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Trade Induced Technical Change?
The Impact of Chinese Imports on Innovation,
Diffusion and Productivity
Nick Bloom (Stanford)
Mirko Draca (Warwick)
John Van Reenen (LSE & CEP)
Chicago
April 2015
What is the impact of Southern trade on
Northern technical change?
• Limited empirical evidence, partly from lack of micro data and
partly due to a lack of North-South trade natural experiments
• Theoretical literature also inconclusive (e.g. ambiguous
effects of competition on innovation & adoption)
• But this is a major economic and political issue because of
the rapid growth of Chinese & low-wage country imports
15
Low-wage % of imports in Europe and the US
10
All low
wage
0
5
China
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
Low wage countries list from Bernard, Jensen and Schott (2006). Countries <5% GDP/capita of US 1972-2001.
Clear political
importance – for
example tires trade
war in 2010
Major election
issue in 2012
Summary: we study the impact of Chinese
imports on technology in Europe (1/2)
Use panel data on ~90,000 firms & establishments in 1990s &
2000s in 12 EU countries. We find that higher threat of Chinese
imports leads to:
A) Within-firm increase in volume of innovation (patenting and
R&D) as well as greater IT, productivity & management quality
B) A reallocation of jobs towards higher tech firms (as measured
by patents, TFP, IT, etc.)
So there is aggregate technological upgrading in the North from
liberalization with a lower wage country like China
Summary: we study the impact of Chinese
imports on technology in Europe (2/2)
Results robust to using IV strategy of quota relaxation post
China’s entry into the WTO
Overall magnitudes moderate but rising: China “accounts” for:
• ≈ 15% of increase in IT, patents & productivity 2000-2007
Suggests the impact on innovation is another positive outcome
(alongside static gains) from low wage country trade
Some econometric ‘case-studies’ illustrate
our results
Freeman and Kleiner (2005) look at a large
US shoe maker’s response to increasing
low wage country competition
Bartel, Ichniowski & Shaw (2007) on US
valve manufacturers’ response to cheaper imports
Bugamelli, Schivardi & Zizza (2008) Italian manufacturers
reactions to Chinese import threats
All find very similar changes:
• Increased innovation to develop new product ranges
• Investment in IT, worker skills and management practices
Seems to fit a ‘Trapped-Factor’ innovation model
(Bloom, Romer, Terry & Van Reenen, 2014)
Caveat: Main empirical analysis is partial equilibrium
Build Endogenous Growth GE model with “Trapped factors”
– Some factors of production with firm-specific investment
(skilled labor, management time, specific capital, etc.)
– China shock reduces profits from old products
– Lower opportunity cost to shifting trapped factors to
innovate in new products Chinese are not making
– Amplifies the standard positive effect on innovation from
larger market size
Consistent with case-studies & results from the paper:
1. Import competition increases innovation
2. Mainly within the narrowly impacted industry
3. Main response from imports from low skilled countries
Related Literature
• Trade & Productivity. De Locker & Goldberg (2014) survey;
Empirics: Pavcnik (2002), Trefler (2004), Dunne et al (2008)
Theory: Heterogeneous firms Melitz (2003), Arkollakis et al
(2008, 2010); Redding & Melitz (2015); product switching:
Bernard et al (2007); offshoring Grossman & Rossi-Hansberg
(2008)
• Trade & Innovation. Empirics: Bustos (2011), Acemoglu et al
(2015); Theory: market size Krugman (1980), Grossman &
Helpman (1992), Eaton & Kortum (2002), Atkeson & Burstein
