1. INNOVATION AMONG SMEs IN MALAYSIAN MANUFACTURING: AN ANALYSIS USING FIRM-LEVEL DATA
Seyed Mehrshad Parvin Hosseini
School of Social Sciences
Universiti Sains Malaysia
Penang
This paper is based on data being analyzed for my PhD thesis. I thank my supervisor, Prof. Suresh Narayanan, for his advice and guidance in preparing this paper. Without his assistance this paper would not have been possible.

2. INTRODUCTION
The 2005 Census of Establishments and Enterprises indicated that 99 per cent of the 519,000 business establishments in Malaysia were small and medium-sized enterprises (SMEs) (MIER, 2000).
SMEs are important in Malaysia because they form the bulk of all establishments in the country and they are predominantly domestically owned and managed (Abdullah: 2002; Rasiah, Malakolunthu, 2010).
Innovation has been identified as being critical in leading Malaysia out of the middle income trap (NEM, 2010) but innovative activity in the country is not only low but confined to foreign –owned firms located in the electronics and electrical sector (NEM, 2010: 53).
For the purposes of this paper innovation is defined as new or significant changes to goods or services; production or delivery methods; marketing methods and organizational methods (OECD, 2005).

3. OBJECTIVE
The objective of this paper is to examine the characteristics of SMEs that innovate and their level of innovation (divided into three stages: non-innovative, moderately innovative and highly innovative).

4. DATA
SME level data were obtained from the Productivity and Investment Climate Survey the Malaysian Department of Statistics and the World Bank in 2007 with data for the reference year 2006.

5. METHOD
The firm-level survey covered a broad range of topics but 20 questions pertained directly or indirectly to innovation. These were used to classify firms into one of the three stages of innovation: non-innovative, moderately innovative and highly innovative. The stage of innovation, the dependent variable, was therefore ordered from ‘none’ to ‘moderate’ and ‘high’.

6. The only data we have is categorical in nature with a 0 for non-innovative firms, 1 for moderately innovative firms and 2 for highly innovative firms. Since the stage of innovation was ordered, the ordered probit model was used for the analysis (See Greene, 2004 for further details).

7. The firm-level covariates were age of firm, export ORIENTATION, foreign firm ownership, firm size, government ownership and SUBSECTOR of activity. The choice was guided by empirical evidence drawn from similar or broadly related studies elsewhere.

8. Table 1 : Descriptive statistics of our sample data
Independent Variables
No innovation
Moderately
innovative
Highly innovative
Total
Age of establishment (x>15)
181
(56.56)
162
(55.48) 64
(47.76)
407
(54.56)
Export orientation More than 10% of sales exported= 1 and zero otherwise 91
(28.44)
133
(45.55) 82
(61.19)
306
(41.02)
Foreign-Owned and Joint venture firms =1 and zero otherwise 48
(15.00) 78
(26.71) 29
(21.64)
155
(20.78)
If government owns 30% or more of share of the company =1 and Zero if otherwise 1
(0.31) 4
(1.37) 2
(1.49) 7
(0.94)
Medium size dummy if number of employees are more than 50 and less than 150 =1 and zero if otherwise 73
(22.81)
(49.32) 71
(52.99)
(38.61)
Sample size (pooled)
320
(42.90)
292
(39.14)
134
(17.96)
746
(100)
Note: Number in quotations ( ) are in percentage

9. Table 2 Results of the ordered probit estimation
Dependent variable (level of innovation)
Coefficients
Std. Error
P>z
Age of establishment more than 15 years **
0.0866**
0.028
Export more than 10 % of sales
0.4567***
0.0918***
0.000
Government owns (30% or more share of the company)
0.5926
0.4239
0.162
Foreign firms and joint ventures
0.0111
0.1086
0.918
Medium size dummy (more than 50 and less than 150 =1 and zero if otherwise)
0.5158***
0.0902***
Food Processing
0.2649
0.856
Textiles
0.3317
0.929
Garments
0.2967
0.541
Wood and Wood Products
0.3501
0.232
Chemicals & Chemical Products
0.3001
0.724
Rubber and Plastic
0.0092
0.2654
0.972
Machinery and Equipment
0.2903
0.659
Electrical Machinery & Apparatus & Office, Accounting, Computing Machines
0.3833
0.920
Electronics (Equipment & Components)
0.3374
0.890
Furniture
0.2885
0.285
*Significant at the 10% level; **Significant at the 5% level; ***Significant at the 1% level.

10. Moderately innovative
Table 3: Marginal effects (for a discrete change of dummy variables from 0 to 1)
Dependent variable (level of innovation)
No innovation
Moderately innovative
Highly innovative
Age of establishment more than 15
0.0743
(0.027)***
(0.029)***
(0.03)***
Export more than 10 % of sales
0.0604
0.1154
(0)***
Government owns (30% or more share of the company) *
0.0249
0.1830
(0.097)
(0.424)
(0.238)
Foreign and joint venture firms
0.0016
0.0027
(0.92)
Medium size dummy
0.0648
0.1325

11. Moderately innovative
Dependent variable (level of innovation)
No innovation
Moderately innovative
Highly innovative
Food Processing
0.0188
(0.857)
Textiles
0.0116
(0.928)
Garments
0.0718
(0.542)
(0.589)
(0.503)
Wood and Wood Products
0.1658
(0.226)
(0.317)
(0.124)
Chemicals & Chemical Products
0.0419
(0.726)
(0.741)
(0.711)
Rubber and Plastic
0.0014
0.0022
(0.976)
Machinery and Equipment
0.0506
(0.66)
(0.689)
(0.638)
Electrical Machinery & Apparatus & Office, Accounting, Computing Machine
0.0152
(0.92)
Electronics (Equipment & Components)
0.0184
(0.889)
(0.897)
Furniture
0.1224
(0.285)
(0.358)
(0.215)

12. The findings suggest competition is a key driver to innovation.
larger firms tend to invest more in R&D than do small ones.
Government participation and foreign ownership was positively associated with innovation but not in a significant manner.
there is no statistically significant difference in level of innovation by industrial subsectors.

13. The end
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