spatial linkages and export processing location in china...

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Spatial Linkages and Export Processing Location in China: A Province-Level

Analysis

Alyson C. Ma,a Ari Van Assche,b,*

a University of San Diego

b HEC Montréal and CIRANO

*Corresponding author. HEC Montréal, Department of International Business, 3000 Chemin de

la Côte-Sainte-Catherine, Montréal (Québec), Canada H3T-2A7. Phone: (514)340-6043. Fax:

(514)340-6987. E-mail: [email protected].

Abstract

This paper analyzes whether spatial linkages matter for a location’s attractiveness for export

processing. Relying on detailed export processing trade data for 29 Chinese provinces in 8

industries during the period 1997-2008, we investigate whether a Chinese province’s proximity to

international suppliers and international buyers affects its attractiveness as an export processing

location. We find that the upstream and downstream spatial linkages both have a strong and

independent explanatory power. We also explore what factors explain the influence of these

spatial linkages.

Keywords: export processing, proximity, distance supplier access, market access, port efficiency,

China.

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1. INTRODUCTION

In the past two decades, many developing countries have set up export processing zones (EPZs)

to integrate their economies into global markets and to spark export-led growth. Designed to

attract export-oriented foreign direct investment, these zones offer a combination of duty

exemptions, tax incentives and streamlined business administration to draw multinational firms to

the country’s low-wage workforce. Between 1986 and 2006, the number of EPZ has

mushroomed, growing from 176 in 47 countries to more than 3,500 EPZs globally in well over

130 countries (Boyenge, 2007).

To date, EPZs have had a mixed record of success (Engman, Onodera and Pinali, 2007).

Jayanthakumaran (2003) and Milberg and Amengual (2008) surveyed EPZs across multiple

countries and observed that they led to the generation of exports, employment and foreign

exchange earnings in many countries, but by no means in all. Johansson and Nilson (1997) found

evidence that EPZs played a catalytic role for domestic firms in Malaysia, but found negative

externality effects in Mexico and the Dominican Republic. Adding to this, a new concern is that

the rapid proliferation of EPZs across the developing world is undermining an EPZ’s

effectiveness as an economic development strategy. Sargent and Matthews (2009) provided

evidence that rising competition from Chinese EPZs has led to a high mortality rate of Mexico’s

maquiladora firms. Using data on EPZs across Chinese municipalities, Wang (2013) showed that

zones which were developed in earlier waves attracted more foreign direct investment and

achieved larger agglomeration economies than those developed in later rounds when there was

stronger competition among EPZs for export-oriented FDI.

In this context, an important question for academics and policymakers is: which factors determine

an EPZ’s attractiveness as an export processing location? While there have been many case

studies that investigate the successes and failures of specific EPZs, there have been relatively few

studies that have empirically analyzed this question across EPZs. Notable exceptions are the

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studies by Woodward and Rolfe (1993) and Hanson, Mataloni and Slaughter (2005) that have

focused on the role of location-specific factors on the attractiveness of an export processing

location. Woodward and Rolfe (1993) used survey data from EPZs across various countries in the

Caribbean Basin and found that labor costs and tax incentives were key determinants of an EPZ’s

attractiveness. Hanson, Mataloni and Slaughter (2005) used firm-level data on U.S.

multinationals and found that U.S. subsidiaries conducted their export processing activities in

locations with lower wages for less-skilled labor, lower corporate income tax rates, and lower

trade costs. Sargent and Matthews (2004, 2009), then again, suggested that spatial linkages such

as an EPZ’s proximity to customers may be an additional driver of an EPZ’s attractiveness, and

especially in industries with more regional production networks.

In this paper, we contribute to the literature by setting up a comprehensive framework to examine

the relative importance of location-specific factors and spatial linkages on an EPZ’s attractiveness

for export processing. We start off by setting up a theoretical model in which a tension between

comparative advantage forces and spatial linkages lies at the heart of a location’s attractiveness

for export processing. Comparative advantage determines to what extent a location has a

production cost advantage compared to other EPZs. The comparative advantage force, however,

is weakened if an EPZ is situated further away from its upstream suppliers and its downstream

buyers. This is because dealing with suppliers and buyers that are more distant implies higher

distance-related trade costs such as transportation costs, time-related costs and coordination

problems. In line with Sargent and Matthews’ (2004, 2009) suggestion, a prediction that comes

out of the model is that, besides location-specific factors, proximity to international suppliers and

buyers should also enhance a location’s attractiveness as an export processing location.

To empirically investigate the predictions of the model, we use a large-scale dataset of Chinese

export processing activities across 29 provinces, 8 industries and 12 years (1997-2008). In our

benchmark results, we show that both proximity to international suppliers and markets have a

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strong and independent explanatory power on a Chinese province’s attractiveness as an export

processing location. These results are robust to the inclusion of a wide set of province-specific

and port-specific control variables.

Furthermore, we show that the role of spatial linkages on export processing differs across

countries. Specifically, spatial linkages matter more in “heavy” industries with products that have

a high weight to value ratio than in “light industries” with products that have a low weight to

value ratio. Finally, we show that spatial linkages matter differently for foreign-owned export

processing activities than for Chinese-owned export processing activities.

Our paper is organized as follows. In section 2, we set up our theoretical model. Section 3 then

describes the data and the empirical strategy that we adopt. Sections 4, 5 and 6 discuss the results.

Section 7 concludes.

