Sources of productivity improvement in industrial clusters: The case of the prewar Japanese silk-reeling industry

Regional Science and Urban Economics 46 (2014) 27–41

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Sources of productivity improvement in industrial clusters: The case of the prewar Japanese silk-reeling industry☆ Yutaka Arimoto a,1, Kentaro Nakajima b,⁎, Tetsuji Okazaki c a b c

Institute of Developing Economies, 3-2-2 Wakaba, Mihama-ku, Chiba-shi, Chiba 261-8545, Japan Faculty of Economics, Tohoku University, 27-1 Kawauchi Aobaku, Sendai 980-8576, Japan Faculty of Economics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Miyagi 113-0033, Japan

a r t i c l e

i n f o

Article history: Received 13 December 2012 Received in revised form 27 January 2014 Accepted 16 February 2014 Available online 24 February 2014 JEL classification: R12 O18 L10

a b s t r a c t We examine two sources of productivity improvements in localized industrial clusters of the silk-reeling industry in prewar Japan. Agglomeration improves the productivity of each plant through positive externalities which shift plant-level productivity distribution to the right. Selection expels less productive plants through competition, which truncates the distribution on the left. We find evidence of agglomeration effects that benefit less productive plants and selection effects in clusters. Here, a cluster is defined by the density of own-industry plants within an area. The results complement previous studies that find positive agglomeration effects in the most productive firms, but no selection effects in cities (Combes et al., 2012; Accetturo et al., 2011). Our results suggest that the sources of productivity improvements in localized industrial clusters might be different from those in cities. © 2014 Elsevier B.V. All rights reserved.

Keywords: Agglomeration Selection Heterogeneous firms Productivity

1. Introduction Plants in the same industry are often concentrated within a region, forming a cluster. Examples of industrial clusters include the high-tech cluster in the Silicon Valley (U.S.), the information technology cluster ☆ This is a much-revised version of a paper released in the CEI discussion paper series #2010–11. The former title was “Agglomeration or Selection? The Case of the Japanese Silk Reeling Industry in 1908–1915.” We are grateful to Toshihiro Matsumura, Noriaki Matsushima, and Yasusada Murata for kindly providing invaluable comments and suggestions. We also thank Gilles Duranton, Janet Hunter, Ryo Horii, Ryo Kambayashi, Daiji Kawaguchi, Takashi Kurosaki, Masaki Nakabayashi, Kaoru Sugihara, Tomohiro Machikita, Raffaele Paci, Osamu Saito, Ryuhei Wakasugi, Kazuhiro Yamamoto, and participants of the meetings of the Socio-Economic History Society at Hiroshima University, ARSC at Kushiro Public University of Economics, JEA at Kyoto University, NARSC at San Francisco, WEHC at Utrecht University, and seminars at Hitotsubashi University, KISER, Kyushu University, Nihon University, RIETI, Tohoku University, the University of Tokyo, and University of Tsukuba for their comments and discussion. The authors greatly appreciate the financial support provided by the Japan Society for the Promotion of Science (#21330064 and #22223003). Arimoto gratefully acknowledges the financial support provided by the Japan Society for the Promotion of Science (#1810041), and Nakajima the financial support provided by the Japan Society for the Promotion of Science (#21730181 and #25380275). We thank Xu Hangtian for his excellent research assistance. The usual disclaimer applies. ⁎ Corresponding author. Tel./fax: +81 22 795 6305. E-mail addresses: [email protected] (Y. Arimoto), [email protected] (K. Nakajima), [email protected] (T. Okazaki). 1 Tel.: +81 43 299 9760.

http://dx.doi.org/10.1016/j.regsciurbeco.2014.02.004 0166-0462/© 2014 Elsevier B.V. All rights reserved.

in Bangalore (India), automobile clusters in Detroit (U.S.) and Toyota (Japan), and the silk fabric cluster in Como (Italy). In addition, developing countries sometimes have export-oriented industrial clusters that specialize within a particular industry. For instance, there is a metal working cluster in Kenya (Sonobe et al., 2011a) and a garment cluster in China (Sonobe et al., 2011b). Several studies have reported that plants in industrial clusters are more productive than those that are not (Nakamura, 1985; Henderson, 2003; Martin et al., 2011; Di Giacinto et al., 2012). The higher level of productivity of plants in industrial clusters has long been explained by agglomeration effects, which refer to localization economies and urbanization economies. Localization economies (or Marshall–Arrow–Romer (MAR) economies; see Glaeser et al. (1992)) arise from the agglomeration of own-industry plants. Productivity increases by transferring knowledge and innovative ideas among densely agglomerated workers, easing matching through thick labor markets, or reducing transaction costs resulting from repeated transactions with proximate firms. Urbanization economies (or Jacobs economies; see Jacobs (1969)) arise from a diversity of industries or from the city size itself. In this case, the diversity of workers and the knowledge spillovers from outside the industry promote innovation and improve productivity. More recently, plant selection has been suggested as another factor that could improve productivity in the context of international trade

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Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

Fig. 1. Map of Japan and the density of silk-reeling plants in 1908.

and spatial economics (Melitz, 2003; Melitz and Ottaviano, 2008; Behrens et al., 2009; Baldwin and Okubo, 2006). This theory suggests that intensified competition expels less productive plants, and consequently, only the relatively productive plants survive. Thus, clusters have a higher average regional productivity, even if a cluster does not improve the productivity of each plant. Empirically, Syverson (2004) found that higher demand density truncates the productivity distribution in the lower tail in the ready-mixed concrete industry. This implies that low-productivity producers are less likely to survive under increased competition. Furthermore, Corcos et al. (2010) have identified selection effects induced by a trade policy. Recent studies have attempted to identify which of these two effects, agglomeration effects or selection effects, are in place. In a pioneering work, Combes et al. (2012) developed an innovative method to measure the magnitudes of the two effects, and found that agglomeration effects best explain the higher productivity in larger cities, defined by population density, in France. Following Combes et al. (2012), Accetturo et al. (2011) too found agglomeration effects in populated Italian labor market areas for most of the industries. These two studies suggest that agglomeration effects have a stronger impact within cities. In this study, we examine which of the two sources of productivity improvement, agglomeration or selection, explain the productivity advantages of industrial clusters. We do so by studying the case of the silk-reeling industry in Japan in the early 20th century, which possessed two key advantages for the purposes of this work. First, there were a number of silk-reeling clusters, of which those in central Japan were the most famous. At the same time, in addition to the clustered plants, numerous silk-reeling plants existed across Japan. This distribution of plants enables us to exploit sufficient regional variations for an empirical analysis. Second, the homogeneity of the products and production technologies enables us to compute the productivity of each plant

with minimum noise. The silk-reeling plants produced a single homogenous product, raw silk, almost all of which was exported at the price determined by the international market. The production technologies were relatively simple, and there was little difference in the quality of labor (they were mostly young women). We also have input and output measures in physical quantities, rather than in monetary terms. These attributes free us from the usual problems of price dispersion, heterogeneous markups, and unobserved prices when estimating productivity. To frame our empirical study, we develop a nested model of agglomeration and selection through competition over input procurement. Spatial economics and international trade literature mostly rely on monopolistic competition in the output market to model competition among heterogeneous firms (Melitz, 2003; Syverson, 2004; Combes et al., 2012). However, the silk-reeling plants in our study were price takers in the international market. Instead of competing in the output market, the plants aggressively competed over input procurement (cocoons). To consider this feature, our model is based on competition over input procurement, a modification of Syverson's (2004) selection model on competition over the sales of homogeneous output. We model this competition by assuming that the marginal cost of cocoons increases with the total production of silk (thus, the total procurement of cocoons) in a region. Using this model, we show that a region with lower entry costs facilitates entry and generates a cluster, but the increase in competition for cocoons causes selection. Consequently, only highly productive plants can survive. We then introduce an agglomeration economy in the spirit of Fujita and Ogawa (1982) and Lucas and Rossi-Hansberg (2002), which assumes that a plant's productivity increases with the total number of operating plants in a region. Our model produces theoretical predictions identical to those of Combes et al. (2012), which enables us to draw a distinction between agglomeration and selection effects by examining the characteristics of plant-level productivity distributions.

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

b) Agglomeration 0.5 0.4 0.3

Density

0.0

0.1

0.2

0.3 0.2 0.0

0.1

Density

0.4

0.5

a) Selection

-4

-2

0

2

4

-4

-2

Case 1

0

2

4

2

4

Case 2

c) Both selection and agglomeration

0.5 0.4 0.3 0.1 0.0

0.0

0.1

0.2

0.3

Density

0.4

0.5

d) Neither effect

0.2

Density

29

-4

-2

0

2

4

-4

Case 3

-2

0

Case 4 Fig. 2. Four considerable cases of cluster effects.

