Scale Economies and the Structure of Trade and Industrial Policy

Transcription

1 Scale Economies and the Structure of Trade and Industrial Policy Ahmad Lashkaripour Volodymyr Lugovskyy Indiana University Purdue University, March / 58

2 An Old Question An old question dating back to Smith (1776), Mill (1848), Graham (1923), Sraffa (1926), What is the optimal design of trade/industrial policy? Which industries should governments protect/promote? 2. How large are the gains from trade/industrial policy? 2 / 58

3 What Determines Policy Outcome/Design? Historically, (i) comparative advantage and (ii) scale economies have been identified as the two pillars of trade/industrial policy. The welfare effects of policy from the perspective of workhorse trade models (ACR) % W i = ( e i,k ψ k % industry size i,k + 1 ) % import intensity ɛ i,k k k ψ k (scale elasticity): degree of scale economies ɛk (trade elasticity): degree of comparative advantage 3 / 58

4 What Determines Policy Outcome/Design? Historically, (i) comparative advantage and (ii) scale economies have been identified as the two pillars of trade/industrial policy. The welfare effects of policy from the perspective of workhorse trade models (ACR) % W i = ( e i,k ψ k % industry size i,k + 1 ) % import intensity ɛ i,k k k ψ k (scale elasticity): degree of scale economies ɛk (trade elasticity): degree of comparative advantage 3 / 58

5 Dominant Approach to Trade Policy Analysis To analyze trade/industrial policy we need estimates for ɛ k (trade elasticity) and ψ k (scale elasticity). There are an extensive set of estimates for ɛ k Broda and Weinstein (2006); Simonovska and Waugh (2014); Caliendo and Parro (2014); Soderbery (2015) But the scale elasticity is arbitrarily normalized 1. ψ k = 0 in PC setups (Caliendo and Parro, 2014) 2. ψ k = 1/ɛ k in MC setups (Ossa, 2014) 4 / 58

6 Dominant Approach to Trade Policy Analysis To analyze trade/industrial policy we need estimates for ɛ k (trade elasticity) and ψ k (scale elasticity). There are an extensive set of estimates for ɛ k Broda and Weinstein (2006); Simonovska and Waugh (2014); Caliendo and Parro (2014); Soderbery (2015) But the scale elasticity is arbitrarily normalized 1. ψ k = 0 in PC setups (Caliendo and Parro, 2014) 2. ψ k = 1/ɛ k in MC setups (Ossa, 2014) 4 / 58

7 Dominant Approach to Trade Policy Analysis To analyze trade/industrial policy we need estimates for ɛ k (trade elasticity) and ψ k (scale elasticity). There are an extensive set of estimates for ɛ k Broda and Weinstein (2006); Simonovska and Waugh (2014); Caliendo and Parro (2014); Soderbery (2015) But the scale elasticity is arbitrarily normalized 1. ψ k = 0 in PC setups (Caliendo and Parro, 2014) 2. ψ k = 1/ɛ k in MC setups (Ossa, 2014) 4 / 58

8 This Paper We compute the optimal design of and the gains from two types of policies for 32 major economies. industry-level tariffs industry-level production subsidies Key step: develop a methodology to jointly identify ψ k and ɛ k for 15 major industries in the WIOD. 5 / 58

9 This Paper We compute the optimal design of and the gains from two types of policies for 32 major economies. industry-level tariffs industry-level production subsidies Key step: develop a methodology to jointly identify ψ k and ɛ k for 15 major industries in the WIOD. 5 / 58

10 Main Findings 1. The optimal design of policy is extremely sensitive to the industry-level scale elasticity. 2. Industrial subsidies deliver greater gains than tariffs 0.9% vs. 2.9% gains in real GDP for the average economy 3. The gains form protectionist policies relatively favor low scale-intensive economies like Brazil, Greece, and Russia whereas further trade liberalization relatively favors high scale-intensive economies like Korea, Taiwan and the Netherlands. 6 / 58

11 Related Literature Empirical work on scale economies: Head and Riess (2000), Head and Mayer (2003), Davis and Weinstein (2003), Antweiler and Trefler (2002), Somale (2015), Costinot, Donaldson, Kyle and Williams (2016). Bartelme et al. (2017) Quantitative analyses of trade policy Markusen and Wigle (1989); Balistreri et al. (2011); Caliendo and Parro (2014); Costinot and Rodríguez-Clare (2014); Ossa (2014); Caliendo et al. (2015); Ossa (2016). Theory Costinot and Rodríguez-Clare (2014) Kucheryavyy et al. (2016) 7 / 58

12 Theoretical Framework 8 / 58

13 A Generic Trade Model N countries (index i, j) K industries (index k) Many firms (index ω) in each industry. General Utility aggregator across industries W i = U i (Q i,1,..., Q i,k ) General non-parametric cost function c ω,k (q, w i ) = c ω,k (q, w i ) + f ω,k (w i ) wi : vector of factor prices 9 / 58

