real Amount Blocked in Sick Units

6000

5000

4000

3000

increased steadily from 17 per cent to 26 per cent by the end

of the decade. However, the growth of sick units was arrested

during the liberalisation phase. The number of SSI sick units

(Rs crore)

registered a negative growth rate from 42 per cent per year in the 1980s to –4 per cent per year in the 1990s. The real11 and

2000

nominal amount outstanding registered a drastic decline in the 1000

post-reform period. Thus, the share of sick SSI units in the total

0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

amount blocked declined to 19 per cent by 2002.

As mentioned earlier, a precise, overall comparison of sickness

Year

in the pre- and post-reform decade is difficult to make, owing to the definitional and accounting problems associated with the Economic Survey data on sickness in the small-scale sector. However, due to the wide gap in the two growth rates one can expect a declining trend in sickness in the small-scale sector, which is also reflected in the decreasing share of sick units in the total number of SSIs functioning in the country.

Nevertheless, the average annual growth rate in the number of non-SSI units falling sick remained almost constant at 5 per cent, both in 1982-88 and 1996-2002. But, the post-reform period – 1996-2002 – has undeniably exhibited a declining trend compared with the pre-reform period, with reference to the real and nominal amount blocked in the non-SSI sector (including weak units), as shown in Table 1. The nominal amount blocked in the non-SSI division during the two periods grew at 21 per cent and 12 per cent respectively. In real terms, it was 13 per cent and 8 per cent. Nevertheless, the economic burden of SICA units (whose net worth was completely eroded) continues, even though the rate of growth of such sick units has decreased drastically. The real amount outstanding in these units grew at

8.6 per cent and 7.4 per cent respectively during the pre- and post-reform period.

Also, a meagre 1 per cent of sick non-SSI units account for around 75-80 per cent of the economic burden caused by all sick units in both decades, whereas the remaining 99 per cent of the sick units belonging to the SSI sector, account for, at the most, only one-fourth of the total amount blocked. The level of sickness in the non-SSI sector, defined as the percentage of sick units to

Amount blocked in SSI units Amount blocked in SICA units

-

Amount blocked in all non-SSI units including weak unitsAmount blocked in all sick units

Source:Same as in Table 1.

total non-SSI units, showed an increasing trend, whereas the corresponding figures for the SSI sector exhibited a decreasing trend (Figure 5). The sick non-SSI units constituted around 2.4 per cent of the total number of non-SSI units12 both at the end of 1988 and at the end of 1999. This means that the level of sickness has not come down in the non-SSI sector. On the other hand, the percentage of sick SSI units in the total number of working SSI units declined in the post-reform period. At the end of 1988, around 15 per cent of the SSI units were sick. This came down by to around 7.5 per cent in 1998, and rose to 10 per cent in 1999, when recession-induced sickness in the SSI sector began. SSI sickness seems to have stabilised during the post-liberalisation period. It could be that their harvest was already done, particularly because their failure rate was around 42 per cent in the previous decade.

The gravity of the situation in the non-SSI sector can also be understood from the data produced by BIFR and IDBI, given in Figures 6 and 7. In contrast to the Economic Survey reports on sickness, the average annual growth rate of non-SSI sickness as per BIFR records (this corresponds to SICA units’ growth rate in Economic Survey) is 18 per cent during 1991-2002. BIFR records detail the non-SSI sickness (Table 2). As per BIFR records, till December 2002, 4,318 cases (all large and medium

Figure 5: Ratio of Sick Units

(a) Sick SSI units as a percentage of total SSI untis (b) Percentage of non-SSI sick untis

16.00

2.50

14.00

2.00

12.00

1980198319861989199219951998 Units (Per Cent)Units (Per Cent)

10.00 1.50

8.00

6.00

4.00 1.00

0.50

2.00

0.00

0.00

1981198319851987198919911993199519971999

Year Year Percentage of sick SSI untis

Percentage of non-SSI sick untis

Source:Computed using government data on sickness and number of SSI and non-SSI units.

scale units) had been registered with it, since the board’s inception in 1987. Altogether, this accounted for an accumulated loss of Rs 89,050.67 crore. Of this 28 per cent was due to only 190 PSUs (4 per cent of the total). The sick units affected a staggering 22 lakh employees. The overstaffing of PSUs resulted in around 45 per cent of the employees being thrown out from the public sector alone, though these accounted for only 28 per cent of the accumulated loss and around 4 per cent of the total number of sick units. After 1992, PSUs have not been referred to BIFR on a large-scale. The tremendous public criticism regarding the efficiency of PSUs towards the end of the1980s must have led to some amount of streamlining, resulting in higher performance levels. In addition, there has been a change in government policy in favour of selling off PSUs to private entrepreneurs. Thus, the disinvestment spirit may also be responsible for this decline.