Our empirical analyses are based on the quantile approach developed by Combes et al. (2012), which estimates the agglomeration and selection effects from productivity distributions. Intuitively, the agglomeration effect will shift the distribution to the right by improving the productivity of all plants in a region. On the other hand, the selection effect truncates the distribution on the left by expelling less productive plants from the market. The quantile method developed by Combes et al. (2012) examines how much of the difference in the productivity distributions of clusters and non-clusters can be explained by relative agglomeration (right-shift and dilation of the distribution) and selection (left-truncation of the distribution). Our main empirical conclusion is that the greater productivity in the prewar Japanese silk-reeling clusters can be explained by agglomeration effects, which benefited less productive plants, and selection effects. Here, we define a cluster by the density of silk-reeling plants within an area. The results are robust to different spatial units, considering economies of scale and accessibility to input and output markets. Our results suggest that selection can also play an important role in improving the productivity in localized industrial clusters, and that we should consider both agglomeration and selection when discussing the advantages of localization. This study contributes to understanding the sources of localization economies. Several studies have identified the positive productivity

impact of the count or density of own-industry plants (Nakamura, 1985; Henderson, 2003; Martin et al., 2011; Di Giacinto et al., 2012). To our knowledge, ours is the first study to examine both agglomeration and selection effects together in an effort to explain the productivity advantages of localized clusters. Our study is also closely related to other studies that examine the productivity advantages of cities. For example, employment density is known to have a positive effect on productivity in both the U.S. (Ciccone and Hall, 1996) and in the EU countries (Ciccone, 2002). Excellent reviews of existing studies on spatial concentration and productivity can be found in Rosenthal and Strange (2004) and Melo et al. (2009). In the context of cities, Combes et al. (2012) and Accetturo et al. (2011) considered both agglomeration and selection effects, and found that the agglomeration effect explains most of the productivity advantages of densely populated cities in France and Italy, respectively. While these studies focus on the productivity advantages of cities, as measured by demand density (employment or population density), we focus on localized industrial clusters as measured by own-industry plant density. Local demand density did not play a role in our case because almost all the raw silk was exported. Instead, the number of plants was a crucial element in plants' survival, as a result of competition over inputs. Given these two industry characteristics, our case is probably

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Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

Table 1 Descriptive statistics. Variable

Mean

Std. dev.

Min.

Panel A: Plant-year level Output (kin ≈ 0.6 kg) Number of pots Number of female workers Cocoon use (koku ≈ 180.39 l) Plant age (years since foundation) Water power dummy Steam power dummy

6580.42 75.65 79.35 1286.22 12.29 0.38 0.57

13086.2 102.79 112.81 10374.43 10.22 0.49 0.50

38 10 3 6 0 0 0

Panel B: county-year level Number of plants Plant density Output (kin ≈ 0.6 kg) Number of pots Number of female workers Cocoon use (koku ≈ 180.39 l) Plant age (years since foundation) Water power dummy Steam power dummy

7.115 0.031 6599.106 77.288 81.252 1135.801 11.3 0.201 0.729

12.935 0.145 7704.873 66.514 68.31 1333.351 6.19 0.318 0.344

more relevant to export-oriented industries that use natural resource inputs or cheap labor force. Our results complement the findings of Combes et al. (2012) and Accetturo et al. (2011), who found agglomeration effects in the most productive firms but no selection effects in cities. In contrast, we find agglomeration effects in less productive plants, and selection effects. Our findings suggest that the sources of productivity improvement in cities and localized industrial clusters may be different. However, the purpose of this paper is not to give a final conclusion for the discussion on the source of productivity improvement in industrial clusters. Instead, we aim to show, based on a case study, that both the selection effect and the agglomeration effect can improve productivity. The rest of this paper is organized as follows. The next section gives an overview of the Japanese silk-reeling industry in the early 20th century. Section 3 provides a theoretical explanation of industrial clusters and plant-level productivity. Section 4 describes the estimation strategy, and Section 5 describes the data. Section 6 provides our main results, and Section 7 discusses the robustness of our findings. Finally, Section 8 concludes the paper. 2. Silk-reeling industry in pre-war Japan 2.1. Silk-reeling as the major exporting industry The silk-reeling industry was one of the major industries in prewar Japan.2 In 1908, this industry employed 24.4% of the total factory workers in Japan,3 and its product, raw silk, occupied 26.6% of the country's total exports (Ministry of International Trade and Industry, 1962; Toyo Keizai Shinposha, 1927). The basic market condition was factored by the growth of the silk weaving industry in the U.S. (Nakabayashi, 2003). The U.S. silkweaving industry introduced its mass production system in the 1860s, and preferred homogeneous raw silk in large lots. Meeting this demand was a challenge for the traditional hand-reeling plants in both Japan and China. By adopting the machine-reeling technology, the emerging silkreeling industry in Japan met the demand in the U.S. and, as a result, grew rapidly. In 1908, the ratio of exports to production was 98.9% with respect to machine-reeled raw silk, with 74.0% of exports going to the U.S. (calculated from the statistics in Nakabayashi, 2003, pp. 468–462). 2 Of the many studies on the development of the silk-reeling industry in Japan, Ishii (1972) and Nakabayashi (2003) are basic references. 3 The denominator is the number of total factory workers in 1909 (Ministry of International Trade and Industry, 1962).

1 0.0002 181 10 11 30 0 0 0

Max.

Obs

300700 1784 1878 676861 88 1 1

4497 4647 4648 4497 4644 4648 4648

178.705 2.465 65100 731.5 582 10831.5 46 1 1

369 369 368 369 369 369 369 369 369

2.2. Market structure A distinctive feature of the silk-reeling industry was that it was composed of numerous small and medium-sized plants, and its product market was very competitive. In 1908, there were 2786 silk-reeling plants. Even the largest plant accounted for only 4.3% of the total silk production in Japan, and the median value of the market share was 0.058% (Ministry of Agriculture and Commerce, 1910). Since these plants were very small, and almost all of their product was exported, they were basically price-takers and could sell as much raw silk as they wanted to (at the market price) in Yokohama, the main exporting port.

2.3. Silk-reeling clusters The silk-reeling plants formed several clusters. The clusters in the Nagano, Aichi, and Gifu prefectures in the central region of Japan were the largest, occupying 37.5% of the silk-reeling plants in 1908. Fig. 1 depicts the density (number of plants/area [km2]) of silk-reeling plants in 1908. The dark colored areas represent the prefectures where silkreeling plants were densely located. For instance, the Nagano prefecture, the largest cluster, had over 18 plants per km2. Note that silk-reeling was quite wide-spread in Japan, and more than half of the counties had more than one silk-reeling plant in 1908. Why did silk-reeling plants localize in these clusters? It is partly because of the natural conditions that lowered the opportunity cost of entering the industry. Among these clusters, the one in Suwa County in the Nagano Prefecture was the largest (Ministry of Agriculture and Commerce, 1910). One of the most common reasons given by major plant owners for the development of the silk-reeling industry in the Suwa cluster was that they could not secure a living by agriculture alone, as their land holdings were small and the soil was infertile. Some respondents also cited a lack of good alternative occupations as a reason (Hirano Village Office, 1932, pp. 560–562). Since the silk-reeling clusters were formed in infertile, remote, and mountainous regions, they were also highly specialized. For example, manufacturing in Suwa County was almost completely dominated by the silk-reeling industry, which occupied 99.1% of the total manufacturing employment (Ministry of Agriculture and Commerce, 1921). The corresponding figures for the second and third largest clusters— Shimo-Ina County and Kami-Ina County of the Nagano Prefecture— were 98.5% and 96.7%, respectively. Another reason for silk-reeling plants localizing in these cluster was the tradition of hand-reeling silk. Hand-reeling reduced the sunk-entry

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

31

Table 2 Estimation result of plant-level productivity. ln(capital)

ln(labor)

ln(intermediates)

ln(age)

Water power dummy

0.152⁎⁎ (0.056)

0.153⁎⁎ (0.074)

0.719⁎⁎ (0.076)

0.056⁎⁎ (0.022)

0.053⁎ (0.032)

Steam power dummy

Constant

No. obs.

R2

0.004 (0.035)

2.170⁎⁎ (0.134)

4479

0.955

Note: The dependent variable is the log of physical output measured in weight. “Capital” is the number of pots, “labor” represents the number of female workers, “intermediates” is the physical quantity of cocoon used measured in volume, and “age” represents plant age (years since foundation). The model is estimated by fixed effects model. Robust standard errors are in parentheses. ⁎ Significant at the 10 percent level. ⁎⁎ Significant at the 5 percent level.

cost of machine-reeling, because some of the know-how for handreeling was also useful for machine-reeling. We checked the correlation of the spatial distribution between machine- and hand-reeling plants at the county level, and found that it was positive (0.19). 2.4. Production According to Duran (1913), the production of raw silk using machine-reeling technology during that period typically proceeded as follows. A silk-reeling plant purchased cocoons from sericulture peasants. Cocoons were boiled to unwind the cocoon filaments. Then, from a group of boiled cocoons, unwound filaments were bundled and reeled onto a small moving reel that was powered by water, steam, electricity, or gas. Owing to its relatively simple production process, the productivity and survivability of a silk-reeling plant essentially depended on the procurement and management of the two basic inputs, namely cocoons and labor. 2.5. Competition over inputs Competition for the procurement of cocoons was severe in clusters, raising input prices in the clusters, but also in adjacent prefectures, or prefectures even farther away (Hirano, 1990; Ishii, 1972, ch. 4; Matsumura, 1992). This competition was amplified by the temporal and technological constraints that raw cocoons had to be transported quickly or dried appropriately to ensure their quality and condition, as they perished in moist environments. Drying was also necessary to prevent the metamorphosis of pupae. As Kajinishi ed. (1964) states, “As the silk-reeling industry developed in Suwa, competition for raw materials and work force became harsh among plants, while loss due to transportation costs and damage of cocoons increased” (pp. 304–306). Indeed, in March 1908, the price of spring cocoons per koku (≈180.39 l) was 42.7 yen in prefectures with a silk-reeling plant density greater than the median, but was 39.8 yen in other prefectures (Ministry of Agriculture and Commerce, 1910). With regard to labor, most silk-reeling workers were young women. As the enormous labor demand in clusters forced plants to hire workers from surrounding rural areas, the plants would pay a fixed cost for boarding, food, and even education, which increased the unit labor cost. Moreover, because silk-reeling required some experience and skill, recruiting and training new workers were expensive. As a result, poaching trained workers from other plants became prevalent in clusters, and gradually proliferated throughout the country (Kambayashi, 2001; Nakabayashi, 2003; Tojo, 1990). In spite of this background, the wages paid to silk-reeling workers did not substantially increase, on average, because of the abundant supply of young unemployed women in the agricultural areas around the clusters. For example, the sample mean and median of the daily wage silk-reeling workers in Suwa county in 1908 were 0.24 and 0.25 yen, respectively (Nakabayashi, 2003, p. 259). At the same time, the average daily wage of female agricultural workers was 0.23 yen in the same year (Statistics Bureau, Management and Coordination Agency, 1988). These data imply that the unit labor cost for an average female silk-reeling

