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MICRO, SMALL AND MEDIUM ENTERPRISES AND ECONOMIC GROWTH

Tulus Tambunan

WORKING PAPER SERIES NO. 14 CENTER FOR INDUSTRY AND SME STUDIES

FACULTY OF ECONOMICS, UNIVERSITY OF TRISAKTI

October 2006

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I. Introduction

From a worldwide perspective, it has been recognized that micro, small and medium enterprises

(MSMEs) play a vital role in economic development, as they have been the primary sources of

job/employment creation and output growth, not only in less developed countries (LDCs) but also in

developed countries. In Piper’s (1997) dissertation, for instance, it states that 12 million or about 63.2%

of total labor force in the United States (US) work in 350,000 firms employing less than 500 employees,

which considered as MSMEs. According to Aharoni (1994), MSMEs make up more than 99% of all

business entities and employ more than 80% of total workforce in the country. These enterprises, often

called the foundation enterprises, are the core of the US industrial base (Piper, 1997). MSMEs are also

important in many European countries. In the Netherlands, for example, these enterprises account for

95% or more of total business establishments (Bijmolt and Zwart, 1994). As in the US, also in other

industrialized/OECD countries such as Japan, Australia, Germany, French and Canada, MSMEs, and

particularly small and medium enterprises (SMEs), are an important engine of economic growth and

technological progress (Thornburg, 1993).

In LDCs, MSMEs have also a crucial role to play because of their potential contributions to

improvement of income distribution, employment creation, poverty reduction, industrial development,

rural development, and export growth. For this reason, governments in these countries have been

supporting their MSMEs extensively through many different programs, with subsidized credit schemes

as the most important component. International institutes such as the World Bank, the Asian

Development Bank (ADB) and the United Nation Industry and Development Organisation (UNIDO)

and many donor countries through bilateral co-operations have also played a crucial role in empowering

MSMEs in these countries.

The development of micro, small and medium enterprises (MSMEs) and changes over time in their

employment and output shares, output composition, market orientation and location are usually thought to

be related to many factors, including the level of economic development, changes in real income per

capita, population growth, and progress in technology. Given this thought, the most important question

addressed in this chapter is whether there is a general or a systematic pattern of transformation of MSMEs

over time in the course of economic development or economic growth. More specifically, whether

MSMEs will die out or grow along with the increase in real income per capita.

This paper addresses this particular question. For this purposes, the paper reviews some empirical

studies and analyses secondary data on the development of these enterprises in some countries, including

Indonesia.

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II. Natural Development

Economic development creates a natural place for development and growth of enterprises of all sizes of

establishment (micro, small, medium as well as large). The size of a business establishment depends on a

variety of factors, of which two most important ones are market and technology (Panandiker, 1996). With

respect to the first factor, if the market is small or very small, only small- or micro-scale economic

activities, and hence micro enterprises (MIEs) and small enterprises (SEs) will be viable. The market size

itself is determined by the level of real income per capita and the size of population that together determine

the actual number of buyers.

In the manufacturing industry, MSMEs produce a variety of goods that, due to their nature or

characteristics, can be grouped into two categories, i.e. consumer goods and industrial goods. With regard

to the first category, MSMEs can be manufacturers of final products sold in the market. They survive and

grow in competition with large enterprises (LEs) manufacturing similar products. That is because MSMEs

differentiate their products by nature or acquire. With that, they create a niche market for themselves. For

instance, in many LDCs many MSMEs, particularly MIEs and SEs, are specialized in a variety of simple

items made by hand such as handicrafts which are outside the competitive area of similar items but more

sophisticated and produced with machines by LEs. In such circumstances the MSMEs have a better chance

to survive and hence to growth and develop further. While, these enterprises will be out priced in the

market if they try to compete with LEs for exactly the same products when the economic scale of output

prescribes a large industry and it depends on modern technologies.

With respect to the second category, MSMEs manufacture products for other manufacturers. They are

often ancillaries to LEs. It is observed in recent years that the relationships between MSMEs

(particularly small and medium enterprises; SMEs) and LEs in many countries have become

increasingly important because of the trend towards, what Richard (1996) called “diverticalization”.

LEs, in order to remain competitive, increasingly focus on core competence and buy in other products

and services. Through such production-linkages mainly in the form of subcontracting, the MSMEs are

often exposed to the muscle power of the large ones leading to unpleasantness and problems for the

MSMEs. One obvious problem is that many MSMEs as the LEs’ suppliers have the difficulties to meet

the tight schedules and product specifications (Semlinger, 1993). The problem, which is mainly technical,

management and organization in nature, is observably not only in LDCs but also in developed countries.

Kaplinsky (1994) for instance found in a number of countries that MSMEs face the difficulties to deliver

products “just in time” and with high standards of quality as required increasingly by LEs.1

1 For more studies on this issue, see Hakansson and Johanson (1993), Kaplinsky (1994), Sydow (1992), and Semlinger (1993).

