May 3, 2009

Industrial Policy Cuts Two Ways:
Evidence from Cotton Spinning Firms in Japan, 1956–1964 *Kozo Kiyota
Faculty of Business Administration, Yokohama National University
Tetsuji Okazaki †Faculty of Economics, University of Tokyo

Abstract
A number of studies have revealed that the effect of industrial policy on productivity
growth is negative.

Is this because industrial policy fails to control the activities offirms, or because it can effectively control them?

This paper attempts to answer thesequestions, using firm-level data from the cotton spinning industry in Japan for theperiod 1956–64.
period.

It has been determined that industrial policy cut two ways during thisIndustrial policy effectively controlled the output of cotton spinning firms,

which contributed to the establishment of a stable market structure during the period.
On the flip side, such policy constrained the reallocation of resources from lessproductive large firms to more productive small firms. Combined with the negative
productivity growth in large firms during this period, industrial policy resulted innegative industry productivity growth.
JEL classification: D21 (Firm Behavior), D4 (Market Structure and Pricing), L5(Regulation and Industrial Policy), N0 (Economic History), K2 (Regulation andBusiness Law)
Keywords:

Industrial

Policy;Regulation;

Reallocation;

Productivity

Growth;Endogeneity*

We would like to thank the editor Sam Peltzman and an anonymous referee for helpful comments.
Kozo Kiyota gratefully acknowledges the financial support received from the Japan Society for the
Promotion of Science (JSPS) 2006 Postdoctoral Fellowship for Research Abroad. The usual
disclaimers apply.
†
Corresponding author. Address: 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; Tel.: +81 3
5841 5530; Fax: + 81 3 5841 5521. E-mail: okazaki@e.u-tokyo.ac.jp

1.

Introduction
Whether industrial policy contributes to productivity growth is an importantquestion in the fields of industrial organization, law and economics, and development
economics (see Noland and Pack 2003; Rodrick 2007, Chapter 4). Among industrial
policies applied in various periods and countries, one of the most controversial policies
is the Japanese industrial policy during the postwar period.1

This controversy arises

because the "success" of some Japanese industrial policies has been used to justify"targeting" policies in other countries, including the United States. 2

Because of this,several studies have investigated the effects of Japanese industrial policy (see Kiyota
and Okazaki 2005). An important lesson from previous studies is that, while Japaneseindustrial policy may have contributed to the growth of labor productivity, it did notcontribute to the growth of total factor productivity (TFP). For example, Beason and
Weinstein (1996) examined the effects of industrial policy on industry-level TFP growth
in Japan. They revealed that industrial policy did not have a significant positive effecton productivity growth. Kiyota and Okazaki (2005) utilized firm-level data in Japan.
They found that industrial policy had a positive effect on labor productivity, but not on

1

The Japanese government implemented an industrial policy to control international trade,investment, technology imports, and foreign exchange (Noland and Pack 2003, pp. 23–37).
2"In fact, it is the success of Japanese targeting that is often used as the justification for targeting in

the United States" (Beason and Weinstein 1996, p. 286).

Note that whether or not Japaneseindustrial policy was a "success" is still controversial, because total factor productivity would havefallen more without industrial policy.

In this paper, the focus is on the mechanisms behind thenegative relationships between industrial policy and industry productivity growth. A thoroughevaluation of industrial policy is beyond the scope of this paper.

1

TFP.
Even though these previous studies were insightful, there is not yet a clear
answer to why industrial policy produced a negative effect on TFP. This uncertainty isa result of two contrasting interpretations of the results. The first is that industrial
policy failed to control the activities of firms and, therefore, lowered industry
productivity growth.

The second is that the government was able to control theactivities of firms and, therefore, lowered industry productivity growth. The former
interpretation implies that the problem lies in implementation, rather than in policy
design, whereas the latter explanation implies that the problem lies in the policy design
itself.

Without clarifying these differences, it is difficult to assess accurately theeffects of industrial policy.
To answer this question, the ways in which industrial policy affects the activities
of firms need to be examined.

However, most of the previous studies that have dealt

with productivity have focused on the correlation between industrial policy measuresand productivity growth, without focusing on specific changes in a firm's activities.
Moreover, previous studies on the effect of industrial policy on productivity have paid
little attention to the legal framework and historical background of the time period,
which makes it difficult to identify which activities were truly controlled by industrial
policy.

Indeed, adequate discussion on the effect of industrial policy on a firm's

activities is missing from this literature strand. 3
3

On the other hand, a number of historical studies have provided a detailed examination of theinstitutional and historical background to industrial policy, many of them focusing on the cotton
spinning industry (Korenaga 2000, 2002, 2004 and 2005; Peck, Levin, and Goto 1987; Saxonhouse
1979; Yamazawa 1978, 1981).

However, these studies are basically descriptive and their scope2This paper examines the effect of industrial policy on industry productivity
growth.

One of the major contributions of this paper is to provide a missing element inthis discussion by presenting an explanation of how industrial policy affects a firm's
activities, and why industrial policy lowers industry productivity growth.

This paper

also attempts to address explicitly the possible endogeneity between industrial policy
and firm characteristics, which has not yet been explored in the literature on industrial
policy. The focus is on the cotton spinning industry in Japan in the period 1956–64, an
industry in which a key legal framework was implemented to control the output ofcotton spinning firms during this time frame.
This paper also contributes to the literature that examines the effects of policychange on productivity. In the international trade literature, on one hand, a number ofstudies, such as Bernard and Jensen (1999) and Kimura and Kiyota (2006), haveestimated productivity at the firm level, have found productivity differences acrossfirms within the same industry (that is, firm heterogeneity), and attempted to link the
differences to firm characteristics such as exporting activities.

Although many

theoretical models that motivate these examinations have in mind some exogenous
policy shocks such as trade liberalization that affects firms differently (for example,
Melitz 2003), most of the empirical studies do not actually identify particular policy
shocks. In the industrial policy literature, on the other hand, a number of studies haveexamined the effects of particular industrial policies as noted. However, only a few
studies utilize firm-level data.

This paper covers the shortcomings of previous studies

by utilizing firm-level data and identifying a particular policy change.

does not extend to a discussion of the link between industrial policy and productivity growth.

3

Focusing on the cotton spinning industry in Japan provides at least fouradvantages.

First, the data cover almost all firms in the industry, which provides theopportunity to examine the effects on both small firms and large firms.

Second,detailed information on physical inputs and outputs are available. This study thus
avoids some of the potential problems in using monetary variables as proxies for inputs
or outputs. 4

Third, precise information on industrial policy is also available from theliterature on economic history and from historical records. Finally, the lessons drawn
from industrial policy in Japan have implications for industrial policy in other countries,
particularly developing countries (for example, Noland and Pack 2003).
This paper is organized as follows.

Section 2 reviews industrial policy in the

cotton spinning industry in Japan. Section 3 explains the empirical model and data.
Section 4 presents the estimation results.

2.

The conclusions are presented in Section 5.

