Motivation

Multi-product Firms and Trade Liberalization

1

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

1MotivationInstitutions that distort the efficient allocation of resources can have sizeable effects on aggregate outcomesHsieh 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 institutionGain from removal of the trade barrier itself22China 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 2005Quotas 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 gainEliminating actual institution accounts for ~70% of overall gainReplacing actual institution with auction raises productivity ~13%33Related LiteratureGrowing 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 misallocationBanerjee 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)

44OutlineAuction-allocation model of quota licenses

Data and Identification Strategy

Evidence of misallocation

Political allocation and counterfactual exercise

Conclusion

55Overview of Auction-Allocation ModelSame basic structure as in Melitz/ChaneyTwo countries, one industryMonopolistic competition, CES utilityFirms 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:

6

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

Analytical solutions to model not possible when aod > 06Three Empirical Implications of Quota RemovalExport growth following quota removal is driven by the intensive marginHigh productivity firms are most constrained under quotasTheir 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 pricesIncumbents prices fall as the license fee goes to zeroBut removal of license fee allows high price (i.e., low-productivity) firms to enter(Will come back to quality variant of model later)

77OutlineAuction-allocation of quota licenses

Data and Identification Strategy



Conclusion

88Quotas Under the MFA/ATCDuring the Uruguay Round (early 1990s), the US, EU and Canada committed to a schedule for withdrawing textile and clothing quotas in four phasesAt the start of 1995, 1998, 2002 and 2005

Chinas 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 performanceBlack-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)

99Aggregate Chinese Textile & Clothing Exports10Notes: Quota-bound = any export constrained by a quota; quota-free = other textile and clothing goods not bound by quotasQuota-free exports rise 29% in 2005Quota-bound exports rise 119% in 2005QuotasRelaxedQuota-BoundQuota-Free10Firm-Level Chinese Customs DataValue and quantity exported By firm, HS8 product, destination country and yearFocus on 2003-2005 exports to US, EU and Canada

Observe exporters ownership typeSOE: state-owned enterpriseDomestic: 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 Mens cotton pajamas to US/Canada Quota-free: not subject to quota by subset of US/EU/Canada Mens cotton pajamas to EU1111Identification StrategySampleStart with 547 HS8 products are subject to quotas by US/EU/CanadaDrop the 188 of these that are subject to quotas by all three countriesThe remaining 359 HS8 products are our sample

Difference-in-differences comparisonQuota-bound (treatment) vs quota-free (control) for 2004-5 versus the same difference for 2003-4Changes 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

1212Quota-Bound vs Quota-FreeCompare treatment and control groups pre- and post-reformSOE share differs substantially ex ante, but not ex post

132002200320042005(1)(2)(3)(4)Quota-Boundhd0.084***0.089***0.090***-0.0200.0190.0190.0200.017Constant0.675***0.596***0.534***0.421***0.0130.0130.0140.012Observations9329439491,016R-squared0.020.020.020.00

13Regression SpecificationWhere DYhdt is Change in market share of incumbent SOEsChange in market share of privately owned entrants Etc.

Just report 3: quota-bound vs quota free in 2004-5 versus 2003-4Full regression results in appendix

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

14

14OutlineAuction-allocation of quota licenses

Identification Strategy



Conclusion

1515Decompositions (DY)Quantity market share changesBy margins of adjustment, ownershipExamine quantity growth to avoid price effectsCant aggregate quantity across HS8, so compute changes for each HS8-country pair and then average across pairs, by groupThe tables I show you will be these averages

Price changes By margins of adjustment, ownership1616Margins of AdjustmentIntensive: Incumbent: firm exports same HS8 to same country in both t-1 and t (note: EU considered single country)

ExtensiveExiter: firm exports HS8-country in t-1 but not tEntrant: firm exports HS8-country in t but no exports in t-1Adder: firm exports HS8-country in t but not t-1 AND was an exporter of some other HS8-country in t-1

