1 trade liberalization and embedded institutional reform: evidence from chinese exporters amit k....

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Trade Liberalization and Embedded Institutional Reform: Evidence from Chinese Exporters

Amit K. Khandelwal, Columbia Business SchoolPeter K. Schott, Yale School of ManagementShang-Jin Wei, Columbia Business School

Motivation• Institutions that distort the efficient allocation of resources can have

sizeable effects on aggregate outcomes– Hsieh and Klenow (2009): aggregate Chinese productivity nearly doubles

if capital and labor are properly allocated among existing firms

• Trade barriers distort resource allocation along both “intensive” and “extensive” margins

• Institutions that manage trade barriers can cause additional distortions

• Key idea: productivity gains from trade liberalization may be larger than expected if institutions that manage the trade barriers are inefficient:– Gain from removal of the “embedded institution”– Gain from removal of the trade barrier itself

2

China and The Multifiber Arrangement (MFA)

• This paper examines the distortions associated with institutions that manage quota licensing

• The global MFA restricted Chinese exports of textile and clothing to the US, EU and Canada until 2005– Quotas were assigned by the Chinese government

• Our question: were quotas assigned to the most productive firms?– Comparison of quota-bound vs quota-free goods before/after 2005

suggests entrants are more productive than incumbents, i.e., the most productive firms were not allocated licenses

• Use key feature of empirical analysis to simulate “political allocation” and compute contribution of eliminating licensing to overall gain– Eliminating actual institution accounts for ~70% of overall gain– Replacing actual institution with auction raises productivity ~13%

3

Related Literature

• Growing literature on misallocation – Hsieh and Klenow (2009), Brandt et al. (2010), Dollar and Wei (2007),

Restuccia and Rogerson (2010), Alfaro et al. (2008)

• Extensive-margin misallocation– Banerjee and Duflo (2005), Banerjee and Moll (2010), Buera et al.

(2010), Chari (2010)

• Inefficient implementation of quotas; studies of MFA/ATC – Krishna and Tan (1998), Anderson (1985)– Harrigan & Barrows (2009), Brambilla et al (2010), Bernhofen et al.

(2011)

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Outline

• Auction-allocation model of quota licenses

• Data and Identification Strategy

• Evidence of misallocation

• “Political allocation” and counterfactual exercise

• Conclusion

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Overview of Auction-Allocation Model

• Same basic structure as in Melitz/Chaney– Two countries, one industry– Monopolistic competition, CES utility– Firms are heterogeneous in productivity (j)– Exporting requires fixed and iceberg trade costs (t)

• Firms optimize under quantity restriction – Quota license fee is like a per-unit trade cost (aod) to export from origin

country o to destination country d (Irrazabal et al. 2010)

• Price of variety with productivity j:

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aod > 0 imposes a disproportionate penalty on high productivity (i.e., high j) firms

Analytical solutions to model not possible when aod > 0

Three Empirical Implications of Quota Removal

• Export growth following quota removal is driven by the intensive margin– High productivity firms are most constrained under quotas– Their exports jump disproportionately as quotas are removed

• Low-productivity enter because license fee goes to zero when quotas are removed– (Depends on TFP distribution: if density of very high TFP firms is high enough, there will be

no entry and the lowest TFP firms will exit)

• Incumbents and entrants make opposing contributions to export prices– Incumbents’ prices fall as the license fee goes to zero– But removal of license fee allows high price (i.e., low-productivity) firms

to enter– (Will come back to quality variant of model later)

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Outline

• Auction-allocation of quota licenses

• Data and Identification Strategy



• Conclusion

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Quotas Under the MFA/ATC

• During the Uruguay Round (early 1990s), the US, EU and Canada committed to a schedule for withdrawing textile and clothing quotas in four phases– At the start of 1995, 1998, 2002 and 2005

• China’s quotas on goods in first three phases were relaxed in early 2002 following its entry into the WTO in late 2001

• We focus on the final phase

• Chinese quotas were allocated by the government – Details are scarce but predominantly on the basis of “past performance”– Black-market sales of licenses complicates our analysis; appears to be a

bigger issue during the 1980s than our sample period (Moore, 2002)– (More about this later)

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Aggregate Chinese Textile & Clothing Exports