Industrial Policy and the Cotton Spinning Industry: A Historical Review of theLegal Framework
Until trade liberalization in the 1960s, foreign exchange allocation was a basic

tool for industrial policy in Japan.

The Foreign Exchange and Foreign Trade

Administration Law of 1949 prescribed that foreign exchange should be concentrated in
the government and that the government should draw up a foreign exchange budget toallocate foreign exchange quotas. The basic purpose of this system was to maintain abalance of payments by saving foreign exchange; however, it was utilized for industrial
4

For example, Klette and Griliches (1996) pointed out that the use of deflated firm-level sales as aproxy for firm-level output can be biased when used to estimate the coefficients of the production
function.

4

policy purposes by the government, specifically the Ministry of International Trade andIndustry (MITI).
For example, for industries that depended upon imported raw materials, MITI
could virtually control the production of each firm through foreign exchange allocations,which, in turn, supported the administrative guidance by MITI.

In addition, foreignexchange for some raw material imports was allocated according to the export
performance of each firm that used them (the export link system).

This system was apowerful tool for the promotion of exports.
However, as the Japanese economy recovered from the damage caused by WWII,
and increased its presence in the world economy, foreign countries, in particular theUnited States, increased pressure on the Japanese government to liberalize trade. In
1959, at the General Meeting of the General Agreement on Tariffs and Trade (GATT) in
Tokyo, the United States representative requested that the Japanese government remove
import restrictions.

To cope with this pressure from abroad, the Japanese cabinet

decided on an action plan for trade liberalization in 1960 and, in practice, tradeliberalization proceeded rapidly in the early 1960s (Fukao 1989; Nakakita 1993).
The cotton spinning industry, the focus of this paper, was deeply involved in thisforeign exchange allocation system.

Its raw materials (inputs), raw cotton, as well as

its products (outputs), cotton yarn, were classified as goods that could not be imported
without foreign exchange allocation by MITI.

In other words, the cotton spinningindustry was not only protected from international competition through foreignexchange allocations for cotton yarn, but domestic competition was also regulated by
MITI through foreign exchange allocations for raw cotton (Japan Spinners' Association
1962, pp. 226–270; Korenaga 2000).

In the early 1960s, as part of a general policy

5change, the cotton spinning industry experienced trade liberalization. In April 1961,
the import of raw cotton was liberalized and government intervention in the cotton
spinning industry by means of foreign exchange allocations was discontinued.
Meanwhile, production and equipment levels in the cotton spinning industry
increased rapidly. When World War II ended in 1945, the 10 largest firms dominatedthe cotton spinning industry, which came to be called "judai-bo."

After the war, manysmaller firms entered the industry and were called "shin-bo" and "shinshin-bo."These

smaller firms, as well as "judai-bo", contributed to the expansion of the industry, in theareas of both production and equipment.
Consequently, from the mid 1950s, the cotton spinning industry was faced withexcess capacity. To deal with this problem, an equipment registration system wasintroduced in 1956 through the Law on Temporary Measures for Textile Industry
Equipment (Sen'i Kogyo Setsubi Rinji Sochi Ho).

This law required that all firms

register their cotton spinning equipment with the government as equipment to be used in
specific categories of products and that only the registered equipment should be used to
manufacture those products.
Every year, the government could approve new registrations if the capacity ofregistered equipment was likely to be insufficient to meet the forecasted, anticipateddemand of the following year.

If the capacity of registered equipment was likely to

exceed anticipated demand in the following year, the government could instruct thefirms to cooperate by suspending the use of excess equipment. In reality, no new
registrations were approved, and hence the number of registered spindles did notincrease after 1957 (Japan Spinners' Association, various half year issues).
In April 1959, the government decided that there existed 1.28 million excess

6spindles and instructed the firms to suspend their use, in accordance with the Law on
Temporary Measures for Textile Industry Equipment.

The suspension rate was defined

as a percentage of the registered spindles, and was 7.5% for the firms with registeredspindles of 7,000 or less.

For firms with more than 7,000 registered spindles, thesuspension rate was 7.5% for up to 7,000 spindles and 15% for any spindles over 7,000(Japan Spinners' Association 1962, p. 100). This means that the operable spindles foreach firm beginning in 1959 were reduced by at least 7.5%, depending on the number ofthe spindles they had registered.

The suspension rate was reduced in October 1961.

The new suspension rate was 6% for the firms with registered spindles of 7,000 or less.
For firms with more than 7,000 registered spindles, the new suspension rate was 6% for
up to 7,000 spindles and 12% for any spindles over 7,000 (Japan Spinners' Association
1961, pp. 43–44).
The role of the equipment controls increased after trade liberalization because,in the past, the government could effectively regulate the production of each firm
through foreign exchange allocations for raw cotton (Japan Spinners' Association 1962,
pp. 100–101).

Hence, in 1959, the Japan Spinners' Association put forward a proposal

requesting that a new "industrial order" be prepared to replace the foreign exchange
allocation system, which had been "the central pillar for the regulation of the cotton
industry" (Japan Spinners' Association 1962, pp. 84–85).
This proposal was reflected in the July 1960 amendments to the Law on
Temporary Measures for Textile Industry Equipment.

Two major reforms were

implemented in this revision. First, the government was given the authority to orderfirms to suspend the use of their equipment to allow for short-term adjustments in
production, in addition to making long-term adjustments.

7

Second, fines wereimplemented to deal with firms that did not comply with these new government
directives (Japan Spinners' Association 1962, pp. 88–94).
In October 1964, the amended law was replaced by the New Law on Temporary
Measures for Textile Industry Equipment.

This new law was proposed as a step

toward deregulation of the cotton spinning industry.
production adjustments was deleted.

First, the clause on short-term

Short-term production adjustments were left tothe "depression cartel" provisions contained in the Anti-monopoly Law, which required
Fair Trade Committee approval. Second, a new "scrap and build" clause was added.
This allowed firms that disposed of suspended equipment to register new equipment up
to half the value of the disposed.

This clause targeted the promotion of rapid

modernization of equipment to enhance the international competitiveness of the industry.
The cotton spinning industry was expected to have become internationally competitiveand fully deregulated by the time the new law expired four years later in 1968 (Japan
Spinners' Association 1979, pp. 57–64).

As it was, after a two-year extension, the newlaw was abolished in June 1970 (ibid p. 106 and p. 149). 5
One may be concerned that a focus on this industrial policy is an examination ofan unusual case.

However, it is indeed in line with the literature.

For example, fromthe Schumpeterian view, industrial policy could provide greater incentives to invest in
5

Government intervention related to textile equipment continued until the early 1980s and was forthe purpose of structural improvement of the industry. In August 1967, the Law on Temporary
Measures for Structural Improvement of Specific Textile Industries was enacted.

The mainobjectives of this law were: 1) to dispose of excess equipment, 2) to increase firm scale, and 3) toupdate equipment.

This law was followed by the Law on Temporary Measures for Structural

Improvement of Textile Industries in 1974.

For more details, see Korenaga (2002).

8

new technology and processes, such as subsidizing technology development.

This inturn stimulates competition among firms and could lead to rapid growth in productivity(see Aghion, Dewartripont, and Rey 1997).