14 A Generic Trade Model Firm ω in industry k sets price p i,k for output by solving max π ω,k = p ω,k q ω,k c ω,k (q ω,k, w i ) q Exports are subject to iceberg barriers: p ωi,k = τ ji,k p ω,i Total income: Y i = k ω p ω,kq ω,k Given Y i and {p ωi,k } the indirect utility can be stated as W i = V i (Y i, p ωi,k ) 10 / 58

15 Gains from Trade Policy d ln W i = Change in welfare in response to policy shock {dτ ji,k } ln Vi ln Y i dy i k,j,ω ( λ ωi,k d ln τ ji,k ψ ω,k d ln q ω,k + ln c ) ω,k d ln w j 1 + ψ ω,k ln w j Scale-driven effects are governed by the scale elasticity ψ ω,k ln q ω,k ln c ω,k 1 11 / 58

16 Gains from Trade Policy d ln W i = Change in welfare in response to policy shock {dτ ji,k } ln Vi ln Y i dy i k,j,ω ( λ ωi,k d ln τ ji,k ψ ω,k d ln q ω,k + ln c ) ω,k d ln w j 1 + ψ ω,k ln w j Scale-driven effects are governed by the scale elasticity ψ ω,k ln q ω,k ln c ω,k 1 11 / 58

17 Gains from Trade Policy d ln W i = Change in welfare in response to policy shock {dτ ji,k } ln Vi ln Y i dy i k,j,ω ( λ ωi,k d ln τ ji,k ψ ω,k d ln q ω,k + ln c ) ω,k d ln w j 1 + ψ ω,k ln w j Changes in international factor prices are governed by the trade elasticity ɛ ji,k = ln X ji,k/x ii,k ln τ ji,k 11 / 58

18 In the generic model ɛ ji,k and ψ ω,k can be variable and endogenous Two assumptions about (i) demand within industries, and (ii) entry = ɛ ji,k and ψ ω,k reduce to structural industry-level parameters. 12 / 58

19 Two Practical Assumptions A1. Nested CES demand structure within industries ( ) (ϑk +1) ( ) (θk +1) pωi,k Pji,k q ωi,k = ϕ ωi,k Q i,k P ji,k P i,k A1 can arise from nested-ces preferences or within-industry specialization à la Eaton and Kortum (2002). ϑ k is an industry-wide measure of firm market power θ k reflects the degree of international CA or taste for variety 13 / 58

20 Two Practical Assumptions A2. Free entry: p ω,k q ω,k = c ω,k (q ω,k, w i ) A2 is motivated by evidence in Hall (1989) and Rotemberg and Woodford (1999) that there are no significant pure profits in the US. We provide additional evidence for A2 based on firm-level trade. 14 / 58

21 A1 + A2 + profit-max. = ψ ω,k AC ω,k MC ω,k 1 = p ω,k MC ω,k 1 = p ω,k MR ω,k 1 = 1 ϑ k A2 (absent selection effects) = ɛ ji,k ln X ji,k/x ii,k ln τ ji,k = θ k 15 / 58

22 Step 1: Estimating ψ k and ɛ k 16 / 58

23 Estimating Equation Reformulate the demand structure specified by A1. (set θ k = ɛ k and ϑ k = 1/ψ k ) x ωi,k = ϕ ωi,k χ i,k p ɛ k ωi,k ( λω ji,k ) 1 ɛk ψ k χ i,k : industry-market FE ϕ ωi,k variety-specific demand shifter λ ω ji,k : within-national market share 17 / 58

24 Estimating Equation Reformulate the demand structure specified by A1. (set θ k = ɛ k and ϑ k = 1/ψ k ) x ωi,k = ϕ ωi,k χ i,k p ɛ k ωi,k ( λω ji,k ) 1 ɛk ψ k χ i,k : industry-market FE ϕ ωi,k variety-specific demand shifter λ ω ji,k : within-national market share 17 / 58

25 Estimating Equation Reformulate the demand structure specified by A1. (set θ k = ɛ k and ϑ k = 1/ψ k ) x ωi,k = ϕ ωi,k χ i,k p ɛ k ωi,k ( λω ji,k ) 1 ɛk ψ k χ i,k : industry-market FE ϕ ωi,k variety-specific demand shifter λ ω ji,k : within-national market share 17 / 58

26 Estimating Equation Reformulate the demand structure specified by A1. (set θ k = ɛ k and ϑ k = 1/ψ k ) x ωi,k = ϕ ωi,k χ i,k p ɛ k ωi,k ( λω ji,k ) 1 ɛk ψ k χ i,k : industry-market FE ϕ ωi,k variety-specific demand shifter λ ω ji,k : within-national market share 17 / 58

