The dynamics of agglomeration

7.1. Changes in the pattern of industrial agglomeration

It is important to note that the maturity of each industry varies according to their different stages of development. The growth of the number of firms and their location choice in an industry affects the degree of agglomeration. On the other hand, industries that experienced contractions would also become dispersed or localised due to firms closing down. Dumais et al. (2002) indicates that the degree of industrial agglomeration rarely changes from 1972 to 1992 in the US. The influence of historical background on agglomeration is also found in France and the UK. Moreover, Dumais et al. (2002) also show empirical findings that the birth of new firms in an industry is likely to weaken

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agglomeration while the closure of old firms enhances it. However, as a developing country, the structure change of industrial agglomeration in China has experienced a tremendous change in the last decade. There are only two 4-digit industry can be matched remain as the top twenty most agglomerated industries from 1998 to 2007 in table 1.9. The reason of such big change is quite mixed by only looking at the impacts of geographic concentration over time.

In this section, we examine the dynamic for each industry in two ways: running an OLS regression between several estimators and tracing the location changes of the twenty most agglomerated industries in China. The ''New entrants", "Exitors", "One-year",

"Survivors" are the percentage of those new entries firms, exit firms, only one-year stay in the dataset firms and surviving firms as we defined in the data description. The job creation and destruction ratio is also applied to investigate if the business cycle of an industry has an impact on the agglomeration at the industry level. The 2-digit industry level data are also used as controlled variables. In section 7.2, we further investigate trends of top twenty most agglomerated industries by examining changes on industrial top clustering location in each five-years time period. The comparison between industrial top agglomerated regions and other regions is also applied. we trace the location changes of the top twenty most agglomerated industries in the first and second time period.

We follow Devereux et al. (2004) and define the "New Entrance" ratio as employment by

"New entrance" firms divided by the total number of firms. The "Exitors" ratio is defined as the total number of workers from "Exitor" firms over total employment. The

"One-year" ratio is defined as the total number employed by "One-year" firms over the total number of employment in the year. We also define the “Survivors” ratio as employment by survivor firms over total employment. To calculate the job creation and destruction rate, we define the job creation rate as the total number of workers employed in new entrants plus the increase in employment in existing enterprises divided by total employment. The job reduction rate is defined as the number of workers employed in

"Exitors" plus the reduction in employment in enterprises decreasing their workforce

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divided by the total employment. Since we have two time periods, we look at the impact of these ratioson industrial agglomeration for all 4-digit industries using two three-year time-period panel regressions.

(30)

(31)

(32)

(33)

(34)

(35)

Equations (30) to (35) provide the OLS regressions which help us to investigate the dynamics of agglomeration in China. As a developing country, the equation will have a different meaning compared with developing countries. The early industrial geographic concentration is mainly characterised as geographical concentration within a certain region by many industries and the location of each industries are also random. After the policy is applied and productivity competition between enterprise and industries, the location is gradually formed as an industrial agglomeration. Taking the TEDA (Tianjin Economic and development area) as an example, the early entrants combined many un-related industries with different characteristics such as instant noodles, food and beverages, piano manufacture etc. After raising the capital investment limits to the entrant and the costs control by the multinational entrances (multinational enterprises), the low-tech industries gradually moved to other small Economic Technological Development areas or other regions. TEDA has now become a real high-tech industry economic zone. Pharmaceutical products, exchange equipment (Motorola), and aircraft

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(Airbus) are the main industries geographically concentrated in the region.

[Table 1.17 about here]

[Table 1.18 about here]

Tables 1.17 and 1.18 give the coefficient values between the MS index at 4-digit industry and county level and various measures of the dynamic characteristics of firms. Table 1.17 shows that only the survival rate has a negative impact on the MS index while other variables show a positive impact on the agglomeration. The positive impacts from one-year firms and the MS index represent the one-year firms also enhancing their agglomeration in a short time period but which could not survive or perform due to strong competition between firms within the same industry over longer time period. The job creation and destruction rate also reflects the localisation phenomenon in manufacturing industries over the time period. Combine with previous results, the positive impacts from the rate of new entrants, exitors and the MS index implies that the industries in China become more agglomerated through rapid restructuring of industry's patterns. The industry development and changes bolster the process of industrial agglomeration from 1998 to 2002.

