1

Modern manufacturing, technology, organization, and

economic growth across the world today: evidence

from country panel data

Elias Sanidas and Wonkyu Shin

Seoul National University

Abstract

Over the recent four decades, the set of modern manufacturing, technology, and organizational

innovations has been important in shaping a new industrial era. Based on the Lean Production

System (LPS), as a one of the most significant organizational cum technical innovations in

industrial development, modern manufacturing has started in Japan and subsequently evolved

in many industrialized economies, such as the USA, Korea and other countries. Its main

characteristics are concentrated on a concerted effort to reduce any type of waste in production,

and hence reductions in inventories and defects are some of its main consequences. There has

been a prolific literature on the benefits of LPS on a microeconomic basis for various firms and

industries in a multitude of countries, by using both primary (survey) and secondary data.

However, so far there has not been any specific evidence on a macroeconomic basis (hence by

using secondary data only). Consequently, this paper endeavors to capture and document the

effects of LPS on macro-economic growth based on a comprehensive panel country-level

empirical analysis. Using 30 years of relevant panel data from 1979 to 2008 and for 69

countries across the globe, we use panel econometric techniques such as fixed effects, GMM,

and Hausman-Taylor models to assess the LPS’s impact on economies. For this assessment we

use a carefully chosen and well-established proxy, the inventories to sales ratio, in order to

measure LPS. Thus, we found that LPS has played a significant role in the economic

development of most developed countries. The effects of LPS on economic growth are therefore

a significant feature of modern manufacturing, which cannot be neglected when considering

industrial policy in various countries, especially those such as Korea that are still in the process

of establishing themselves in the international competitive arena as new strong economic

powers. Our paper, overall, includes micro and macro issues in order to assess macro-economic

growth.

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1. Introduction

It is well known that the mass production system that took place in the USA during the

second industrial revolution during the period of approximately 50 years from 1870 to 1920 has

been gradually replaced by modern manufacturing techniques which started with Toyota in

Japan since the 1960s. By the first decade of the 21st century, there is evidence that many

countries in the world, especially advanced ones, are using modern techniques of technology

and organization usually termed under the umbrella of ‘flexible production’, just-in-time (JIT)

and quality control, lean production, and so on. A prolific literature covers all these historical

developments in journals and books in various disciplines. Thus, in economics, Milgrom and

Roberts (1990, p. 511) have already stated that “…Manufacturing is undergoing a revolution.

The mass production model is being replaced by a vision of a flexible multiproduct firm that

emphasizes quality and speedy response to market conditions while utilizing technologically

advanced equipment and new forms of organization…” These authors have shown through

rigorous models of complementarities that we should expect to see a simultaneous change in

several variables such as lower prices, higher quality in production, lower inventories, fewer

defects, lower wastage, and so on (all this will be further explained below).

Thus, we want to emphasize from the outset that one of the most important

consequences of this modern manufacturing system (henceforth called lean production system

or LPS) is the continuous through time reduction in inventories. This reduction is independent

of the business cycle as we shall see later. We will hence use inventories (as a ratio to sales in

order to take into account the size factor) as a good proxy of the LPS. This proxy has been used

by many other researchers, especially on a micro basis (firm or sector level). We will justify the

use of this proxy further below.

The aim of our paper is to provide empirical evidence as to whether our sample of xx

countries have been modernizing their economies overall by adopting principles of the LPS. On

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a macro-economic basis our study is the first one to attempt such an empirical study. On a micro

economic basis, there has already been substantial evidence about the adoption of LPS in firms

and sectors in countries such as Japan, Korea, and the USA. However, it is not obvious a priori

that if LPS is adopted in some firms and sectors, then that means that LPS is also adopted

overall (or on average) in the relevant countries. Especially, this is even less evident on the

macro basis as the proxy which researchers use on a micro basis (inventories to sales ratio) can

effectively be used on macro basis. The reason for this latter argument is that if inventories

diminish in some firms and sectors then they might increase in some others. We will examine all

this in section 2 where we present the theoretical background of our paper.

In section 3 we present our data details and empirical results. There we will use panel

data econometric techniques such as fixed effects, GMM, and the Hausman Taylor model with

our dependent variable being the growth rate in GDP and the independent variables being the

usual variables used in such models (such as initial GDP) and our proxy for LPS. In section 4

we conclude.

2. Theoretical background

There are two issues which will assist us in clarifying our proposed hypotheses to test.

The first one is related to technology which is so important in influencing economic growth and

the second one is related to the behavior of inventories. Regarding the former, we will adopt the

United Nations Centre on Transnational Corporations (UNCTC, 1985, p. 119) definition:

‘Technology may be embodied in the form of capital goods, such as machinery, equipment and

physical structures; or it may be disembodied in such forms as industrial property rights

unpatented know-how, management and organization (authors’ emphasis) and design and

operating instructions for production systems’.

Accordingly, technology has two different aspects: one that is embodied in the form of

physical products (technical innovations; hereafter referred to as “TIs”) and the other is

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disembodied in such as know-how, managerial, operational and organizational technology

(organizational innovations; hereafter referred to as “OIs”). This view of distinguishing between

embodied and the disembodied technology (or TIs and OIs) is shared by many scholars; for

example Brown (1968), Peters (1980), Romer (1990), Tomer (1990), Nelson (1996), Sanidas

(2004, 2005, 2006), Marshall (1890), Schumpeter (1934, 1942), and others. The distinction

between TIs and OIs concerning technology will assist us in better understanding the

functioning and consequences of LPS.

Effectively, modern production is heavily based on OIs which we can also call

organizational process innovations (such as just-in-time, kanban, and kaizen), as against

technological process innovations (Edquist et al, 2001). A first succinct but holistic definition of

JIT is given in Harrison A. (1994, p.175). This author distinguishes three areas of excellence

regarding the JIT philosophy or, as it is also called, ‘Lean Production’, or ‘World Class

Manufacturing’:

Techniques are systematically put in place to attack all sources and causes of waste.

Everybody is included and participates in the JIT process and management.

Continuous improvement searches for the ideal case of zero scrap, defects, and

inventories.

Womack et al (1990, p. 13) sheds some light on the definition of the lean production system

(LPS):

“...Lean production (a term coined by IMVP researcher John Krafcik) is ‘lean’ because it uses

less of everything compared with mass production- half the human effort in the factory, half the

manufacturing space, half the investment in tools, half the engineering hours to develop a new

product in half the time. Also, it requires keeping far less than half the needed inventory on site,

results in many fewer defects, and produces a greater and ever growing variety of products...”

Imai (1997, p. 45) summarizes the benefits from implementation of JIT/QC as follows

(explanation of Japanese words in the quotation are provided below).