27 Estimating Equation Reformulate the demand structure specified by A1. (set θ k = ɛ k and ϑ k = 1/ψ k ) x ωi,k = ϕ ωi,k χ i,k p ɛ k ωi,k ( λω ji,k ) 1 ɛk ψ k χ i,k : industry-market FE ϕ ωi,k variety-specific demand shifter λ ω ji,k = Sales of Variety ωi,k Total Sales of all Varieties from Country j 17 / 58

28 Estimating Equation Reformulate the demand structure specified by A1. (set θ k = ɛ k and ϑ k = 1/ψ k ) x ωi,k = ϕ ωi,k χ i,k p ɛ k ωi,k ( λω ji,k ) 1 ɛk ψ k Key Features: Can be estimated with the vector of firm-level prices, p ω,ih, and sales, x ω,ih. Estimation does not require GE assumptions about cost structure and firm selection. 18 / 58

29 Data Universe of Colombian import transactions: Each observation identifies: (i) firm id, (ii) transaction date, and (ii) HS10 product. The data set reports: f.o.b. value, quantity, freight charge, and import tax price = f.o.b. price + freight + tax 19 / 58

30 Pooled Estimation Estimating equation in logs ln x ω,k = ɛ ln p ω,k + (1 ψɛ) ln λ ω j,k + χ k + ϕ ω,k ω, kt: firm ω HS10 category k year t x ω,kt : sales data p ω,kt : unit price data λ ω j,kt : within-national market share Recovering α 20 / 58

31 Pooled Estimation Include year subscript t and write in stochastic form: ln x ω,kt = θ ln p ω,kt + (1 α) ln λ ω j,kt + χ kt + ϕ ω,k + ε ω,kt ω, kt: firm ω HS10 category k year t x ω,kt : sales data p ω,kt : unit price data λ ω j,kt : within-national market share Recovering α 20 / 58

32 Pooled Estimation Include year subscript t and write in stochastic form: ln x ω,kt = θ ln p ω,kt + (1 α) ln λ ω j,kt + χ kt + ϕ ω,k + ε ω,kt ω, kt: firm ω HS10 category k year t x ω,kt : sales data p ω,kt : unit price data λ ω j,kt : within-national market share Recovering α 20 / 58

33 Pooled Estimation Include year subscript t and write in stochastic form: ln x ω,kt = θ ln p ω,kt + (1 α) ln λ ω j,kt + χ kt + ϕ ω,k + ε ω,kt ω, kt: firm ω HS10 category k year t x ω,kt : sales data p ω,kt : unit price data λ ω j,kt : within-national market share Recovering α 20 / 58

34 Identification Strategy Take first differences to eliminate the firm-product FE ln x ω,kt = θ ln p ω,kt + (1 α) ln λ ω j,kt + χ }{{ kt + ε } ω,kt HS10-year FE Identification Challenge: ln p and ln λ maybe correlated with the demand shock ε ω,ht. Identification Strategy: use plausibly exogenous cost-shifters as instruments. 21 / 58

35 Identification Strategy Take first differences to eliminate the firm-product FE ln x ω,kt = θ ln p ω,kt + (1 α) ln λ ω j,kt + χ }{{ kt + ε } ω,kt HS10-year FE Identification Challenge: ln p and ln λ maybe correlated with the demand shock ε ω,ht. Identification Strategy: use plausibly exogenous cost-shifters as instruments. 21 / 58

36 Identification Strategy Take first differences to eliminate the firm-product FE ln x ω,kt = θ ln p ω,kt + (1 α) ln λ ω j,kt + χ }{{ kt + ε } ω,kt HS10-year FE Identification Challenge: ln p and ln λ maybe correlated with the demand shock ε ω,ht. Identification Strategy: use plausibly exogenous cost-shifters as instruments. 21 / 58

37 Main Instrument Compile an external database on monthly exchange rates. Variety-specific measure of exposure to agg. exchange rate shocks: 12 x ω,kmt 1 z ω,kt = E jmt x ω,kt 1 m=1 x ω,kmt 1 x ω,kt 1 : share of month m sales in the prior year. Ejmt : agg. exchange rate movement in month m. z ω,kt shares similarities with a Bartik instrument. Exposure to aggregate monthly exchange rate shocks depends on the monthly-composition of sales. 22 / 58

38 Main Instrument Compile an external database on monthly exchange rates. Variety-specific measure of exposure to agg. exchange rate shocks: 12 x ω,kmt 1 z ω,kt = E jmt x ω,kt 1 m=1 x ω,kmt 1 x ω,kt 1 : share of month m sales in the prior year. Ejmt : agg. exchange rate movement in month m. z ω,kt shares similarities with a Bartik instrument. Exposure to aggregate monthly exchange rate shocks depends on the monthly-composition of sales. 22 / 58