In both BIFR and IDBI data sources, as in the Economic Survey data, non-SSI sickness showed a sudden jump in 1997, with the onset of the recession. The Economic Survey shows that SSI sector sickness shot up only two years later, in 1999 (Figure 1) and again declined. This time lag can be attributed to the flexibility and superior staying power of small industries in a depression phase. Many studies by SIDO and NCAER13 have shown that technological advancement has made SSIs more efficient in the post-liberalisation period, thus reducing their failure rate.14 In the recessionary years, their smallness itself is shown to be a blessing in disguise, especially when they are technically efficient.

Thus, while the SSI sector appeared relatively immune to the recession, the non-SSI sector fell easy prey to it. The latter was heavily responsive to the problem years. This is reflected in non-SSI sector sickness trends, where a clear upward shift starts from 1987 in the pre-reform period (the SICA units sickness jumps in 1986, soon after the introduction of policy reforms by the Rajiv Gandhi government and from 1997 in the post-reform period, when the manufacturing sector growth rate declined by half (from

14.1 per cent in 1996 to 7.3 per cent in 1997). In 1997 itself, as per BIFR records, there was a 140 per cent increase in the incidence of sickness. In other words, the number of sick units increased by a factor of 1.4 from the previous year. Until then it was on a declining phase. In 1996, only 97 firms had been registered with BIFR. The next year the figure shot up to 233. The upward spiral reached its highest level in 2002, when the manufacturing sector registered a growth rate of just 2.7 per cent, with 559 firms registering with BIFR (However, IDBI and the Economic Survey are indicative of a recovery in 2002). From this we can infer that the recession/external factors and the incidence of sickness are showing a powerful correlation. It is time to concentrate on the factors that make our industries vulnerable to external factors.

Another factor of importance is the declining potential viability of these sick units (Table 3). The data shows that in all the sectors the number of potentially viable units has been reduced to almost half over 10 years. The amount blocked on account of viable units has also shown a substantial reduction during this period. In the non-SSI sector, in 1990 the outstanding credit that could be redeemed was 44 per cent of the total amount blocked in that division. In 2002, the amount that could be redeemed from potentially viable units is a mere 19 per cent. This is where the difference between the pre- and post-reform period lies. Now, once in the grip of sickness it is not easy for a firm to survive. Product and technological obsolescence can be one reason for the reduced viability of units. Also, in order to save face, the firms must have allowed sickness to grow by hiding it through creative accounting. Thus, even revised definitions to detect sickness at the incipient stage have not improved the viability of firms. Perhaps the revised definitions must have helped the financial institutions in retrieving some of the loan amount extended. Hence, winding up, rather than rehabilitation, appears as the best fire-fighting strategy in these cases.

Table 2: Registered Cases of Sick Large-Scale Units, December 31, 2002

Private Public Total Registered Cases
No of Cases 4128 190 4318
Net worth (Rs crore) Accumulated losses (Rs crore) Workers 35733.45 64429.35 1239891 11064.62 24621.32 997302 46798.07 89050.67 2237193

Source:BIFR.

Figure 6: Non-SSI Sickness 600 500 400 300 200 100

and pharmaceuticals constitute around one-third of the total sick units in the chemicals sector, proclaiming a crisis in the pharmaceuticals division marred by skewed patent regimes. A time series plot shows a sudden upsurge in 1997 in all these sectors. Besides, 85 service sector units also appeared on the sickness map given by IDBI, in 1997.

Industries that figured prominently on the sickness map are those that received lesser assistance from financial institutions during the decade.16 Yet it cannot be stated emphatically that sickness in these industries was due to decreased flow of financial assistance, given that the composition of sick industries has not

No of companies registered1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

changed in the pre- and post-reform periods. The causality can

be the other way round. The dismal performance of these units

Year Total number of sick untis

must have forced FIs to reduce their assistance to them in the

Source: BIFR.

wake of increased efforts to cut down their NPAs. If the declining

Figure 7: Sickness, According to IDBI Data D/S ratio (amount disbursed/amount sanctioned, which fell from 80 per cent in 1986 to 62 per cent in 2001) can be taken as an indicator of stricter scrutiny of investment proposals and an even

No of Firms 800 700 600 500 400 300 200 100 0

stricter project follow-up, it can be said that the poor performance of

Tabel 4: Industry Share in Total Number of Sick Units and Amount Blocked 1994-2002

Industry Per Cent Share in Number of Sick Units Per Cent Share in Amount Blocked
Textiles Metals Chemicals and products Food products 25.5 15.6 15.0 9.5 11.4 17.3 13.7 4.2

1994

1995

1996

1997

1998

1999

2000

2001

2002

Electrical 7.3 3.4 Services 5.3 4.5

Year Number of non-SSI sick untis Source: Report on Development Banking.