(2010); competition (Raith, 2002; Schmidt, 1997); Aghion et al,
2005); learning (Coe & Helpman, 1995; Acharya & Keller,
2008); Inputs (Goldberg et al, 2010a,b),
• Impact of China. Bernard Jenson & Schott (2006); Ossa &
Hsieh (2010); Levchenko & Zhang (201)
• Low wage countries & labour markets: Machin & Van
Reenen (1998), Autor, Dorn & Hanson (2013, 2014)
Data
Within plant/firm effects
Reallocation effects between plants/firms
Extensions & Robustness
Innovation and Productivity data: firm panel
• European Patent Office data (patents and citations) matched to
population of public and private firms (living & dead) from BVD
AMADEUS for 12 European countries 1996-2005
• France, Italy, Spain and Sweden have good materials data so
estimate production functions to get TFP (separately by
industry). Use de Loecker (2011) version of Olley-Pakes
• Harte Hanks plant survey on IT. Focus on computers per
worker as consistent across time & countries, but also use
other software-based (e.g. ERP)
• Smaller samples:
– Subset 459 quoted firms reporting R&D for 5+ years
– 1,576 firms with management data
Trade data: UN Comtrade
• Trade data at 6-digit level product matched to 4-digit SIC
using Feenstra, Romalis, & Schott (2006) concordance
• Our main measure is IMPCH = (Chinese Imports/All Imports):
• Well measured annually at 4-digit SIC level
• Also use import penetration measures
– Chinese imports/apparent consumption
– Chinese imports/production
Chinese Imports into EU by SIC-2, 2000-2005
Food
Tobacco
Chemicals
Transport
20
21
28
37
30
29
33
24
27
38
35
26
22
32
36
34
39
31
23
25
Textiles
Fabricated metals
Toys
Leather
Apparel
Furniture
0
.02
.04
.06
5-year change in export share, 2000 to 2005, for our sample
.08
.1
Data
Within plant/firm effects
Reallocation effects between plants/firms
Extensions & Robustness
Basic Technology Equation
=
Yijkt α IMP
ln
CH
jkt
patents, IT, R&D,
TFP, management
Example: For IT
i = plants (22,957)
j = industries (366)
k = countries (12)
jk = 2,816 cells
t = 2000,…,2007
+ f kt + ηi + ε ijkt
Chinese import
share
Fixed Effects
fkt : country*time dummies
Cluster at industry-by-country (jk)
Estimate in (5-year) long differences
Table 1A: Within Firm OLS Results
(1)
(2)
(3)
ΔlnPATENT
Δln(IT/N)
ΔTFP
5 year diffs
5 year diffs
0.321***
(0.102)
0.361**
(0.076)
0.257***
(0.072)
1.213**
(0.549)
0.891***
(0.337)
Sample
period
# units
2005-1996
2007-2000
2005-1996
2007-1996
2010-2004
8,480
22,957
89,369
459
1,576
# ind*cty
clusters
Obs
1,578
2,816
1,210
196
579
30,277
37,500
292,167
1,626
3,607
Method
Change in
Chinese
Imports
Δln(R&D)
5 year diffs 5 year diffs
ΔManagement
3 year diffs
Notes: SE clustered by industry-country, Country-year dummies included. Estimate
TFP separately by industry (on 1.4m obs). Use Olley-Pakes (1996)/de Loecker
(2011). Management data from Bloom and Van Reenen (2010), management mean
(std dev.) is 3.09 (0.59). Because of short-panel run regressions in 3-year diffs.
Endogeneity - use MFA policy experiment
• The Multi Fiber Agreement (1974) restricted apparel and
textile exports from developing countries
• When China entered the WTO in 2001 it gained access to
this phased abolition, occurring between 2002 and 2005
(Brambilla, Khandelwal & Schott, 2010)
• As Chinese textile products came off quota there was huge
surge of imports into EU (led to “bra-wars”)
• 31,052 patents in the textile & apparel sub-sample
• Use (value weighted) proportion of HS6 products covered by
a quota in each 4 digit SIC in 2000
IV with share of SIC4 on quota under MFA –
almost random - making this a great instrument
|
% industry covered by
ussic |
quotas (all stages)
4-digit |
Mean
Freq.
