on Firm-Level Price Data

Transcription

1 The Pro-Competitive Effect of Imports from China: an Analysis on Firm-Level Price Data Matteo Bugamelli, Silvia Fabiani and Enrico Sette (Bank of Italy, Economic Research Department) Brown Bag Lunch Meeting, MEF 15 aprile 2009

2 QUESTIONS Does product competition from China affects firms pricing strategies in advanced countries? Does increased import penetration of Chinese products cause a reduction of firms prices and markups? Are these effects stronger in less tech advanced sectors where competition is more price-based? Are more productive firms more capable to limit price and squeeze margins?

3 BACKGROUND: THEORY Pro-competitive effect of trade theoretically well-grounded (Bernard et al, 03; Melitz, 03; Melitz & Ottaviano, 08): stronger foreign competition is followed by price reductions due to both decreases in markups and increases in average firm productivity (reallocation effects)

4 BACKGROUND: EMPIRICS Cross-country data (Alcalà & Ciccone, 04). Trade and growth Sectoral data. Auer & Fischer (08): US sectors more exposed to import penetration from emerging countries recorded higher productivity growth, lower price inflation. Chen et al. (07): increased European imports rise industry productivity, reduce markups, slow down production prices

5 BACKGROUND: EMPIRICS ON FIRM- LEVEL DATA One-off trade liberalization (among others, Pavcnik, 02). Effects on aggregate productivity growth and margins US plant data (Bernard, Jensen & Schott, 2006a,b): effects of reduction in inbound trade costs & increase in imports from low wage countries on aggregate productivity growth, firms exit. Effects stronger in labor intensive industries

6 BACKGROUND: EMPIRICS ON CHINA Bloom, Draca & Van Reenen (08): using plant-level data from 11 EU countries, find that Chinese import competition reduces employment growth (large effect) and increases propensity to adopt ICT (small effect) and plant exit Broda & Romalis (08): using US household data on non-durable consumption (94-05), find that inflation for poorest HH has been 6 pp smaller than that for richest HH thanks to cheaper imported goods. China contributed for about a third of the total effect

7 THIS PAPER Focuses on price effects of foreign competition using firm-level data (complements Bernard et al.) Allows for sectoral and firm heterogeneity Uses a direct measure of firm-level price Use a well-defined measure of foreign (price) competition: imports from China large and temporally well-defined shock mostly price-based competitive pressure concentrated in Italy s comparative adv sectors

8 QUESTIONS & ANSWERS Does product competition from China affects firms pricing strategies in advanced countries? YES Does increased import penetration of Chinese products cause a reduction of firms prices and markups? YES Are these effects stronger in less tech advanced sectors where competition is more price-based? YES Are more productive firms more capable to limit price and squeeze margins? YES

9 EMPIRICAL SPECIFICATION [1] Optimal pricing under imperfect competition: p i, t = μ i, t * c i, t Take logs and first-differences: Δ log p i, t = Δ log μi, t + Δlogc ci, t Observe Δlog(p) Need to proxy for costs and mark-ups

10 EMPIRICAL SPECIFICATION [2]: MARKUPS Markup is function of: time-invariant a t sector component related to technology firm-level demand (cyclical markups) competition (domestic and foreign, the latter broken down between import penetration and share of Chinese imports) firm size Taking logs and FD Δ log μ = α + βδ + i, t o DEM i, t + γ 0ΔDCOMPs, t γ + 1 ΔIMPENs, t + γ 2ΔCHINA _ ITs, t + δδsizei, t εi, t

11 EMPIRICAL SPECIFICATION [3] : UNIT COSTS Unit costs are function of: unit wages unit input costs (intermediate inputs) labor productivity Taking logs and FD year dummies: common shocks sector dummies: sectoral trends Δ log ci, t = α 2 + κδ log Wi, t 1 + θδ log IC i, t 1 ε Δ log LPROD i, t 1 + ψ t + μ s + η i, t +

12 EMPIRICAL SPECIFICATION [4]: MIXED MODEL Combine previous 2 equations Lagged values: 1-year delay in price adjustment in the face of shocks (Fabiani et al., 2007) Mixed model due to sector fixed effects in cost function (results hold also in fully differenced model) Δ log + κδ logw pi, t = α + βδ log DEM i, t 1 + γ 0DCOMPs, t 1 + γ IMPEN 1 s, t 1 i, t 1 + γ CHINA _ 2 + θδ log IC i, t 1 IT s, t 1 + δδ log SIZE + εδ log LPROD i, t 1 i, t 1 + ψ + μ + η t s i, t

13 EMPIRICAL SPECIFICATION [5]: ENDOGENEITY Potential endogeneity of share of Chinese imports over total Italian a imports: entry of Chinese products may be stronger in sectors where Δprice is higher Instrument: share of world imports from China. Not affected by evolution of Italian firms productivity and prices but by push factors (industrial developments and trade policies in China)

