Quantifying Upstreamness in East Asia:

Transcription

1 Quantifying Upstreamness in East Asia: Insights from a Coasian model of Production Staging Thibault Fally and Russell Hillberry University of Colorado-Boulder and World Bank

2 Introduction Motivation Production processes seem increasingly complex Many suppliers involved (e.g. car and aircraft industries) Growth of trade in intermediate goods, production chains reorganized across several countries Great unbundling, vertical specialization,... Many examples: barbie doll, ipod, Airbus, etc.... But lack of quantitative analysis on many aspects of fragmentation: Length of production chains? Position along production chains? General-equilibrium effects of Trade? Not just across countries: across plants

3 Introduction Why should we care about vertical fragmentation? Multiplier effects: Gains from trade are larger if production is more fragmented More opportunities to trade Trade induces not only a decrease in prices for consumer but also a decrease in producer costs if access to cheaper/various inputs Effects of trade costs on trade Magnifies the effects of trade costs (Yi 2003) Vertical specialization endogenous to trade costs (Yi 2010) Co-location and agglomeration forces Vertical linkages as a source of gains from agglomeration (Marshall) and co-location (Krugman and Venables, 1996) Vertical linkages and economic development (O-Ring theory) Business cycles transmission, etc.

4 Introduction Trade-related questions on production staging 1. Do richer countries have a comparative advantage: in more fragmented industries? in industries that are closer to final demand? 2. Does foreign sourcing: create new opportunities to fragment production? to reorganize production chains? or is it simply a substitute to domestic sourcing? 3. Role of within-country transaction costs vs. international trade costs? 4. Structural changes in East Asia: Role of China?

5 Introduction Wanted: Indexes on length and position on production chains: 1. To measure the extent of vertical fragmentation 2. To examine industry evolution depending on position along chains 3. To test whether and why countries specialize at different stages Looking at fragmentation across plants, not just across countries The ultimate geographic unit is the plant Fragmentation within country also matters International trade: substitute to intra-national trade? Theoretical framework requires: Endogenous number of production stages, allowing for more than 2. Domestic and foreign sourcing, the latter being constrained by the extent of fragmentation across plants Parsimonious model: for calibration, estimation, and counterfactuals

6 Introduction Wanted: Indexes on length and position on production chains: 1. To measure the extent of vertical fragmentation 2. To examine industry evolution depending on position along chains 3. To test whether and why countries specialize at different stages Looking at fragmentation across plants, not just across countries The ultimate geographic unit is the plant Fragmentation within country also matters International trade: substitute to intra-national trade? Theoretical framework requires: Endogenous number of production stages, allowing for more than 2. Domestic and foreign sourcing, the latter being constrained by the extent of fragmentation across plants Parsimonious model: for calibration, estimation, and counterfactuals

7 Introduction Wanted: Indexes on length and position on production chains: 1. To measure the extent of vertical fragmentation 2. To examine industry evolution depending on position along chains 3. To test whether and why countries specialize at different stages Looking at fragmentation across plants, not just across countries The ultimate geographic unit is the plant Fragmentation within country also matters International trade: substitute to intra-national trade? Theoretical framework requires: Endogenous number of production stages, allowing for more than 2. Domestic and foreign sourcing, the latter being constrained by the extent of fragmentation across plants Parsimonious model: for calibration, estimation, and counterfactuals

8 Introduction Main objectives 1. Develop two indexes of production staging (Fally, 2012) Describe the position of industries or plants on the value chain Depends on the number of plants involved sequentially 2. Here: generalize previous indexes to international framework Account for differences in production staging across countries Examine the number of countries involved sequentially Examine the position of countries in GVCs And compute these indexes using IO tables for Asia (IDE-JETRO)

9 Introduction Objectives (cont d) 3. Theoretical framework: Combine models with predictions on position on value chains Predictions on production staging across countries and plants Based mainly on Kikuchi, Nishimura and Stachurski (2012), can be combined with Costinot, Vogel and Wang (2012). 4. Quantitative analysis: Calibration (in the future: estimation) Counterfactuals: trade costs decrease, with and without China, etc.