------------+---------------------------------------------------2211 |
.77447796
210 (BROADWOWEN FABRIC, COTTON)
2221 |
.23278008
63
(BROADWOWEN FABRIC, SILK)
2231 |
.02347782
134 (BROADWOWEN FABRIC, WOOL)
2321 |
.86472106
32
(MEN'S AND BOYS' SHIRTS)
2322 |
1
22
(MEN'S AND BOYS' UNDERWEAR)
2323 |
.78554922
26
(MEN'S AND BOYS' NECKWEAR)
2325 |
.05432023
10
(MEN'S AND BOYS' TROUSERS)
2329 |
.74802500
12
(MEN'S AND BOYS' CLOTHING NEC)
2337 |
.48232245
137 (WOMEN’S AND GIRLS SKIRTS)
2339 |
0
22
(WOMEN’S AND GIRLS CLOTHING NEC)
IV with share of SIC4 on quota under MFA –
almost random - making this a great instrument
|
% industry covered by
ussic |
quotas (all stages)
4-digit |
Mean
Freq.
------------+---------------------------------------------------2211 |
.77447796
210 (BROADWOWEN FABRIC, COTTON)
2221 |
.23278008
63
(BROADWOWEN FABRIC, SILK)
2231 |
.02347782
134 (BROADWOWEN FABRIC, WOOL)
2321 |
.86472106
32
(MEN'S AND BOYS' SHIRTS)
2322 |
1
22
(MEN'S AND BOYS' UNDERWEAR)
2323 |
.78554922
26
(MEN'S AND BOYS' NECKWEAR)
2325 |
.05432023
10
(MEN'S AND BOYS' TROUSERS)
2329 |
.74802500
12
(MEN'S AND BOYS' CLOTHING NEC)
2337 |
.48232245
137 (WOMEN’S AND GIRLS SKIRTS)
2339 |
0
22
(WOMEN’S AND GIRLS CLOTHING NEC)
Table 2A: IV estimates using changes in EU
textile & clothing quotas - IT
Method
ΔChinese Imports
Δln(IT/N)
ΔChinese
Imports
Δln(IT/N)
OLS
First Stage
IV
1.284***
(0.172)
Quotas removal
1.851***
(0.400)
0.088***
(0.019)
Sample period
2005-2000
2005-2000
2005-2000
Number of units
2,891
2,891
2,891
industry clusters
83
83
83
2,891
2,891
2,891
Observations
SE clustered by 4 digit industries, Country-year and site type dummies included.
All columns using just textiles and apparel sample
Table 2A- Cont: IV estimates using changes in EU
textile & clothing quotas – Patents and TFP
Method
ΔlnPATENTS
ΔlnPATENTS
Δln(TFP)
Δln(TFP)
OLS
IV
OLS
IV
0.902***
(0.087)
1.629**
(0.326)
ΔChinese
Imports
ΔChinese
Imports
1st Stage F-stat
Sample period
Units
Industry clusters
Observations
1.160***
(0.377)
1.864*
(1.001)
24.1
11.5
2005-1999
2005-1999
2005-1999
1999-2005
1,866
1,866
12,247
12,247
149
149
177
177
3,443
3,443
20,625
20,625
SE clustered by 4 digit industries, Country-year dummies included
Maybe quota sectors had pre-existing trends?
Table 3: Control for these & results robust
(1)
Dependent Variable:
Quotas removal
(SIC4*year>=2001)
Firm-specific trends?
Sample period
Number of units
Number industry
clusters
Observations
(2)
Δln(PATENTS) Δln(PATENTS)
(5)
(6)
ΔTFP
ΔTFP
0.129**
(0.063)
0.216**
(0.105)
0.143***
(0.018)
0.178***
(0.037)
No
Yes
No
Yes
2005-1992
2005-1992
2005-1992
2005-1992
2,435
2,435
16,495
16,495
159
159
187
187
14,768
14,768
55,791
55,791
SE clustered by 4 digit industries, Country-year and dummies included.
