small firm “presence” in indian manufacturing

World Development, Vol. 20, No. 9, pp. 1377-1389,1992. 0305-750x/92 $5.00 + 0.00

Printed in Great Britain. Pergamon Press Ltd

Small Firm “Presence” in Indian Manufacturing

IRA N. GANG* Rutgers University, New Brunswick, New Jersey

Summary. - This paper employs a pooled cross-section time-series model to examine the determinants of the relative importance of registered small firms in Indian manufacturing. These determinants include both industry-specific and economy-wide characteristics. Small firms perform relatively better in an expansionary macroeconomy and in industries with less vertical integration and economies of scale in management, and where small firms are able to overcome their inherent cost disadvantages. The evidence suggests the more capital intensive the industry, the greater small firm presence.

1. INTRODUCTION

In this paper we look at the “relative importance” or “presence” of small firms in manufacturing. We employ two measures to capture the presence of small firms: small firms’ share of output and small firms’ share of value added. We are interested in suggesting several reasons why we observe substantial interindustry variation in small firm presence.

Stylized stories of the development process give an important role to small modern manufacturing firms, as opposed to household manufacturing. Smaller firms are viewed as good absorbers of surplus labor and are important in achieving the goal of an equitable distribution of income (Kashyap, 1988). The smaller firms grow relatively faster at first because of flexibility, subcontracting facilities and differentiated products. Larger firms, however, eventually make up a greater share of these measures because of scale economies, managerial efficiency, better access to finance and infrastructure and a favorable tariff structure (Anderson, 1982).

How true is this stylized story? Kashyap (1988) argues that the Indian experience is at variance with this idealized picture, but offers no facts to substantiate this point. We begin by looking at how small manufacturing firms have performed relative to large firms. This paper goes on to develop, examine and test hypotheses on why small firms may do relatively better - or worse - than large firms.

In fiscal year 1982-83 small units of employment in India produced 17.5% of the organized

sector manufacturing output, 12.4% of value added, and provided 22.4% of employment. Compared to 10 years earlier, this represented virtually no change in value added or output share, and a 2% increase in employment share. Moreover, while from 1973-74 to 1982-83 the average share of the organized manufacturing sector’s value added, output and employment accounted for by small firms in India was 11.8, 17.0 and 20.6%, respectively, there is tremendous interindustry variation in these figures. Table 1 further shows that small firms are much more important in some industries than in others. Moreover, the change in presence during 1973- 74 to 1982-83 shows substantial interindustry variation - increasing greatly in some, decreas- ing in others, and, still in others, barely changing.

The answer to the question of how small firms have fared relative to large firms is far from clear. If the Indian case fits the stylized model, then it must be that India is at a transition point where large is about to overtake small. But this is unlikely - indeed in some industries, small has increased relative to large. Moreover, the absolute presence of small differs widely by industry.

*This paper was completed while the author was visiting the Indian Statistical Institute-Delhi Centre. He thanks ISI-Delhi for its kind hospitalitv, and the American Institute of Indian Studies and Rutgers Universitv for their financial suonort. He also thanks the referee, T. C. Anant, Arinham Das-Gupta and participants in seminars at ISI-Delhi and Delhi School of Economics for their comments, and Gail Alterman for her research assistance. Final revision accepted: January 21, 1992.

1377

1378 WORLD DEVELOPMENT

Table 1. Some characteristics of Indian small firm manufacturing (percentage share of organized sector, 1973-74 to 1982-83)

Industry Value added output Employment

a Food (20.21)* Beverages & tobacco (22) Cotton textiles (23) Wool & synthetics’ (24) Jute, hemp & mesta (25) Other textile products (26) Wood (27) Paper (28) Leather (29) Rubber petroleum plastic coal (30) Chemical (31) Nonmetallic minerals (32) Basic metals & alloys (33) Metal products (34) Nonelectrical machinery (35) Electrical machinery (36) Transportation equipment (37) Other manufacturing (38) Gas and steam (41) Water works and supply (42) Cold storage (741) Repair services (97)

All industries (weighted)

verage 19.8 24.6

3.7 12.6

1.0 36.1 44.3 17.0 25.1 11.7 10.0 13.3 8.3

27.7 15.1 9.4 4.5

23.1 50.4 28.2 87.1 24.4

11.8

change average -6.2 32.9 20.8 27.5

1.0 9.0 -10.3 19.0

0.4 -7.3

7.9 3.8

-1.4 -4.1 -3.1 -6.1 -1.5

3.6 -1.8

1.1 0.8

-7.1 37.8

2.1 -12.2

7.1

0.3

4.0 44.4 54.0 21.1 33.1

8.6 15.0 7.1

13.6 38.9 17.8 13.9 6.9

31.7 -8.7 36.0 -4.1 47.0 26.7 37.9 0.0 37.1 -6.7 36.3 -14.3 87.5 -15.7 85.8 -13.4 34.9 12.1 25.7 3.8

17.0 -0.9 20.6 1.7

change -5.4 13.3

1.8 -6.6

0.6 -11.5

6.7 55.6 2.3 1.8 26.1 -1.9

-1.7 29.9 0.6 -5.3 30.3 1.3 -1.3 21.7 I.6 -2.4 28.8 4.4 -2.8 16.3 0.0

3.5 43.9 2.3 -2.1 26.1 -0.4

1.8 16.5 3.6 1.8 11.3 - 1.0

average change 22.5 -10.8 49.0 22.5

9.7 0.2 20.7 -5.7

1.0 0.3 39.1 -7.9

Source: Constructed from data drawn from the Annual Survey of Industries, 1973-74 to 1982-83 and from Chandhok et al. (1990). For information on the definition of “small,” see the text discussion. *Numbers in parentheses are India’s National Industrial Classification codes.

The thrust of this paper deals with why these differences in small firm presence arise.

One simple answer is that government policy in India has worked to encourage small firms in some industries and not in others. The Industrial Policy Resolution of 1956 called for support for small firms in order to promote industrial decen- tralization and increase employment. This was reiterated in the Industrial Policy Statement of December 1977. Policy measures to promote small firms include exemption from excise and other taxes, preferential pricing (for example, in sales to public sector firms), quantitative restrictions on the output of some large firms, and the reserving of particular products for production by small firms (see Little, Mazumdar and Page, 1987, pp. 26-29). These policies may have differential effects among industries - promp- ting small firms more vigorously in some, less so in others. Incentives, however, have also been provided to large firms and public sector undertakings (Ghosh, 1988). Foremost among these is India’s byzantine structure of import and export regulations, licensing procedures, quantitative

restrictions and high tariffs. It is difficult to see what the general thrust of government policy has been - to encourage small or to encourage large firms - even within one industry.

Alternatively, one might argue small firms succeed where they are relatively more efficient than large firms. This approach suffers two weaknesses. First, studies on the relative efficiency of small firms in India, and especially those of Page (1984) and Goldar (1988), fail to establish a firm systematic relationship between firm size and technical efficiency. Second, if we were able to establish a link between size and efficiency, we would then be left with the difficulty of explaining why firms of the ineffi- cient size still exist. Understanding the relative efficiency of small firms thus does not explain why we witness such tremendous variation among industries in small firm presence. Factors other than technical efficiency must be affecting the relative importance of small firms.

