SME Innovation in the Malaysian Manufacturing Sector

Abstract

This paper examines the determinants of innovation amongst small and medium enterprises in the Malaysian manufacturing sector using firm-level data. For small-sized firms, younger firms are more likely to innovate compared to older firms. However, for medium-sized and large-sized firms, older firms are more likely to innovate. The extent of foreign ownership is not an important determinant of innovation. Small-sized firms with more employees are more likely to innovate. Medium-sized firms that produce for domestic market tend to be more innovative. In terms of ownership structure, medium-sized firms that are public limited companies are less likely to innovate. The relationship between technological characteristics of industry and firms' likelihood to innovate appear to be complex. Higher market concentration is associated with higher probability to innovate for medium-sized firms.

Full text

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SME Innovation in the Malaysian Manufacturing Sector
Cassey LeeLee Chew Ging
The University of Nottingham Malaysia CampusThe University of Nottingham Malaysia Campus
Abstract
This paper examines the determinants of innovation amongst small and medium enterprises
in the Malaysian manufacturing sector using firm-level data. For small-sized firms, younger
firms are more likely to innovate compared to older firms. However, for medium-sized and
large-sized firms, older firms are more likely to innovate. The extent of foreign ownership is
not an important determinant of innovation. Small-sized firms with more employees are more
likely to innovate. Medium-sized firms that produce for domestic market tend to be more
innovative. In terms of ownership structure, medium-sized firms that are public limited
companies are less likely to innovate. The relationship between technological characteristics
of industry and firms’ likelihood to innovate appear to be complex. Higher market
concentration is associated with higher probability to innovate for medium-sized firms.
Citation: Lee, Cassey and Lee Chew Ging, (2007) "SME Innovation in the Malaysian Manufacturing Sector." Economics
Bulletin, Vol. 12, No. 30 pp. 1-12
Submitted: September 10, 2007. Accepted: October 26, 2007.
URL: http://economicsbulletin.vanderbilt.edu/2007/volume12/EB-07L60001A.pdf

Page 2

1. Introduction
Small and Medium Enterprises (SMEs) have a significant presence in the Malaysian
manufacturing sector. Around 90 percent of the total establishments in the manufacturing
sector are SMEs1. These firms account for about 29 percent and 33 percent of the total
output and employment in the manufacturing sector, respectively. Acknowledging the
importance of SMEs to the manufacturing sector, the Malaysian government has made
some effort to promote the development of SMEs in the sector. In the recent Eighth
Malaysia Plan (2001-2005) about 42 percent (or RM1.09 billion) of the development
allocation for industrial development was allocated for SME development.

The government’s emphasis has been on developing more resilient SMEs via the
transformation from labor intensive operations to ones based on capital, knowledge and
technology, including the ability to innovate, design and develop new products and
processes2. Some of the government programs that have been implemented to bring about
this transformation include the following:

? The Industrial Technical Assistance Fund (ITAF) to provide incentives for
product and process improvement.
? The Technology Acquisition Fund.
? ICT Grants where a matching grant of up to RM500,000 per company was
provided for purchase of hardware and the accompanying software.

Such emphasis on technology upgrading of SMEs is not unique to Malaysia. The role of
SMEs in national innovation systems and the importance of technological change and
innovation in creating opportunities for SMEs are explicitly recognized in the Bologna
Charter on SME Policies (which was adopted on 15 June 2000). More specifically, the
Bologna Charter calls for governments to consider implementing SME policies that will:

? improve SMEs’ ability to manage innovation,
? reduce financial barriers to innovation in SMEs, and
? improve SMEs’ access to national and global innovation networks.

Despite the importance of innovation in SMEs (or SME innovation, in short), there is
relatively little empirical research on the subject, particularly in developing countries.
The paper hopes to partially address this gap by analyzing the experience of Malaysia.
This is made possible by the implementation of national innovation surveys in Malaysia
the recent years.

This paper begins with a brief review of the existing literature on SME innovation in
Section 2. This is followed by a general discussion of SME innovation in Malaysia in

1 Figure based on 2001 census data and the official definition of SME. The official
definition of SME is summarized and compared with selected definitions used by other
countries and organizations in Appendix A.
2 Quoted from the Eighth Malaysia Plan, p.262.