All columns using just textiles and apparel sample
*No significant correlation between quota IV and pre-WTO change in observables
like TECH, industry size, wages, etc. (see Table A3)
Other ways of controlling for unobserved
technology shocks
• Include three digit industry trends (Table 1B)
• Use level of Chinese imports share in 1999 in all EU & US
four digit industry as IV for subsequent growth (Table 8)
– Trade analogue of Card’s “ethnic enclave” instrument
• Include lagged dependent variable (Table A4)
• Find supportive results to the quota IV approach
Data
Within plant/ firm effects
Reallocation effects between plants/firms
Extensions & Robustness
C) Employment Equation
If high TECH plants “protected”
from Chinese imports then γn > 0
Employment
growth
∆=
ln N ijkt γ [TECH ijkt −5 * ∆IMP
n
α ∆IMP
n
CH
jkt
expect αn < 0
CH
jkt
]+
+ δ TECH ijkt −5 + ∆f + ∆ε ijkt
n
expect δn > 0
n
kt
n
EMPLOYMENT GROWTH BY INITIAL IT INTENSITY
.1
0
-.1
-.2
Employment Growth
Top Quintile China
Import Growth
Bottom Quintile China
Import Growth
1
2
3
4
5
IT LOW
IT HIGH
Quintiles of initial IT Intensity
1
2
3
4
5
IT LOW
IT HIGH
Quintiles of initial IT Intensity
Table 4A: High Tech firms shed less jobs when faced
with rising Chinese imports
Dependent Variable:
TECH Measure:
Chinese Import Growth
Ln(pat stock/worker) at t-5
Ln(pat stock/worker) at t-5*
Chinese imports growth
IT intensity (t-5)
(IT/N) (t-5)*Chinese Imp Growth
Δln(N)
Patents
-0.434***
(0.136)
0.348***
(0.049)
1.434**
(0.649)
Δln(N)
IT
-0.379***
(0.105)
Δln(N)
TFP
-0.377***
(0.096)
0.230***
(0.010)
0.385**
(0.157)
Ln(TFP) at t-5
Ln(TFP) at t-5*
Chinese import growth
Clusters
1,375
2,816
Observations
19,844
37,500
SE clustered by country- industry, all standard additional controls included
0.136***
(0.012)
0.795***
(0.307)
1,210
292,167
C) Survival Equation
If high TECH plants partially
“protected” from effect of Chinese
imports then γs > 0
Survival
s
CH
=
∆
SURVIVAL
γ
TECH
IMP
[
*
ijkt
ijkt −5
jkt ] +
α ∆IMP
s
CH
jkt
expect αs < 0
+ δ TECH ijkt −5 + ∆f + ∆ε ijkt
s
expect δs > 0
s
kt
s
Table 4B: High tech firms more likely to survive
Chinese imports
Dependent Variable
TECH measure:
Change in Chinese Imports
Ln(patent stock/workert-5) *Change
in Chinese Imports
(IT/N) t-5*Change in Chinese Imports
Survival
Survival
Survival
patents
-0.079
(0.051)
0.288**
(0.138)
IT
-0.182**
(0.072)
TFP
-0.208***
(0.050)
0.137
(0.112)
ln(TFPt-5) *Change in Chinese Imports
0.110*
(0.059)
IT Intensity (IT/N)t-5
Ln(patent stock/workert-5)
-0.002
(0.006)
-0.008
(0.009)
Ln(TFPt-5)
Observations
7,985
28,624
-0.003
(0.003)
60,883
SE clustered by up to 2,863 country- industry pairs, lagged size & all standard
additional controls
Magnitude calculation
Table 6: Aggregate effect of trade on technology, 20002007, % of Technology Measure that Chinese trade
‘accounts for’
Measure
Within (%) Between (%) Exit (%)
Total (%)
Patents
5.1
6.7
2.1
13.9
IT Intensity
9.8
3.1
1.2
14.1
TFP
9.9
2.4
0.2
12.5
Notes: calculated for the regression sample using OLS coefficients
Table A5: Comparison of Industry level results to
firm results confirms simulated magnitudes
Panel A: Industry-level regressions
Dependent Variable:
Change in Chinese
Imports
Sample period
Observations
Δln(PATENTS)
Δln(IT/N)
Δln(TFP)
0.368*
(0.200)
0.399***
(0.120)
2005-1996
6,888
2007-2000
7,409
0.326***
(0.072)
