1 Trade Liberalization and Embedded Institutional Reform Evidence from Chinese Exporters Amit K Khandelwal Columbia Business School Peter K Schott Yale School of Management ShangJin Wei Columbia Business School ID: 768271
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1 Trade Liberalization and Embedded Institutional Reform: Evidence from Chinese Exporters Amit K. Khandelwal, Columbia Business School Peter K. Schott, Yale School of Management Shang-Jin Wei, Columbia Business School
Motivation Institutions 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 firmsTrade barriers distort resource allocation along both “intensive” and “extensive” marginsInstitutions that manage trade barriers can cause additional distortionsKey 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 governmentOur 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 licensesUse 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%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 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)4
Outline Auction-allocation model of quota licenses Data and Identification Strategy Evidence of misallocation“Political allocation” and counterfactual exerciseConclusion5
Overview of Auction-Allocation ModelSame basic structure as in Melitz /Chaney Two 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 a od > 0 imposes a disproportionate penalty on high productivity (i.e., high j ) firms Analytical solutions to model not possible when a od > 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 removedLow-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)7
OutlineAuction-allocation of quota licenses Data and Identification Strategy Evidence of misallocation “Political allocation” and counterfactual exerciseConclusion8
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 2001We focus on the final phaseChinese 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)9
Aggregate Chinese Textile & Clothing Exports 10 Notes: Quota-bound = any export constrained by a quota; quota-free = other textile and clothing goods not bound by quotas Quota-free exports rise 29% in 2005Quota-bound exports rise 119% in 2005QuotasRelaxedQuota-BoundQuota-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 CanadaObserve 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 EU11
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 countriesThe remaining 359 HS8 products are our sampleDifference-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 quotas12
Quota-Bound vs Quota-Free Compare treatment and control groups pre- and post-reform SOE share differs substantially ex ante, but not ex post132002200320042005 (1) (2) (3) (4) Quota- Bound hd 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-squared0.02 0.02 0.02 0.00
Regression Specification Where D Yhdt is Change in market share of incumbent SOEsChange in market share of privately owned entrants Etc.Just report α3: quota-bound vs quota free in 2004-5 versus 2003-4Full regression results in appendixAlso do “placebo” diff-in-diffs for prior year, i.e., 2003-4 versus 2002-3Also add country-product FEs to control for underlying trends14
OutlineAuction-allocation of quota licenses Identification Strategy Evidence of misallocation “Political allocation” and counterfactual exerciseConclusion15
Decompositions (DY) Quantity market share changes By margins of adjustment, ownership Examine quantity growth to avoid price effectsCan’t 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 averagesPrice changes By margins of adjustment, ownership16
Margins 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 17
18 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)MarginAllSOEDomesticForeign Incumbents -0.122 Net Entry Adders 0.116 New Exporters 0.037 Exiters -0.031 Total Net Entry 0.122 Total 0.000 Notes: Bold indicates statistical significance at conventional levels.
19 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)MarginAllSOEDomestic Foreign Incumbents -0.122 Net Entry Adders 0.116 New Exporters 0.037 Exiters -0.031 Total Net Entry 0.122 Total 0.000 Notes: Bold indicates statistical significance at conventional levels.
20 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)MarginAllSOEDomesticForeign Incumbents -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 0.055 Notes: Bold indicates statistical significance at conventional levels.
Decompose Change in Market Shares(Summary of Diff-in-Diff Terms from Regression ) 21 Difference-in-Differences(Quota-Bound vs Quota-Free, 2004-05 vs 2003-04)MarginAllSOEDomestic Foreign Incumbents -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 0.055 Notes: Bold indicates statistical significance at conventional levels.
22 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)MarginAllSOEDomestic Foreign Incumbents -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 0.055 Notes: Bold indicates statistical significance at conventional levels.
23 Pre-Reform “Placebo” Market-Share Decomposition Pre-Reform Difference-in-Differences (Quota-Bound vs Quota-Free, 2003-04 vs 2002-03)MarginAllSOEDomesticForeign Incumbents -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 -0.018 Notes: Bold indicates statistical significance at conventional levels.
