Session 2: External trade indices Unit value indices vs price indices: Pros and Cons of collection methods
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1 Session 2: External trade indices Unit value indices vs price indices: Pros and Cons of collection methods 1. Sources of information 2. Unit value vs specific price indices 3. Comparison of indices in the EU 4. Way forward Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 1/28
2 1. Data sources 2 main possible sources of information: Customs data Unit Value Indices (UVI) Enterprise-based price surveys - Survey of transactions Specific price Indices (SPI) Other used practices: Specific sources for some products (quotations): electricity, water, petroleum, gas Proxies: PPI, CPI Partner countries (mirror price indices) Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 2/28
3 2. Unit values vs. specific prices 2.1.a Advantages of unit values Low cost: basic data already available Exhaustive coverage Value based on real transaction prices Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 3/28
4 2. Unit values vs. specific prices Main issues: 2.1.b Problems with unit values Lack of detail and heterogeneity of items at the lowest level of the product classification Example of refrigerators (UNSD Strategies for the Measurement of External Trade Indices 1981 ) small Medium Large All sizes Period q p v q p v q p v q p v Initial ,7 17 Current ,6 46 Overstatement of price increase by unit value of 35% Quality changes not taken into consideration Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 4/28
5 2. Unit values vs. specific prices 2.1.b Problems with unit values Other issues: Changes in the product classification (HS) Misreporting of values (e.g., transfer pricing not detected by Customs) Misreporting of quantities Consignments with mixed products Arbitrary definition of outliers detection rules Sensitivity to outliers detection process [Within the EU: absence of intra-eu customs data, missing quantities, ] Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 5/28
6 2. Unit values vs. specific prices 2.1.b Problems with unit values : lack of harmonisation between countries UNIT VALUE INDEX VOLUME INDEX Belgium Paasche Laspeyres Denmark Fisher (chained) Fisher (chained) France Paasche (chained) Laspeyres Germany Paasche (IVU) Laspeyres Greece Paasche Laspeyres Ireland Fisher Fisher Italy Fisher Fisher Netherlands Fisher (IVU) Fisher Portugal Paasche (chained) Laspeyres Spain Paasche (chained) Laspeyres United Kingdom Laspeyres Laspeyres EUROSTAT Fisher (chained) Fisher (chained) Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 6/28
7 2. Unit values vs. specific prices 2.2.a Advantages of price surveys Precise definition of products Quality changes can be taken into consideration Low volatility of outputs Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 7/28
8 2. Unit values vs. specific prices 2.2.b Problems with price surveys Sampling scheme must ensure a good coverage and representativeness (traders, products) Size of sample (traders, items) and sampling errors Definition of the value (eg. for exports: FOB value, basic price, invoice value ) Quoted prices may differ from real transaction prices Price of purchase (imports) often more difficult to collect than price of sale Higher volatility of import flows Estimation of weights Resource and cost Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 8/28
9 3. Comparison UVI/SPI on EU data 3.1. The study Study carried out by Eurostat on the basis of data provided by 4 Member States computing both kind of indices (prior to the 2005 EU legislation) Datasets: Monthly data on Imports with SPIs and UVIs; CPA 3 digits (close to CPC) Methodology: SPIs as a reference Measures: ratio UVI/SPI, discrepancy, variability / instability Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 9/28
10 1100: Crude petroleum and natural gas - Finland 300 SPI UVI UVI/SPI janv-96 avr-96 juil-96 oct-96 janv-97 avr-97 juil-97 oct-97 janv-98 avr-98 juil-98 oct-98 janv-99 avr-99 juil-99 oct-99 janv-00 avr-00 juil-00 oct-00 janv-01 avr-01 juil-01 Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 10/28