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“…Opportunities for cost reduction on-site may be expressed in terms of muda. The

best way to reduce costs in gemba is to eliminate excess use of resources. To reduce

costs, the following seven activities should be carried out simultaneously, with quality

improvement being the most important. The other six major cost-reduction activities

may be regarded as part of the process quality in a broader sense:

1. Improve quality.

2. Improve productivity.

3. Reduce inventory.

4. Shorten the production line.

5. Reduce machine downtime.

6. Reduce space.

7. Reduce lead-time.

These efforts to eliminate muda will reduce the overall cost of operations…”

Note that muda means waste and gemba means shop floor or work place in general. Quality

refers to both process quality and gemba quality. The former includes the quality of work in

developing, making, and selling products or services. The latter refers to managing resources

and it includes the five M’s, namely man, machine, material, method, and measurement.

To further appreciate the OIs introduced in the LPS it would be useful to compare the mass

production system main characteristics with those of the LPS. First, Table 1 presents the general

comparison between JIT/QC system and non-JIT/QC system. Second, Table 2 presents a

comparison between three production systems, including LPS.

Table 1 Comparison between the JIT/QC and non-JIT/QC systems

Characteristics JIT/QC System Non- JIT/QC System

Labor division Flexible work teams Rigid work segmentation

Setting standards Standardization methods Standardization methods

Inventories Low inventories (high stocks are a

waste)

High inventories (large stocks

add flexibility)

Discipline Self-discipline of workers Discipline imposed through

strict hierarchical organization

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Production runs Small batch sizes Long runs

Planning flow Last stage first First stage first

Set up times Frequent Infrequent

Operating control Decentralized Centralized

Interdependence Increased Lowered

Source: Based on information collected from Harrison A. (1994), McMillan (1996),

Schonberger (1982, 1986, 1996).

Table 2 Characteristics of three main production systems Factory system Mass production/big

business system

Scientific management,

Fordism and LPS

1. Transaction costs reduced Transaction costs reduced Transaction costs reduced

2. Better quality control for a

larger production

Development of core

competences

Quality of products

increased

3. More specialization Division between

Entrepreneurs and

managers

Greater division of labor on

the shop floor

4. Inventories decreased Transport costs reduced Reduction in wasteful

efforts, materials, time

5. Improvements in scheduling Moral hazard reduced Cooperation between

employees increased

6. Increased coordination Increased coordination Increased coordination

7. Intensification of efforts Organizational and

technical non-separabilities

More control of labor by

management via planning

8. Improved supervision Supervision more difficult Improved supervision

9. Creation of Externalities Establishment of standards

10. Greater size of markets Develop a science for each

element of a person’s work

11. New TIs New TIs Variety of asset

specifications decreased

Source: based on Chandler (1997), Sanidas (2005).

In general, OIs are linked with production systems and have an important role to play in

economic growth and development. However, OIs are not only linked with production systems.

Thus, Blomstrom, Lipsey and Zejan (1996) reexamined the rate of per-capita growth with post-

World War II data and found no evidence that fixed investment (or equipment investment). They

concluded that emphasis should be put on the importance of institutions, economic policies,

foreign direct investment and the efficient use of investments (which are all related to OIs) as

the cause of economic growth. Hall and Jones (1998), in their empirical study, took a step-by-

step approach to the analysis of the determinants of economic growth across countries and they

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provided evidence of a causal effect of social infrastructure (institutional quality) on worker’s

productivity growth, given that social-infrastructure encourages the adoption and

implementation of TIs and OIs.

Sachs and Vial (2001) examine the competitiveness of Latin America Countries (LAC)

with five factors including technology (TIs for this case), institutional quality and openness, and

explain the LAC weak economic performance. They emphasize the necessity of wise use of

natural resources (which are rather of the institutional or of OIs for this case) for the countries to

grow their national competitiveness. Denison (1962) explicitly distinguishes between OIs and

TIs and discusses about OIs’ possible great impact on productivity; Mansfield (1968) lists OIs,

namely new techniques of organization, marketing, and management, as a source of growth;

Lucas (1993) concludes his paper by analyzing the economic miracles of East Asia, with an

emphasis on learning-by-doing growth through human capital accumulation, if OIs (roles of

workers and managers) properly operate; Romer (1993, 1994) suggests that ideas (potentially

OIs) can positively affect economic growth; Stiglitz (1996) explains the East Asian miraculous

growth with governmental OIs; Chandler (1997) discusses managerial aspects of OIs; Hall

(2000, p. 1) discusses OIs through some modeling and says that “when an economy is well-

organized, it is more productive”.

Pack (1994) views OIs as improvements in knowledge (such as organization routines,

rearrangement of the flow of material in a factory, better management of inventory, or other

changes that do not require knowledge to be embodied in new equipment) impacting on the

annual rate of productivity. Pack (1994) introduces issues of shortcomings and difficulties of

endogenous theory to construct his empirical work. He suggests some theoretical

accommodation to the endogenous theory according to the reality of economic growth through

new business practices, OIs. Thus, the annual rate of productivity improvement can be

interpreted in improvements in both TIs and OIs (Pack 1994). Other studies that have

incorporated OIs in the empirical models are those by Baily and Gersbach (1995), Lieberman

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and Demeester (1999), Sanidas (2005) and Singh (2008) who include OIs in their empirical

growth models.

Over the last 30 years, OIs (such as those incorporated in the LPS and influenced by

institutions and other societal structures) have been prevailing in many developing and

developed countries, playing a significant role in economic growth. It seems that OIs have been

adapted and assimilated by developing countries and particularly the NIEs according to their

own circumstances by imitating the OIs in advanced countries such as the United States and

Japan (Hausmann and Rodrik 2003). Thus the implementation and localization of OIs may vary

across countries, yet their consequences and results will much be the same. As a consequence of

the implementation of OIs, economies reduce unnecessary investments, minimize inventories

and avoid bearing redundant costs in production. Productivity in firms (or countries) may yield

a great increase in responsiveness to both supply and demand through drastic reduction in

transaction and other types of opportunity costs.

Inclusion of OIs in the production function relative to the inclusion of TIs is rather rare.

For instance, Menard (1996, p. 291) remarked,‘…Thus, the production function should include

three related inputs: capital, labor, and organizational capabilities: Q = Q (K, L, O)…’ (p. 291).

In addition, Lydall (1998, p.33) says, ‘…The productive enterprise needs to make use of many

technologies besides the physical. These may be classified under the headings of commercial,

financial, and organizational technologies…’ A general way of incorporating the above

organizational inputs (OIs) in the production function is shown in the following equation.