2005-1996
5,660
Panel B: Firm-level (firms allocated to single SIC4)
Dependent Variable:
Change in Chinese
Imports
Years
Observations
Δln(PATENTS)
Δln(IT/N)
Δln(TFP)
0.171**
(0.082)
2005-1996
30,277
0.361**
(0.076)
2007-2000
37,500
0.164***
(0.051)
2005-1996
241,810
Data
Within plant/firm effects
Reallocation effects between plants/firms
Extensions & Robustness
Extensions & Robustness
• Dynamic Selection (we bound the bias)
• General Equilibrium
• Heterogeneity of China effects
– Within firm innovation effects strongest in industries
where there are more “trapped factors”
• Other low wage countries (yes, similar to China)
• Lawyer effects on patents (find no evidence for this)
• Offshoring (find some effect on IT and TFP)
• “I-Pod” story (firms innovate here to produce in China – but
imports reduce profits)
• Exports (find small & insignificant effects)
• Product Switching
Table 7: Dynamic Selection - Worst Case Lower
Bounds
Δln(PAT)
Δln(PAT)
Baseline
0.321***
(0.102)
Worst case
Lower Bound
0.271**
(0.098)
Number of units
8,480
8.732
Number of clusters
1,578
1,662
Observations
30,277
31,271
Dependent Variable:
Change in Chinese
Imports
Use Manski & Pepper (2000) idea of imputing lower bound of zero patents to all the
firms who exited to calculate worst case lower bound. Similar results for Negative
Binomial model & using quota IV
What about general equilibrium – does that
reduce China’s estimated impact?
Bloom, Romer, Terry & Van Reenen (2014) detail model of
impact of falling trade costs with China on global Growth in a
GE model of endogenous growth with “trapped factors”
Calibrating this model to 1996-2007 we find that China raises:
- Long-run global growth in OECD by ≈ 0.2% (market size)
- Short-run (10 year) growth by further ≈ 0.2% (trapped factors)
Quantitatively important (equivalent to 16% ↑ in consumption)
GE effects magnify the impacts we find in micro data
Any evidence for trapped factor mechanism?
Looks at heterogeneity of Chinese import effect
• We want to capture the firm specific ‘trapped’ capacity to
innovate
• Two measures of ‘trapped factors’
– Industry wage premia (estimated via UK LFS)
– Measured firm-level TFP (can be show to incorporate
trapped factors)
• We allow China effect to be heterogeneous across
firms/industries depending on these proxies
The heterogeneity of the China treatment effect
consistent with trapped factor mechanism
Change Chinese Imports
0.321***
(0.102)
Industry wage premia
Change Chinese Imports
* Industry Wage premia
TFPt-5
0.202***
(0.092)
-0.411***
(0.069)
2.467***
(1.171)
0.284*
(0.157)
-0.287***
(0.050)
1.464***
(0.462)
Change Chinese Imports
*TFPt-5
Number of units
Number of clusters
Number of Observations
-2.466***
(0.848)
8,480
1,578
30,277
8,480
1,578
30,277
5,014
1,148
5,014
1,148
14,500
14,500
Is there something specific about China?
No: similar to all low-wage countries
Δln(Patents)
Dependent variable:
Change in Chinese
0.182***
(0.074)
Imports
Change in Non-China
Low Wage Imports
Change in All Low
Wage country Imports
Change in High Wage
Country Imports
Change in World
Imports
Observations
0.063
(0.125)
0.182***
(0.073)
0.152
(0.128)
0.106***
(0.040)
0.003
(0.019)
29,062
29,062
29,062
0.004
(0.019)
29,062
Low wage countries list taken from Bernard et al (2006). Defined as countries <5%
GDP/capita relative to the US 1972-2001.