On the flip side, if industrial policy forces

firms to reduce capital stocks by taking spindles equipment out of operation, industrial
policy will discourage firms to invest in new technologies and thus can cause a negative
effect on productivity. 6

3.

In the following sections, this issue is explored in more detail.

Empirical Model and Data

3.1. Estimation of the Production Function and Productivity
The econometric analysis on the dynamic framework is based upon firm profitmaximizing behavior, developed by Olley and Pakes (1996) and extended by Levinsohn
and Petrin (2003) (LP).

Specifically, we estimate the following Cobb–Douglas

production function:y nt = β 0 + β k k nt + β l l nt + β e ent + β m mnt + ε nt ,(1)

where y nt , k nt , l nt , ent , and mnt are the natural logs of output, capital stock,
employment, energy, and materials of firm n in year t , respectively; and ε nt isunobserved by researchers and consists of two components, with one being productivity
level ω nt and the other being an error term η nt .

TFP is obtained from:

TFPnt = exp(ω nt ) .

6This type of policy came to be widely applied in Japan in the 1970s and 1980s, when manyindustries were faced with overcapacity following the decline of economic growth and the sharp risein oil prices (Bureau of Industrial Policy, Ministry of International Trade and Industry ed. 1983,
1988).

9

A firm maximizes the expected value of its current and future profits, and caneasily adjust labor, energy, and material inputs. However, capital is a quasi-fixed
input. 7

Capital in use in year t is assumed to be known at the beginning of the year,

and thus capital is another state variable. Note that productivity may be observed by
the firm, although it is unobserved by the researchers. If the firm's private knowledgeof its productivity affects its choice of inputs, there exists a correlation between the
choice of inputs and productivity. As pointed out in Marschak and Andrews (1944),
such a correlation causes a simultaneity problem.

This simultaneity violates theordinary least squares (OLS) conditions for unbiased and consistent estimation.
LP suggest a method for overcoming the simultaneity problem in production
function estimation by using a material input as a proxy for the unobserved productivity
component.

Let the demand function for materials be mnt = mnt (ω nt , k nt ) , and theinverse of the optimal function with respect to ω nt be ω nt = ω nt (mnt , k nt ) .

This

implies that:y nt = β l l nt + β e ent + φ nt (mnt , k nt ) + η nt ,

(2)

where

φ nt (mn t , kn t ) = β 0 + β k k nt + β m mnt + ω nt (mnt , kn t ) .

(3)

Assuming that φ nt ( mnt , k nt ) can be approximated by a third-order polynomial andadding some technical assumptions, the consistent estimators of the production function
are obtained.

We employ the LP estimation to overcome the simultaneity problem toestimate the production function.
7

Note that this assumption is consistent with the historical review in Section 2 because the Japanese

government partly controlled some of the equipment.

10

3.2. Data
The main source of data is Statistics on the Japanese Cotton Spinning Industry(Menshi Boseki Jijo Sankosho), published biannually by the Japan Spinners' Association(various half year issues). 8

The strength of these statistics is their wide sample

coverage and reliability, as well as the comprehensiveness of the information they
provide.

These statistics cover the majority of cotton spinning firms in Japan. At thesame time, they include quantitative information such as output, labor input, material
input, energy usage, and capital stock figures.

However, information on sales andinvestment is not available.
From these statistics, a longitudinal (panel) data set was constructed for theyears 1956 to 1964.

This covers the entire period of when the Law on Temporary

Measures for Textile Industry Equipment was enforced; hence, consistent data on
equipment available exists from Statistics on the Japanese Cotton Spinning Industry.
Firms for which output or input information is not available, or is not positive, havebeen excluded.

Firms that existed for only one year have also been excluded. The

total number of sample firms exceeded 120 for every year. The numbers of workers
covered in the dataset are 92,430 in 1956, and 76,405 in 1964, which are 71% and 68%,
respectively, of all the regular workers in the cotton spinning industry in Japan. 9
Output is defined as the weighted average of 12 output commodities. 10
8WeightThe Japan Spinners' Association changed its name from the All Japan Cotton Spinners' Associationin 1964.
9

The total number of regular workers was 129,738 in 1956 and 111,549 in 1964 (MITI 1958, 1966).

10

The 12 commodities are: 1) cotton yarn, 2) blended spinning cotton yarn, 3) spun rayon yarn, 4)11

is defined as the sales share of each commodity.

As mentioned above, sales data are

not available from Statistics on the Japanese Cotton Spinning Industry. Unit price data
have been obtained from the Bank of Japan (1956–1964) to calculate sales. 11
are defined as labor, capital stock, material input, and energy usage.

InputsLabor is definedas the sum of production workers and white collar workers. Because data on whitecollar workers are not available for the periods before 1958, the number of white collar
workers was estimated using the proportion of white collar workers to the total numberof workers in 1959.

Capital stock is defined as the number of operating spindles.12

Material input is defined as the weighted average of the eight materials. 13

Weight iswaste cotton yarn, 5) blended waste cotton yarn, 6) tokubo, 7) viscose staple yarn, 8) mixed spun
rayon yarn, 9) blended synthetic yarn, 10) synthetic yarn, 11) cotton waste, and 12) rayon staple
waste.
11

Note that the unit price depends on a "count," which is a unit that indicates the thickness of yarn.

The higher the count, the thinner the yarn.

Thinner yarn is regarded as being of higher quality and,

therefore, a higher count means a higher unit price.

Although the unit price is available only for

20-count yarn in Statistics on the Japanese Cotton Spinning Industry, the conversion rate (therelationship between unit price and count) is available for cotton yarn and spun rayon yarn (Fujino,
Fujino, and Ono 1979).

The unit prices for cotton yarn and spun rayon yarn have been adjusted,based on this conversion rate.

This means that the unit price differs across different firms.

For theconversion rate, see Table A1.
12

The number of operating spindles refers to the average number of spindles actually operated in acertain year. Its upper limit was the number of operable spindles: the registered spindles minus
spindles that were not suspended or out of order.

Note that whereas no additional registration wasapproved after 1957, the number of operating spindles could change within the limit.
13

The eight inputs are: 1) cotton, 2) rayon staple, 3) viscose staple, 4) waste cotton yarn, 5)12

defined as the share of material costs. Energy usage is defined as the weighted averageof the three energy inputs. 14

The cost of energy is used for the weight.

Unit pricedata for the material inputs have been obtained from the Bank of Japan (1956–1964), as
have unit price data for energy usage (Bank of Japan 1964). 15
Figure 1 presents the output share of the 10 largest firms, namely "judai-bo," for1956–1979. Except in 1964–65, when the New Law on Temporary Measures for
Textile Industry Equipment was enacted, the share of "judai-bo" remained stable fromthe late 1960s.

The cotton spinning industry maintained a stable market structure fortwo decades in the sense that the share of these large firms remained almost constantthroughout the period. Note that the coverage of Statistics on the Japanese Cotton
Spinning Industry changed when the New Law on Temporary Measures for Textile
Industry Equipment was enacted in 1965. 16

To exclude this effect, the period prior to1965 was focused upon.
polyester, 6) vinyl, 7) nylon, and 8) acrylic.
14

The three energy inputs are: 1) coal, 2) electricity, and 3) heavy oil.

