Discussion Papers In Economics
And Business
Human capital spillovers from Special Economic
Zones: evidence from Yangtze Delta in China

Zhaoying Lu
Discussion Paper 21-02

April 2021

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANHuman capital spillovers from Special Economic
Zones: evidence from Yangtze Delta in China
Zhaoying Lu†
April 23, 2021

Abstract
This paper evaluates the effects of a place-based program in the Yangtze Delta ofChina—Special Economic Zones (SEZs). It takes into account spatial dependence toexamine whether the human capital of SEZs exerts positive influences on the productivity of the local industry. The empirical results find that the local industry benefits
from the human capital of SEZs. The spillover effects are not only confined to own
counties but also neighboring counties. Indeed, SEZs contribute more to the productivity of neighboring counties than the one in the hosting county itself. Moreover,positive spillover effects of the human capital of SEZs still hold for the growth of
productivity. Furthermore, the productivity of a region is similar to the one of thesame industry in proximity.

Keywords: Special Economic Zone, Spatial Analysis, Human Capital, Spillovers,
Yangtze Delta.
JEL classification: C21, C23, R10, O21†

Graduate School of Economics, Osaka University, 1-7, Machikaneyama-cho, Toyonaka, Osaka, 5600043 , JAPAN. E-mail: u571686b@econ.osaka-u.ac.jp1

1

Introduction

A growing number of “place-based” policies are implemented in geographically targetedareas to improve economic performances (Neumark and Simpson, 2015). For instance,
Enterprise Zone programs in the US and the European Union; Special Economic Zone
programs in developing countries. Such policies are thrown into doubt that they are simply a form of reallocating resources for arbitrage benefits (Glaeser and Gottlieb, 2008;
Greenstone and Looney, 2010). On the other hand, some literature supports implementing
the policies and provides rationales for them (Kline and Moretti, 2014).
The question arises whether rationales of the policies in developed countries such as
knowledge spillovers still hold for developing countries. Knowledge spillovers are expected to produce positive effects on the local economy, more specifically, bringing additional people who have more skills can improve the other’s productivity through sharingof knowledge and faster technology adoption (Moretti, 2010). Knowledge spillovers fromadditional human capital in targeted areas are crucial for policy-makers to understand theefficiency of the policies, especially for developing countries that have constraint resources
to allocate.
Among developing countries, this paper focuses on place-based policies in China—
Special Economic Zone (SEZ) program. China has been implemented SEZ program asa crucial part of Chinese economic reforms since 1978. In the last decades, the government has cost a huge amount of money and allocated millions of acres to set up SEZs
for regional developments1 . Empirical literature finds that implementing the policies has
positive impacts for the firms located within SEZs or the cities hosting the SEZ program(Wang, 2013; Alder et al, 2013; Zheng et al, 2017; Lu et al, 2019). However, few attempts
were made to study the extent of geography diffusion of the impacts in China, assumingthat the spillovers of SEZs are only limited to their considered regions.
The relation of knowledge spillovers and regional growth can be investigated in a geographical dimension (see Döring and Schnellenbach, 2006). A common mechanism fora transfer of knowledge is the mobility of individuals and the trade of goods, which carry1

As of 2006, there were 1,568 SEZs in more than 270 cities that cover 9,949 square kilometers in total(Zheng et al, 2017).

2

production-related knowledge with them (e.g. Matusik and Hill, 1998), and notably, would
also allow a spilling over of embodied knowledge. A particularly interesting mechanismof embodied knowledge spilling over is the transfer of human capital (i.e. a skilled worker
commanding tacit knowledge) from one firm to another. We define the kind of spillovers
as “human capital” spillovers in this paper. Human capital spillovers do not only occur
within one particular region but also transfer to neighboring ones (Lopez-Bazo et al, 2004;
Fischer et al, 2009).
In the context of the SEZ program, we need to consider spillover effects beyond thetargeted zones. Some literature evaluates the impacts of place-based policies in the further
scope (Rosenthal and Strange, 2008; Zheng et al, 2017). As a development policy widely
accepted in many countries, the externalities from targeted zones to the local economy
should also be included for policy-makers to do a cost-benefit analysis. Corresponding
preferential policies such as tax deduction and subsidy are provided by the government to
attract skilled labor in order to stimulate regional growth. However, entrants of targetedzones that have higher human capital may put local firms at disadvantage due to suchpolicies. They may poach away locally trained talent, and crowd out firms outside thezones in the same industry.
This paper aims to address two important questions on human capital spillovers from
SEZs to the local economy. First, does the human capital of SEZs diffuse positive externalities to local industries in the same region? Second, will the human capital of SEZs
benefit the same industry in neighboring regions? Only considering the regions which
hosting the SEZ program may overestimate the magnitude of the externalities of SEZs.
Bringing additional skilled labor to SEZs is suspected to be simply a form of reallocating
resources to another location. On the contrary, attracting firms that relatively have higher
human capital is likely to spill over knowledge or advanced technology to stimulate theproductivity of local industries.
This paper builds on the research estimating the human capital spillovers at the regional
level. In many studies, they set the human capital of a state or of a county as an explanatory
variable and confirm the existence of human capital externalities (Lopez-Bazo et al, 2004;
Ramos et al, 2010). To evaluate the spillovers of SEZs to the local economy, we set thehuman capital of the firms within SEZs as an explanatory variable. In addition, we focus
3