Q= f (M, K, L, O, T) (1)

In this production function, all inputs (M: materials, K: capital, L: labour, O: OIs, T:

TIs) participate in the output Q in an autonomous way. Thus, changes in O cause changes in Q,

even if the other inputs do not vary. In addition, if we adopt the usual calculus results regarding

the marginal products of output with respect to each one of the inputs, we can obtain from

Lagrangian maximization of Q the cost or shadow price of each input.

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Regarding the behavior of inventories, we just saw that on a micro basis we expect

from the LPS implementation that many firms and sectors achieve a continuous decrease in

inventories. This trend is also supported independently by other scholars who attempted to

theoretically understand the long term trend of inventories. Thus, Milgrom and Roberts (1990, p.

523) have shown through models of complementarities (as rigorously shown in their article) that

“…one expects to see a pattern of the following sort linking changes in a wide range of

variables:

Lower prices,

Lower marginal costs,

More frequent Product Redesigns and Improvements,

Higher Quality in Production, Marked by Fewer Defects,

Speedier Communication with Customers and Processing of Orders,

More Frequent Setups and Smaller Batch Sizes, with Correspondingly Lower

Levels of Finished-Goods and Work-In-Process Inventories and of Back

Orders per Unit Demand,

Speedier Delivery from Inventory,

Lower Setup, Wastage, and Changeover Costs,

Lower Marginal Costs of Product Redesign...”

The complementarities between all these variables are due to indivisibilities and hence

non-convexities. As these authors mention (ibid, p. 515) “these non-convexities then explain

why the successful adoption of modern manufacturing methods may not be a marginal decision”.

As these authors conclude, due to these complementarities the expected trend would be “to find

an increasing proportion of manufacturing firms adopting the modern manufacturing strategic

cluster that we have described” (ibid, p. 527). We will in our paper empirically check this trend

by adopting one of the most important consequences of modern manufacturing, that is, a long

term trend in diminishing inventories.

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This trend is also related to the production smoothing as seen in LPS and JIT systems.

The objective of this smoothing “…is to reduce the variability of the production rate at the final

stage of manufacturing operations so as to create a stable demand stream for the other

manufacturing operations at the preceding stages. Therefore, production smoothing is a key

element of TPS, and, hence, a key component of the JIT philosophy…” (Yavuz and Akcali,

2007, p. 3580). Many techniques have been devised in order to achieve production smoothing

and hence efficient inventories planning within the context of LPS and JIT as Yavuz and Akcali

(2007) show in their article. Although production smoothing primarily examines volatility or

variance of production, it is nonetheless important to link production smoothing to inventories

and their trend to reduce over time (see also Morton et al, 1990; Konig and Seitz, 1991). Thus,

Morton et al (1990) conclude that subcontracting (a special feature of JIT) reduces the

variability of production and inventory. It is worth noting that Wen (2005, p. 1542) showed that

“…under the production smoothing motive, the covariance between inventory investment and

sales is negative at all cyclical frequencies…” This, at least supports the main consequence of

the LP cum JIT structures that as the sales increase in the long term, inventories decrease1.

Also on a theoretical level, Bils and Kahn (2000, p. 477) have shown that “...a persistent rise in

real marginal cost, absent intertemporal substitution, creates a persistent reduction in inventory

holdings relative to expected shipments”.

For a very comprehensive and recent treatment of input and output inventories

behavior in a general equilibrium macro-economic model, see Iacoviello et al (2010); these

authors relate the importance of LPS and JIT/QC in contributing to the persistent decline of

input inventories to GDP ratio through time. It is worth summarizing these authors’ findings

here because they provide strong theoretical and empirical evidence for our arguments. These

1 The author Wen (2005) did not have in mind these structures of LP and JIT, but it is not unreasonable to

deduct the above conclusion from his proposition.

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scholars have constructed a dynamic stochastic general equilibrium model that takes into

account both input and output inventories, both goods and services sectors, depreciation, and

other desirable features of a comprehensive model. Input inventories (which are materials and

work-in-progress and constituting about 75% of total inventories) are very countercyclical,

contrary to output inventories which are mildly procyclical. As clearly shown in their graphs the

input inventories to GDP ratio has been steadily declining since about the mid 1980s in the USA.

Through Bayesian estimation methods, Iacoviello et al (2010) successfully capture the

countercyclicality of the input inventory to output ratio and found that the elasticity of

substitution between input inventories and fixed capital in the production function is much

smaller than unity. Through their general equilibrium model, the authors derive the steady-state

ratios of input and output inventories ratios which are a function of several parameters such as

the weight of input inventories in the CES aggregate, the elasticity of substitution, and so on.

These parameters are determined by “... new methods of inventory management like just-in-time

production or flexible manufacturing system...” (ibid, p. 7), which have been eminent since

1984 (p. 24). As the authors emphasize (ibid, p. 13), “...the prevailing view in the literature is

that a decline in (M+F)/Yg or M/Yg likely resulted from improvements in inventory

management and production techniques, such as “just-in-time” production, “flexible

manufacturing systems”, and “material resources planning”...” (M and F stand for input and

output inventories respectively; Yg stands for GDP)2.

There is considerable empirical evidence that on a micro basis, firms and industries,

through adopting LPS or similar production systems saw their inventories decreasing over a

long period of time, beyond business cycles fluctuations. Thus, Chen et al (2005) found that

2 The authors then quote several papers related to this statement.

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American companies reduced inventories between 1981 and 2000 (see also Chen et al (2007)

for an extension of this conclusion to retail and wholesale industries). For a direct relationship,

Bo (2004) has found that inventory stock is negatively associated with fixed investment for

Dutch firms. For more evidence on a micro basis, see further below for more articles when we

examine the proxy for representing the LPS or JIT or similar systems. On a macro basis, Chikan

et al (2005), Chikan and Tatrai (2003), and Chikan and Kovacs (2009) have empirically shown

that inventories to GDP ratio decreases over time for many countries. These authors refer to the

LPS in order to explain this decrease. Williams (2008) and Elder and Tsoukalas (2006) brought

evidence of the declining inventories to GDP ratio in the UK.

3 Data, proxies, and econometric results

a) Data

Except for the institutional variables (they come from WGI) all other data come from

the World Development Indicators (WDI) database provided by the World Bank. The period

examined is from 1979 to 2008. The number of countries that report the data relevant for our

analysis vary from year to year. We obtained consistent data only for 69 countries (see Appendix

2 for the country list); low and lower income countries especially where their development does

not include a strong industrial sector are excluded on purpose, while countries with substantial

industrial production are included. However, some developing countries such as Vietnam,

Indonesia, Philippines, Tunisia and Ukraine are all included in the analysis, leading to quite a

balanced panel dataset.