39 Main Instrument Compile an external database on monthly exchange rates. Variety-specific measure of exposure to agg. exchange rate shocks: 12 x ω,kmt 1 z ω,kt = E jmt x ω,kt 1 m=1 x ω,kmt 1 x ω,kt 1 : share of month m sales in the prior year. Ejmt : agg. exchange rate movement in month m. Additional IVs from the literature: tariff +VAT (Caliendo and Parro, 2014). count of competing varieties (Khandelwal, 2010). Details 22 / 58

40 Pooled Estimation Manufacturing Non-Manufacturing Variable (log) IV OLS IV OLS Price, ɛ *** 0.070*** *** 0.031*** (0.119) (0.001) (0.300) (0.003) Within-national share, 1 α 0.367*** 0.885*** 0.186*** 0.858*** (0.013) (0.001) (0.031) (0.008) Weak Identification Test Under-Identification P-value No. of Product-Year Groups 21,418 21,418 8,902 8,902 Observations 1,135,849 1,135, , ,631 R-squared / 58

41 Pooled Estimation Manufacturing Non-Manufacturing Variable (log) IV OLS IV OLS Price, ɛ *** 0.070*** *** 0.031*** (0.119) (0.001) (0.300) (0.003) Within-national share, 1 α 0.367*** 0.885*** 0.186*** 0.858*** (0.013) (0.001) (0.031) (0.008) Weak Identification Test Under-Identification P-value No. of Product-Year Groups 21,418 21,418 8,902 8,902 Observations 1,135,849 1,135, , ,631 R-squared / 58

42 Pooled Estimation Manufacturing Non-Manufacturing Variable (log) IV OLS IV OLS Price, ɛ *** 0.070*** *** 0.031*** (0.119) (0.001) (0.300) (0.003) Within-national share, 1 α 0.367*** 0.885*** 0.186*** 0.858*** (0.013) (0.001) (0.031) (0.008) Weak Identification Test Under-Identification P-value No. of Product-Year Groups 21,418 21,418 8,902 8,902 Observations 1,135,849 1,135, , ,631 R-squared Manufacturing: ψ = ɛ/α 0.31 Non-Manufacturing: ψ = ɛ/α / 58

43 Pooled Estimation Manufacturing Non-Manufacturing Variable (log) IV OLS IV OLS Price, ɛ *** 0.070*** *** 0.031*** (0.119) (0.001) (0.300) (0.003) Within-national share, 1 α 0.367*** 0.885*** 0.186*** 0.858*** (0.013) (0.001) (0.031) (0.008) Weak Identification Test Under-Identification P-value No. of Product-Year Groups 21,418 21,418 8,902 8,902 Observations 1,135,849 1,135, , ,631 R-squared Manufacturing: ψ = ɛ/α 0.31 Non-Manufacturing: ψ = ɛ/α / 58

44 Our Estimates in Perspective α = trade elasticity scale elasticity 0.6 Perfectly competitive models: α = 0 Monopolistically competitive models: α = 1 25 / 58

45 Our Estimates in Perspective α = trade elasticity scale elasticity 0.6 Perfectly competitive models: α = 0 Monopolistically competitive models: α = 1 25 / 58

46 Industry-Level Estimation Estimated Parameter Sector ISIC4 codes ɛ k 1 α k ψ k Obs. Weak Ident. Test Agriculture & Mining , (1.281) (0.120) (0.151) Food , (0.923) (0.044) (0.167) Textiles, Leather & Footwear , (0.231) (0.019) (0.019) Wood , (1.134) (0.201) (0.386) Paper , (2.002) (0.132) (0.182) Petroleum , (0.188) (0.091) (1.434) Chemicals , (0.190) (0.022) (0.060) 26 / 58

47 Industry-Level Estimation Estimated Parameter Sector ISIC4 codes ɛ k 1 α k ψ k Obs. Weak Ident. Test Agriculture & Mining , (1.281) (0.120) (0.151) Food , (0.923) (0.044) (0.167) Textiles, Leather & Footwear , (0.231) (0.019) (0.019) Wood , (1.134) (0.201) (0.386) Paper , (2.002) (0.132) (0.182) Petroleum , (0.188) (0.091) (1.434) Chemicals , (0.190) (0.022) (0.060) 27 / 58

48 Industry-Level Estimation (continued) Estimated Parameter Sector ISIC4 codes ɛ k 1 α k ψ k Obs. Weak Ident. Test Rubber & Plastic , (0.484) (0.069) (0.056) Minerals , (0.241) (0.103) (0.188) Basic & Fabricated Metals , (0.254) (0.031) (0.026) Machinery , (0.286) (0.043) (0.048) Electrical & Optical Equipment , (0.091) (0.014) (0.132) Transport Equipment , (0.159) (0.017) (0.067) N.E.C. & Recycling , (0.516) (0.072) (0.852) 28 / 58

