Evaluation and estimation of the impact of organizational
technology on productivity of Korean firms and sectors
Jisun Lim, Elias Sanidas, Keun Lee
Abstract
It is a well known fact that technology innovation is a key element of economic growth. However, researchers
usually dismiss the organizational part of technology innovation and emphasize only the technical part oftechnology innovation. Technology innovation includes not only technical innovation but also organizationalinnovation as well.
The just-in-time and quality control (JIT/QC) process is one of the most influential organizational innovations
which has been widely used in various industries and many countries. Therefore, it is worthwhile to examine
economic growth in terms of this organizational innovation. Sanidas (2004, 2005, 2006) already studied therelationship between organizational innovation and economic growth using both Japan and USA cases.
However, Korea has not been studied yet and is worthy to study for the following two reasons. First, Korea isstill a developing country unlike USA and Japan. It can give us different results coming from different industrial
or cultural backgrounds. Second, Korea has achieved a very high economic growth in a very short period oftime. All this should present a very good case for reaching some explicit and direct conclusions for theusefulness and importance of JIT/QC in a fast developing country such as Korea.
This study uses the same methodology as per Sanidas (2004, 2005). Accordingly, the inventory to sales ratio
will be used as a well justified proxy for JIT/QC implementation on sectors and we will regress this proxy on
Korean sectoral TFP (Total Factor Productivity). Nevertheless, we use the KIS data base which is firm level
data and not sectoral level data directly since it contains financial statements, having many critical variables forour study; this data base contains all Korean firms listed on KOSDAK (Korean stock market). So, we use firmlevel analysis and do the sectoral level analysis by grouping these firms according to their industrial codes.
We expect that the JIT/QC organizational innovation has a significant impact on changes in productivity of
Korean firms and sectors. Furthermore, using firm level panel data, we can be more confident with the
hypothesis than previous research.

Introduction
1.1 Motivation of this study
There is plenty of evidence that technological innovation is one of the most important factors of
economic growth. Technological innovation basically consists of two major concepts; 1) technicalinnovations and 2) organizational innovations. Technical innovations are based on new technical1findings but there are more conditions required for technical innovation such as enhancing theworking procedure and the overall condition of a group. On the other hand, however, organizationalinnovation is the intentional effort firms to change an organization for enhancing the overall
efficiency of the group by finding and solving problems of the organization. There are many
interesting issues between organizational innovations and technical innovations. However, we aregoing to concentrate more on the relationship between organizational innovations and productivity ofa firm in this paper by considering a variety of other influencing factors such as technical innovations.

Unlike technical innovation, organizational innovation has not been studied by many researchers
because of its measurement problems. In other words, many researchers ignore the importance oforganizational innovations in economic growth, not because it is less important than technicalinnovations, but it is difficult to find out things to measure the organizational innovations.
Nonetheless Sanidas (2005) has brought the issues in regard of organizational innovations into themajor economic analysis because of its importance in economic growth. He believes that theorganizational innovation is as important reason for the growth in productivity as technicalinnovations.

His most important finding is that JIT/QC system, one of the most important organizationalinnovations, greatly improves the productivity of firms and sectors: He proves his hypothesis bymeans of inventory to sales ratio, a proxy for JIT/QC implementation. His research has concentrated
on sectoral analysis and on a limited number of explanatory variables. In our paper we will use dataon a firm basis and include more explanatory or controlling variables. Therefore, we use firm-level
panel data in order to see how the Korean manufacturing firms, the actual body of JIT/QC
implementation, responds to the organizational innovations. Furthermore, we solve the endogeneity
problem more efficiently by using Fixed Effect (FE) and System GMM models.

2

2.

Productivity and its measurement

2.1

Productivity and its measurement

Productivity is how many outputs one can produce with the same amount of inputs. Therefore, ahighly productive firm can produce more outputs with the same amount of inputs. This indicates that
productivity growth is a desirable phenomenon for the society since only society can spend moregoods than before but also firms can have more efficient production system which makes theircontinuous growth possible. There are mainly two broad concepts for the productivity: 1) Labor
productivity (LP) and 2) Total factor productivity (TFP). LP is calculated as output per worker, while
TFP is output residual which is not explained by tangible inputs. Both measurements are widely
accepted but TFP is better measurement for our research with the following two reasons.

Since TFP is an output residual, it reflects the impact of unobservable factors such as organizationalinnovations or technical innovations better than LP does. Sanidas (2005) briefly explained therelationship between TFP and LP in order to show the superiority of TFP over LP with the following
formula.
TFP= (Y/L) – b (K/L) = (Y/K) + (1-b)(K/L)(2.1)

LP= (Y/L) = (K/L) + (Y/K)(2.2)

While,

TFP, LP and all terms in brackets are expressed as growth rates (first differences in natural logs). The
constant b is capital’s share in total revenue, Y is production and L is labor. Equation (1) can beinterpreted as follows: Capital productivity (Y/K) expresses (is a proxy of) disembodied technology,
or OIs in the recent analysis, whereas (K/L) expresses embodied technology, or TIs in the recent
analysis (Reati, 2001). Consequently, according to equation (2), TFP is either the difference between
labor productivity (Y/L) and a fraction of embodied technology (K/L=TI), or the addition ofdisembodied technology (Y/K=OI) and a fraction of embodied technology (K/L=TI).

3

To sum, we first choose TFP as our indicator for the productivity of Korean manufacturing firms andsectors. This is because TFP might be able to catch the impact of organizational innovations better
than LP and it is more suitable to observe manufacturing industry which is the target space of theresearch. However, we can also check the result with LP or other productivity calculation method inorder to enhance the robustness of our result.

2.2. Modeling TFP and its estimation
There are numerous ways to calculate TFP. However, it can be classified by two broad concepts
according to Richard and Kenneth (2004): 1) growth accounting method and 2) index number method.
However, our method is more similar to the index number method in order to see a long-run trend of
the productivity. Suppose there is a Cob-Douglas production function:(2.3)
Y is gross output, A is total factor productivity (TFP), L is labor input, K is capital input and M ismaterial input; α , α , αare shares of labor, capital, and material respectively. Since we suppose

constant returns 1 to scale (CRS), α

α

α

1.

We can easily generate the equation (2.4) and (2.5) from the equation (2.3):
lnY

lnA

α lnL

α lnK

α lnM

(2.4)
(2.5)

Furthermore, α , α and α are determined by the following equations below in accordance withfirm’s profit maximization behavior:

1

α

PL L
P Y

α

PK K
P Y

For the justification of the research, you can see Sanidas book p223
4

α

PM M
P Y

PL is a price of labor input, PK is a price of capital input, PM is a price of material input and P is a
price of output, while L is a labor input, K is a capital input, M is a material input and Y is output.

Organizational issues and its measurement
3.1 Organizational issues and its measurement
There have been several attempts to prove the relationship between organizational innovations andproductivity. Baily and Gersbach (1995) wants to explain the difference of labor productivity byorganizational innovations. Furthermore, Brynjolfsson and Hitt (2000) points out that organizationalinnovation is a major driver of IT accomplishment and there might have been serious productivity loss
if there was no organizational innovations followed by technical innovations. Recently the JIT/QC(just-in-time/quality control) system is one of the most important organizational innovations firms andsectors. Tomer (2001) pointed out that “JIT/QC is the high performance work system which requires
fundamentally and radically different organizations and employer-employee relationships.” JIT/QC
which is also linked as LPS (lean production system) enhances the productivity by reducing all thewaste, satisfying customers, lowering cost, and improving quality. These became all possible since
JIT/QC system produces goods right after customer’s order. For this reason, the overall firms cannot
only remove inventories but also reduce all the waste and the overall cost of keeping and managing
inventories. Moreover, firms produce goods suiting for the taste of their customers so it improves thequality of products and achieve the customer satisfaction.