Chinese imports normalized by domestic production
The lawyer effect?
Maybe firms just patenting more to protect IP?
So investigate this in three ways:
• R&D: seem to spend more on innovation
• Cites/patents: should drop if more marginal ideas patented.
We find the opposite
• Timing of patents: if this is simply a legal response should
happen immediately (or in advance), while it is an innovation
response more likely to be lagged (which is what we find,
Table A14)
What about offshoring instead?
Is effect all driven by firms offshoring low value inputs to China?
Investigate this by generating a Chinese offshoring proxy
(based on Feenstra-Hansen, 1999)
• Weight Chinese imports/apparent consumption by SIC 4digit input-output tables (US 2002 tables)
• Proxies how much Chinese imports are increasing for each
industry averaged across its sourcing industries
Table 1D: Offshoring matters for more
compositional measures (IT and TFP) but not
innovation
Dependent Variable
Change in Chinese Imports
Offshoring (ΔChinese Imports
in source industries
Observations
ΔPATENTS
0.313***
(0.100)
0.173
(0.822)
30,277
Δln(IT/N)
0.279***
(0.080)
1.685***
(0.517)
37,500
ΔTFP
0.189***
(0.082)
1.396***
(0.504)
30,608
Note: We also find bigger jobs shakeout for firms who have branches in China
The IPod effect: innovate because of low
costs? No, imports reduce industry profits
Dependent Variable:
Δln(Employment)
Δln(EU
Producer
Prices)
Δln(Profits
/Sales)
Change in Chinese
Imports
-0.411***
(0.133)
-0.453**
(0.217)
-0.112**
(0.052)
Observations
8,788
259
5,372
Industry-country pairs
1,913
131
2,295
Aggregation
SIC4
SIC2
SIC4
2005-1996
2006-1996
2007-2000
Years
Notes: Estimation by OLS in 5 year long differences, SE clustered by industrycountry pair, country-year dummies included,
Is this just exporting to China?
No exporting per se has little effect
Dependent Variable
Change in Chinese Imports
Change in Exports to China
Number of Observations
ΔPATENTS
0.322***
(0.100)
-0.243
(0.200)
30,277
Δln(IT/N)
0.361***
(0.076)
0.028
(0.118)
37,500
Δln(TFP)
0.254***
(0.072)
-0.125
(0.126)
292,167
Conclusions
Empirics
• Find trade-induced increases in innovation, IT and TFP
• Occurs within and between plants and firms
• Relatively large and growing: China “accounts” for ~15% of
increase in aggregate IT, patents and TFP and rising
• Other low-wage countries trade similar effect, but high-wage
countries trade appears to have no effect
Model
• Trapped-factors model seems to be best fit
– firms have trapped factors that keep them in the industry
– so innovate new products to escape competition
Back Up
Trapped Factor Model
• Set Up
– Endogenous Growth with North (R&D performing high
tech) and South
– Consider that in short run some factors are trapped in a
firm (specific due to sunk costs irreversibilities)
– Parameter with Northern barriers to Southern trade
import changes over time. Consider a shock to this
• Results
– Standard result that lowering trade barriers increases
innovation/growth through market size effect
– Trapped factors re-deployed from production to
innovation sector in firms affected by shock. This gives
an extra short-run kick to innovation
Table 1B: Include SIC-3 trends
Dependent variable:
Change in Chinese
Imports
Number of Units
Number of country by
industry clusters
Observations
Δln(PATEN
TS)
0.191*
(0.102)
8,480
Δln(IT/N)
ΔTFP
0.170**
(0.082)
22,957
0.128**
(0.053)
89,369
1,578
2,816
1,210
30,277
37,500