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With respect to technology, as explained in Panandikaer (1996), if the economic size dictated by

technology is large, the MSMEs will be out competed in the market because they cannot produce

efficiently due to lack of economies of scale. For instance, in electronics industries, the state of the art of

technology may indicate a large size, so LEs are viable. But, neither the market nor the technology is fixed

for all time. They constantly change. In the last 5 to 10 years, the world has witnessed that innovations in

technology, at least in some fields like bio, processing of materials, information, telecommunication, TV,

satellite, fax, cellular, phones and pagers, computer and automation, are indeed rapid. Many LEs

experienced serious problem in adapting themselves to that changing technologies and hence business

environment in terms of making shifts in planned production and changes in planned investment and labor

division (including the recruitment of new workers with certain high skills needed by new technology)

and, so many of them are left behind. In such circumstances, the MSMEs have a better chance to survive.

To sum up, the above discussion suggests that the size and its changes over time of an enterprise

depend on a variety of considerations; two amongst them are the market and technology. This implies that

a complex interaction between demand-side factors (for example, market) and supply-side factors (for

example, technology) affects the development of MSMEs. Thus, based on this theoretical framework

supported by some evidence, the analysis of MSMEs’ development should be approached from the

supply- and demand-sides of the industries or sectors where the MSMEs operate. With this analytical

approach, the main supply-side and demand-side determinant factors, the interactions between the factors,

and effects of the individual as well as interacted factors on the MSMEs’ development can be better

examined.

III. Engine for Economic Growth: ‘Pro’ and ‘Contra’ Arguments

In discussing industrial systems and the role of MSMEs within the systems and their pattern of overall

development in LDCs, attention usually focuses on seminal articles by Hoselitz (1959), Staley and Morse

(1965), and Anderson (1982), among some others. Their works often classified as the ‘classical’ theories

on MSMEs’ development. The ‘modern’ theories, on the other hand, include the works of Berry and

Mazumdar (1991) and Levy (1991) in the newly industrializing countries in East Asia like Taiwan and

South Korea, and the literature on flexible specialization thesis based on many experiences from MSMEs

in Western European countries. These theories explicitly place emphasis on the importance of

subcontracting networks and the economic benefits of agglomeration and clustering for the development

of MSMEs.

The ‘classical’ theories predict that advantages of MSMEs will diminish over time and LEs will

eventually predominate in the course of economic development marked by the increase in income. While,

the ‘modern’ theories based on experiences from many European and other developed countries showing

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The ‘classical’ theories seem to get less supports as many international aid agencies, including the

World Bank, since the 1980s have been giving direct or indirect supports to MSMEs to accelerate

economic growth and to reduce poverty in LDCs. For example, over the last five years, the World Bank

Group approved more than US$10 billion in MSME support programs, including US$1.5 billion in 2002

(World Bank, 2002, 2004).2Also many non-government organizations (NGOs) from donor countries

that have regional offices in many LDCs (including Indonesia), such as the Swisscontact and the Asia

Foundation (TAF) have been actively involved in assisting MSMEs in these countries.

The World Bank gives three core arguments in supporting MSMEs in LDCs, which in line with the

arguments of the ‘modern’ paradigm on the importance of MSMEs in the economy (World Bank, 2002,

2004). First, MSMEs enhance competition and entrepreneurship and hence have external benefits on

economy-wide efficiency, innovation, and aggregate productivity growth. Second, MSMEs are

generally more productive than LEs but financial market and other institutional failures and not-

conducive macroeconomic environment impede MSME development. Third, MSMEs expansion boosts

employment more than LEs growth because MSMEs are more labor intensive. In other words, the

World Bank believes that direct government support for MSMEs in LDCs help these countries exploit

the social benefits from their greater competition and entrepreneurship, and their MSMEs can boost

economic growth and development.

The above arguments do not mean, however, that LEs are not important, or MSMEs can fully

substitute the role of LEs in the economy. Even, there are skeptical views from many authors about this

World Bank’s pro-MSME policy. Some authors stress the advantages of LEs and challenge the

assumptions underlying this pro-MSME policy. Specifically, LEs may exploit economies of scale and

more easily undertake the fixed costs associated with research and development (R&D) with positive

productivity effects. A growing body of studies suggests that MSMEs do not boost employment. Those

are LEs which provide more stable and therefore higher quality jobs with positive ramifications for

poverty alleviation. For instance, Davis et al.(1993) find that while gross rates of job creation and

destruction are higher in MSMEs, there is no a systematic relationship between net job creation and firm

size. Research findings from Biggs and Shah (1998) on MSMEs in Sub-Saharan Africa show that LEs

were the dominant source of employment creation in the manufacturing sector. Little et al. (1987) and

Snodgrass and Biggs (1996) find that MSMEs are neither more labor intensive, nor better at job creation

than LEs. Obviously, evidence from these studies rejects the view that MSMEs are the engine of job

formation. 3

2 About 80% of World Bank programs involve financial assistance to MSMEs, while 20% are technical assistance for MSMEs and supports for institutions which give assistance to MSME in many LDCs. 3 See also many other works by e.g. Biggs (2002); Pagano and Schivardi (2001); and Brown, et al. (1990).