Our approach is to note the coexistence of small and large firms in the same industry, and examine what factors determine their relative

shares. We specify a model of small firm presence in a developing economy. While most earlier studies on small firms have been restricted to employing a cross-section (White, 1982; Acs and Audretsch, 1989) or a time series (Highfield and Smiley, 1987, we make use of panel data to incorporate both industry specific and economy- wide factors. In addition, we examine the determinants of small firm presence within the textile and engineering subsectors of manufacturing.

e.g., if the number of factories is less than 50 in a less industrialized state. Note that once a firm is in the census sector its status is not altered for four years, though changes in employment may warrant it. The published factory sector statistics are derived by summing the census sector plus the completely enumerated industries of the noncensus sector plus two times the sampled units belonging to the sample sector.’

In the next section we discuss the data to both explain our measure of “small” and so that the constraints placed upon our model are clear. Section 3 then discusses the model and its empirical implementation. We analyze our results in section 4 and offer some observations in the concluding section 5.

2. THE SAMPLE

The AS1 provides comprehensive data on the registered factory sector since 1959. A significant change in coverage and accounting, however, took place starting with the 1973-74 survey, so our data series starts with that year. Moreover, as of the AS1 198>84, the publication of detailed results pertaining to the census sector was discon- tinued (Government of India, National Accounts Statistics, 1989, p. 83). This then defines the time frame of our study - 1973-74 to 1982-83. We can arrive at a consistent data set on the sample sector by taking the difference between the factory and census sectors for this period.

The main source of our data is the Annual Survey of Industries (ASI), which provides consistent industry level data series across industries and over time. The AS1 covers the organized manufacturing sector, i.e., factories registered under the Factories Act of 1948 and geographically covers the whole Indian union. These are all factories employing 10 or more workers and using power, or factories employing 20 or more workers but not using power on any working day of the preceding 12 months. This is referred to as the factory sector and is now conveniently available in Chandhok et al. (1990). AS1 data on any firm relate to the accounting year which ends on any day between April 1 and the following March 31 (the Indian financial year). Data are gathered at the plant level and then aggregated.

The factory sector is divided into two parts. The data on these two parts are not conveniently gathered in any one source or easily accessible location. The census sector consists of firms with 50 or more employees using power, or 100 or more employees, not using power. Until 198% 84, data for the census sector were published (Government of India, ASI, various years). The noncensus or sample sector consists of all firms with 10-49 workers, using power, or 20-99 workers, not using power (for a thorough discussion of the sample sector of the ASI, see Ramachandran, 1988). Sample sector data are not published. In the period under consideration the census sector is completely enumerated each year while 50% of the firms in the sample sector are queried in a given year. All electricity undertakings irrespective of size of employment are enumerated as are some sample sector firms,

The sample sector provides a convenient and ready definition of the small firm in the organized sector of manufacturing: firms that use power and have lo-49 employees and firms that have not used power at any time during the past 12 months and have 2&99 employees. This is the definition of small firm we adopt here.’ In fact this is just one definition of small firms that exists in the Indian context. Small firms can include a wide range of manufacturing units which vary in the size of employment, capital investment and the value of output as well as in the level of organization, technology, source of power and type and quality of products. Small units belong to three subsectors: (a) traditional village industries including handicrafts, (b) small unregistered household and nonhousehold units not covered by the Factories Act of 1948, and (c) registered factories (see the thorough discussion in Suri, 1988). Ours is an operational definition falling under (c). We are therefore investigating the determinants of relative importance of the organized or registered small firm.

We look at 22 two-digit level industries drawn from the ASI. The list is contained in Table 1.” We chose the two-digit level as the unit of analysis as it allows complementary industry level data to be included in the analysis (e.g., the wholesale price deflators). It avoids the problem of where to assign multiproduct firms. The data for each series were deflated by the appropriate price index with 197&71 as the base year. The capital series are obtained using 1973 as the base stock and deflating increments to this stock with the machine and equipment deflator. There is much debate over the measurement of capital


stock; our measure is one standard and our results were robust with regard to several alterna- tives.

3. MODEL

Our data are for 22 industries, with 10 years of data (1973-74 to 1982-83) for each industry. With it we try to explain the share of industry output and value added accounted for by small factories, defined by the number of people they employ. We specify a model that accounts for differences in behavior over the 22 industries as well as over time within an industry.

We proceed from the assumption that in each industry there is a minimum efficient firm size (MES) - the firm size at which economies of scale are exhausted and firms of that size do not suffer a unit cost disadvantage relative to larger ones. We also ask why the MES varies from industry to industry and why factories may vary from MES within the same industry. Our immediate predecessor is White (1982) who examines a cross-section of US manufacturing firms and offers insights into the determinants of small firm activity relative to that of large firms.

That industries differ in their MES can be explained by a number of technological and nontechnological variables. White (1982) suggests the capital-labor ratio, the value added to output ratio (as a measure of vertical integration), the degree of fixed transactions costs, advertising, uncertainty and whether the firm is in a basic industry. Firms at sizes deviating from MES indicate the existence of a variety of barriers-to-entry, government policy interven- tion, the existence of geographically separate markets, or monopolistic competition (White, 1982).

In examining the determinants of small firm presence (i.e., the determinants of MES and deviations from MES) we are able to take advantage of cross-section and time-series nature of our data. We distinguish industry-specific from economy-wide characteristics. The industry- specific features vary across industries and over time, and are intended to capture the relevant industry characteristics contributing to small firm presence. The economy-wide traits which vary over time but not across industries, set out the general economic environment. It is of interest to ask whether certain general economic environ- ments favor the presence of small or large firms.

We capture the presence of small firms using two alternative measures: the share of value added contributed by small firms (VASHARE) and the share of output contributed by small

firms (OUTSHARE). It is useful to think of each industry as composed of two firms: a large firm and a small firm. The first measure is then simply the small firm’s value added over the sum of small plus large firm value added. The second measure can be similarly computed with respect to output.

We argue that small firm presence depends on the industry’s capital intensity, degree of vertical integration, economies of scale in management, the relative productivity of small firms, uncertainty, the presence of the unregistered sector, various economy-wide features, and industry- specific fixed effects. We can now discuss each of our hypotheses on the determinants of small firm presence and the variables by which we opera- tionalize it.

(a) Capital intensity

The more capital intensive, the higher the MES and the less likely small firms will flourish (White, 1982). The value of capital to the employment ratio for the industry is employed as an indicator of barriers-to-entry in terms of scale and technology. Here we distinguish two such ratios and estimate separate equation’s: the fixed capital to employment ratio (KFIXEMP) and the invested capital to employment ratio (KINVEMP). The difference between them is that KINVEMP includes inventories. KFIXEMP better captures the technological aspect, though KINVEMP has at least one part of it valued at that years prices.