One of the most important waste is firm’s inventory which is reflection of efficiency of a firm.
Therefore, we conclude that the key feature of JIT/QC system is low inventory ratio 2 (for more details

2

The inventory to sales ratio generally reflects an efficiency of a firm. The lower the ratio, the more efficientfirms become, and therefore productivity increases. Reduction in this ratio over time is due to an important5

you can Sanidas, 2005, which includes as a extensive literature review on this). This lies in the factthat all the advantages of JIT/QC system are reflected in a low inventory ratio. Furthermore,
Lieberman et al. (1999) who evaluates the relationship between inventory and the productivity levelof a firm, concludes that JIT/QC system plays a considerably important role in reducing its inventory
level and improves the productivity level of a firm. 3 Besides Lieberman et al., there are still many
researchers such as Callen et al. (2000), Mitenburg (1993), and Jakes and Power (1993) who haveconsiderable interests in JIT/QC system and its impact on firm performances. To sum, we use
inventory to sales ratio as a proxy for JIT/QC implementation, one of the important organizationalinnovations, and examine its effect on firm performances to account for the hypothesis,“organizational innovation greatly influence the productivity of a firm.”

3.2 Implementation of the JIT/QC system in Korea
JIT, one of the “Japanese production systems”, was first introduced in Korea at the end of 1980s by
Hyundai automobile company. Lee (1995) analyzed why Hyundai, the biggest automobile company in
Korea, introduced the system and summarized reasons in three perspectives. First, Hyundai faced
demand shock at the end of 1980s. Hyundai had no choice to prevent such a drastic demand change inNorth America market since Hyundai was based on mass production system. Moreover, first union ofHyundai was established in 1987 and it made Hyundai more rigid and fixed. Even some negative
signs for Hyundai, domestic market came to be bigger and bigger since the motorization period cameto Korea. To overcome both exogenous and endogenous shock in 1980s and catch an opportunity on
an emerging domestic market, Hyundai had to come up with more flexible system and decided to
introduce JIT. However, Hyundai was not the only company having such problems in Korea. As welast mentioned, it was a worldwide phenomenon and most of Korean companies had the sameorganizational innovation, the JIT/QC system; however any other system that resembles the JIT/QC system canalso be a cause of reduction in this inventory to sales ratio.
3

He wonders which factors determine the inventory level of a firm and finds that customer communication andparticipation of employees in its problem solving process is the most important element to reduce the inventory
level of a firm. Therefore, he conclude that JIT/QC system significantly contributes the low inventory ratio.
6

problem with Hyundai. Daewoo, the third largest automobile company in Korea, had more serious
problems based on Daewoo itself. Daewoo was a joint company with GM and it made Daewoo muchharder to dare with problems instantly. Finally, Daewoo decided to become independent from GM and
implemented JIT in 1992. At the starting point with automobile industry, JIT quickly spread andadapted to other industries such as electronics, ship-buildings, and heavy industries which arecharacterized by assembly industry needing many components to complete a single product. Kim et al(1997) did the survey on 64 different firms of sizes and sectors in Korea and concluded “Korean
manufacturers widely use JIT but the degree of JIT implementation is different on each company.” Itmeans almost all major companies in Korea, including Samsung, LG, Hyundai, GS etc., competitively
adopted JIT in 1990s even though there were some differences in times and degrees. 4 In order toincrease the competitiveness of a company, many industries in Korea, not only manufacturingindustries, have been trying to adopt JIT. Yoo (2001) studied the performance of JIT implementation
on Korea service industry and revealed that many service firms in Korea did not yet implement JIT
but it is very important task for service industry to adopt JIT since service industries are the dominantsector in Korea and its importance would be bigger and bigger.

To sum up, Korea had experienced rapid growth after the Korean War but later reached some growth
limitation and worldwide difficulties at the end of 1980s. To overcome these difficulties in the late1980s, Korean companies decided to adopt JIT. However, they partially failed to improve firm
performances in the first place, because they just imported JIT and they misunderstood it: JIT is notjust a system, which is explicit, but it also philosophy, which is tacit. Therefore, organizationalinnovation should be needed for implementing JIT, not the process innovation or systemic innovation.
Fortunately, there was a chance to revise the result in 1997. Through the financial crisis in 1997, many
Korean companies found the true value of JIT and realized its importance in management system.
Therefore, they started to implement JIT as a new strategy for global leading companies after 1997

4

Newspapers in 1990s were also checked and provide further evidences for this.
7and emphasized its importance to their employees, too. 5 As a result, JIT was transformed to fit theirindustrial characteristics and now it has been widely used no matter what the sector is.

There are some special reasons for why Korean companies should adopt JIT: Korea has better NIS(National Innovation System) for JIT that Korea is the country which can maximize the profit from
JIT. (Rephrasing) First, Korea is strong in IT (Information Technology) industry. In the past, JIT wasbased on KANBAN, card system, but now it uses computer system for collecting or sending data.
Korea can manipulate and use better systems for JIT like RFID and we can expect it will giveconsiderable effects on firm’s profit from this. Second, Korea industries are consisted of big anddiversified companies like Samsung, LG or Hyundai called Chaebols: it means needs for JIT is moreprevalent in Korea. Already, top firm dominance in Korea is over 60% and it proves the need for JIT.
Chaebols have so many subsidiary firms, locating in distant places, that they need faster and moreaccurate communication method for collecting and sending data for JIT. Moreover, JIT not only helps
to increase firm performance but also have the positive side-effects on relating industries. Therefore,
JIT will be more widely used in Korea for better performances and outcomes.

4

Empirical analysis

4.1 Data and variables(1) Data

We mostly use the data established by Jung in 2009. 6 He already calculated all the importantcomponents of TFP such as labor, capital and other explanatory variables. Based on his calculation,
we made lnTFP and some other independent variables. The sample firms are all the listed firms and5This shows that Korea companies now understand what the true meaning of JIT is and how they try to do forit.
6

We so much appreciated to Moo Sup, Jung since he did great works to establish the dataset we use. Jung’s
data is based on firm level data, KIS (Korea Information Service) and it has financial statements of all the listed
and delisted firms and he calculated the firm level TFP and other explanatory variables depending on this. For
more information about the data, you can find in his 2009 doctoral thesis, ‘Sectoral Systems of Innovation’.
8

delisted firms in manufacturing industry during the sample periods. This firm sample includes also thefirm observations found in the period before the firm is listed. We exclude the firms under 8 years andsome important missing or extreme values.
Table 4-1 Firms by sector and year in the sample
ICPA
code
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

Industry name

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

20095

sum

Food and kindred products
Textile mill products
ApparelLumber and wood
Furniture and fixtures
Paper and allied
Printing publishing and allied
ChemicalsPetroleum and coal products
Leather
Stone clay glass
Primary metal
Fabricated metal
Machinery non-electElectrical machinery
Motor Vehicles
Transportation equipment
and ordnanceInstruments
Rubber and misc plastics
Total