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Arguments against the pro-MSME policy also come from emerging empirical evidence which

supports the view that firm size responds to national institutional conditions. Beck et al. (2002), for

instance, find that countries with good banking systems or well-developed financial institutions tend to

have more LEs than MSMEs, because successful firms face no financial constraints and so they can

grow to their efficient sizes. Similarly, evidence from Kumar, et al.’s (2001) research indicates that

countries with better legal institutions, as measured by judicial system efficiency, tend to have more LEs

than MSMEs. From their study in Côte d’Ivoire, Sleuwaegen and Goedhuys (2002) find that restrained

access to inputs, especially credit, results in a bi-modal firm size distribution in the ‘missing middle’

with MSMEs on one tail of the size distribution growing less and LEs on the other tail growing faster

than in developed economies. Thus, all this evidence suggests that institutional development is

associated with countries having more LEs than MSMEs.

Another important argument is on the validity of considering firm size as an exogenous determinant

of economic growth. Kumar et al. (2001) posit that natural resource endowments, technology, policies,

and institutions help determine a nation’s industrial composition and optimal firm size. You (1995)

states that whether a good can be manufactured most economically in MSMEs or in LEs, it would

depend on three things: type of goods produced, kinds of raw materials and other endowments used, and

methods of production adopted. From a country perspective, it means that different countries with

different endowments have different comparative advantages in the production of goods: in some

countries they can be produced efficiently in LEs while in other countries they are most economically to

be produced in smaller scale. Similarly, as argued by Caves et al. (1980), the volume of external trade of

a country also determines the optimal firm size in the country: countries that are open to international

trade may have a larger optimal firm size than those that are less integrated internationally.

IV. MSME Growth-Economic Growth IV.1 Analytical Framework

From the ‘modern’ theories perspective, MSMEs have two important roles to play simultaneously:

to accelerate economic growth through the growth of their output contributions to gross domestic

product (GDP), and to reduce poverty through employment creation and income generation effects of

their generated output growth. In addition to these direct effects, theoretically, MSME have also indirect

effects on economic growth and poverty reduction through their ‘growth-linkage’ effects. Output and

employment increases in MSME lead output and employment to increase in the rest of the economy

through three main linkages: production (forward and backward), investment, and consumption.

Thus, hypothetically, there is a positive correlation between output growth in MSMEs and economic

growth, which can be less or more than proportional rate (Figure 1). The following simple equation

describes this hypothesis:

dYN = a0 + a1dYMSME + ai Xi + e (1)

where,

dYN = the annual growth rate (%) in national income or real GDP per capita (can be in log form.);

dYMSME = the annual growth rate (%) in output in respectively MSME;

Xi = a set of controls postulated as economic growth determinants;

ai = a vector of coefficients on the variables in X (i = 3,4,….,n);

e = a random variable that has mean 0 at fixed values of the predictor variables, commonly referred to as

the regression residual or the error component in the equation that captures the effects of time

invariant and time-varying unobserved variables.

Figure 1: Hypothesis on the Correlation between Output Growth in MSME and Economic

Growth: Output growth in MSMEs (%)

proportional growth

more than proportional growth

less than proportional growth

450 Economic growth (%)

Output growth in MSME can be originated from four sources: growth in number of establishments

(with constant employment and output per unit of establishment), growth in number of workers (with

constant number of establishment and labor productivity), growth in output or productivity (with

constant number of establishment and total workers per firm), or a combination of the first three sources.

IV.2 Some Findings

Empirical studies examining the link in growth between MSMEs and the economy are, however,

limited. One interesting study is from Beck et al. (2003) who provide the first cross-country empirical

study on the link between SMEs (MIEs are excluded) and economic growth using database on the share

of manufacturing employment accounted for by SMEs from a large number of both developed/rich and

less developed/poor countries in all contingent This chapter presents and discusses their findings briefly.

For the analysis, they construct two measures of SME size. The first measure (SME250) is the share of

the SME in the total official labor force in manufacturing with a level of 250 employees is taken as the

cutoff for the definition of a SME. This variable provides them with a consistent measure of firm size

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distribution across countries. The second measure (SMEOFF) is the share of the SME in total official

labor force in manufacturing when the official country definition of SME is used, with the official

country definition varying between 100 and 500 employees. This variable takes into account that

economic and institutional country characteristics might determine characterization of a firm as SE, ME

or LE. They use only SME employment in manufacturing because no cross-country data are available

for the share of SMEs in other sectors, such as agriculture and services. They also restrict their definition

of SMEs to formal enterprises and exclude informal enterprises, which may represent an important

component of output in some countries (like Indonesia and India). However, in their study they

incorporate estimates of the size of the informal sector relative to the formal sector in each country in

their sample.

