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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.

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Presentation on theme: "Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, Diffusion and Productivity Nick Bloom (Stanford) Mirko Draca (Warwick) John."— Presentation transcript:

1 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

2 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

3 Low wage countries list from Bernard, Jensen and Schott (2006). Countries <5% GDP/capita of US 1972-2001. Low-wage % of imports in Europe and the US China All low wage

4 Clear political importance – for example tires trade war in 2010

5 Major election issue in 2012

6 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

7 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 Summary: we study the impact of Chinese imports on technology in Europe (2/2)

8 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

9 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 Seems to fit a ‘Trapped-Factor’ innovation model (Bloom, Romer, Terry & Van Reenen, 2014)

10 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)

11 Data Within plant/firm effects Reallocation effects between plants/firms Extensions & Robustness

12 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

13 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 IMP CH = (Chinese Imports/All Imports): Well measured annually at 4-digit SIC level Also use import penetration measures –Chinese imports/apparent consumption –Chinese imports/production

14 Chinese Imports into EU by SIC-2, 2000-2005 5-year change in export share, 2000 to 2005, for our sample Leather Apparel Furniture Toys Food Tobacco Chemicals Transport Fabricated metals Textiles

15 Data Within plant/firm effects Reallocation effects between plants/firms Extensions & Robustness

16 Basic Technology Equation Chinese import share patents, IT, R&D, TFP, management Fixed Effects f kt : country*time dummies Cluster at industry-by-country (jk) Estimate in (5-year) long differences Example: For IT i = plants (22,957) j = industries (366) k = countries (12) jk = 2,816 cells t = 2000,…,2007

17 (1)(2)(3) ΔlnPATENTΔln(IT/N)ΔTFPΔln(R&D)ΔManagement Method5 year diffs 3 year diffs Change in0.321***0.361** 0.257***1.213**0.891*** Chinese Imports (0.102)(0.076)(0.072)(0.549)(0.337) Sample period 2005-19962007-20002005-19962007-19962010-2004 # units8,48022,95789,3694591,576 # ind*cty clusters 1,5782,8161,210196579 Obs 30,27737,500292,1671,6263,607 Table 1A: Within Firm OLS Results 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.

18 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

19 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)

20 | % 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

21 Δln(IT/N) ΔChinese Imports Δln(IT/N) MethodOLSFirst StageIV ΔChinese Imports 1.284*** 1.851*** (0.172)(0.400) Quotas removal 0.088*** (0.019) Sample period2005-2000 Number of units2,891 industry clusters83 Observations2,891 Table 2A: IV estimates using changes in EU textile & clothing quotas - IT SE clustered by 4 digit industries, Country-year and site type dummies included. All columns using just textiles and apparel sample

22 ΔlnPATENTS Δln(TFP) MethodOLSIVOLSIV ΔChinese Imports 0.902*** (0.087) 1.629** (0.326) ΔChinese 1.160***1.864* Imports(0.377)(1.001) 1 st Stage F-stat 24.1 11.5 Sample period2005-1999 1999-2005 Units1,866 12,247 Industry clusters149 177 Observations3,443 20,625 Table 2A- Cont: IV estimates using changes in EU textile & clothing quotas – Patents and TFP SE clustered by 4 digit industries, Country-year dummies included

23 Maybe quota sectors had pre-existing trends? Table 3: Control for these & results robust (1)(2)(5)(6) Dependent Variable:Δln(PATENTS) ΔTFP Quotas removal 0.129**0.216**0.143***0.178*** (SIC4*year>=2001) (0.063)(0.105)(0.018)(0.037) Firm-specific trends? NoYesNoYes Sample period2005-1992 Number of units2,435 16,495 Number industry clusters 159 187 Observations 14,768 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)

24 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

25 Data Within plant/ firm effects Reallocation effects between plants/firms Extensions & Robustness

26 C) Employment Equation expect α n < 0 Employment growth If high TECH plants “protected” from Chinese imports then γ n > 0 expect δ n > 0

27 -.2 -.1 0.1 1234512345 Quintiles of initial IT Intensity EMPLOYMENT GROWTH BY INITIAL IT INTENSITY Bottom Quintile China Import Growth IT HIGH IT LOW Top Quintile China Import Growth Quintiles of initial IT Intensity Employment Growth

28 Dependent Variable:Δln(N) TECH Measure:PatentsITTFP Chinese Import Growth-0.434*** -0.379*** -0.377*** (0.136) (0.105) (0.096) Ln(pat stock/worker) at t-50.348*** (0.049) Ln(pat stock/worker) at t-5*1.434** Chinese imports growth(0.649) IT intensity (t-5)0.230*** (0.010) (IT/N) (t-5)*Chinese Imp Growth0.385** (0.157) Ln(TFP) at t-5 0.136*** (0.012) Ln(TFP) at t-5* 0.795*** Chinese import growth(0.307) Clusters1,3752,8161,210 Observations19,84437,500292,167 Table 4A: High Tech firms shed less jobs when faced with rising Chinese imports SE clustered by country- industry, all standard additional controls included