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Section 3. In Section 4, we examine the determinants of SME innovation in a more
rigorous fashion via firm-level econometric analysis. Section 5 concludes.

2. SME Innovation: A Brief Review of the Literature
This section briefly reviews some of the recent literature related to SME innovation. The
literature on SME-related innovation is very diverse. Each paper tends to focus on
different aspects of innovation, within the context of different sectors, and in specific
countries. This is partly due to the significant variations in the nature of data that are used
in the different studies.

Acs and Audretsch (1998) uses four-digit industry-level data to empirically examine
innovation in small and large firms in the US. The innovation data in their study was
constructed from innovations identified in technology, engineering and trade journals.
The authors find that R&D is positively related to innovation and negatively related to
market concentration. However, such innovation activities of small and large firms
respond to different technological and economic environments.

Tether (1998) uses data on innovation from the United Kingdom that are based on
information gathered from the Queen’s Award applications. The author suggests that the
value of innovation (impact of innovation on sales) should be an important measure of
innovativeness. He finds that the value of innovation is higher for large firms compared
to small firms.

Clarysse and van Dierdonck (1998) looks at the Flemish experience in the textile and
chemical industries. They are particularly interested in the impact of innovation
strategies. Overall, the authors find that innovative SMEs do not necessarily create more
employment. However, the picture is more complex if innovation strategies are further
classified in different categories e.g. ‘Porterian’ (market focus), Schumpeterian (creative-
destructors) and resource-based (core competencies). For example, only Schumpeterian is
related to employment creation.

Motohashi (2001) uses plant-level data from Japan to evaluate SME innovation policy in
Japan. His analysis suggests that government policies have impact on new firms as well
as on existing firms. In particular, participation in SME promotion schemes has positive
impact on SMEs’ sales growth rates. Due to the volatility of small firms, he also
suggests that government policy should be targeted towards these firms.

Harris et al. (2003) investigates firm-level innovation using panel probit estimation based
on the data taken from the Confidentialised Unit Record File for the Business
Longitudinal Survey of Australian firms. The authors find that larger firms are more
likely to innovate. Past profitability, export activity and whether firm is a “start-up” have
no effect on the likelihood of innovation. They also show that inter-firm network and the
presence of business planning have a positive effect on the likelihood of innovation.

Mole et al. (2001) examines barriers to the adoption and deployment of technology
within electronic and engineering SMEs in the UK manufacturing sector. The authors
find that competition is an important factor in prompting firms to adopt new technology.

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They also find evidence that larger firms are more likely to adopt new technologies. In
terms of sources of information for innovation – trade journal is an important source.

Romijin and Albu (2001) attempts to explain the innovativeness of small high-technology
firms in the United Kingdom. They emphasize on the role external factors such as
scientific institutions that can foster and nurture technology firms. Government
innovation policy should thus focus on promoting linkages between SMEs and scientific
institutions.

Masurel et al. (2003) takes an entirely different research route in SME innovation by
studying that the perception of SME entrepreneurs on innovation. Entrepreneurs tend to
believe that they themselves are the most critical factor for innovation. Other important
factors include product advantages, marketing activities and pre-development – all which
are within the control of firms and entrepreneurs.

Rouvinen (2002) studies the characteristics of product and process innovators among the
Finnish manufacturing firms. He uses probit method to estimate process innovation
equation and product innovation equation with the data of the Community Innovation
Survey conducted by Statistics Finland. He finds that the ability to benefit from inward
spillover has a symmetric effect on these two types of innovations. Cooperation with non-
academic is significant in both equations, but cooperation with universities and non-profit
research organizations is only significant in product innovation equation.

Rolfo and Calabrese (2003) examines evidence on the impact of Italian aid programs that
are aimed at fostering technological innovation in SMEs. The overall finding on the
impact of industrial policy on SME innovation appears to be a negative one. This is
attributed to policy clashes with the capacity of SMEs to absorb innovation due to lack of
technical structures (technical office, design department, R&D laboratory) and staff
capable of interacting with research bodies.

3. SME Innovation in Malaysia: Some Recent Evidence from Survey Data
Information on SME innovation at the firm level in Malaysia is only available recently.
The most important source of information is the National Survey of Innovation (NSI)
carried out by the Ministry of Science, Technology and Innovation, Malaysia (MOSTI).
This section reviews the state of innovation across different classes of firm size as
observed in NSI.