Results for the second five-years time period is completely opposite, all of the regressions in table 1.18 show a negative correlation between the MS index and other estimators, excluding the survivor rate. It implies the firms and employment become more geographical stable and still maintain the increase of industrial agglomeration.

Firms locate in the industrial agglomerated regions are more competitive than others and keep taking advantage from Marshall's externalities during the process of firm growth and development.

The different coefficient in the first and second time period help to describe the dynamics of industrial agglomeration in China in this decade. In the first time period, the

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explosive growth on manufacturing industries in the coastal regions attract employee from the whole country. The degree of agglomeration has reached its peak during this time period. However, once all firms have decided their geographic locations in the coastal regions, they compete each other on productivity, profits, exporting orders and high-skilled or low cost employees. These competition or even excessive competition lead to death and move out of firms in the industrial agglomerated regions and therefore decrease the magnitude of industrial agglomeration. The low proportion of new entrants in the top agglomerated regions also partially implies the excessive industrial cluster and limited resources including labour, natural advantage and production costs in those existing agglomerated regions. Firms need to seek for other regions where have lower costs, less competition and sufficient resources and that may have negative impacts on the degree of agglomeration. Moreover, the increasing number of firms together with a shrinking on average firm size suggest that smaller firms may cluster within the agglomerated regions. Smaller firm size may potentially given a smaller Herfindahl index.

Therefore, the magnitude of industrial agglomeration may still rise up with a smaller industrial concentration value and a remained geographic concentration value.

7.2. The location changes of the top most agglomerated industries

In tables 1.19 and 1.20, we examine the impact of new entry firms on the extension of the agglomeration of the twenty most agglomerated industries by tracing the location of new entry firms during two time periods. Tables 1.19 and 1.20 show the percentage of new entry firms located in the top agglomerated regions in the first year of each time period, the number of new entry firms, geographic concentration (G) and γ value for the new entry.

[Table 1.19 about here]

[Table 1.20 about here]

From 1998 to 2002, the number of new entry firms for each industry goes from 5 to 643;

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ten industries have new entry firms located in the top agglomerated region in 1998. The percentage of new entry firms located in the top agglomerated region rises from 11% to 48%. Knives and scissors (SIC-3484), with 48% of new entry firms located in the top agglomerated region, exhibits strong localisation, which is similar to the finding in the UK. It also reflect the enquire of skills and the nature of knowledge spillovers effects' impact industrial agglomeration of o cutlery production. About 40% of new entry firms in the precious stones and jade mining industry (SIC-1095) – the top most agglomerated industry – are located in the top agglomerated region. Moreover, the γ value for the new entrant is also the largest among the twenty most agglomerated industries. On the other hand, exchange equipment (SIC-4112), with only 11% new entries located in the top region, has the smallest γ value, although its geographic concentration value G is not low in the table. The top two industries with the most number of new entries are the silk industry and paper industry. However, the proportion of new entries located in the top regions for the two industries are relatively low. Hence, the new entrants would have a great impact on agglomeration and our new finding of the industry becoming more agglomerated can be explained as the prosperity of new agglomerated regions rather than the increase of new entries in the old agglomerated region.

In 2007, sixteen industries were among the top twenty agglomerated industries, with new entries preferring to locate in the top region. However, the share of new entries located in the top region is relatively small in general. The silk industry experienced a huge increase in the number of new entries but very few of these were located in the traditional top agglomerated region. Moreover, although two of the natural resources extraction industries have very few new entry firms, these often prefer to locate in the traditional cluster region, showing that the extent of agglomeration for those industries is still high. This implies that the geographic exploration of natural resources and extraction is localised in the new region.

Home electronic ventilation appliances (SIC-3953), household kitchen appliances (SIC-3954) and other household electrical appliances (SIC-3959) have small γ values in

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table 1.13 and their share of new entry firms locating in the top agglomerated region is quite small as well. This shows that the location of new entries into these household electronic appliances manufacturing industries goes against agglomeration.

We find that the characteristics of each industry determine the impact that the new entries have on the extent of agglomeration in the dynamic trends. The region of agglomeration for natural resources extraction industries is highly determined by the location of the raw material and a new cluster region may be formed if a new mining region is explored. Some labour-intense industries, such as the silk industry and household electronic appliances with a significant number of new entries, are located in different regions to extent the scale of the industry.