on the human capital spillovers in the same industry rather than across all the industries,
supposing that the observation in proximity has high similarity in the same industry.
This study builds on the spatial econometric literature which allows us to examine
spillovers in three channels (see LeSage and Pace, 2009; Elhorst, 2014)). Specifically,an increase in the human capital of a SEZ is likely to affect its own region (direct effect)
and may affect the neighboring regions (indirect effect). Moreover, spatial regression canmeasure impacts passing through the neighboring regions and back to the region itself(feedback loops). These effects seek to investigate both intra- and inter-regional effects ofhuman capital spillovers on regional productivity. In the context of the SEZ program, it
should be examined that whether the externalities arising from a SEZ have impacts on thegeographical proximal regions. However, to the best of the author’s knowledge, existing
literature focuses on the impacts of SEZs in an intra-region dimension that SEZs exert
influences on the hosting regions in China. To fill this gap, the paper adds to the existing
literature by considering spatial proximity. Both intra- and inter-regional examinations arenecessary for policymakers to revise the cost-benefit analysis of the SEZ program.
This paper answers the question of whether the externalities of human capital in SEZs
are able to raise local productivity. From the empirical results, we conclude that the humancapital of SEZs contributes to diffusing knowledge and thus improving productivity in thesame region. In addition, this paper further addresses whether the spillover effects of
SEZs can benefit neighboring regions in spatial proximity as well. The positive spillovers
to wider geographic scope imply that the rising proportion of college-educated workers in
SEZs does not harm the firms in the proximity, instead, the driving effect of human capital
to facilitate local firms to improve productivity is evident.
The rest of this paper is organized as follows. Section 2 presents related literature thatmainly focuses on place-based policies in developing countries. Section 3 introduces the
background of the SEZ program released in China. Section 4 describes the data. Section 5 and Section 6 present estimation models and empirical results. The conclusion issummarized in the last section.

4

2

Related Literature

2.1

Literature on Place-based Policies

Place-based policies in developed countries differ from those in developing countries onboth policy goals and implement measures. To contrast empirical evidence for similartypes, this paper directly relates to place-based policies operated in developing countries.
In developing countries, implementing place-based policies is a way to stimulate thelocal economy, as well as to promote development within the zones. Johansson and Nilsson (1997) find that SEZs have positive effects on exports in Hong Kong, Malaysia, Singapore, and Sri Lanka. In particular, they highlight the performances of SEZs in Malaysia
for attracting foreign investors who transfer knowledge to local markets. Wang (2013)
and Alder et al. (2013) find that SEZs exert positive influences on the local economy
with respect to employment, export, and foreign direct investment in China. Since using
macro-level data, they evaluate the influences on the whole SEZ-operating counties, donot separate firms in zones with those outside zones in a county. Zheng et al. (2017) andLu et al. (2019) distinguish firms located within SEZs and those outside zones by using
geocoded firm-level data. Zheng et al. (2017) find that in several major cities of China
SEZs have positive spillovers with respect to wage, employment, and productivity for
non-SEZ firms located nearby SEZs. Ciżkowicz et al. (2017) also find that implementing
place-based policies creates jobs for the firms outside zones in Poland.
With regards to research methods, existing literature studying place-based policies canbe divided into two types. One is literature that summarizes experiences and rationales
of implementing place-based policies based on case studies and interviews. Zeng (2011)
describes experiences in China and discusses keys of success and faced opportunities.
Farole (2011) introduces experiences in Africa and possible reasons for those outcomes.
The other one is literature that employs econometric models to estimate the effects ofplace-based policies. Many empirical studies evaluate the impacts of place-based policies
by a quasi-experimental approach such as the difference-in-difference method (See Wang,2013; Alder et al, 2013; Zheng et al, 2017; Lu et al, 2019). These studies that allow forcomparisons of treatment groups and appropriate control groups make valid identificationof causal effects. The quasi-experimental approach is broadly applied to investigate the5impacts of place-based policies in both developing and developed countries (See Busso
et al, 2011). Except for this kind of approach, spatial econometric models are used toassess the spillover effects of place-based policies by taking into account the geographicalproximity of the observations (Ciżkowicz et al., 2017).