(b) Vertical integration (VAOUT)

The more vertically integrated the industry the more production takes place within a firm and the smaller small firm presence. Value added to output ratio for the industry captures vertical integration (White, 1982).

(c) Economies of scale in management (WOREMP)

If an industry’s sales structure, regulatory environment, or technology is one in which there are significant economies of scale in management, small firm presence is reduced. As the ratio of workers to employees rises to one for the industry, the number of administrative and sales staff relative to production workers declines. Large firms may maintain a large sales and administrative staff to deal better with, for

SMALL FIRM “PRESENCE” 1381

example, government regulations. As a propor- tion of total employees, however, the size of the sales and administrative staff in large firms is less than the average for all firms.

(d) Smnll firms relative productivity (RELPROD)

This is a measure of output per employee of small firms vis-hi-vis the industry. RELPROD exceeding one suggests small firm productivity exceeds that of large firms and that small firms are overcoming the inherent cost disadvantages they face. This variable captures the nonmeasured inputs applied to production, as well as age and knowledge of productive techniques by firms.

(e) Uncertainty (UNCERT)

If output deviates significantly from its ex- pected value, small firms suffer relative to large firms as they are less able to absorb these shocks, for example, small firms are less able to hold inventories. We capture uncertainty in a very simple way by constructing an index - the absolute value of deviations of actual output from predicted output, where predicted output is retrieved from an autoregressive process with a lagged dependent variable.

(f) Presence of unregistered sector (UNREG)

Our data are on the registered or organized manufacturing industry. Recently the unregistered sector has received attention as an important component of economic life in India. We partially correct for spillover effects and choices in and out of the small firm and unregistered sectors by including the unregistered firms share of the value added of all firms in our equations.

(g) Economy-wide variables

These variables are included to capture elements of the general economic environment that might reasonably be related to small sector importance. Highfield and Smiley (1987) in their work on new business starts propose two scena- rios relevant to our study. In a robust, expansionary economy small firms might expand their output and value added relative to large firms if they are more responsive and flexible. On the other hand, in recessionary times small firms may

expand their output relative to large firms if they act to take up the slack left by large firm reductions in output. We use two variables to capture the macroeconomic climate, inflation (INFLATE) as captured by the rate of change of the implicit domestic product deflator and the real growth of gross national product (GNPGROW).

Our other economy-wide variable is presence of public sector firms (PUBLIC) as measured by the percentage of real GDP that comes from public sector enterprises.

(h) Dummy variables

Dummy variables that take on the value of one for a specific industry, zero otherwise, are included to capture industry specific but time irrele- vant effects. These include government policies favoring small over large industries (see the discussion in Little, Mazumdar and Page, 1987, pp. 26-29), the extent of industry specific import protection, credit availability, whether the market is local or national, and so on. Because a constant term is included, one industry dummy variable (IND20,21) in the series is excluded.

We estimate the model using the pooled cross- sectionally heteroskedastic and timewise (first order) autoregressive procedure in SHAZAM 6.2 (White et al., 1990) with constant rho, based on the model outlined in Kmenta (1986, pp. 616 625). Throughout we assume there is a common vector of slope coefficients across industries and over time, but allow for industry-specific fixed effects in the cross-section, employing separate dummy variables for each industry to allow for industry specific intercept terms. The inclusion of the economy-wide variables which vary over time eliminates the need for dummy variables for time period, as they account for the over-time variation.

There are several caveats which should be kept in mind. Neither of our capital measures ade- quately captures the age of the capital; some authors argue that smaller enterprises employ capital equipment which is older than that employed by larger firms (see the discussion in Page, 1984, on this point). In some industries, e.g. textiles, however, there is evidence that the opposite is true. In addition, the structure of the AS1 sampling, discussed above, may bias the result. To the extent these aspects vary systematically between small and large firms they will bias the result. In fact, since the data are aggregated, product lines may differ between small and large firms, which we cannot detect using our data. Finally, we are dealing here with registered firms


- if firms systematically avoid registration, for example, to avoid paying taxes, then a selection bias problem may arise.

4. RESULTS

We look at the determinants of the presence of small firms, where presence is measured alternatively by share of output (OUTSHARE) and the share of value added (VASHARE). We also examine the alternative results we get when using invested or fixed capital as our measure, and when we include or exclude industry specific dummy variables.

The analysis is conducted in several parts, presented in Tables 2-5. We first examine the results on the 22 industries. Data on the unregistered sector are only available on a subset of 15 industries, leaving 15 cross-sections to examine when we include UNREG in our analysis. Fin- ally, often the textile industries and engineering industries are examined independently. We do that here as well, effectively allowing the slope coefficients to be fixed among those industries, but to differ from other industries.

(a) Entire sample results (22 industries, 10 years)

We first center our attention on equations (1) and (2) in Table 2. Several trends clearly emerge when we examine the determinants of small firm presence where share of output (OUTSHARE) is the measure of importance. Small firm presence is relatively more important in industries that are less vertically integrated and where there are no significant economies of scale in management (VAOUT and WOREMP are both negative and significant). Public sector involve- ment (PUBLIC) and uncertainty in production (UNCERT) are not statistically significant (mar- ginally). Thus there is no evidence indicating that the bureaucratic environment and unexpected fluctuations in output play a role in determining output share. The relative productivity of small firms, however, is positive and significant. In industries where small firms are able to overcome inherent cost disadvantages, for example, by increasing nonmeasured inputs or knowledge of productive techniques, they have greater presence.

The macroeconomic climate which favors small firms is generally expansionary with higher growth (GROWGNP) and inflation (INFLATE) both positive and significant. Small firms benefit more from better times, their behavior is more procyclical than large firms. Perhaps they are

more flexible; larger firms are unable to adjust rapidly to changing economic conditions. Note that this is in contrast to Highfield and Smiley’s (1987) finding that firm entry was generally increased during slack periods. Furthermore, it contradicts Kashyap’s (1988) assertion that in India the “position” of small firms is counter- cyclical.

Capital intensity, i.e., technology and the existence of barriers-to-entry and further expansion, play a surprising role. The coefficient is significant and positive, indicating that the more capital intensive the industry, the greater the presence of small firms. What can explain this? First, note that our definition of small is employment based. It may be that small firms of this nature have a bias towards choosing technologies that are capital intensive. At least this is what our data indicate and this supports Kashyap’s assertion that small firms are not always relatively labor intensive and capital saving. What is more surprising is that in the regressions in which the industry-specific constant terms have been constrained to zero (not reproduced here), our other coefficient results hold up but the coefficient on capital intensity is negative and significant. If we interpret the constrained regression to be an aggregate relationship, then capital intensity may capture scale effects and can be interpreted as a barrier to small firm presence. Once the industry- specific fixed effects are accounted for, we see that small firms have increased presence where capital intensity is higher.

The industry dummy variables capture a host of other industry-specific, time-invariant effects. They are jointly significant, and it is clear these effects differ greatly between industries. More- over, when the industry dummy variables are constrained to be zero, the significance of our other coefficients are unchanged; of course, without the dummy variables the R-square falls drastically.

