Testing Localization of Chinese Food Industries:
Evidence from Microgeographic Data
Wenchao WU a,1, Shaosheng JIN b, and Suminori TOKUNAGA ca. Graduate School of Life and Environmental Science, University of Tsukuba, Japan.
b. School of Management, Zhejiang University, China.
c. Faculty of Economics and Business Administration, Reitaku University, Japan.

1. Introduction
Ever since the publication of the Principles of Economics (Marshall, 1890), the agglomeration of economic
activity has been fascinating generations of economists. As Krugman (1991) put it, “The most striking featureof the geography of economic activity… is surely concentration.”
Generally speaking, the food industry is transportation intensive; therefore, the extent of localization is
not high, in relative terms. Research in the UK (Duranton and Overman, 2005) and Japan (Nakajima, Saito
and Uesugi, 2012) found very low percentages of food industries to be localized. Previous research also
showed that the degree of industrial concentration in the food sector is low in China (Li, Shi and Jin, 2008).
However, these conclusions are based on provincial aggregate data and discrete measurements.
Applying the continuous measurement of the kernel density function model, this study aims to assess
the extent of agglomeration for the 50 four-digit Chinese food industries using microgeographic data. This
introduction is followed by a. review of literature Section 3 presents the data and empirical strategy. Section
4 shows the main results and the last section concludes the study.

2. Literature review
To assess the geographic distribution of economic activity, economists have developed several indices.
The representative of the first generation indices are Gini, Isard, and Herfindahl indices. These measurements
are improved by the second generation, e.g., EG index (Ellison and Glaeser, 1997) and D index (Mori,
Nishikimi and Smith, 2005).
However, these indices have drawbacks. Firstly, they rely on discrete, divided geographical units. In
other words, the dots representing individual firms are transformed into units in boxes. Although computation
is simpler by aggregating data, information is wasted. In addition, discrete space limits the analysis to only
one spatial scale: usually county, region, or state. Consequently, results vary with spatial scales, rendering
them incomparable. Motivated by satisfying the properties that an ideal agglomeration index should have
(Combes, Mayer and Thisse, 2008), Duranton and Overman (2005) proposed a new index (DO index
henceforth) which provides continuous space using microgeographic data. Some studies have alreadyadopted this method (Duranton and Overman, 2008; Ellison, Glaeser and Kerr, 2010; Koh and Riedel, 2012;
Nakajima, Saito and Uesugi, 2012; Barlet, Briant and Crusson, 2013).
Empirically, Akune and Tokunaga (2005) and Tokunaga and Akune (2005) measure agglomeration of
the food industry in Japan using the EG index and analyze the dynamics of agglomeration from 1980 to 2000.
Jin and Tokunaga (2009) evaluate agglomeration of the Chinese food industry using the D and EG indices.
Li, Shi, and Jin (2008) measure agglomeration of the food industry in China using discrete measurements.
The results indicate a low degree of concentration across the food sector in China. However, as pointed out
earlier, measurements based on aggregate data and discrete space are proven to be biased.

3. Data and empirical strategy
3.1 Data
This research relies on data from the Second National Economic Census of China, conducted by the National
Bureau of Statistics of China in 2008. The National Economic Census Dataset is, perhaps, the mostcomprehensive for Chinese manufacturing so far. It covers all existing firms regardless of size or ownership.
The dataset contains 1,890,513 firms in the manufacturing sector. Each observation representing one firm
provides information on the firm’s location, post code, establishment year, number of employees, capitalstock, revenue, ownership, business status, and industry classification code2, including 2-digit, 3-digit, and4-digit codes. Address and post codes help to match the geographic location of firms.
1 Corresponding author. Email address: zju.wwc.2008@163.com
2 Industry classifications follow the Industrial Classification for National Activities (ICNA) of China (GB/T 4754-2002).

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In this study, we focus on three 2-digit industries, C13, C14, and C15, which represent food processing,food manufacturing, and beverage production, respectively. These 2-digit industries include 19 3-digit
industries, which could be classified into 50 4-digit industries.
Since the calculation of DO’s localization index requires exact geographic data, we use Google Maps
API for geocoding, in which the address of each firm is converted to longitude and latitude. Approximately
0.65% of the observations are unable to be matched with a geographic location or have no employee number
data. This left us with a population of 189,152 firms in the 3 two-digit food industries.
Figures 1(a)–(d) map this location information for our illustrative industries: Yellow Wine (C1523),
Canned Aquatic Food (C1452), Liquid Milk and Milk Products (C1440), and Aquatic Products Freezing(C1361). Each dot on the map represents a firm, and the size of the dot reflects firm size. Here, we use thenumber of employees as a proxy for firm size. As we can observe from the map, the Yellow Wine (C1523)
and Aquatic Products Freezing (C1361) industries seem to be localized, Liquid Milk and Milk Products(C1440) are dispersed, whereas Canned Aquatic Food (C1452) appears fairly random. We continue using
these industries for illustrative purposes in the methodology section.