worker in the largest cluster was approximately the same as that of the average agricultural worker. Regional migration of silk-reeling workers was minor, as these young women only worked for couple of years before marriage. Ishii (1972) showed the data that over 70% of silk-reeling workers in Suwa came from regions within the prefecture. In general, worker mobility in prewar Japan was quite low due to labor barrier formed by the “stem-family” system and pre-war patriarchy, keeping the number of farm households remained constant with a constant family size (Hayashi and Prescott, 2008). 3. Theoretical framework In this section, we present an illustrative model to demonstrate that lower sunk-entry costs can foster entry and generate a cluster, but also induce fierce selection. In this case, the mechanism of selection is competition over inputs rather than demand-side factors, which are more common in selection models based on monopolistic competition. This is because the plants in our case are price-takers producing a homogenous good. We based our model on Syverson's (2004) selection model to fit into the context of the Japanese silk-reeling industry. We then follow Combes et al. (2012) to incorporate agglomeration effects. 3.1. Market structure We consider the entry and production decisions of silk-reeling plants that procure cocoons from farmers, reel the silk, and sell the final product. Output is sold in the export market (Yokohama) at an exogenous price, p, set by the international market. Therefore, plants are price-takers, and can sell as much as they wish in Yokohama. We assume that the production of qi silk by plant i entails labor cost hiqi, input purchase cost w(Q)qi for cocoons, and fixed cost f. A plant uses hi units of labor to produce one unit of output (raw silk). We further assume that all workers have the same skills, but that the value of hi differs across plants. Following the discussion on the female labor market in that period in Section 2, we normalize the wage paid to a unit of labor to unity. Input purchase price w(Q) can be considered as an aggregate inverse supply function, where Q = ∑ qi is the total output in the region (i.e., factor demand). We assume that w(Q) increases with Q because greater silk production requires a greater demand for cocoons. Therefore, unlike the plants modeled in Syverson (2004) that have demand density as the key variable, plants in our model do not compete over output sales, but rather over input purchases (cocoons). The profit of a plant can be represented as πi ¼ pqi −hi qi −wðQ Þqi − f : For simplicity, we assume a linear marginal input purchase price as w(Q) = wQ: πi ¼ pqi −hi qi −wQ qi −f :

ð1Þ

This model has two stages. In stage one, each potential plant decides whether to pay a sunk-entry cost, s, to enter the market. After the

32

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

TFP 1915 1.5 0

0

.5

1

Density

2 1

Density

2

3

TFP 1908

-3

-2

-1

0

1

2

-2

-1

TFP 1908 Clustered

Non-clustered

2

3

Non-clustered

.6 .4 .2

.4

.6

Density

.8

1

.8

Output per pot 1915

0

0

.2

Density

1

Clustered

Output per pot 1908

0

2

4

6

8

0

2

Output per pot 1908 Clustered

4

6

8

Output per pot 1915

Non-clustered

Clustered

Non-clustered

Output per worker 1915 .6 .4 0

0

.2

.2

.4

.6

Density

.8

1

.8

Output per worker 1908

Density

0

TFP 1915

0

2

4

6

8

0

2

Output per worker 1908 Clustered

4

6

8

Output per worker 1915

Non-clustered

Clustered

Non-clustered

Fig. 3. Kernel densities of plant-level productivity.

payment, draws productivity hi from distribution g(h) with h a plant i support 0; h , where h is an arbitrary upper bound. In stage two, each plant decides its level of production, qi, given its productivity parameter, hi, forming an expectation of total output in a region of E(Q). The expected profit of an entrant plant at stage two is Eðπ i Þ ¼ ðp−hi Þqi −wEðQ Þqi −f :

ð2Þ

 pffiffiffiffiffiffi2 ^ h  i h−h wf iþ ^ E πi hi jhi ≤ h ¼ −f : w

ð3Þ e

V ¼ and as such, the optimal output of a plant with marginal cost hi is 

qi ðhi Þ ¼

p−hi −wEðQ Þ : w

ð7Þ

We assume a free-entry condition so that plants enter the market (i.e., pay s and draw hi) until the expected value of entry is equal to zero:

The first-order condition with respect to qi is ∂Eðπ i Þ ¼ p−hi −wqi −wEðQ Þ ¼ 0; ∂qi

pffiffiffiffiffiffi ^ þ wf from Eq. (6) into E(π ) yields Substituting p−wEðQ Þ ¼ h i ^ operating profit, conditional on hi ≤ h:

ð4Þ

Substituting qi  into the expected profit yields

Z

2 3 pffiffiffiffiffiffi2 ^ h−h þ wf 6 7 −f 5g ðhÞdh−s ¼ 0: 4 w 0 ^ h

ð8Þ

^ is the value that solves this expression, and is a funcEquilibrium h tion of g(h) and parameters w, f, and s. 3.2. Entry cost, selection effect, and emergence of clusters

½p−hi −wEðQ Þ2 −f : E½π i ðhi Þ ¼ w

ð5Þ

The expected profit decreases with hi. Therefore, a critical productiv^ choose not to pro^ exists such that entrants drawing h N h ity draw, h, i duce. This cutoff level of productivity can be solved by setting E(πi) = 0: pffiffiffiffiffiffi ^ ðEðQ ÞÞ ¼ p−wEðQ Þ− wf : h

ð6Þ

We now consider regions that are symmetric except for their entry cost, s. We interpret s as the opportunity cost of starting-up a silkreeling plant. Based on the historical information in Section 2, we assume that s varies across regions owing to its first nature and the fact that plants tend to occur in infertile, remote, and mountainous regions. We show that a region with a lower s imposes fiercer competition (i.e., a lower cutoff unit labor requirement), but can support a large number of operating plants, resulting in a cluster.

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

33

Table 3 Descriptive statistics of plant-level productivity. Measure of productivity

Clusters vs. non-clusters

Obs.

Mean

Interquantile range

10th percentile

25th percentile

50th percentile

75th percentile

90th percentile

All Clusters Non-clusters

2232 1096 1136

−0.026 0.001 −0.052 (4.685)

0.224 0.196 0.240

−0.267 −0.222 −0.311

−0.136 −0.089 −0.167

−0.012 0.013 −0.038

0.088 0.107 0.072

0.182 0.190 0.175

All Clusters Non-clusters

2233 1097 1136

3.945 4.049 3.845 (8.449)

0.707 0.619 0.805

3.204 3.360 3.011

3.624 3.778 3.448

4.025 4.073 3.947

4.331 4.398 4.253

4.566 4.605 4.541

All Clusters Non-clusters

2234 1097 1137

3.923 4.036 3.814 (9.442)

0.659 0.570 0.739

3.161 3.384 3.016

3.634 3.796 3.480

3.999 4.061 3.912

4.293 4.367 4.219

4.507 4.561 4.430

All Clusters Non-clusters

2247 1098 1149

0.026 0.031 0.020 (0.906)

0.284 0.270 0.296

−0.272 −0.255 −0.292

−0.102 −0.092 −0.108

0.062 0.056 0.069

0.183 0.178 0.188

0.285 0.288 0.284

All Clusters Non-clusters

2263 1103 1160

4.169 4.202 4.137 (2.261)

0.868 0.880 0.868

3.222 3.353 3.117

3.778 3.783 3.772

4.290 4.306 4.263

4.646 4.663 4.640

4.933 4.942 4.913

All Clusters Non-clusters

2263 1103 1160

4.138 4.173 4.104 (2.492)

0.833 0.847 0.810

3.239 3.298 3.193

3.762 3.770 3.753

4.261 4.295 4.224

4.595 4.617 4.564

4.862 4.893 4.824

Panel A: 1908 TFP

Output per pot

Output per worker

Panel B: 1915 TFP

Output per pot

Output per worker

Note: TFP is estimated by the fixed effects estimation. The t-values of the t-test are presented in parentheses. The null hypotheses of the t-test are that average productivity is the same for clusters and non-clusters.