To assessing the relationship between SMEs and economic growth, they control for an array of

country-specific factors, and they adopt two different analytical approaches. The first approach is a

simple ordinary least squares (OLS) framework with the following growth regression:

Yi,2000-Yi,1990 = αYi,1990 + βSMEi +γXi + εi (2)

where Y is the log real GDP per capita; Yi,2000-Yi,1990 = the average annual growth rate in real GDP per

capita averaged over the period 1990-2000; Yi,1990 = initial income; SME = one of the two indicators of

the size of the SME; X = a set of conditioning information, i = the country index; and ε = the white-

noise error term. In the analysis, they include initial income to control for convergence effects and

secondary school enrolment to capture human capital accumulation. They also include several policy

variables, such as government expenditures as a share of GDP, the share of exports and imports in GDP,

the inflation rate, the black market premium and the share of credit to the private sector by financial

institutions in GDP. Except for Y, all data are averaged over the 1990s.

As the analysis with the OLS approach is prone to simultaneity problems, to assess the robustness of

the results, the analysis uses IV regressions (two stage instrumental variable regressions/2SLS) as the

second approach to extract the exogenous component of SME development. In selecting instrumental

variables for the SME size, the analysis focuses on exogenous national characteristics that theory and

past empirical findings suggest influence the performance of SMEs. The model uses legal origin since

cross-country analyses differences in legal systems influence the contracting environment with

implications on corporate finance and hence SME and LE formation and growth. In particular, countries

with a recent socialist legal heritage had legal institutions that were not encouraging of entrepreneurship

and new firm formation. The model also includes information on natural resource endowments since

many studies show that endowments influence government policies and institutions that shape the

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competitiveness environment.4Further, the analysis includes data on religious composition and ethnic

diversity since some authors argue that Catholic and Muslim countries tend to foster vertical bonds of

authority that hurt corporate finance, while other studies show that ethnic diversity tends to reduce the

provision of public goods, including the institutions that support business transactions and the

contracting environment. Finally, they include continent dummies for countries in sub-Sahara Africa

(SSA) and Latin America to control for geographic and cultural characteristics not captured by any of

the other variables.5

The model also uses an array of different instrumental variable sets to assess the robustness of the

results, as there is no obvious, ideal instrumental variable set. In selecting the appropriate set of

instruments for drawing inferences about the impact of SMEs on economic growth, they provide test

statistics.6

Table 1 shows the OLS results based on equation (2). Specifically, the regression equation

estimated is the real growth rate of GDP per capita over the period 1990-2000 = α + β1 initial income

(i.e. the real GDP per capita in 1990) + β2 SME (measured by their share of manufacturing

employment) +β3 education (indicated by gross secondary school enrollment in percentages) + β4

government consumption (measured by the share of general government final expenditure in GDP) + β5

inflation (= the log difference of the consumption price index/CPI) + β6 black market premium (= the

overvaluation of the official relative to the black market exchange rate in percentages) + β7 trade

(measured by total exports and imports as a percentage of GDP) + β8 private credit (= claims of banks

and other financial institutions on the private sector, as a share of GDP) + β9 informal (= the unofficial

economy as a percentage of GDP). Log values of all right hand side variables were used.

Table 1 presents four specifications of equation (2). Specifications (1) and (3) use SME250 and

specifications (2) and (4) use SMEOFF as the indicator of SME. Only specifications (3) and (4) include

a measure of the size of the informal sector, besides other exogenous variables mentioned above. They

use measures of informal activity rather than informal labor force because very few countries have data

on the size of the labor force employed by the informal sector.

As can be seen, the share of SME employment in total employment in manufacturing is associated

with higher rates of GDP growth. SME250 and SMEOFF enter significantly and positively at the one-

percent significance level. These results are robust to controlling for a large number of other potential

determinants of economic growth. Their robustness checks show that that both SME indicators continue

to enter significantly in the growth regressions even when controlling for the business environment and

institutional development and also for the importance of informal economy (while the measure of the

informal economy does not enter significantly). The analysis also shows that the relation between SMEs

4 See further their paper (Beck et al.,2003) for the list of these studies. 5 See further Beck et al. (2003) for their discussion on these studies. 6 For more detail, see Beck et al. (2003).

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and economic growth is robust to leaving out transition economies and countries in Sub-Saharan Africa.

The coefficient size suggests not only a statistically significant but also economically meaningful

relationship between the importance of SMEs in an economy and its GDP per capita.