III Composition of Sickness

(a) Industry-wise distribution of sick units: An industrywise study showed that the composition of sickness has not changed much.15 Most of the sick units in the 1980s were from textiles, metals and chemicals sectors. Both IDBI and BIFR data are in general agreement regarding the top four industries on the sickness map. The shares of each industry as per the IDBI reports and BIFR records are given in Tables 4 and 5. The jute industry figured prominently in terms of its share of workers displaced, even though it accounted for very few sick units and accumulated losses. Similarly, BIFR records place the fertiliser industry in terms of accumulated losses, even though it accounts for less than 1 per cent of the total number of sick units. IDBI reports puts power projects as the main contender in the amount blocked. In the food products division, sugar and vanaspati and vegetable oils together accounted for around 70 per cent of the amount blocked and 50 per cent of the number of units. Similarly, drugs

Paper 5.2 2.9 Transport equipments 5.0 2.8 Machinery 4.7 2.4 Cement 4.3 6.0 Electricity 2.1 18.2 Fertiliser 2.1 2.8

Source:IDBI Report on Development Banking, various issues.

Table 5: Industry Share in Sickness 1987-2002

(Per cent)

Industry Share in Share in Total Share in Per Cent of

Total Number Net Worth Accumulated Workers

of Sick Units of Sick Units Losses Displaced

Textiles Metals Chemicals Food products Electrical and electronics Paper and pulp Rubber goods Transport Jute Leather and leather goods Fertilisers

Source:BIFR. 17.81 13.66 15.16 26.41

15.84 14.14 14.61 8.28

12.58 12.65 12.04 5.48

10.15 7.40 6.92 4.41

6.14 5.24 6.69 3.23

5.77 2.31 2.16 2.81

1.60 0.88 0.85 0.50

1.51 1.34 1.52 1.62

1.23 0.27 0.99 6.70

1.16 1.69 1.52 0.47

0.86 5.62 6.92 0.85

Table 3: Viability Statistics of Sick Units

(as percentage of total)

1990 No SSI Sick units 2001 Amt No Amt 1990 No Non-SSI Units 2001 Amt No Amt Total 1990 2001 No Amt No Amt
Potentially viable Non-viable Viability not yet decided 7.5 91.4 1.1 24.3 71.8 3.9 5.2 90.4 4.4 8.9 87.5 3.6 37.1 42.6 20.3 43.7 30.3 26 12.45 55.89 31.65 18.53 33.45 48.02 7.8 90.9 1.3 38.7 41.1 20.2 5.3 89.9 4.8 16.8 42.9 40.3

Source:Economic Survey, Trend and Progress of Banking in India.

these industries led to their lower share in the total assistance funds.

(b) State-wise distribution of sickness: In the 1980s, Maharashtra, Gujarat and West Bengal figured prominently in terms of the number of sick units as well as amount blocked. However, the post-reform period points to the entry of Andhra Pradesh and Tamil Nadu into the top three or four positions in terms of the largest number of sick units. This is so in both BIFR and IDBI data, for all the time periods mentioned. IDBI data also establish their predominance in the amount blocked. According to IDBI records, till 2001 Andhra Pradesh had the highest figure of

Table 6: Share of States and Regions in Distribution of Sickness, December 31, 2002

State Per Cent of Per Cent Share Per Cent Per Cent of Sick Units in Net worth of Share in Workers Sick Units Total Losses Displaced

Maharashtra 20 22 21 12 Tamil Nadu 10 6 6 6 Andhra Pradesh 10 6 6 9 Gujarat 9 10 10 9 Uttar Pradesh 8 6 6 7 West Bengal 7 17 15 26 NCT Delhi 5 9 9 3 Karnataka 5 3 3 4 Bihar 2 5 812 Northern region 26 24 22 16 Southern region 28 16 16 20 Western region 34 36 35 24 Eastern region 12 25 26 40

Source:BIFR.