We first consider the effect of entry cost s on the cutoff marginal ^ The implicit function theorem implies that labor requirement h.   ^ − ∂V e =∂s dh ¼ Z ¼ ^ ds ∂V e =∂h 2

w N0: pffiffiffiffiffiffi ^ h−h þ wf g ðhÞdh

^ h 0

ð9Þ

Regions with a lower start-up cost have a lower cutoff labor unit ^ This relationship implies that competition is more severe: requirement, h. high-cost (low-productivity) plants are not profitable in regions with a lower start-up cost. Next, we investigate the effect of s on the number of plants in a region. Let Ne denote the number of entrant plants that have paid s and drawn hi, ^ Then, and let Np denote the number of producing plants with hi ≤ h. ^ h

Np ¼ Ne

0

  ^ : g ðhÞdh ¼ Ne G h

ð10Þ

By applying Eq. (4), the total production in a region can be represented

Q ¼ Ne

^ h 0

 qi g ðhÞdh

Z ¼ Ne

^ h 0

Ne

  ^ 1 þ Ne G h

Z

^ h 0

p−h g ðhÞdh: w

ð12Þ

pffiffiffiffiffiffi ^ p−h− wf Ne ¼ Z ^  h pffiffiffiffiffiffi ^ h−h þ wf g ðhÞdh:

ð13Þ

0

^ yields Partial differentiation with respect to h

∂Ne ¼ ^ ∂h

Z h^  pffiffiffiffiffiffipffiffiffiffiffiffi   ^ ^ − ðp−hÞg ðhÞdh− p−h− wf wf g h 0 b0: "Z ^ #2 h pffiffiffiffiffiffi ^ h−h þ wf g ðhÞdh

ð14Þ

^ (fiercer selection) implies more entry. Therefore, a lower h   ^ , the effect of the entry cost on the number Now, since N p ¼ Ne G h of producing plants is expressed by   dh ^   ^ dNp dNe dh ^ ^ þN g h ¼ G h : e ^ ds ds ds dh

  p−h ^ : g ðhÞdh−Ne EðQ ÞG h w

Since Q = E(Q) in equilibrium, we can represent Q as a function of Ne ^ and h:   ^ ¼ Q Ne ; h

pffiffiffiffiffiffi ^ p−h− wf : w

0

as Z

Q¼

^ By applying Eqs. (11) and (12), we can represent Ne as a function of h and g(⋅), as well as parameters {p, w, f}, as follows:

Implication 1. (selection effect)

Z

Additionally, rearranging Eq. (6) yields

ð11Þ

ð15Þ

The overall effect of s on Np consists of two effects. The first, represented by the first term, is the entry effect, which is negative. That is, a lower value of s means more plants will enter the market. The second, represented by the second term, is the competition effect, which is positive because ddsh^ N0 from Eq. (9). Since a lower s induces competition, the number of plants that can produce after entry will be smaller. The

34

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

Table 4 Main estimation results. Panel A: above vs. below median density (1)

(2)

(3)

(4)

(5)

(6)

Measure of productivity

TFP

TFP

Output per pot

Output per pot

Output per worker

Output per worker

Year

1908

1915

1908

1915

1908

1915

A (relative agglomeration)

0.227 [0.122] 0.916 [0.054] 0.025 [0.031] 2232 1058 1174 0.949

0.184⁎ [0.058] 0.920⁎

0.256 [0.216] 0.945 [0.047] 0.030⁎ [0.014] 2229 1104 1125 0.917

0.242⁎ [0.017] 0.978⁎

[0.027] 0.021 [0.027] 2220 1045 1175 0.845

0.211⁎ [0.089] 0.979 [0.021] 0.127⁎ [0.017] 2276 1067 1209 0.967

[0.007] 0.051⁎ [0.020] 2242 1077 1165 0.946

0.290 [0.192] 0.957 [0.041] 0.004 [0.016] 2243 1087 1156 0.933

Panel B: above vs. below 75-percentile density A (relative agglomeration) 0.117 [0.220] D (relative dilation) 0.957 [0.103] S (relative selection) 0.197⁎ [0.079] Total observations 2232 Observations in clusters 532 Observations in non-clusters 1700 0.981 R2

0.217 [0.195] 0.912 [0.081] 0.083 [0.052] 2240 559 1681 0.948

0.244⁎ [0.055] 0.973 [0.016] 0.161⁎ [0.032] 2206 501 1705 0.979

0.245 [0.208] 0.936 [0.045] 0.123⁎ [0.016] 2179 508 1671 0.971

0.177⁎ [0.075] 0.988 [0.018] 0.141⁎ [0.026] 2248 533 1715 0.979

0.255 [0.162] 0.940 [0.034] 0.108⁎ [0.016] 2259 558 1701 0.960

D (relative dilation) S (relative selection) Total observations Observations in clusters Observations in non-clusters R2

Note: Estimation method proposed by Combes et al. (2012) is used. Standard errors are calculated by 200 bootstrapping iterations. TFP is estimated by the fixed effects estimation. Bootstrapped standard errors are in square brackets. ⁎ Significant at the 5 percent level.

aggregate effect of a low entry cost on the number of producing plants depends on the relative magnitude of the (positive) entry effect and the (negative) competition effect. However, we can show that the entry effect always dominates the competition effect. Substituting Eqs. (13) and (14) into Eq. (15) yields    dh ^ dN p dN e  ^ ^ ¼ G h þ Ne g h ^ ds ds dh "    2  Z ^ G h ^ −g h ^ − p−h ¼

"Z

^ h 0

#

h   pffiffiffiffiffiffi  iZ ^ þ wf g h ^ GðhÞdh − G h

^ h 0

#2

pffiffiffiffiffiffi ^ h−h þ wf gðhÞdh

^ h 0

GðhÞdh

^ dh b0: ds

ð16Þ       ^ 2 ^ −g h ^ ∫h GðhÞdh is positive because from Eq. (9), G Here, G h 0   h i     ^ ≥GðhÞ for all h∈ 0; h ^ ,G h ^ Ng h ^ , and dh^ N0. Therefore, the overall h ds expression is negative. Implication 2. (endogenous clusters) A lower entry cost, s, means more plants will enter the market (entry effect), but also imposes fiercer competition after entry, which reduces the number of producing plants (competition effect). The former always dominates the latter, and therefore, the number of producing plants is greater in a region with a lower s. 3.3. Agglomeration effect and productivity distributions Following Combes et al. (2012), we now introduce the agglomeration effect, which improves plants' productivity through the interaction between adjacent operating plants. We model this effect by assuming that, when a plant interacts with Np plants, the effective units of labor supplied by an individual worker become a(Np), where a(0) = 1, a′ N 0, and a″ b 0. Then, a plant with labor requirement hi reduces the number of its workers to l(hi) = qihi/a(Np), at a total cost of a(Np)l(hi) = qihi. Thus, each plant's maximization problem is unchanged.

Given this agglomeration effect, the logarithm of a plant's productivity ϕi can be derived as follows: 2 3 h  i hq i q i i  5 ¼ ln a Np − ln ðhi Þ: ϕi ¼ ln ¼ ln 4 ð17Þ l q h =a N i i

p

Then, the density function of the log productivities is as follows:   8 ^ ; ^ ≡ A− ln h > 0 for ϕbϕ > >   < A−ϕ A−ϕ g e f ðϕÞ ¼ e ð18Þ > ^   > for ϕ ≥ ϕ; > : ^ G h where A ≡ ln[a(Np)]. As discussed above, each plant's maximization problem is unchanged, regardless of the presence of the agglomeration effect. Thus, substituting ^ obtained from Eq. (8) into the equilibrium cutoff labor requirement, h, Eq. (18) yields the equilibrium distribution of plant productivities. From this productivity density function and the assumption of a′ N 0, it is clear that an increase in the number of operating plants shifts the distribution to the right, while maintaining its form. Implication 3. (agglomeration effect) An increase in the number of operating plants in region Np shifts the productivity distribution to the right. The result of our model with respect to the productivity distribution is identical to that of Combes et al. (2012), although the theoretical set up is different. Thus, we can apply their novel empirical strategy. To do so, we introduce some additional notation. We introduce regions into the model explicitly using the index of r. Let F(ϕ) be the corresponding cumulative density function of f(ϕ). The proportion  of plants that cannot survive in region r is de^r is the cutoff productivity in region r. ^ r , where h fined as Sr ≡ 1−G h Let Ar ≡ ln[a(Npr)] be the agglomeration effect in region r, where Npr is the number of producing plants in the region. The underlying cumulative density function of the log productivities across all

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

^ r →∞ regions when there are no selection or agglomeration effects (h and Ar = 0, ∀ r) is defined as follows:   −ϕ e ; F ðϕÞ ≡ 1−G e

ð19Þ −ϕ

because ϕ = 0, and if Ar = 0, then, h = e and change of variables. Then, the cumulative density function of the log productivities for surviving firms in region r is defined as follows: ( F r ðϕÞ ¼ max 0;

)

e F ðϕ−Ar Þ−Sr : 1−Sr

ð20Þ

Because the results of productivity distribution from our model are identical to those of Combes et al. (2012), we can consider the following four polar cases with respect to the channels of productivity improvement, as in the Combes et al. (2012) model. For simplicity, we consider two regions r = c (clusters) and r = n (non-clusters). Case 1. Only the selection effect matters When there is no agglomeration effect, only selection affects productivity. In this case, a(Np) = 1 holds for any value of Np. However, se^n , where h ^c ( h ^ n ) is the cutoff unit labor ^ c bh lection implies that h requirement in region c (n). This raises the log productivity cutoff in ^ . This case is represented in Fig. 2(a). The solid line ^ Nϕ the clusters: ϕ c n represents the log productivity distribution in clusters, while the dashed line represents the same for non-clusters. Note that the log productivity distribution in clusters is left-truncated. Case 2. Only the agglomeration effect matters In this case, only the agglomeration effect improves plants' productivity. To eliminate the selection effect, we impose sc = sn = s, where sc (sn) is the fixed entry cost in region c (n). Then, the intention of selection ^n ^c ¼ h is the same in the both clusters and non-clusters, and therefore, h and Npc = Npn, where Npc (Npn) is the number of plants in region c (n). To establish clusters and non-clusters, we assume Nc N Nn by exogenous reasons that fall outside the scope of our model. Only firms in clusters benefit from increased worker interaction, ln[a(Npc)] N ln[a(Npn)]. Thus, the log productivity simply shifts to the right, while maintaining its distribution form. This case is shown in Fig. 2(b). Case 3. Both the selection and agglomeration effects matter In this case, the fixed entry costs are different between clusters and non-clusters, sc b sn, and the concentration of workers improves productivity, a′ N 0 and a″ b 0. Thus, both left-truncation by selection and a right-shift by agglomeration occur within clusters. This case is shown in Fig. 2(c). Case 4. Neither effect matters In this case, the fixed entry costs are the same for all regions and the concentration of workers does not improve productivity, a(Np) = 1. Then, the log productivity distribution is common across regions, and there is no difference in the productivities across regions. This case is shown in Fig. 2(d). 4. Empirical strategy As we have shown in the previous section, our theoretical results in the plant-level log of the productivity distribution are identical to those of Combes et al. (2012). Thus, we can apply their quantile approach to measure the selection and agglomeration effects. This section briefly describes our empirical strategy. Before describing the estimation strategy, we address the concern that the impact of the agglomeration effect varies across plants. For example, high-productivity plants would tend to benefit more rapidly from agglomeration economies. To include this effect, we redefine the agglomeration economies to introducing heterogeneity in the benefits