41
11
12
3
5
15
1
64
3
5
23
37
9
15
43
24

42
17
13
3
6
22
1
64
3
5
25
38
13
18
58
31

38
19
16
2
6
23
1
62
4
6
25
42
15
19
64
37

41
17
16
2
8
25
2
62
4
7
22
47
18
23
69
37

41
18
18
2
9
23
2
55
4
8
21
49
21
24
69
43

39
20
16
3
6
27
2
61
4
8
23
52
24
23
81
41

37
19
16
4
7
27
2
59
3
8
23
53
21
26
78
41

37
21
17
4
7
30
2
67
4
8
23
54
26
29
92
43

39
21
17
4
8
30
2
69
4
8
22
55
23
30
93
45

39
22
14
4
6
31
2
67
4
8
22
59
28
26
99
41

40
21
19
4
7
28
3
71
4
8
23
59
24
35
101
43

42
21
15
4
6
28
4
77
4
9
25
63
28
47
132
42

47
22
20
4
9
32
4
86
3
9
26
65
28
56
155
52

50
20
27
4
13
32
5
99
4
9
27
67
28
54
177
53

44
22
24
4
11
28
6
94
3
8
26
68
23
59
181
50

45
23
23
4
10
28
4
86
4
9
26
68
25
52
168
51

48
22
22
4
11
29
5
87
4
9
29
69
26
54
168
48

44
25
23
4
10
27
4
80
3
8
26
70
29
60
180
52

44
23
25
5
9
27
4
74
4
9
23
70
30
66
184
49

49
25
22
4
8
31
6
83
5
8
24
71
29
69
189
52

47
24
24
4
8
31
6
86
5
8
24
71
29
72
184
52

894
433
399
76
170
574
68
1,553
80
165
508
1,227
497
857
2,565
927

7

8

8

8

8

7

5

6

6

7

6

8

8

8

6

6

6

7

5

7

6

143

5
10
333

5
10
382

5
11
403

4
12
424

3
12
430

5
11
453

7
14
450

9
17
496

10
15
501

9
15
503

13
16
525

15
21
591

11
22
659

14
26
717

15
25
697

13
29
674

18
25
684

15
24
691

16
23
690

17
22
721

16
22
719

225
382
11,743

Notes: ICPA is the abstract of International Comparison of Productivity among Asian countries and it is a kindof industry classification method.

(2) Dependent variable:,

The dependent variable, representing the productivity, is lnTFP which is calculated in section 2,
equation (2.5).

(4.1)

α

PL L
,α
P Y
α

P K
,α
P Y
α

α

PM M
P Y

1)

According to this formula, we generate lnTFP by using real sales for Y, number of employees for L,
amount of real capital stock for K, and amount of real material for M Also, we use nominal salary of
employees for

PL L , nominal material cost for PM M, and nominal sales for P*Y . Above all,9

most important thing in here is that we calculate α as 1
assume ααααα instead of α

PK K
P Y

since we

1 which means Constant Returns to Scale (CRS).

The summary of relevant statistics is shown in table 4-2. The average lnTFP has been increased until
1996 but decreased in1997-1998. 7

Table 4-2 Basic statistics of lnTFP of Korean manufacturing firms
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Total

N
333
382
403
424
430
453
450
496
501
503
525
591
659
716
697
674
684
691
690
721
719
11742

Min
-0.2208083
0.1240976
0.1830527
0.0919996
0.1521869
0.1801595
0.2761996
0.2602434
0.2243188
0.2670184
0.2986575
0.3065836
0.1547144-0.1819205-0.0126298
0.1034023-0.1289452-1.236561-0.5199544
0.044456-0.852604-1.236561

Ave
1.37987
1.446067
1.501747
1.573021
1.720657
1.768846
1.832516
1.887486
1.91578
1.892634
1.878173
1.968786
1.850326
1.629648
1.684207
1.692368
1.682121.748631.762732
1.801422
1.824905
1.748789

Max
3.655658
4.525209
7.681687
7.500089
4.714948
6.627207
6.320239
5.577044
6.31553
6.743624
5.583751
6.746835
7.197788
5.158665
5.429958
5.911024
5.806043
6.349205
6.728796
6.875051
7.840692
7.840692

S.D
0.6652139
0.6634652
0.7152628
0.7270731
0.7657983
0.8612827
0.8221902
0.8198778
0.8210027
0.7973706
0.8137102
0.8635861
0.8646484
0.7510954
0.7366585
0.8035699
0.7563073
0.8228802
0.8414477
0.8961543
1.018244
0.8259959*Notes: ‘N’ denotes the number of firms of the year.
‘Min’ denotes the minimum level of lnTFP of the year.
‘Ave’ denotes the average level of lnTFP of the year.
‘Max’ denotes the maximum level of lnTFP of the year.
‘S.D’ denotes the standard deviation of lnTFP of the year.

7

The financial crisis of East Asia occurred in 1997 and productivity might be affected by the crisis.
10

Graph11.

Avverage lnTF
FP of Koreann manufactu
uring firms from 1985 to 2005

(3) IndependentIt variables
Organizzational innovation
The impportance of organizationnal innovatiions and its impact on firm perforrmances aree alreadydiscusseed in section33. To reflect its impact onn the producctivity of a fiirm, we use ffist lagged innventory
to saless ratio as a proxy for JIT/QC impplementationn, one of thee most impoortant organ
nizationalinnovatiions of the recentrfirms. 8 Our hypotthesis is thatt this proxy might move toward the opposite
way witth its dependdent variable.. In other woords, lnTFP wouldw
be higgh when therre is low inventory tosales rattio is low.

4 Basic sttatistics of organizational innovattion (inventtory to salees ratio) of Korean
Table 4-3
manufaacturing firm
ms
Yeaar
1985
1986
1987
1988
1989
1990
1991
1992

N
3333
3882
4003
4224
4330
4553
4550
4996

Min
0.0160597
0.00017373
0.0101791
0.00051579
0.00064191
0.00051404
0.00050866
0.00011682

Mean
0..1530377
0..1488465
0..1452755
0..1436668
0..1580199
0..1520546
0..1579913
0..1577134

8Max
0.4587215
0.4988559
0.4765949
0.4805244
0.4877077
0.4654154
0.4993039
0.4991425

S.D.
0.0825283
0.0853205
0.0823643
0.0828182
0.0943394
0.0909525
0.0979253
0.0997336

We usee first lagged inventoryi
to sales ratio as a proxy for org
ganizational innnovation sincce normally innventory
is measurred at the end of year and itts impact is reeflected on thee next year.
11

1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Totaal

5001
5003
5225
5991
6559
717
6997
6774
6884
6991
6990
7221
719
1177430.00094726
0.00080384
0.00014349
0.00009594
0.00004924
0.00013573
0.00020081
0.00020842
0.00003967
0.00002979
0.00002071
0.00003857
0.00001744
0.00001744

0..1550362
00.149444
0..1544389
0..1486228
0..1520245
0..1374854
0..1350695
0..1441873
0..1446189
0..1449994
0..1453651
0..1399202
0..1414903
0..1470927

0.498036
0.4945502
0.4979129
0.4840247
0.4828551
0.4998363
0.4798976
0.48816
0.4831915
0.499683
0.4923962
0.4992245
0.4930248
0.4998363

0.1005645
0.0938321
0.0944955
0.0976862
0.0955592
0.0969608
0.0892584
0.0935725
0.0957718
0.0979012
0.0950484
0.092623
0.0978475
0.0941481

Notes: ‘N
N’ denotes thhe number of firms of thee year.
‘‘Min’
denotees the minimuum level of lnTFP
lof the year.
‘‘Ave’
denotes the averagee level of lnT
TFP of the yeear.
‘‘Max’
denotees the maxim
mum level of lnTFP of thee year.
‘‘S.D’
denotess the standarrd deviation ofo lnTFP of thet year.