Countries that are more likely to have lenan production were selected in our sample, as

this is what we are aiming at measuring; however, to check the robustness of our findings

against selection bias, we did analyze a larger number of countries (74) including very small

sized or island countries where lean production is unlikely to take place. We found similar

results in terms of the signs of IRIs’ coefficients and hence consistence with our results for 69

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countries (the results for the 74-country sample are not shown here). Also please note that our

69-country sample contains both developed and developing countries, and thus with this mix of

nations we might be able to discern whether globalization of the LPS might be part of our

findings (if developed countries have lower inventories per GDP, then developing countries

might follow as well in this decreasing trend). Table 3 shows the summary statistics of the

control and target variables included in all our models.

Table 3 Descriptive Statistics of variables

Variable Description Name Obs. Mean Std. Min Max

Real GDP Growth Rate (annual %) a

RGGR 392 2.05 3.28 -14.28 10.44

Inventories Ratio to Investment a IRI 373 0.04 0.07 -0.20 0.40

Inventories Ratio to Investment (1year lag) b IRI_LAG 374 0.04 0.07 -0.21 0.43

Inventories Ratio to GDP a IRG 383 1.79 3.58 -4.29 26.85

R&D expenditure (% of GDP) c RAD 383 1.15 0.98 0.05 4.18

Institutional Quality Index c INS 414 0.58 0.83 -1.03 1.85

Rule of Law Index c RL 414 0.57 0.94 -1.32 1.88

Corruption Control Index c CC 414 0.62 1.02 -1.30 2.34

Regulation Quality Index c RQ 414 0.69 0.82 -1.54 1.86

Government Effectiveness Index c GE 414 0.72 0.88 -1.08 2.12

Political Stability Index c PS 414 0.37 0.78 -1.82 1.45

Voice and Accountability Index c VA 414 0.49 0.93 -1.58 1.62

Log of Real GDP per-capita a LNRGPC 392 8.75 1.15 5.29 10.63

Log of Real GDP per-capita (initial year) d LNRGPC_INI 379 8.71 1.15 5.14 10.57

Log of Investment (% of GDP) a LNI 391 3.15 0.22 2.48 3.84

Log of Gov’t Consumption (% of GDP) a LNGC 389 2.77 0.36 1.51 3.63

Log of Openness (Trade/GDP) a LNOP 386 4.26 0.59 2.63 6.09

Inflation, consumer prices (annual %) a INFP 374 56.78 305 -2.99 4735

Log of Secondary Schooling (% gross) a LNSSE 399 4.37 0.35 2.11 5.05

Population, Total a LNPOP 413 16.31 1.61 12.35 20.99

*Note: Data from the World Bank’s WDI (World Development Indicators) and WGI (Worldwide

Governance Indicators)

a-Calculated using average of non-overlapping five-year periods (1979-2008).

b-Calculated using average of non-overlapping five-year periods (1978-2007) since one year lag is used.

c-Calculated using average of 10-years (1999-2008) due to lack of data.

d-Initial year for each one of the 6 periods (1979, 1984, 1989, 1994, 1999 and 2004).

b) Analysis for the proxy for LPS: the IRI or IRG

In our empirical work, we shall proxy “OIs” with inventories to sales ratio (ISR).

Although quality increases in tandem with decreasing inventories under the impact of LPS (see

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for example Alles et al, 2000 for links between reducing inventories and increasing quality), we

do not have readily available a measure of quality improvement on a country basis. The proxy

ISR has been already established in literature as a good proxy for the JIT/QC or LPS. Thus,

Lieberman and Demeester (1999) who evaluate the relationship between inventory reduction

and productivity growth, conclude that JIT/QC system plays a considerably important role in

reducing its inventory level and improving the productivity level of a firm.

Furthermore, Nakamura M. and Nakamura A. (1989) viewed inventory/sales ratio as

main decision variables and mentioned “understanding inventory/sales ratio seems particularly

relevant since many US firms are beginning to implement the just-in-time production and

inventory system that some see as the key to the typically lower inventory/sales ratio of

Japanese firms.” Biggart and Gargeya (2002) wrote an article for the impact of JIT on inventory

to sales ratio and conducted their research on various types of inventories. They were

particularly interested in JIT system and found it really contributes to decrease the inventory

level of firms. Hence they found that total inventory to sales ratio and raw material inventory to

sales ratio significantly decreased.

More recently, Capkun et al. (2009) studied the relationship between inventory and

financial performance in manufacturing companies and they suggested that JIT and other types

of related techniques lower inventories and improve the productivity of the firms. They

mentioned that “recent research on the relationship between a managerial focus on improving

operations and performance has been concentrated primarily on JIT” and “the overwhelming

majority of studies show the positive effect of JIT implementation on earnings and financial

performance through the increase in productivity and inventory efficiency.” In addition, Irvine

(2003) observed the long term trends in US inventory to sales ratio and concluded that inventory

holding in the US has become much more efficient from the mid-1980s and this was the result

of adoption JIT and other inventory control systems by durable goods manufacturers.

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Several other researchers use inventory to sales ratio as a proxy for JIT/QC

implementation. For example, Swamidass (2007) used inventory to sales ratio to see the effect

of Toyota production system (TPS) on US manufacturing during 1981-1998; Keane and

Feinberg (2005) and Dalton (2009) use inventory to sales ratio as a proxy for adoption of

improved logistics and JIT; Kros et al. (2006) used inventory turnover to measure the impact of

JIT inventory system on OEM suppliers. More recently, Sanidas (2011a) used the ISR as an

independent variable representing OIs (JIT/QC) together with R&D representing TIs to gauge

the positive impact of technology (both OIs and TIs) on 18000 Korean manufacturing firms’

growth in productivity. Sanidas (2004, 2005, 2011b) has provided further evidence of the impact

of ISR on American, and Korean sectors. Other important references of scholars having used the

inventory to sales ratio are Ramey and Vine (2004), Bairam (1996), and Salem and Jacques

(1996). In conclusion, it is established in the literature to use inventory to sales ratio as a proxy

for JIT/QC implementation and related OIs in research.

The above literature review shows that, on a micro basis there is evidence that ISR has

been decreasing in many firms and sectors in countries where the LPS has been implemented.

However, not all firms and not all sectors experience this decreasing long term trend (see for

example, Sanidas, 2011). Hence, there arises the following question: on a macroeconomic basis

what is the overall trend of the ISR? A priori we do not have a readily available answer,

although from the discussion in the previous section we saw that the overall trend is also

decreasing in some countries such as the USA. A side effect of our paper would be to check this

overall decreasing trend for several countries (of our sample).