2.2

Literature on Human Capital Spillovers

Human capital spillovers and accumulation of knowledge are regarded as catalysts forthe development of a city (Lucas, 1988). More generally, the human capital of others inclose proximity can raise everyone’s human capital and increase firm productivity, throughsharing of knowledge and faster adoption of innovations (Moretti, 2010). Lots of literature
measures human capital by the educational level, such as the share of workers with a college degree or comparable education, because positive spillovers are more likely to diffuse
from more highly educated workers, due to the knowledge they possess and perhaps thework they do. Glaeser and Saiz (2004) find that human capitals benefit the local economy
in the term of wage, patents, and economic growth rate. Human capital is used to estimate
the spillover effects not only in its own region but also in the spatial dimension. Ramos etal. (2010) investigate the effects of human capital spillovers on regional productivity andgrowth, not only for the considered region but also for the neighboring ones. Baltagi et al.
(2016) estimate firm-level productivity spillovers in China’s chemical industry considering
the neighbor’s skilled labor ratio.

3

Background

This section introduces the background of SEZ programs implemented in China. SEZs are
geographically designated areas by the government that is aimed to stimulate economic
growth in their jurisdiction. They are subject to different regulations than other areas
within the same country. Generally, the term “SEZ” is a zone that includes some common
characteristics such as—it is located in geographically designated areas; it offers benefit
to investors within the zone; it has separate customs areas; it has single administrations(World Bank, 2009).

6

The SEZ program has been implemented over the last 40 years as a crucial part ofChinese economic reforms since 1978. Before the reforms, China was an isolated socialist
state dominated by central planning. Instead of carrying on previous policies, the creationof SEZs in the initial stage was an experiment to test for the market-oriented economy. By
1980, the first four SEZs were approved to set up in Shenzhen, Zhuhai, Shantou, and Xiamen. Since these regions were eastern coastal areas near Hong Kong, Macao, and Taiwan,
the designation at that time took into account both economic and political factors. As the
success of these regions, additional 14 coastal regions were designated as SEZs in 1984
to gain further access to foreign markets and investment. The next wave of SEZs in the
1990s gradually began to extend from eastern coastlines towards inland regions, especiallyafter Deng Xiaoping’s southern tour to SEZs in 1992. By 2001, SEZs were established
one after another covering all provinces across China. As of 2006, SEZs account for 1,568
occupying 9,949 acres of land in total.
By administrative levels, SEZs are divided into two categories: national-level andprovincial-level. The former one is conducted by the central government and the latter
one is directed by the provincial governments. National-level SEZs grant more autonomy and enjoy more privileges than provincial-level SEZs. Only authorized by the central
government or the governments which have provincial administrative level are legal SEZs
in China. The others that were not approved by the central or provincial governments
violated related laws and regulations were abolished in 2006.
Although there are some differences between specific privileges of national- and provinciallevel, SEZs are all granted market-oriented freedom and offered preferential benefits. The
SEZ program is given greater autonomy to adjust related regulations along the basic linesof national ones by removing some constraints within the scope of the zones. Also, thegovernment provides a series of preferential policy packages for foreign and domestic investors which enter the zones as the following2 :1. Tax deduction. In General, the policy deducts corporate income tax rate to 15-24%
for firms in SEZs relative to 33% for ordinary domestic firms outside SEZs. Also, customs
2

See Wang (2013), Alder et al. (2013), Zheng et al. (2017) and respective provincial government websites for details.

7duty is exempted and duty-free allowances of intermediate inputs are offered for the firms
located in SEZs.
2. Land use discount. The land is owned by the government and the land use right isstrictly regulated in China. Different from many countries, officials can allocate land onlarge scale and convert agricultural land for industrial purposes when necessary. Land use
rights for industrial purposes are granted for domestic and foreign investors which enter
the SEZs. Besides land use rights, land use fees are discounted for entrants relative to thefirms outside zones.
3. Special treatment for a loan. The state-owned bank looses lending policies andgives priority to the firms in SEZs to apply for a loan.
4. Procedure simplification. For potential investors who enter the zones, procedures
are simplified and approved for high-speed.
5. Property protection. The government commits to the investors who enter the zones
that all of their private properties are under-protected.
Besides these preferential policies, the government also makes a great effort to improve
infrastructures of zones such as roads, ports, electricity, gas, water, telecommunications,and other service facilities. Furthermore, to attract skilled human capital, SEZs offer an
extra personal income tax deduction, allowances, and Hukou registration priority benefit
to a highly qualified individual3 .
Each SEZ has its administrative committee which performs management functions
within its geographical scope. Administrative committees are not under the control of local governments, they are directly controlled by the state or the provincial governments.
They direct and administer affairs of SEZs on the behalf of the government such as projectapproval, local taxation, land management, public facilities planning, financial revenue,
personnel, environment protection, and so on. For example, administrative committees
take responsibility to attract investors from domestic or abroad that meet the standards of
local development. Each administrative committee has the right to decide which investors
could enter the zone. They offer a bundle of preferential policies and negotiate with po3

In China, each citizen is categorized by location of origin and further classified in a rural or urban
Hukou.

8tential entrants for details. After these negotiations, the investors decide whether to enterthe zone or not.