171718Decompose 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)MarginAllSOEDomesticForeignIncumbents-0.122Net Entry Adders0.116 New Exporters0.037 Exiters-0.031 Total Net Entry0.122Total0.000Notes: Bold indicates statistical significance at conventional levels.1819Decompose 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)MarginAllSOEDomesticForeignIncumbents-0.122Net Entry Adders0.116 New Exporters0.037 Exiters-0.031 Total Net Entry0.122Total0.000Notes: Bold indicates statistical significance at conventional levels.1920Decompose 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)MarginAllSOEDomesticForeignIncumbents-0.122-0.106-0.013-0.003Net Entry Adders0.116-0.0110.0710.056 New Exporters0.037-0.0030.0350.005 Exiters-0.031-0.027-0.001-0.003 Total Net Entry0.122-0.0410.1050.058Total0.000-0.1470.0920.055Notes: Bold indicates statistical significance at conventional levels.20Decompose Change in Market Shares(Summary of Diff-in-Diff Terms from Regression)21Difference-in-Differences(Quota-Bound vs Quota-Free, 2004-05 vs 2003-04)MarginAllSOEDomesticForeignIncumbents-0.122-0.106-0.013-0.003Net Entry Adders0.116-0.0110.0710.056 New Exporters0.037-0.0030.0350.005 Exiters-0.031-0.027-0.001-0.003 Total Net Entry0.122-0.0410.1050.058Total0.000-0.1470.0920.055Notes: Bold indicates statistical significance at conventional levels.2122Decompose 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)MarginAllSOEDomesticForeignIncumbents-0.122-0.106-0.013-0.003Net Entry Adders0.116-0.0110.0710.056 New Exporters0.037-0.0030.0350.005 Exiters-0.031-0.027-0.001-0.003 Total Net Entry0.122-0.0410.1050.058Total0.000-0.1470.0920.055Notes: Bold indicates statistical significance at conventional levels.2223Pre-Reform Placebo Market-Share DecompositionPre-Reform Difference-in-Differences(Quota-Bound vs Quota-Free, 2003-04 vs 2002-03)MarginAllSOEDomesticForeignIncumbents-0.016-0.0010.006-0.021Net Entry Adders0.0170.002-0.0050.020 New Exporters-0.024-0.010-0.013-0.001 Exiters0.0240.0240.015-0.015 Total Net Entry0.0160.016-0.0030.003Total0.0000.0150.002-0.018Notes: Bold indicates statistical significance at conventional levels.23

24Each line is the lowess-smoothed relationship between initial market share and subsequent change24

25Quota relationships are steeper, especially for SOEs25Price Changes Before/After Quota Removal26

Quota-Bound Exports26Decompose change in the overall MFA price between 2004-5 by margin and compare with OTCwhere {f,h,d,t} index {firm,product,country,year}

Quantity-weighted avg log export price

Product-country price change

Overall = Incumbents + Net Entrants27Export Price Decomposition

27Distribution of Prices, by Margin28

28Distribution of Prices, by Margin(Comparison Groups)29

29Distribution of Prices, by Margin(Comparison Groups)30

3031Decompose Price ResponseDifference-in-Differences(Quota-Bound vs Quota-Free, 2004-05 vs 2003-04)MarginAllSOEDomesticForeignIncumbents (I) Within-0.037-0.023-0.009-0.005 Across-0.049-0.028-0.012-0.008Entrant (N)-0.069-0.021-0.0500.002Exiter (X)0.0510.0220.0280.000Net Entry (N-X)-0.120-0.044-0.0780.002Total-0.206-0.095-0.100-0.012Extensive Share0.5820.4590.786-0.1673132Decompose Price ResponseDifference-in-Differences(Quota-Bound vs Quota-Free, 2004-05 vs 2003-04)MarginAllSOEDomesticForeignIncumbents (I) Within-0.037-0.023-0.009-0.005 Across-0.049-0.028-0.012-0.008Entrant (N)-0.069-0.021-0.0500.002Exiter (X)0.0510.0220.0280.000Net Entry (N-X)-0.120-0.044-0.0780.002Total-0.206-0.095-0.100-0.012Extensive Share0.5820.4590.786-0.167DTotal = DIncumbents + DEntrants - DExiters

Price change holding market share fixed3233Decompose Price ResponseDifference-in-Differences(Quota-Bound vs Quota-Free, 2004-05 vs 2003-04)MarginAllSOEDomesticForeignIncumbents (I) Within-0.037-0.023-0.009-0.005 Across-0.049-0.028-0.012-0.008Entrant (N)-0.069-0.021-0.0500.002Exiter (X)0.0510.0220.0280.000Net Entry (N-X)-0.120-0.044-0.0780.002Total-0.206-0.095-0.100-0.012Extensive Share0.5820.4590.786-0.1673334Pre-Reform Placebo Diff-in-Diff (Prices)Pre-Reform Difference-in-Differences(Quota-Bound vs Quota-Free, 2003-04 vs 2002-03)MarginAllSOEDomesticForeignIncumbents (I) Within-0.018-0.014-0.0040.000 Across0.0070.0070.003-0.003Entrant (N)-0.019-0.0030.005-0.021Exiter (X)0.0270.048-0.013-0.008Net Entry (N-X)-0.046-0.0510.018-0.013Total-0.058-0.0580.017-0.017Extensive Share0.8010.8781.0680.80434Quality 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 checkApproach is similar to Hummels and Klenow (2005), Khandelwal (2010), Hallak and Schott (2011)

Find similar results.