4 Average inventory tot sales ratioo of Korean manufacturring firms frrom 1985 to 2005
Graph4-2.

an manufactturing firmss from 1985 to 2005
Graph44-3. Minimum inventoryy to sales rattio of Korea

12Graphs 4-2, and 4-3 explicitly show us that low inventory level is not only caused by the economic
condition of the year but also more considerably influenced by JIT/QC implementation. This isbecause the average inventory to sales ratio has continuously decreased since the early 1990s which
was the starting point of JIT/QC implementation, even though there were small ups and downs during
the period. The fluctuation has been largely influenced by the economic condition of the year.
Furthermore, minimum level of inventory to sales ratio shows us more explicitly that inventory tosales ratio dropped due to the firm’s intentional effort to reduce its inventory level. Therefore,
inventory to sales ratio functions appropriately as a proxy for JIT/QC implementation andorganizational innovation for our research.

However, here is one more thing to consider. When we use inventory to sales as a proxy for theorganizational innovation, we should use the first lagged one for the research. This is because
organizational innovation needs time to be implemented and also we need to inventory level at the endof the year. Therefore, we specifically use the first lagged variable of inventory to sales ratio as aproxy for the organizational innovations.

Innovation capability
Innovation capability is considered as one of the most important factors for the productivity. Normally,
R&D expenditure and the number of patents are most widely used to measure the innovation
capability of a firm (Solow (1957), Griliches (1964, 1973, 1979, 1980, 1984, 1986)), Scherer (1965)).
However, R&D expenditure and patents are strongly correlated with each other so that only need tochoose one for the research. We choose R&D expenditure to sales ratio as a proxy for the innovation
capability of a firm. This is because 1) patents are not normally collected by all the sample firms
during the sample period and 2) patents have so many variations cross the sample firms throughout
the years. On the other hand, R&D expenditure to sales ratio is easy to be found and calculated in its
financial statements and has no such a huge variation across the firms throughout the years.
13Efficiency wage
‘Efficiency wage hypothesis’ believes that each firm has an incentive to give high salary to theirworkers in order to increase their productivity. Normally people prefer to work for firms paying theiremployees high salary. So, the firm willing to pay high salary can choose efficient workers and inducethem put intense effort on work. Therefore, employees would not attempt to leave such a company. As
a result, such a firm can lower the transaction cost. High salary prevents the labor union as shown inthe case of Samsung 9 and gives sociological reward to their workers. Many researchers such as
Stiglitz (1974,1976, 1984) , Solow (1979) and Dickens (1986) supported the hypothesis and there
have been so many empirical works in various ways. Therefore, we refer higher salary to efficiency
wage and use firm’s relative salary level, in a comparison with to its industry level, (average salary ofa firm- average salary of the industry) / average salary of the industry, as a proxy for efficiency wageof each firm.

Learning by education‘Learning by education’ is the next step of ‘Learning by doing’. People can improve their skills when
they learn. Therefore, it is no surprise that ‘Learning by education’ is the most common way toenhance one’s ability. Based on the hypothesis, we use education expenditure for sales to sales ratio asa proxy for learning by education. From the model, we can see whether firm’s investment in its humanresource brings a good result to the firm or not.

K/L
To control technical innovation, we use K/L, real capital stock to the number of employees, as a proxy
for technical innovation in case of using labor productivity (LP) as a proxy for the productivity.

9

Samsung does not have a labor union and many people point out that one of the biggest reason for this is they
have the highest salary than any other groups in Korea.
14

However, K/L should be negatively correlated with its productivity level in case of TFP since K and L
both are the input factors lowering the TFP and usually K grows faster than L. 10
The summary of descriptive statistics is shown in table 4-4 and correlation among the variables isshown in table 4-5. Also, we can check the partial correlation of the variables in table 4-6. Just note
that: 1) There is no apparent problems of multicollinearity. 2) Partial correlation of first lagged
inventory to sales ratio is negative which is indicative of the result we expect.

Table 4-4

Descriptive table of independent variables

Variables
Organizational innovation(inventory sales ratio)
Innovation capability(R&D expenditure sales ratio)
Efficiency wage
(salary gap ratio)
Learning by education(education expenditure for sales)
K/L(captital stock/num. of employees)

Level

N

Min

Mean

Max

S.D.

Firm

11743

0.00020.14710.49980.0941

Firm

11743

0.00000.01070.29240.0230

Firm

11696

-0.6300-0.01780.79000.2432

Firm

10822

0.00000.00030.00090.0003

Firm

11743

443

97438

1974873

143787

Notes (1): ‘N’ denotes the number of firms of the year.
‘Min’ denotes the minimum level of lnTFP of the year.
‘Ave’ denotes the average level of lnTFP of the year.
‘Max’ denotes the maximum level of lnTFP of the year.
‘S.D’ denotes the standard deviation of lnTFP of the year.
Notes (2): since variables are not measured by an integrated unit, it is meaningless work to compare
its coefficient value across the variables.

lnTFP is calculated as lnY α lnL α lnK α lnM. Therefore, lnTFP increases only if L increased morethan K or L decreased less than K. However, normally K increases and L decreases when technology develops,
so usually K/L gives negative sign to the lnTFP.
10

15

Table 4-5 Correlation among the variables
1

2

3

4

5

6

Productivity

1

1.00

L.Productiviry

2

0.87

1.00

Organizational innovation

3

0.07

0.12

Innovation capability

4

0.15

0.14

0.11

1.00

Efficiency wage

5

-0.06

-0.11

-0.11

-0.03

1.00

Learning by education

6

0.10

0.08

-0.04

0.12

0.08

1.00

K/L

7

-0.30

-0.27

-0.06

-0.08

0.23

0.04

7

1.00

1.00

Notes (1): correlations between explanatory variables are not that significant.
Notes (2): Productivity is measured by lnTFP and L.lnTFP means first lagged variable of lnTFP.
Organizational innovation is measured by the first lagged variable of inventory/sales.
Innovation capability is measured by R&D expenditure/sales.
Efficiency wage is measured by average salary of a firm/average salary of the sector-1.
Learning by education is measured by education expenditure /sales.

Table 4-6

Partial correlations with dependent variable
Variable

Partial correlation
0.86
-0.08

Significance
0.00
0.00

Learning by education

0.06
0.05
0.10

0.00
0.00
0.00

K/L

-0.17

0.00

L.lnTFP
Organizational innovation
Innovation capability
Efficiency wage

Notes: dependent variable is lnTFP
L.lnTFP means first lagged variable of lnTFP.
Organizational innovation is measured by the first lagged variable of inventory/sales.
Innovation capability is measured by R&D expenditure/sales.
Efficiency wage is measured by average salary of a firm/average salary of the sector-1.
Learning by education is measured by education expenditure /sales.