35

of agglomeration. By introducing heterogeneity,  −ðD −1Þ agglomeration econo, where Dr ≡ ln[d(Np)], my a(Np) can be rewritten as a Np h r d(0) = 1, d′ N 0, and d″ b 0. Then, the log productivity of a plant with unit cost h in region r is denoted by ϕr ðhÞ ¼ ln

qr ðhÞ ¼ Ar −Dr ln ðhÞ: lr ðhÞ

ð21Þ

Then, we can write the cumulative density function of the log of the productivity in a surviving plant in region r as   9 8 < e F ϕ−A −Sr = D : F r ðϕÞ ¼ max 0; : 1−Sr ; r

ð22Þ

r

This implies that the agglomeration effect not only shifts the distribution to the right by Ar, but also dilates the distribution by Dr. However, selection eliminates a fraction, Sr, of entrants. Based on the extended model, the estimation strategy is as follows. As in the previous section, we consider two regions, r = c (clusters) and r = n (non-clusters), and the cumulative density functions in each region as     9 9 8 8 < e < e F ϕ−A F ϕ−A −Sc = −Sn = D D ; and F n ðϕÞ ¼ max 0; : F c ðϕÞ ¼ max 0; : : 1−Sc ; 1−Sn ; c

c

n

n

Next we introduce relative parameters as A = Ac − DAn, D = Dc/Dn, and S = (Sc − Sn)/(1 − Sn). Intuitively, if we obtain A N 0, there is a larger right-shift in clusters than in non-clusters. Dilation by agglomeration is captured by D. If we obtain D N 1, clusters have a larger dilation of their distribution than non-clusters. Selection is captured by S. If S N 0, there is more elimination of entrants in clusters than in non-clusters. By introducing these relative parameters, we can express the cumulative density functions Fc(ϕ) and Fn(ϕ) in terms of the other expression in such a way that clarifies their relationship. If Sc N Sn, Fc can also be obtained from Fn by dilating D, shifting by A, and left-truncating a share of S, as follows:  F ðϕ−AÞ−S F c ðϕÞ ¼ max 0; n D : 1−S

ð23Þ

If Sc b Sn, Fn can also be obtained from Fc by dilating D1, shifting by −DA, −S , as follows: and left-truncating a share of 1−S  −S F ðDϕ þ AÞ−1−S : F n ðϕÞ ¼ max 0; c −S 1−1−S

ð24Þ

Then, to estimate the relative parameters A, D, and S, we rewrite the cumulative density functions as quantiles, and then combine them. If S N 0, Eq. (23) can be rewritten in quantile form as λc ðuÞ ¼ Dλn ðS þ ð1−SÞuÞ þ A;

ð25Þ

−1 where λc(u) ≡ F−1 c (u) is the uth quantile of Fc and λn(u) ≡ Fn (u) is the uth quantile of Fn. Furthermore, if S b 0, Eq. (24) can be rewritten as

λn ðuÞ ¼



1 u−S A λc − : D 1−S D

ð26Þ

By combining these two equations and using change of variable −S −S Þ þ ½1− maxð0; 1−S Þu, we obtain u → r S (u), where r S ðuÞ ¼ maxð0; 1−S the following expression: λc ðr S ðuÞÞ ¼ Dλn ðS þ ð1−SÞr S ðuÞÞ þ A:

ð27Þ

This equation implies how the quantiles of the productivity distribution in clusters relate to those in non-clusters through relative parameters

36

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

Table 5 Constrained specification. Panel A: S = 0 (1)

(2)

(3)

(4)

(5)

(6)

Measure of productivity

TFP

TFP

Output per pot

Output per pot

Output per worker

Output per worker

Year

1908

1916

1908

1916

1908

1916

A (relative agglomeration) D (relative dilation)

0.241⁎ [0.095] 0.913⁎

0.257⁎ [0.104] 0.897⁎

0.846⁎ [0.192] 0.847⁎

0.422⁎ [0.163] 0.909⁎

0.921⁎ [0.249] 0.827⁎

Total observations Observations in clusters Observations in non-clusters R2

[0.042] 2232 1058 1174 0.935

[0.041] 2220 1045 1175 0.771

[0.049] 2276 1067 1209 0.980

[0.035] 2229 1104 1125 0.626

[0.064] 2242 1077 1165 0.967

0.140⁎ [0.139] 0.985 [0.031] 2243 1087 1156 0.798

S (relative selection)

0.038 [0.122] 0.060⁎

−0.016 [0.058] 0.050⁎

0.145 [0.089] 0.065⁎

0.029 [0.216] 0.038⁎

0.107⁎ [0.017] 0.081⁎

0.049 [0.192] 0.017⁎

Total observations Observations in clusters Observations in non-clusters R2

[0.027] 2232 1058 1174 0.911

[0.019] 2220 1045 1175 0.850

[0.016] 2276 1067 1209 0.954

[0.011] 2229 1104 1125 0.914

[0.028] 2242 1077 1165 0.897

[0.008] 2243 1087 1156 0.904

Panel B: D = 1 A (relative agglomeration)

Note: Estimation method proposed by Combes et al. (2012) is used. Standard errors are calculated by 200 bootstrapping iterations. TFP is estimated by the fixed effects estimation. Bootstrapped standard errors are in square brackets. ⁎ For A and S significantly different from 0 at the 5 percent level, for D significantly different from 1 at the 5 percent level.

A, D, and S. Then, we define mθ(u) = λc(rS(u)) − Dλn(S + (1 − S)rS(u)) + A, where θ = (A, D, S). Finally, we estimate θ by minimizing the meansquare error on mθ, as

^θ ¼ arg min θ

Z

1 0

^ θ ðuÞ2 du ; ½m

ð28Þ

^ θ ðuÞ is the empirical counterpart of mθ(u), obtained by replacing where m λc and λn with their estimators.4 This implementation compares the two distributions asymmetrically. That is, we compare the quantiles of region c's productivity distribution to the quantiles of the truncated, dilated, and shifted distribution of region n. Combes et al. (2012) added a further step to obtain a robust estimator able to symmetrically treat the quantiles of the two distributions. Combining Eqs. (25) and (26) into one equation and using change of variable u→er S ðuÞ, where er S ðuÞ ¼ maxð0; SÞ þ ½1−maxð0; SÞu, we obtain another equality condition:

er ðuÞ−S 1 A e θ ðuÞ ¼ λn er S ðuÞÞ− λc S þ : m D 1−S D

ð29Þ

  ^ D; ^ ^S Using this condition, Combes et al. (2012)'s estimator ^θ ¼ A; which is what we actually estimate in this study, is described as follows: ^θ ¼ arg min MðθÞ; where M ðθÞ Z 1h Z 1 θ i2 b ^ θ ðuÞ2 du þ e θ ðuÞ du; ½m ¼ m 0

0

See Combes et al. (2012) for details on obtaining these estimators.

5.1. Data We compiled the micro-data of the census of silk-reeling plants, Zenkoku Seishi Kojo Chosa, for the two data points 1908 and 1915. The data include plant-level information on plant name, location, year of foundation, number of pots, number of workers, number of business days per year, types of power used, physical input of cocoons measured by volume (koku ≈ 180.39 l), and physical output of raw silk measured by weight (kin ≈ 0.6 kg). The dataset covers both hand-reeling and machine-reeling plants. Here, we focus on machine-reeling plants because hand-reeling and machine-reeling are different techniques and serve different markets. We matched plants over the two periods based on plant name, location, and year of foundation, and constructed an unbalanced panel dataset. There were 2385 machine-reeling plants in 1908 and 2263 in 1915. In all, 910 plants that existed in 1908 survived until 1915. The extent of agglomeration is measured by regional own-plant density, computed as the number of silk-reeling plants per area (km2). The regional areas in each county or prefecture were obtained from the Geographical Information System (GIS) data for 1937, from “Taisho-Showa Gyoseikai Data.”5 Table 1 reports the descriptive statistics of our data. Plant-year level statistics are provided in Panel A. In Panel B, we report descriptive statistics of the county-year average for county-years with at least one silkreeling plant. 5.2. Measures of plant-level productivity

ð30Þ

b e θ ðuÞ, obtained by replacing e θ ðuÞ is the empirical counterpart of m where m the true quantiles, λc and λn by their estimators. The standard errors for the estimated parameters are obtained by 200 bootstrap iterations, including re-estimating plants' productivities. 4

5. Data, and measures of plant-level productivity

As discussed in the previous section, we focus on the shape of the productivity distributions to distinguish the channels of productivity improvement effects. For this purpose, we first estimate the productivity of each plant. We use the total factor productivity (TFP) as the primary measure of plant-level productivity. We also use output per pot 5 The data are published by Yuji Murayama, Division of Spatial Information Science, Graduate School of Life and Environmental Sciences, University of Tsukuba, URL: http:// giswin.geo.tsukuba.ac.jp/teacher/murayama/data_map.html.