If instead share of value added (VASHARE) is taken to be the measure of the presence of small firms, the results are largely consistent (see equations 3 and 4 in Table 2). The notable exception is when the share of the public sector in the economy (PUBLIC) is positive and significant, and RELPROD is now not significant.

(b) Unregistered sector sample (15 industries, 10 years)

The data for the unregistered sector variable are only available on 15 industries - the four textile industries aggregated into a single industry and the other industries (i.e., excluding gas and

Tab

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2.

Reg

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resu

lts

(22

indu

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es)

Dep

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vari

able

=

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are

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Dep

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=

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are

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valu

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by

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rms

Equ

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n

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Equ

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Equ

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190

DF

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ror

190

DF

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cien

t er

ror

190

DF

co

effi

cien

t er

ror

190

DF

KF

IXE

MP

0.

3723

7 0.

1393

7 2.

6718

-

- -

0.23

172

0.11

039

2.09

91

-

KIN

VE

MP

-

0.14

392

0.09

407

1.52

98

- -

0.09

024

0.07

512

1.20

13

VA

OU

T

-0.3

5644

0.

0955

8 -3

.729

1 -0

.322

67

0.09

656

-3.3

415

-0.2

2562

0.

0846

6 -2

.664

8 -0

.217

41

0.08

804

-2.4

693

UN

CE

RT

0.

0205

7 0.

0210

3 0.

9779

0.

0199

2 0.

0216

5 0.

9197

-0

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23

0.01

925

-1.5

181

-0.0

2912

0.

0197

8 -1

.471

7 W

OR

EM

P

-0.2

5511

0.

1040

9 -2

.450

7 -0

.297

88

0.11

020

-2.7

032

-0.2

3518

0.

1048

4 2.

2432

-0

.248

18

0.10

986

-2.2

591

RE

LP

RO

D

0.00

680

0.00

401

1.69

38

0.00

612

0.00

395

1.54

85

-0.0

0032

0.

0016

1 -0

.201

6 -0

.000

34

0.00

172

-0.1

955

PU

BL

IC

0.00

063

0.00

106

0.59

24

0.00

018

0.00

120

0.14

75

0.00

170

0.00

095

1.78

93

0.00

152

0.00

106

1.43

65

INF

LA

TE

0.

0912

0 0.

0330

3 2.

7608

0.

1024

9 0.

0340

8 3.

0069

0.

0780

5 0.

0304

8 2.

5606

0.

0846

2 0.

0311

4 2.

7168

_

GR

OW

GN

P

0.00

109

0.00

048

2.24

99

0.00

122

0.00

049

2.44

89

0.00

107

0.00

045

2.33

78

0.00

117

0.00

046

2.52

48

IND

22

0.03

451

K

0.03

097

1.11

43

0.03

168

0.03

311

0.95

67

0.10

584

0.03

665

2.88

73

0.10

480

0.03

898

2.68

85

IND

23

-0.1

5769

0.

0223

3 -7

.059

1 -0

.158

09

0.02

315

-6.8

284

-0.1

0502

0.

0215

3 -4

.877

2 -0

.104

54

0.02

275

-4.5

937

t IN

D24

-0

.094

97

0.01

928

-4.9

253

-0.0

9424

0.

0200

0 -4

.711

3 -0

.037

31

0.02

222

-1.6

788

-0.0

3571

0.

0230

3 -1

.550

2 IN

D25

-0

.190

95

0.03

593

-5.3

145

-0.1

9270

0.

0367

0 -5

.250

8 -0

.100

88

0.02

834

-3.5

588

-0.1

0234

0.

0299

1 -3

.421

6 3

IND

26

0.15

714

0.02

307

6.81

07

0.15

204

0.02

287

6.64

75

0.19

037

0.01

923

9.89

61

0.18

757

0.01

994

9.40

25

&z

IND

27

0.26

997

0.02

375

11.3

640

0.26

294

0.02

420

10.8

620

0.28

216

0.02

178

12.9

540

0.27

959

0.02

281

12.2

540

IND

28

-0.0

7032

0.

0245

2 -2

.867

3 -0

.061

88

0.02

475

-2.4

994

-0.0

0095

0.

0234

4 0.

0407

0.

0073

3 0.

0242

2 0.

3024

IN

D29

0.

0356

2 0.

0194

7 1.

8293

0.

0293

6 0.

0203

7 1.

4408

0.

0775

6 0.

0194

2 3.

9926

0.

0738

5 0.

0201

7 3.

6600

z

IND

30

-0.2

9220

0.

0264

6 -1

1.04

10

-0.2

6865

0.

0264

2 -1

0.16

70

-0.1

1990

0.

0233

5 -5

.133

4 -0

.104

22

0.02

381

-4.3

763

g

IND

31

-0.2

5096

0.

0446

2 -5

.623

3 -0

.214

06

0.04

480

-4.7

779

-0.1

5333

0.

0345

0 -4

.444

3 -0

.127

59

0.03

509

-3.6

361

IND

32

-0.1

3629

0.

0220

0 -6

.193

6 -0

.129

12

0.02

248

-5.7

432

-0.0

2885

0.

0246

5 -1

.170

1 -0

.022

64

0.02

057

-0.8

802

6 IN

D33

-0

.241

68

0.03

382

-7.1

459

-0.2

1367

0.

0358

7 -5

.955

9 -0

.151

08

0.02

937

-5.1

438

-0.1

3180

0.

0313

4 -4

.204

9 IN

D34

0.

1065

6 0.

0203

9 5.

2255

0.

1007

2 0.

0213

2 4.

7224

0.

1050

5 0.

0191

3 5.

4912

0.

1035

9 0.

0203

6 5.

0868

IN

D35

-0

.118

92

0.02

344

-5.0

730

-0.1

2770

0.

0260

1 -4

.908

5 -0

.037

18

0.02

315

-1.6

061

-0.0

3927

0.

0255

8 -1

.534

7 IN

D36

-0

.176

19

0.02

400

-7.3

409

-0.1

9053

0.

0302

9 -6

.289

7 -0

.107

88

0.02

318

-4.6

522

-0.1

1352

0.

0281

8 -4

.027

1 IN

D37

-0

.208

09

0.02

228

-9.3

386

-0.2

1856

0.

0250

3 -8

.731

5 -0

.126

30

0.02

247

-5.6

199

-0.1

2998

0.

0250

2 -5

.194

9 IN

D38

0.

0457

3 0.

0268

9 1.

7008

0.

0406

3 0.

0283

2 1.

4345

0.

0661

4 0.

0280

8 2.

3551

0.

0661

4 0.

0298

3 2.

2164

IN

D41

0.

0263

9 0.

0728

5 0.

3622

0.

1147

6 0.

0613

4 1.

8707

0.

2238

7 0.

0777

0 2.

8811

0.

2810

5 0.

0720

6 3.

9000

IN

D42

0.

0306

8 0.

0469

7 0.

6532

0.

0831

6 0.

0419

9 1.

9804

0.

0513

7 0.