2. MODEL

In this section, we set up a theoretical model in the spirit of Head and Mayer (2006) and Redding

and Venables (2004) to investigate the determinants of an EPZ’s attractiveness as an export

processing location. Consider a world that consists of 1,… , EPZs, j = 1, ... , S countries,

and a single industry with monopolistic competition. Within the industry, each firm produces a

single variety y which it assembles in an EPZ i and then exports to the final consumer in country

j. For simplicity, we assume that all firms located in EPZ i are symmetric. Consumers are

spread over different countries, and we assume that there are ad valorem trade costs, 1,

between any EPZ i and country j. Focusing on a single EPZ i, the relationship between the

domestic price and the price that consumers pay in country j, ,then equals:

. (1)

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On the demand side, we assume that consumers in country j have a constant elasticity of

substitution across product varieties denoted by and spend an amount on the industry’s

output. Given these preferences, country j’s demand for variety y produced in EPZ i equals:i

. (2)

where is the price index for the industry’s output in country j:

∑ . (3)

In a symmetric equilibrium with firms in EPZ i, the value of total exports from EPZ i to

country j equals:

≡ . (4)

By aggregating EPZ i’s exports across all destination markets, EPZ i’s total exports amounts to:

∑ = , where (5)

∑ . (6)

Redding & Venables (2004) refer to the term as “market access,” while Harris (1954) and

Head & Mayer (2006) define it as “market potential.” Since our empirical analysis uses the

estimation procedure developed by Redding & Venables (2004) to capture , we stick to the

term market access throughout this paper. Market access simply measures the sum of market

capacities of the countries to which EPZ i exports, weighted down by their respective distance-

related trade costs. For EPZ i, market access will be greater if it is located closer to countries that

have a large market capacity.

On the supply side, we assume that export processing firms in EPZ i use a combination of low-

skilled labor and intermediate goods as inputs to conduct their processing activities, and incur a

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fixed cost. Intermediate inputs can either be procured locally or imported. Labor has a price in

EPZ i and an input share ; intermediate inputs have a price index and an input share 1 .

Technological capabilities may differ across EPZs and we capture this is parameter . Given the

monopolistic competition market structure in the industry, a firm in EPZ i charges the following

price for its variety:

, where (7)

∑ . (8)

Following Redding and Venables (2004), we refer to as EPZ i’s supplier access, which

measures the price index that needs to be paid for imported inputs. From equation (8), an EPZ’s

supplier access is higher if it s located closer to countries with lower production costs.

If we insert equation (7) into equation (5) and rearrange, we obtain an expression for EPZ i’s total

sales related to export processing activities:

, (9)

where is a constant that captures an EPZ’s technology level. Log-linearizing equation

(9) then allows us we obtain an equation that can be used for regression analysis:

ln ln ln 1 ln 1 ln ln . (10)

The equation suggests that an EPZ’s sales value related to export processing activities depends on

both location-specific variables and spatial linkages. First, it depends on location-specific

variables such as the technology level ( ), number of firms ( ) and production factor costs ( ).

Second, it depends on a location’s spatial linkages with international suppliers and buyers as

measured by market access ( ) and supplier access ( ). An EPZ’s exports are higher if they

have greater market access, that is, with low trade costs to importing regions with high spending.

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Furthermore, an EPZ’s exports are higher if they have greater supplier access, that is, where

inputs can be bought at low prices due to low distance-related trade costs from suppliers.

3. DATA AND METHODS

To empirically validate the predictions of the model, we exploit a unique dataset on China’s

processing trade regime from 1997-2008 that was compiled by the General Administration of

Customs of the People’s Republic of China. The Chinese government introduced the processing

trade regime in the mid-eighties in order to simultaneously attract foreign direct investment and

promote exports. Under the customs regime, export processing plants located in China (both

Chinese-owned and foreign-owned) are granted duty exemptions on imported raw materials and

other inputs as long as they are used solely for export purposes. The regime is more far-reaching

than similar systems in many other East Asian economies. China’s concessionary provisions are

not geographically limited within strictly policed export processing zones, but rather apply in its

entire territory (Naughton, 2006). As a result, China’s processing trade regime has become an

important contributor to its overall trade performance. Between 1997 and 2008 the share of

processing exports in China’s total manufacturing exports has fluctuated between 48% and 60%,

while the share of processing imports in total imports has hovered around 45%.

As is shown in table 1, foreign-owned export processing plants play a dominant role in China’s

processing trade regime. Their share of processing exports has increased from 64.4% in 1997 to

84.7% in 2008. In all manufacturing industries, their share of processing exports exceeded 50%

in 2008.

[Table 1 about here]

The Chinese processing trade data have a number of features that are relevant for the purposes of

the present analysis. First, the dataset provides information on trade related to export processing

activities for the universe of Chinese provinces and industries. In our analysis, we include all first

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level administrative divisions in Mainland China except Tibet, which has very little processing

exports, and Chongqing, which became a directly administered city in 1997 and for which data

only became available in 2001. This yields 29 locations comprising 26 provinces and 3 directly

administered cities (Beijing, Tianjin and Shanghai). For simplicity, we refer to all these

administrative divisions as provinces. In addition, they are disaggregated by product category,

thus allowing us to conduct our analysis across the eight manufacturing industries listed in Table

1.ii

Second, for each Chinese province’s export processing activities, the dataset identifies the source

countries and value of its processing inputs and the destination countries and values of its

processed goods. Since the processing trade regime requires that imported processing inputs are

required to be used for the manufacture of processing exports, the dataset provides a unique

spatial mapping of a processing location’s linkages with international suppliers and international

buyers.iii

To measure a province’s attractiveness as an export processing location in a specific industry and

year, we use the natural log of province i’s processing exports in industry k and in year t,

. We investigate the determinants of a province’s attractiveness as an export processing

location by estimating the following model for the period 1997-2008, which is a generalization of

equation (10):

, (11)

where are industry-year fixed effects; is a vector of spatial linkages; is a vector of

province-specific (i.e. location-specific) control variables; is a vector of port-specific control

variables; and is a stochastic error.