10Notes: Quota-bound = any export constrained by a quota; quota-free = other textile and clothing goods not bound by quotas

2000 2001 2002 2003 2004 20052.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

China's Quota vs Quota-Free Exports to US/EU/Can

Quota-Free Quota-Bound

$ B

illio

n

Quota-free exports rise 29% in 2005Quota-bound exports rise 119% in 2005 Quotas

Relaxed

Quota-Bound

Quota-Free

Firm-Level Chinese Customs Data

• Value and quantity exported – By firm, HS8 product, destination country and year– Focus on 2003-2005 exports to US, EU and Canada

• Observe exporter’s ownership type– “SOE”: state-owned enterprise– “Domestic”: privately-owned domestic firm – “Foreign”: privately-owned foreign firm

• Create two sets of HS8-country (hd) groups : – Quota-bound: subject to quota until 2004 by subset of US/EU/Canada

• “Men’s cotton pajamas” to US/Canada– Quota-free: not subject to quota by subset of US/EU/Canada

• “Men’s cotton pajamas ” to EU

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Identification Strategy

• Sample– Start with 547 HS8 products are subject to quotas by US/EU/Canada– Drop the 188 of these that are subject to quotas by all three countries– The remaining 359 HS8 products are our sample

• Difference-in-differences comparison– Quota-bound (“treatment”) vs quota-free (“control”) for 2004-5 versus

the same difference for 2003-4– Changes in control group account for trends in textile-and-clothing

supply (e.g., privatization) or demand (e.g., preferences)– Attribute any differential response to the removal of quotas

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Quota-Bound vs Quota-Free

• Compare treatment and control groups pre- and post-reform– SOE share differs substantially ex ante, but not ex post

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2002 2003 2004 2005(1) (2) (3) (4)

Quota-Boundhd 0.084 *** 0.089 *** 0.090 *** -0.020 0.019 0.019 0.020 0.017

Constant 0.675 *** 0.596 *** 0.534 *** 0.421 ***

0.013 0.013 0.014 0.012

Observations 932 943 949 1,016 R-squared 0.02 0.02 0.02 0.00

Regression Specification

• Where DYhdt is – Change in market share of incumbent SOEs– Change in market share of privately owned entrants – Etc.

• Just report α3: quota-bound vs quota free in 2004-5 versus 2003-4– Full regression results in appendix

• Also do “placebo” diff-in-diffs for prior year, i.e., 2003-4 versus 2002-3• Also add country-product FEs to control for underlying trends

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Outline

• Auction-allocation of quota licenses

• Identification Strategy



• Conclusion

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Decompositions (DY)

• Quantity market share changes– By margins of adjustment, ownership– Examine quantity growth to avoid price effects

• Can’t aggregate quantity across HS8, so compute changes for each HS8-country pair and then average across pairs, by group

• The tables I show you will be these averages

• Price changes – By margins of adjustment, ownership

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Margins of Adjustment

• Intensive: – Incumbent: firm exports same HS8 to same country in both t-1 and t

(note: EU considered single country)

• Extensive– Exiter: firm exports HS8-country in t-1 but not t– Entrant: firm exports HS8-country in t but no exports in t-1– Adder: firm exports HS8-country in t but not t-1 AND was an exporter of

some other HS8-country in t-1

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Decompose Change in Market Shares(Summary of Diff-in-Diff Terms from Regression)

Difference-in-Differences(Quota-Bound vs Quota-Free, 2004-05 vs 2003-04)

Margin All SOE Domestic ForeignIncumbents -0.122

Net Entry

Adders 0.116

New Exporters 0.037

Exiters -0.031

Total Net Entry 0.122

Total 0.000Notes: Bold indicates statistical significance at conventional levels.

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Margin All SOE Domestic ForeignIncumbents -0.122

Net Entry

Adders 0.116

New Exporters 0.037

Exiters -0.031

Total Net Entry 0.122

Total 0.000Notes: Bold indicates statistical significance at conventional levels.

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Margin All SOE Domestic ForeignIncumbents -0.122 -0.106 -0.013 -0.003

Net Entry

Adders 0.116 -0.011 0.071 0.056

New Exporters 0.037 -0.003 0.035 0.005

Exiters -0.031 -0.027 -0.001 -0.003

Total Net Entry 0.122 -0.041 0.105 0.058

Total 0.000 -0.147 0.092 0.055Notes: Bold indicates statistical significance at conventional levels.


