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Estimating Import Price Elasticities, Adjusting for Quality A.Fougeyrollas, N. Lancesseur, T. Thanagopal ERASME (Ecole Centrale Paris) WIOD Meeting - May.

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Presentation on theme: "Estimating Import Price Elasticities, Adjusting for Quality A.Fougeyrollas, N. Lancesseur, T. Thanagopal ERASME (Ecole Centrale Paris) WIOD Meeting - May."— Presentation transcript:

1 Estimating Import Price Elasticities, Adjusting for Quality A.Fougeyrollas, N. Lancesseur, T. Thanagopal ERASME (Ecole Centrale Paris) WIOD Meeting - May 25 th -26 th -27 th 2011 - IPTS, Sevilla

2 feedbacks Exchange rates can be different between sectors and if we use output, or Intermediate consumption or Value added as a benchmark. Output minus intermediate consumption dont always give the right Value added. 2

3 Outline The following estimations are a very preliminary work Two questions : Does the three demand types exhibits the same price elasticities ? If we take quality effects into account, are the elasticities modified ? 3

4 I. First model I.I. Specification (1) M imports, P relative prices and DT total demand addressed to the sector i country of origin, s industry d demand type (Consumption ….) 4

5 I. First model I.II. Methodology (1) For testing we pooled imports coming from 7 countries (DEU, BEL, ITA, JAP, USA, NLD and ESP) Fixed effect approach : least squares dummy variable (LSDV) regression model. We define a set of dummy variables where is equal to 1 in the case of an observation relating to individual i and 0 otherwise. The model can be rewritten as follows: 5

6 I. First model I.III. Methodology (2) We noticed autocorrelation that for some sectors errors (using the Breush-Godfrey test) corrected using Cochrane-Orcutt procedure 6

7 I. First model I.III. Results (1) 7 ConsumptionGFCFIntermediate Consumption Price elasticityR²Price elasticityR²Price elasticityR² Textiles and Textile Products 2.12***0.87-- 0,47***0.98 Wood and Products of Wood and Cork 2.17***0.88-- 0,120.96 Coke, Refined Petroleum and Nuclear Fuel 1.13***0.89-- 0,92***0.92 Chemicals and Chemical Products 1.080.34-- 1,94**0.95 Other Non-Metallic Mineral 1.83***0.73-- 0,65***0.91 Basic Metals and Fabricated Metal 1.19**0.910.470.9 0,160.85 Electrical and Optical Equipment***0.95 1,91***0.92 Transport Equipment 2.78***0.760.8***0.91 0,72***0.91

8 I. First model I.IV. Results (2) The price elasticities of import differ depending on the type of demand The elasticities are higher in the case of final consumption (compared to IC and GFCF) 8

9 Import price elasticity tends to be underestimated since the prices do not take into account the quality effects Quality innovation is becoming more and more important particularly in developed countries, as such, ignoring the influence of product quality might bias import price elasticity and therefore debates on trade competitiveness. 9 II. Second model II.I. Introduction

10 We used a gravity model (Bergstrand [2002]) and we introduced a quality proxy. This model concerns exclusively imports demanded by consumers. reflects the value of manufactures imports from country i (exporter) to j (importer) for goods produced by the sector s 10 II. Second model II.II. Specification

11 reflects export potential of country j in sector s refers to the price index of importer in sector s is a proxy for the quality of foreign goods records the distance between the two trading partners refers to the importer-industry fixed effects refers to the elasticity of substitution between domestic and foreign goods 11 II. Second model II.II. Specification (2)

12 Export potential: Theory says (Melitz [2003]) that the probability of a firm to export is increasing with its size. Hence, we wanted to use a data which could capture this information but it does not exists, so we use the number of employees in the sector as a proxy. Price index Prices are derived from unit values i.e they are obtained by dividing the value of trade by its volume 12 II. Second model II.III. Variables (1)

13 Quality proxy variable: The knowledge variable of the NEMESIS model is used: it is calculated as the R&D expenditures (EUKLEMS data) of a sector and R&D expenditures from other sectors (spillovers) are added via technology flow matrices. The quality of products should improve with the R&D expenditures of the corresponding sector. Thus, it should also increase the competitiveness on the international markets of this sector. Distance Distance is obtained by calculating the distance between the largest city in the country (CEPII data) 13 II. Second model II.III. Variables (2)

14 We use a sample of 9 countries (DE, DK, ES, FI, FR, IE, IT, NL, UK) Time frame: 1996-2003 Cities considered for distance: Essen, Copenhagen, Madrid, Helsinki, Paris, Dublin, Rome, Amsterdam and London Data obtained for 18 sectors (only goods sectors) Data sources: cepii website, EUKLEMS, Wiod 14 II. Second model II.IV. Sample

15 We did two panel regressions, pooling imports by country and then by sector Three estimation method: OLS (with fixed effect) 2sls (using lag as instruments) Poisson regression : to account for missing trade values (Westerland and Wilhelmsson [2006]) The following tables concern OLS results because they were the most robust 15 II. Second model II.V. Methodology

16 16 NameInitial σR-squareAdjusted σR-squareQuality Agriculture0.7070.2610.7150.291-0.050 GasDistribution2.4410.2142.5320.2110.624 Refined Oil-0.4340.131-0.5880.1040.259 Electricity0.4630.2770.3960.2780.301 Ferrous and Non-Ferrous Metals0.7610.1320.9620.1041.382 Non-metallic mineral products0.8920.1781.1130.1910.837 Chemicals-0.5760.1340.8640.1761.869 Metal Products0.4650.1820.9630.1860.540 Agricultural and Industrial Machines-0.6820.2860.0210.2360.965 Office Machines0.4990.4460.5000.441-0.019 Electrical Goods-0.1420.2210.5920.2361.202 Transport Equipment-0.0570.1641.7190.1750.992 Food, Drink and Tobacco0.0080.2210.2740.1930.235 Textiles, Cloth and Footwear1.8170.1322.1250.1430.273 Paper and Printing Products-0.0840.1600.4660.2310.628 Rubber and Plastic1.2170.1541.7480.1680.401 Other Manufactures-0.8120.327-0.2650.3230.583

17 Adjusting for quality does increase the import price elasticities under all estimations except for the Poisson estimation For most of the sectors, the impact of quality innovation is positive and highly significant A 1% increase of product quality leads to higher imports varying between 0.2% and 2%. Distance, as expected, varies negatively and significantly with trade flows 17 II. Second model II.VI. Results (1)

18 18 Importing Country Initial Import Price Elasticity R-square Adjusted Import Price Elasticity R-square DE (Germany)0.7960.2381.1240.534 DK (Denmark)0.4180.2940.4870.421 ES (Spain)-0.7460.453-0.5630.487 FI (Finland)0.1690.2830.2300.354 FR (France)0.7870.2960.8870.485 IE (Ireland)1.2910.2451.5570.454 IT (Italy)-0.2470.2630.2320.425 NL (Netherlands)1.4810.2931.5770.450 UK (United Kingdom)0.2180.1401.2430.424

19 All the countries in the sample do improve their import price elasticities when they have been adjusted for quality effects In conclusion of the second section, although it was a very preliminary work, we showed with all these model that the import price elasticities were underestimated when the quality was not taken into account. It means that industrial policies should also enhance R&D expenditure to improve quality of products as well as competitiveness. 19 II. Second model II.VI. Results (2)

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