49 Summary of Estimates High-ψ sectors: 1. Electrical & Optical Equipment 2. Transport Equipment 3. Petroleum Low-ψ sectors: 1. Agriculture & Mining 2. Paper 3. Minerals 29 / 58

50 Summary of Estimates High-ψ sectors: 1. Electrical & Optical Equipment 2. Transport Equipment 3. Petroleum Low-ψ sectors: 1. Agriculture & Mining 2. Paper 3. Minerals 29 / 58

51 Step 2: Quantifying the Gains from Policy 30 / 58

52 Our estimation of ψ k and ɛ k did not impose any particular structure on the supply side Our GE analysis, however, requires more structure One Hicksian composite factor of production Symmetric firms (Krugman, 1980) c i,k (q; w i ) = w i z i,k q + w i f e k 31 / 58

53 A1 + A2 + symmetric cost Q ji,k = χ ji,k L ψ kɛ k j,k p (ɛ k+1) j,k P ɛ k i,k E i,k Q ji,k : total demand facing country j in market i L j,k size labor-force in country j industry k χ ji,k : fundamentals invariant to policy E i,k : total expenditure on industry k 32 / 58

54 Trade Policy 33 / 58

55 Industry-Level Import Tariffs Country i imposes industry-level tariffs Q ji,k = χ ji,h L ψ kɛ k j,k [(1 + t ji,k ) p j,k ] (ɛk+1) P ɛ k i,k E i,k Tariff revenue: R i,k = j k t ji,kp j,k Q ji,k

56 Welfare effects of tariff shocks (ρ i = E i /w i L i ) d ln W i = ln V i ln Y i ( d ln ρ i + k +e i,k ψ k d ln L i,k e i,k ɛ k d ln λ ii,k ) Optimal tariff Home (h) on ROW(f) 1 + t fh,k = (1 + γ fh,k ) (1 + t h ) ( 1 εh hf,k λ hh,k λ ε h λ ff,k fh,k γ hh,k 1 + t hf.k h λ hh,k ) γji,k = ψ k 1+ψ k r ji,k : inverse export supply elasticity ε i jj,k, ε i jj,k : own- and cross-price elasticity in market i 35 / 58

57 Welfare effects of tariff shocks (ρ i = E i /w i L i ) d ln W i = ln V i ln Y i ( d ln ρ i + k +e i,k ψ k d ln L i,k e i,k ɛ k d ln λ ii,k ) Optimal tariff Home (h) on ROW(f) 1 + t fh,k = (1 + γ fh,k ) (1 + t h ) ( 1 εh hf,k λ hh,k λ ε h λ ff,k fh,k γ hh,k 1 + t hf.k h λ hh,k ) γji,k = ψ k 1+ψ k r ji,k : inverse export supply elasticity ε i jj,k, ε i jj,k : own- and cross-price elasticity in market i 35 / 58

58 Welfare effects of tariff shocks (ρ i = E i /w i L i ) d ln W i = ln V i ln Y i ( d ln ρ i + k +e i,k ψ k d ln L i,k e i,k ɛ k d ln λ ii,k ) Optimal tariff Home (h) on ROW(f) 1 + t fh,k = (1 + t h ) 1 ɛ kλ hh,k ψ k 1+ɛ k λ hh,k ψ k +1 r hh,k γfh,k 0 for most economies εh hf,k ε h ff,k λ hh,k λ fh,k = ɛ kλ hh,k 1+ɛ k λ hh,k 35 / 58

59 Industrial Policy 36 / 58

60 Industry-Level Production Subsidies Country i offer industry-level production subsidies Q ij,k = χ ii,k L ψ kɛ k i,k [(1 s i,k ) p i,k ] (ɛk+1) P ɛ k j,k E j,k Subsidy cost: R i,k = j k s i,kp j,k Q ji,k 37 / 58

61 Welfare effects of subsidy shocks (ρ i = E i /w i L i ) d ln W i = ln V i ln Y i ( d ln (1 s i ) + k +e i,k ψ k d ln L i,k e i,k ɛ k d ln λ ii,k ) Optimal subsidy 1 s h,k = ε h,k (1 + ψ k ) 1 (1 + t h ) r hf,k [1 + ε f hh,k ψ k 1+ψ k ε h,k ] + r hh,k ε h hh,k ε i jj,k, εi jj,k : own- and cross-price elasticity in market i 38 / 58