3.1. Variables of Interest: Spatial linkages

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To determine the proximity of export processing activities in a specific Chinese province,

industry and year to international suppliers and buyers, we use an estimation procedure that has

been developed by Redding & Venables (2004) and can be derived directly from the theoretical

model.iv

The estimation procedure consists of two steps. First, a set of gravity equations is estimated on

China’s processing exports and imports to determine which countries are the most important

suppliers of processing inputs to China as a whole and buyers of processed goods from China as

as a whole in a specific industry. Second, the estimated coefficients from the gravity equations

are used to calculate each province’s spatial proximity to these international suppliers and buyers.

Step 1: Identification of countries’ supplier and market capacity

In a first step, we estimate a set of gravity models on bilateral processing imports and exports to

determine a country’s capacity of supplying processing inputs to China and buying processed

goods from China.

Supplier capacity. To estimate a country’s capacity to supply inputs to China’s processing trade

regime, we for each year-industry combination estimate a gravity equation on the log of a

Chinese province i’s bilateral processing imports from source country j, :

, (2)

where are country-fixed effects; are province-fixed effects; captures a

province’s internal distance to the closest Chinese seaport; reflects the external distance

between the Chinese seaport and the source country; and is a stochastic error. For internal

distance, we use Google Maps to calculate the seaport that is closest from a Chinese province’s

capital.v For external distance, we use Searates.com to calculate the distance in nautical miles

along maritime shipping routes from the Chinese seaport used by a province to the largest seaport

of a country. vi

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From equation (1), the estimated coefficients on the country fixed effects, , provide a measure

of a country’s supplier capacity in an industry and year, with a higher coefficient suggesting that

a country is a more important supplier of processing inputs to China.

Market capacity. To estimate a country’s capacity as a buyer of goods processed in Chinese

export processing plants. Specifically, we for each industry-year combination estimate a gravity

equation on the log of a Chinese province i’s bilateral processing exports to country j , :

. (3)

The parameter estimate of the destination country dummy, , determines country j’s market

capacity, with a higher coefficient indicating that a country is a more important buyer of

processed goods from China.

Step 2: Calculation of spatial linkages

The estimated coefficients of the gravity equations can next be used to calculate each Chinese

province’s proximity to potential international suppliers and buyers in a specific industry and

year. We construct three distinct measures of spatial linkages: travel time to port, supplier access,

and market access.

Travel time to port. We treat a province’s distance from a major Chinese seaport as a separate

independent variable in our analysis. To measure this, we use the driving time on Google Maps

between a province’s capital and its closest major seaport as a measure of a province’s distance to

port.

Supplier access. The Chinese seaport’s proximity to international suppliers of processing inputs

is calculated as the distance-weighted sum of countries’ supplier capacities using the following

formula (Redding & Venables, 2004):

∑ (3)

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where and are the estimated coefficients from equation (1), measures

the supplier capacity of country j in industry k and year t and is the estimated bilateral

distance friction between seaport i and country j in industry k and year t. Supplier access has an

intuitive interpretation: in a specific industry and year, a province has a larger supplier access if it

uses a seaport that is on average located closer to countries with large supplier capacities.

Market access. A measure of a Chinese seaport’s market access is similarly constructed as the

appropriately distance-weighted sum of the market capacities of all destination countries of

processing exports:

∑̂

. (4)

In a specific industry and year, a Chinese province has a larger market access if it uses a seaport

that is located closer to countries with large estimated market capacities.

3.2. Province-Specific Control variables

We include a set of province-specific controls that prior literature has identified as important

drivers for the attractiveness of a manufacturing location.

Labor costs. Labor costs are generally cited as the principal motive for firms to localize

manufacturing activities, and especially in labor-abundant countries such as China (Farrell, 2005;

Lewin, 2005). According to Banister & Cook (2011), hourly compensation costs in China’s

manufacturing was only 4 percent of those in the United States in 2008, thus creating an

important incentive for firms to arbitrage labor cost differences by offshoring to China. To

capture labor costs in a province, we use the average wage of staff and workers in manufacturing

from China Statistical Yearbook as a measure of unskilled labor costs in a Chinese province.

Human capital. The availability of human capital may also influence the attractiveness of a

manufacturing location (Farrell & Grant, 2005; Jensen & Pedersen, 2011). Access to better

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educated and trained personnel should enhance the management of manufacturing activities, and

to coordinate them with other value chain stages overseas. Furthermore, human capital should be

an important input for firms specializing in more sophisticated value chain activities.vii To

measure the abundance of human capital in a Chinese province, we use the share of population

with a secondary education from the various years of the China Statistical Yearbook.

Local supplier access. Accessibility to local suppliers should enhance a Chinese province’s

attractiveness as an export processing destination. It should allow firms to save on transport costs

and costly delays of sourcing inputs within China, therefore reducing production costs and

increasing flexibility (Amiti & Javorcik, 2008). Furthermore, it may lead to important factor

market externalities and technology spillovers (Ellison, Glaeser & Kerr, 2010). To measure local

supplier access, we follow Amiti & Javorcik (2008) and Leamer (1997) by using the GDP share

of a province i weighted by its circular area: ,∗ , where , is China’s

GDP in year t, is province i’s area in square kilometres, and is a mathematical constant.

Note that this measure, which we will call distance-weighted GDP share, is both an indicator of

the size and of the density of economic activity in the province. Large and sparsely populated

provinces will have a smaller local supplier access than small and densely populated provinces.

