Transcript 产业政策、现有比较优势与企业表现

very preliminary
Comparative Advantage and the Effects of
Place-Based Policies: Evidence from
China’s Export Processing Zones
Zhao Chen#, Sandra Poncet*, Ruixiang Xiong#
#China
Center of Economic Studies, Fudan
University
*Paris School of Economics (University of Paris 1)
2015/7/8
and CEPII
I. Introduction
• Industry
policies,
place-based
policies and Economic Zones in China
• No conclusive conclusions about the
effectiveness
of
place-based
policies (Moretti, 2010, Busso et al., 2013)
• Difficulties
in
evaluation
of
industry policies (Krugman, 1983)
– How to measure industry policy
– How to identify the causality
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I. Introduction
• In this paper
– The effects of export-processing zones
(EPZs)
• Clear policy purpose
• The role of comparative advantage
– DID estimation using a quasi-experiment
of EPZs in China.
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II. Brief literature review
• The effect of industry policies (Cai, Harrison and Lin, 2011)
– Tariff policy has positive impact on TFP of industries
with comparative advantage
– Comments:
• Tariff policy & TFP
• Comparative advantage: exporting firms
• Policy of protection vs. policy of promotion
II. Brief literature review
• Policy evaluation of Economic Zones
• City-level data (Wei, 1995; Wang, 2013; Alder et al., 2013)
• Firm-level data (Head and Ries, 1996; Schminke and Van
Biesebroeck, 2013)
• Comments:
– policy at city-level, no within city difference
– Few concern about the heterogeneous impact
• This paper:
– Policy difference at city-industry level
– Comparative advantage
III. Background of China’s EPZs
• Aim: promote exports by preferential
policies
• Establishment
:
Establishment year
Number of EPZs
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2000
15
2001
3
2002
8
2003
13
2005
18
total
57
III. Background of China’s EPZs
• Only some industries chosen as key industries
could enjoy preferential policies
• Preferential policies
 in EPZs ： free VAT, free trade for imported
components; facilitate firm’s exporting
outside ： tax reimbursement when providing
firms in EPZs with intermediate goods
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IV. Data
• Data source
– China annual survey of manufacturing
firms from 1998 to 2007
• Sample
– To make the cities more comparable, we
only include the cities having EPZs by
2005
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3. Data
• Data cleaning
Basic cleaning (Brandt et al., 2012, Nie
et al., 2012)
Industry classification adjustment (Yang
et al., 2013)
Administrative division adjustment (Bao
et al., 2013)
Price deflator
Exporting firms (1998-2007)
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4. Empirical Results
OLS：Ycijt=αc+βi+δt+ψTc+θKci+ρTc·Kci+Xjtλ+εcijt
FE：Ycijt=γj+δt+ψTc+θKci+ρTc·Kci+Xjtλ+εcijt
Y :the naturallogarithm of export value;
c，city；i，3 digits industry；j,firm ，t,year；
T c：before E PZ s’ establishm ent equals 0,otherw ise 1；
K ci：not key industry equals 0，otherw ise 1；
X jt：firm and city levelcontrolvariables；
ε
cijt：random error term ；
ρ: the effects of place-based policy.