62 Welfare effects of subsidy shocks (ρ i = E i /w i L i ) d ln W i = ln V i ln Y i ( d ln (1 s i ) + k +e i,k ψ k d ln L i,k e i,k ɛ k d ln λ ii,k ) Optimal subsidy 1 s h,k = ε h,k (1 + ψ k ) 1 (1 + t h ) r hf,k [1 + ε f hh,k ψ k 1+ψ k ε h,k ] + r hh,k ε h hh,k ε i jj,k, εi jj,k : own- and cross-price elasticity in market i 38 / 58

63 Welfare effects of subsidy shocks (ρ i = E i /w i L i ) d ln W i = ln V i ln Y i ( d ln (1 s i ) + k +e i,k ψ k d ln L i,k e i,k ɛ k d ln λ ii,k ) Optimal subsidy under autarky 1 s h,k = ψ k 38 / 58

64 Quantitative Strategy Use micro-estimated ψ k and ɛ k Trade, revenue, and expenditure share data (WIOD 2012) 32 major economies 15 tradable industries + an aggregate of the service industry Employ the hat-algebra methodology (Dekle et al. 2007) and the MPEC approach (Su and Judd, 2012) to compute the optimal industry-level tariffs/subsidies for each country. 39 / 58

65 Optimal Design of Trade Policy Estimated α k α k =1 α k =0 Brazil China Tax Rate (%) Mining Mfg. NEC Paper Agriculture Mineral Rubber Plastic Machinery Wood Textiles Chemicals Metal Food Transport Eq. Electrical Eq. Petroleum Mining Paper Agriculture Mineral Rubber Plastic Textiles Machinery Chemicals Tax Rate (%) Wood Metal Mfg. NEC Food Transport Eq. Petroleum Electrical Eq. Germany USA Tax Rate (%) Tax Rate (%) Machinery Rubber Plastic Paper Mineral Agriculture Metal Chemicals Mfg. NEC Textiles Mining Wood Food Electrical Eq. Transport Eq. Petroleum Paper Agriculture Mining Mfg. NEC Mineral Machinery Rubber Plastic Textiles Chemicals Metal Wood Food Transport Eq. Electrical Eq. Petroleum 40 / 58

66 Optimal Design of Industrial Policy Estimated α k α k =1 α k =0 Brazil China Subsidy Rate (%) Agriculture Paper Mining Metal Mineral Mfg. NEC Wood Rubber Plastic Textiles Food Machinery Chemicals Transport Eq. Electrical Eq. Petroleum Textiles Rubber Plastic Paper Mineral Mfg. NEC Subsidy Rate (%) Agriculture Metal Machinery Mining Wood Chemicals Food Transport Eq. Electrical Eq. Petroleum Germany USA Subsidy Rate (%) Subsidy Rate (%) Transport Eq. Textiles Chemicals Rubber Plastic Metal Machinery Mining Paper Mfg. NEC Mineral Agriculture Wood Electrical Eq. Food Petroleum Paper Agriculture Machinery Transport Eq. Mfg. NEC Metal Rubber Plastic Mineral Mining Chemicals Textiles Wood Food Electrical Eq. Petroleum 41 / 58

67 Avg. gains from import tariffs 0.88% α k = 0: 1.48% α k = 1: 1.80% Avg. gains from industrial subsidies 2.94% α k = 0: 1.33% α k = 1: 10.83% Avg. gains from multilateral trade liberalization 2.36% α k = 0: 2.30% α k = 1: 2.16% 42 / 58

68 Avg. gains from import tariffs 0.88% α k = 0: 1.48% α k = 1: 1.80% Avg. gains from industrial subsidies 2.94% α k = 0: 1.33% α k = 1: 10.83% Avg. gains from multilateral trade liberalization 2.36% α k = 0: 2.30% α k = 1: 2.16% 42 / 58

69 For each economy, measure the degree of specialization in scale-intensive industries as (Kucheryavyy et al. 2016) Scale-Intensity i = 16 k=1 ( ψk ψ ψ ) β k log β i,k r i,k, High scale-intensive economies: Brazil, Greece, Russia Low scale-intensive economies: Korea, Taiwan, Netherlands 43 / 58

70 Gains from Liberalization vs. Protection across Countries Gains from Optimal Production Subsidies 6 RUS % Gains (net of αk=0 case) DNK GRC AUS CAN BRA USA MEX FRA ROM PRT AUT GBR IND JPN CHN IDN TUR ITA POL ESP DEU HUN FIN CZE SWE SVK BEL NLD IRL KOR Scale Intensity of the Economy TWN 44 / 58

71 Gains from Liberalization vs. Protection across Countries Gains from Optimal Import Tariffs 1 GRC % Gains (net of αk=0 case) 0 1 DNK AUS MEX ROM RUS PRT BRA USA IND CAN IDN TUR GBR JPN FRA ESP ITA POL CHN AUT SWE HUN FIN DEU CZE BEL NLD SVK IRL 2 KOR Scale Intensity of the Economy TWN 45 / 58