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

37

Table 6 Alternative spatial units: prefecture-level. Panel A: above vs. below median density (1)

(2)

Measure of productivity

TFP

TFP

Output per pot

Output per pot

Output per worker

Output per worker

Year

1908

1915

1908

1915

1908

1915

A (relative agglomeration)

0.101 [0.107] 0.962 [0.046] 0.081⁎ [0.035] 2232 1119 1113 0.936

−0.512 [0.323] 1.189 [0.126] 0.175⁎ [0.056] 2248 1043 1205 0.941

0.065 [0.044] 0.993 [0.012] 0.213⁎ [0.030] 2209 1095 1114 0.965

0.331 [0.372] 0.918 [0.077] 0.074⁎ [0.031] 2234 1026 1208 0.924

0.181⁎ [0.027] 0.968⁎

0.125 [0.087] 0.967 [0.020] 0.082⁎ [0.014] 2250 1009 1241 0.910

−0.506⁎ [0.099] 1.196⁎

0.267 [0.177] 1.005 [0.040] 0.154⁎ [0.040] 2201 445 1756 0.976

0.093 [0.119] 1.007 [0.026] 0.157⁎ [0.023] 2245 516 1729 0.983

D (relative dilation) S (relative selection) Total observations Observations in clusters Observations in non-clusters R2

Panel B: above vs. below 75-percentile density A (relative agglomeration) 0.288⁎ [0.048] D (relative dilation) 0.892⁎ S (relative selection) Total observations Observations in clusters Observations in non-clusters R2

[0.025] 0.043 [0.027] 2232 448 1784 0.938

[0.047] 0.169⁎ [0.035] 2241 518 1723 0.925

(3)

(4)

(5)

[0.008] 0.141⁎ [0.012] 2229 1070 1159 0.983 0.151⁎ [0.064] 1.032⁎ [0.013] 0.099⁎ [0.032] 2217 438 1779 0.980

(6)

0.217⁎ [0.027] 0.9998 [0.008] 0.105⁎ [0.017] 2320 557 1763 0.992

Note: Estimation method proposed by Combes et al. (2012) is used. Standard errors are calculated by 200 bootstrapping iterations. TFP is estimated by the fixed effects estimation. Bootstrapped standard errors are in square brackets. ⁎ For A and S significantly different from 0 at the 5 percent level, for D significantly different from 1 at the 5 percent level.

(capital productivity) and output per worker (labor productivity) as alternative measures of plant-level productivity. In the Japanese silkreeling industry, output per pot and output per worker were conventionally used as measures to evaluate plant-level productivity. To estimate TFP, we specify the following plant-level production function: yit ¼ βk kit þ βl lit þ βm mit þ δZ it þ ωit þ ϵit ;

ð31Þ

where yit is the log of the physical output, measured in weight of plant i in year t, kit is the log of the capital input (number of pots), lit is the log of the labor input (number of female workers), mit is the log of the quantity of intermediate input (cocoons used, measured in volume), Zit is the vector of plant i's observable characteristics, and ωit and ϵi are productivity terms unobservable to econometricians. With regard to plant i's observable characteristics, (Zit), we include the log of the plant age, as well as dummy variables indicating the use of water power or steam power. While ϵit is also unobserved by plants before they make their input decisions, ωit is observable. We assume that ωit = ωi; that is, the observable productivity for plants does not change throughout the study period (seven years). Given this assumption, we estimate this production function using the fixed effects model.6 The estimation results are reported in Table 2. The coefficients of ln(capital) and ln(worker) are in expected signs and are statistically significant. The coefficient of ln(intermediate) is positive, but not significantly different from zero. We interpret the ^ kit −β ^ lit þ β ^ mit −^δZ it as the TFP of plant i in period t. residual yit −β k l m

5.3. Distribution of plant-productivity in clusters and non-clusters We use a county as a unit of observation of regional productivity density. All counties in Japan are classified into two groups based on regional plant densities (number of plants per km2 in a region). We regard counties with plant density above median as clustered counties, and the other counties as non-clustered counties. Fig. 3 depicts the kernel densities of the log of the TFP for clusters and non-clusters.7 The solid line shows the density of clustered prefectures and the dashed line shows the density of non-clustered prefectures. In each figure, the estimated density in the lower tail of the distribution is lower for clustered prefectures than it is for non-clustered prefectures, while the density in the higher tail of the distributions is similar for both groups. Moreover, the shapes and positions of the two distributions seem to be similar, except for the lower tails. There is no clear sign that the distribution of clustered prefectures shifts to the right. These observations are confirmed by the descriptive statistics of the productivity distributions shown in Table 3. Panel A represents the statistics for 1908 and Panel B represents those for 1915. The mean productivities are significantly higher in clusters for every productivity measure and period, indicating that plants located in clusters were indeed more productive than those in nonclusters. The percentiles in the clusters are much higher than in the non-clusters in the lower tails (the 10th and 25th percentiles), but quite similar in the higher tails. This suggests that the distribution did not shift to the right, but was truncated on the left.

6. Main results In this section, we report our estimation results. We present our robustness checks in the following section.

6 An alternative way of estimating the TFP is the structural approach proposed by Levinsohn and Petrin (2003) which relaxes the assumption of ωit = ωi. However, a limitation of our data set is that the two periods are seven years apart, making it difficult to apply this method.

7 We estimate the density functions using the Epanechnikov kernel with optimal bandwidth.

38

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

Table 7 Robustness to scale economies: restricting plants to below-median employment. (1)

(2)

(3)

(4)

(5)

(6)

Measure of productivity

TFP

TFP

Output per pot

Output per pot

Output per worker

Output per worker

Year

1908

1915

1908

1915

1908

1915

A (relative agglomeration)

0.294⁎ [0.041] 0.897⁎

0.212 [0.461] 0.910 [0.194] 0.017 [0.088] 2241 518 1723 0.935

0.229 [0.135] 0.973 [0.032] 0.053⁎

0.218 [0.118] 0.940 [0.031] 0.112⁎

0.201 [0.157] 0.984 [0.034] 0.096⁎

[0.026] 2201 445 1756 0.934

[0.039] 2245 516 1729 0.964

0.116 [0.108] 1.028 [0.024] 0.164 [0.085] 2217 438 1779 0.983

D (relative dilation) S (relative selection) Total observations Observations in clusters Observations in non-clusters R2

[0.020] −0.019 [0.030] 2232 448 1784 0.964

[0.036] 2320 557 1763 0.968

Note: Estimation method proposed by Combes et al. (2012) is used. Standard errors are calculated by 200 bootstrapping iterations. TFP is estimated by the fixed effects estimation. Bootstrapped standard errors are in square brackets. ⁎ For A and S significantly different from 0 at the 5 percent level, for D significantly different from 1 at the 5 percent level.

6.1. Main results The estimation results are shown in Table 4. We present our estimates separately for the three measures of productivity (TFP, output per pot, and output per worker), each for two periods (1908 and 1915). We use the county as the main spatial unit to define clusters. Panel A uses the median own-plant-density per area as the criterion for defining clusters and non-clusters. In this case, counties above the median density are considered clusters. The estimates of relative-agglomeration (A) are all positive, and they are statistically significantly different from zero at the 5% level in three out of six specifications. This implies that, compared to non-clusters, the productivity distribution of the clusters is shifted to the right. In other words, the mean productivity was higher in the above-median density counties than in the less dense counties. The estimates of relative-dilation (D) are statistically significantly different from 1 in two cases, but note that they are all less than 1. Thus, the distribution of plant productivity is less dilated in denser counties. Given A N 1, this implies that less productive firms benefit more from being in denser counties. The estimates of relative selection (S) are all positive, and statistically significant for three specifications. This suggests that, in denser counties, there is a tendency for the distribution to be truncated at the lower tail, and thus plants are more likely to be severely selected. In Panel B, we use a stricter criterion for defining clusters, namely densities above the 75th percentile. The general tendencies are similar to the median criterion. We find some positive agglomeration effect, less dilation, and relative selection in denser counties. In particular, we find stronger evidence of selection than when we used the median criterion. Here, the estimates of S are larger and are significant in five specifications (compared to three). To summarize, plants were more productive in clusters with higher own-plant density. This was driven by agglomeration effects, which helped less productive plants, and selection effects in clusters. This finding suggests that tougher competition between plants in their own industry do play a role in improving productivity in localized industrial clusters. Our finding of selection effects contrasts with the findings of Combes et al. (2012) and Accetturo et al. (2011) who only found agglomeration effects in French and Italian cities, respectively. Our results also differ from those of Combes et al. (2012) and Accetturo et al. (2011) in finding that D b 1, rather than D N 1. Why do less productive plants benefit more than more productive plants by being in denser counties? We can make some conjectures,

based on historical facts. The two major innovations in the Japanese silk-reeling industry in this period were improvements in silk-reeling machines and the introduction of a new management system. First, while the majority of the reeling machines had two spools until the end of the 1890s, in the 1900s, three-spool reeling machines became prominent in the Suwa district. Then, in the following decade, these were replaced by four-spool reeling machines (Nakamura and Molteni, 1994, p. 36). As this technological change was simple and easy to imitate, less productive plants in clusters had a chance to catch-up through imitation. Another major innovation was the introduction of the performancebased wage system. A distinctive characteristic of this system was that each worker's wage was determined based on her performance relative to the average of all the workers in the same plant. Performance was evaluated using several measures, including labor productivity, cocoon productivity, and product quality, which were strategic factors for the competitiveness and profitability of the plant. This wage system was devised by Nakayama Co. in the Suwa district in the 1870s, spread within the district, and then later became popular in other districts (Ishii, 1972, pp. 291–307; Nakabayashi, 2003, pp. 241–277). This anecdote suggests that imitation of innovation was not difficult, especially within a cluster.