(a) Yellow Wine (C1523)(b) Canned Aquatic Food (C1452)(c) Liquid Milk and Milk Products (C1440)(d) Aquatic Products Freezing (C1361)

Figure 1. Location of firms for illustrative industries

3.2 Empirical approachThis section briefly introduces the empirical approach. The general idea of DO’s method is comparing theactual distribution of bilateral distances between any two firms with a randomly drawn set of bilateral
distances.

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Step 1: Estimating the kernel density
For industry A with n firms, the Euclidean distances between every pair of firms are calculated. This generates𝑛(𝑛 − 1)⁄2 unique bilateral distances. Also, the weight of each firm is considered. With 𝑒𝑖 denoting thenumber of employee of firm i, the estimator of the density of bilateral distances at distance d is
 d  di , j 
n 1
n
1
(1)
K d  e i  e  j  f 


n 1
n
i 1  j i 1h e i  e  j 
 h 
i 1

j i 1

where d i , j is the Euclidean distance between firms i and j, h is the bandwidth, and f is the kernel function3.
Step 2: Choosing counterfactuals
To report the significance of the result, we choose the relevant counterfactuals as the benchmark with which
the kernel density should be compared. Since the exact distribution of the distance among the population is
unknown, we have to rely on the Monte Carlo approach to construct the counterfactuals.
The analysis is only informative when controlling for the overall tendency of manufacturing toagglomerate. That is to say, the distribution of firms is inhomogeneous across space. Therefore, it is natural
to consider the set of all existing “sites” S currently occupied by all the firms.
Following Duranton and Overman (2005) and Nakajima, Saito, and Uesugi (2012), we run 1,000
simulations for each investigation. In each simulation, we sample as many sites as there are firms in the groupof interest. The sampling is done without replacement for each time. For an industry A with n firms, we
generate the counterfactuals Am for m = 1, 2, …, 1000 by sampling n elements without replacement from
S, so that each trial could be regarded as a random relabeling of firm sites.
Step 3: Global confidence intervals
Next, we determine the confidence intervals. The distance under consideration is from zero to three hundred
kilometers4. We neglect distances greater than 300 km, since any significantly high density of distance in thisrange could be interpreted as dispersion and thus be redundant for our purpose.
On the basis of the concept of a local confidence interval, we construct the global confidence interval.
Since there are correlations between distances, the idea is to return to the simulated industries and look for
the upper and lower local confidence intervals such that 5% of the randomly drawn densities lie above theupper band and 5% lie below the lower band5.
Following this procedure, we define the upper global confidence band K A  d  and lower global
confidence band K  d  . If, for an industry A, K A  d  > K A  d  for at least one d ∈[0,300], then this industryA

is defined as globally localized at the 5% confidence level. On the contrary, when K A  d  < K  d  for atAleast one d ∈[0,300] and K A  d  ≤ K A  d  for every d ∈[0,300], industry A is said to be dispersed (at 5%
confidence level). Similar to the local localization indices, we can define an index of global localization: A  d   max K A  d   K A  d  ,0

(2)

and an index of dispersiond 300ifA d   0
 max K A  d   K A  d  ,0
(3)
 A d   d 00otherwiseAll computations, including the calculation of distance, estimation of kernel density, runningsimulations, and constructing the confidence bands, is manipulated using R. The whole procedure relies on
the package “dbmss,” developed and maintained by Marcon, Gabriel, Stephane, and Florence (2014).
Figure 2 demonstrates the illustrative examples. The solid lines in the figures represent k-density
estimation of observed industry, i.e., the real industry distribution. The two dashed lines are the upper and

3 Following Silverman (1986), the Gaussian kernel with optimal bandwidth is adopted.
4 Previous research in the UK and Japan investigate distances from 0–180 km. Considering the fact that China has a territory

larger than the UK or Japan, we expand the range to 300 km.
5 For more details on constructing the global confidence bands, see Duranton and Overman (2005, 2008) and the KdEnvelope
functions in R package “dbmss” (Marcon, Gabriel, Stephane and Florence, 2013, 2014).
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lower global confidence bands constructed from 1,000 simulations.