29 C) Survival Equation expect α s < 0 Survival If high TECH plants partially “protected” from effect of Chinese imports then γ s > 0 expect δ s > 0

30 Dependent Variable Survival TECH measure: patentsITTFP Change in Chinese Imports -0.079-0.182**-0.208*** (0.051)(0.072)(0.050) Ln(patent stock/worker t-5 ) *Change in Chinese Imports 0.288** (0.138) (IT/N) t-5 *Change in Chinese Imports 0.137 (0.112) ln(TFP t-5 ) *Change in Chinese Imports 0.110* (0.059) IT Intensity (IT/N) t-5 -0.002 (0.006) Ln(patent stock/worker t-5 ) -0.008 (0.009) Ln(TFP t-5 )-0.003 (0.003) Observations 7,98528,62460,883 Table 4B: High tech firms more likely to survive Chinese imports SE clustered by up to 2,863 country- industry pairs, lagged size & all standard additional controls

31 Magnitude calculation Table 6: Aggregate effect of trade on technology, 2000- 2007, % of Technology Measure that Chinese trade ‘accounts for’ MeasureWithin (%)Between (%)Exit (%)Total (%) Patents 5.16.72.113.9 IT Intensity 9.83.11.214.1 TFP 9.92.40.212.5 Notes: calculated for the regression sample using OLS coefficients

32 Dependent Variable:Δln(PATENTS)Δln(IT/N)Δln(TFP) Change in Chinese Imports 0.368*0.399***0.326*** (0.200)(0.120)(0.072) Sample period2005-19962007-2000 2005-1996 Observations6,8887,4095,660 Dependent Variable:Δln(PATENTS)Δln(IT/N)Δln(TFP) Change in Chinese Imports 0.171**0.361**0.164*** (0.082) (0.076) (0.051) Years2005-19962007-2000 2005-1996 Observations 30,27737,500241,810 Table A5: Comparison of Industry level results to firm results confirms simulated magnitudes Panel A: Industry-level regressions Panel B: Firm-level (firms allocated to single SIC4)

33 Data Within plant/firm effects Reallocation effects between plants/firms Extensions & Robustness

34 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

35 Dependent Variable:Δln(PAT) Baseline Worst case Lower Bound Change in Chinese 0.321*** 0.271** Imports(0.102) (0.098) Number of units8,480 8.732 Number of clusters1,578 1,662 Observations30,277 31,271 Table 7: Dynamic Selection - Worst Case Lower Bounds 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

36 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

37 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

38 Change Chinese Imports0.321*** 0.202*** 0.284*-2.466*** (0.102)(0.092)(0.157)(0.848) Industry wage premia -0.411*** (0.069) Change Chinese Imports 2.467*** * Industry Wage premia (1.171) TFP t-5 -0.287*** (0.050) Change Chinese Imports 1.464*** *TFP t-5 (0.462) Number of units8,480 5,014 Number of clusters1,578 1,148 Number of Observations 30,277 14,500 The heterogeneity of the China treatment effect consistent with trapped factor mechanism

39 Dependent variable:Δln(Patents) Change in Chinese 0.182***0.0630.182*** Imports (0.074)(0.125)(0.073) Change in Non-China 0.152 Low Wage Imports (0.128) Change in All Low 0.106*** Wage country Imports (0.040) Change in High Wage 0.0030.004 Country Imports (0.019) Change in World Imports Observations29,062 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 Is there something specific about China? No: similar to all low-wage countries

40 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)

41 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 4- digit input-output tables (US 2002 tables) Proxies how much Chinese imports are increasing for each industry averaged across its sourcing industries

42 Dependent VariableΔPATENTSΔln(IT/N)ΔTFP Change in Chinese Imports 0.313*** 0.279***0.189*** (0.100)(0.080)(0.082) Offshoring (ΔChinese Imports0.1731.685***1.396*** in source industries(0.822)(0.517)(0.504) Observations30,27737,50030,608 Table 1D: Offshoring matters for more compositional measures (IT and TFP) but not innovation Note: We also find bigger jobs shakeout for firms who have branches in China

43 Dependent Variable:Δln(Employment)Δln(EU Producer Prices) Δln(Profits /Sales) Change in Chinese-0.411***-0.453**-0.112** Imports(0.133)(0.217)(0.052) Observations8,7882595,372 Industry-country pairs1,9131312,295 AggregationSIC4SIC2SIC4 Years2005-19962006-19962007-2000 The IPod effect: innovate because of low costs? No, imports reduce industry profits Notes: Estimation by OLS in 5 year long differences, SE clustered by industry- country pair, country-year dummies included,