4.2 Regression models
As foreshadowed previously we will now make the following hypothesis so that we can test it
empirically.
Hypothesis: Organizational innovation is positively correlated with the productivity level of a firm.
There are mainly three things to be considered for the regression. First, we should check and control

16

the economic condition of the year. 11 This is because the key variable, inventory to sales ratio, theproxy for organizational innovation, is strongly influenced by the economic condition of the year.
However, by the number of the simple regression, we found the fact that the economic condition of
the year does not significantly influence the productivity of a firm. 12 Therefore, we do not need toconcentrate on this issue as long as we use inventory to sales ratio as a proxy for the organizationalinnovation.

Second, we should consider the individual differences of firms which can cause trouble with the error
term. Even though we focus only on the firms in manufacturing sectors, there are still many different
characteristics depending on firms and sectors. Therefore, we should either include the individual
dummies or do other things to solve the problems. We use the Fixed Effect (FE) model in this caseand extract the individual term by demeaning the value from the original model. However, there arestill endogeneity problem caused by its first lagged dependent variable and inventory to sales ratio.

Therefore, we finally use the system GMM to solve the the endogeneity problems of the model. There
are mainly two major reasons for the endogeneity problems, unobserved common factors and twoway causation. To solve the endogeneity problem caused by the unobserved common factors, we use
the Fixed Effect (FE) model. However, the endogeneity problem occured by the two-way causation
still remains even though we use the FE model. Therefore, we should find another way to solve bothendogeneity problems. However, GMM is the only way to solve the endogeneity problem of the
dynamic panels. Therefore, we use System GMM which is more efficient model than GMM to prove
the hypothesis.

11

For example, economic boom or crisis is the best illustration for economic conditions.

12

We did the simple regression to know whether productivity is influenced by low level of inventory due to theeconomic condition of the year. However, low inventory level due to the economic boom in 1985-1989 did not
significantly influence the productivity level in 1985-1989. On the other hand, low inventory level due to the
JIT/QC implementation significantly enhances the productivity level.
174.2.1

Simple regression model

JIT/QC started to be implemented in Korea at the end of 1980s. Therefore, we can briefly recognize
the impact of organizational innovations on firm’s performance by comparing before and after the
implementation of JIT/QC. The period before JIT/QC implementation is 1985 to 1987 and the period
after JIT/QC implementation is 1988 to 2005.

Dependent variable is lnTFP and independent variables are first lagged lnTFP and first lagged
inventory to sales ratio: we use the first lagged lnTFP to control the autocorrelation problems and firstlagged inventory to sales ratio is used for measuring the impact of organizational innovation.

,

,

,

(4.2)

From the regression, we can find out the fact that there is no strong relationship between inventory tosales ratio and the productivity level of a firm. 13 In other words, the economic condition of the year
does not seriously affect the productivity level of a firm by lowering the inventory level. This isbecause inventory to sales ratio in 1985-1987 was fully influenced by the economic condition of theyear, 14 but it failed to enhance the productivity of a firm significantly.

On the other hand, however, inventory to sales ratio in 1988-2005 has a strong negative relationshipwith its productivity level: this is what we have expected as the productivity of a firm increases when
the inventory level is low. Since inventory to sales ratio in 1988-2005 is influenced not only by the
economic condition of the year but also by JIT/QC implementation which started to be implemented

13

T-statistics of organizational innovation indicates that organizational innovation in 1985-1987 is not
significant, which is correct expected result of us.
14
Inventory to sales ratio is fully influenced by economic condition in 1985-1987 since JIT/QC was not yet
introduced in Korea. However, 1985-1987 is economic boom in Korea since 1988 is the year which held theSeoul Olympic in Korea, so we can check from the graph 4-2 the inventory to sales ratio dropped in 1985-1987.
18

in Korea at the end of 1980s, we can generalize the fact that low inventory to sales ratio due to the
JIT/QC implementation increases the productivity of firm significantly. In other words, organizationalinnovation enhances the productivity of a firm significantly.

Table 4-7 Simple regression before and after JIT/QC implementation
Variable

Level

BeforeAfter

Past level of Productivity

Firm

0.87**

0.88**(32.68)(105.82)

Firm

-0.25
(-1.41)-0.30**(-6.24)Firm

0.26**

0.25**(L.lnTFP)
Organizational innovation(L.inventory/sales)
Constant(7.58)(18.26)

Obseravaions

661

9956

Adjusted R-square

0.8

0.76

legend: + p<.1; * p<.05; ** p<.01

Notes (1): Before JIT/QC implementation is 1985-1987.
After JIT/QC implementation is 1988-2005.
Notes (2): L.lnTFP means first lagged variable of lnTFP.
Organizational innovation is measured by first lagged variable of inventory to sales ratio.

Table 4-8 Simple regression for finding the year in which JIT/QC started to be implemented
Variable

Level

Past level of Productivity

Firm

1985-1988

1985-2005

0.84**

0.87**

0.90**

0.88**(32.68)(46.33)(105.82)

Firm

0.07
(0.23)-0.25(-1.41)-0.26+(-1.90)-0.30**(-6.24)Firm

0.24**

0.26**

0.24**

0.25**(L.inventory/sales)
Constant

1985-1987(17.78)(L.lnTFP)
Organizational innovation

1985-1986(4.47)(7.58)(8.25)(18.26)

Obseravaions

311

661

1032

9956

Adjusted R-square

0.77

0.8

0.81

0.76

legend: + p<.1; * p<.05; ** p<.01

Notes (1): L.lnTFP means first lagged variable of lnTFP.
Organizational innovation is measured by first lagged variable of inventory to sales ratio.
Notes (2): We also did the same regression for 1985-1989 and got more strong results than 1985-1988.
However, 1985-1988 result already give us the strong result, so we did not add in here.

194.2.2

Panel Data Analysis(1) Pooled OLS Model (N: 9125 firms, T: 20 years)

Now we want to prove the hypothesis with other explanatory variables which are already proved to bevalid in other studies to explain the productivity. Therefore, we use lnTFP , as a dependent variable
for the productivity of a firm and independent variables are 1) inventory to sales of a firm, 2) R&D
expenditure / sales of a firm, 3) average wage of a firm / average wage of the sector – 1, 4) education
expenditure for sales /sales of a firm and 5) capital stock/ number of employees of a firm. However,
we also put the first lagged dependent variable to adjust the autocorrelation problem of the model andindustry and time dummy variables to control the industry and time effect.

,

¾

,

, ,

,

.

Independent variables: ∑5i 1 βi Xi,f,t
①
②
③
④
⑤

Organizational innovation: first lagged (inventories/sales) of a firm
Innovation capability: R&D expenditures/ sales of a firm
Efficiency wage: Average wage of the firm/Average wage of the sector – 1
Learning by education: Education expenditure for sales /sales of a firm
K/L: capital stock/ num. of employees(2) Fixed Effect (FE) Model (N: 9125 firms, T: 20 years)

Each firm has all different characteristics and they determine the way to produce, and affect the
productivity level of a firm. Therefore, we should take account the individual effects of each firm into
the model. Fortunately, we use panel data for our research and there are many effective ways tocontrol the individual effect in panel data analysis. We control the individual effects by putting
20

individual intercept into the model and transforming the equation either using Fixed Effect (FE) or
Fist Difference (FD) model. Since any individual effects of a firm are not considered in Equation (4.3),
we make the new model, equation (4.4), which adds individual intercepts (αin the presence of thecommon intercept α .