Table 7: State Ranking according to Number of Sick Units andAmount Blocked as per IDBI and BIFR Data

State Number of Units Amount-wise BIFR IDBI BIFR IDBI BIFR 1987-2002 1994- 2002 1994-2001 1994-2002 1987-2002

Maharashtra 1 1 1 1 1 Andhra Pradesh 2 2 3 2 8 Tamil Nadu 3 42 6 7 Gujarat 4 34 3 3 Uttar Pradesh 5 5 5 4 6 West Bengal 6 8 7 10 2 Karnataka 7 9 9 9 10 Delhi 8 N A 6N A 4 Madhya Pradesh 9 6 8 8 8 Rajasthan 10 7 10 7 9 Bihar very low very low 5 Punjab low 10 low 5 11

Note: In case of number of sick units, the share of each state for 1994-2002 in BIFR data is also given for comparison. However, data on amount blocked for 1994-2002 was not available with BIFR, and is not given here.

Source:BIFR, IDBI Reports on Development Banking.

Table 8: Average Annual Growth Rate of Sick Units

(Per Cent)

State 1987-1991 1992-1996 1997-2002 1991-2002
Maharashtra -19 -9 62 26
Tamil Nadu -11 4 99 54
Andhra Pradesh -3 -13 52 20
Gujarat -27 26 35 24
Uttar Pradesh 56 1 25 41
West Bengal -28 9 75 37
Delhi 13 -30 62 10
Karnataka -11 -14 60 23
Bihar 7 23 67 60
All India -15 -6 41 18

Source:BIFR.

amount outstanding in sick units in 2002 it was pushed to the second place. Tamil Nadu comes in fourth.17 However, BIFR records that cover the period 1987-2002 do not place these two states in any significant position with regard to the economic burden of sickness. The traditional seats of sickness – Maharashtra, Gujarat and West Bengal – figure high in BIFR data series, due to the wider time range. Also, the capital intensity of the projects (which is reflected in the net worth of companies) that have gone bust is quite high for these states, resulting in their having a larger share of accumulated losses and workers displaced. Bihar, for instance, figures in the top-five list in terms of economic losses, though the number of units constitute just 2 per cent of the total sick units. Region-wise and state-wise distribution of sickness and a comparative ranking of states as per the two data sets, if IDBI and BIFR, are given in Tables 6 and 7. The growth rate of sick units over different time periods is shown in Table 8. The supremacy of the southern states in the IDBI data is due to the severe shakeout that took place in these states in association with recession, which is captured well in their time period – 19942002. For instance, if we look at BIFR data, among states with a significant share in the total number of sick units (Maharashtra, Andhra Pradesh, Tamil Nadu, Gujarat and Uttar Pradesh), Tamil Nadu registered an annual growth rate of 99 per cent during 19972002, when the national average was just 41 per cent. Andhra Pradesh occupies third position with 52 per cent. Similarly, when there was a 140 per cent increase in 1997 from that in the previous year, at the national level, the corresponding figure for AP and TN was 300 per cent and 480 per cent. This is greater than the most industrialised state of Maharashtra. Thus, southern states were the worst hit during the recent shakeout. When the national average of sickness was around 18 per cent, (for 1991-2002, as given by BIFR), the average annual growth rate of sickness for Tamil Nadu was 54 per cent. For AP, the corresponding figure was 20 per cent. This shows that southern states that embraced the new policy reforms in spirit and content were badly hit by the global recession.

Apart from this, BIFR data series provides a region-wise analysis on employment, net worth and accumulated losses of failed units. Just as industrialisation was localised, sickness is also localised. In the northern region, UP and Delhi accounted for around 60-63 per cent of sick units and accumulated losses. UP alone has 46 per cent of the workers displaced. The share of individual states in sickness represents an even higher concentration in other regions. For instance, in the southern region Tamil Nadu and Andhra Pradesh together accounted for 71 per cent of the total sick units and 74 per cent of accumulated losses. Andhra Pradesh alone has 35 per cent of the sick units and 36 per cent of the accumulated losses. Tamil Nadu contributes 36 per cent of sick units and 37 per cent of the accumulated losses in this region. However, 44 per cent of the workers displaced are from AP. In the eastern region, West Bengal dominates with 65 per cent of the sick units and unemployed workers and 57 per cent of losses. Next comes Bihar, which accounts for 16 per cent of units and 30 per cent of accumulated losses. This can be due to the greater presence of sick PSUs in Bihar. These two states, account for 83-86 per cent of the sickness in the eastern region. In the western region, Maharashtra leads, followed by Gujarat, with around 57 per cent and 26 per cent respectively in terms of the number of sick units. These two states together account for 83 per cent of the sick units and 86 per cent of the accumulated losses.