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Net Entry

Adders 0.116 -0.011 0.071 0.056

New Exporters 0.037 -0.003 0.035 0.005

Exiters -0.031 -0.027 -0.001 -0.003

Total Net Entry 0.122 -0.041 0.105 0.058


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Net Entry

Adders 0.116 -0.011 0.071 0.056

New Exporters 0.037 -0.003 0.035 0.005

Exiters -0.031 -0.027 -0.001 -0.003

Total Net Entry 0.122 -0.041 0.105 0.058


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Pre-Reform “Placebo” Market-Share Decomposition

Pre-Reform Difference-in-Differences(Quota-Bound vs Quota-Free, 2003-04 vs 2002-03)

Margin All SOE Domestic ForeignIncumbents -0.016 -0.001 0.006 -0.021

Net Entry

Adders 0.017 0.002 -0.005 0.020

New Exporters -0.024 -0.010 -0.013 -0.001

Exiters 0.024 0.024 0.015 -0.015

Total Net Entry 0.016 0.016 -0.003 0.003

Total 0.000 0.015 0.002 -0.018Notes: Bold indicates statistical significance at conventional levels.

-.8

-.6

-.4

-.2

0

Cha

nge

in M

ark

et S

har

e, 2

004

-5

0 .2 .4 .6 .8 1Market Share, 2004

Quota-Free SOE Quota-Free Domestic Quota-Free Foreign

Note: Market shares computed with respect to all firms in 2004.

Lines Generated by Lowess SmoothingChange in Market Share vs Initial Level

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Each line is the lowess-smoothed relationship between initial market share and subsequent

change

-.8

-.6

-.4

-.2

0

Cha

nge

in M

ark

et S

har

e, 2

004

-5

0 .2 .4 .6 .8 1Market Share, 2004

Quota-Free SOE Quota-Free Domestic Quota-Free Foreign

Quota-Bound SOE Quota-Bound Domestic Quota-Bound Foreign

Note: Market shares computed with respect to all firms in 2004.

Lines Generated by Lowess SmoothingChange in Market Share vs Initial Level

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Quota relationships are steeper, especially for

SOEs

Price Changes Before/After Quota Removal

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-.2

-.1

0.1

Per

cent

Quota-Free Exports Quota Exports

2002-3 2003-4 2004-5 2002-3 2003-4 2004-5

Note: Product-countries in first and ninety-ninth percentiles are dropped from each distribution.

By Group and YearAverage Price Change

Quota-Bound Exports

• Decompose change in the overall MFA price between 2004-5 by margin and compare with OTC– where {f,h,d,t} index {firm,product,country,year}

• Quantity-weighted avg log export price

• Product-country price change

ΔOverall = ΔIncumbents + ΔNet Entrants

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Export Price Decomposition

Distribution of Prices, by Margin

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0.2

.4.6

.8D

ensi

ty

-2 0 2 4Ratio

2005 Incumbents 2005 Entrants 2004 Exiters

First and ninety-ninth percentiles are dropped from each distribution.

By MarginDistribution of 2005 Quota-Bound Prices

Distribution of Prices, by Margin(Comparison Groups)

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0.2

.4.6

.8D

ensi

ty

-2 0 2 4Ratio



By MarginDistribution of 2004 Quota-Bound Prices

Distribution of Prices, by Margin(Comparison Groups)

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0.2

.4.6

.8D

ensi

ty

-2 -1 0 1 2 3Ratio



By MarginDistribution of 2004 Quota-Free Prices

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Decompose Price Response


Margin All SOE Domestic ForeignIncumbents (I)

Within -0.037 -0.023 -0.009 -0.005

Across -0.049 -0.028 -0.012 -0.008

Entrant (N) -0.069 -0.021 -0.050 0.002

Exiter (X) 0.051 0.022 0.028 0.000

Net Entry (N-X) -0.120 -0.044 -0.078 0.002

Total -0.206 -0.095 -0.100 -0.012

Extensive Share 0.582 0.459 0.786 -0.167

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Decompose Price Response



Within -0.037 -0.023 -0.009 -0.005

Across -0.049 -0.028 -0.012 -0.008

Entrant (N) -0.069 -0.021 -0.050 0.002

Exiter (X) 0.051 0.022 0.028 0.000

Net Entry (N-X) -0.120 -0.044 -0.078 0.002

Total -0.206 -0.095 -0.100 -0.012

Extensive Share 0.582 0.459 0.786 -0.167

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Pre-Reform “Placebo” Diff-in-Diff (Prices)