0641

5 0.

8008

0.

0826

1 0.

0630

6 1.

3099

IN

D74

1 0.

5537

2 0.

0331

5 16

.699

0 0.

5724

5 0.

0329

3 17

.382

0 0.

6663

8 0.

0310

9 21

.432

0 0.

6824

5 0.

0318

7 21

.413

0 IN

D97

0.

1032

6 0.

0309

4 3.

3370

0.

1011

4 0.

0313

2 3.

2288

0.

1008

3 0.

0277

6 3.

6321

0.

1012

6 0.

0288

3 3.

5114

C

ON

STA

NT

0.

5089

0 0.

0855

0 5.

9519

0.

5483

8 0.

0896

9 6.

1135

0.

3467

8 0.

0835

9 4.

1483

0.

3591

8 0.

8629

9 4.

1621

*Equ

atio

n (1

):

rho

= 0.

2334

9,

Bus

e R

sq

uare

=

0.96

21,

DW

=

1.52

63,

Run

s te

st

norm

al

stat

istic

=

-3.0

299,

lo

g of

th

e lik

elih

ood

func

tion

= 47

0.10

5.

tEqu

atio

n (2

):

rho

=

0.26

470,

B

use

R

squa

re

= 0.

9582

, D

W

= 1.

5057

, R

uns

test

no

rmal

st

atis

tic

= -2

.957

5,

log

of

the

likel

ihoo

d fu

nctio

n =

467.

030.

SE

quat

ion

(3):

rh

o =

0.

2106

7,

Bus

e R

sq

uare

=

0.96

98,

DW

=

1.67

73,

Run

s te

st

norm

al

stat

istic

=

-2.1

529,

lo

g of

th

e lik

elih

ood

func

tion

= 47

3.75

3.

t;

PEqu

atio

n (4

):

rho

=

0.24

428,

B

use

R

squa

re

= 0.

9640

, D

W

= 1.

7057

, R

uns

test

no

rmal

st

atis

tic

= -2

.423

4,

log

of

the

likel

ihoo

d fu

nctio

n =

471.

150.

8


steam, water works, cost storage, repair services). We rearranged our data set in order to incorporate UNREG and redo the analysis. These results are presented in Table 3. Our results vary from those above.

Where we are concerned with explaining OUTSHARE, again UNCERT and PUBLIC are insignificant and inflation is significant. PUBLIC is negative and significant in equation (4) with KINVEMP. In equations (3) and (4) capital intensity, vertical integration, economies of scale in management and growth are also insignificant.

The relative productivity of small firm employees (RELPROD) makes a difference - the more productive they are the greater small firm presence. The more small firms are able to overcome their inherent cost disadvantages, the more important they are. In addition, the unregistered sector does influence small firm presence: the larger the relative size of the unregistered sector, the larger small firm presence. If we think of the unregistered sector as generally small shops outside the reach of the Factories Act, some of this activity spills over to have an impact on the presence of small firms. UNREG is put in as a control variable, i.e., given an unregistered sector of a certain size, how do the other independent variables affect small firm presence.

Turning now to VASHARE determinants, there are two switches compared to our OUT- SHARE analysis at the .05 level of significance. UNREG is not a determinant of VAOUT and VASHARE regains its significance. Moreover, PUBLIC is negative and significant when KFIXEMP and not with KINVEMP. Moreover, while the dummy variables are jointly significant, few individuals coefficients differ from zero, providing some indication that the forces picked up by these variables are netting out.

How can we reconcile our results from the 22- industry sample and the 15industry sample. In our pooling, we are assuming the slope coefficients are the same for all industries at all times. This is a fairly strong assumption, and the variation we are witnessing may be its manifesta- tion.

(c) Textiles and engineering

Often the textiles and engineering industries, as the two more important groupings of manufacturing industries, are given separate treatment in the analysis of Indian industries. We do that here, effectively allowing the slope coefficients for textile and engineering industries to be different than other industries.

The textile industries consist of the following

four industries: cotton textiles; wool, synthetics and fiber textiles; jute, hemp and mesta textiles; and textile products. Table 4 contains the results from these regressions. We briefly summarize these results here. VAOUT, UNCERT, PUBLIC and RELPROD are not significant in the determination of OUTSHARE. The macro- climate conducive to increasing relative importance of small firms is expansionary. KFIXEMP and KINVEMP are again significant and positive. This corresponds well to the capital- intensive nature of small firm production in organized textile manufacturing (e.g., the power- loom sector). WOREMP is negative and significant. The industry dummy variables are jointly significant. The results coincide fairly well with the 22-industry case.

In explaining VASHARE for both measures of capital, only capital intensity is significant. Moreover, it is again positive (but not when industry dummies are omitted).

We turn now to the five engineering industries in Table 5: basic metal and alloys; metal products and parts, machinery and machine tools and parts; electrical machinery; and transport equipment. We find consistent results for OUTSHARE and VASHARE. Generally, we see here that the less vertically integrated the industry, the fewer economies of scale in management exist, and the more productive small firms are relative to large firms, the more important are small firms in the industry’s output and value added statistics. There is some indication that small firms do relatively better than large firms when the economy is robust.

5. CONCLUSION

The fortunes of small units of employment varied greatly during the decade 1973-74 to 1982-83. In some industries they grew relative to large firms, in some they shrunk. Moreover, the presence of small firms in the industry at the beginning of the period does not seem to be an indicator of small firm performance over the period.

We tackled the question of why in different industries small firms are responsible for different shares of output and value added. To do this we employed a pooled cross-section time series data on 22 industries over 10 years drawn largely from the Annual Survey of Industries (ASI). We used two measures of the relative importance of small firms: the share of total output produced by small firms and the share of total value added contributed by small firms. Our measure of small is a firm which employs lW9

Tab

le

3.

Reg

ress

ion

resu

lts

(15

indu

stri

es)

Dep

ende

nt

vari

able

=

sh

are

of

outp

ut

by

smal

l fi

rms

Dep

ende

nt

vari

able

=

sh

are

of

valu

e ad

ded

by

smal

l fi

rms

Equ

atio

n

1*

Equ

atio

n

2t

Equ

atio

n

3$

Equ

atio

n

48

Est

imat

ed

Sta

nda

rd

T-R

atio

E

stim

ated

S

tan

dard

T

-Rat

io

Est

imat

ed

Sta

nda

rd

T-R

atio

E

stim

ated

S

tan

dard

T

-Rat

io

Var

iabl

e co

effi

cien

t er

ror

126

DF

co

effi

cien

t er

ror

126

DF

co

effi

cien

t er

ror

126

DF

co

effi

cien

t er

ror

126

DF

KF

IXE

MP

0.

1514

6 0.

1308

2 -1

.157

8 -

- -0

.029

19

0.12

389

-0.2

356

- -

KIN

VE

MP

-

-0.1

1241

0.

0807

1 -1

.392

7 -

- -0

.101

84

0.07

559

-1.3

471

VA

OU

T

-0.0

4155

0.