4

Data

The main data is firm-level data from the Annual Survey of Industrial Firms (ASIF) conducted by the National Bureau of Statistics of China (NBS). The ASIF data covers allstate-owned firms and non-state-owned firms with an annual turnover exceeding five million yuan (approximately $700,000), and those firms occupy over 90% of the total industrial outputs of China in 2004. The ASIF data contains more than 100 variables, providing
information on industrial output, intermediate input, total employment, industry affiliation, and geographic location. Since covering comprehensive variables, the data has been
widely used in empirical literature such as Brandt et. al. (2011).
The ASIF data in 2004 is more comprehensive than other years’ ASIF data. Besides thebasic information, the data in 2004 include the level of employee education. To evaluate
the spillover effects of human capital, we exclusively use the 2004 data. Our paper focuseson manufacturing firms in Yangtze Delta, which is made up of three province-level areas
in east-central China: Shanghai City, Jiangsu, and Zhejiang Provinces. Yangtze Delta is
the most industrially developed and wealthiest area in China. Although occupying about
2% of the total land area, Yangtze Delta contributes more than 21% of GDP in China. In
this paper, we focus on manufacturing firms in Yangtze Delta which includes 84,290 firms.
The information of SEZs is from notices of the government. One is “The review ofSpecial Economic Zones in China”, which was published by the National Development
and Reform Commission and the Ministry of Land and Resources in 2006. It contains
authorized SEZ’s name, type, authorized time, and occupied area. The other one is “thenotice of four directions” which includes geographic information on SEZs released by the
Ministry of Land and Resources of China. It has detail information on the geographic
boundary of SEZs which covers specific villages, roads, or coasts. The data has been used
to related research such as Zheng et al. (2017) and Lu et al. (2019).
The ASIF data does not report any information about SEZs, however, it contains eachfirm’s address and geographic location code. The geographic location consists of a 12-digit
9geographic code, which provides location information at the most disaggregated level. It
consists of district (or county), jiedao (streets or avenues), and juweihui (communities orvillages). Following Zheng et al. (2017), each firm’s geographic location code and theexact geographic boundaries of SEZs can be used to identify whether a firm is located in
SEZs or not. By comparing geographic location code and boundaries, there are about 15%of the firms located in SEZs.
To investigate intra- and inter-regional spillovers from SEZs, we define region as acounty in this paper. All variables are aggregated at the county-level by using firm-level
data information. In particular, we define regional productivity as the log of value-added
per employed worker of each industry in county i (yik ). We assume that the regional productivity function depends on three factors. First, we employ the level of education asa measure of human capital. hik denotes the share of labor who have college degrees orabove for the firms located in the zones of industry k in county i; eduik denotes the average
numbers of schooling years for the firms outside the zones of industry k in county i. Second, we use physical capital into the function to describe regional productivity: the log ofcapital stock per employed worker of each industry in county i (capik ). Third, we control
for competition index (comik ) and industrial diversity index (divik ). The strength of competition of an industry in a local market will influence regional productivity. Fierce competition may decrease regional productivity due to diminishing marginal returns. Meanwhile, intensive competition forces local firms to adopt advanced techniques to improve
productivity. In this paper, we define a competition index as follows,
comik = ln(1/Hik )where Hik =revenueikf 2f ∈Ωik ( revenueik ) denotes an Herfindahl index of sales revenue concentra-Ption of industry k in county i; revenueikf is sales revenue of firm f belongs to industry k
in county i; revenueikf denotes the sum of sales revenue of industry k in county i.
Additionally, we control for industrial diversity index (divik ). As noted by Jacobs,
productivity of an industry in a region can be influenced by different industries and themore diverse the more likely to improve regional productivity. Following Marrocu et al.
(2013) and Henderson (1997), an industrial diversity index is defined as the follows,10divik = ln(1/empik0)2 )empi − empik
k6=k0
X

(

where empi denotes total employment in county i; empik denotes the total employment ofindustry k in county i.
In Figure 1, we present the counties hosting SEZ program in Yangtze Delta. SEZhosting counties are mainly distributed on the east coast or along the Yangtze River. There
are as many as 108 SEZ-hosting counties in Yangtze Delta in 2004, which means SEZhosting counties occupy about two-thirds of all the counties in the area.
[Insert Figure 1 here]

5

Model Specification

In this paper, we introduce spatial dependence to analyze the spillover effects of the human capital of SEZs to their own counties and the neighboring ones. Considering spatial
dependence to examine the effects of SEZs to intra- and inter-region is plausible because
a positive shock on the productivity of an industry in a county could also be transferredto the same industry in the nearby counties. Moreover, the spatial lag of explanatory variables can also be included because externalities arising from neighbor’s human capital orother neighbor characteristics could also play a role in determining the productivity of thelocal industry.
There are two advantages to employ spatial regressions. First, spatial regressions cantake into account the spatial lag of the dependent variable and independent variables to describe the outcome of interest. The introduction of spatial dependence allows observations
to have associations with each other and to explore the relationship between a county andthe neighboring counties. In addition, spatial regression captures spillover effects throughthree channels. An increase in SEZs will affect the SEZ-hosting county itself (direct effect) and possibly affect the neighboring counties (indirect effect). Furthermore, spatial
regression exploits impact passing through the neighboring counties and back to the counties themselves (feedback effect). The inclusion of spatial information makes it possible toassess the effects of the SEZ program more comprehensively through multiple channels.
11