MOSTI has been carrying out innovation surveys at the national-level in Malaysia on a
bi-annual or tri-annual basis since the mid-1990s. The methodology for these surveys is
based primarily on the approach adopted in the Community Innovation Survey (CIS) that
have been conducted in Europe since the early 1990s. The first NSI survey (NSI-1) was
conducted in 1995 (covering the period 1990-1994), the second (NSI-2) in 2000
(covering 1997-1999) and the latest (NSI-3) in 2002/2003 (covering the period 2000-
2001). The definition of ‘innovation’ that is used in these surveys comes from the
OECD’s Oslo Manual as well as their variations in the CIS surveys. In these surveys, two
types of innovation are identified, namely, product innovation and process innovation.

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For the purpose of comparison with other countries, we adopt the EU definition of SMEs,
namely:

? Small-sized firm is defined as a firm having less than 50 employees.
? Medium-sized firm is defined as a firm having at least 50 employees but less than
250 employees.
? Large-sized firm is defined as a firm having at least 250 employees.

Table 1 summarizes the number of innovating and non-innovating firms in the NSI-2 and
NSI-3. Horizontal Percentage provides the percentages of innovating and non-
innovating firms for each class of firms and total. Vertical Percentage describes, among
the innovating firms, how many percentage of them belongs to each class of firms. We
also supply similar information for non-innovating firms. Generally, we find the
following patterns:

? The proportion of innovating small-sized firms is smaller than medium-sized
firms.
? The proportion of innovating medium-sized firms is smaller than large-sized
firms.

The above results hold despite differences in samples in both surveys; the NSI-3 has
more small-sized firms in the sample compared to NSI-2.

[Insert Table 1 here]

Table 2 summarizes the number of innovating and non-innovating firms by industry.
Clearly, the distribution of firms across different firm size and industries are fairly
uneven. This is a function of both the survey response rates in each categories as well as
firm size distribution across different industries.

[Insert Table 2 here]

There are significant differences in the pattern of innovation rates between firms in each
class of firm size across different industries. This is shown in Figure I.

[Insert Figure I here]

4. Econometric Analysis of the Determinants of Innovation
4.1 Data and Methodology
In this section, we carry out empirical analysis using the NSI-3 data to find out whether
there are differences between small, medium and large-sized firms. However, due to
unavailability of some observations, only a subset of the NSI-3 data is used. Market
concentration data are computed from data collected by the Ministry of Domestic Trade
and Consumer Affairs, Malaysia3.


3 Fore more details, see Lee (2004).

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As discussed earlier in this study we are interested in explaining why a firm innovates.
The dependent variable used in this study is binary. Let us defined our dependent variable
as

1iffirminnovates
0otherwise
?

The model essentially describes the probability that
class of binary response models of the form

P(1
=
| Xt)=F(Xt’β β β β)=F(Zt)

where F is a strictly increasing function taking on values strictly between zero and one, Xt
is the column vector of full set of explanatory variables associated with firm t, in which
one of them takes a value equals to one and β β β β is the column vector of all parameters. The
variables to be included as explanatory variables will be discussed in details later. We use
both the cumulative distribution function of the standard normal and logistic distribution
function as the function F. The cumulative distribution function of the standard normal
leads to the probit model. The logistic distribution function results in the logit model.
Additional discussion of these models can be found in monograph by Maddala (1983)
and two surveys by Amemiya (1981, 1984).

The significance of each explanatory variable would be tested by the usual t-test. But, the
sample size should be sufficient large as it relies on the asymptotic expressions for the
variances. Therefore, the t-test follows approximately the standard normal distribution.
Two goodness of fit measures: McFadden R2 (1974) and perfectly correctly predicted are
used to assess the accuracy with which the model approximates the data set. Detailed
discussion of these two measures can be found in Verbeek (2004) and Wooldridge
(2003).