,

∑

,

,

, ,
,

,

. )

,

However, equation (4.4) is not an appropriate model for OLS estimation since individual effect term(αis correlated with other explanatory variables. Furthermore, random effect estimation also canbring some inefficient results to us since individual firm’s characteristics are also correlated with its
dependent variable, productivity level. Therefore, we should find another method to estimateparameters. Fixed effect (FE) model is the most common and simple way to solve this kind ofproblem by removing the individual effect term from the model. Therefore, we use FE model for the
research, but unfortunately we lose our time-invariant variables such as industry dummies (θduring the process.

(3) Regression Results with Pooled OLS, FE and RE

The regression results are very satisfactory in overall. However, there are still several interesting
things to discuss.

First, the past productivity level is influence the most powerfully the present levelof the productivity. This is because the productivity, usually considered as an equivalent to thetechnology, is not the one which is easily disappeared or discarded. Technology is not easily discarded
since it usually embodies people in a firm and is continuously kept by the people. Therefore, theproductivity of today is continuously influenced by the past level of it unless there is a big paradigm
shift making all the past experiences become useless.
21

Second, efficiency wage, innovation capability, learning by education and organizational innovations
are all the important factors for the productivity. However, there are some other important points to bementioned. All the variables including organizational innovation and innovation capability become
stronger and more significant only after controlling the individual effect by using FE or RE model. In
other words, organizational innovation and other variables are heavily influenced by the individual
characteristics of a firm, which are unobservable, but should be controlled. This result implies thateach firm differs in characteristics which suit only for the company. Therefore, so many things should
be considered when they want to change their policy to improve the productivity.

To sum up, no matter what the model we use, we consistently have the good result with otherimportant explanatory variables for the productivity. Therefore, we almost get to the point that“organizational innovation is positively correlated with the productivity level of a firm” but there is
still one more thing that is not yet solved but should be solved.

Table 4-9. Regression results with lnTFP and explanatory variables
Variable

Level

OLS

FE

RE

Past productivity level

Firm

0.85**

0.58**

0.76**(87.02)(33.98)(59.81)

Organizational innovation

Firm-0.31**(-5.71)-0.56**(-7.07)-0.47**(-7.08)Innovation capability

Firm

1.68**

1.06*

3.01**(2.55)(5.41)(3.56)

0.18**

0.59**

0.33**

Efficiency wage

Firm

(10.14)(18.36)(13.97)

Learning by education

Firm

44.93*

76.21**

56.62**(2.50)(3.64)(2.83)

K/L

Firm-0.00**-0.00**-0.00**(-10.84)(-9.39)(-10.97)

constant

Firm

0.32**

0.75**

0.46**(13.75)(25.40)(15.03)9125

Observation

9125

9125

Adjusted R-square

0.77

0.44

Wooldridge test statistics

235.06 (0.00)

Hausman test statistics

386.66 (0.00)

legend: + p<.1; * p<.05; ** p<.01

Notes (1): Productivity is measured by lnTFP and L.lnTFP means first lagged variable of lnTFP.
Organizational innovation is measured by the first lagged variable of inventory/sales.
22

Innovation capability is measured by R&D expenditure/sales.
Efficiency wage is measured by average salary of a firm/average salary of the sector-1.
Learning by education is measured by education expenditure /sales.
Notes (2): OLS means Ordinary Least Square model while FE means Fixed Effect model and RE
means Random Effect model. Industry and year dummy variables are included in themodel. 15 All estimates in table are from white-heteroskedasticity-consistent covariancematrix estimator. Wooldridge test justify the regression result with the first lagged
dependent variable 16 and Hausman test justify the regression result with RE.

4.2.3

Dynamic Panel Data Analysis(1) System - Generalize Method of Moment (GMM) model
To eliminate the endogeneity problem caused by the unobserved common factors, we use FE model.
However, we still have the endogeneity problem due to the two-way causation and it is very difficult
to find the instrumental variables (IV) outside of the model. To solve this problem, GMM needs to be
developed. GMM tries to find instruments for endogenous variables in its model. Therefore, it is
widely used for its convenience. GMM is based on the First Difference (FD) Model. We can directly
generate the equation (4.6) from the equation (4.4).
∆

,

∆

,

∑

∆

, ,

∆

∆

,

(4.5)

Even though we transform the equation (4.4) to (4.5), we can see that the first lagged timedifferencing variable (∆ TFP ,) still generate the endogeneity problem with its error term (∆,,which is called two-way causation. Therefore, we should find another method to solve theendogeneity problem of FD model and use Generalized Moment Method (GMM) instead of FD.
Arellano and Bond (1991) developed GMM method from the Anderson and Hsiao which uses only
the second lagged variable (lnTFP ,(lnTFP , , … , lnTFP ,15

as its IV while GMM uses every possible moment conditions

to find its’ consistent estimates.

We also include the result with dummy variables in Appendix 4-1.

16

Wooldridge test(2002) is the test for autocorrelation in panel data. H is no first order autocorrelation. A
significant test statistic indicates the presence of serial correlation.
23

∆

,

∆

,

∑

∆

, ,

∆

,

for

t.

(4.6)

However, GMM method can also generate the biased result by excluding some important timeinvariant variables. Therefore, we decide to use System GMM which uses both time differencing
equation and level equation at the same time. Therefore, System GMM basically uses both equation(4.4) and (4.6). However, System GMM uses differenced variables as its instruments for level
equation. To eliminate the endogeneity problem of the model, we use various variables in the modeland use its lagged differencing and lagged variables as its IV.

(2) Regression results for an electric machinery industry

We do the case study first before we apply the System GMM into all the sample firms in
manufacturing industry. This is because we need confidence in the research even if there are no
endogeneity problems between the dependent and independent variables. We decide to choose theelectric machinary industry for our case study to measure the impact of organizational innovations on
the performance of firms since 1) electric machinery industry is one of the most representative
industries for the JIT/QC implementation and 2) it has so many sample firms that it has a fewer
variation than others.

On the other hand, we do not choose motor vehicle industry for our case study, even though motor
vehicle industry is the most famous industry for the JIT/QC implementation. The major reason is thatmotor vehicle industry is dominated by the small number of Chaebols which means that the result
would be largely affected by only the large number of small firms in the sector. Unfortunately,
however, we cannot assure the productivity enhancement on small firms in car industry. This isbecause many articles already pointed out that small firms in motor industry have harder time than
before since they are heavily depending on the small number of large firms and they have no choice24whether to increase or decrease their inventory ratio. Therefore, the inventories that used to be in
Chaebols go directly to small firms under Chaebols. So, we worry that many small firms under
Chaebols might give us the wrong result.