24 Each line is the lowess -smoothed relationship between initial market share and subsequent change
25 Quota relationships are steeper, especially for SOEs
Price Changes Before/After Quota Removal 26 Quota-Bound Exports
Decompose 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 priceProduct-country price changeΔOverall = ΔIncumbents + ΔNet Entrants27Export Price Decomposition
Distribution of Prices, by Margin 28
Distribution of Prices, by Margin(Comparison Groups) 29
Distribution of Prices, by Margin(Comparison Groups) 30
31 Decompose Price Response Difference-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 -0.008 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
32 Decompose Price Response Difference-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 -0.008 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 D Total = D Incumbents + D Entrants - D ExitersPrice change holding market share fixed
33 Decompose Price Response Difference-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 -0.008 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
34 Pre-Reform “Placebo” Diff-in-Diff (Prices) Pre-Reform Difference-in-Differences (Quota-Bound vs Quota-Free, 2003-04 vs 2002-03)MarginAllSOEDomesticForeign Incumbents (I) 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 checkApproach is similar to Hummels and Klenow (2005), Khandelwal (2010), Hallak and Schott (2011)Find similar results….35
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 productsLog quality isQuality-adjusted prices:
Decompose Quality-Adjusted Price Response 37 Difference-in-Differences (Quota-Bound vs Quota-Free, 2004-05 vs 2003-04)MarginAllSOEDomesticForeign Incumbents (I) 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) 38 Pre-Reform Difference-in-Differences (Quota-Bound vs Quota-Free, 2003-04 vs 2002-03)MarginAllSOEDomestic Foreign Incumbents (I) 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 addedCapital coefficient = 1 - labor coefficientAmong textile/clothing exportersAverage SOEs is 1/4 to 1/3 as productive as the average domestic and foreign firm, respectivelyConsistent with literature39
Coarse, Back-of-Envelope Productivity Calculation40
41 Ownership Mean TFP Relative Market Share Change TFP ChangeSOEs1.57-0.147-0.231Private Enterprises3.19 0.092 0.293 Foreign Enterprises 2.73 0.055 0.150 Overall 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 Coarse, Back-of-Envelope Productivity Calculation
OutlineAllocation of quota licenses via an auction MFA Background, Identification Strategy Evidence of misallocation “Political allocation” and counterfactual exerciseConclusion42
Decomposing Productivity Gains 43 Political AllocationAuction AllocationNo Quota We want to decompose the overall productivity gain from quota removal into two parts
Decomposing Productivity Gains 44 Political AllocationAuction Allocation No Quota We want to decompose the overall productivity gain from quota removal into two parts Part due to removal of licensing regime
Decomposing Productivity Gains 45 Political AllocationAuction Allocation No Quota We want to decompose the overall productivity gain from quota removal into two parts Part due to removal of licensing regime Part due to removal of quota
Decomposing Productivity Gains 46 Political AllocationAuction AllocationNo Quota We want to decompose the overall productivity gain 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 47 Choose parameters of the no-quota scenarioElasticity of substitution σ=4 (from Broda et al. 2006)Country sizesFixed and variable trade costsLog 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 48
Numerical Solutions for Auction-Allocation Scenario 49 Use 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 removedSolve for endogenous license fee that clears the marketThis license price is ~10% of the average price of an exporterRe-compute aggregate export TFP
TFP vs Market Share, No Quota vs Auction Allocation 50Disproportionate 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 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 shareDecompose aggregate price decline between political allocation and “no quota” allocation as we did in empirical tablesCalculate contribution of price decline attributed to net extensive marginChoose ρ to match observed 67.5% extensive-margin contribution to quality-adjusted price decline (at ρ=0.15) 51
Political Allocation Market Share 52 r = 1
Political Allocation Market Share53 r = 0.95
Political Allocation Market Share54 r = 0.85
Political Allocation Market Share55 r = 0.75
Political Allocation Market Share56 r = 0.65
Political Allocation Market Share57 r = 0.55
Political Allocation Market Share58 r = 0.45
Political Allocation Market Share59 r = 0.35
Political Allocation Market Share60 r = 0.25
Political Allocation Market Share61 r = 0.15
Political Allocation Market Share 62 r = -0.15
Political Allocation Market Share 63 r = -0.25
Political Allocation Market Share 64 r = -0.35
Political Allocation Market Share 65 r = -0.45
Political Allocation Market Share 66 r = -0.55
Political Allocation Market Share 67 r = -0.65
Political Allocation Market Share 68 r = -0.75
Political Allocation Market Share 69 r = -0.85
Political Allocation Market Share 70 r = -1.00
Decomposing the Overall Productivity Gain Weighted-Average TFP 4.21 3.431.50Political 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 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 72
Sensitivity of Numerical Solutions 73 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.
ConclusionsContributions 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 economyAggregate productivity gain from quota removal larger than what one would predict solely from trade liberalization74