Table 1: SME Employment and Growth with a Simple OLS Method

GDP per capita growth

1 2 3 4 SME250 SMEOFF Informal Observations Adj R2

2.211*** (0.467)

47 0.652

2.126*** (0.472)

64 0.611

2.017*** (0.534)

0.254 (0.795)

39 0.633

2.389*** (0.489) 0.404

(0.621) 49

0.671 Notes: *** indicates significance levels of 1 percent. Robust standard errors are given in parentheses. Source: Beck et al. (2003).

The results in Table 1 indicate a very robust relationship between the SME and the rate of economic

growth, suggesting that countries with higher share of SMEs in their economy (in this case measured by

their employment share in manufacturing) tend to have higher rates of economic growth than countries

with lower share of SMEs. The results with a simple OLS framework, however, are subject to concerns

that a large economic share of SME is a characteristic of successful economies, but not a causal force, or

the results do not support the view that SMEs exert a causal impact on long-run economic growth. In

other words, the SME-economic growth relationship is not robust to controlling for simultaneity bias.

As explained earlier, to assess whether simultaneity biases drive the strong association between the

importance of SME in the economy and economic growth, the model used IV regressions with five

different sets of instrumental variables for both SME250 and SMEOFF and statistics to assess the

validity of each set of instruments. Table 2 shows the result. In this model, they extract the exogenous

component of SME by using five different sets of instrumental variables. The analysis carries out two

stage instrumental variable regressions (2SLS). In the first stage regression equation, SME250 (or

SMEOFF ) is formulated as a function of legal origin (using dummy), namely British legal origin,

French legal origin, and Germany legal origin, and other exogenous national characteristics including

transition, latitude (is the capital’s latitude in absolute terms), and religious. The second stage regression

equation is the same as the one used in Table 1.7

7 For more detail explanations about the results including statistic tests used, see further Beck et al. (2003).

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Table 2: SME Employment and Growth with 2SLS Method GDP per capita growth

1 2 3 4 5 6 7 8 9 10 Institutional Development Business environment F-test F-test (extra) C-statistic Observations Adj R2 (first stage)

5.17 (4.95)

0

59 0.787

2.50 (1.96)

0 0.001 0.57 59

0.786

3.60 (3.01)

0 0

0.53 59

0.807

-1.52 (1.82)

0 0.001 0.35 59

0.793

5.94 (3.93)

0 0

0.98 59

0.786

3.43* (1.98)

0

50 0.664

1.42** (0.63)

0 0

0.15 50

0.768

1.84** (0.88)

0 0

0.18 50

0.711

1.004 (0.86)

0 0.004 0.014

50 0.66

3.003** (1.29)

0 0

0.94 50

0.66 Note: *,** and *** indicate significance levels of 10,5 and 1 percent respectively Source: Beck et al. (2003).

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In summary, the findings presented in Table 2 show that the instrumental variable fails to reject the

view that SMEs do not exert a causal impact on economic growth. This study with two different

analytical approaches yields thus twin findings. The first approach with OLS regressions shows that

SMEs are associated with economic growth. While, the second one with 2SLS regressions that control

for simultaneity bias shows that SMEs are not robustly linked with growth are consistent with the view

that a large economic share of SME is a characteristic of fast growing economies, but not a determinant

of this rapid growth.

Alternatively, the following simple way may also give some clue about the pattern of growth of

MSMEs in the course of income increases. This time with SME (not included MIEs) in selected

countries in the Asia-Pacific region where data are available. Table 3 shows the overall growth rates of

SMEs in establishment (A) and total workers employed in them (B), and annualized simple (non

compound) growth rate in GDP (based on World Bank figures) in these countries. At a glance, the

figures do not show a clear pattern of the relationship between SME growth and annual growth of GDP.

The sampled countries can be categorized in two groups, namely low- and high-growing economies. As

can be seen, the growth rates of SME in establishment as well as employment vary among countries not

only between the two but also within the groups of countries.

Table 3: Growth Rates of SME Establishment (A), Their Employment (B) and GDP in

Selected Asian-Pacific Countries (%) 1990-1999 or 2000

SME Country/Economy3)

A B

GDP

Australia Brunei Darussalam Canada Chile China Hong Kong, China Indonesia Japan South Korea Malaysia Mexico New Zealand PNG Peru Singapore Chinese Taipei (Taiwan) USA

5.86 3.71 1.01 6.07 -1.18 0.73 4.10 -0.89 5.78 0.00

11.91 2.90 0.00 2.17 8.95 3.63 2.52

4.13 0.00 1.72 0.00 1.04 -0.91 0.00 -0.06 -0.07 0.00

11.18 5.60 0.00 2.52

10.58 2.30 0.88

2.92 3.39 1.62 5.98 9.69 3.72 4.15 1.84 6.21 5.96 2.91 1.90 3.88 3.44 7.16 5.85 3.06

Sources: APEC (2003) and World Bank database for annual growth of GDP.