To prove the hypothesis, we basically use the same model with simple regression model in (4.2.1) andinclude some important explanatory variables which are already explained in panel data analysis in(4.2.3). 17 We only consider the first lagged dependent variable (lnTFP(t-1)) and organizayionalinnovation (inventory to sales ratio (t-1)) as endogenuous and treat year dummy variables andinnovation capability (R&D expenditure to sales ratio (t)) as exogenuous. Therefore, only (lnTFP(t-1))
and (inventory to sales ratio (t-1)) are estimated by GMM method and other explanatory variables
such as learning by education or K/L is estimated by instrumental variables coming from the outsideof the model.

Export to sales ratio is used instead of education expenditure to sales ratio and thenumber of patents is used instead of K/L.

The regression result shows us that organizational innovation is still very important factor for the
productivity enhancement. In this model, the technical part such as innovation capability and K/L is
not that important factor for the productivity enhancement. It is an unexpected result because
technical part is normally considered as more important factors for the productivity of a firm inelectric machinery industry. However, the result clearly shows us that organizational part is more
important factor for the firms in electric machinery industry. Therefore, they should make more effort
on organizational innovation.

17

We include and exclude variables except some important key variables such as organizational innovation andinnovation capability which we should see the result. This is because most of variables in the model has
endogeneity problem with its dependent variable so it is impossible to extract the endogeneity problem keepingwith all the variables in the model.
25

Table 4-10. Regression results of electrical machinery industry
VariableLevelsystem GMM

Past productivity level

Firm

0.27+
(1.70)

Organizational innovation

Firm

Innovation capability

Firm-2.57**(-3.39)0.54(1.35)

Efficiency wage

Firm

2.29**

K/L

Firm-0.00*(3.46)(-2.26)constant

Firm

0.98**(2.71)

Observation

8454

Sargan test statistics

40.94 (0.161)

Hansen test statistics

26.33 (0.788)

legend: + p<.1; * p<.05; ** p<.01

Notes (1): Productivity is measured by lnTFP and L.lnTFP means first lagged variable of lnTFP.
Organizational innovation is measured by the first lagged variable of inventory/sales.
Innovation capability is measured by R&D expenditure/sales.
Efficiency wage is measured by average salary of a firm/average salary of the sector-1.
Notes (2): Sargan test indicates that there is no endogeneity problem in the model, while Hansen test
indicates that subsets of instruments which we use for the model are valid. Sargan test is
not robust but not weakened by many instruments, but Hansen test is robust but can be
weakened by many instruments. Since we have a good result from the both test, we do notneed to worry about the instruments no matter what we used for the model.
Notes (3): We did not attach all the other variables since they are used as instrument variables for themodel. These are year dummy variables, industry dummy variables, export to sales ratio
and number of patents.

(3) Regression results for all firms in manufacturing industry

Based on the result from the electric machinery industry, we apply the same approach to all the firms
in manufacturing industry. This time we include more dummy variables which are not explained by
the model but make other variables exogenuous. We generate the interaction term between industry
dummies and efficiency wage, and industry dummies and K/L. Then we treat the first lagged
dependent variable, organizational innovation as endogenuous and R&D expenditure to sales ratio and26the interaction term between industry dummies and K/L as exogenuous. We also introduce the export
to sales ratio and education expenditure to sales ratio as instruments for the model.

The regression result shows us that both organizational part and technical part are two major
important factors for enhancing the productivity level of a firm. However, efficiency wage is notapplied the case of overall manufacturing industries when we get rid of the endogeneity problems. In
other words, efficiency wage effect which indicates higher salary than other firms in the same industry
might not be the cause of enhancing the productivity level but the result of high productivity of a firm.

To sum up, even though not many variables are important factors for enhancing the productivity levelof a firm, organizational innovation is a very important factor for the productivity enhancement inoverall manufacturing industry in Korea along with the technical innovations.

Table 4-11. Regression results of all sample firms in manufacturing industry
VariableLevelsystem GMM

Past productivity level

Firm

0.65**(5.42)

Organizational innovation

Firm

Innovation capability

Firm

Learning by education

Firm-1.64**(-2.63)
2.48**(3.89)
46.83

K/L

Firm

constant

Firm(0.78)-0.00**(-3.25)0.98**(2.71)

Observation

8454

Sargan test statistics

56.95 (0.263)

Hansen test statistics

48.21 (0.585)

legend: + p<.1; * p<.05; ** p<.01

Notes (1): Productivity is measured by lnTFP and L.lnTFP means first lagged variable of lnTFP.
27

Organizational innovation is measured by the first lagged variable of inventory/sales.
Innovation capability is measured by R&D expenditure/sales.
Learning by education is measured by education expenditure /sales.
Notes (2): Sargan test indicates that there is no endogeneity problem in the model, while Hansen test
indicates that subsets of instruments which we use for the model are valid. Sargan test is
not robust but not weakened by many instruments, but Hansen test is robust but can be
weakened by many instruments. Since we have a good result from the both test, we do notneed to worry about the instruments no matter what we used for the model.
Notes (3): We did not attach all the other variables since they are used as instrument variables for themodel. These are year dummy variables, industry dummy variables, export to sales ratio,
number of patents, interaction term between industry dummy variables and efficiency
wage, and interaction term between industry dummy variables and K/L.

5.

Conclusions

5.1. Concluding remarks

To show that organizational innovation strongly enhances the productivity level of Korean
manufacturing firms and sectors, we use the firm-level data analysis. We use three different levels of
empirical methods. First, Pooled regressions, secondly, panel data with fixed effect and random effectvariants and system GMM. Each one of these methods has its own merits and in particular system
GMM takes account the endogeneity problem. We find consistently good result as expected according
to our theoretical considerations as explained above in previous sections.

As the most important result we found that organizational innovation is consistently important factor
for the productivity level along with the technical innovation. In section 4, we used several different
econometric approaches and use other explanatory variables to find the true relationship between
organizational innovation and the productivity level of a firm. Thus, we consistently have a robust
result with other explanatory variables no matter what the model we use. Therefore, the impact oforganizational innovation on firm performances definitely has strong relationship with its productivity
level.
28

On the other hand, organizational innovation takes time to be reflected on its productivity level, while
technical innovation is directly reflected on it. For this reason, we use the first lagged inventory tosales ratio as our proxy for organizational innovation. However, it is an interesting fact that we cannot
prove the hypothesis with the present value of inventory to sales ratio. It indicates that organizationalinnovation is the major reason for enhancing the productivity. This is because organizationalinnovation takes time to be reflected and low inventory level does not directly enhance theproductivity level of a firm.

Also, each firm has its unobserved common factors but they do not significantly change the results. In
addition, the explanatory variables we use for the pooled OLS are already calculated as a form ofcontrolling the size effect of the firms. 18 However, still it is hard to deny the fact that explanatory
variables are very powerful factors as to explain the dependent variable, since the correlation between
explanatory variables and productivity becomes stronger and more significant when we take accountthe unobserved common factors into the model. Therefore, firms would effectively enhance theirproductivity level when they take account their abilities and situations which are different with others.

On the other hand, different industry needs different types of approach to enhance the productivity
level. This is based on our research that technical innovation is important factor for the overall
manufacturing industry but not in electric machinery industry, which is represented as high-techindustry. Generally, it is easy to think that high-tech industry needs more technical innovations toimprove the productivity level: it means technical innovation is more important factor for the
productivity enhancement in high-tech industry. However, the result shows us that organizationalinnovation is much more important in high-tech industry. This is plausible result since every firm in
high-tech industry try to have more technical technologies to improve their productivities, while very18

We use the real sales and number of employees to control the size effect of a firm and divide explanatory
variables by either real sales or number of employees.
29few of them might understand the importance of organizational innovation and try to have it.