Government efficiency. A location’s institutional quality should bolster its attractiveness as an

export processing location. Locations with a good governance infrastructure that strengthens the

freedom of transaction, secures property rights, and ensures transparency in government and legal

processes should improve the attractiveness of doing business in the location (Globerman &

Shapiro, 2003; Liu, Feils & Scholnick, 2011). We follow the methodology proposed by Cole,

Eliott, & Zhang (2008) and Tang & Tang (2004a,b) to quantify the government efficiency of a

Chinese province in each year. The government efficiency index aggregates 39 separate

indicators from the China Statistical Yearbook that cover the quality of a province’s public

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services, public goods, government size, and national welfare (see appendix B for more details).

A higher index number implies that a province has greater government efficiency.

3.3. Port-Specific Control Variables

Port characteristics may also influence a province’s attractiveness as an export processing

location. If a port is more efficient, it allows export processing plants to more cheaply and

rapidly link with its international suppliers and buyers, therefore increasing its attractiveness as an

export processing location. Clark, Dollar & Micco (2004) show that higher port efficiency

significantly reduces shipping costs. Similarly, Blonigen and Wilson (2008) estimate that

improved port efficiency increases a port’s trade volumes. In this paper, we use two indicators of

port efficiency.

Port size. Due to economies of scale at the seaport level, larger ports tend to also be more

efficient (Clark, Dollar & Micco, 2004). This may come from the fact that larger ports are used

by larger ships, or because they create more competition amongst shipping companies. To

measure port size, we compile data on the number of berths for productive use that are

operational in a Chinese seaport from China Data Online.

Port congestion. Ports that are more congested tend to lead to higher handling costs and more

delays, therefore making them less efficient (Sanchez et al., 2003). To measure port congestion,

we use data from China Data Online to calculate a Chinese port’s volume of throughput per berth.

Finally, we in all our regressions include industry-year fixed effects to control for industry-

specific shocks that are common to all provinces. Descriptive statistics for our dependent and

independent variables are presented in Table 2.


4. BENCHMARK RESULTS

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The empirical results from our estimation of equation (1) with the inclusion of industry-year fixed

effects are presented in Table 3. All standard errors are robust to heteroskedasticity and clustered

at the port-industry-year level.viii We find no evidence that errors are serially correlated over

time. Since we use gravity equations to estimate the independent variables supplier and market

access, the residuals of these gravity equations may affect the residuals in our estimation

equations (1)-(3). As Head & Mayer (2006) point out, this may invalidate the standard errors, but

it has no impact on the estimated coefficient. To correct for this, we follow Redding & Venables

(2004) and Fally, Paillacar, & Terra (2010) and use bootstrap to obtain unbiased confidence

intervals.


Column (1) of Table 3 provides the results without supplier access and market access. The R-

square of this specification is 0.657. Supplier access and market access are included in columns

(2) and (3), respectively. Column (4) consists of the findings with the full specification with an

increased R-square of 0.664. Since our results are consistent across the four specifications, we

will focus the discussion on our baseline specification in column (4), which includes all three

spatial linkages.

First and foremost, we find that spatial linkages significantly affect a province’s attractiveness as

an export processing location. In particular, the negative coefficient on the travel time to port

variable suggests that provinces located closer (in travel time) to a major seaport are more

attractive as export processing locations. To give a concrete example, this implies that the 53

hours of travel time from Urumqi in Xinjiang to the Shanghai port acts as an important deterrent

for the localization of export processing plants in the Northwestern province. Furthermore, the

positive coefficients on supplier access and market access imply that provinces that use ports

which are closer to international suppliers and markets are more attractive export processing

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locations. Together, these results validate the importance of spatial linkages for a province’s

attractiveness as an export processing location.ix Namely, internal distance given by travel time

and external distance estimated by supplier and market access significantly affect a province’s

amount of processing exports.

Our province-specific control variables generally take on the expected sign and are significant.

The positive and significant coefficient on the percent of the population with a secondary degree

suggests that access to more educated personnel is necessary to successfully engage in export

processing activities. The positive and significant coefficient on a province’s distance-weighted

GDP share indicates that greater access to intra-provincial suppliers increases the number of

processing activities that gravitate to a province. We also find that better-quality government

efficiency positively affects the attractiveness of a province as an export processing location. The

only result that is not as expected is manufacturing wages, which shows a positive impact on the

localization of export processing activities. This result, however, is in line with the findings of

other micro-data studies which show that firms seem insensitive to local wages in choosing a

location for foreign investment (see Liu, Lovely & Ondrich, 2010 for an overview). It also is in

line with Bunyaratavej, Hahn & Doh’s (2007) finding that a country is more likely to be a

destination of services offshoring as the average wage of a country increases. One reason for this

result may be that average manufacturing wages also capture productivity effects, with provinces

with higher wages also having more productive workers.

Our findings in table 3 suggest that port-specific factors also influence a province’s attractiveness

as an export processing location. While port size is statistically insignificant, provinces that use

more congested seaports are found to be less attractive as export processing locations.

5. HEAVY VERSUS LIGHT INDUSTRIES

We next investigate which factors explain the role of spatial linkages on export processing.