1186

3 -0

.350

2 -0

.041

79

0.11

911

-0.3

509

-0.5

2649

0.

1416

7 -3

.716

3 -0

.474

94

0.14

013

-3.3

893

UN

CE

RT

0.

0218

3 0.

0235

5 0.

9268

0.

0189

1 0.

0237

7 0.

7953

-0

.005

34

0.02

462

-0.2

167

-0.0

0994

0.

0242

1 -0

.410

5 W

OR

EM

P

-0.0

4647

0.

0731

3 -0

.635

3 -0

.037

75

0.07

549

-0.5

000

0.07

277

0.09

323

0.78

05

0.11

451

0.09

418

1.21

59

RE

LP

RO

D

0.11

264

0.01

337

8.42

40

0.11

105

0.01

342

8.27

17

0.04

665

0.01

148

4.06

16

0.04

637

0.01

132

4.09

58

INF

LA

TE

0.

0540

3 0.

0285

9 1.

8893

0.

0547

3 0.

0287

4 1.

9041

0.

0809

9 0.

0292

8 2.

7659

0.

0770

5 0.

0288

6 2.

6695

P

UB

LIC

-0

.001

26

0.00

114

-1.1

076

-0.0

0151

0.

0011

8 -1

.277

1 -0

.001

62

0.00

111

- 1.

4503

-0

.002

14

0.00

113

-1.8

987

GR

OW

GN

P

0.00

042

0.00

039

1.06

14

0.00

042

0.00

040

1.03

48

0.00

040

0.00

042

0.94

88

0.00

032

0.00

042

0.77

78

UN

RE

G

0.18

588

0.09

069

2.04

94

0.18

947

0.09

080

2.08

65

0.09

643

0.09

494

1.01

56

0.11

410

0.09

299

1.22

70

IND

22

0.02

085

0.03

856

0.54

08

0.01

699

0.03

896

0.43

59

0.12

525

0.04

221

2.96

73

0.10

770

0.04

244

2.53

75

IND

TE

X

-0.1

6426

0.

0246

4 -6

.664

2 -0

.167

44

0.02

518

-6.6

475

-0.0

5977

0.

0264

6 -2

.258

6 -0

.074

78

0.02

674

-2.7

960

IND

27

0.17

438

0.05

880

2.96

56

0.17

236

0.05

905

2.91

86

0.28

484

0.06

051

4.70

67

0.26

769

0.05

980

4.47

58

IND

28

-0.0

1113

0.

0240

5 -0

.462

6 -0

.014

85

0.02

371

-0.6

263

0.11

407

0.02

584

4.41

47

0.11

022

0.02

540

4.33

95

IND

29

-0.0

3389

0.

0421

4 -0

.804

2 -0

.033

28

0.04

186

-0.7

948

0.04

695

0.04

361

1.07

64

0.03

695

0.04

265

0.86

61

IND

30

-0.0

3523

0.

0403

6 -0

.872

6 -0

.034

75

0.03

855

-0.9

013

0.01

739

0.04

013

0.43

33

0.04

006

0.03

822

1.04

79

IND

31

-0.0

0298

0.

0460

4 -0

.647

3 -0

.000

80

0.04

277

-0.0

188

0.03

323

0.04

285

0.77

53

0.06

514

0.03

954

1.64

72

IND

32

-0.0

8842

0.

0298

7 -2

.959

8 -0

.092

43

0.03

026

-3.0

543

0.04

935

0.03

230

1.52

76

0.04

090

0.03

235

1.26

39

IND

33

-0.0

1487

0.

0462

3 -0

.321

6 -0

.009

81

0.04

534

-0.2

163

0.01

284

0.04

544

0.28

26

0.04

327

0.04

444

0.97

34

IND

34

0.10

092

0.00

316

3.19

19

0.10

268

0.03

162

3.24

73

0.16

294

0.03

190

5.10

68

0.15

778

0.03

123

5.05

22

IND

35

-0.0

1834

0.

0241

8 -0

.758

3 -0

.011

48

0.02

523

-0.4

550

0.10

066

0.02

504

4.01

95

0.10

922

0.02

523

4.32

87

IND

36

-0.0

5858

0.

0292

1 -2

.005

4 -0

.044

55

0.03

268

- 1.

3627

0.

0361

5 0.

0277

9 1.

3007

0.

0555

4 0.

0304

1 1.

8260

IN

D37

-0

.120

06

0.02

522

-4.7

597

-0.1

1241

0.

0262

3 -4

.284

5 -0

.002

49

0.02

514

-0.0

988

0.00

395

0.02

516

0.15

68

IND

38

-0.0

0346

0.

0527

6 -0

.655

7 -0

.002

88

0.05

290

-0.0

544

0.13

105

0.05

643

2.32

20

0.12

100

0.05

549

2.18

03

CO

NS

TA

NT

0.

1543

6 0.

0825

0 1.

8813

0.

1586

9 0.

0828

9 1.

9143

0.

1122

8 0.

0984

8 1.

1400

0.

0888

4 0.

0980

6 0.

9059

*Equ

atio

n

(1):

rh

o =

0.

4302

0,

Bu

se R

squ

are

=

0.93

95,

DW

=

1.

7748

, R

un

s te

st n

orm

al s

tati

stic

=

-0

.768

1,

log

of

the

lik

elih

ood

fun

ctio

n

=

392.

569.

tE

quat

ion

(2

):

rho

=

0.42

884,

B

use

R

squ

are

=

0.93

90,

DW

=

1.

7806

, R

un

s te

st n

orm

al s

tati

stic

=

-0.9

327,

lo

g of

th

e li

kel

ihoo

d fu

nct

ion

=

39

2.54

3.

$Equ

atio

n

(3):

rh

o =

0.

2445

4,

Bu

se R

squ

are

=

0.94

73,

DW

=

1.

9692

, R

un

s te

st n

orm

al

stat

isti

c =

-0

.819

3,

log

of

the

lik

elih

ood

fun

ctio

n

=

382.

336.

P

Equ

atio

n

(4):

rh

o =

0.

2463

6,

Bu

se R

squ

are

=

0.94

85,

DW

=

1.

9722

, R

un

s te

st n

orm

al s

tati

stic

=

-0.4

895,

lo

g of

th

e li

kel

ihoo

d fu

nct

ion

=

38

3.37

2.

Tab

le

4.

Reg

ress

ion

re

sult

s -

text

ile

ind

ust

ries

Dep

ende

nt

vari

able

=

shar

e of

ou

tput

by

sm

all

firm

s D

epen

dent

va

riab

le

= sh

are

of

valu

e ad

ded

by

smal

l fi

rms

Equ

atio

n 1*

E

quat

ion

27

Equ

atio

n 3$

E

quat

ion

49

Est

imat

ed

Stan

dard

T

-Rat

io

Est

imat

ed

Stan

dard

T

-Rat

io

Est

imat

ed

Stan

dard

T

-Rat

io

Est

imat

ed

Stan

dard

T

-Rat

io

Var

iabl

e co

effi

cien

t er

ror

’ 28

D

F co

effi

cien

t er

ror

28

DF

coef

fici

ent

erro

r 28

D

F co

effi

cien

t er

ror

28

DF

KFI

XE

MP

1.51

360

0.72

550

-2.0

863

- 1.