The following scatter diagrams (Figures 2 and 3) may give a more comprehensive picture of the

relationship. The first scatter diagram indicates an upward regression line, suggesting that the growth of

SME establishment correlates positively with the GDP growth, although the regression line appears

rather weak (not 100% linear). However, this scatter diagram is much better than that with respect to

SME employment. As can be seen in Figure 3, the regression line seems to have a negative slope. To

test the relationship statistically, the following simple regression equation is used:

dY1990-1999/2000 = a1 + a2SMEi + ξ (3) where, dY1990-1999/2000 = the annual growth rate of GDP averaged over the period 1990-1999/2000; SME

= one of the two measures (A and B in Table 3); and ξ = the white-noise error term.

Figure 2: Scatter Diagram on Overall Growth of SME Establishment and Annual Growth of

GDP, 1990-99/00

0

2

4

6

8

10

12

-5 0 5 10 15

Annual growth rate of GDP (%)

Ove

rall

grow

th o

f SM

E

esta

blis

hmen

t (%

)

Source: see Table 3

Figure 3: Scatter Diagram on Overall Growth of SME Employment and Annual Growth of GDP, 1990-99/00

0

2

4

6

8

10

12

-5 0 5 10 15

Annual growth rate of GDP (%)

Ove

rall

grow

th o

f SM

E

empl

oym

ent (

%)

Source: see Table 3

Table 4 presents findings of the test. Based on t-statistic, the regression coefficients of the overall

growth rates of SME in total establishment and total workers employed in them are statistically not

significant, although, the SME establishment-GDP relationship is positive, as generally expected in the

‘flexible specialization’ thesis. The SME employment-GDP relationship, on the other hand, is negative.

As with the research’s findings in Beck et al. (2003), this analysis yields also twin findings, not from

utilizing two different analytical approaches, but from using two different measures of SME growth.

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Since the data used in this analysis are cross-section, not time series, the negative correlation may

suggest that in the low-growing economies the employment share of SMEs is higher than that in the

high-growing economies; or according to the ‘classical’ theoretical framework, in the poor countries

SMEs as a source of employment are more important than that in the rich countries.

Table 4: The Relationship between SME Growth and GDP Growth: Some Statistical Indicators

Statistic Indicators

SME Establishment-GDP (% growth)

SME Employment-GDP (% growth)

SME coefficient Std. Error t-statistic

Probability R2

S.E. of regression S.D. dependent variable Durbin-Watson statistic

F-statistic

0.013074 0.158181 0.082650 0.9352

0.000455 2.240198 4.334118 1.772963 0.00681

-0.018279 0.153250 -0.119276

0.9066 0.000948 2.239647 2.169557 1.789229 0.014227

This analysis and that from Beck et al., however, have some limitations because of data used, namely

cross-country on one particular time, not time series data on a long distance of subsequent periods. In

fact, based on cross-country data, regression coefficients as in these studies do not show how the

economy (or GDP) responses to MSME. The results only give some idea about the correlation between

the two variables. The impact of ‘something’ on ‘something else’ cannot be observed in one period, as

the impact itself is a dynamic process in nature, crossing a range of periods. Moreover, the nature as

well as the size of the impact of MSME on income may vary among countries due to differences in

many domestic factors, including economic structure, policy environment, and characteristics of and

problems facing MSME.

Unfortunately, time series data on MSME from individual countries are hard to find. Only some,

mainly developed countries have such data, but only since 1980s or 1990s. For instance, in Japan, the

MITI provides annual data from 1989/90 on manufacturing sector by size of enterprises with respect to

total units, employment and value added contribution. With respect to the share of SMEs (units with 4-

19 workers; thus not including MIEs) in manufacturing value added, the regression analysis with a

simple OLS approach finds that the impact of SME on GDP growth (in billions of US dollar) is positive

but, statistically, insignificant. The GDP growth is measured at current price, both in absolute value (dY)

and relative value (dY%), as shown in Figures 4 and 5 respectively. From the perspective of total

number of units and total workers employed in them, SMEs correlate positively with GDP growth in

value as well as percentage, as can be seen in Figures 6 to 9. However, the coefficients are also not

significant, statistically.

Figure 4: Scatter Diagram on Share of SME in Manufacturing Value Added and Change of GDP in Value, Japan, 1991-2000

-800

-600

-400

-200

0

200

400

600

800

11.5 12 12.5 13 13.5 14 14.5

SME (%)

Cha

nge

in G

DP

(bill

.of U

S$)

dY = -2947.501 + 0.391 SME (2601.2) (202.2)* (-1.13) (1.20)**

Note: * = std. error; ** = t-statistics Source: for SME: MITI (2002); for GDP (OECD database)

Figure 5: Scatter Diagram on Share of SME in Manufacturing Value Added and Change of GDP

in %, Japan, 1991-2000

-15

-10

-5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Period: 1991-2000

%

SME (%) Growth in GDP (%)

dY% = -83.54 + 0.48 SME (57.96) (4.51) (-1.44) (1.53)