Recent economics studies show that transportation costs continue to be a key barrier to

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international trade (Clark, Dollar & Micco, 2004; Hummels, 2007), and especially for “heavy”

products with a high weight to value ratio that are more expensive to transport (Hummels, 2007;

Harrigan 2010). In line with these studies, we can use variations across industries to determine if

transportation-related costs can help explain the role of spatial linkages on the localization of

export processing activities. In Table 4, we show that the average weight/value ratio of

processing exports in 2008 differs significantly and intuitively across the eight industries included

in the dataset. For instance, in the sectors footwear & headgear and minerals & wood, the

weight/value ratio exceeds 10 kg per U.S. dollar of processing exports, suggesting that they are

relatively heavy industries where transportation costs constitute a significant fraction of

production costs.x In contrast, the weight/value ratio for machinery, electrical (mostly computers

and other high value electronics products) is less than 0.1 kg per U.S. dollar of processing

exports, making it a light industry where transportation costs only constitute a small fraction of

production costs.


If transportation costs are a key driver of the spatial concentration of export processing activities

in the proximity of its international suppliers and buyers, we should find that spatial linkages

enhance a province’s attractiveness as an export processing location more in heavy industries

with a high weight/value ratio than in light industries with a low weight/value ratio. To test for

this, we create a dummy variable heavy that takes on the value 1 if in 2008 the weight/value ratio

of processing exports in an industry is larger than 5, and 0 otherwise.xi Our prediction will be

verified if spatial linkages matter more in the heavy industries (heavy=1) than in the light

industries (heavy=0).

We present the results of our analysis in Table 5. In column (1), we copy the baseline results of

column (4) in Table 3. In columns (2) and (3) we then estimate equation (1) for heavy and light

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industries, respectively. In general, the results in columns (2) and (3) are similar to the baseline

specification in column (1). Namely, the coefficient of travel time to port is negative and

significant for both heavy and light industries, while supplier access and market access are

positive and significant for both types of industries. More importantly, the results support our

prediction since the magnitude of the coefficients on the spatial linkages is larger in absolute

terms for heavy industry and smaller for light industry as compared to the baseline results. A

Wald test shows that the coefficients on the spatial linkages variables are statistically different

between the heavy and light specifications at the 1 percent level. To further test our prediction,

we interact the dummy variable heavy with the three measures of spatial linkages. The findings,

available in column (4), show that the coefficient on the interaction of heavy with travel time to

port is negative and significant, while the interaction of heavy with supplier access and market

access are positive and significant. In support of our prediction, these results indicate that spatial

linkages are especially important for the attractiveness of an export processing location in heavy

industries than in light industries.


There are also intuitive differences in the impact of province-specific and port-specific variables

between heavy and light industries. While in light industries a province’s attractiveness as an

export processing location increases with unskilled wages and with the availability of human

capital, these two factors have no significant influence in heavy industries. This result is in line

with the fact that light industries tend to produce higher value products such as electronics that

more intensively require skilled workers. Port congestion, then again, is only found to matter in

heavy industries and not in light industries.

6. FOREIGN-OWNED VERSUS CHINESE-OWNED PROCESSING PLANTS

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We finally investigate if spatial linkages matter differently across firm types. For this purpose,

we further disaggregate China’s processing trade by firm-type and re-estimate our supplier access

and market access measures for each combination of firm type-industry-year separately. As such,

our dependent variable as well as our supplier access and market access measures now not only

vary by year-industry, but by firm type-year-industry, thereby leading to an almost doubling of

our observations. We then create a dummy variable foreign that takes on the value 1 if

processing exports are conducted by a foreign-owned plant and 0 otherwise.

Table 6 provides the estimation results by firm type. In column (1), we present the results

pooling the two firm types. In columns (2) and (3) we then estimate equation (1) for Chinese-

owned and foreign-owned processing plants, respectively. In general, the results are similar to the

baseline results in Table 4. Both for Chinese-owned and foreign-owned export processing plants,

all three spatial linkages significantly affect a province’s attractiveness as an export processing

location. Interestingly, we find that spatial linkages are more important for foreign-owned

processing plants than for Chinese-owned processing plants. We can reject the null hypothesis at

the 1 percent level that the coefficients on the three measures of spatial linkages are equal in

columns (2) and (3). In column (4), the three measures of spatial linkages are interacted with the

dummy variable for foreign. We find that the coefficient on the interaction term

∗ is negative and significant, while the coefficients on the

interaction terms ∗ and ∗ are

positive and significant. Taking into account the industry-year fixed effects in the regression,

these results suggest that, within an industry and year, spatial linkages matter more for foreign-

owned export processing plants than for Chinese-owned export processing plants.


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The coefficients on the province-specific and port-specific control variables are relatively similar

across the two firm types. A notable exception is that foreign-owned export processing plants

tend to gravitate more to provinces with higher wages, and to provinces that use less congested

ports. Chinese-owned processing plants agglomerate in provinces that use larger ports.

Cultural factors can explain the greater role of spatial linkages for foreign-owned enterprises than

for Chinese-owned enterprises. To understand this, it is important to recognize two important

stylized facts related to foreign-owned export processing plants in China. First, the large brunt of

foreign-owned export processing plants in China originate from South Korea, Hong Kong,

Taiwan, and Japan (Yu & Tian, 2012). Second, foreign-owned firms from East Asian countries

tend to agglomerate in the provinces that are closest to their country of origin (Kang & Lee, 2007;

Schroath, Hu & Chen, 1993, Yu & Tian, 2012). Hong Kong and Taiwanese firms, for example,

tend to concentrate their subsidiaries in the Southern region of China, while Japanese and Korean

firms tend to concentrate their subsidiaries in the Northeastern provinces. Taken together, these

two stylized facts suggest that foreign-owned export processing plants in a Chinese province

generally share a common country of origin with suppliers and buyers in neighboring countries.

This shared country of origin generates a commonality in cultural understanding, references and

norms, which generates increased trust (Dyer & Chu, 2000; Jiang et al., 2011) and stronger ties

(Granovetter, 1973). These advantages related to cultural proximity can induce foreign-owned

plants to disproportionately deal with international suppliers and buyers in neighboring countries.