In this paper, we divide manufacturing firms into 28 industries by 2-digit level classification, for the reason that the same industry clusters together and has similar characteristics in proximity. We estimate human capital spillovers of SEZs on the local economy via
a Spatial Durbin Model (SDM) as follows,y = α + W yδ + Xβ + W Xθ + ε
X = [h

edu

cap

com

div]

(1)

where y is regional productivity for industry k in county i; W is row-standardized inverse
weight matrix within 150 kilometers, otherwise zero; h denotes the share of labor who
have college degrees or above for the firms located in the zones in county i; edu controls
the average numbers of schooling years for the firms outside the zones of industry k incounty i; cap is the capital stock per employed worker of each industry in county i;
com and div denote competition index and industrial diversity index; ε is the error term,
normally distributed by (0, σ 2 ).
The coefficient of h describes the spillover effects from the human capital of SEZs
on regional productivity. If the coefficient of h is significantly positive, which meansthat a 1% increase in the human capital of SEZs stimulates regional productivity. Also,
if the estimate δ is statistically significant, it implies that a spatial autocorrelation exists
and employing spatially lagged dependent variable is meaningful. Furthermore, if thecoefficient of W h is significantly positive, it implies that the human capital of SEZ in acounty is similar to the one in neighboring counties located in close distance.
With respect to the selection of models, we compare SDM models with other spatial
regression models. As noted by LeSage and Pace (2009), SDM models nest most of theother specifications like Spatial Error Model (SEM) and Spatial Autoregressive Model(SAR). Hence, we estimate the SDM models and then compare them with SEM or SAR
models by Likelihood Ratio (LR) test. The estimation results obtained from these models
can be used to test the hypothesis:
H0 : θ = 0
H0 : θ + δβ = 0.
12

The former one examines the hypothesis of whether the SDM model can be simplified
to forms of SAR model. If the null hypothesis is rejected, it demonstrates that the SDM
model better describes the data; the latter one is used to examine between SDM and SEM
model. If the null hypothesis is rejected, it means that the SDM model cannot be simplified
to the SEM model. Since the SDM model is a generated form nesting the other two models,
it is better favored than the two models in the case that either of the two hypotheses isrejected.
As suggested by LeSage and Pace (2009), we measure average direct and indirect
effects to explain the marginal effects of the explanatory variable. Average direct effect,measured by taking an average of the own derivatives for the counties themselves, which
captures the effects of SEZs to its own county. The average indirect effect is calculatedby the average of derivatives with respect to neighboring counties, which measures thespillovers of SEZs to neighboring counties. To illustrate, we take the partial derivative ofthe dependent variable with respect to h as follows,∂y= S(W ) = (In − δW )−1 (In β1 + W θ1 )∂h(2)where In are 3815 × 3815 idempotent matrices. From equation (4), the partial derivative
is not only depends on β1 , but also θ1 , δ, and spatial weight matrix. By definition, theaverage indirect effect is calculated by the average of diagonal elements of S(W ), andthe average indirect effect is measured by the average of off-diagonal elements of S(W ).
From the above equation, we can capture spillover effects through three channels. An
increase in human capital in SEZs will affect the firms in the considered county itself(direct effect) and arouse spillovers passing through neighboring counties and back to thecounties themselves (feedback effect). Furthermore, growth in human capital in county i
may affect the neighboring counties (indirect effect).
Some literature suggests that one needs to estimate the effects of human capital on
growth rate (Lopez-Bazo, 2004; Ramos et al, 2010) because the human capital used inmost literature is a stock variable which may arouse the problem of endogeneity. Criticize
causality from productivity to human capital does not apply to the estimation of growthrate in this case. To investigate the relation of the growth of regional productivity, we
13transform equation (1) as follows4 ,∆y = α + W ∆yδ + y−1 γ + Xβ + W Xθ + ε
X = [h

edu

cap

com

div]

(3)

where ∆y denotes the growth rate of regional productivity from the period of 2003 to2004 for industry k in county i; y−1 denotes the regional productivity for industry k incounty i in 2003.
Equation (3) evaluates the effects of human capital on the growth of regional productivity controlling for the neighbors, lagged value of the productivity, and other variables.
If the estimate δ is significantly positive, it implies that the growth of regional productivity
depends on the one in the neighbors. Also, the coefficient of h describes the spillover
effects from the human capital of SEZs on the growth of regional productivity when controlling for lagged productivity and other explanatory variables. Furthermore, if the coefficient of W h is significantly positive, it implies that the human capital of SEZ in a county
is similar to one another that is located in close distance.

