estimating import price elasticities, adjusting for quality
DESCRIPTION
Estimating Import Price Elasticities, Adjusting for Quality. Fougeyrollas , N. Lancesseur, T. Thanagopal ERASME (Ecole Centrale Paris) WIOD Meeting - May 25 th -26 th -27 th 2011 - IPTS, Sevilla. feedbacks. - PowerPoint PPT PresentationTRANSCRIPT
Estimating Import Price Elasticities, Adjusting for Quality
A. Fougeyrollas, N. Lancesseur, T. Thanagopal
ERASME (Ecole Centrale Paris)WIOD Meeting - May 25th-26th-27th 2011 - IPTS, Sevilla
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 don’t always give the right Value added.
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Outline The following estimations are a very preliminary
workTwo questions :
Does the three demand types exhibits the same price elasticities ?
If we take “quality effects” into account, are the elasticities modified ?
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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 ….)
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I. First modelI.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:
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I. First modelI.III. Methodology (2)
We noticed autocorrelation that for some sectors errors (using the Breush-Godfrey test)
corrected using Cochrane-Orcutt procedure
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I. First modelI.III. Results (1)
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Consumption GFCF Intermediate Consumption
Price elasticity R² Price elasticity R² Price elasticity R²
Textiles and Textile Products 2.12*** 0.87 - - 0,47*** 0.98
Wood and Products of Wood and Cork 2.17*** 0.88 - - 0,12 0.96
Coke, Refined Petroleum and Nuclear Fuel 1.13*** 0.89 - - 0,92*** 0.92
Chemicals and Chemical Products 1.08 0.34 - - 1,94** 0.95
Other Non-Metallic Mineral 1.83*** 0.73 - - 0,65*** 0.91
Basic Metals and Fabricated Metal 1.19** 0.91 0.47 0.9 0,16 0.85
Electrical and Optical Equipment 0.3 0.4 1.37*** 0.95 1,91*** 0.92
Transport Equipment 2.78*** 0.76 0.8*** 0.91 0,72*** 0.91
I. First modelI.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)
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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.
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II. Second modelII.I. Introduction
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
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II. Second modelII.II. Specification
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
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II. Second modelII.II. Specification (2)
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
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II. Second modelII.III. Variables (1)
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)
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II. Second modelII.III. Variables (2)
We use a sample of 9 countries (DE, DK, ES, FI, FR, IE, IT, NL, UK)
Time frame: 1996-2003Cities 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
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II. Second modelII.IV. Sample
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
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II. Second modelII.V. Methodology
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Name Initial σ R-square Adjusted σ R-square Quality
Agriculture 0.707 0.261 0.715 0.291 -0.050
GasDistribution 2.441 0.214 2.532 0.211 0.624
Refined Oil -0.434 0.131 -0.588 0.104 0.259
Electricity 0.463 0.277 0.396 0.278 0.301
Ferrous and Non-Ferrous Metals 0.761 0.132 0.962 0.104 1.382
Non-metallic mineral products 0.892 0.178 1.113 0.191 0.837
Chemicals -0.576 0.134 0.864 0.176 1.869
Metal Products 0.465 0.182 0.963 0.186 0.540
Agricultural and Industrial Machines -0.682 0.286 0.021 0.236 0.965
Office Machines 0.499 0.446 0.500 0.441 -0.019
Electrical Goods -0.142 0.221 0.592 0.236 1.202
Transport Equipment -0.057 0.164 1.719 0.175 0.992
Food, Drink and Tobacco 0.008 0.221 0.274 0.193 0.235
Textiles, Cloth and Footwear 1.817 0.132 2.125 0.143 0.273
Paper and Printing Products -0.084 0.160 0.466 0.231 0.628
Rubber and Plastic 1.217 0.154 1.748 0.168 0.401
Other Manufactures -0.812 0.327 -0.265 0.323 0.583
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
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II. Second modelII.VI. Results (1)
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Importing Country Initial Import Price
Elasticity
R-square Adjusted Import Price
Elasticity
R-square
DE (Germany) 0.796 0.238 1.124 0.534
DK (Denmark) 0.418 0.294 0.487 0.421
ES (Spain) -0.746 0.453 -0.563 0.487
FI (Finland) 0.169 0.283 0.230 0.354
FR (France) 0.787 0.296 0.887 0.485
IE (Ireland) 1.291 0.245 1.557 0.454
IT (Italy) -0.247 0.263 0.232 0.425
NL (Netherlands) 1.481 0.293 1.577 0.450
UK (United Kingdom) 0.218 0.140 1.243 0.424
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.
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II. Second modelII.VI. Results (2)