5136

0 0.

7255

0 2.

0863

-

- K

INV

EM

P 0.

8862

1 0.

2865

7 3.

0925

-

0.88

621

0.28

657

3.09

25

VA

OU

T

0.05

706

0.12

573

0.45

38

0.07

968

0.11

804

0.67

51

0.05

706

0.12

573

0.45

38

0.07

968

0.11

804

0.67

51

F U

NC

ER

T

-0.0

2473

0.

0314

9 -0

.785

2 -0

.025

19

0.02

985

-0.8

436

-0.0

2473

0.

0314

9 -0

.785

2 -0

.025

19

0.02

985

-0.8

436

WO

RE

MP

-1.6

4190

0.

6192

3 -2

.651

5 -

1.78

040

0.60

269

-2.9

541

- 1.

6419

0 0.

6192

3 -2

.651

5 -1

.780

40

0.60

269

-2.9

541

g

RE

LPR

OD

-0

.002

05

0.00

161

-1.2

709

-0.0

0215

0.

0015

3 -1

.402

9 -0

.002

05

0.00

161

-1.2

709

-0.0

0215

0.

0015

3 -1

.402

9

INFL

AT

E

0.09

211

0.05

815

1.58

39

0.08

667

0.05

558

1.55

93

0.09

211

0.05

815

1.58

39

0.08

667

0.05

558

1.55

93

$

PUB

LIC

-0

.000

42

0.00

176

-0.2

394

0.00

037

0.00

158

0.23

51

-0.0

0042

0.

0017

6 -0

.239

4 0.

0003

7 0.

0015

8 0.

2351

s

GR

OW

GN

P 0.

0018

3 0.

0009

2 1.

9730

0.

0019

1 0.

0009

0 2.

1150

0.

0018

3 0.

0009

2 1.

9730

0.

0019

1 o.

OtI

O90

2.

1150

z

IND

24

-0.0

2509

0.

0375

5 -0

.668

0 -0

.038

51

0.03

460

-1.1

127

-0.0

2509

0.

0375

5 -0

.668

0 -0

.038

51

0.03

460

-1.1

127

IND

25

0.03

523

0.02

690

1.30

95

0.03

768

0.02

353

1.60

11

0.03

523

0.02

690

1.30

95

0.03

768

0.02

353

1.60

11

IND

26

0.27

706

0.04

321

6.41

12

0.25

214

0.03

949

6.38

36

0.27

706

0.04

321

6.41

12

0.25

214

0.03

949

6.38

36

CO

NST

AN

T

1.44

700

0.53

801

2.68

95

1.51

920

0.52

479

2.89

49

1.44

700

0.53

801

2.68

95

1.51

920

0.52

479

2.89

49

*Equ

atio

n (1

):

rho

= 0.

3470

8,

Bus

e R

sq

uare

=

0.97

65,

DW

=

1.68

08,

Run

s te

st

norm

al

stat

istic

=

1.68

32,

log

of

the

likel

ihoo

d fu

nctio

n =

111.

310.

T

Equ

atio

n (2

):

rho

= 0.

2731

1,

Bus

e R

sq

uare

=

0.98

34,

DW

=

1.68

66,

Run

s te

st

norm

al

stat

istic

=

1.03

58,

log

of

the

likel

ihoo

d fu

nctio

n =

113.

093.

$E

quat

ion

(3):

rh

o =

0.20

805,

B

use

R

squa

re

= 0.

9933

, D

W

= 1.

6795

, R

uns

test

no

rmal

st

atis

tic

= 0.

6407

, lo

g of

th

e lik

elih

ood

func

tion

= 12

6.74

4.

DE

quat

ion

(4):

rh

o =

0.19

075,

B

use

R

squa

re

= 0.

9931

, D

W

= 1.

6752

, R

uns

test

no

rmal

st

atis

tic

= 0.

0161

, lo

g of

th

e lik

elih

ood

func

tion

= 12

7.30

5.

Tab

le

5.

Reg

ress

ion

resu

lts

- en

gine

erin

g in

dust

ries

Dep

ende

nt

vari

able

=

shar

e of

ou

tput

by

sm

all

firm

s D

epen

dent

va

riab

le

= sh

are

of

valu

e ad

ded

by

smal

l fi

rms

Equ

atio

n l*

E

stim

ated

St

anda

rd

Var

iabl

e co

effi

cien

t er

ror

KFI

XE

MP

-0.0

4865

0.

2752

4 K

INV

EM

P -

VA

OU

T

-0.4

3499

0.

2219

3 U

NC

ER

T

0.00

599

0.05

959

WO

RE

MP

-0.1

7496

0.

1034

5 R

EL

PRO

D

0.05

373

0.02

013

INFL

AT

E

0.09

501

0.04

511

PUB

LIC

-0

.001

39

0.00

149

GR

OW

GN

P o.

OcQ

75

0.00

060

IND

34

0.26

328

0.06

057

IND

35

0.06

742

0.05

767

IND

36

0.00

956

0.05

039

IND

37

-0.3

0096

0.

0619

4 C

ON

STA

NT

0.

3364

8 0.

1170

4

T-R

atio

37

D

F

-0.1

767

-

-1.9

600

0.10

05

- 1.

6912

2.

6691

2.

1064

-0

.930

2 1.

2430

4.

3466

1.

1690

0.

1896

-0

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7 2.

8748

Equ

atio

n 2t

E

stim

ated

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anda

rd

coef

fici

ent

erro

r

-

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0.

1477

2 -0

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74

0.21

302

-0.0

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0.

0569

4 -0

.179

34

0.10

398

0.05

051

0.02

027

0.10

669

0.04

342

-0.0

0187

0.

0016

4 0.

0008

4 0.

0005

9 0.

2410

0 0.

0468

1 0.

0490

4 0.

0409

6 0.

0003

5 0.

0276

7 -0

.049

49

0.04

305

0.37

459

0.11

816

T-R

atio

37

D

F

-

-0.7

331

-1.7

873

-0.2

707

- 1.

7248

2.

4912

2.

4568

-1

.134

3 1.

4108

5.

1474

1.

1971

0.

0128

-1

.149

4 3.

1702

Equ

atio

n 3$

E

stim

ated

St

anda

rd

coef

fici

ent

erro

r

0.24

390

0.25

603

-0.6

9066

0.

2135

5 -0

.017

34

0.04

604

-0.1

2362

0.

1065

8 0.

0390

4 0.

0191

5 0.

0700

0 0.

0441

2 -0

.000

36

0.00

142

0.00

079

o.oo

o59

0.27

091

0.05

643

0.15

776

0.05

320

0.07

504

0.04

688

0.06

574

0.05

714

0.21

387

0.11

182

T-R

atio

37

D

F

0.95

26

-

-3.2

341

-0.3

766

-1.1

599

2.03

82

1.58

66

-0.2

546

1.32

30

4.80

05

2.96

50

1.60

05

1.15

05

1.91

26

Equ

atio

n 40

E

stim

ated

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anda

rd

coef

fici

ent

erro

r

- -

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8006

0.