Though the southern states, mainly AP and TN, had made an entry into the sickness map in terms of the number of units, BIFR records show that their share in the economic loss is the least for the whole of India. Surprisingly, the eastern region, with the left-oriented, labour protective West Bengal has contributed to around 40 per cent of the workers displaced, in spite of having only 12 per cent the total number of sick units. This has happened because of the failure of big projects. This should be a matter of concern for the West Bengal government.

IV Performance of BIFR

BIFR was set up primarily to provide support to sick units through the speedy disposal of cases for winding up or rehabilitation. But BIFR’s performance had come under heavy criticism almost since its inception. The Goswamy Committee, set up in 1993, criticised the preference of the board for rehabilitation rather than winding up, which resulted in undue delay without results. Even after the setting up of the debt recovery tribunals for the speeding up of the process, things have not improved at BIFR. Now steps have taken to wind up the organisation. The year-wise performance of BIFR is given in Table 9. Of the total 4,318 cases registered, hardly 9 per cent were revived by the end of December 2002. Winding-up order was served for 25 per cent of the cases. Another 24 per cent were dismissed as nonmaintainable. The remaining cases are awaiting a decision. CMIE has pointed out that around 36 per cent of the cases were pending in 2001, and a mere 1.06 per cent could actually turn around.18 This gives credibility to the argument that BIFR only served to delay the liquidation process. In business circles the board came to be known as the “board for industrial funeral rites”. A proposal was made in budget 2001-02 to repeal SICA and amend the Companies Act in order to set up a national company law tribunal to which may be assigned the disputes now handled by Company Law Board, BIFR, AAIFR and the high court. The Companies Amendment Act 2003 came into effect on January 3, based on the recommendations of the Balakrishna Eradi committee, which was set up to examine the law relating to insolvencies and winding up of companies. With this BIFR and its appellate authority, AAIFR, lost the rationale for their existence. Coupled with this comes the new ordinance, the Securitisation and Reconstruction of Financial Assets and Enforcement of Security Interest Ordinance

– promulgated in June 2002 (and regularised into an act in November 2002), which gives creditors the power to takeover the assets/management of the defaulting company without the intervention of the court. These firefighting policies indicate a marked shift in the responsibility of industrial restructuring from secured creditors and financial institutions to the defaulting debtor firms.

V Conclusion

In this paper, an attempt has been made to trace the trends and patterns in industrial sickness during the pre- and post-reform period, with focus on large and medium-scale units. In the 1980s, widespread inefficiencies in industrial establishments, sponsored by government policies, resulted in a steady increase in sickness. This was true in the small-scale sector – the most protected division of industry – during 1982-88, when around 15 per cent of the total SSI units were sick. During this period, SSI sickness was growing at 42 per cent per annum. The decade of the 1980s was thus a decade of SSI sickness. Coming to the 1990s, one can see that industrial sickness has been reduced. Though this cannot be stated emphatically owing to the definitional and accounting problems associated with data on SSI sickness, it holds true in the case of the non-SSI sector. The rate of growth of sickness has slowed down in the non-SSI sector. But this doesn’t give us relief. After 1997, all industrial divisions and all states recorded an unprecedented continuing increase in sickness. Amidst claims that India would come out unscathed from recession, more and more firms are closing down. In 2002, there was a 21 per cent growth in the incidence of non-SSI sickness, as per BIFR records. In addition, a comparison of preand post-reform non-SSI sickness shows that the economic burden of sick units whose net worth had eroded completely (SICA), did not show any remarkable reduction in the post-reform period. Compared with the SSI sector the reduction in the number of SICA units was not remarkable. Though the non-SSI sector forms only a meagre 1 per cent of the total sick units, almost 75-80 per cent of the amount blocked by them. Also, 2.4 per cent of the total non-SSI units in the country were sick, both at the end of 1988 and at the end of the year 1999, when the corresponding figure indicated a reduction in the SSI sector. Thus, in general, the post-reform period is a period of large and medium-scale sickness.