We postulate that the probability of innovating is influenced by the following factors:

a) Firm characteristics such as age of firm (AGE), extent of local ownership (OWN)
measured by the percentage share of local equity ownership, firm size measured
by total employees (SIZE), and the percentage of sales derived from exports
(EXPORT).
b) Type of ownership structure – measured by dummies to represent partnership
(PARTNER), private limited (PRIVATE) and public limited (PUBLIC). Sole-
proprietorship is used as the reference category.
c) Industry characteristics such as market concentration and technological levels of
industry.6 Market concentration is measured by Herfindahl-Hirschman Index
(HHI). Technological levels of industry are measured by dummies to represent
medium-low technology (MEDLOW), medium-high technology (MEDHIGH) and
high technology (HIGH) where low technology level acts as the reference
category.

t
t
Y
?
=?
(1)
1
=
tY . In this study, we consider a
tY (2)

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The following specification of model is estimated by probit and logit models:

Zt=Xt’β β β β
=β0+β1AGEt+β2OWNt+β3SIZEt+β4EXPORTt+β5PARTNERt+β6PRIVATEt
+β7PUBLICt+β8MEDLOWt+β9MEDHIGHt+β10HIGHt+β11HHIt+εt (4)

4.2 Results
The maximum likelihood results are summarized in Table 3. The interpretations are
provided below:

[Insert Table 3 here]

Age of Firms
For small-sized firms, younger firms are more likely to innovate compared to older firms.
However, for medium-sized and large-sized firms, older firms are more likely to
innovate. This implies market entry related innovation may be important only for small
firms.

Extent of Local Ownership
The extent of local vs. foreign ownership is not an important determinant of innovation.
This applies for SMEs of all categories.

Firm Size
The firm size is statistically significant at the 5 % level only for small-sized firms. The
positive sign for the coefficient of this variable indicates that small-sized firms with more
employees are more likely to innovate compared to small-sized firms with less
employees.

Share of Export in Sales
Interestingly, the variable representing percentage share of export in sales is only
statistically significant at the 5 % level for medium-sized firms. The negative sign of the
coefficient indicates that medium-sized firms that produce for domestic market tend to be
more innovative than medium-sized firms producing for export markets.

Types of Ownership
Overall, the results indicate that ownership structure matters in innovation only in the
case of medium-sized firms. Medium-sized firms that are public limited companies are
less likely to innovate compared to sole-proprietorship medium-sized firms. The dummy
variable representing partnership for the case of large-sized firms has been excluded
because in our sample none of the large-sized firms has the ownership structure which is
partnership.

Types of Industry by Technological Characteristics
The results on the relationship between technological characteristics of industry and
firms’ likelihood to innovate appear to be complex. Small and medium-sized firms in
low-technology industry are more likely to innovate compared to their counterparts in
medium-high technology industry. Interestingly, large-sized firms in medium-low

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technology industry are more likely to innovate compared to their counterparts in low
technology industry but small-sized firms in the same technology industry are less likely.
Small-sized firms in high technology industry are more likely to innovate.

Market Concentration
The market concentration variable is only statistically significant at the 5 % level for
medium-sized firms. The positive sign for the coefficient indicates that higher market
concentration is associated with higher probability to innovate for medium-sized firms.

5. Conclusion
In recent years, governments have become interested to implement SME policies that are
aimed at improving SMEs’ ability to innovation. Despite such initiatives, there is a lack
of rigorous empirical work on the determinants of innovations amongst SMEs,
particularly in the developing countries. This study attempts to address the lack of
empirical work on SME innovation in developing countries by undertaking an
econometric analysis of the Malaysian experience using firm-level data.

This study indicates that the innovation amongst SMEs in the Malaysian manufacturing
sector is very complex subject matter. In terms of firm size, younger small-sized firms
are more likely to innovate compared to older small-sized firms. In contrast, for medium-
sized and large-sized firms, older firms are more likely to innovate. As determinants of
innovation, some variables are only statistically significant for firms in specific class size,
for example, firm size (positively, for small-sized firms), export market orientation
(negatively, for medium-sized firms), ownership structure (medium-sized firms) and
market concentration (positively, for medium-sized firms).

The relationship between technological characteristics of an industry and the likelihood
of firms belonging to that industry to innovate also appears to be very complex. SMEs in
low-technology industry are more likely to innovate compared to their counterparts in
medium-high technology industry. Interestingly, large-sized firms in medium-low
technology industry are more likely to innovate compared to their counterparts in low
technology industry.