15

The unit price is available only at the industry level. For a more detailed description of thedefinition and the source of unit price data, see Table A1.
16

Until 1964, Statistics on the Japanese Cotton Spinning Industry surveyed the equipment classified

into cotton spinning by the Law on Temporary Measures for Textile Industry Equipment of 1956,
and it also surveyed the outputs and inputs of that equipment.

As the new law was enacted in

October 1964 and the classification of equipment was changed, the Statistics on the Japanese Cotton
Spinning Industry expanded the coverage of the survey to include equipment for other new outputs
such as spun rayon yarn in 1965, when the coverage of the survey on outputs and inputs was
expanded as well.

That is why the output share of the large firms declined in 1965 (Figure 1). For

more details, see the Japan Spinners' Association (1960, page I).

13

=== Figure 1 ===Table 1 reports the descriptive statistics for 1956–1964. To measure the effectsof the New Law on Temporary Measures for Textile Industry Equipment (that is,industrial policy), the ratio of suspended spindles was used, which is defined as the ratioof the number of suspended spindles to the total registered spindles. 17

Because thesuspended spindles ratio was different across different firms, the ratio allows the captureof the industrial policy effect at the firm level.

=== Table 1 ===There are four major findings in this table.

First, labor productivity grewsteadily from 1956 to 1964. Labor productivity increased during the industrial policy
period, increasing from 5.04 in 1959 to 5.86 in 1964.

Second, the cotton spinningfirms started using synthetic yarn as an intermediate input from 1959.

17

The ratio of

The data on the suspended spindles for the end of 1964 are not available in the second half of theyear issue of Menshi Boseki Jijo Sankosho for 1964, reflecting the change in the legal framework in
October 1964.

Hence, for 1964, we use the number of the suspended spindles at the end of June

1964, available in the first half of the year issue of Boseki Jijo Sankosho for 1964. The ratio of
suspended spindles should be between 0.075 and 0.15 for 1959–1960 and between 0.06 and 0.12 for
1961–1964. However, in some firm-years, the ratios were outside these ranges.

Because it is

impossible to identify whether firms mistakenly reported these figures or broke the law, the analysis
was conducted assuming that these figures were reported correctly.

14synthetic yarn to material inputs increased from 0% in 1958 to 7% in 1968.

This result

may support the view that the introduction of synthetic yarn improved the productivityof the cotton spinning firms. 18

Third, the industrial policy controlled the amount ofequipment used by firms after 1959.

On average, the ratio of suspended spindles is

6.8–8.6% after the suspension of spindles started in 1959. Finally, the correlation
between the suspended spindles ratio and firm size (measured by the capital stock)ranges between 0.40 and 0.48.

This implies that the suspended spindles ratio is notjust a proxy for firm size but that it may be related to firm size.
These results seem to suggest that, through the regulation on equipment,
industrial policy made a significant contribution to raising labor productivity while also
controlling firm output to maintain a stable market structure.

Indeed, the growth inlabor productivity and the maintenance of a stable market structure are surprising,considering the fact that the cotton spinning industry was faced with a substantialchange in the technological environment.

An example of these changes is thewidespread adoption of synthetic fiber as an intermediate input, given that the Japanese
economy experienced rapid growth during the 1960s and a severe recession in the early
1970s.

Note, however, that labor productivity may not be a good indicator of

productivity, because labor productivity increases not through an increase in theefficiency of workers, but through an increase in other types of inputs, such as capital or18

For example, Nagahama (1965, pp. 5–6) stated that the synthetic yarn enabled the cotton spinningfirms to control the quality of staple more easily, which improved firm productivity. However,none of the previous studies has statistically examined the relationship between the use of thesynthetic yarn and firm productivity.

Section 4.2 addresses this issue in more detail.

15

To control the effects of the other factors, the next section estimates TFP. 19

energy.

4.

Results

4.1. Firm-Level Productivity
Table 2 presents the estimates of the input coefficients from the production
function.

The production function was estimated using all cotton spinning firms

included in the source data for 1956–64 (unbalanced panel), based on OLS and LP
estimation. 20

The estimates of the coefficients derived from LP estimation are

significantly different from those derived from OLS.

While the OLS results reject thenull hypothesis that the production function has constant returns to scale, the LP
estimation results do not reject the null hypothesis.

This implies that the coefficientsare biased downward in OLS estimation and, therefore, LP estimation successfully
eliminates the simultaneity bias.

These results also suggest that the Japanese

government does not necessarily target increasing returns to scale in the sector.

=== Table 2 ===Table 3 presents the (unweighted) annual average level of growth in laborproductivity and TFP between 1956 and 1964. TFP is obtained from LP estimation.
Table 3 also reports levels and growth rates for both large and small firms. 21
19In

As noted, investment data are not available from this data source. The LP estimation method isapplied to estimate TFP.
20

Table A2 presents entry and exit patterns.

21

To simplify the discussion, large firms are known as "judai-bo", and "shin-bo" and "shinshin-bo"

16Section 2, the year identified was when the industrial policy was enacted (that is, 1959).
Productivity growth was compared before and during the times the industrial policy wasenacted, to assess the productivity implications of industrial policy.

=== Table 3 ===Three findings stand out from this table. First, labor productivity grew rapidly,
even after the industrial policy was enacted. The annual average growth rate was
5.59% for 1956–59 and 2.76% for 1959–64. Second, by contrast, TFP did not show
significant growth.

The annual average rate of TFP growth was 0.65% and 0.27% for

the years 1956–59 and 1959–64, respectively. As noted in the introduction, theseresults are consistent with the findings of previous studies, such as Beason and
Weinstein (1996) and Kiyota and Okazaki (2005).
Finally, both labor productivity and TFP growth rates for large firms wereconsistently lower than those for small firms.

For example, the TFP growth rate forsmall firms was 0.70% for 1956–59, whereas the rate for large firms was –0.01%.
Moreover, the TFP growth rate for large firms for 1959–64 was –0.72%.

This result issurprising when combined with the knowledge that large firms maintained around a
48% market share between 1957 and 1964 (Figure 1 and Table 1), despite theirproductivity decreasing during this period.

In addition, it is also interesting to notethat the difference in TFP growth between large and small firms is not necessarily
attributable to the catch-up process of small firms.

are small firms.

17This is because on average, theTFP level of small firms is greater than that of large firms throughout this period.
Note that Table 3 simply presents the difference in productivity growth before
and during industrial policy.

Hence, it is not clear whether industrial policy has

created significant negative effects on the efficiency of the cotton spinning firms.

The

results clearly confirm that the output share of large firms was not supported by either
efficiency or by an unobservable effort that would have been captured by TFP. This
result implies that industrial policy effectively controlled a firm's activities.

The next

section examines in more detail the implications of these findings for industry
productivity growth.