3535Quality-Adjusted PricesPut quality in CES preferences

Quantity demanded for each variety

Impose = 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:

36Decompose Quality-Adjusted Price Response37Difference-in-Differences(Quota-Bound vs Quota-Free, 2004-05 vs 2003-04)MarginAllSOEDomesticForeignIncumbents (I) Within-0.055-0.026-0.011-0.018 Across0.001-0.005-0.0020.007Entrant (N)-0.072-0.026-0.029-0.018Exiter (X)0.0400.0320.0070.000Net Entry (N-X)-0.112-0.058-0.036-0.018Total-0.166-0.088-0.049-0.028Extensive Share0.6750.6530.7360.63937Pre-Reform Placebo Diff-in-Diff (QA Prices)38Pre-Reform Difference-in-Differences(Quota-Bound vs Quota-Free, 2003-04 vs 2002-03)MarginAllSOEDomesticForeignIncumbents (I) Within0.0180.013-0.0050.010 Across-0.018-0.010-0.001-0.006Entrant (N)0.0040.005-0.0050.004Exiter (X)-0.007-0.004-0.002-0.001Net Entry (N-X)0.0110.009-0.0030.005Total0.0120.012-0.0080.009Extensive Share0.9320.7680.3200.58138Coarse, Back-of-Envelope Productivity CalculationIdentify 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 addedCapital coefficient = 1 - labor coefficient

Among textile/clothing exportersAverage SOEs is 1/4 to 1/3 as productive as the average domestic and foreign firm, respectivelyConsistent with literature

3939Coarse, Back-of-Envelope Productivity Calculation40

4041OwnershipMean TFPRelative Market Share Change TFP ChangeSOEs1.57-0.147-0.231Private Enterprises3.190.0920.293Foreign Enterprises2.730.0550.150Overall0.213Multiply changes in market share by each ownership types 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 Coarse, Back-of-Envelope Productivity Calculation41OutlineAllocation of quota licenses via an auction

MFA Background, Identification Strategy



Conclusion

4242Decomposing Productivity Gains43Political AllocationAuction AllocationNo QuotaWe want to decompose the overall productivity gain from quota removal into two parts43Decomposing Productivity Gains44Political AllocationAuction AllocationNo QuotaWe want to decompose the overall productivity gain from quota removal into two partsPart due to removal of licensing regime44Decomposing Productivity Gains45Political AllocationAuction AllocationNo QuotaWe want to decompose the overall productivity gain from quota removal into two partsPart due to removal of licensing regimePart due to removal of quota

45Decomposing Productivity Gains46Political AllocationAuction AllocationNo QuotaWe want to decompose the overall productivity gain from quota removal into two partsPart due to removal of licensing regimePart 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 scenariosNo quotaAuction allocationPolitical allocation: a perturbation of the auction-allocation model that matches our empirical evidence of misallocation

46Numerical Solutions for No-Quota Scenario47Choose parameters of the no-quota scenarioElasticity of substitution =4 (from Broda et al. 2006)Country sizesFixed and variable trade costsLog Normal productivity distribution, LN(,q)

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

Simulate productivity draws, compute cutoffs, total exports, market shares and prices47TFP vs Market Share Under No Quotas48

48Numerical Solutions for Auction-Allocation Scenario49Use the no-quota scenario but impose the quota restrictiveness observed in dataExport quantities jump 161% in quota-bound versus quota-free goods when quotas are removed

Solve for endogenous license fee that clears the marketThis license price is ~10% of the average price of an exporter

Re-compute aggregate export TFP

49TFP vs Market Share, No Quota vs Auction Allocation50

Disproportionate penalty on high-TFP firms50Numerical Solutions for Political-Allocation ScenarioFirms have second, political draw Correlation with TFP

Re-assign market shares from auction-allocation based on this drawAssign 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 productivityLow 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 marginChoose to match observed 67.5% extensive-margin contribution to quality-adjusted price decline (at =0.15)

5151Political Allocation Market Share52

r = 152Political Allocation Market Share53

r = 0.9553Political Allocation Market Share54








r = 0.1561

Political Allocation Market Share62r = -0.1562








Political Allocation Market Share70r = -1.0070Decomposing the Overall Productivity GainWeighted-Average TFP

4.21

3.43

1.50

Political AllocationAuction AllocationNo Quota39% of total gain71% of total gainMoving from auction-allocation to no quotas increase aggregate productivity by 23%Moving from political- to auction allocation increases aggregate productivity by 127%71Illegal Subcontracting?Unobserved illegal subcontracting can lead to over-estimation of the role of the extensive margin as former subcontractors enter under their own name

ButIt is illegalLittle evidence in 2004 production data of exports>productionEntrants are small and numerous, whereas entering subcontractors would likely be largeMajority 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 stringentVery few firms shrink or exit after quotas (even among SOEs), as firms who had used subcontractors might be expected to

7272Sensitivity of Numerical Solutions73

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 margins contribution to the overall price decline when quotas are removed. Dashed vertical lines indicate the observed contribution of the extensive margin from Table 7. 73ConclusionsContributions 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 liberalization7474

Motivation

Documents

Transcript of Motivation