292,167
Table A3: Regressing 2000-1995 changes
on 2000 Quota IV, Industry Level
QUOTA
Observatio
ns
Δln
(PATENTS)
Δln(TFP)
Δln(Output/
Labor)
Δln(Capital/
Labor)
Δln(Labor)
Δln(Wages)
-0.263
0.010
0.006
0.059
-0.053
0.032
(0.195)
(0.023)
(0.041)
(0.071)
(0.035)
(0.023)
203
115
115
114
113
116
Table 8: Initial Conditions IV
Dependent Variable
Method:
Change in Chinese
Imports
Initial Condition IV
First-stage F-Statistic
Sample period
Number of Units
#of industry clusters
Observations
(1)
(2)
(3)
Δln
Δln
Δln(IT/N)
(PATENTS) (PATENTS)
OLS
IV
OLS
0.321***
0.495**
0.361***
(0.224)
(0.117)
(0.106)
95.1
(4)
(5)
(6)
Δln(IT/N)
ΔTFP
ΔTFP
IV
0.593***
(0.252)
OLS
0.257***
(0.087)
IV
0.507*
(0.283)
38.7
2005-1996
2005-1996
2007-2000
2007-2000
8,480
304
30,277
8,480
304
30,277
22,957
371
37,500
22,957
371
37,500
14.5
20051996
89,369
354
292,167
2005-1996
89,369
354
292,167
Summary Statistics: 12 Country Panel
Austria
Denmark
Finland
France
Germany
Ireland
Italy
Norway
Spain
Sweden
Switzerland
United Kingdom
Computer
Intensity
0.50
0.69
0.59
0.55
0.52
0.63
0.55
0.72
0.49
0.60
0.60
0.64
Employment
352
148
173
243
435
196
222
131
175
161
179
270
Number of
Sites
1067
510
677
2911
3679
350
2630
362
1018
1168
1346
3567
Output Quotas rather than Input Quotas
matter most
Dependent Var.
Method
Output Quota
Removal
Δln(IT/N)
Δln(IT/N)
Means
Reduced Form
1.284***
(0.172)
Reduced Form
0.133***
(0.045)
(standard dev)
0.094
(0.232)
0.311
(0.342)
0.031
(0.041)
Input Quota
Removal
Observations
2,891
2,891
Notes: Input quotas are calculated using the Feenstra-Hansen method but using
quotas instead of import flows. 489 SIC4 clusters.
List of low wage countries
Albania
Angola
Bangladesh
Benin
Bolivia
Burkina Faso
Burundi
Cambodia
Cameroon
Central African Rep
Chad
China
Comoros
Congo
Djibouti
Egypt
Equatorial Guinea
Ethiopia
Gambia
Ghana
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
India
Indonesia
Ivory Coast
Lao People's Dem. Rep.
Madagascar
Malawi
Mali
Mauritania
Mongolia
Morocco
Mozambique
Nepal
Nicaragua
Niger
Nigeria
Pakistan
Papua New Guinea
Philippines
Rwanda
Senegal
Sierra Leone
Sri Lanka
Sudan
Suriname
Syria
Tanzania
Togo
Uganda
Viet Nam
Yemen
Zambia
Zimbabwe
Low wage countries list taken from Bernard, Jensen and Schott (2006).
They defined these as countries with less than 5% average per capita
GDP relative to the Unites States in the period between 1972-2001.
Tab A10 China associated with skill upgrading
(wage bill share of college educated in UK)
Sample
All
All
All
Textiles
&
Apparel
Textiles
&
Apparel
Method
OLS
OLS
OLS
OLS
IV
Change in Chinese
0.144**
0.099**
0.166**
0.277***
Imports
(0.035)
(0.043)
(0.030)
(0.053)
Change in IT intensity
F-test of excluded
IVs
Observations
0.081**
0.050*
(0.024)
(0.026)
9.21
204
204
204
48
48
SE are clustered by 74 SIC3 industries; 2006-1999, UK LFS data, IV is height of quota pre-WTO;
all columns control for year dummies, regressions weighted by industry employment in 1999
Table A11: INDUSTRY SWITCHING
Dependent
variable
ΔChinese
Imports
IT intensity (t-5)
Plant
Switches
Industry
0.138***
(0.050)
Plant Switches
Industry
0.132***
(0.050)
-0.018**
(0.008)
Switched Industry
Employment
growth
Observations
32,917
-0.002
(0.006)
32,917
Δln(IT/N)
Δln(IT/N)
0.466***
(0.083)
0.025**
(0.012)
0.023*
(0.012)
32,917
32,917
“Switched Industry” is a dummy if a plant switched its main
four digit industry over a five Year period. SE clustered by
country*industry pair. 2000-2007.