Source: see Figure 4

15

Figure 6: Scatter Diagram on Total Units of SME in Manufacturing and Change of GDP in Value, Japan, 1989-2000

-800000

-600000

-400000

-200000

0

200000

400000

600000

800000

0 5000 10000 15000 20000 25000 30000

SME (unit)

Chan

ge o

f GD

P (v

alue

)

dY = -842.95 + 0.23 SME (1324.4) (0.06) (-0.64) (0.75)

Source: see Figure 4

Figure 7: Scatter Diagram on Total Units of SME in Manufacturing and Change of GDP in %, Japan, 1989-2000

-15

-10

-5

0

5

10

15

20

0 5000 10000 15000 20000 25000 30000

SME (unit)

Chan

ge o

f GDP

(%)

dY% = -23.1 + 0.27 SME (30.68) (0.001) (-0.75) (0.9)

Source: see Figure 4

16

Figure 8: Scatter Diagram on Total SME Employment in Manufacturing and Change of GDP in Value Japan, 1989-2000

-800000

-600000

-400000

-200000

0

200000

400000

600000

800000

0 500000 1E+06 2E+06 2E+06 3E+06 3E+06

SME (employment)

Chan

ge o

f GDP

(val

ue)

dY = -1151.3 + 0.27 SME (1148.99) (0.001) (-0.795) (0.9)

Source: see Figure 4 Figure 9: Scatter Diagram on Total SME Employment in Manufacturing and Change of GDP in

% Japan, 1989-2000

-15

-10

-5

0

5

10

15

20

0 500000 1000000

1500000

2000000

2500000

3000000

SME (employment)

Chan

ge o

f GDP

(%)

dY% = -32.61 + 0.33 SME (33.3) (0.000) (-0.98) (1.12) Source: see Figure 4

Next, evidence from South Korea is more interesting, in the sense that not only the estimated

regression coefficients are insignificant from the statistical perspective as in the case of Japan, but some

coefficients are negative. It is positive with respect to the relationship between total unit of SME and

GDP growth in value (Figure 10), but it is negative in terms of percentage growth (Figure 11). In the

case of employment, both coefficients are positive (Figures 12 and 13), but in the case of value added,

both coefficients are negative (Figures 14 and 15).

17

Figure 10: Scatter Diagram on SME in Manufacturing (unit) and Change of GDP in Value, South Korea,1989-2000

-100000

-50000

0

50000

100000

150000

200000

1 2 3 4 5 6 7 8 9 10

Period: 1981-1997

Val

ue (m

ill. o

f US$)

and

uni

ts

Change of GDP (value) SME (unit)

dY = 30476.34 + 0.078 SME (68300.4) (0.88) (0.45) (0.22) Source: KFSB (1998). Figure 11: Scatter Diagram on SME in Manufacturing (unit) and Change of GDP in %, South

Korea,1989-2000

-200

20406080

100120140160180

0 50000 100000 150000

SME (unit)

Cha

nge

of G

DP

(%)

dY% = 63.06 - 0.2 SME (61.7) (0.001) (1.02) (-0.6)

Source: see Figure 10 Figure 12: Scatter Diagram on SME Employment in Manufacturing and Change of GDP in value,

South Korea,1989-2000

18

0

500

1000

1500

2000

2500

-1E+05

-50000

0 50000 1E+05

2E+05

2E+05

Change of GDP (value)

SME

empl

oym

ent (

000)

dY = -46046 + 0.299 SME (104267.7) (57.41) (-0.44) (0.89)

Source: see Figure 10 Figure 13: Scatter Diagram on SME Employment in Manufacturing and Change of GDP in %,

South Korea,1989-2000

-200

20406080

100120140160180

0 500 1000 1500 2000 2500

SME employment (000)

Chan

ge o

f GDP

(%)

dY% = 23.3 + 0.02 SME (100.4) (0.06) (0.232) (0.06)

Source: see Figure 10 Figure 14: Scatter Diagram on SME Value Added (bill. Won) in Manufacturing and Change of

GDP in Value, South Korea,1989-2000

19

-100000

-50000

0

50000

100000

150000

200000

1 2 3 4 5 6 7 8 9 10

Period: 1989-2000

Chan

ge o

f GDP

(val

ue) a

nd

SM

E va

lue

adde

d

Change of GDP (value) SME (value added)

dY = 55949.1 - 0.112 SME (38797.9) (0.696) (1.44) (-0.32)

Source: see Figure 10 Figure 15: Scatter Diagram on SME Value Added (bill. Won) in Manufacturing and Change of

GDP in %, South Korea,1989-2000

-200

20406080

100120140160180

0 20000 40000 60000 80000 100000

SME value added

Chan

ge o

f GDP

(%)

dY% = 58.2 - 0.33 SME (33.9) (0.001) (1.72) (-0.97)

Source: see Figure 10

Now, for Indonesia, this chapter presents two cases. The first case uses regional data from 2002 on

total number of SEs in manufacturing industry (called small-scale industries or SSIs) and real per capita

Gross Regional Domestic Product (GRDP) in 71 Kabupaten/Kota in Java, South Sumatera and South

Sulawesi. Data on SSIs are from Potensi Desa (PODES) 2003 (BPS) and data on GRDP are from

Indonesia Human Development Report 2004 published (published by BPS, BAPPENAS and UNDP).