In conclusion, we find many interesting points from the research. First we realize the importance oforganizational innovations together with technical innovations: even though electric machineryindustry did not give us very significant result on technical innovations, we can also check thattechnical innovation is a very important factor for the productivity for all firms and manufacturingindustries. Furthermore, we recognize the important difference between organizational innovation andtechnical innovation: organizational innovation needs time to be reflected on its productivity level
while technical innovation does not. Moreover, we find the reason we should consider the individual
characteristics of firms and finally come to know that why different industries need different factors
for enhancing the productivity level.

5.2

Discussion and Policy implication

The critical point of this analysis is that we should be careful about policy recommendations. For
example, very probably organizational innovation is more required than technical innovation in hightech industry 19 . However, this is not normally what we think of the high-tech industry. Brynjolfssonand Hitt (2000) insist that ‘organizational complements such as new business process, new skills andnew organizational and industry structures as a major driver of the contribution of information
technology (IT).’ Therefore, we should thoroughly think and decide the factors before we make thestrategy for better business performance.

Also, organizational innovation should be treated differently since it is very different with technicalinnovation. Usually technical innovation improves the productivity level of a firm once it is
implemented to a firm. On the other hand, organizational innovation might not be properly
implemented or even lower the productivity level of a firm if the technology is not appropriate for a

19

Examination of more industries will confirm this conclusion for electric machinery industry.
30

firm. Therefore, firms should find and search the characteristics of their organizations first and thendecide whether they take the organizational technology or not. Furthermore, they should approach
more carefully for this and try to be implemented by all people in a firm, not by just a few of them,and do not make a hasty conclusion with its impact on productivity level.

In addition, to keep the people employed in a firm is the most important thing in order to prevent the
productivity loss of a firm. This is because the past productivity level is the most important factor of
the present level of it. Technology cannot exist by itself. It means technology combines with its
human capital and decends to the next period with its people. This effect even exceeds the overall
productivity gain by education or other activities to improve the productivity.

In conclusion, organizational innovation is very different type of technology innovation comparing
with the technical one but it also considerably contributes to the productivity growth of a firm. In
other words, it significantly contributes to economic growth. However, the study for organizationalinnovation has been rarely done by very few economists because of it measuring problems but itshould be studied because it is major factor of economic growth. Therefore, we should understand its
importance in economic growth and try to develop and find things for the organizational innovation.
From the research, we provide evidence that organizational innovation has been significantly
improved the productivity of Korean manufacturing firms and sectors. Moreover, we realize thatorganizational innovation should be accompanied by the correct diagnostic of the firms and active
support from the top and active response from the bottom. However, once organizational innovationachieved and implemented to the firms it would greatly enhance the productivity of firms
continuously and consistently.

Appendix 4-1 Regression result with dummy variables
Variable

OLS
31

FE

RE

L.lnTFP

0.85**

0.58**

0.76**(first lagged dependent variable)-87.02-33.98-59.81

Organizational innovation-0.31**-0.56**-0.47**(inventory to sales ratio, firm level)(-5.71)(-7.07)(-7.08)Innovation capability

1.06*

3.01**

1.68**(R&D expenditure to sales ratio)

-2.55

-5.41

-3.56

Efficiency wage

0.18**

0.59**

0.33**(salarygap ratio)-10.14-18.36-13.97

Learning by education

44.93*

76.21**

56.62**(education expenditure for sales to sales ratio)

-2.5

-3.64

-2.83

K/L

-0.00**-0.00**-0.00**(capital stock/num. employees)(-10.84)(-9.39)(-10.97)

Industry 7

0.01

0

0.04

(food and kindred)

-0.49

(.)

-0.95

8

0

0

0.03

(textile and mill product)

-0.02

(.)

-0.62

9

0.02

0

0.05

(apparel)

-0.9

(.)

-1.04

10

-0.04

0

0.02

(lumber and wood)(-1.17)

(.)

-0.37

11

0.02

0

0.03

(furniture and fixtures)

-0.91

(.)

-0.76

12

-0.05

0

-0.04

(paper and allied)(-0.67)(.)(-0.29)

13

0.04*

0

0.08**(chemicals)

-2.27

(.)

-2.8

14

0.01

0

0.01

(patroleum and coal products)

-0.34

(.)

-0.14

15

-0.07**

0-0.07+(leather)(-2.79)(.)(-1.81)16

0.06**

00.11**(stone clay glass)

-2.62

(.)

-3

17

-0.07**

0-0.09**(primary metal)(-3.96)(.)(-3.36)

18

0.02

0

0.07+

32

(fabricated metal)

-0.93

(.)

-1.91

19

0

0.03

(machinary non-elect)-0.01(-0.21)

(.)

-0.89

20

0

0

0

(electrical machinary)(-0.22)

(.)

-0.1

21

0

0.03

(motor vehicles)-0.01(-0.47)

(.)

-0.8

22

0.02

0

0.04

(transportation equipment)

-0.35

(.)

-0.56

23
(instruments)

0.01

0

0.05

-0.19

(.)

-0.95

24

0

0

(rubber and misc plastics)
Year 1986(-0.12)-0.04+

(.)
-0.03

0.04
-0.9
-0.04+(-1.75)(-1.33)(-1.73)

0

0

(.)

0.05*

1987
1988

0.05*

(.)
0.06**

1989-2.14
0.12**-2.64
0.15**-2.45
0.13**

1990-5.08
0.06**-6.21
0.12**-5.69
0.08**

1991-2.64
0.09**-5.12
0.17**-3.75
0.12**

-4.13

-5.63
0.09**

1992

0.04*

-7.39
0.15**

1993

-1.99
0.04*

-6.9
0.15**-4.14
0.09**

-2.13

-6.89

-4.29

1994

0.03

0.14**

0.07**

1995

-1.42
0.01

-6.5
0.12**-3.67
0.06**

-0.43

-5.47

-2.63

0.05**

0.16**

0.10**

-2.67

-7.62

-5.09

-0.06*(-2.56)

0.05*

-0.01
(-0.50)-0.24**(-10.73)-0.15**(-6.56)-0.19**(-8.87)0.07**

0.08**

0.09**

1996
1997
1998
1999
33

-1.97

-3.11

-3.54

-3.96

0.03

0.06*

0.05*

-1.18

-2.39

-2.21

0.02

0.05*

0.05*

-0.87

-2.12

-2.04

0.07**

0.12**

0.10**

-3.2

-4.93

-4.5

0.04+

0.12**

0.08**

-1.84

-4.78

-3.61

0.03

0.10**

0.07**

-1.24

-3.64

-2.7

0.06*

0.13**

0.10**

-2.1

-4.76

-3.66

0.32**

0.75**

0.46**-13.75-25.4-15.03

9125
0.44

9125

Adjusted R-square

9125
0.77

Wooldridge test statistics

235.06 (0.00)

2000
2001
2002
2003
2004
2005
constant
Observation

Hausman test statistics

386.66 (0.00)

legend: + p<.1; * p<.05; ** p<.01

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