72 Gains from Liberalization vs. Protection across Countries Gains from Multilateral Liberalization % Gains (net of αk=0 case).5 0 DNK AUS MEX IDN AUT POL ITA BRA FRA CAN ESP CHN TUR JPN GBR IND PRT USA NLD CZE BEL FIN DEU SWE SVK HUN IRL TWN KOR.5 GRC ROM RUS Scale Intensity of the Economy 46 / 58

73 Beyond Policy: International TFP Differences 47 / 58

74 Income-Size Elasticity Puzzle Income-size elasticity = ln Real Income per capita ln Population Size Quantitive trade models predict a strong & positive Income-size elasticity (Ramondo et al. 2016) ( T F P = A (Population) ψ 1 Trade ) 1 ɛ GDP The factual income-size elasticity: weakly negative! Data: / 58

75 Standard Krugman 1 Model Prediction Standard Krugman Model Data Real per capita Income (log, US=1) Population (log) 49 / 58

76 Krugman + Domestic Trade Frictions Ramondo et al. (2016) Krugman w/ doemstic trade costs 1 Model Prediction Data Real per capita Income (log, US) Population (log) 50 / 58

77 Krugman + DTF + Estimated ψ Krugman w/ domestic trade costs & estimated scale elasticity 1 Model Prediction Data Real per capita Income (log, US=1) Population (log) 51 / 58

78 Concluding Remarks Scale economies are consequential to the optimal design of and gains from industrial/trade policy. Future Directions: 1. Combining external and internal economies of scale 2. Interacting scale economies and IO linkages 52 / 58

79 Concluding Remarks Scale economies are consequential to the optimal design of and gains from industrial/trade policy. Future Directions: 1. Combining external and internal economies of scale 2. Interacting scale economies and IO linkages 52 / 58

80 Thank you

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84 Nested Krugman Inner nest: across firms, within countries (η h ). Outer nest: across countries (σ h ). Nested CES demand function ( ) pωi,h /φ 1 ηh ( ) 1 σh ω,h Pji,h x ωi,h = E i,h P ji,h P i,h Variety ωi, h: sold by firm ω to market i in product h. θh σ h 1 ψ h 1/ (η h 1) Back 54 / 58

85 Nested Krugman Inner nest: across firms, within countries (η h ). Outer nest: across countries (σ h ). Nested CES demand function ( ) pωi,h /φ 1 ηh ( ) 1 σh ω,h Pji,h x ωi,h = E i,h P ji,h P i,h Variety ωi, h: sold by firm ω to market i in product h. θh σ h 1 ψ h 1/ (η h 1) Back 54 / 58

86 Nested Krugman Inner nest: across firms, within countries (η h ). Outer nest: across countries (σ h ). Nested CES demand function ( pωi,h /φ ω,h x ωi,h = P ji,h ) 1 ( ψ h Pji,h P i,h ) θh E i,h Variety ωi, h: sold by firm ω to market i in product h. θ h σ h 1 ψ h 1/ (η h 1) Back 54 / 58

87 Nested Krugman Inner nest: across firms, within countries (η h ). Outer nest: across countries (σ h ). Nested CES demand function ( pωi,h /φ ω,h x ωi,h = P ji,h ) 1 ( ψ h Pji,h P i,h ) θh E i,h Variety ωi, h: sold by firm ω to market i in product h. θ h σ h 1 ψ h 1/ (η h 1) Back 54 / 58

88 Nested Krugman Inner nest: across firms, within countries (η h ). Outer nest: across countries (σ h ). Nested CES demand function ( pωi,h /φ ω,h x ωi,h = P ji,h ) 1 ( ψ h Pji,h P i,h ) θh E i,h Variety ωi, h: sold by firm ω to market i in product h. θ h σ h 1 ψ h 1/ (η h 1) Back 54 / 58

89 Nested Krugman Inner nest: across firms, within countries (η h ). Outer nest: across countries (σ h ). Nested CES demand function ( pωi,h /φ ω,h x ωi,h = P ji,h ) 1 ( ψ h Pji,h P i,h ) θh E i,h Variety ωi, h: sold by firm ω to market i in product h. Armington: ψ h 0 Krugman: ψ h = 1/θ h Back 54 / 58

90 Nested EK EK with multiple firms within each country: Prod./Quality drawn from a nested Fréchet distribution. ϑh : within-country Fréchet parameter θh across-country Fréchet parameter Nested demand function ( ) pωi,h /φ ϑh ( ) θh ω,h Pji,h x ωi,h = E i,h P ji,h P i,h Variety ωi, h: sold by firm ω to market i in product h. ψh 1/ϑ h Back 55 / 58