6.2. Constrained specifications In our main results, we found both left-truncation (S N 0) and less dilation (D b 1) within clusters. To examine the importance of these two effects, we follow Combes et al. (2012) and re-estimate the model using a constrained specification that sets S = 0 (no selection) or D = 1 (no dilation). Panel A in Table 5 reports the estimates with the restriction of S = 0. The estimates of D are less than unity and statistically significant, except for column (6). The magnitudes of D are less than the estimates in Table 4. This suggests that, when S is constrained to zero, the selection effect arises as D b 1. A smaller pseudo-R2 in four out of six specifications implies that ignoring S leads to a misspecification. Panel B in Table 5 reports the estimates after imposing the restriction of D = 1. Here, the estimates of S are positive and significant in every column. The magnitudes of A are less than the estimates in Table 4 and are not statistically different from zero, with the exception of column (5). This suggests that, when dilation is constrained to unity, the selection effect arises more strongly. The pseudo-R2 s values are smaller in four out of six specifications, which emphasize the importance of considering dilation.

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

39

Table 8 Robustness to proximity to Yokohama: restricting to prefectures within 300 km radius from Yokohama. (1)

(2)

(3)

(4)

(5)

(6)

Measure of productivity

TFP

TFP

Output per pot

Output per pot

Output per worker

Output per worker

Year

1908

1915

1908

1915

1908

1915

A (relative agglomeration)

S (relative selection)

0.092 [0.172] 0.976 [0.079] 0.196⁎

0.146 [0.140] 0.930 [0.063] 0.090⁎

0.122 [0.102] 1.017 [0.022] 0.160⁎

0.112 [0.066] 0.988 [0.015] 0.074⁎

−0.004 [0.166] 1.038 [0.036] 0.172⁎

0.246 [0.139] 0.950 [0.031] 0.071⁎

Total observations Observations in clusters Observations in non-clusters R2

[0.078] 1546 449 1097 0.984

[0.036] 1667 556 1111 0.969

[0.036] 1568 431 1137 0.981

[0.021] 1627 461 1166 0.978

[0.038] 1576 438 1138 0.988

[0.020] 1692 532 1160 0.925

D (relative dilation)

Note: Estimation method proposed by Combes et al. (2012) is used. Standard errors are calculated by 200 bootstrapping iterations. TFP is estimated by the fixed effects estimation. Bootstrapped standard errors are in square brackets. ⁎ For A and S significantly different from 0 at the 5 percent level, for D significantly different from 1 at the 5 percent level.

7. Robustness checks 7.1. Alternative spatial units In the main analysis, we chose the county as the spatial unit. We believe that this is a natural choice given the communication and transportation technology at the time, which affects agglomeration or selection effects. However, it is possible that these effects may also appear in larger spatial units. To check this, Table 6 shows the results of using the prefecture as the spatial unit. We define prefectures above the median (Panel A) or 75th percentile (Panel B) plant density as clusters. The results reconfirm our main finding of positive and significant S in most cases. The agglomeration effects (A) are less clear. The estimates of A are generally smaller and fewer cases are statistically significant when compared to the main results. We also find D N 1 in a number of cases when using the 75th percentile criterion (Panel B). These results could imply that, while competition over inputs were severe, even at the prefecture level, agglomeration effects, such as technical spillovers, were relatively limited within a narrower spatial extent.

conditions, is the high transportation cost to Yokohama. Plants close to Yokohama may simply be more productive and less severely selected because of lower transportation costs. By the same token, better access to input (cocoon) markets can potentially relax the selection effect. To examine whether our results are robust to the proximity of input and output markets, we first restrict our samples to the plants in the 17 prefectures that have their prefectural capital within 300 km of Yokohama (see Fig. 1). 8 Here, clusters are defined as those counties above the 75th percentile density, as before. The estimates reported in Table 8 reconfirm the presence of the selection effect. This suggests that selection in clusters was severe, even among prefectures within a similar proximity to Yokohama. Table 9 reports the estimates after restricting the data to samples that have better access to the cocoon markets. In this case, we first developed a measure of market potential to the cocoon market at the prefectural level in period t, defined as follows: CocoonMarketPotentialrt ¼

XY st drs s

ð32Þ

Silk-reeling plants require large setup costs and are likely to benefit from economies of scale. To check whether our results are not solely driven by economies of scale, Table 7 shows the estimates after restricting the samples to plants below the median number of workers. Here, clusters are defined as those counties above the 75th percentile in density. The results confirm that selection effects and less dilation also occur in small-scale plants.

where Yst is the physical cocoon output (koku) in prefecture s in period t, drawn from the Annual Statistical Report of the Ministry of Agriculture and Commerce (1910, 1917), and drs is the great circle distance between prefecture r and s. This measure is the inverse distance weighted sum of the cocoon outputs. We then restricted our sample to those plants in prefectures above the median CocoonMarketPotential. Here, clusters are defined as those counties above the 75th percentile of density. We again find a positive and significant selection parameter, S, for every measure of productivity and period. This implies that the selection effect is not driven by proximity to input markets.

7.3. Market access

7.4. Sorting (plant relocation)

In our main results, we did not consider the accessibility of input or output markets. In monopolistic competition models, such as those of Melitz and Ottaviano (2008) and Combes et al. (2012), large markets, or better access to markets (lower transportation costs) intensify selection. This is because a larger market means better opportunity for profit, which increases the number of operating plants. Such a mechanism does not work in our setting because plants are all price-takers. However, market access could affect the selection of plants, since proximity to the output market, which in our case is the Yokohama port, can offset the higher costs associated with congestion and tougher competition. Indeed, the Bureau of Commerce, Ministry of Agriculture and Commerce (1912) pointed out that the primary reason for the lack of a large silk-reeling cluster in the Tohoku region (Northeast in Japan), despite a conducive environment in terms of temperature and weather

One concern on the interpretation of the left-truncation in the clusters is that less productive plants might relocate to non-clusters to escape from severe competition. Unfortunately, not much is known about plant relocation after startup. However, anecdotes suggest that plant relocations were uncommon and, if any, they were conducted by major entities with high productivity. The notion of starting a silkreeling plant in a non-cluster in a different prefecture to avoid congestion in the original cluster was pioneered by Katakura-gumi, one of the major silk producers in the Suwa district in 1898. Other large producers soon followed. However, these cases did not represent a

7.2. Scale economies

8 Fukushima, Nigata, Tochigi, Ibaragi, Chiba, Tokyo, Kanagawa, Saitama, Gumma, Yamanashi, Nagano, Shizuoka, Aichi, Gifu, Toyama, Mie, and Ishikawa satisfy this condition and include the major clusters.

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Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41

Table 9 Robustness to access to cocoon markets: restricting to prefectures above median cocoon market potential. (1)

(2)

(3)

(4)

(5)

(6)

Measure of productivity

TFP

TFP

Output per pot

Output per pot

Output per worker

Output per worker

Year

1908

1915

1908

1915

1908

1915

A (relative agglomeration)

0.092 [0.062] 0.965 [0.027] 0.111⁎

0.171 [0.098] 0.991 [0.023] 0.128⁎

0.395 [0.203] 0.907⁎

S (relative selection)

0.196 [0.145] 0.926 [0.065] 0.174⁎

0.156 [0.097] 1.004 [0.025] 0.097⁎

0.133 [0.174] 0.969 [0.039] 0.111⁎

Total observations Observations in clusters Observations in non-clusters R2

[0.067] 1867 446 1421 0.986

[0.030] 2054 593 1461 0.957

[0.034] 1825 438 1387 0.970

[0.026] 1878 440 1438 0.972

[0.019] 2047 508 1539 0.962

D (relative dilation)

[0.042] 0.120⁎ [0.022] 2074 550 1524 0.986

Note: Estimation method proposed by Combes et al. (2012) is used. Standard errors are calculated by 200 bootstrapping iterations. TFP is estimated by the fixed effects estimation. Bootstrapped standard errors are in square brackets. ⁎ For A and S significantly different from 0 at the 5 percent level, for D significantly different from 1 at the 5 percent level.

relocation of a plant, but were startups of branch plants. In addition, they were carried out by large silk producers with higher productivity (Hirano Village Office, 1932, vol. 2, p. 233; Kajinishi ed, 1964, p. 303). Startups of productive branch plants in non-clusters would right-shift the productivity distribution in the non-clusters, but would not affect the distribution in the clusters. In this sense, plant relocation is unlikely to be the driving force of the left-truncation of the productivity distribution in clusters. 7.5. Competition with hand-reeling In our analyses, we concentrated on the machine-reeling plants. However, hand-reeling plants did still exist. Indeed, in our dataset, there were still 401 hand-reeling plants in 1908 (whereas the number of machine-reeling plants was 2385). We argue that these handreeling plants were unlikely to affect the productivity distribution of machine-reeling plants and, therefore, our analyses. This is because machine- and hand-reeling plants did not compete in the output market, since hand-reeled silk could not satisfy the quality and standard required in the international market, and was produced for domestic consumption. On the other hand, machine-and hand-reeling plants did compete for cocoons. Since we defined clusters and non-clusters based on the number of machine-reeling plants only, plants in our definition of non-clusters might have faced severe competition when purchasing cocoons if the region contained many hand-reeling plants. However, this is not the case. The number of machine- and handreeling plants at the county-level shows a mild positive correlation coefficient of 0.19, suggesting that the number of plants in our nonclusters is not under-valued.