6

Empirical results[Insert Table 1 here]
Table 1 reports the results of spatial regression models using regional productivity as

the dependent variable. To diagnose the existence of spatial dependence, we first estimate
equation (1) using linear ordinary least squares (OLS) models without spatial effects. The
estimated coefficients of the spatially lagged dependent variable in the SAR and SDM
model are significant at the 1% significance level, which suggests the existence of spatial
dependence. In addition, estimated coefficients of h from (1) to (3) are all significantly
positive. In the third column, the coefficient of h amounts to 0.7936, which implies that a
1% increase in the human capital of SEZs will lead to 0.7936% improvement in regional
productivity. Furthermore, the spatial lag of h shows significantly positive in the third
4

See Ramos et al (2010) and Lopez-Bazo (2004) for the derivation of the equation (3).

14column, which implies that the human capital in a zone is similar to the one in the neighborzones.
Likelihood ratio (LR) tests are proceeded to compare the SDM model with SAR and
SEM models. As presented in Table 1, the first hypothesis whether the SDM model canbe simplified to the SAR model should be rejected at the 1% significance level; the second
hypothesis whether the SDM model can be replaced by the SEM model should also be
rejected. Thus, we come to a conclusion that the SDM model best describes the data andthe remains of this paper employs the SDM model to calculate average direct, indirect
effects, and robustness checks.
[Insert Table 2 here]
Table 2 reports direct, indirect, and total effects of the SDM model in equation (1). As
noted in the previous section, one cannot interpret the estimate β as a partial derivativewith respect to the explanatory variable, one should calculate the estimates of direct, indirect, and total effects to interpret the marginal effects. In particular, the direct effect of thehuman capital of SEZs on regional productivity is positive and similar to the coefficient of
h. In addition, the feedback loops calculated by the difference between them are insignificantly different from zero, so that feedback loops can be neglected. The result implies
that the regional productivity benefits from the human capital of SEZs in the consideredcounties, however, the spillovers from SEZs pass through neighboring ones are not backto the considered counties.
From the second column, we find that the indirect effect of the human capital of SEZs
on the neighboring counties is significantly positive. The estimation result indicates that a
1% increase in the human capital of SEZs will lead to a 1.7844% increase in the regional
productivity of the neighbors. The neighboring counties benefit from the human capital of
SEZs more than two times the one in the considered counties. Because the indirect effects
are cumulative impacts from all other neighboring counties, aggregating indirect effects
from the neighborhood would lead to a larger magnitude than the direct effect itself.
[Insert Table 3 here]15

Table 3 presents sensitivity checks of our estimates with alternative spatial weight matrices. We employ alternative weight matrices based on cut-off distance from 160 kilometers to 200 kilometers respectively. In particular, we present the derivative with respect tothe variable h and compare it with the results in Table 2 which used the cut-off distance as
150 kilometers. The direct effect estimates are not affected by alternative weight matrices,and the indirect effect estimates are relatively constant with alternative weight matrices.
[Insert Table 4 here]
The empirical results of equation (4) using OLS, SAR, as well as SDM models areshown in Table 4 respectively. As the estimates of equation (1), the ones of the spatially
lagged dependent variable in SAR and SDM model are significant at the 1% significancelevel and suggest the existence of spatial lag of the growth of productivity. Also, the estimates of human capital are significantly positive at a 5% significance level. The magnitudeof the coefficient for human capital accounts for 0.5634 in the SDM model, which means
a 1% increase in the human capital of SEZs will lead to a 0.5634% improvement in thegrowth of regional productivity when controlling for the lagged value of the productivity
and other variables. Moreover, the spatial lag of human capital is significantly positive as
the one in Table 1.
[Insert Table 5-Table 8 here]
From Table 5 to Table 8, we present the estimated results of the alternative explanatory
variable: entrants of SEZs. Firms located within SEZs include entrants and incumbent
firms. Since incumbent firms were established before SEZs were introduced, they strictly
were not supposed to be called “additional” firms or people to the zones. In addition, theincumbent firms are unable to enjoy preferential policies like the entrants (see Zheng etal). For these reasons, we drop them from the sample and focus on the entrants to evaluate
whether human capital spillovers of SEZs have positive effects on regional productivity.
In Table 5, we report the estimates of equation (1) replaced h with the human capital ofentrants in SEZs, and find that the magnitude of the estimates remains virtually unchanged
relative to the ones in Table 1. In Table 6 and Table 7, we report average direct, indirect16

effects and the estimates with alternative weight matrices based on the results of the SDM
model in Table 5. In Table 8, we present the estimates of equation (3) with the humancapital of entrants in SEZs. From Table 5 to Tables 8, the estimates of the human capitalof entrants are a little smaller than the ones from Table 1 to Table 4 because the numberof entrants occupies 77% of all the firms in SEZs. Since entrants are a subsample of all
the firms in SEZs, it is reasonable that the estimates are smaller than the ones in previous
estimation results.
To sum up, our results find that local firms benefit from the human capital of SEZs in
the same industry. Positive spillover effects are not only confined to own counties but alsoneighboring ones. In addition, we find that the human capital of SEZs benefits the growthof regional productivity. Moreover, positive externalities of human capital still hold forthe sample of entrants.
There are two possible explanations for the estimated results. Both of them are beyond
the scope of this paper and should be carefully investigated in further research. First, mostfirms that enter SEZs are engaged in new productive activities rather than simply reallocating capital and labor from elsewhere. Those firms bring additional people especiallyrelatively highly skilled labor to the local industry. They generally have their own competitive advantages and do not compete with lower technology firms. Positive spillovers
which are from higher-skilled labor to lower ones lead to faster new technology adoption. In addition, unlike a few multinational firms which have little connection with thelocal industry, the firms in SEZs establish strong linkages with the local industry like subcontracting (Zeng, 2011). This would facilitate interactions with skilled labor of SEZs andtechnical upgrades for local firms, and thus seems likely to improve productivity.