4.2. Productivity and Industrial Policy
To examine the effects of industrial policy on productivity, we employ themethodology that is used to examine the effects of trade liberalization on productivity.
Similar to Pavcnik (2002), and Amiti and Konings (2007), the possible links between
industrial policy and firm-level productivity are specified as follows:ω nt = γ 0 + χ i + χ t + γ 1 ( suspended spindles ratio) nt + γ 2 ( synthetic yarn) nt + υ nt ,(4)

where χ i denotes firm-specific fixed effects that capture unobserved firm-level
heterogeneity, χ t denotes year-fixed effects that control for industry-level shocks overtime (for example, demand shocks), suspended spindles ratio denotes the ratio of
suspended spindles that captures the effects of industrial policy, and υ nt denotes theerror term.

As a control variable, a synthetic yarn-material input ratio synthetic yarn

was included to control for the effects of the introduction of synthetic fiber. The
suspended spindles ratio and the synthetic yarn ratios are the same as those used in18

Table 1.
Table 4 presents the regression results. Columns (1) and (2) indicate theresults of the fixed-effects and random-effects models, respectively. The synthetic
yarn–material ratio shows significantly positive coefficients, which is consistent withthe anecdotal evidence discussed in Section 3.2.

On the other hand, the suspended

spindles ratio does not have a significant sign. Note however, that the suspended
spindles ratio could be endogenous as Section 2 found that the suspension rate wasdifferent according to the number of registered spindles. Another regression was run
in order to address the possible endogeneity of industrial policy.

The ratio of

suspended spindles is treated as an endogenous variable, while capital stock and otherexogenous variables are instrumental variables (IV). 22=== Table 4 ===Column (3) presents the two-step efficient GMM estimator.

Firm-specificfixed effects are included to control for the unobservable heterogeneity of firms.23

The

endogenous (GMM distance) test statistic rejects the null that the suspended spindles
ratio can be treated as exogenous at the 95% level.

In the first stage regression results,

the Kleibergen–Paap underidentification Lagrange multiplier (LM) test rejects the null
hypothesis that the equation is underidentified at the 99% level.

The weakidentification Wald F statistic exceeds 10, "the rule of thumb" of Staiger and Stock
22

Other exogenous variables are the synthetic yarn-material ratio and year dummies.

23

The time-invariant "judai-bo" (that is, large firms) specific effects are wiped out by the firm-

specific fixed effects.

19(1997).

The null hypothesis that the instrument is weak is rejected at the significant

critical value.
These results suggest that the instruments have some validity.

The regression

results indicate that, once the endogeneity of industrial policy is controlled for, thecoefficient of the suspended spindles ratio turns out to be significantly negative. The
result implies that industrial policy produces negative effects on TFP. The coefficientsof the OLS fixed- and random-effects models are biased because of the endogeneity. 24

4.3. Industry Productivity Growth
Figure 1 and Table 1 indicate that industrial policy effectively controlled theoutput of cotton spinning firms.

Table 3 shows that TFP growth in large firms was

negative during this period, and Table 4 indicates that industrial policy produced
significant negative effects on the productivity of firms.

Note that these figures arederived from an (unweighted) arithmetic average of firm-level productivity.

Weextend the analysis to assess the implications for overall industry productivity growth.
As in Olley and Pakes (1996), the aggregate productivity level is defined as theweighted average of firm-level productivity, with the share of industry output defined as24

One may be concerned that the results are influenced by the firms whose suspended spindles

ratios are outside the ranges (see footnote 23).

An alternative ratio was constructed that takes theupper limit value (0.15 for 1959–1960 and 0.12 for 1961–1964) if the ratio exceeds the upper limit
value and takes the lower limit value (0.075 for 1959–1960 and 0.06 for 1961–1964) if the ratio
becomes smaller than the lower limit.
ratio with this alternative ratio.

A regression was then run, replacing the suspended spindles

It was found that the regression results were qualitatively similar ineach case.

20the weight:ω t ≡ ∑n s nt ω nt ,(5)

where s nt is the share of firm n 's output in the cotton spinning industry in year t .
Note that aggregate productivity growth is driven not only by continuing firms but also
by entering/exiting firms.

To incorporate the effect of entry/exit into the aggregate

productivity growth, following Foster, Haltiwanger, and Krizan (2001), the aggregate
productivity growth was decomposed using a modified version of Baily, Hulten, andCampbell (1992).
Specifically, the aggregate productivity growth Δωt is decomposed into fivecomponents: 25 1) "within effect," which captures the contribution of each firm's own
productivity growth; 2) "between effect" or "reallocation effect," which captures thechanges in output shares; 3) "covariance effect," which captures the correlation between
the changes in the shares and the productivity growth; 4) "entry effect," which captures
the effects of entry, and 5) "exit effect," which captures the effects of exit.

To examine

the difference in the productivity growth between large and small firms, the within andbetween effects were further decomposed for large and small firms, respectively.26
Table 5 presents the decomposition of the industry productivity growth.
major findings are threefold.

The

First, the industry productivity growth was positive

between 1956 and 1959, but turned negative between 1959 and 1964. In addition, theaggregate industry growth moved in tandem with the within effect.

This finding

suggests that the industry productivity growth can be attributed to each firm's own
25

See the Appendix for an explanation of how the decomposition is computed.

26

A similar decomposition exercise can be found in Baily, Halten, and Campbell (1992).

21

productivity growth.
the small firms.

Second, the large firms demonstrated a lower within effect than

The within effects of the large firms were –0.01% for 1956–59 and –

0.44% for 1959–64.

The within effects of the small firms were 0.35% and 0.04% for

1956–59 and 1959–64, respectively.

=== Table 5 ===Third, the between effect did not contribute to industry productivity growth,
especially after 1959.

This result implies that the reallocation of resources from theless productive large firms to the more productive small firms was ineffective during
this period.

Industrial policy effectively controlled the output of the firms.

Simultaneously, however, this control also restricted the reallocation of resources acrossthese same firms.

Combined with the negative TFP growth of the large firms,

industrial policy resulted in overall negative industry productivity growth.

5.

Conclusion
This paper examined the effect of industrial policy on industry productivity

growth.

A detailed historical review of the industrial policies within the cotton

spinning industry was first provided, in which the government introduced a major legal
framework: the suspension of equipment after 1959.

This measure was designed toindirectly control the output of cotton spinning firms.
Using firm-level data from the cotton spinning industry for 1956–64, estimations
were then made on firm productivity and the industry as a whole, both before andduring the industrial regulation period. The major findings are summarized as follows.

22First, industrial policy effectively controlled the output of cotton spinning firms.

The

output share of the 10 largest firms, which are called "judai-bo," remained stable fromthe late 1950s, after the Law on Temporary Measures for Textile Industry Equipment
was enacted.

The market share of these large firms remained almost constantthroughout this period.
Second, cotton spinning firms experienced low total factor productivity (TFP)
growth from 1956 to 1964.

The (unweighted) annual average growth rate was 0.27%.

The TFP growth rate for large firms was consistently lower than the rate for small firms.
The annual average TFP growth rate for large firms for 1959–64 was –0.72%, while therate for small firms was 0.35%.