10 Introduction Literature International fragmentation of production Hummels, Ishii, Yi (2001), Feenstra (1998), Johnson and Noguera (2010, 2012), Koopman et al. (2012), Baldwin and Taglioni (2011) Theory: Helpman (1984), Yi (2003, 2010), Baldwin and Venables (2010), Costinot, Vogel and Wang (2011) Domestic fragmentation Fort (2011) Syverson and Hortacsu (2011) Input-output literature: e.g. Dietzenbacher (2009) Kikuchi, Nishimura and Stachurski (2012) Vertical linkages and... economic development: Kremer (1993), Jones (2010) business cycles: Bems, Johnson and Yi (2010)

11 Introduction Outline of the presentation Definitions and properties Asian input-output tables, Stylized facts, country and industry comparisons Theoretical framework Calibration exercise

12 Definitions Definition: Number of production stages (Fally, 2012) Measure of the number of stages to produce good i: N i = 1 + j µ ij N j where µ ij is the direct IO coefficient of input j for the production of i (amount if input j needed to produce one dollar of good i) Proposition: N i = n n v i (n) with v i (n): share of value of good i coming from n stages upstream: n v i(n) = 1 v i (1) = V i /Y i v i (2) = j µ ijv j (1), etc.

13 Definitions Definition: Distance to final demand Measure of the number of stages between production of good i and final consumption: D i = 1 + j ϕ ij D j = n n s i (n) ϕ ij : share of production of i purchased by industry j. s (n) i : share of production of i that goes through n stages before reaching final demand. If Y i, M i and X i denote production, import and export of i, we get: ϕ ij = Y j Y i + M i X i. µ ji For later: multi-country generalization of N and D

14 Definitions Properties of these indexes Captures the length of snakes, not the number of spider legs Does not depend on whether a supplier is affiliated or not Mirror assumption for foreign sourcing: Implicitly assumes N F i = N i N i = 1 + j (µ D ij N j + µ F ij N F j ) 1 + j (µ D ij + µ F ij )N j Positively depends on the price of inputs relative to final goods Reflects fragmentation across plants even in the same industry Closed economy: i C i N i i C i = i V i D i i V i = i Y i i V i GO VA

15 Definitions Generalization of N and D Goal: Tracking stages across countries Number of stages to produce i in country c: N ic = 1 + j,s µ ijcs N js µ ijcs : amount of j from country s used to produce i in country c. Nb of stages btw production of i in country c and final consumption: D ic = 1 + j,s ϕ ijcs D js ϕ ijcs : share of i in country c purchased by industry j in country s.

16 Definitions Index of international fragmentation Embodied Nb. of border-crossings to produce i in country c: N ic = j,s µ ijcs [ N js + 1(if i j) ] µ ijcs : amount of j from country s used to produce i in country c. Nb. of border-crossings between production of i in country c and final consumption: D ic = 1 + j,s [ ϕijcs D js + 1(if i j) ] ϕ ijcs : share of i in country c purchased by industry j in country s.

17 Data Asian International Input-Output Tables Developed by IDE-JETRO (Japan Ministry of Commerce) Cover: USA, JPN, SGP, CHN, TWN, KOR, MYL, THA, IDN, PHL 1975, 1990, 2000 Tables with 45 industries in panel (76+ industries in 1990 and 2000) 4-D Input-output matrix with coefficients µ ijcs depending on: Output industry i Input industry j Destination country c Input country source s (deviates from proportionality assumption: see Puzello 2012) NB: More aggregated than BEA tables for the US (430 industries for 2002)

18 Data Examples Table: Industries with the largest embodied Nb. of stages Embodied production stages N Slaughtering, meat and dairy products Non-ferrous metal Iron and steel Motor cycles Synthetic resins and fiber Weaving and dyeing Electronic computing equipment Spinning Motor vehicles Knitting 2.730

19 How sausages are made? What you never wanted to know: Sausages N = D = Petroleum N = D = μ= 0.43 Meat packing N = D = Industrial chemicals N = D = μ= 0.07 μ= 0.21 μ= 0.72 Meat animals N = D = Feed grains N = D = Fertilizers N = D = μ= 0.12 μ= 0.25 μ= 0.12

20 Data Examples Table: Industries with the largest distance to final demand Stages to final demand D Iron ore Basic industrial chemicals Iron and steel Other metallic ore Synthetic resins and fiber Non-ferrous metal Chemical fertilizers and pesticides Crude petroleum and natural gas Non-metallic ore and quarrying Pulp and paper 3.179

21 Data Fragmentation and characteristics of industries (US data, Fally 2012) Table 6: Pairwise correlations Measure: Production Stages to Stages final demand Specificity * * R&D intensity * Capital intensity * Skill intensity * * Advertising intensity * Productivity Financial Dependence * 0.233* Top 4 share Notes: Data for 1992; * significant at 1%. NB: zero correlation between N i and D i

22 Fragmentation in Asia - Tradables only

23 Electronics: 8% of Asian exports in % in 2000

24 Theoretical framework Model Production staging across and within borders: With finite and endogenous number of firms involved sequentially Endogenous range of tasks performed by each firm (firm scope) Parsimonious model for calibration and counter-factual analysis Mostly based on Kikuchi, Nishimura and Stachurski (2012) Our contribution: Putting the model into a general-equilibrium framework with trade Differs from the literature: Yi (2003, 2010), Johnson and Moxnes (2012): two production stages Krugman and Venables (1996): Infinite number of suppliers. Costinot, Vogel and Wang (2012): Firm boundaries are irrelevant. Countries differ in rates of mistakes which determine their position on GVCs.