Tab A14 Dynamics - Patent effect largest at long lags
PANEL A: PATENTS, Δln(PATENTS)
5-year lag of Chinese Imports Change
∆IMP
CH
t −5
(1)
0.328***
(0.110)
4-year lag of Chinese Imports Change
(2)
(3)
(4)
(5)
∆IMP
3-year lag of Chinese Imports Change
0.402***
(0.120)
∆IMP
CH
t −3
2-year lag of Chinese Imports Change
0.333***
(0.113)
∆IMPt CH
−2
1-year lag of Chinese Imports Change
0.074
(0.136)
0.314***
(0.102)
∆IMPt CH
−1
Contemporaneous Chinese Imports Change
CH
Number of country-industry pairs
Number of Firms
Observations
1,578
8,480
30,277
1,578
8,480
30,277
1,578
8,480
30,277
(7)
0.013
(0.163)
0.280*
(0.149)
-0.005
(0.178)
0.394***
(0.110)
CH
t −4
∆IMPt
(6)
1,578
8,480
30,277
1,578
8,480
30,277
0.321***
(0.102)
1,578
8,480
30,277
-0.069
(0.145)
0.203
(0.163)
1,578
8,480
30,277
Table A13 Results on IT appear broadly robust to
using other ICT diffusion measures
Databases
Growth in Chinese
Import Share
0.002
(0.070)
Highest Quintile
Growth in Chinese
Import Share
Quintile 4
0.030**
(0.010)
Quintile 3
0.043***
(0.010)
Obs.
0.013***
(0.005)
24,741
0.034**
(0.014)
0.021
(0.013)
0.006
(0.005)
-0.008
(0.013)
0.014***
(0.005)
0.010**
(0.005)
0.024***
(0.011)
24,741
0.249***
(0.083)
0.040
(0.034)
0.020**
(0.010)
Quintile 2
Groupware
ERP
24,741
24,741
-0.018
(0.013)
24,741
24,741
Note: All changes in long (5-year) differences. Includes country, year and site-type
controls. All standard-errors clustered by country and SIC-4 cell
Table A4 Controlling for lagged technology
Dep. variable:
Quotas removal
*I(year>2000)
Include lagged dependent
variable(t-5)?
IV lagged dependent variable?
Years
Number of units
Number of industry clusters
Observations
(1)
Δln(PATENTS)
(2)
Δln(PATENTS)
(3)
ΔTFP
(4)
ΔTFP
0.207**
(0.098)
0.490***
(0.157)
0.201***
(0.038)
0.204***
(0.047)
No
No
2005-1995
675
104
6,075
Yes
Yes
2005-1995
675
104
6,075
No
No
2005-1995
675
104
3,107
Yes
Yes
2005-1995
675
104
3,107
Find Cites/Patents constant as Chinese imports
rise – so no evidence patent quality falling
Dependent variable
Growth in Chinese
Imports
Cites/
Patent
Cites/
Patent
Cites/
Patent
Log(1+Cites/
Patent)
OLS
OLS
OLS
OLS
0.090
(0.242)
0.027
(0.843)
0.082
(0.242)
0.090
(0.242)
Log (Patents)
0.020***
(0.007)
4 digit industry controls
Yes
n/a
n/a
n/a
Firm fixed effects
No
Yes
Yes
Yes
Country-year fixed
effects
Yes
Yes
Yes
Yes
21,273
21,273
21,273
21,273
Observations
SE are clustered by firm