Therefore, the aggregate (weighted) industry

productivity growth turned negative between 1959 and 1964.

Third, industrial policy

had negative effects on firm productivity. Besides, as a consequence of industrial
policy, the reallocation effect did not work during this period.

Combined with thenegative TFP growth rates of the large firms, industrial policy resulted in overall
negative productivity growth in the cotton spinning industry.
What would the TFP record have been without the policy intervention?
Although our estimates cannot precisely answer this question, it is useful to discuss thecounterfactual evidence.

As was indicated in Figure 1, industrial policy made asignificant contribution to the maintenance of a stable market structure for two decades
in the sense that the output share of the 10 largest firms remained almost constantthroughout the period. Based on the fact that the productivity level of these firms was
lower than that of other small firms (Table 3), the output share of the 10 largest firms
would have declined without industrial policy. On the flip side, as was observed inseveral studies such as Olley and Pakes (1996), deregulation would cause a reallocation23

of capital toward more productive firms.

This implies that the output share of firms

with higher productivity would increase. The growth of overall industry productivity
could mark a positive in the cotton spinning industry.
One important policy implication that can be drawn from this analysis is thatindustrial policy often cuts two ways.

Industrial policy may be effective in controllinga firm's activities, such as output levels.

This control, however, implies that theindustrial policy also constrains the reallocation of resources across the firms.

Thistype of policy will be effective when large firms achieve high rates of productivity
growth.

Otherwise, the policy will fail.

Policymakers should recognize thatindustrial policy can be a double-edged sword.
Future research should include a more thorough investigation of the validity of
Levinsohn–Petrin (LP) estimation in analyzing the effects of policies, as an important
extension.

LP estimation assumes perfectly competitive input markets.

However,various policies such as industrial policy and trade policy could distort the input markets
and generate some imperfectly competitive environments.

This may cause biases inthe LP estimator. It is also important to control for the quality of the inputs, such as
the difference between high- and low-quality capital to estimate firm productivity more
precisely.. Further investigation on the endogeneity of industrial policy is anotherimportant extension. To conduct such an analysis, it is imperative that the quality andcoverage of the firm-level data and policy variables be improved and expanded.

Appendix.

Decomposition of Industry TFP Growth

To decompose the industry productivity growth Δωt , we follow the method
used by Foster, Haltiwanger, and Krizan (2001) as follows:24

Δω t ≅ ∑n∈C s nt −1 Δω nt + ∑n∈C (ω nt −1 − ω t −1 )Δs nt + νρ ΔsΔω(A1)

+ ∑n∈N s nt (ω nt − ω t −1 ) − ∑n∈X s nt −1 (ω nt −1 − ω t −1 ),where ν ={ ∑ Δs }{ ∑ Δω }(> 0) ; ρ
n∈C

2
nt

n∈C

2
nt

ΔsΔω

is the correlation between increases

in the firm's output share and its productivity growth; and C , N , and X represent
the set of continuing firms, entering firms, and exiting firms, respectively.
The first term indicates each firm's own productivity growth, weighted by theindustry share of each firm in year t − 1 .

This is sometimes called the "within" effect.

The second term represents the changes in the shares, weighted by the deviation of the
productivity level from the industry average level in year t − 1 .

The second term

captures the changes in the shares, which is sometimes called the "between" effect, or
alternatively, the "reallocation" effect.

The third term captures the correlation between

the changes in the shares and the productivity growth, which is called the "covariance"
effect.

The fourth and last terms represent the effects of entry and exit, respectively.

We use equation (A1) to decompose the industry productivity growth.

References
Aghion, Philippe, Mathias Dewatripont, and Patrick Rey. 1997. "Corporate Governance,
Competition Policy and Industrial Policy." European Economic Review 41: 797805.
Amiti, Mary and Jozef Konings. 2007. "Trade Liberalization, Intermediate Inputs, andProductivity: Evidence from Indonesia." American Economic Review 97: 16111638.
Baily, Martin Neil, Charles Hulten and David Campbell. 1992. "Productivity Dynamics
in

Manufacturing

Plants."Brookings

Paperson

Economic

Activity(Microeconomics) 1992: 187-249.
Bank of Japan (Statistics Department). 1956-64. Oroshiuri Bukka Tokei Nenpo

25

(Wholesale Price Index Annual). Tokyo: Bank of Japan. (In Japanese)
Beason, Richard and David E. Weinstein. 1996. "Growth, Economies of Scale, andTargeting in Japan (1955-1990)." Review of Economics and Statistics 78: 286295.
Bernard, Andrew B. and J. Bradford Jensen. 1999. "Exceptional Exporter Performance:
Cause, Effect, or Both?" Journal of International Economics 47: 1-26.
Bureau of Industrial Policy, Ministry of International Trade and Industry. ed. 1983.

Sankoho no Kaisetsu: Aratana Sango Chosei he Mukete (Commentary on the
Temporary Law for Structural Adjustment of Specific Industries: Toward a New
Adjustment). Tokyo: Tsusho Sangyo Chosakai. (In Japanese)
Bureau of Industrial Policy, Ministry of International Trade and Industry. ed. 1988.

Sangyo Kozo Tenkan Enkatsukaho no Kaisetsu (Commentary on the Law for
Moderating the Structural Transformation of Specific Industries). Tokyo: Tsusho
Sangyo Chosakai. (In Japanese)
Foster, Lucia, John Haltiwanger and C.J. Krizan. 2001. "Aggregate Productivity
Growth: Lessons from Macroeconomic Evidence." in New Developments in

Productivity Analysis, 303-372, edited by Charles R. Hulten, Edwin R. Dean andMichael J. Harper. Cambridge, MA: University of Chicago Press.
Fujino, Shozaburo, Shiro Fujino and Akira Ono. 1979. Choki Kiezai Tokei 11 Sen'i

Kogyo (Estimates of long-term economic statistics since 1868, vol.11, Textiles).
Tokyo: Toyo Keizai Shinposha. (In Japanese)
Fukao, Mitsuhiro. 1989. "Nihon no Kawase Kanri no Jiyuka to Kokusaisyushi Kouzou
no Henka (Liberalization of Foreign Exchange Controls and Changes in Balanceof Payments in Japan)." Kin'yu Kenkyu 8: 1-64. (In Japanese)
Japan Spinners' Association. various half year issues. (Menshi) Boseki Jijo Sankosho(Statistics on the Japanese Spinning Industry). Tokyo. (In Japanese)
Japan Spinners' Association. 1961. "Kajo Men Seiboki no Shori nitsuite, 3 (On Disposalof Excess Spinning Machines, 3)." Monthly Report of Japanese Cotton Spinning

Industry 178: 43-45 (In Japanese).
Japan Spinners' Association. 1962. Sengo Boseki Shi (The Postwar History of the Cotton

Spinning Industry). Osaka: Japan Spinners' Association. (In Japanese)
Japan Spinners' Association. 1979. Zoku Sengo Boseki Shi (Continuation of the Postwar
26

History of the Cotton Spinning Industry). Osaka: Japan Spinners' Association.
(In Japanese)
Kimura, Fukunari and Kozo Kiyota. 2006. "Exports, FDI, and Productivity: Dynamic
Evidence from Japanese Firms." Review of World Economics 142: 695-719.
Kiyota, Kozo and Tetsuji Okazaki. 2005. "Foreign Technology Acquisition Policy andFirm Performance in Japan, 1957-1970: Micro-Aspect of Industrial Policy."