Nevertheless, on a macro basis, we do not have a readily available series of sales in

order to construct the ISR. We will then use two alternatives for sales, namely, gross domestic

investment and GDP (as many other researchers have done so). We shall call “IRI” the

inventories to investment ratio and “IRG” the inventories to GDP ratio, defined as follows:

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IRI jt= ΔINVjt−1 + ΔINVjt

GCF jt , and IRGjt=

ΔINVjt−1 + ΔINVjt

GDP jt, (2)

where INV3 stands for national inventories, and GCF

4 stands for gross capital formation, the

index ‘j’ stands for country, and ‘t’ stands for year.

Some basic descriptive statistics on IRI and IRG for the sample countries are shown in

Table 4. An important comment in this Table is that more developed countries have the tendency

to have a lower ISR (either IRI or IRG).

Table 4 Some basic descriptive statistics on IRI and IRG

Country List IRI IRG

Obs Mean S.D Min Max Obs Mean S.D. Min Max

Algeria 6 0.11 0.09 0.01 0.25 6 3.63 2.81 0.44 8.08

Argentina 1 0.08 . 0.08 0.08 3 17.45 1.94 15.21 18.64

Australia 6 0.01 0.00 0.01 0.02 6 0.29 0.10 0.15 0.41

Austria 6 0.03 0.00 0.03 0.03 6 0.73 0.13 0.60 0.95

Belarus 4 0.08 0.09 0.00 0.20 4 2.51 2.78 0.01 6.29

Belgium 6 0.04 0.03 0.02 0.10 6 1.03 0.81 0.37 2.62

Brazil 6 0.02 0.02 -0.02 0.04 6 0.36 0.39 -0.28 0.81

Bulgaria 6 0.11 0.15 -0.20 0.21 6 4.00 3.13 -1.33 7.19

Canada 6 0.01 0.02 -0.02 0.03 6 0.23 0.45 -0.34 0.63

Chile 6 0.04 0.03 0.01 0.08 6 1.06 0.40 0.57 1.63

China 6 0.14 0.07 0.05 0.22 6 5.19 2.65 1.79 8.49

Colombia 5 0.07 0.06 -0.03 0.12 6 7.34 9.00 1.54 23.01

Costa Rica 6 0.01 0.11 -0.17 0.17 6 0.65 2.27 -2.50 4.22

Croatia 3 0.10 0.05 0.06 0.15 3 2.25 0.74 1.54 3.03

Cyprus 6 0.06 0.04 0.01 0.10 6 1.81 1.10 0.19 2.82

Czech Republic 4 0.02 0.04 -0.03 0.06 4 0.62 1.04 -0.88 1.55

Denmark 6 0.03 0.02 0.00 0.05 6 0.60 0.32 0.13 1.00

Estonia 5 0.08 0.05 0.05 0.17 5 2.54 1.33 1.41 4.76

Finland 6 0.03 0.03 -0.02 0.07 6 0.73 0.79 -0.29 1.98

France 6 0.02 0.01 0.01 0.02 6 0.35 0.13 0.16 0.52

Gabon 4 0.02 0.02 0.00 0.04 4 0.69 0.59 0.16 1.42

Germany 6 0.00 0.02 -0.02 0.02 6 0.10 0.38 -0.37 0.60

Greece 5 0.05 0.04 0.00 0.09 5 3.17 4.72 0.03 11.48

Hong Kong SAR, China 6 0.04 0.03 0.01 0.08 6 1.12 0.84 0.22 2.10

Hungary 6 0.10 0.03 0.04 0.14 6 2.63 0.67 1.58 3.65

Iceland 6 0.01 0.01 -0.01 0.02 6 0.16 0.25 -0.14 0.55

Indonesia 6 0.06 0.09 -0.04 0.16 6 2.37 1.78 0.57 4.45

Ireland 6 0.02 0.01 0.01 0.04 6 0.55 0.21 0.35 0.80

Israel 6 0.03 0.03 0.01 0.09 6 0.74 0.77 0.23 2.29

3 Inventories are raw materials, work-in-progress goods, and final goods held by firms to meet temporary

or unexpected fluctuations in production or sales (WDI 2010). 4 GCF are assets that include land improvements (fences, ditches, drains, and so on); plant, machinery,

and equipment purchases; and the construction of roads, railways, and the like, including schools, offices,

hospitals, private residential dwellings, and commercial and industrial buildings.