The SSI sector sickness seems to have stabilised during the post-reform period, probably because it had already taken its toll. Also, while the non-SSI sickness showed a sudden jump in 1997 with the onset of recession, such a jump is visible in the SSI sector only after two years, that is in 1999. This can be an indicator

Table 9: BIFR Yearwise Performance, as on December 31, 2002

Total Number Cases Disposed of during the Year

Year of Cases Growth Rate Cases Cases Winding up Dismissed Registered over the under Revived Recoduring the Previous Year Revival mmended

Year (Per Cent)

1987 311 0 0 0 8
1988 298 -4.18 0 1 12 29
1989 202 -32.21 0 1 31 78
1990 151 -25.25 3 3 43 44
1991 155 2.65 4 4 47 28
1992 177 14.19 8 7 30 42
1993 152 -14.12 9 13 64 59
1994 193 26.97 10 37 79 48
1995 115 -40.41 22 25 64 29
1996 97 -15.65 29 93 85 25
1997 233 140.21 13 36 85 22
1998 370 58.80 13 21 50 36
1999 413 11.62 13 10 65 70
2000 429 3.87 10 37 153 158
2001 463 7.93 50 47 133 118
2002 559 20.73 74 34 143 252
Total 4318 258 369 1084 1046
Per cent
of the total 5.97 8.55 25.10 24.22

Notes: 1 Format earlier adopted was indicating cases revived in the year of registration. As a company normally takes five/seven years to be revived, the new format indicates companies revived in the year in which net worth became positive and companies were discharged from the purview of SICA. 2 Figures of companies revived after the successful implementation of scheme as well as those where net worth became positive at the inquiry stage itself have been clubbed together. 3 Above figures are according to calendar year.

Source: BIFR.

of the greater resilient power of small firms compared with large firms. With technical efficiency in production, smallness can be an advantage during a period of fluctuations.

The industrial sickness of the1990s catches our attention in many ways. Trends in the post-reform period sickness indicate the extent of creative accounting among firms. This is reflected in the percentage of weak units, which registered a drastic decline. Not only that, the potential viability of sick units also declined. If 44 per cent of the total amount blocked could be retrieved from sick units in 1990, only 19 per cent of the amount can be retrieved at present. This decline in potential viability can also be attributed to fast-changing product preferences and technologies. However, this shows that even with continuous revisions in the definition of sickness, it was not possible to detect and repair sickness at the incipient stage. BIFR, the body set up for just this purpose, turned out to be a colossal failure, with hardly 9 per cent of the cases referred it turning around. Even this is a doubtful figure. The poor performance of BIFR calls for its closure. The direction of the present policy reforms to shift the burden of corporate restructuring from creditors to defaulting firms, needs to be appreciated, though one can differ in the minute details.

One matter of concern is the vulnerability of firms to external imbalances and sudden policy shifts. It needs to be explored why our industries fall easy prey to such developments. There is something that is common behind the sudden upsurge in sickness in 1986 and 1997 in the non-SSI sector. This means that we need to focus on the capital structure of our firms, which is believed to be the factor that makes a firm vulnerable to outside events. One other interesting factor is the emergence of the southern states – Andhra Pradesh and Tamil Nadu – to prominent positions on the sickness map. The incidence of sickness in these states seems to be highly responsive to the onset of recession. It has to be kept in mind that these are the two states that embraced liberalisation in spirit and content. So, it is of utmost importance to look at the factors that must have caused the sudden upsurge in sickness since 1997. One also needs to ascertain whether there has been any shift in the factors behind the sickness in the 1980s and 1990s.

lli

Email: economics@assumptioncollege.ac.in

Notes

1 In the Indian context only a small-scale unit is defined and what is not defined or the residual is termed as non-SSI unit or as large and mediumscale unit. There is no official definition for medium-scale unit and hence for small- and medium-scale unit (SME). The present definition of smallscale unit carries with it an investment limit of Rs 1 crore in plant and machinery, provided it is not owned by or controlled by a subsidiary of any other industrial undertaking.

2 In the Economic Survey, for some years the exact data on sickness was missing. These figures were plugged in from the data appeared in the Handbook of Industrial Policy and Statistics, RBI Report on Trends and Progress of Banking in India, Report on Currency and Finance, etc. The data was showing consistency in all these sources.

3 There were around 1,25,000 non-existent and non-traceable SSI units at the end of March 1990 (which implies a 43 per cent growth over the previous year) with an outstanding bank credit of Rs 240 crore. The number of non-traceable units alone for the rest of the years can be identified from other sources but the amount outstanding against them could not be ascertained. The fundamental problem of whether a non-traceable unit should be counted as sick or not is difficult to answer since both the possibilities– unit running successfully or it must have closed its operations

– do exist. More important is the question of how much their presence affects the calculated growth rates. For instance, if the non-traceable and non-existent data is included for 1996-2002, will the average annual growth rate of small-scale industry sickness increase or decrease or remain as such? As long as this question cannot be tackled, the comparison of small-scale industry sickness for both the decades cannot be done.

4 Therefore, in the pre-reform period one can have six-year data on smallscale industry sickness, from 1982 to 1988. The post-reform period data exist for the period 1991-2002.