International Journal of Industrial Organization 23: 563-386.
Klette, Tor Jakob and Zvi Griliches. 1996. "The Inconsistency of Common Scale
Estimators When Output Prices Are Unobserved and Endogenous." Journal ofApplied Econometrics 11: 343-361.
Korenaga, Takafumi. 2000. "Sengo Nihon no Gaika Yosan Seido to Men Bosekigyo:
Men Bosekigyo ni Taisuru Yunyu Genmen yo Gaika Shikin no Wariate Seisaku(The Foreign Exchange Budget System and the Cotton Spinning Industry in
Postwar Japan: Foreign Exchange Allocation Policy for Cotton Spinning
Industry Imports of Raw Cotton)." Keizaigaku Ronshu 66: 16-36. (In Japanese)
Korenaga, Takafumi. 2002. "Bosekigyo niokeru Kozochosei Enjo Seisaku no Tenkai:
1960 Nendai Kohan Kara 80 Nendai Zenhan Made (The Policy of Structural
Adjustment in the Cotton Spinning Industry from the Late-1960s to the Early1980s)." Seinan Gakuin Daigaku Keizaigaku Ronshu 37: 1-15. (In Japanese)
Korenaga, Takafumi. 2004. "Bosekigyo no Suitai to Shijo Kozo: Sannyu Taishutsu
Kodo to Setsubi Toshi Kodo Karano Sekkin I (The Decline of the Cotton
Spinning Industry and Market Structure: Approached from an Entry, Exit andInvestment Perspective, Part I)." Seinan Gakuin Daigaku Keizaigaku Ronshu 38:
57-90. (In Japanese)
Korenaga, Takafumi. 2005. "Bosekigyo no Suitai to Shijo Kozo: Sannyu Taishutsu
Kodo to Setsubi Toshi Kodo Karano Sekkin II (The Decline of the Cotton
Spinning Industry and Market Structure: Approached from an Entry, Exit andInvestment Perspective, Part II)." Seinan Gakuin Daigaku Keizaigaku Ronshu,
39: 57-77. (In Japanese)
Levinsohn, James and Amil Petrin. 2003. "Estimating Production Functions Using
Inputs to Control for Unobservables." Review of Economic Studies 70: 317-341.
Marschak, Jacob and William H. Andrews, Jr. 1944. "Random Simultaneous Equations
27

and the Theory of Production." Econometrica 12: 143-205.
Melitz, Marc J. 2003. "The Impact of Trade on Intra-Industry Reallocations andAggregate Industry Productivity." Econometrica, 71: 1695-1725.
Ministry of International Trade and Industry (MITI). 1958. Census of Manufactures,1956 issue (Kogyo Tokei Hyo, 1956). Tokyo: MITI. (In Japanese)
Ministry of International Trade and Industry (MITI). 1966. Census of Manufactures

1964 issue (Kogyo Tokei Hyo, 1964). Tokyo: MITI. (In Japanese)
Nagahama, Kiyohiko. 1965. "Sogo Sen'i Meka ni yoru Gosei Sen'i no Seisan to Shohi"(Production and Consumption of Synthetic Fibers by General Textile Producers)

Nihon Bouseki Geppou (Montyly Report of Japan Spinners' Association) 221: 219. (In Japanese)
Nakakita, Toru. 1993. "Boeki to Shihon no Jiyuuka Seisaku (Trade and Capital
Liberalization Policy)." In Sengo Nihon no Keizai Kaikaku – Shijyo to Seifu(Economic Reform in Postwar Japan: Market and Government), 275-306, edited
by Yutaka Kosai and Jyuro Teranishi. Tokyo: University of Tokyo Press. (InJapanese)
Noland, Marcus and Howard Pack. 2003. Industrial Policy in an Era of Globalization:

Lessons from Asia. Washington, D.C.: Peterson Institute.
Olley, G. Steven and Ariel Pakes. 1996. "The Dynamics of Productivity in the
Telecommunications Equipment Industry." Econometrica 64: 1263-1297.
Pavcnik, Nina. 2002. "Trade Liberalization, Exit, and Productivity Improvements:
Evidence from Chilean Plants." Review of Economic Studies 69: 245-276.
Peck, Merton J., Richard C. Levin and Akira Goto. 1987. "Picking Losers: Public Policy
toward Declining Industries in Japan." Journal of Japanese Studies 13: 79-123.
Rodrik, Dani. 2007. One Economics, Many Recipes: Globalization, Institutions, andEconomic Growth. Princeton, NJ: Princeton University Press.
Saxonhouse, Gary. 1979. "Industrial Restructuring in Japan." Journal of JapaneseStudies 5: 273-320.
Staiger, Douglas and James H. Stock. 1997. "Instrumental Variables Regression with
Weak Instruments." Econometrica 65: 557-586.
Yonezawa, Yoshie. 1978. "Sen'i Sangyo to Hikakugyo ni okeru Sangyo Chosei seisaku
to sono Hyoka (Structural Adjustment Policy in the Textile Industry and Leather
28

Industry, and its Evaluation)." Nihon Keizai Kenkyu 7: 53-70. (In Japanese)
Yonezawa, Yoshie. 1981. "Sen'i Sangyo no Sangyo Chosei (Structural Adjustment in the
Textile Industry)." In Nihon no Sangyo Chosei (Industrial Adjustment in Japan),148-176, edited by Sueo Sekiguchi. Tokyo: Nihon Keizai Shinbunsha. (InJapanese)

29

Figure 1. Output Share of the Top 10 Largest Firms (Judai-bo), 1956-1979
60.0%

50.0%

40.0%

30.0%

20.0%

10.0%

0.0%
1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979

Table 1. Descriptive Statistics
Output shareof the top 10
largest firms(judai-bo)

Labor
Outputproductivity capital ratio

1956
0.53
4.50
1957
0.49
4.79
1958
0.48
4.65
1959
0.49
5.04
1960
0.49
5.30
1961
0.48
5.74
1962
0.47
5.52
1963
0.47
5.62
1964
0.47
5.86
Note: n.a. stands for not available.
Source: See main text.