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4. Empirical Results
• How to define comparative advantage (CA):
– Qci = 1, if location entropy for industry i in city c > 1
before EPZ establishment, otherwise 0
• Regression
– Full sample
– Subsample with CA
– Subsample without CA
– Triple-interaction term with Qci
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4.1 Basic model
(1)
Full-sample
(2)
Without
CA
(3)
With
CA
(4)
Fullsample
(5)
Without
CA
(6)
With
CA
OLS
OLS
OLS
FE
FE
-0.0317
0.0640***
(0.0291)
(0.0217)
0.0367
0.123***
-0.0477
-0.0106
-0.0547
FE
0.0594**
*
(0.0326)
(0.0344)
(0.0360)
(0.0191)
-0.0332
0.00541
-0.0414
(0.0337)
(0.0364)
(0.0394)
0.0663**
0.00903
0.0830*** 0.104***
(0.0255)
(0.0299)
(0.0283)
(0.0157)
(0.0303)
(0.0169)
-0.909***
-1.388***
-0.404
-0.247
-0.328*
-0.229
(0.232)
(0.143)
(0.283)
(0.243)
(0.169)
(0.313)
Cluster-City
YES
YES
YES
YES
YES
YES
Obs
338,211
97,966
240,245
338,211
97,966
240,245
R-squared
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Number
0.912
0.912
0.913
0.919
0.916
0.921
T
K
T*K
Constant
4.2 long-run effects
Reference group: n = - 5 [-7, -6, -5]
(n=-4) * Kci
(n=-3) * Kci
……
(n= 4) * Kci
(n= 5) * Kci
-.2
0
.2
.4
4.2 long-run effects: full
sample
-4
-3
-2
-1
0
1
2
years since(to) EPZs' establishment
Estimates
95% lower bound
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3
95% upper bound
4
5
-.1
0
.1
.2
.3
4.2 long-run effects: sub-sample
w/o CA
-4
-3
-2
-1
0
1
2
years since(to) EPZs' establishment
Estimates
95% lower bound
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3
95% upper bound
4
5
-.2
0
.2
.4
4.2 long-run effects: subsample with CA
-4
-3
-2
-1
0
1
2
years since(to) EPZs' establishment
Estimates
95% lower bound
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3
95% upper bound
4
5
5. Robustness checks
Ycijt =γj +δt +φ Tc +θ K ci +ω Q ci + ρ Tc ·K ci +ϑ Tc · Q ci +π K ci · Q ci
+ τ Tc ·K ci · Q ci +𝐗 jt ∆+εcij
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5.1 Using interaction terms（FE）
T
T*K
T*Q
T*K*Q
Constant
Cluster-city
Observations
R-squared
Number of panelid
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-0.0185
(0.0325)
0.0451
(0.0319)
-0.0549*
(0.0330)
0.0811**
(0.0333)
-0.241
(0.240)
YES
338,211
0.919
75,197
5.2 Considering firm’s relocation
(1)
(2)
(3)
full-sample
Without CA
With CA
-0.0481**
-0.0192
-0.0539**
(0.0194)
(0.0282)
(0.0225)
0.108***
0.0376
0.128***
(0.0159)
(0.0287)
(0.0172)
-0.265
-0.356**
-0.253
(0.217)
(0.162)
(0.276)
Cluster-City
YES
YES
YES
Observations
277,610
79,517
198,093
R-squared
0.919
0.916
0.921
Number of panelid
56,090
16,555
39,535
T
T*K
Constant
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5.3 Omitted governance
abilities（FE）
(1)
(2)
(3)
Full-sample
Without CA
With CA
-0.0169
-0.0115
-0.0149
(0.0347)
(0.0441)
(0.0378)
0.0873***
0.00393
0.108***
(0.0281)
(0.0434)
(0.0311)
-0.0469
-0.144
-0.0499
(0.269)
(0.147)
(0.364)
Cluster-City
YES
YES
YES
Observations
158,340
45,687
112,653
R-squared
0.920
0.918
0.921
Number of panelid
34,569
10,233
24,336
T
T*K
Constant
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5.4 Higher export intensity firms
（FE）
Prologis（2008）
(1)
(2)
(3)
Full-sample
Without CA
With CA
-0.0524**
-0.00726
-0.0652**
(0.0256)
(0.0354)
(0.0266)
0.110***
0.0346
0.130***
(0.0215)
(0.0457)
(0.0231)
-0.539
-0.570**
-0.546
(0.436)
(0.225)
(0.554)
Cluster-City
YES
YES
YES
Observations
166,809
47,361
119,448
R-squared
0.930
0.932
0.930
Number of panelid
38,766
11,512
27,254
T
T*K
Constant
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6. Conclusions and implications
• Conclusions
Average effects
Overall: 10.4%
Industries with CA：12.3%; otherwise
effects
no
Long-run effects
Industries with CA：from 9.8% to 24.4%
Otherwise no effects
• Policy implication：
local initial conditions are important
when making place-based policies
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Thank You！
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