7

Conclusion

This paper evaluates the spillover effects of SEZs in Yangtze Delta using 2004 manufacturing data. Taking into account spatial proximity, this paper quantifies the spillover
effects of the human capital of SEZs on regional productivity. One major finding is that
local firms benefit from the human capital of SEZs in the same industry. The spillover
effects are not only confined to own counties but also neighboring counties. Indeed, SEZs
17

contribute more to the regional productivity of neighboring counties than the one in the
hosting county itself. Moreover, positive spillover effects of the human capital of SEZs
still hold for the growth of regional productivity.
This paper answers the question of whether the externalities of human capital in SEZs
are able to raise regional productivity. From the empirical results, we conclude that the human capital of SEZs contributes to improving productivity. In addition, this paper further
answers whether the spillover effects of human capital can benefit neighboring regions inspatial proximity as well. The positive spillovers to wider geographic scope imply thatthe rising proportion of college-educated workers in SEZs does not harm the firms in theproximity, instead, the driving effect of human capital to facilitate local firms to improve
productivity is evident.
Although this paper sheds light on the spillover effects of human capital in SEZs, there
are remaining issues for further research. We can make an extension to firm-level data
which would enable us to analyze inter-firm issues in spatial econometric models that havebeen discussed in recent literature (Baltagi et al, 2016; Hashiguchi and Tanaka, 2015). In
addition, we can identify spatial spillovers of human capital in own- and other industry
sectors as Autant-Bernard and LeSage (2011) examining for MAR and Jacobs knowledge
externalities. The magnitudes of them are crucial for understanding the spillover effectsof human capital in SEZs on local productivity. The experience in the Yangtze Delta region needs to be carefully examined and put into the specific situation of other countries
and regions whereas the evidence of the region has positive spillovers towards the local
economy. We leave the remaining issues to further research and evaluate whether the conclusion still holds.

18

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21

Figure 1: SEZ-hosting counties in Yangtze Delta

22

Table 1. Estimation Results for Regional Productivity

h
edu
cap
com
div

OLS

SAR

SDM

(1)

(2)

(3)

0.9407(9.9236)0.1180(8.4971)
0.4788(41.1389)-0.0001(-0.0043)
0.0576(2.4902)0.8035(9.0136)0.1151(8.8304)
0.3759(31.6789)
0.0021(0.2141)0.0249(1.1462)0.42750.62700.4419(22.1670)
0.43300.5882

3,815

3,815

0.7936(2.6442)0.1335(1.4495)
0.3993(24.2615)-0.0134(-0.7696)-0.0019(-0.1518)
0.3890(15.4114)-0.1369(-0.4929)-0.1358(-4.7920)
0.0095(0.4294)0.1518(7.0856)0.5449(8.8201)
0.44990.580371.60(0.00)
58.63(0.00)
3,815

W*h
W*edu
W*cap
W*com
W*div
W*y
R squared
Std. error
LR test spatial lag
LR test spatial error
No. of obs.
Note: t-statistics are reported in parentheses.
Numbers in parentheses for LR tests are p-values.

23

Table 2. Average Direct, Indirect, and Total Effects EstimateshDirectIndirect

Total

(1)

(2)

(3)

0.8292(2.7129)0.1272(1.3693)0.4029(23.3717)-0.0128(-0.7410)
0.0048(0.3883)

edu
cap
com
div

1.78442.6137(4.3177) (4.0456)-0.1621-0.0349(-0.2643) (-0.0551)
0.18750.5904(1.7817) (5.2583)
0.0050-0.0077(0.1129) (-0.1602)
0.32880.3336(5.5349) (5.5227)

Note: t-statistics are reported in parentheses.

Table 3. Robustness checks with alternative spatial weight matrices
partial derivatives with respect to human capital

160 kilometer
170 kilometer
180 kilometer
190 kilometer
200 kilometer
Note: t-statistics are reported in parentheses.