Within -0.018 -0.014 -0.004 0.000

Across 0.007 0.007 0.003 -0.003

Entrant (N) -0.019 -0.003 0.005 -0.021

Exiter (X) 0.027 0.048 -0.013 -0.008

Net Entry (N-X) -0.046 -0.051 0.018 -0.013

Total -0.058 -0.058 0.017 -0.017

Extensive Share 0.801 0.878 1.068 0.804

Quality Downgrading?

• Might expect prices to decline due to quality downgrading in response to quotas (Aw and Roberts 1986; Boorstein and Feenstra 1991; Harrigan and Barrows 2009)

• We see prices fall in the data, but declines are concentrated among privately owned entrants (assumed to be more productive)

• Nevertheless, we can compute quality-adjusted prices to check– Approach is similar to Hummels and Klenow (2005), Khandelwal (2010),

Hallak and Schott (2011)

• Find similar results….

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Quality-Adjusted Prices

• Put quality in CES preferences

• Quantity demanded for each variety

• Impose σ = 4, use dt fixed effects to capture price index/income, h fixed effect compares quantities and prices within products

• Log quality is

• Quality-adjusted prices:

Decompose Quality-Adjusted Price Response

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Within -0.055 -0.026 -0.011 -0.018

Across 0.001 -0.005 -0.002 0.007

Entrant (N) -0.072 -0.026 -0.029 -0.018

Exiter (X) 0.040 0.032 0.007 0.000

Net Entry (N-X) -0.112 -0.058 -0.036 -0.018

Total -0.166 -0.088 -0.049 -0.028

Extensive Share 0.675 0.653 0.736 0.639

Pre-Reform “Placebo” Diff-in-Diff (QA Prices)

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Within 0.018 0.013 -0.005 0.010

Across -0.018 -0.010 -0.001 -0.006

Entrant (N) 0.004 0.005 -0.005 0.004

Exiter (X) -0.007 -0.004 -0.002 -0.001

Net Entry (N-X) 0.011 0.009 -0.003 0.005

Total 0.012 0.012 -0.008 0.009

Extensive Share 0.932 0.768 0.320 0.581

Coarse, Back-of-Envelope Productivity Calculation

• Identify textile and clothing exporters in the Annual Survey of Industrial Production– (Match with trade data is imperfect)

• Calculate TFP of each firm assuming Cobb-Douglas, constant returns to scale – Labor coefficient is the share of wages in value added– Capital coefficient = 1 - labor coefficient

• Among textile/clothing exporters– Average SOEs is 1/4 to 1/3 as productive as the average domestic and

foreign firm, respectively– Consistent with literature

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40

0.5

11

.5D

ensi

ty

.125 .25 .5 1 2 4 8 16 32TFP

SOE Domestic Foreign

First and ninety-ninth percentiles are dropped from each distribution. Collective firms are excluded.

by Ownership TFP, Textile & Clothing Exporters

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Ownership Mean TFPRelative Market Share Change TFP Change

SOEs 1.57 -0.147 -0.231Private Enterprises 3.19 0.092 0.293Foreign Enterprises 2.73 0.055 0.150Overall 0.213

Multiply changes in market share by each ownership type’s mean TFP to gauge TFP gain from reallocation of 21.3%

(Calculation assumes homogenous firms within ownership type)

These numbers are from the market share table


Outline

• Allocation of quota licenses via an auction

• MFA Background, Identification Strategy



• Conclusion

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Decomposing Productivity Gains

43

Political Allocation

Auction Allocation

No Quota• We want to decompose the overall productivity gain

from quota removal into two parts


44


Auction Allocation


from quota removal into two parts– Part due to removal of licensing regime


45


Auction Allocation


from quota removal into two parts– Part due to removal of licensing regime– Part due to removal of quota


46


Auction Allocation


from quota removal into two parts– Part due to removal of licensing regime– Part due to removal of quota