44 Dependent VariableΔPATENTSΔln(IT/N)Δln(TFP) Change in Chinese Imports 0.322***0.361***0.254*** (0.100) (0.076)(0.072) Change in Exports to China-0.2430.028-0.125 (0.200) (0.118)(0.126) Number of Observations 30,277 37,500292,167 Is this just exporting to China? No exporting per se has little effect

45 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

46 Back Up

47 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

48 Table 1B: Include SIC-3 trends Dependent variable: Δln(PATEN TS) Δln(IT/N)ΔTFP Change in Chinese 0.191*0.170**0.128** Imports (0.102)(0.082)(0.053) Number of Units8,48022,95789,369 Number of country by industry clusters 1,5782,8161,210 Observations30,27737,500292,167

49 Table A3: Regressing 2000-1995 changes on 2000 Quota IV, Industry Level Δln (PATENTS)Δln(TFP) Δln(Output/ Labor) Δln(Capital/ Labor)Δln(Labor)Δln(Wages) QUOTA-0.2630.0100.0060.059-0.0530.032 (0.195)(0.023)(0.041)(0.071)(0.035)(0.023) Observatio ns203115 114113116

50 Table 8: Initial Conditions IV (1)(2)(3)(4)(5)(6) Dependent Variable Δln (PATENTS) Δln (PATENTS) Δln(IT/N) ΔTFP Method:OLSIVOLSIVOLSIV Change in Chinese0.321***0.495**0.361***0.593*** 0.257***0.507* Imports (0.117) (0.224) (0.106)(0.252)(0.087)(0.283) Initial Condition IV First-stage F-Statistic 95.1 38.7 14.5 Sample period2005-1996 2007-2000 2005- 1996 Number of Units8,480 22,957 89,369 #of industry clusters304 371 354 Observations 30,277 37,500 292,167

51 Computer Intensity EmploymentNumber of Sites Austria0.503521067 Denmark0.69148510 Finland0.59173677 France0.552432911 Germany0.524353679 Ireland0.63196350 Italy0.552222630 Norway0.72131362 Spain0.491751018 Sweden0.601611168 Switzerland0.601791346 United Kingdom0.642703567 Summary Statistics: 12 Country Panel

52 Output Quotas rather than Input Quotas matter most Dependent Var.Δln(IT/N) Means MethodReduced Form (standard dev) Output Quota 1.284***0.133***0.094 Removal(0.172)(0.045)(0.232) Input Quota0.3110.031 Removal(0.342)(0.041) Observations2,891 Notes: Input quotas are calculated using the Feenstra-Hansen method but using quotas instead of import flows. 489 SIC4 clusters.

53 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.

54 SampleAll Textiles & Apparel Textiles & Apparel MethodOLS IV Change in Chinese0.144**0.099**0.166**0.277*** Imports(0.035)(0.043)(0.030)(0.053) Change in IT intensity0.081**0.050* (0.024)(0.026) F-test of excluded IVs 9.21 Observations204 48 Tab A10 China associated with skill upgrading (wage bill share of college educated in UK) 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

55 Dependent variable Plant Switches Industry Δln(IT/N) ΔChinese 0.138***0.132***0.466*** Imports (0.050) (0.083) IT intensity (t-5) -0.018** (0.008) Switched Industry 0.025** (0.012) 0.023* (0.012) Employment growth -0.002 (0.006) Observations 32,917 Table A11: INDUSTRY SWITCHING “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.

56 Tab A14 Dynamics - Patent effect largest at long lags

57

58 Table A13 Results on IT appear broadly robust to using other ICT diffusion measures Databases ERP Groupware Growth in Chinese Import Share 0.002 (0.070) 0.040 (0.034) 0.249*** (0.083) Highest Quintile Growth in Chinese Import Share 0.020** (0.010) 0.013*** (0.005) 0.034** (0.014) Quintile 4 0.030** (0.010) 0.006 (0.005) 0.021 (0.013) Quintile 3 0.043*** (0.010) 0.014*** (0.005) -0.008 (0.013) Quintile 2 0.024*** (0.011) 0.010** (0.005) -0.018 (0.013) Obs.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

59 Table A4 Controlling for lagged technology

60 Find Cites/Patents constant as Chinese imports rise – so no evidence patent quality falling Dependent variable Cites/ Patent Cites/ Patent Cites/ Patent Log(1+Cites/ Patent) OLS Growth in Chinese Imports 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 Yesn/a Firm fixed effects NoYes Country-year fixed effects Yes Observations 21,273 SE are clustered by firm


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