25 Theoretical framework Autarky Partial Equilibrium (Kikuchi et al. 2012) Kikuchi et al. (2012) setting: Range s [0, 1] of (ordered) tasks to be performed sequentially. Final goods: t = 1, Most upstream goods: t = 0 (price = 0) Firm completing stages s through t incurs convex costs c(t s) +δp(t s) price of intermediate goods at stage t s where δ > 1 reflects transaction costs. Price at stage t under perfect competition: F.O.C: p(t) = min {δp(t s) + c(s)} s c (s down ) = δc (s up ) Key result: Downstream firms are larger, i.e. perform a larger range of tasks

26 Theoretical framework Extended framework Extension of Kikuchi et al (2012). Four main changes: 1. Cross-border trade costs τ ij > 1 in addition to transaction costs δ i 2. Costs in terms of labor, with endogenous wages in general equilibrium 3. One production chain (snake) for each final good, indexed by k. 4. Labor requirement: c i (k, s) = exp(sθ i (k)) 1 where parameter θ i (k) varies by country and chain k Random snake-specific θik drawn from Frechet distribution θi parameterizes country i s average cost F i (θ) = 1 exp [ ( θ/ θ i ) κ ] For later: Add some spiders to the snakes.

27 Theoretical framework Mixed-Complementarity Programing (GAMS) Minimization schedule: min p F i (k) = p i (k, 1), with arbitrage conditions: p i (k, t) δ i p i (k, t s) + w i c i (k, s) p i (k, t) τ ji p j (k, t) Lagrangians q i (k, t, t s) for the first constraint are quantities of intermediate goods needed for each unit of final good Lagrangians x ij (k, t) for second constraint are trade flows Optimization implies the goods market clearing condition: s up q i (k, t, s up )+ j x ji(k, t) s down δ i q(k, s down, t)+ j τ ijx ij (k, t)

28 Theoretical framework General equilibrium Closing the model: Cobb-Douglas Utility: U i = k log qf i (k) Labor market clearing: k,t,s q i(k, t, s)c i (k, t s) L i General-equilibrium effects: Average θ i negatively associated with wages at equilibrium Variations in θ i (k) across countries and product varieties (chains) generate gains from trade in final goods AND intermediate goods Low- θ i countries have larger firm size Low- θ i countries have a comp. advantage in downstream industries Computational limitations: currently limited at 20 elementary stages (t) and 20 product varieties (k).

29 Calibration Table : Parameter choices and moments to match Parameters: Moments to match: Average θ i USA 1.93 GDP per capita USA 35,080 by country SGP 2.61 (PWT 7) SGP 32,808 JPN 2.38 JPN 26,721 TWN 3.38 TWN 21,891 KOR 4.16 KOR 17,208 MYS 6.54 MYS 7,917 THA 8.36 THA 5,178 IDN IDN 2,549 CHN CHN 2,442 PHL PHL 2,210 Dispersion coeff κ Simonovska and for θ ki across k s All 6.14 Waugh (2010) All 6.14 Labor supply Total value-added in tradeable goods (...) (...) in tradeable goods (...) (...) Transaction cost δ i Others 15% US Distrib. margin Others 15% SGP 5% GO/VA for SGP SGP 4.66 Border cost All 15% Trade/output ratio All 23%

30 Calibration and counter-factual exercises Results: average theta and wages Log theta PHL CHN IDN THA MYS KOR TWN SGP JPN USA Log wage

31 Calibration and counter-factual exercises Results: GO/VA ratio and wages PHL CHN CHN IDN PHL IDN THA THA MYS MYS KOR KOR TWN TWN JPN JPN SGP SGP USA USA Log wage GO/VA (model) GO/VA (data)

32 Calibration and counter-factual exercises Results: N data vs. model CHN Average N (model) TWN IDN JPN PHL USA KOR THA MYS SGP Average N (data)

33 Calibration and counter-factual exercises Results: Upstreamness D IDN PHL CHN PHL IDN CHN THA THA MYS MYS KOR KOR SGP TWN TWN SGP JPN USA JPN USA Log wage Index D* (model) Dstar_manuf

