Zhao Chen # , Sandra Poncet * , Ruixiang Xiong #
description
Transcript of Zhao Chen # , Sandra Poncet * , Ruixiang Xiong #
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) and CEPII23/4/22
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 :
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Establishment year Number of EPZs
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) Matching key industries
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4. Empirical ResultsOLS:Ycijt=αc+βi+δt+ψTc+θKci+ρTc· Kci+Xjtλ+εcijt
FE:Ycijt=γj+δt+ψTc+θKci+ρTc· Kci+Xjtλ+εcijt Y: the natural logarithm of export value;
c,city;i,3 digits industry;j, firm,t, year;
Tc:before EPZs’ establishment equals 0, otherwise 1;
Kci:not key industry equals 0,otherwise 1;
Xjt:firm and city level control variables;
εcijt: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) (2) (3) (4) (5) (6)
Full-sampleWithout
CAWith CA
Full-sample
WithoutCA
With CA
OLS OLS OLS FE FE FE
T -0.0477 -0.0106 -0.0547
-0.0594*
** -0.0317
-0.0640**
*
(0.0326) (0.0344) (0.0360)(0.0191
) (0.0291) (0.0217)K -0.0332 0.00541 -0.0414
(0.0337) (0.0364) (0.0394)
T*K0.0663** 0.00903
0.0830***
0.104*** 0.0367 0.123***
(0.0255) (0.0299) (0.0283)(0.0157
) (0.0303) (0.0169)
Constant -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 YESObs 338,211 97,966 240,245 338,211 97,966 240,245
R-squared 0.912 0.912 0.913 0.919 0.916 0.921Number of
panelid 75,197 22,464 52,733
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4.2 long-run effects
Reference group: n = - 5 [-7, -6, -5]
(n=-4) * Kci
(n=-3) * Kci ……(n= 4) * Kci (n= 5) * Kci
4.2 long-run effects: full sample
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-.2
0.2
.4R
eg
ressio
n c
oe
ffic
ien
ts:
% c
ha
ng
e in
exp
ort
va
lue
-4 -3 -2 -1 0 1 2 3 4 5years since(to) EPZs' establishment
Estimates 95% upper bound
95% lower bound
4.2 long-run effects: sub-sample w/o CA
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-.1
0.1
.2.3
Re
gre
ssio
n c
oe
ffic
ien
ts:
% c
ha
ng
e in
exp
ort
va
lue
-4 -3 -2 -1 0 1 2 3 4 5years since(to) EPZs' establishment
Estimates 95% upper bound
95% lower bound
4.2 long-run effects: sub-sample with CA
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-.2
0.2
.4R
eg
ressio
n c
oe
ffic
ien
ts:
% c
ha
ng
e in
exp
ort
va
lue
-4 -3 -2 -1 0 1 2 3 4 5years since(to) EPZs' establishment
Estimates 95% upper bound
95% lower bound
5. Robustness checks
Ycijt =γj+δt+φ Tc+θ Kci+ω Qci + ρ Tc·Kci+ϑ Tc · Qci+π Kci · Qci + τ Tc·Kci · Qci+𝐗jt∆+εcij
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5.1 Using interaction terms ( FE )
T -0.0185(0.0325)
T*K 0.0451(0.0319)
T*Q -0.0549*(0.0330)
T*K*Q 0.0811**(0.0333)
Constant -0.241(0.240)
Cluster-city YESObservations 338,211
R-squared 0.919Number of panelid 75,197
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5.2 Considering firm’s relocation
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(1) (2) (3)
full-sample Without CA With CA
T -0.0481** -0.0192 -0.0539**
(0.0194) (0.0282) (0.0225)
T*K 0.108*** 0.0376 0.128***
(0.0159) (0.0287) (0.0172)
Constant -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
5.3 Omitted governance abilities ( FE )
(1) (2) (3)
Full-sample Without CA With CA
T -0.0169 -0.0115 -0.0149
(0.0347) (0.0441) (0.0378)
T*K0.0873*** 0.00393 0.108***
(0.0281) (0.0434) (0.0311)
Constant -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
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5.4 Higher export intensity firms ( FE ) Prologis ( 2008 )
(1) (2) (3)
Full-sample Without CA With CA
T -0.0524** -0.00726 -0.0652**
(0.0256) (0.0354) (0.0266)
T*K0.110*** 0.0346 0.130***
(0.0215) (0.0457) (0.0231)
Constant -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
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6. Conclusions and implications
• Conclusions Average effects
Overall: 10.4%Industries with CA : 12.3%; otherwise no
effectsLong-run effects
Industries with CA : from 9.8% to 24.4%Otherwise no effects
• Policy implication :local initial conditions are important when
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Thank You !
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