24

DirectIndirect

Total

(1)

(2)

(3)

0.8259(2.2841)0.8208(2.3427)0.8097(2.1759)
0.8253(2.1373)
0.8338(2.7775)

1.8798(3.5512)2.0949(3.6255)
2.1802(3.6839)2.5176(3.6122)2.6302(3.6747)2.7058(3.3450)
2.9157(3.4873)
2.9900(3.4007)3.3429(3.4160)
3.4641(3.5306)

Table 4. Estimation Results for Growth of Regional Productivity

h
edu
cap
com
div
y−1

OLS

SAR

SDM

(1)

(2)

(3)

0.5914(7.0428)0.0509(3.9129)
0.3663(31.9542)-0.0056(-0.5996)
0.0543(2.6740)-0.5438(-37.7554)

0.5967(7.1602)
0.0551(4.2719)0.3545(31.1575)-0.0067(-0.7204)
0.0522(2.5907)-0.5537(-38.7224)

W*h
W*edu
W*cap
W*com
W*div
W*y
R squared
Std. error
LR test spatial lag

0.35740.5274LR test spatial error
No. of obs.

3,459

Note: t-statistics are reported in parentheses.
Numbers in parentheses for LR tests are p-values.

25

0.5634(2.2175)
0.0666(0.5722)0.3407(6.0845)-0.0160(-0.2643)
0.0232(1.7109)-0.5655(-6.0849)
0.5889(3.2629)-0.0780(-0.2871)
0.0409(1.5522)0.0042(0.1916)0.1747(5.2314)0.21590.1770(103.8699) (3.2380)
0.33670.34850.5234
0.521429.88(0.00)
37.54(0.00)
3,459
3,459

Table 5. Estimation Results for Regional Productivity
replace with the human capital of entrants

h
edu
cap
com
div

OLS

SAR

SDM

(1)

(2)

(3)

0.8553(9.1292)
0.1164(8.3480)
0.4844(41.8230)
0.0000(0.0024)
0.0662(2.8699)

0.7324(8.3205)
0.1137(8.6950)0.3804(32.1716)
0.0022(0.2271)
0.0329(1.5189)

0.42530.62820.4439(22.2332)
0.4304
0.5890

3,815

3,815

0.7236(2.4393)0.1317(1.4570)0.4048(24.7774)-0.0139(-0.8167)
0.0067(0.5311)0.3297(13.5335)-0.1281(-0.4669)-0.1375(-4.8362)
0.0134(0.6095)
0.1564(7.2730)
0.5469(8.9368)
0.44680.581270.58(0.00)
56.02(0.00)
3,815

W*h
W*edu
W*cap
W*com
W*div
W*y
R squared
Std. error
LR test spatial lag
LR test spatial error
No. of obs.
Note: t-statistics are reported in parentheses.
Numbers in parentheses for LR tests are p-values.

26

Table 6. Average Direct, Indirect, and Total Effects Estimates
replace with the human capital of entrantshDirectIndirect

Total

(1)

(2)

(3)

0.7553(2.4954)0.1258(1.3803)
0.4085(23.9274)-0.0132(-0.7767)
0.0140(1.1222)

edu
cap
com
div

1.58092.3362(3.9907) (3.6479)-0.1412-0.0153(-0.2326) (-0.0245)
0.19280.6014(1.8346) (5.3778)
0.0129-0.0003(0.2855) (-0.0064)
0.35090.3649(5.6364) (5.7503)

Note: t-statistics are reported in parentheses.

Table 7. Robustness checks with alternative spatial weight matrices
partial derivatives with respect to human capital of entrants

160 kilometer
170 kilometer
180 kilometer
190 kilometer
200 kilometer
Note: t-statistics are reported in parentheses.

27

DirectIndirect

Total

(1)

(2)

(3)

0.7497(2.1065)0.7450(2.1552)
0.7342(1.9955)
0.7480(1.9755)
0.7567(1.9988)

1.6790(3.3197)
1.8699(3.3179)
1.9009(3.1498)
2.1362(3.4030)
2.3084(3.3872)2.4287(3.0532)
2.6150(3.1437)2.6352(2.9280)
2.8843(3.1002)
3.0652(3.1613)

Table 8. Estimation Results for Growth of Regional Productivity
replace with the human capital of entrants

h
edu
cap
com
div
y−1

OLS

SAR

SDM

(1)

(2)

(3)

0.5305(6.3964)0.0495(3.7936)0.3700(32.3987)-0.0055(-0.5895)
0.0601(2.9635)-0.5420(-37.6190)

0.5361(6.5122)0.0537(4.1489)
0.3582(31.6024)-0.0066(-0.7112)
0.0580(2.8837)-0.5520(-38.5840)

0.35580.52800.2159(103.8232)
0.33490.5241

3,459

3,459

0.5138(2.0380)
0.0650(0.6978)
0.3442(10.1529)-0.0163(-0.4542)
0.0282(2.2371)-0.5636(-10.2150)
0.5361(5.5386)-0.0752(-0.2980)
0.0427(1.6886)
0.0056(0.2640)0.1876(7.3179)0.1780(3.2749)
0.34850.521429.58(0.00)
37.23(0.00)
3,459

W*h
W*edu
W*cap
W*com
W*div
W*y
R squared
Std. error
LR test spatial lag
LR test spatial error
No. of obs.
Note: t-statistics are reported in parentheses.
Numbers in parentheses for LR tests are p-values.

28