It is important to stress that Chinese-owned export processing plants may also have an advantage

of dealing with neighboring-country suppliers and buyers due to cultural factors. Since foreign-

owned firms from East Asian countries agglomerate in the provinces that are closest to their

country of origin, Chinese-owned plants are more likely to develop familiarity and weak ties with

foreign firms from neighboring countries. A Chinese-owned plant located in Northeast China,

for example, may have a cultural advantage of dealing with Korean suppliers and buyers due to

20

the large concentration of Korean-owned firms in China’s Northeast. This advantage, however,

should be less large than that for foreign-owned export processing plants since Chinese-owned

processing plants do not share a common country of origin with neighboring-country suppliers

and buyers.

7. CONCLUDING REMARKS

Our paper set out to investigate which factors drive an EPZ’s attractiveness as an export

processing location. Using data on export processing activities across 29 Chinese provinces and 8

industries for the period 1997-2008, we found systematic evidence that both location-specific

factors and spatial linkages determine a location’s competitiveness in drawing export processing

activities. Specifically, we found that location-specific factors such as a province’s production

factor costs, institutional quality, local supply base and port efficiency all improve a province’s

ability to attract export processing activities. In addition, we found that a province’s proximity to

international suppliers and buyers is a significant driver of its attractiveness for export processing.

We next examined if the role of spatial linkages differs by industry and found this to be the case.

Spatial linkages are a significantly more important driver of an EPZ’s attractiveness in “heavy”

industries with a high weight-to-value ratio than in “light” industries with a low weight-to-value

ratio. Our study therefore provides supporting evidence for Sargent and Matthews’ (2009) call to

distinguish between proximity-dependent and proximity-independent industries when discussing

competition between EPZs. In proximity-independent industries, an EPZ’s competitiveness is

primarily driven by location-specific factors. In proximity-dependent industries, an EPZ’s

competitiveness is significantly affected by spatial linkages.

Finally, we examined if an export processing plant’s country-of-origin affects the role of spatial

linkages. Within industry-year combinations, we found that spatial linkages matter more for

foreign-owned processing plants than for Chinese-owned processing plants. We conjectured that

21

this result reflects the fact that EPZ’s have an easier time attracting foreign firms from countries

with which it has pre-existing cultural and historical ties.

Our results have important implications for policymakers. They suggest that the usefulness of

EPZs to create export-led growth is heterogeneous across countries and heavily depends on the

EPZ’s geographic and cultural/historical proximity to countries with key supply bases and large

markets. There are nonetheless policy actions that local governments can take to enhance its

export processing operations. Improved port efficiency, government efficiency and availability of

human capital, for example, are found to all enhance a province’s attractiveness as an export

processing location.

Our empirical findings also provide a new perspective on China’s dramatic success as an

exporting nation. They suggest that China’s success in attracting export-oriented foreign direct

investment may not be entirely due to home-grown factors (location-specific factors), but also the

result of the strong economic performance of its neighboring East Asian countries such as Japan,

South Korea and Taiwan. Specifically, China’s proximity to countries with key supplier and

buyer capacities may have given its EPZs the competitive edge needed to strongly develop.

22

Appendix: Computation of Government Efficiency

To measure government efficiency, we follow Cole et al. (2009) and Tang & Tang (2004a,b) by

first collecting the 39 indices listed in the table below from China’s Statistical Yearbook. Next,

we used the following three steps to create the overall index of provincial government efficiency:

1. Each index is standardized using the following formula: ,where STDij is

the standardized value of index j in province i, Xij is the original value of the index j in

province i, is the mean value of X; S is the standard error of the mean.

2. The mean is used to average the STD values for each province within each sub-factor.

The resulting STD value for each sub-factor is once again averaged, standardized, and

normalized.

3. The weighted mean is then used to aggregate the sub-factors and each of the four factors.

The weights of each sub-factor and factor follows Tang and Tang (2004b) and Cole et al.

(2009) and are presented in the table below. This allows us to compute the aggregated

STD values and corresponding ranks for our 29 provinces for the period 1997-2008.

23

Factors Sub Factors Indices Government public services (17 indices, weight = 0.4)

Education, science & technology, culture, and public health services (11 indices, weight = 0.55)

1. Per capita government expenditures for science & technology promotion (yuan)

2. Rate of products with excellent quality (%) 3. Patent (inventions, utility models and design) applications granted

(item/100,000 persons) 4. Per capita transaction value in technical market (yuan) 5. Inverse of student-teacher ratio of primary schools 6. Inverse of student-teacher ratio of secondary schools 7. Inverse of illiterate and semi-illiterate rate (%) 8. Share of government appropriation for education in GDP 9. Institutions for culture and art (unit/100,000 persons) 10. Beds in health institutions (unit/100,000 persons) 11. Employees in health institutions (person/100,000 persons)

Public security services (2 indices, weight = 0.15)

12. accidents (traffic, fires, and pollution, case/100,000 persons) 13. Losses in accidents (yuan)

Meteorological services (2 indices, weight = 0.15)

14. Agro-meteorological services stations (unit/100,000 persons) 15. Earthquake monitoring stations (unit/100,000 persons)

Social security services (2 indices, weight = 0.15)

16. Urban community welfare facilities (unit/10,000 persons) 17. Rural social security network (unit/10,000 persons)

Government public goods (11 indices, weight = 0.3)

Social infrastructure (5 indices, weight = 0.5)