17

Italy 6 0.01 0.01 -0.01 0.03 6 0.29 0.24 -0.15 0.58

Japan 6 0.01 0.01 0.00 0.02 6 0.35 0.22 -0.09 0.47

Kazakhstan 4 0.00 0.13 -0.18 0.10 4 0.61 2.96 -3.40 3.29

Korea, Rep. 6 0.01 0.02 -0.03 0.04 6 0.51 0.61 -0.55 1.11

Latvia 5 0.13 0.11 0.06 0.32 6 5.17 4.96 1.35 14.76

Lithuania 4 -0.02 0.07 -0.11 0.06 4 0.10 1.12 -1.08 1.60

Macedonia, FYR 4 0.11 0.13 -0.09 0.20 4 2.42 2.60 -1.42 4.13

Malaysia 6 -0.01 0.04 -0.07 0.02 6 -0.39 0.96 -1.71 0.60

Malta 6 0.02 0.06 -0.08 0.10 6 0.75 1.25 -1.05 2.70

Mexico 6 0.13 0.06 0.07 0.19 6 3.16 1.42 1.58 4.60

Namibia 6 0.00 0.08 -0.15 0.06 6 0.69 1.11 -1.08 2.33

Netherlands 6 0.00 0.01 -0.01 0.02 6 0.09 0.23 -0.17 0.44

New Zealand 6 0.04 0.01 0.02 0.05 6 0.82 0.23 0.53 1.17

Norway 6 0.05 0.04 -0.03 0.09 6 1.08 1.02 -0.81 2.04

Oman 1 0.01 . 0.01 0.01 5 18.10 9.00 5.46 26.85

Panama 6 0.10 0.09 -0.05 0.19 6 2.36 1.88 -0.49 4.90

Paraguay 6 0.07 0.03 0.04 0.12 6 1.58 0.82 0.92 3.18

Peru 6 0.04 0.03 0.02 0.10 6 1.06 0.99 0.37 2.96

Philippines 6 0.01 0.03 -0.05 0.05 6 0.34 0.72 -0.76 1.45

Poland 5 0.10 0.09 0.02 0.25 5 3.12 3.03 0.49 7.46

Portugal 6 0.02 0.01 0.01 0.04 6 0.59 0.36 0.21 1.27

Romania 4 0.12 0.19 0.01 0.40 5 3.52 4.83 0.20 11.84

Russian Federation 4 0.14 0.05 0.10 0.20 4 3.64 2.12 1.98 6.76

Saudi Arabia 6 0.04 0.05 -0.04 0.08 6 1.01 0.87 -0.08 2.19

Singapore 6 -0.01 0.08 -0.14 0.07 6 0.31 2.42 -2.47 3.19

Slovak Republic 5 0.02 0.06 -0.05 0.10 5 1.92 3.44 -1.06 7.32

Slovenia 4 0.02 0.07 -0.08 0.09 4 0.81 1.62 -1.30 2.65

South Africa 6 0.02 0.04 -0.04 0.06 6 0.51 0.81 -0.67 1.30

Spain 6 0.02 0.01 0.01 0.03 6 0.41 0.16 0.18 0.66

Sweden 6 0.02 0.02 0.00 0.05 6 0.29 0.26 0.03 0.77

Switzerland 6 0.02 0.01 0.00 0.04 6 0.48 0.38 0.03 1.16

Tunisia 6 0.04 0.02 0.00 0.07 6 1.11 0.68 0.13 1.89

Turkey 6 0.02 0.04 -0.04 0.08 6 0.38 0.80 -0.82 1.46

Ukraine 4 0.09 0.10 -0.01 0.21 4 2.74 3.24 -0.29 6.72

United Arab Emirates 6 0.04 0.02 0.01 0.05 6 0.93 0.35 0.36 1.21

United Kingdom 6 0.01 0.03 -0.03 0.03 6 -0.45 1.90 -4.29 0.51

United States 6 0.02 0.01 0.01 0.04 6 0.41 0.21 0.25 0.68

Uruguay 6 0.08 0.05 0.02 0.14 6 1.24 0.94 0.28 2.89

Venezuela, RB 6 0.02 0.10 -0.13 0.15 6 1.26 1.76 -1.32 3.77

Vietnam 4 0.08 0.02 0.06 0.10 5 7.37 6.78 1.67 15.28

Average (All Countries) 373 0.04 0.07 -0.19 0.40 383 1.79 3.58 -4.29 26.85

Figures 1A, 1B, and 1C show the yearly trend of IRI for selected countries such as the

developing China, India and Mexico, the Newly Industrialized Economies (S. Korea, Hong

Kong and Singapore) and the developed countries (United States, Germany and Japan); Figures

2A, 2B, and 2C show the corresponding trends for IRG. In these Figures we can see that IRI and

IRG are different according to a country’s economic development and growth. Thus we can

observe, for example, that even though IRI and IRG seem to be fluctuating along with business

18

cycles, most countries’ IRIs for the selected developed countries and NIEs have been drifting

downwards in the long run; exceptionally China’s IRI has been decreasing more substantially

(Naughton, 2007). However, Mexico and India’s IRI do not show much overall decreasing

tendency but on the contrary they have been increasing from the early 2000s.

Figure 1A IRI yearly trend for selected countries (USA, Germany and Japan)

Figure 1B IRI yearly trend for selected countries (Korea, Hong Kong and Singapore)

Figure 1C IRI yearly trend for selected countries (China, India and Mexico)

19

Figure 2A IRG yearly trend for selected countries (USA, Germany and Japan)

Figure 2B IRG yearly trend for selected countries (Korea, Hong Kong and Singapore)

Figure 2C IRG yearly trend for selected countries (China, India and Mexico)

20

In order to see more closely the link between the level of IRI and economic

development, Figure 3 shows a scatter-graph of IRI and real GDP per capita (taking the natural

logarithms of 5-year averages of lnRGPC) for the last period 2003-2008. As expected the two

variables are negatively correlated. The correlation coefficient between these two variables is in

the range from -0.12 to -0.43 depending on the examined period. High income countries tend to

have lower IRI while low-income countries tend to have higher IRIs. Figure 3 also shows that,

unsurprisingly, the IRI values for Germany (GER), United States (USA), Japan (JPN), South

Korea (KOR) and Singapore (SGP) are smallest since they are leading countries implementing

OIs and lean-production. Some emerging economics such as China (CHN), India (IND), and

Turkey (TUR) that used to have the largest IRI values in the first period (1979-1983) have

smaller IRIs in the sixth period (2003-2008) due to rapid industrialization and economic

development in recent years.

Figure 3 LnGPC and IRI for the 6th period (5-year average: 2003-2008)

21

c) OIs and Economic Growth

Table 5 shows regressions based on fixed effects (FE) model5. The random effects (RE)

model assume that individual country-specific effects are randomly treated (uncorrelated with

other regressors in the model) whereas the FE model relaxes this assumption and controls for

unobserved individual heterogeneity correlated with independent variables. However, since our

explanatory variables are not free from the endogeneity problem and violate the RE assumptions

in the regression, our conclusions are based on the FE model (in any case the Hausman test

confirms our choice of FE as being the right model to use). Hence, overall, as usually, the RE

model does not perform well in such cases. The time span used in these regressions is 30 years

(1979-2008) in terms of six periods (by taking 5-year average for each one of the 6 periods) as

previously mentioned. GDP growth rate is regressed on IRI, IRI lagged, IRI2, IRI

2 lagged, IRG,

IRG2 and control variables. The latter include initial per-capita GDP as a factor controlling for

the effect of the initial level of a country’s economic development. We also include investment,

5 As usually, the random effects model does not perform well in such cases.

DZA

BLR

BEL

BRA

BGR

CAN

CHLCHN

CRI

HRV

CYPCZE

DNK

FIN

FRA

GRC

HUN

IDN

IRLISR

ITAJPN

KAZ

KOR

LTU

MKD

MLT

MEX

NAM

NLD

NZL

NOR

PAN

PRY

PER

PHL

POL

PRTROM

RUS

SAU

SGP

SVK

SVN

ZAF

ESPSWE

CHETUN

TURUKR

ARE

GBR

USA

URYVEN

VNM

LVA

EST

MYS

GER

ISLAUS

AUTHKG

-.1

0.1

.2.3

IRI

6.5 7.5 8.5 9.5 10.5 11.5

lnrgpc

22

education and population to account for standard factors of economic growth. Furthermore, to

control for other macroeconomic factors, inflation and year dummies are included in all

specifications6.

Regression shown in column (1) in Table 5 is our basic model without the effect of OIs;

it displays correct signs of coefficient for all variables in accordance with the literature. In

columns (2) - (7), we introduce IRI or IRI_lag and IRI2 or IRI

2_lag, as well as IRG and IRG

2.