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Appendix A: Definitions of SME for Manufacturing Sector
Small
Employees:
? 5 & ? 50
Revenues:
? RM250,000 & < RM10
milllion
Country
Malaysia
Medium
Employees:
? 51 & ? 150
Revenues:
? RM10 million & ? RM25
milllion

Employees:
51 - 200
Fixed Assets:
> Baht 50 million & ? Baht 200
million
Employees:
? 50 & < 250

Employees:
51 - 300
Total Assets / Sales:
> USD 3 million & ? USD 15
million
Thailand Employees:
? 50
Fixed Assets:
? Baht 50 million

Employees:
< 50

Employees:
? 50
Total Assets / Sales:
? USD 3 million

EU
World Bank

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Table 1. Innovation in the Manufacturing Sector, 1997-2001
NSI-2 (1997-1999)
Innovating Non-
Innovating

26 207
99 427
64 134
189 768


NSI-3 (2000-2001)
Non-
Innovating
Total Innovating Total
Number
Small
Medium
Large
Total

Horizontal
Percentage
Small
Medium
Large
Total

Vertical
Percentage
Small
Medium
Large
Total
Source: NSI-2 and NSI-3
Note: We exclude firms with no information on firm size.






















233
526
198
957


125
72
66
263


357
69
61
487


482
141
127
750


11.2%
18.8%
32.3%
19.7%


88.8%
81.2%
67.7%
80.3%


100.0%
100.0%
100.0%
100.0%


25.9%
51.1%
52.0%
35.1%


74.1%
48.9%
48.0%
64.9%


100.0%
100.0%
100.0%
100.0%


13.8%
52.3%
33.9%
100.0%
27.0%
55.6%
17.4%
100.0%
24.3%
55.0%
20.7%
100.0%
47.5%
27.4%
25.1%
100.0%
73.3%
14.2%
12.5%
100.0%
64.3%
18.8%
16.9%
100.0%

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Table 2. Innovation in the Manufacturing Sector by Industry, 2001
Small
Code Industry 0 1
15
Beverages 65 16
16 Tobacco Products 1 0
17 Textiles 2 5
18 Wearing Apparel 54 26
19 Leather Products 5 1
Wood Products Except
Furnitures 25 2
21 Paper and Paper Products 6 3
Publishing, Printing and
Recorded Media 27 21
Coke, Refined Petroleum
Products 0 1
Chemicals and Chemical
Products 8 3
Rubber and Plastic
Products 12 6
Other Non-Metallic
Mineral Products 17 7
27 Basic Metals 10 2
Fabricated Metal
Products 55 9
Machinery and
Equipment N.E.C. 30 3
Office, Accounting and
Computing Machinery 1 3
Electrical Machinery and
Apparatus N.E.C 2 5
Radio, TV and
Communication
Equipment 0 0
Medical, Precision
Instruments 0 1
Motor Vehicles, Trailers
and Semi Trailers 1 3
Other Transport
Equipment 6 0
Furniture; Manufacturing
N.E.C. 29 7
37 Recycling 1 1
Total 357 125 482 69 72 141 61 66 127 487 263 750
Source: NSI-3
Note: 0 = Non-Innovation, 1 = Innovation, T = Total
Medium
0 1
Large
1
Total
1 T T 0 T 0 T
Food Products and
81
1
7
80
6
12 14
0
1
6
1
26
0
3
8
2
3
1
0
14

5
2
1
1

8
3
1
15
0
80
2
3
74
6
35
2
8
29
2
115
4
11
103
8
0
2
2
1
20
27
9
6
3
1
1
7
4
6
1
4
2
10
3
37
10
7
6
44
16
22
48 0 5 5 1 4 5 28 30 58
23
1 0 0 0 0 0 0 0 1 1
24
11 9 7 16 2 4 6 19 14 33
25
18 11
8 19 4 6 10 27 20 47
26
24
12
3
2
3
4
6
6
2
4
4
0
6
4
22
16
14
6
36
22
28
64 7 14
21 3 5 8 65 28 93
29
33 4 0 4 4 1 5 38 4 42
30
4 3 1 4 3 3 6 7 7 14
31
7 0 3 3 4 4 8 6 12 18
32
0 0 0 0 2 9 11 2 9 11
33
1 0 0 0 1 2 3 1 3 4
34
4 0 1 1 1 5 6 2 9 11
35
6 0 0 0 1 3 4 7 3 10
36
36
2
1
0
5
0
6
0
4
0
1
0
5
0
34
1
13
1
47
2