91 Nested EK EK with multiple firms within each country: Prod./Quality drawn from a nested Fréchet distribution. ϑh : within-country Fréchet parameter θh across-country Fréchet parameter Nested demand function ( ) pωi,h /φ ϑh ( ) θh ω,h Pji,h x ωi,h = E i,h P ji,h P i,h Variety ωi, h: sold by firm ω to market i in product h. ψh 1/ϑ h Back 55 / 58

92 Nested EK EK with multiple firms within each country: Prod./Quality drawn from a nested Fréchet distribution. ϑh : within-country Fréchet parameter θ h across-country Fréchet parameter Nested demand function ( pωi,h /φ ω,h x ωi,h = P ji,h ) 1 ( ψ h Pji,h P i,h ) θh E i,h Variety ωi, h: sold by firm ω to market i in product h. ψh 1/ϑ h Back 55 / 58

93 Nested EK EK with multiple firms within each country: Prod./Quality drawn from a nested Fréchet distribution. ϑh : within-country Fréchet parameter θ h across-country Fréchet parameter Nested demand function ( pωi,h /φ ω,h x ωi,h = P ji,h ) 1 ( ψ h Pji,h P i,h ) θh E i,h Variety ωi, h: sold by firm ω to market i in product h. ψh 1/ϑ h Back 55 / 58

94 Nested EK EK with multiple firms within each country: Prod./Quality drawn from a nested Fréchet distribution. ϑh : within-country Fréchet parameter θ h across-country Fréchet parameter Nested demand function ( pωi,h /φ ω,h x ωi,h = P ji,h ) 1 ( ψ h Pji,h P i,h ) θh E i,h Variety ωi, h: sold by firm ω to market i in product h. ψh 1/ϑ h Back 55 / 58

95 Nested EK EK with multiple firms within each country: Prod./Quality drawn from a nested Fréchet distribution. ϑh : within-country Fréchet parameter θ h across-country Fréchet parameter Nested demand function ( pωi,h /φ ω,h x ωi,h = P ji,h ) 1 ( ψ h Pji,h P i,h ) θh E i,h Variety ωi, h: sold by firm ω to market i in product h. Standard EK: ψh 0 Back 55 / 58

96 Income-Size Elasticity: Data Income Size Elasticity Global Trade Openness.05.6 Elasticity Trade to GDP Year.2 Back 56 / 58

97 Other Instrument: Borrowed from the Literature 1. Trade tax: import tariff + Colombia VAT Caliendo and Parro (2014) use import taxes as an exogenous cost shifter to identify the trade elasticity. 2. Instruments for ln λ ω j,ht (from Khandelwal (2010)): No. of country j firms serving the Colombian market in product category h No. of HS10 product categories actively served by firm ω. Return 57 / 58

98 Other Instrument: Borrowed from the Literature 1. Trade tax: import tariff + Colombia VAT Caliendo and Parro (2014) use import taxes as an exogenous cost shifter to identify the trade elasticity. 2. Instruments for ln λ ω j,ht (from Khandelwal (2010)): No. of country j firms serving the Colombian market in product category h No. of HS10 product categories actively served by firm ω. Return 57 / 58

99 Recovering α = ψθ ln x ω,ht = θ ln p ω,ht + (1 ψɛ) ln λ ω j,ht + χ ht + φ ω,h + ε ω,ht Basic idea: import varieties can be classified as 1. low-λ varieties (imported from thick market like Chinese) 2. high-λ varieties (imported from thin market like Taiwan) If consumers discount low-λ varieties = 1 α > 0 Return 58 / 58

100 Recovering α = ψθ ln x ω,ht = θ ln p ω,ht + (1 α) ln λ ω j,ht + χ ht + φ ω,h + ε ω,ht Basic idea: import varieties can be classified as 1. low-λ varieties (imported from thick market like Chinese) 2. high-λ varieties (imported from thin market like Taiwan) If consumers discount low-λ varieties = 1 α > 0 Return 58 / 58

101 Recovering α = ψθ ln x ω,ht = θ ln p ω,ht + (1 α) ln λ ω j,ht + χ ht + φ ω,h + ε ω,ht Basic idea: import varieties can be classified as 1. low-λ varieties (imported from thick market like Chinese) 2. high-λ varieties (imported from thin market like Taiwan) If consumers discount low-λ varieties = 1 α > 0 Return 58 / 58

102 Recovering α = ψθ ln x ω,ht = θ ln p ω,ht + (1 α) ln λ ω j,ht + χ ht + φ ω,h + ε ω,ht Basic idea: import varieties can be classified as 1. low-λ varieties (imported from thick market like Chinese) 2. high-λ varieties (imported from thin market like Taiwan) If consumers discount low-λ varieties = 1 α > 0 Return 58 / 58