5 The data on non-SSI sector in the Economic Survey, till 1986, follows a classification – large and medium. After that, in line with the SICA definition, non-SSI sector sickness was classified into non-SSI weak units (those units with 50 per cent erosion of net worth and a current ratio less than one) and non-SSI sick units (whose net worth has been eroded completely) as defined in SICA 1985. This was corrected by taking data from CMIE, which supplies the data on non-SSI sick units for the period before 1987. This was adopted because the data provided by CMIE for the subsequent years on non-SSI sick units was in agreement with the data that appeared in the Economic Survey. Trusting this method of compilation the problems with non-SSI sector are circumvented. Since BIFR and other sources of data, say, IDBI report on development banking gives data only for non-SSI sick units whose net worth has been eroded completely, special emphasis is given to the data for non-SSI sick units, which will be termed as SICA units (whenever it refers to data from Economic Survey) from now onwards, in order to avoid confusion.

6 The definition of sickness has undergone changes over the years. In the beginning, for defining a unit as sick it needed to have been registered for seven years and should have incurred losses continuously for two years including the current year and its net worth must have been wiped out completely by the accumulated losses. The latest definition of sick units is such that the repeated default of the loan amount is enough to categorise it as a sick unit. Technically speaking, the loan account of the unit has to become a non-performing asset. (NPA is defined as an advance that has not been serviced “as a result of past dues” accumulating for 180 days or more. Once the advance becomes doubtful, the loan account is reviewed by the banks. At present, for NPA, a 90-day criterion is adopted.) With the new law, definition of sick units now include in its ambit, the companies which fail to repay debts within any three consecutive quarters on demand for its repayment by creditor(s) of such companies.

7 In the SIDBI study, done in (1999), the nominal asset values have been deflated by the wholesale price index for machinery and transport equipment at 1970-71 prices. Thus, the 1991 definition of SSI as a unit with Rs 6 million implied a real investment of Rs 1.18 million. The change from previous years (Rs 1.06 million in 1985) was insignificant. However, the definition of 1997 (that is, Rs 30 million) implied a real investment of Rs 3.17 million, which was significantly higher than the earlier figures.

8 In all these, average annual growth rates are taken. Compound growth rates were not taken due to the very low R2 values recorded, signifying its inappropriateness. However, there was not much difference between the compound growth rates and the corresponding average annual growth rates.

9 While Economic Survey reports sickness according to the information provided by commercial banks, IDBI reports on sickness are based on the information provided by the major term-lending development financial institutions. BIFR records are based on sick firms’ references to it. BIFR and IDBI data sources give data on only non-SSI sickness, that is, SICA units. BIFR data starts from 1987 onwards (the year of its inception). IDBI data starts from 1994 and is compiled from ICICI, IDBI and IFCI

– the three major term-lending institutions in India – published in the IDBI Report on Development Banking. Compared with BIFR data, the FI’s reports on sickness have more coverage, since some of the units do not come under BIFR’s purview. Computer software and granite units are cases to this effect as there is always a confusion regarding the term “industry”, for whose sickness BIFR was created. Many a time, references by these kinds of firms are rejected by BIFR. Not only that, in recent years the reference to BIFR itself has been made optional. However, the pattern exhibited in BIFR records and IDBI records are similar. This goes hand in hand with the trends exhibited by the Economic Survey data on non-SSI SICA units. One limitation of IDBI records on sickness is that data from only 1994 onwards is available.

10 The Goswamy Committee (1993) which looked into the issue of industrial sickness admitted that: Decades of high tariff, quotas, licensing restrictions, barriers to entry, and irrational excise duties have been instrumental in fostering widespread inefficiencies in large and medium-scale private sector factories. These have nurtured a perverse environment in which, irrespective of ownership, an inefficient firm is never penalised for being systematically uncompetitive (p 2). In a less protected scenario, firms cannot systematically have higher variable cost and higher fixed cost, compared to other firms in the industry, and yet continue to survive. In India, such firms not only survive, but often do so outside the ambit of SICA and BIFR. It implies that our market structure and past government policies provided sufficient cushion to production and cost inefficiencies. In a more economically competitive situation, many of the seemingly healthy firms have the potential of turning sick (pp 10-11).

11 The prices for deflating the nominal amounts were taken from RBI: www.rbi.org.in/sec7/25853.pdf

12 The number of total non-SSI units was calculated by deducting 7.5 per cent of the units from the data on factories registered under the Factories Act, where 7.5 per cent is the number of SSI units registered under the Factories Act. The number of total SSI units was taken from the Handbook of Industrial Policy and Statistics. The number of total non-SSI units could be calculated only till 1999.