34 Calibration and counter-factual exercises Counterfactual 1 10% reduction in trade costs: τ ij 1 = 0.9. (τ ij 1)

35 Calibration and counter-factual exercises Counterfactual 1: 10% reduction in trade costs Table : Counterfactual 1): 10% decrease in border trade costs Country Welfare GO/VA GO/VA gains ratio ratio (% change) Benchmark Counterfact USA SGP JPN TWN KOR MYS THA IDN CHN PHL

36 Calibration and counter-factual exercises Counterfactual 1: 10% reduction in trade costs Table : Counterfactual 1): 10% decrease in border trade costs Country D* D* Export/output (ranked) Benchmark Counterfactual Benchmark Counterfact. USA SGP JPN TWN KOR MYS THA IDN CHN PHL ALL:

37 Calibration and counter-factual exercises Counterfactual 2 10% reduction in transaction costs: δ i 1 = 0.9. (δ i 1)

38 Calibration and counter-factual exercises Counterfactual 2: 10% reduction in transaction costs Table : Counterfactual 2): 10% decrease in transaction costs Country Welfare GO/VA GO/VA gains ratio ratio (% change) Benchmark Counterfact USA SGP JPN TWN KOR MYS THA IDN CHN PHL

39 Calibration and counter-factual exercises Counterfactual 2: 10% reduction in transaction costs Table : Counterfactual 2): 10% decrease in transaction costs Country D* D* Export/output (ranked) Benchmark Counterfactual Benchmark Counterfact. USA SGP JPN TWN KOR MYS THA IDN CHN PHL ALL:

40 Calibration and counter-factual exercises Counterfactual 3 Counterfactual 3: 10% increase in Chinese productivity θ CHN = θ CHN /1.1

41 Calibration and counter-factual exercises Counterfactual 3: 10% increase in Chinese productivity Table : Counterfactual 3): 10% increase in Chinese productivity Country Welfare GO/VA GO/VA gains ratio ratio (% change) Benchmark Counterfact USA SGP JPN TWN KOR MYS THA IDN CHN PHL

42 Calibration and counter-factual exercises Counterfactual 3: 10% increase in Chinese productivity Table : Counterfactual 3): 10% increase in Chinese productivity Country D* D* Export/output (ranked) Benchmark Counterfactual Benchmark Counterfact. USA SGP JPN TWN KOR MYS THA IDN CHN PHL ALL:

43 Calibration and counter-factual exercises Counterfactual 4 Counterfactual 4: 10% increase in Chinese transaction costs

44 Calibration and counter-factual exercises Counterfactual 4: 10% reduction in Chinese transaction costs Table : Counterfactual 4): 10% decrease in Chinese transaction costs Country Welfare GO/VA GO/VA gains ratio ratio (% change) Benchmark Counterfact USA SGP JPN TWN KOR MYS THA IDN CHN PHL

45 Calibration and counter-factual exercises Counterfactual 4: 10% reduction in Chinese transaction costs Table : Counterfactual 4): 10% decrease in Chinese transaction costs Country D* D* Export/output (ranked) Benchmark Counterfactual Benchmark Counterfact. USA SGP JPN TWN KOR MYS THA IDN CHN PHL ALL:

46 Calibration and counter-factual exercises Counterfactual 5 Counterfactual 3: Removing China (i.e. increasing trade costs to and from China to infinity)

47 Calibration and counter-factual exercises Counterfactual 5: Removing China Table : Counterfactual 5): Removing China Country Welfare GO/VA GO/VA gains ratio ratio (% change) Benchmark Counterfact USA SGP JPN TWN KOR MYS THA IDN PHL

48 Calibration and counter-factual exercises Counterfactual 5: Removing China Table : Counterfactual 5): Removing China Country D* D* Export/output (ranked) Benchmark Counterfactual Benchmark Counterfact. USA SGP JPN TWN KOR MYS THA IDN PHL ALL:

49 Conclusion Future work Model: combine determinants of country positions on chains: 1. Kikuchi et al. (2012) 2. Costinot, Vogel and Wang (2012) 3. Differences in factor intensities along production chains 4. Exogenous differences in productivity (Yi, 2010) Exploiting cross-sectoral variations: Match trade flows and indexes N and D at the sector level Estimating instead of calibrating... And testing each model against the others

50 Conclusion Conclusion In this paper, we develop indexes of production staging: Generalizing indexes from Fally (2012) and Antras et al. (2012) that can be computed with multi-country Input-Output Tables. Implemented here using Asian Input-Output Tables We develop a new theoretical framework With both Foreign and Domestic sourcing With an endogenous and finite number of firms in each chain Endogenous value-added (range of tasks) for each firm Parsimonious framework, allowing for calibration in GE. Further work: Estimation, Incorporating alternative models, Improving computing method, Exploiting sectoral data, etc.