(a) Yellow Wine (C1523)(b) Canned Aquatic Food (C1452)(c) Liquid Milk and Milk Products (C1440)(d) Aquatic Products Freezing (C1361)

Figure 2. Kernel density and global confidence bands for illustrative industries.

In Figures 2(a) and (d), we can see that the observed k-density lies over the upper global confidenceband, which implies that the Sugar Refinery industry (C1340) exhibits a localization pattern. Intuitively, from
Figure 1(a), we can see that most firms cluster in the Yangtze River delta. In Figure 2(b), the observed density
is enveloped by the confidence bands. This suggests that the corresponding industry does not deviatesignificantly from randomness. In Figure 2(c), the solid line lies below the lower global confidence band.
Therefore, the Liquid Milk and Milk Products (C1440) industry is defined as dispersed.

4. Results
We repeat the procedure above for each industry. Results show that 21 of the four-digit food industries are
localized whereas 15 are dispersed. The remaining 14 do not deviate significantly from randomness.
Compared with the results of the baseline analysis, the number of localized industries increases while thenumber of dispersed ones decreases. More industries are shown to be randomly distributed. Table 1 shows
the number of localized and dispersed industries under each two-digit industry. Interestingly, localization of
the food industry is significantly higher than that in some other countries. Duranton and Overman (2005)
found that out of 30 four-digit food industries in UK, only one industry is localized. Similarly, among 40
four-digit food industries in Japan, only three exhibit localization (Nakajima, Saito and Uesugi, 2012).
Table 1. Number of localized and dispersed industries in each two-digit food industry
Two-digit industry
No. of four-digit No. of localized No. of dispersed
industries
industries
industries
C13
Food Processing
17
12
3
C14
Food Manufacturing
20
5
6
C15
Beverage Production
13
4
6
Total
50
21
15

Figure 3 demonstrates the number of globally localized and dispersed industries at each distance.
Although it seems more industries are globally dispersed at most distances, it does not contradict previous
findings that more industries are localized (21 versus 15). The rationale is that one industry is defined as
localized if it is localized for at least one unit distance.

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(a) Global localization(b) Global dispersion

Figure 3. Number of global localized and dispersed industries

We define the measure of localization at each distance d as Γ(𝑑) ≡ ∑𝐴 Γ𝐴 (𝑑). Similarly, the measure of
the extent of cross-industry dispersion by distance is defined as Ψ(𝑑) ≡ ∑𝐴 Ψ𝐴 (𝑑). Figure 4 demonstrates
the index of global localization and dispersion by distance when accounting for the weight. Figure 4(a) showsthat the extent of localization is much greater at small distances. The distances with significantly higher
agglomeration are less than 70 km. Therefore, we can infer that the localization of food industries in China
takes place within small areas.

(a) Global localization(b) Global dispersion

Figure 4. Index of global localization and dispersion by distanceTo compare the degree of localization and dispersion among 4-digit industries, we employ the crossdistance measures of localization and dispersion. Following Duranton and Overman (2005), the crossdistance index of localization for industry A is defined as Γ𝐴 = ∑300
𝑑=0 Γ𝐴 (𝑑) and cross-distance index of(𝑑)dispersion as Ψ𝐴 = ∑300
Ψ
.
𝑑=0 𝐴
Table 2. Most localized and most dispersed four-digit food industries
Four-digit industry
Γ or Ψ
Most localized
1523
Yellow Wine
0.220
1361
Aquatic Products Freezing
0.126
1340
Sugar Refinery
0.089
1494
Additive of Food and Fodder
0.070
1310
Grain Mill Products
0.027
Most dispersed
1532
Drinking Water
1320
Feed Processing
1522
Beer
1440
Liquid Milk and Milk Products
1399
Processing of Other Food Not Listed

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0.037
0.031
0.029
0.024
0.023

Table 2 lists the five most localized and five most dispersed industries. Yellow Wine industry (C1523)
is the most localized industry. As we can see from Figure 1(a), most of the firms cluster in the Yangtze Riverdelta. Aquatic Products Freezing (C1361) is the second most localized industry. This is consistent with theimpression from Figure 1(d), where most of its production is located along the coastline of China.

5. Conclusion
On the basis of DO’s model, this study tests the localization of Chinese food industries. Contrary to previous
research based on aggregate data and discrete measurement, we found pronounced localization of Chinese
food industries. The results show that, among the 50 four-digit food industries, 21 of them are localized whenfirm size is considered. Fifteen industries are dispersed and the remaining 14 are randomly located. Yellow
Wine (C1523) and Aquatic Products Freezing (C1361) are the two most localized industries.

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