13 NCAER study in 1996-97 and all-India census of SSI units, 1972 and 1987-88 by SIDO.

14 Theories on declining industries that came up in the 1980s have shown the superior staying power of SSI units when exit becomes an all-ornothing situation in the industry. With technical efficiency in production it is actually the SSI unit that drives the large unit out of business. See Londregan (1989), Ghemawat and Nalebuff (1985), Leiberman (1990).

15 The composition of sick units in the pre-reform period was taken mainly from the existing literatures. Murty (1995), Goswamy (1993).

16 An industry-wise and state-wise analysis of the flow of financial assistance from all development FIs is done. This showed that around 40-50 per cent of the total assistance is concentrated in only two sectors – infrastructure and services. Similarly two states – Maharashtra and Gujarat – accounted for around 35-40 per cent of the total assistance. This happened in spite of a huge increase in the flow of development funds. For instance, there was a 20-fold increase in the amount sanctioned and a 15-fold increase in the actual disbursement of funds during 1986-2001. (The figures will be even higher, if one takes into account the working capital as well as project assistance offered by commercial banks in the country). Another striking fact is that though 80 per cent of the funds sanctioned were disbursed in 1986, there has been a consistent reduction in this ratio (D/S or amount disbursed/amount sanctioned) over the years. In 2001, only 62 per cent of the funds sanctioned were actually disbursed. This can be due to better scrutiny of investment proposals during both preand post-implementation stages of the project, closure of projects halfway through, inability of firms to comply with the hard terms put forward by the FIs, availability of other external funds, etc.

17 Till 2001 December, AP had more share than TN in total sickness in this region. However, in 2002, 71 firms referred to BIFR from TN, double the number of references from AP.

18 This information is provided in CMIE Monthly Bulletin, March 2001.

References

Anant, T C A and Goswamy Omkar (1995): ‘Getting Everything Wrong: India’s Policies Regarding Sick Firms’ in Dilip Mukharjee (ed), Indian Industry: Policies and Performance, Oxford University Press, New Delhi.

Ghemawat, Pankaj and Barry Nalebuff (1990): The Devolution of Declining Industries, Quarterly Journal of Economics, Vol 105, pp 167-86. Reappeared in Applied Industrial Economics, Louis Philips (ed), University Press (1998), Cambridge.

– (1985): Exit, Rand Journal of Economics, Reappeared in Applied Industrial Economics, Louis Philips (ed), Cambridge University Press (1998).

Leiberman, B Marvin (1990): Exit from Declining Industries: ‘Shakeout’ or ‘Stakeout’, Rand Journal of Economics, Vol 21, 4, Winter, pp 538-54. Reappeared in Applied Industrial Economics,Louis Philips (ed), Cambridge University Press (1998).

Londregan (1987): Exit and Entry over the Industry Life Cycle, in Louis Philips (ed), Applied Industrial Economics, Cambridge University Press. Murty, M R (1995): Industrial Sickness in Joint and Private Sectors, Working Paper, Institute for Studies in Industrial Development.

Report of the Committee on Industrial Sickness and Corporate Restructuring (1993): Submitted to the Union Minister of Finance, Government of India, (Known as Goswamy Committee report after its Chairman – Omkar Goswamy).

SIDBI Report on Small-Scale Industries Sector (1999): Small Industries Development Bank of India.

REVIEW OF AGRICULTURE

June 30, 2006

Measuring the Marginal Value of Water and Elasticityof Demand for Water in Agriculture – E Somanathan, R Ravindranath

Is Bt Cotton Cultivation Economically Viable for Indian Farmers? An Empirical Analysis – A Narayanamoorthy, S S Kalamkar

Globalisation and Expanding Markets for Cut-Flowers: Who Benefits? – Sucharita Sen, Saraswati Raju

Lease Farming in Kerala: Findings from Micro Level Studies – K N Nair, Vineetha Menon

Drought, Agricultural Risk and Rural Income:Case of a Water Limiting Rice Production Environment, Tamil Nadu – K N Selvaraj, C Ramasamy

Diversification towards High Value Agriculture:Role of Urbanisation and Infrastructure – P Parthasarathy Rao, P S Birthal, P K Joshi

Institutional Credit, Indebtedness and Suicides in Punjab – P Satish

Farmers’ Suicides and Response of Public Policy:Evidence, Diagnosis and Alternatives from Punjab – Anita Gill, Lakhwinder Singh

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