By using a simple regression equation with GRDP as the dependent variable and total number of

SSIs as the only explanatory variable, it is found (not shown here) that the regression coefficient

20

between the two variables is negative and not significant, statistically. The scatter diagrams from two

different angles may explain this. As can be seen in Figure 16, in terms of real per capita GRDP, most of

the sampled SSIs are found in the lower left corner of the figure, namely in regions with GRDP ranging

from 500 to about 1500 thousands rupiah. There are only two out-layers: one low-income region, which

has the highest number of SSIs in the group (almost 80.000 units), and the other one, a high-income

region with more than 30.000 units.

Figure 16: Scatter Diagram on Real p.c. GRDP and Total Number of SSIs in 71 Kabupaten/Kota in Java, Sumatera and South Sulawesi, 2002

0500

10001500200025003000350040004500

0 20000 40000 60000 80000

SSIs (unit)

Real

GR

DP p

er c

apita

(000

Rp)

Source: BPS and UNDP

The second case uses data from BPS from its annual publication Statistical Year Book of Indonesia

on SSIs with respect to total number of units, total worker employed in them, and total value added. As

shown in Table 5, the long-term trend for the period 1975-2003 shows increases of these three measures

of SSIs; although in some years there were some declines. In 1975, the number of this size group of

enterprises was slightly more than 48,000 and it increased to more than 200,000 units by the end of the

period. Their workers also went up from more than 300,000 people in the beginning of the period to

almost 2 million men by the end of the period. In the same year, the production of SSIs was very small,

as their registered value added was only around 53 billion rupiah, but in 2003, it reached more than 15

trillion rupiah.

With a simple OLS method, the regression’ results (not shown here) do not reveal, however, strong

evidence which supports the ‘modern’ thesis. If the time series include 1998 when the country’s GDP

dropped by 13% and 1999 when the country’s economy started to recover though with a very small

positive rate of growth, all regression coefficients are negative and not significant, statistically. These

results can be explained by the following three figures. Apparently, scatter points in these figures cannot

yield growth trends with a definitive direction. The findings are, however, not different if the crisis

period is excluded in the analysis, as all correlation coefficients are also negative and not significant. 21

One possible reason for these findings is that the negative effect of income growth on SSIs, as

hypothesized in the ‘classical’ thesis, is stronger than the other way around. Table 5: SSIs in Units, Workers Employed, and Value Added, 1975-2003

Year Units Workers Value added (mill. rupiah)

1975 1979 1986 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

48186 113024 94534

124990 168154 190767 228978 241169 194564 225603 240088 230721 238582 255114

343240 827035 770144 952038 1406987 1597799 1915378 2077298 1505604 1779237 1799290 1761510 1767996 1846149

53027 187323 775304 1508544 3220910 3888457 4612438 4802224 6923421 8184064 7847511

12011801 13862151 15180331

Source: BPS..

Figure 17: Scatter Diagram on GDP Growth Rates and Total Number of SSIs, 1975-2003

0

50000

100000

150000

200000

250000

300000

-15 -10 -5 0 5 10

GDP growth rate (%)

SSIs

(uni

ts)

22

Figure 18: Scatter Diagram on GDP Growth Rates and Total Workers in SSIs,

1975-2003

0

500000

1000000

1500000

2000000

2500000

-15 -10 -5 0 5 10

GDP growth rate (%)

SSIs

(wor

kers

)

Figure 19: Scatter Diagram on Index of GDP Growth Rates and Index of SSIs’ Value Added

(1975=100), 1975-2003

0

5000

10000

15000

20000

25000

30000

35000

-300 -200 -100 0 100 200

GDP growth

Valu

e ad

ded

of S

SIs

V. Concluding Remark

In overall, the evidence presented here so far does not lead to a definitive conclusion on whether to

reject or to accept the ‘classical’ or the ‘modern’ hypothesis regarding the evolution of MSMEs in the

course of economic development. Besides data problem, different natures or patterns of relationship

between the level of economic development and the growth of MSMEs by country or region may also

explain these ambiguous findings. In other words, the ‘classical’ thesis may valid only for certain

countries or regions with certain characteristics (e.g. economy, social, geography, community behavior,

and traditional habits); while in other countries or regions, the ‘modern’ thesis gets more supports.

23

24

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