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Table 3. Results


AGE
Small Medium Large
Logit
-0.1478***
(-6.2208)
0.0131
(1.2401)
0.0358**
(2.4093)
-0.0069
(-0.8084)
-0.4172
(-0.6362)
0.5050
(1.3352)
-0.1588
(-0.054)
-0.8549*
(-1.9410)
-1.1865**
(-2.2943)
2.3412*
(1.9163)
2.5351
(1.0694)
-0.0681
(-0.0613)

99.3884***
Probit
-0.0799***
(-6.6517)
0.0074
(1.3218)
0.0189**
(2.2735)
-0.0035
(-0.7431)
-0.2062
(-0.5523)
0.3504
(1.5794)
-0.0594
(-0.0385)
-0.5140**
(-2.0566)
-0.7086**
(-2.4522)
1.3696**
(2.0031)
1.5457
(1.2208)
-0.1129
(-0.1910)

97.1601***
Logit
0.0416*
(1.6535)
-0.0027
(-0.3708)
0.0074
(1.5847)
-0.0145**
(-2.1427)
-0.1439
(-0.0778)
-0.9276
(-0.8029)
-2.9511*
(-1.8774)
0.3087
(0.5914)
-2.6498**
(-2.1602)
-1.1276
(-1.0814)
18.4524**
(2.2013)
-0.7899
(-0.4409)

25.5452**
*
0.1771
69.09
Probit
0.0265*
(1.7623)
-0.0017
(-0.3814)
0.0045
(1.6002)
-0.0089**
(-2.2057)
-0.1162
(-0.1057)
-0.6061
(0.9025)
-1.8145*
(-1.9400)
0.1794
(0.5710)
-1.5551**
(-2.2174)
-0.6656
(-1.0748)
10.8124**
(2.2729)
-0.4304
(-0.4070)

26.0012**
*
0.1803
68.18
Logit
0.0737*
(1.7318)
0.0143
(1.5247)
0.0004
(0.7261)
-0.0080
(-0.8720)

Probit
0.0414*
(1.7626)
0.0083
(1.5594)
0.0002
(0.7864)
-0.0048
(-0.8739)

OWN
SIZE
EXPORT
PARTNER
PRIVATE
0.0723
(0.0529)
-0.8872
(-0.5598)
1.4876*
(1.6946)
-0.0134
(-0.0102)
1.1539
(1.1646)
8.8665
(1.3671)
-2.7510
(-1.5760)

28.6023**
*
0.2864
76.74
0.0080
(0.0098)
-0.5219
(-0.5448)
0.8770*
(1.7583)
-0.0304
(-0.0405)
0.6667
(1.1827)
5.4049
(1.4445)
-1.5522
(-1.5180)

29.0745**
*
0.2911
72.74
PUBLIC
MEDLOW
MEDHIGH
HIGH
HHI
Intercept

LR Test
McFadden R2
Percent
Correctly
Predicted
Log
Likelihood
Number of
Observations
Number of
Innovating
Firms
Note: LR Test is the likelihood ratio test of the overall significance.
*** Significant at the 1% level, ** at the 5% level, * at the 10% level.
Values in parentheses are t-ratio.
0.2703
78.26
0.2643
78.62
-134.1262 -135.2403 -59.3304 -59.1024 -35.6389 -35.4028
276 110 86
106 70 63

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Figure I: Proportion of Innovating Firms (%) Across Various Industries, 2001
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Food Products and Beverages
Tobacco Products
Textiles
Wearing Apparel
Leather Products
Wood Products Except Furnitures
Paper and Paper Products
Publishing, Printing and Recorded Media
Coke, Refined Petroleum Products
Chemicals and Chemical Products
Rubber and Plastic Products
Other Non-Metallic Mineral Products
Basic Metals
Fabricated Metal Products
Machinery and Equipment N.E.C.
Office, Accounting and Computing Machinery
Electrical Machinery and Apparatus N.E.C
Radio, TV and Communication Equipment
Medical, Precision Instruments
Motor Vehicles, Trailers and Semi Trailers
Other Transport Equipment
Furniture; Manufacturing N.E.C.
Recycling
% of Innovating Firms
Small MediumLarge

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References
Acs, Z. and D. Audretsch (1988) “Innovation in Large and Small Firms: An Empirical
Analysis” American Economic Review 78, 678-690.

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