18. State budgetary appropriation in capital construction and innovation (100 million yuan) 19. Local-central government projects ratio of investment in capital construction and innovation (%) 20. Ratio of projects completed and put into use in capital construction and innovation (%) 21. Treatment efficiency of industrial wastewater, waste gas and solid wastes 22. Ratio of area of nature reserves and provincial area (%)

City infrastructure (5 indices, weight = 0.5)

23. Rate of access to gas (%) 24. Public Transportation Vehicles per 10,000 persons in cities (unit) 25. Per capita area of paved roads (sq.m) 26. Per capita green area (sq.m) 27. Public toilets per 10,000 persons (unit)

Government Scale (5 indices, weight = 0.2)

28. Inverse of ratio of workers in government agencies and total pop. (person/10,000 persons) 29. Inverse of ratio of workers in government agencies and total employed persons (%) 30. Inverse of ratio of government consumption and final consumption (%) 31. Inverse of ratio of government expenditures and GDP (%) 32. Inverse of the share of penalty and confiscatory income and income from administrative fees in total government revenue.

National Scale (5 indices, weight = 0.1)

33. Per capita annual net income of rural households (yuan) 34. Per capita annual disposable income of urban households (yuan) 35. Inverse of Engle coefficients of rural households (%) 36. Inverse of Engle coefficients of urban households (%) 37. Inverse of CPI (preceding year = 100) 38. GDP per capita (yuan) 39. Ratio of expenditure on policy-related subsidies and government expenditure (%)

24

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Table 1: Share of foreign-owned processing plants in total processing exports, various manufacturing industries, 1997-2008

Year Machinery, electrical

Miscellaneous Footwear & headgear

Minerals & wood

Chemicals & plastic

Textiles Metals Transport equipment

Total

1997 75.72 61.29 74.34 61.71 65.39 60.00 31.43 51.03 64.44 1998 76.62 62.90 73.67 61.74 65.55 59.89 41.24 54.35 66.40 1999 76.73 62.44 72.12 59.54 64.94 60.16 49.71 57.45 67.47 2000 79.75 65.28 72.23 62.23 67.64 61.75 53.22 64.62 70.90 2001 81.02 66.17 73.22 64.50 67.28 63.00 54.46 64.41 72.52 2002 82.05 69.49 74.01 65.73 67.65 65.10 57.27 68.22 74.99 2003 85.64 76.40 74.96 70.49 68.01 66.39 55.91 67.47 79.00 2004 87.95 79.87 75.62 72.56 69.14 67.10 56.22 73.62 81.72 2005 89.74 83.66 75.63 75.47 69.52 69.15 56.74 71.51 83.82 2006 90.87 85.76 76.45 75.94 70.49 70.25 67.60 62.04 85.14 2007 90.72 85.36 76.53 77.58 70.66 70.29 70.27 60.10 85.14 2008 90.57 85.74 75.60 78.03 71.60 69.78 73.28 57.90 84.66

Source: China Customs Statistics.

29

Table 2: Summary Statistics for benchmark, 1997-2008

Variable Mean Standard dev. Minimum Maximum

Ln(processing exports) 17.81 2.88 5.31 25.85

Ln(travel time to port) 1.74 1.21 0.00 3.99

Ln(supplier access) 2.82 2.63 0.00 9.14

Ln(market access) 2.73 2.43 0.00 8.58

Ln(wages) 2.90 1.05 0.54 4.97

Share of pop. with sec. education 6.47 1.55 0.27 11.94

Ln(distance-weighted GDP share) 9.45 0.54 7.26 10.94

Government efficiency 0.26 1.99 -6.88 7.37

Ln(# of berths) 4.77 0.55 3.09 6.17

Ln(congestion) 5.02 0.63 2.94 6.17

30

Table 3: Baseline results, 1997-2008

Dependent variable: Log of processing exports by province i in industry k and year t

(1) (2) (3) (4)

Ln(travel time to port) -0.84*** -0.86*** -0.85*** -0.86***

[0.05] [0.05] [0.05] [0.05]

Ln(supplier access) 1.86* 1.71*

[0.83] [0.80]

Ln(market access) 3.48*** 2.81**

[1.03] [0.92]

Ln(wages) 1.10*** 1.09** 1.10*** 1.10***

[0.17] [0.16] [0.17] [0.18]

% population with secondary degree 0.08*** 0.08*** 0.08** 0.08**

[0.03] [0.03] [0.03] [0.03]

Government efficiency 0.27*** 0.28*** 0.27*** 0.28***

[0.02] [0.02] [0.02] [0.02]

Ln(distance-weighted GDP share) 0.78*** 0.77*** 0.79*** 0.78***

[0.06] [0.06] [0.06] [0.06]

Ln(# of berths) 0.07 0.07 0.01 -0.04

[0.09] [0.09] [0.09] [0.09]

Ln(congestion) -0.28*** -0.25*** -0.26*** -0.23***

[0.07] [0.07] [0.07] [0.07]

Industry-year fixed effects Yes Yes Yes Yes

Observations 2506 2506 2506 2506

R2 0.657 0.662 0.659 0.664

Notes: Coefficients are reported with bootstrapped standard errors that are robust to heteroskedasticity and that are clustered by industry-year-port. ***, **, * and † denote significance at the 0.1%, 1%, 5% and 10% levels, respectively. Coefficients on constant and industry-year fixed effects not reported.

31

Table 4: Industries classification HS Codes Industry description Weight-value ratio*

28-40 Chemicals & plastic 2.46

41-43, 50-63 Textiles 4.97

44-49, 68-71 Minerals & wood 13.21

64-67 Footwear & headgear 17.77

72-83 Metals 3.22

84-85 Machinery, electrical 0.09

86-89 Transportation 5.99

90-96 Miscellaneous manufacturing 2.11

Source: China Customs Statistics. * the weight-value ratio is expressed in kg per US$.