The results show that IRI and IRI_lag have a positive and significant effect on growth. When

IRI decreases, growth rises and vice versa; in the case of IRI one-year lag (IRI_lag), we can

infer that the effects are larger based on the magnitude of IRI coefficients. Thus, the sign of IRI

or IRG coefficients is negative as expected. When the quadratic form of IRI is included, then it

shows a concave form, hence IRI still decreasing (in a ‘square’ way) in most of the interval of

the period examined (shown by the IRI2 term). On the contrary, the quadratic term of IRG is

insignificant, hence suggesting that IRG is a more linear representation of the LPS impact on

GDP growth than IRI is. It is also worth noting that models in column (6) and (7) exhibit the

highest R2, thus suggesting the quadratic significance of IRI’s impact on GDP growth.

Table 5 The effect of LPS Economic Growth (Fixed Effects Model)

Dependent

Variable:

Real GDP

Growth Rate

Model

(1) (2) (3) (4) (5) (6) (7)

LnRGPC_INI -8.584*** (0.719)

-8.834*** (0.709)

-8.859*** (0.717)

-8.935*** (0.729)

-9.056*** (0.711)

-8.341*** (0.722)

-8.016*** (0.700)

LnGC -2.887*** (0.840)

-2.789*** (0.875)

-2.811*** (0.881)

-3.580*** (0.928)

-3.246*** (0.901)

-3.645*** (0.902)

-3.688*** (0.859)

LnI 2.462*** 3.010*** 3.050*** 2.980*** 3.900*** 2.410*** 2.941***

6 We also included yearly dummy variables which might take into account short business cycle

fluctuations.

23

(0.860) (0.878) (0.894) (0.931) (0.923) (0.915) (0.893)

LnPOP -4.820*** (1.413)

-4.186*** (1.401)

-4.211*** (1.406)

-4.541*** (1.416)

-4.500*** (1.387)

-3.780*** (1.387)

-3.236** (1.336)

LnOP 2.492*** (0.833)

2.334*** (0.843)

2.351*** (0.847)

2.417*** (0.853)

2.503*** (0.836)

2.405*** (0.829)

2.570*** (0.793)

LnSSE -0.112 (0.990)

-0.224 (0.972)

0.230 (0.974)

0.365 (0.990)

0.488 (0.970)

0.419 (0.961)

0.647 (0.921)

INFP -0.0008* (0.000)

-0.0007 (0.000)

-0.0007 (0.000)

-0.0007 (0.000)

-0.0007 (0.000)

-0.0007 (0.000)

-0.0008* (0.000)

IRG -0.193***

(0.060)

-0.216* (0.112)

IRG2

0.002 (0.006)

IRI -6.006**

(2.526)

1.973

(3.111)

IRI_lag -10.474***

(2.259)

-2.288 (2.600)

IRI2

-55.332***

(13.280)

IRI2_lag

-55.896*** (10.052)

Constant 143.02***

(24.657)

132.41***

(24.614) 132.88*** (24.725)

140.28*** (24.935)

137.96*** (24.56)

124.66*** (24.501)

110.10*** (23.689)

No. obs. 361 356 356 351 352 351 351

No. Countries 68 69 69 68 68 68 68

R-sq. 0.486 0.509 0.509 0.506 0.524 0.536 0.586

Note: *denotes statistical significance at the 10%, * * at the 5%, *** at the 1% level. Period effect is

considered in all models. Standard errors are in parenthesis.

Table 6 shows the results of GMM where we also included year dummies to control for

unobserved macroeconomic effects (not shown). Except for population (lnpop), all control

variables are treated as endogenous variables. The estimated coefficient of IRI or IRG is

significant and negative in all specifications and has a magnitude that is comparable to that in

the FE regression results. Overall, all coefficients are comparable in signs and significance with

those of FE regressions.

Table 6 The effect of LPS on Economic Growth (System GMM)

Dependent

Variable:

Real GDP

Growth Rate

Model

(1) (2) (3) (4) (5) (6)

LnRGPC_INI -1.746*** (0.259)

-1.076*** (0.132)

-1.843*** (0.252)

-1.783*** (0.266)

-2.006*** (0.207)

-1.940*** (0.226)

24

LnGC -1.415** (0.691)

-1.417*** (0.400)

-2.406*** (0.630)

-2.001*** (0.558)

-2.557*** (0.608)

-2.207*** (0.581)

LnI 3.875** (1.610)

6.435*** (0.897)

2.364* (1.399)

3.614** (1.524)

2.943** (1.272)

4.311*** (1.284)

LnPOP -0.323 (0.349)

-0.536 (0.336)

-0.750*** (0.283)

-0.613** (0.275)

-0.767*** (0.248)

-0.672** (0.261)

LnOP 1.513** (0.732)

0.546 (0.651)

0.978 (0.706)

0.968 (0.722)

1.017* (0.613)

0.870 (0.588)

LnSSE 4.230*** (0.942)

3.441*** (0.632)

4.853** (0.815)

4.687*** (0.783)

5.425*** (0.739)

5.008*** (0.768)

INFP -0.019*** (0.005)

-0.006*** (0.002)

-0.013** (0.005)

-0.014*** (0.005)

-0.011** (0.005)

-0.014** (0.006)

IRG -0.088** (0.036)

IRI

-10.506** (4.154)

-0.345 (4.007)

-3.992 (2.548)

IRI_lag -10.336***

(3.877)

IRI2

-92.605*** (21.910)

IRI2_lag

-60.315*** (14.333)

Constant -9.880

(9.191)

-13.637

(6.962)

5.419

(8.221)

-1.579

(8.052)

3.027

(7.279)

-1.817

(7.865)

No. obs. 351 356 351 352 351 351

No. Countries 68 69 68 68 68 68

Instruments a 31 43 34 34 38 38

AR(2) b 0.164 0.097 0.221 0.182 0.194 0.203

Hansen-test c 0.513 0.097 0.445 0.482 0.611 0.660

*Notes: *denotes statistical significance at the 10%, * * at the 5%, *** at the 1% level. Standard errors

are in parenthesis. Period dummies not reported.