left. This suggests that the higher plant-level productivity in the silkreeling clusters can be explained by both agglomeration effects, which helped less productive plants, and selection effects. Thus, our results provide new evidence that selection could play a role in improving productivity in localized industrial clusters. These findings are robust to larger spatial units, economies of scale, and accessibility to input and output markets. We also discussed that the findings are not likely to be driven by plant relocation or competition with hand-reeling plants. Our study, which focused on localized industrial clusters, complements the previous findings of Combes et al. (2012) and Accetturo et al. (2011), who examined the sources of productivity improvements in cities. Our findings of agglomeration effects in less productive plants and selection effects contrast with their findings based on cities. In cities, agglomeration effects benefit the most productive plants, but there was no evidence of selection effects. The differences between these findings imply that the sources and mechanisms of productivity improvement may differ between cities and localized industrial clusters (Beaudry and Schiffauerova, 2009). First, since plants in cities enjoy the benefits of urbanization (or Jacobs) economies, agglomeration effects could be stronger than in industrial clusters, which typically lack diversity of industries. Second, by definition, localized industrial clusters are composed by the agglomeration of plants within the same industry and, thus, competition is likely to be more severe. This could be the reason for our finding of selection effects. If this is the case, we would expect to see selection effects in other industrial clusters as well. Localized export-oriented industrial clusters, using local natural resources or that rely on a cheap labor force, could possibly fit our model. Further examination on the presence of selection effects in other industrial clusters and a comparison with cities remains for future research. References

8. Concluding remarks This study attempted to distinguish the two channels through which localized industrial clusters can improve plant-level productivity. We did so by focusing on the Japanese silk-reeling industry between 1908 and 1915. Based on a nested model of selection and agglomeration, we considered the agglomeration effect, which improves the productivity of all plants in a region, and the plant-selection effect, which raises the average regional productivity by expelling less-productive plants through intense competition. Using plant-level data, we were able to distinguish the channels of productivity improvement using the quantile method developed by Combes et al. (2012), which compares the productivity distributions between clusters and non-clusters, defined by own-plant density per area. We found that, compared to non-clusters, the productivity distribution in clusters with a higher own-plant density per area was relatively shifted to the right, as well as being less dilated and truncated on the

Accetturo, A., Di Giacinto, V., Micucci, G., Pagnini, M., 2011. Local Productivity Differences through Thick and Thin: Market Size, Entry Costs and Openness to Trade, mimeo. Baldwin, R., Okubo, T., 2006. Heterogeneous firms, agglomeration and economic geography: spatial selection and sorting. J. Econ. Geogr. 6, 323–346. Beaudry, C., Schiffauerova, A., 2009. Who's right, Marshall or Jacobs? The localization versus urbanization debate. Res. Policy 38, 318–337. Behrens, K., Mion, G., Murata, Y., Südekum, J., 2009. Trade, wages, and productivity. CEPR Discussion Paper Series, #7369. Bureau of Commerce, Ministry of Agriculture and Commerce, 1912. Juyo yushutsuhin kin'yu oyobi unchin ni kansuru chosa (Survey on the finance and transportation costs of major export goods). Bureau of Commerce, Ministry of Agriculture and Commerce, Tokyo (in Japanese). Ciccone, A., 2002. Agglomeration effects in Europe. Eur. Econ. Rev. 46, 213–227. Ciccone, A., Hall, R., 1996. Productivity and the density of economic activity. Am. Econ. Rev. 86, 54–70. Combes, P., Duranton, G., Gobillon, L., Puga, D., Roux, S., 2012. The productivity advantages of large cities: distinguishing agglomeration from firm selection. Econometrica 80, 2543–2594. Corcos, G., Del Gatto, M., Mion, G., Ottaviano, G., 2010. Productivity and Firm Selection: Quantifying the “New” Gains from Trade, mimeo.

Y. Arimoto et al. / Regional Science and Urban Economics 46 (2014) 27–41 Di Giacinto, V., Gomellini, M., Micucci, G., Pagnini, M., 2012. Mapping local productivity advantages in Italy: industrial districts, cities or both? Bank of Italy Economics Working Paper Series #850. Duran, L., 1913. Raw Silk: A Practical Hand-Book for the Buyer. Silk Publishing Company, New York. Fujita, M., Ogawa, H., 1982. Multiple equilibria and structural transition of non-monocentric urban configurations. Reg. Sci. Urban Econ. 12, 161–196. Glaeser, E., Kallal, H., Shainkman, J., Shleifer, A., 1992. Growth in cities. J. Polit. Econ. 100, 1126–1152. Hayashi, F., Prescott, E., 2008. The depressing effect of agricultural institutions on the prewar Japanese economy. J. Polit. Econ. 116, 573–632. Henderson, V., 2003. Marshall's scale economies. J. Urban Econ. 53, 1–28. Hirano, Y., 1990. Kindai Yo-san-gyo no Hatten to Kumiai Seishi (Development of modern sericulture and cooperative silk-reeling). University of Tokyo Press, Tokyo (in Japanese). Hirano Village Office, 1932. Hirano Mura Shi (History of Hirano Village). Hiano Village (in Japanese). Ishii, K., 1972. Nihon Sanshigyo-shi Bunseki (Analysis of Japanese silk industry). University of Tokyo Press, Tokyo (in Japanese). Jacobs, J., 1969. The Economy of Cities. Vintage, New York. Kajinishi, M. (Ed.), 1964. Gendai Nihon Sangyo Hattatsu Shi: Sen'i (History of Modern Japanese Industries: Textile). Kojunsha, Tokyo (in Japanese). Kambayashi, R., 2001. Tokyu Chingin Seido to Ko-jo Toroku Seido, (Grade wage system and registration system of female factory workers). In: Okazaki, T. (Ed.), Torihiki Seido no Keizai-shi (Governing Business Transactions: A Historical Perspective). University of Tokyo Press, Tokyo (in Japanese). Levinsohn, J., Petrin, A., 2003. Estimating production functions using inputs to control for unobservables. Rev. Econ. Stud. 70, 317–341. Lucas, R., Rossi-Hansberg, E., 2002. On the internal structure of cities. Econometrica 70, 1445–1476. Martin, P., Mayer, T., Mayneris, F., 2011. Spatial concentration and plant-level productivity in France. J. Urban Econ. 69, 182–195. Matsumura, S., 1992. Senkan-ki Nihon Sanshigyo-shi Kenkyu (Historical study of Japanese silk-reeling in the inter-war period). University of Tokyo Press, Tokyo (in Japanese). Melitz, M., 2003. The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica 71, 1695–1725. Melitz, M., Ottaviano, G., 2008. Market size, trade, and productivity. Rev. Econ. Stud. 75, 295–316.

41

Melo, P., Graham, D., Noland, R., 2009. A meta-analysis of estimates of urban agglomeration economies. Reg. Sci. Urban Econ. 39, 332–342. Ministry of Agriculture and Commerce, 1910. Twenty-Fifth Statistical Report of the Department of Agriculture and Commerce. Ministry of Agriculture and Commerce, 1917. Thirty-Second Statistical Report of the Department of Agriculture and Commerce. Ministry of Agriculture and Commerce, 1921. Kojo Tsuran (Census of manufacturing plants). Nihon Kogyo Kurabu, Tokyo. Ministry of International Trade and Industry, 1962. Kogyo tokei 50 nen shi (50 years history of manufacturing census), material volume 1. Printing Bureau, Ministry of Finance, Tokyo (in Japanese). Nakabayashi, M., 2003. Kindai Shihonshugi no Soshiki: Seishigyo no Hatten ni Okeru Torihiki no Tochi to Seisan no Kozo (An Organization of Modern Capitalism: The Governance of Trade and the System of Production in the Development of the Silk Reeling Industry). University of Tokyo Press, Tokyo (in Japanese). Nakamura, R., 1985. Agglomeration economies in urban manufacturing industries: a case of Japanese cities. J. Urban Econ. 17, 108–124. Nakamura, M., Molteni, C., 1994. Silk-reeling technology and female labor. In: Nakamura, M. (Ed.), Technology Change and Female Labor in Japan. United Nations University Press, Tokyo. Rosenthal, S., Strange, W., 2004. Evidence on the nature and sources of agglomeration economies. In: Henderson, V., Thisse, J. (Eds.), Handbook of Regional and Urban Economics, Vol. 4. North-Holland, Amsterdam, p. 2119-271. Sonobe, T., Akoten, J., Otsuka, K., 2011a. The growth process of informal enterprises in Sub-Saharan Africa: a case study of a metalworking cluster in Nairobi. Small Bus. Econ. 36, 323–335. Sonobe, T., Hu, D., Otsuka, K., 2011b. Process of cluster formation in China: a case study of a garment town. J. Dev. Stud. 39, 118–139. Statistics Bureau, Management and Coordination Agency, 1988. Nihon Choki Tokei Soran (Historical Statistics of Japan), vol. 4. Japan Statistical Association, Tokyo (in Japanese). Syverson, C., 2004. Market structure and productivity: a concrete example. J. Polit. Econ. 112, 1181–1222. Tojo, Y., 1990. Seishi Domei no Joko Toroku Seido (Worker registration system of silk-reeling association). University of Tokyo Press, Tokyo (in Japanese). Toyo Keizai, Shinposha, 1927. Meiji Taisho kokusei soran (Statistical handbook of Meiji and Taisho Eras). Toyo Keizai Shinposha, Tokyo (in Japanese).