32

Table 5: Heavy versus light industries, 1997-2008

Dependent variable: Log of processing exports by province i in industry k and year t

Baseline

(1)

Heavy

(2)

Light

(3)

Pooled (4)

Ln(travel time to port) -0.86*** -1.05*** -0.75*** -0.74*** [0.05] [0.09] [0.05] [0.05] Ln(travel time to port) * Heavy -0.32*** [0.07] Ln(supplier access) 1.71* 2.78*** 0.13† 0.15*

[0.80] [0.49] [0.08] [0.09] Ln(supplier access) * Heavy 2.65*** [0.66] Ln(market access) 2.81** 5.86* 0.64*** 0.46*** [0.92] [2.81] [0.11] [0.10] Ln(market access) * Heavy 5.46* [2.45]

Ln(wage) 1.10*** 0.37 1.41*** 1.07***

[0.18] [0.23] [0.22] [0.16]

% population with secondary degree 0.08** 0.01 0.11*** 0.08**

[0.03] [0.05] [0.03] [0.03]


[0.06] [0.11] [0.07] [0.06]


[0.02] [0.04] [0.03] [0.02]

Ln(# of berths) -0.04 -0.29 0.13 -0.02 [0.09] [0.20] [0.10] [0.08]

Ln(congestion) -0.23*** -0.50*** -0.13 -0.25***

[0.07] [0.11] [0.08] [0.08]

Industry-Year fixed effects Yes Yes Yes Yes

Observations 2506 2506 1668 2506 R2 0.664 0.664 0.656 0.667


33

Table 6: Foreign-owned versus Chinese-owned processing plants, 1997-2008

Dependent variable: Log of processing exports by province i in industry k under firm type r and year t

Pooled

(1)

Chinese-owned

(2)

Foreign-owned

(3)

Pooled (4)

Ln(travel time to port) -0.88*** -0.71*** -1.04 *** -0.65*** [0.04] [0.06] [0.04] [0.06] Ln(travel time to port) * Foreign -0.45*** [0.05] Ln(supplier access) 0.23*** 0.58** 0.68*** 0.14**

[0.05] [0.21] [0.12] [0.05] Ln(supplier access) * Foreign 0.18*** [0.06] Ln(market access) 0.39*** 0.34* 0.86*** 0.15** [0.04] [0.16] [0.12] [0.06] Ln(market access) * Foreign 0.47*** [0.04]

Ln(wage) 1.10*** 0.71*** 1.30*** 0.86***

[0.17] [0.18] [0.16] [0.16]

% population with secondary degree 0.08*** 0.07* 0.07** 0.06**

[0.02] [0.03] [0.03] [0.02]


[0.06] [0.08] [0.07] [0.06]


[0.02] [0.03] [0.02] [0.02]

Ln(# of berths) 0.18** 0.26** 0.00 0.12 [0.08] [0.10] [0.09] [0.09]

Ln(congestion) -0.11 0.13 -0.39*** -0.13*

[0.07] [0.09] [0.06] [0.06]

Industry-Year fixed effects Yes Yes Yes Yes

Observations 4680 2363 2317 4680 R2 0.578 0.542 0.687 0.587


34

i The demand function is consistent with a utility function of the following form: ∑ . ii Processing exports and imports are compiled at the 8-digit harmonized system (HS) product category. We drop non-manufacturing products (HS chapters 1-26) and follow Feenstra and Wei (2010) by aggregating the manufacturing data into eight manufacturing industries. iii An important source of bias that we need to take into account is the fact that most processing trade with Hong Kong consists of re-exports through the administrative region. This can significantly affect the analysis since it biases the true source country of processing inputs and the true destination country of processing exports that are shipped through Hong Kong. To estimate the true country of origin of processing imports re-exported through Hong Kong and the true destination country of processing exports re-exported through Hong Kong, we link the processing trade data to a data set from the Hong Kong Census and Statistical Office on Hong Kong re-exports (Ma & Van Assche, 2011). All data presented in the paper are adjusted for Hong Kong re-exports. iv The estimation procedure has been has been used by numerous scholars in the fields of economics (Redding, 2010). Fally, Paillacar & Terra (2010) and Ma (2006) have used the estimation procedure to investigate the role of spatial linkages on a country’s or region’s wages. Mayer, Méjean & Nefussi (2010) used it to examine the role of spatial linkages on FDI location decisions. v To identify the ports, we have solely considered the 8 seaports that the American Association of Port Authorities lists among the world’s 25 busiest ports in 2009: Shanghai, Shenzhen, Ningbo, Guangzhou, Qingdao, Tianjin, Xiamen and Dalian. We have excluded Guangzhou and Ningbo due to their close proximity to Shenzhen and Shanghai, respectively. vi Since the dataset from searates.com only provides the distances between major seaports, we were required to exclude land-locked countries from our analysis. This is at a relatively low loss of generality since our sample accounts for more than 95% of China’s processing exports and processing imports, respectively. vii This argument has recently been backed up by theoretical work by Costinot (2009) that shows that more educated workers are complementary sources of traditional comparative advantage forces in complex industries. viii Since the supplier and market access variables vary by port rather than province, clustering is done at the port-industry-year level. ix Our results are consistent with the inclusion of distance in kilometers as a proxy for internal distance rather than travel time. x While Footwear & headgear are generally light, they also are of low value, therefore leading to a high weight-to-value ratio. xi Our results are robust to the use of other thresholds.

spatial linkages and export processing location in china...

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