‘a’ refers to instruments used( for example, LnRGPC_INI, LnGC, LnSSE, IRI, IRI_lag, IRG with

appropriate lags, year dummies of 1983, 1988, 1993, 1998, 2003, 2008 are used as instruments for first

differences and levels equations)

‘b’ refer to Arellano-Bond test for AR(2) in differences ( value)

‘c’ refer to Hansen test of joint validity of instruments ( value)

Due to lack of data for the entire 30-year period (used in our regressions) for the

variables R&D (RAD) and institutional performance (INS), the Hausman-Taylor (HT)

estimation method is applied. This method enables us to estimate the impact of TIs (as proxied

by R&D) and Institutions (INS) in the regressions7. Unsurprisingly, RAD and INS are

7 World Development Indicators (WDI) has consistent R&D data (as % of GDP) available from 1999 to

2008 and Worldwide Governance Indicators (WGI) cover the period from 1996 to 2009. Thus we took

10-year average of R&D expenditure and Institutional quality to be used in the HT model, which allows

us to have consistent and unbiased estimators through the use of instrumental variables technique. In any

25

significantly related with economic growth and the presence of these variables does not affect

much the statistical significance of IRI or IRG in the HT model. This suggests that IRI or IRG

works as a significant independent force on economic growth. The IRI or IRG has a negative

and large coefficient that is statistically significant in all specifications of the HT regression as

shown in Table 7. Overall, the results are similar to those with FE and GMM models.

Table 7 The effect of LPS on Economic Growth (Hausman Taylor Model)

Dependent Variable:

Real GDP Growth

Rate

Model

(1) (2) (3) (4) (5) (6)

Time-

Variant

Exogenous

LnGC -2.301*** (0.790)

-1.927** (0.817)

-2.573*** (0.871)

-2.578*** (0.852)

-2.531*** (0.835)

-3.009*** (0.796)

LnPOP -0.489 (0.751)

-0.323 (0.722)

-0.571 (0.775)

-0.530 (0.757)

-0.181 (0.657)

0.019 (0.569)

LnOP 1.378* (0.737)

1.292* (0.742)

1.416* (0.753)

1.370* (0.736)

1.245* (0.714)

1.292* (0.673)

LnSSE -1.103 (0.867)

-0.675 (0.858)

-0.606 (0.870)

-0.429 (0.850)

-0.443 (0.837)

-0.017 (0.798)

INFP -0.001*** (0.000)

-0.001*** (0.000)

-0.001** (0.000)

-0.001** (0.000)

-0.001** (0.000)

-0.001*** (0.000)

Time-

Variant

Endogenous

LnRGPC

_INI -7.823*** (0.672)

-7.857*** (0.661)

-8.030*** (0.679)

-8.179*** (0.662)

-7.244*** (0.657)

-6.855*** (0.625)

LnI 3.024*** (0.809)

3.573*** (0.824)

3.678*** (0.861)

4.353*** (0.852)

2.956*** (0.841)

3.083*** (0.822)

IRG -0.207***

(0.056)

IRI -7.028***

(2.372)

3.148 (2.962)

IRI_lag

-10.169***

(2.074)

-0.459 (2.380)

IRI2

-65.430*** (12.219)

IRI2_lag

-63.552*** (8.990)

Time-

Invariant

Exogenous

RAD 2.907*

(1.731)

2.484

(1.614) 2.907* (1.731)

2.864* (1.692)

2.280*

(1.413) 2.010* (1.198)

Time-

Invariant

Endogenous

INS 8.067***

(2.625)

7.891***

(2.488) 7.635*** (2.636)

7.814*** (2.576)

7.422*** (2.202)

7.183*** (1.902)

case, using R&D and INS as invariant to time might be close to reality because these two variables do not

change much over time.

26

Constant 68.850***

(14.211) 62.800***

(13.944) 68.650*** (14.816)

66.652*** (14.488)

58.130*** (13.122)

50.803*** (11.811)

No. obs. 334 337 334 334 334 334

Chi2. 275.20 279.62 275.20 319.35 302.50 380.70

Notes: (i) *denotes statistical significance at the 10%, * * at the 5%, *** at the 1% level. Standard errors

are in parenthesis; (ii) Period dummies treated as time variant exogenous is considered in all models,

however, not reported.

4 Conclusion

We used 69 countries (both developed and developing), three econometric methods

(FE, GMM, and HT) for the period of 1979 to 2008. These regressions included several

standard explanatory variables plus the proxy for LPS which is inventories to investment or

inventories to GDP. Consistently, our results indicate that countries maintaining low inventory

level associated either with their investment or GDP level have a higher rate of economic

growth, even after controlling for initial development stage, physical and human capital, and

furthermore, institutional quality and technology levels.

As we explained already, the ratios of inventories to investment or inventories to GDP

represent or depict the new prevailing lean production system (LPS) of many industries in

various countries and especially in more developed ones. As an indication of the mechanism in

this system, countries with lower (higher) levels of IRI or IRG experience higher (lower) GDP

growth. Our results also show that the long run trend of decreasing inventories is strongly

associated with economic growth (this is inferred via our panel models). This association is the

main reason of the previous indication that IRI or IRG are lower in advanced countries than in

developing countries.

Furthermore, our results give us supportive evidence that the lean production system

(which is a major type of OI) has significant implications for effective economic performance.

Unnecessarily large inventories could be seen as a sign of failure in management and efficient

production. In brief, promoting OIs through LPS is crucial to economic growth. Thus, we need

to draw our attention to economic growth in terms of systems in production since recent

27

economic development takes place in situations where OIs are promoted in society and where

the increasing complexity of modern TIs and competitiveness is based more on institutional and

organizational capacity and human capital than on the abundance of natural resources or

physical capital. The implication from our findings is that the TIs, capital, labor and institutions

are not important independently in determining economic growth but are dependent on the

efforts of how countries appropriately organizing their resources.

28

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Appendix 1 Country List

Code Country Name Code Country Name Code Country Name

DZA Algeria HKG Hong Kong SAR, China PER Peru

ARG Argentina HUN Hungary PHL Philippines

AUS Australia ISL Iceland POL Poland

AUT Austria IDN Indonesia PRT Portugal

BLR Belarus IRL Ireland ROM Romania

BEL Belgium ISR Israel RUS Russian Federation

BRA Brazil ITA Italy SAU Saudi Arabia

BGR Bulgaria JPN Japan SGP Singapore

CAN Canada KAZ Kazakhstan SVK Slovak Republic

CHL Chile KOR Korea, Rep. SVN Slovenia

CHN China LVA Latvia ZAF South Africa

COL Colombia LTU Lithuania ESP Spain

CRI Costa Rica MKD Macedonia, FYR SWE Sweden

HRV Croatia MYS Malaysia CHE Switzerland

CYP Cyprus MLT Malta TUN Tunisia

CZE Czech Republic MEX Mexico TUR Turkey

DNK Denmark NAM Namibia UKR Ukraine

EST Estonia NLD Netherlands ARE United Arab Emirates

FIN Finland NZL New Zealand GBR United Kingdom

FRA France NOR Norway USA United States

GAB Gabon OMN Oman URY Uruguay

GER Germany PAN Panama VEN Venezuela, RB

GRC Greece PRY Paraguay VNM Vietnam