14 DATA [1]: SOURCES Bank of Italy survey (Invind): price data, capacity utilization rate (demand). d) Manuf. firms >=50. Annual data Centrale dei Bilanci: wage, input costs, labor productivity, log employees, C-4 concentration ratio OECD-STAN: import penetration Unbalanced panel: About 6,000 obs

15 DATA [2]: TRADE DATA UN-World Trade Analyzer: China s export market shares (over Italian a and world imports) Product breakdown: 4-digits SITC-Rev 3 class mapped into 3-digits Nace-Rev. 1 (Ateco2002) Excluded sectors: subject to strong regulation and taxation or with large swings (tobacco, energy, airplanes, ships, boats )

16 DATA [3]: FIRM-LEVEL DATA ΔlogP is % change in output price ΔlogDEM l is % change in capacity utilization rate ΔlogW is % change in (labor cost/ employees) ΔlogIC is % change in (cost of intermediate inputs/employees) ΔlogLPROD is % change in (value added/employees) ΔlogSIZE is % change in number of employees

17 DESCRIPTIVE STATISTICS Employees (median) Firm age Sales (Mln ) VA/worker (000 ) Yearly Wage (000 ) Export/Sales (%)

18 AVERAGE PRICE CHANGE NA average Invind PPI (Istat) Invind PPI (Istat)

19 SHARE OF IMPORTS FROM CHINA In the world In Italy

20 SHARE OF IMPORTS FROM CHINA Sector In Italy In the world 15 Food and Drinks Textile Clothing Leather Goods Wood Products (excl. Furniture) Paper Publishing Chemical Goods Rubber Products Products from Non-metal Minerals Iron and Steel Metal Products Machine Manufacturing Information Processing Equipment Electric Equipment TV and Communication Equipment Medical and Optical Equipment Cars Other Vehicles Furniture and Other Goods

21

22 BASE REGRESSION IV year-sector OLS IV dummies (2 dig) IV + cluster (firm) CHINAIT_{s,t t-1} ** ** ** (0.04) (0.069) (0.082) (0.079) IMPEN_{s,t-1} -0.07*** *** *** *** (0.02) (0.021) (0.041) (0.022) DCOMP_{s,t-1} (0.01) (0.008) (0.010) (0.009) ΔlogDEM_{i,t-1} 0.02** 0.017** ** (0.01) (0.007) (0.007) (0.007) ΔlogSIZE_{i,t-1} 0.07** 0.070** 0.069** 0.070** (0.03) (0.029) (0.030) (0.029) logic_{i,t-1} t 0.004** 004** 0.004** 004** 0.004** 004** 0.004** 004** (0.00) (0.002) (0.002) (0.002) ΔlogW_{i,t-1} (0.00) (0.002) (0.002) (0.002) ΔlogLPROD_{i,t-1} * * * (0.00) (0.000) (0.001) (0.000) Observations

23 MAIN COEFFICIENTS Chinese share has negative, significant and sizeable effect Import penetration is negative and significant ifi Other coefficients have the expected sign

24 ROBUSTNESS CHINAIT_{s,t-1} -0.22** -0.17** -0.19** -0.17** -0.18* (0.09) (0.07) (0.08) (0.08) (0.10) IMPEN_{s,t t-1} -0.06** -0.06*** -0.06*** -0.06** -0.06** (0.03) (0.02) (0.02) (0.02) (0.03) DCOMP_{s,t-1} ,00 (0.01) (0.01) (0.01) (0.01) (0.01) ΔlogDEM_{i,t-1} 0.03*** 0.02** 0.01** 0.01** 0.01 (0.01) (0.01) (0.01) (0.01) (0.01) ΔlogSIZE_{i,t-1} ** 0.07** 0.08*** 0.09*** (0.03) 03) (0.03) 03) (0.03) 03) (0.03) 03) (0.03) 03) ΔlogIC_{i,t-1} ** 0.004** 0.004** 0.004* (0.00) (0.002) (0.002) (0.002) (0.002) ΔlogW_{i,t-1} * (0.01) (0.002) (0.002) (0.002) (0.002) ΔlogLPROD_{i,t-1} * * (0.01) (0.0002) (0.0002) (0.0003) (0.0002) EXPSHARE_{i,t-1} 0.01** (0.00) Observations R² F - Statistics ti ti 562,08 956, ,3 902,48 794,56

25 ADVANCED COUNTRIES Further robustness exercise: run same regression including share of imports from advanced (IMF definition) countries instead of China s one Its effect is not significantly ifi different from zero [also: all results hold with firm fixed effects]