• In order to do this, we use numerical solutions of the model to compute weighted-average firm productivity under three scenarios– No quota– Auction allocation– Political allocation: a perturbation of the

auction-allocation model that matches our empirical evidence of misallocation

Numerical Solutions for No-Quota Scenario

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• Choose parameters of the no-quota scenario• Elasticity of substitution σ=4 (from Broda et al. 2006)• Country sizes• Fixed and variable trade costs• Log Normal productivity distribution, LN(μ,q)

• Choose (μ,q), iceberg trade costs and ratio of export to domestic fixed cost to match:– Export size distribution– Share of Chinese and U.S. textile and clothing firms that export– U.S. and Chinese import penetration in each others’ markets

• Simulate productivity draws, compute cutoffs, total exports, market shares and prices

TFP vs Market Share Under No Quotas

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Numerical Solutions for Auction-Allocation Scenario

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• Use the no-quota scenario but impose the quota restrictiveness observed in data– Export quantities jump 161% in quota-bound versus quota-free goods

when quotas are removed

• Solve for endogenous license fee that clears the market– This license price is ~10% of the average price of an exporter

• Re-compute aggregate export TFP

TFP vs Market Share, No Quota vs Auction Allocation

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Disproportionate penalty on high-TFP firms

Numerical Solutions for Political-Allocation Scenario

• Firms have second, political draw – Correlation ρ with TFP

• Re-assign market shares from auction-allocation based on this draw– Assign highest market share to the most politically connected firm,

second most connected firm gets second highest share, etc.– Firm prices continue to based on true underlying productivity– Low TFP firms with high political draw get high market share

• Decompose aggregate price decline between political allocation and “no quota” allocation as we did in empirical tables

• Calculate contribution of price decline attributed to net extensive margin– Choose ρ to match observed 67.5% extensive-margin contribution to

quality-adjusted price decline (at ρ=0.15)

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Political Allocation Market Share

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r = 1


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r = 0.95


54

r = 0.85


55

r = 0.75


56

r = 0.65


57

r = 0.55


58

r = 0.45


59

r = 0.35


60

r = 0.25


61

r = 0.15


62

r = -0.15


63

r = -0.25


64

r = -0.35


65

r = -0.45


66

r = -0.55


67

r = -0.65


68

r = -0.75


69

r = -0.85


70

r = -1.00

Decomposing the Overall Productivity Gain

Weighted-Average TFP

4.21

3.43

1.50Political Allocation

Auction Allocation

No Quota

39% of total gain

71% of total gain

Moving from auction-allocation to no quotas increase aggregate productivity by 23%

Moving from political- to auction allocation increases aggregate productivity by 127%

Illegal Subcontracting?

• Unobserved illegal subcontracting can lead to over-estimation of the role of the extensive margin as former subcontractors enter under their own name

• But…– It is illegal– Little evidence in 2004 production data of exports>production– Entrants are small and numerous, whereas entering subcontractors

would likely be large– Majority of quota exporters in 2004 also export goods to non-quota

countries, but would they subcontract both?– Extensive-margin contribution is strong even among “processing”

exports where documentation is more stringent– Very few firms shrink or exit after quotas (even among SOEs), as firms

who had used subcontractors might be expected to

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Sensitivity of Numerical Solutions

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.2.4

.6.8

1

0 .2 .4 .6 .8 1Contribution of Extensive Margin

Relative Political-Allocation TFP

0.2

.4.6

.8

0 .2 .4 .6 .8 1Contribution of Extensive Margin

Contribution of Institutional Reform

Notes: Left panel displays weighted average firm TFP under political allocation as a share of weighted average firm TFP under auction allocation. Right panel traces out the share of overall productivity growth accounted for by institutional reform. Both quantities are plotted against the extensive margin’s contribution to the overall price decline when quotas are removed. Dashed vertical lines indicate the observed contribution of the extensive margin from Table 7.

Conclusions

• Contributions of paper

– Use margins of adjustment to infer misallocation of resources under quotas

– Use key features of data to provide coarse, back-of-the-envelope estimates of the aggregate consequences

– Emphasize “embedded” institutions’ ability to impose an additional drag on the economy

• Aggregate productivity gain from quota removal larger than what one would predict solely from trade liberalization

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1 trade liberalization and embedded institutional reform: evidence from chinese exporters amit k....

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