1430

1 -0

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93

0.20

511

-0.0

4992

0.

0419

7 -0

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10

0.11

482

0.03

730

0.01

854

0.09

622

0.04

299

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0133

0.

0016

4 o.

ooo9

9 0.

0005

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1955

9 0.

0451

5 0.

0910

9 0.

0384

9 0.

0211

1 0.

0260

3 -0

.004

92

0.04

054

0.30

596

0.11

865

T-R

atio

37

D

F

2

-0.5

598

-2.7

202

t

-1.1

891

z

-1.0

983

2.01

15

!z

2.23

80

-0.8

122

1.69

18

4.33

12

K

2.36

60

0.81

11

-0.1

214

2.57

87

*Equ

atio

n (1

):

rho

=

0.19

579,

B

use

R

squa

re

= 0.

9715

, D

W

= 1.

8843

, R

uns

test

no

rmal

st

atis

tic

= -0

.475

7,

log

of

the

likel

ihoo

d fu

nctio

n =

148.

116.

tE

quat

ion

(2):

rh

o =

0.

2162

9,

Bus

e R

sq

uare

=

0.96

94,

DW

=

1.89

43,

Run

s te

st

norm

al

stat

istic

=

-0.6

925,

lo

g of

th

e lik

elih

ood

func

tion

= 14

8.21

0.

SEqu

atio

n (3

):

rho

=

0.17

670,

B

use

R

squa

re

= 0.

9657

, D

W

= 1.

5980

, R

uns

test

no

rmal

st

atis

tic

= -1

.714

6,

log

of

the

likel

ihoo

d fu

nctio

n =

152.

101.

O

Equ

atio

n (4

):

rho

=

0.22

982,

B

use

R

squa

re

= 0.

9602

, D

W

= 1.

6528

, R

uns

test

no

rmal

st

atis

tic

= -2

.286

2,

log

of

the

likel

ihoo

d fu

nctio

n =

152.

030.


people and uses power or 2&99 people and does not use power.

Generally we find small firm presence is greater in an expansionary macroeconomic climate, and lower in industries that are vertically integrated, where there are economies of scale in management and output uncertainty. Some variation in these results are introduced when we look at a subsample of 15 industries. Here we find that economies of scale in management and uncertainty do not matter, while the presence of a significant unregistered sector in the industry and small firms relative productivity adds to small firm presence. In addition, we look specifi- cally at the textile and engineering sectors, though these results must be regarded with caution as they are based only four and five cross- sections, respectively.

Most surprisingly we are able to detect differ-

ences in the overall manufacturing industry relationship between capital intensity and presence (negative) and the positive relationship that we observe when controlling for industry specific fixed effects. Small firms, where size is defined by number of employees, are relatively capital intensive.

By the use of a fixed effects model we are able to hold constant industry variations in government policy. Our results then indicate how changes in government policy will effect the relative importance of small firms. For example, policies which encourage rising aggregate demand or that breakup vertically integrated firms will increase the presence of small firms. As the government of India has often given vocal support to the importance of small firms, this is, of course. of some interest.

NOTES

1. Access to factory-level data is restricted under the the size of the factory, not the size of the firm. To the Collection of Statistics Act, though data on the factory extent multiplant firms have units that fall into the and census sectors are published in a more aggregated sample sector, our results will be biased. For the rest of form. There are some inconsistencies between the the paper we will use the terms plant, factory and firms, published versions of the census and factory sectors. In interchangeably. particular, in the period we consider there are seven data points in which the census sector number exceeded 3. The published material contains 2j industries. the factory sector number. In these cases we treated Here electricity is omitted from our sample as there are one of the sector’s point as a missing observation. no small units.

2. The aggregate industry data we use are based on

REFERENCES

Acs, Zoltan J., and David Audretsch, “Small-firm entry in U.S. Manufacturing,” Economica, Vol. 56 (May 1989), pp. 255-265.

Anderson, Dennis, “Small industry in developing countries: A discussion of issues,” World Develop- menf, Vol. 10, No. 11 (1982), pp. 913-948.

Chandhok, H. L., and The Policy Group. Indiu Database: the Economy (New Delhi: Living Media India, 1990).

Ghosh, Arun, “Government policies concerning small scale industries - An appraisal,” in K. B. Suri (Ed.), Small Scale Enterprises in Industrial Develop- ment: The Indian Experience (New Delhi: Sage. 1988), pp. 299-325.

Goldar, Bishwanath, “Relative Efficiency of Modern Small Scale Industries in India,” in K. B. Suri (Ed.), Small Scale Enlerprises in Industrial Developmenr: The Indian Experience (New Delhi: Sage, 1988). pp. 45-117.

Government of India, National Accounts Slatistics: Sources and Methods (New Delhi: Central Statistical Organisation, Department of Statistics, Ministry of Planning, 1989).

Government of India, Annual Survey of Indusfries. Volume I, Summary Results for the Factory Sector; Volume II, Summary Results for the Census Sector. General Review for the Census Sector (New Delhi: Central Statistical Organisation, Department of Statistics, Ministry of Planning, various years).

Highfield, Richard and Robert Smiley. “New business starts and economic activity: An empirical investiga- tion,” International Journal of Industrial Organiza- lion. Vol. 5, No. 1 (1987), pp. 51-66.

Kashyap, S. P., “Growth of small-size enterprises in India: Its nature and content,” World Development. Vol. 16, No. 6 (1988), pp. 667-681.

Kmenta, J., Elements of Econometrics, 2nd edition. (New York: Macmillan, 1986).

Little, Ian M. D., Dipak Mazumdar, and John M. Page, Jr., Smull Manufacturing Enterprises: A Com- parative Analysis of India and Other Economies (New York: Oxford University Press, 1987).

Page, John M., “Firm size and technical efficiency,” Journal of Development Economics. Vol. 16, No. l-2 (1984). pp. 129-152.

Ramachandran, G., “Data base for small scale indus-

SMALL FIRM “PRESENCE” 1389

tries: An appraisal,” in K. B. Suri (Ed.), Small Scale White, Lawrence _I., “The determinants of the relative Enterprises in Industrial Development: The Indian importance of small business,” Review of Economics Experience (New Delhi: Sage, 1988). and Statistics, Vol. 64, No. 1 (1982), pp. 42-49.

Suri, K. B., “Introduction,” m K. B. Suri (Ed.), Small White, K. J., Wong, S. D., Whistler, D. and Haun, Scale Enterprises in Industrial Development: The S. A., SHAZAM User’s Reference Manual Version Indian Experience (New Delhi: Sage, 1988a). 6.2 (New York: McGraw-Hill, 1990)

Suri. K. B. (Ed.), Small Scale Enterprises in Industrial Development: The Indian Experience (New Delhi: Sage Publications, 1988b).

small firm “presence” in indian manufacturing

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