0.08
0.08
0.08
0.09
0.09
0.09
0.10
0.09
0.09

Arithmetic average of all firms
OutputOutputCapital-labor Synthetic
Suspendedenergy ratio material ratio
ratio
yarn-material spindles ratio
input ratio
0.55
0.54
0.51
0.52
0.51
0.51
0.49
0.46
0.46

0.87
0.87
0.88
0.89
0.90
0.89
0.90
0.90
0.92

62.80
66.66
64.10
64.05
65.66
68.64
63.71
66.24
71.03

0.00
0.00
0.00
0.01
0.02
0.01
0.02
0.04
0.07

0
0
0
0.083
0.086
0.075
0.070
0.068
0.069

Correlation
between thesuspendedratio andcapital stock
n.a.
n.a.
n.a.
0.44
0.48
0.40
0.44
0.48
0.48

Table 2. Estimation Results of Production Function
OLS
Dependent variable: gross output
pg
Independent
variables
CoefficientStandard error Significancelevel
Capital stock-0.003
0.009
0.754
Labor
0.033
0.011
0.003
Energy
0.063
0.016
0.000
Material inputs
0.893
0.012
0.000
Test of constant returns to scale
F -test: F -statistic = 22.5 (p -value = 0.000)
Number of observations
1181
Note: All standard errors are bootstrapped using 1,000 replications.
Source: See main text.

LP estimation
Dependent variable: gross outputg
CoefficientStandard error Significance
level
0.085
0.167
0.612
0.022
0.029
0.452
0.071
0.037
0.055
0.815
0.178
0.000
Wald test: Chi-squared = 0.03 (p -value = 0.859)
1181

Table 3. Productivity Level and Growth(Annual average)
Level
Labor productivity
TFP
All firms
All firms
Large firms Small firms
Large firms Small firms
1956-59
4 83
4.83
4 89
4.89
4 82
4.82
0 75
0.75
0 73
0.73
0 75
0.75
1959-64
5.61
5.21
5.64
0.77
0.72
0.78
GrowthLabor productivity
TFP(%)
All firms
All firms
Large firms Small firms
Large firms Small firms
1956-59
5.59
-0.85
6.14
0.65
-0.01
0.70
1959-64
2.76
0.68
2.93
0.27
-0.72
0.35
Notes: Unweighted arithmetic annual average is reported. Large firms are the top 10 largest firms(judai-bo) while small firms are other firms.
Source: See main text.

Table 4. Effects of Industrial Policy on Productivity
OLS
Dependent variable: ln(TFPit)
Suspended spindles ratio
Synthetic yarn-material ratio

(1)
0.120
[0.107]0.187*[0.104]
1181
Yes
Y
Fixed

(2)
-0.054[0.094]0.219**[0.099]1181
Yes
Y
RandomSuspended spindles ratio
Synthetic yarn-material ratio

IV(3)-0.799*[0.445]0.212**[0.105]1180
YesY
4.31**

Observations
Observations
Year ddummiesiYear ddummiesi
Firm-specific effects
Endogeneity (GMM distance) test
R-squared
Underidentification test
0.017
0.031
31.20***
overall
Kleibergen-Paap rk LM statistic
0.041
0.038
withinWeak identification test
0.041
0.126
64.01
between
Kleibergen-Paap Wald rk F statistic
11.79
Hasuman test statistic
Note: Robust standard errors are in brackets. ***, **, and * indicate statistically significant levels at 1, 5, and10 percent, respectively. The Hausman test tests the null hypothesis that the coefficients estimated by the
random effects estimator are the same as the ones estimated by the fixed effects estimator. For IV estimation,suspension rate is treated as endogenous variable while capital stock and other exogenous variables are as
instruments. Two-step GMM estimation is used for the second stage. In IV estimation, one observation is not
used because of the singleton group.

Table 5. Decomposition of Industry TFP Growth[1] = [2] + [5] [2] = [3][3][4][5] = [6][6][7][8] = [9]
[9]
[10]
[11]
+ [8] + [11]
+ [4]
+ [7]
+ [10]OverallWithinBetweenEntry and Entry
CovarianceExit
productivity
effectLarge
SmalleffectLarge
Small exit effecteffectgrowth
firms
firms
firms
firms
1956-59
0.44
0.35
-0.01
0.35
-0.09
-0.01
-0.08
0.02
0.02
0.00
0.17
1959-64
-0.06
-0.40
-0.44
0.04
-0.26
-0.23
-0.03
0.00
0.02
-0.01
0.60
Note: Large firms are the top 10 largest firms (judai-bo) while small firms are other firms (shin-bo & shinshin-bo). Figure indicates annual
average rate (percent).
Source: See main text.

Table A1. Price Data: Definition and Source
Outputp Y1

Item
Cotton yarn, adjusted byaverage countp Y1' Cotton yarn
p Y2 Blended spinning cotton yarn
p Y3

Definition
exp(0.8739+0.0068*averagecount)×p Y1'
Available from the source(P Y1 +P Y3 )/2

Supun rayon yarn, adjusted by exp(0.9390+0.0034*averagecount)×p Y3'
average countSourceFujino, Fujino and Ono (1979)
Bank of Japan (1956-1964)

Fujino, Fujino and Ono (1979)

p Y3' Supun rayon yarn
p Y4 Waste cotton yarn

Available from the source
P Y1 /P M1 ×P M6

Bank of Japan (1956-1964)

p Y5 Blended waste cotton yarn

(P Y4 +P Y3 )/2

p Y6 Tokubo

P Y3

p Y7 Viscose staple yarn

P Y3

p Y8 Mixed supun rayon yarn

(P Y1 +P Y3 )/2

p Y9 Blended synthetic yarn

(P Y1 +P Y10 )/2

p Y10 Synthetic yarn

p Y10A W A +p Y10B W B +p Y10C W W
Weighti ht W j isi theth shareh off material
t i l
+p
W
cost.
C
Y10C
D

p Y10A Polyester
p Y10B Vinylon

Available from the source

Bank of Japan (1956-1964)

Available from the source

Bank of Japan (1956-1964)

p Y10V Nylon

Available from the source

Bank of Japan (1956-1964)

p Y10D Acrylic

Available from the source
p M5

Bank of Japan (1956-1964)

p Y11 Cotton weesp
p Y12 Rayon staple weesp
Material inputs

P Y3 /P M1 ×P Y4
All Japan Cotton Spinners'
Association (various years)
Bank of Japan (1956-1964)

p M1 Cotton

Available from the sourcep M2 Rayon staple
p M3 Viscose staple

Available from the source
P Y3

p M4 Waste cotton yarn

Available from the source

Bank of Japan (1956-1964)

p M5 Polyester

Available from the source

Bank of Japan (1956-1964)

p M6 Vinylon

Available from the source

Bank of Japan (1956-1964)

p M7 Nylon

Available from the source

Bank of Japan (1956-1964)

p M8 Acrylic
Energy use
p E1 Coal

Available from the source

Bank of Japan (1956-1964)

Available from the source

Bank of Japan (1964)

p E2 Electricity

Available from the source

Bank of Japan (1964)

p E3 Heavy oil

Available from the source

Bank of Japan (1964)

Table A2. Number of Entry, Incumbent, and Exit
Number of firms
Total
1956-57
132
1957-58
135
1958-59
139
1959-60
134
1960-61133
1961-62132
1962-63130
1963-64
125
Source: See main text.

Entry
11
3
4
0
1
1
1
0

Incumbent
121
132
131
132
130
128
124
121

Exit
0
0
5
2
2
3
5
4