26 INDIRECT EFFECT Check whether the elasticity of demand or of labor costs is different as Chinese share is larger Idea: firms reactivity it to shocks may be reduced Add interaction terms

27 INDIRECT EFFECT Demand Wage Demand and wage CHINAIT_{s,t-1} -0.17** -0.17** -0.17** (0.07) (0.07) (0.07) ΔlogDEM_{i,t-1} CHINAIT_{s,t ** -0.48** (0.21) (0.22) IMPEN_{s,t-1} -0.06*** -0.06*** -0.06*** (0.02) (0.02) (0.02) DCOMP_{s,t-1} (0.01) (0.01) (0.01) ΔlogDEM_{i,t-1} 0.03*** 0.02** 0.03*** (0.01) 01) (0.01) 01) (0.01) 01) ΔlogSIZE_{i,t-1} 0.07** 0.07** 0.07** (0.03) (0.03) (0.03) ΔlogIC_{i,t-1} 0.004** 0.004** 0.004** (0.002) 002) (0.002) 002) (0.002) 002) ΔlogW_{i,t-1} * 0.004* (0.002) (0.002) (0.002) ΔlogLPROD_{i,t-1} * (0.0002) (0.002) (0.0002) ΔlogW_{i,t-1} CHINAIT_{s,t-1} (0.11) (0.11) Observations

28 HETEROGENEOUS EFFECTS Is the effect of Chinese competitive pressures different across sectors and firms? Split sectors according to propensity to innovate (Pavitt 1984): traditional, scale intensive, highly innovative (science-based+ specialized suppliers) H1: coefficient significant only in less innovative sectors (more price-based compet.) H2: effect weaker for more productive firms Results support both H1 and H2

29 HETEROGENEOUS EFFECTS IV IV IV CHINAIT_{s,t-1} Traditional -0.16*** -0.28*** (0.07) (0.08) CHINAIT_{s,t-1} Scale Int ** -0.56*** (0.08) (0.14) CHINAIT_{s,t-1} Science + Spec. Suppliers (0.10) (0.16) CHINAIT_{s,t-1} -0.28*** (0.08) CHINAIT_{s,t-1} LPROD_{i,t-1} 1.89*** (0.59) CHINAIT_{s,t-1} LPROD_{i,t-1} Traditional {i t T 127** 1.27** (0.62) CHINAIT_{s,t-1} LPROD_{i,t-1} Scale Int. 7.09*** (2.28) CHINAIT_{s,t-1} LPROD_{i,t-1} Science { } + Spec. Suppliers 1.93 (2.04) Observations R² 0,13 0,13 0,13

30 DIFFERENCED MODEL We also run a fully differenced model, where also sector level variables are expressed in Δlog Sector fixed effects are still there, inherited from the cost equation Estimates capture the effect of an acceleration in the Chinese share Results in line with mixed model

31 DIFFERENCED MODEL OLS IV CHINAIT_{s,t-1} ** (0.08) (0.53) IMPEN_{s,t-1} (0.01) (0.01) ΔDCOMP_{s,t-1} -0,01-0,01 (0.01) 01) (0.01) 01) ΔlogDEM_{i,t-1} 0.02** 0.02** (0.01) (0.01) ΔlogSIZE_{i,t-1} t 0.07** 07** 008** 0.08** (0.03) (0.03) ΔlogIC_{i,t-1} 0.004** 0.004* (0.002) 002) (0.002) 002) ΔlogW_{i,t-1} * (0.002) (0.002) ΔlogLPROD_{i,t-1} t ** 0006** (0.0003) (0.0003) Observations

32 ISSUE # 1 Firms exit Results so far should be interpreted as conditional on firms survival But some firms exit the sample (we do not know why). Survival bias? Use CERVED and identify exiting firms. Two dummy vars: i) more restrictive: only liquidation and failure; ii) less restrictive: also M&A Results hold and the dummy is not significant

33 ISSUE # 2 Firm switching sector Firms may switch sector when competition is fiercer Is it a problem for our sample? We have only 116 switches at 3 digits (out of about 6183 firm-year observations) Its correlation with China share is (p-value 0.38)

34 ISSUE # 3 Measure of p We measure average price change across all goods produced by a firm We can say little about quality upgrade, although h it seems this would work against us

35 ISSUE # 4 import penetration import penetration could be endogenous, too results fully hold when instrumenting ti also lagged Italian import penetration with lagged US import penetration ti

36 CONCLUSIONS Increased entry of Chinese imports have lowered price dynamics and price elasticity to demand in Italy Large effect: 1pp increase in China s mkt share brings to 0.17pp lower price dynamics Reduction in markups Unveiled the mechanism bringing to trade-induced productivity enhancing reallocations: beginning of the Melitz story?