11 1310: Iron ore - Netherlands 400 SPI UVI 350 UVI/SPI janv-95 avr-95 juil-95 oct-95 janv-96 avr-96 juil-96 oct-96 janv-97 avr-97 juil-97 oct-97 janv-98 avr-98 juil-98 oct-98 janv-99 avr-99 juil-99 oct-99 janv-00 avr-00 juil-00 oct-00 janv-01 avr-01 juil-01 oct-01 Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 11/28
12 : Food products and beverages Finland: 1995 = SPI UVI janv-96 avr-96 juil-96 oct-96 janv-97 avr-97 juil-97 oct-97 janv-98 avr-98 juil-98 oct-98 janv-99 avr-99 juil-99 oct-99 janv-00 avr-00 juil-00 oct-00 janv-01 avr-01 juil-01 Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 12/28
13 1500: Food products and beverages Finland: monthly changes 1,05 SPI 1,04 UVI 1,03 1,02 1,01 1,00 0,99 0,98 0,97 janv-96 avr-96 juil-96 oct-96 janv-97 avr-97 juil-97 oct-97 janv-98 avr-98 juil-98 oct-98 janv-99 avr-99 juil-99 oct-99 janv-00 avr-00 juil-00 oct-00 janv-01 avr-01 juil-01 Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 13/28
14 2830: Steam generators, except central heating hot water boilers, Netherlands: 1995 = SPI UVI janv-96 avr-96 juil-96 oct-96 janv-97 avr-97 juil-97 oct-97 janv-98 avr-98 juil-98 oct-98 janv-99 avr-99 juil-99 oct-99 janv-00 avr-00 juil-00 oct-00 janv-01 avr-01 juil-01 Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 14/28
15 : TV and radio transmitters & apparatus for line telephony & line telegraphy, Finland: 1995 = SPI UVI janv-96 avr-96 juil-96 oct-96 janv-97 avr-97 juil-97 oct-97 janv-98 avr-98 juil-98 oct-98 janv-99 avr-99 juil-99 oct-99 janv-00 avr-00 juil-00 oct-00 janv-01 avr-01 juil-01 Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 15/28
16 U V I / S P I d i s c r e p a n c y, F i n l a n d : s t a n d a r d d e v i a t i o n v s a v e r a g e 2 5 % A g g r e g a t e s D i v i s i o n s G r o u p s 2 0 % Standard deviation of discrepancies 1 5 % 1 0 % 5 % 0 % % % % 0 % 1 0 % 2 0 % 3 0 % A v e r a g e d i s c r e p a n c i e s ( + = > U V > S P ) Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 16/28
17 U V I / S P I d i s c r e p a n c y, N e t h e r l a n d s : s t a n d a r d d e v i a t i o n v s a v e r a g e A g g r e g a t e s D i v i s i o n s G r o u p s 2 5 % 2 0 % Standard deviation of discrepancies 1 5 % 1 0 % 5 % 0 % % % % 0 % 1 0 % 2 0 % 3 0 % A v e r a g e d i s c r e p a n c i e s ( + = > U V > S P ) Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 17/28
18 Standard deviation of discrepancies UVI/SPI monthly discrepancy, Sweden: standard deviation vs average 25% Aggregates Divisions Groups 20% 15% 10% 5% 0% -3,0% -2,5% -2,0% -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% 2,0% 2,5% 3,0% Average discrepancies ( + => UV > SP) Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 18/28
19 Normalised average standard deviation of monthly, quarterly, annual and pluriannual UVI / SPI discrepancies by product Green: < 2 % Blue: 2 to 4 % Black: 4 to 8 % Red: > 8 % Code Divisions and groups Month Quarter Annual Whole period 1000 Coal, lignite & peat 22,4% 18,1% 12,1% 7,5% 1010 Coal 21,9% 14,6% 8,2% 5,7% 1020 Lignite 78,2% 40,8% 22,4% 2,1% 1030 Peat 39,9% 31,7% 21,6% 17,4% 1100 Crude petroleum & natural gas 24,2% 17,0% 11,9% 3,2% 1110 Crude petroleum & natural gas 22,0% 16,8% 13,3% 4,3% 1300 Metal ore 50,7% 38,6% 27,4% 7,9% 1310 Iron ore 61,8% 58,4% 42,9% 4,1% 1320 Non-ferrous metal ores, except uranium and thorium ores 37,1% 26,9% 18,1% 4,3% 1400 Other mining and quarrying materials 18,4% 13,0% 7,8% 3,9% 1410 Stone 33,0% 23,4% 13,9% 30,5% 1420 Sand & clay 19,6% 13,6% 9,0% 2,3% 1430 Chemical and fertilizer minerals 33,3% 20,7% 9,7% 5,3% 1440 Salt 68,0% 48,7% 20,7% 13,4% 1450 Other mining and quarrying materials n.e.s. 27,4% 19,3% 10,8% 8,4% 1500 Food products and beverages 5,2% 3,8% 3,2% 1,6% 1510 Meat & meat products 12,6% 10,3% 6,7% 1,1% 1520 Fish & fish products 13,9% 12,5% 10,2% 8,0% 1530 Fruit and vegetables 11,3% 9,2% 6,5% 5,1% 1540 Vegetable and animal oils and fats 16,6% 13,0% 11,0% 15,4% 1550 Dairy products 9,1% 6,4% 4,1% 2,7% 1560 Grain mill products, starches and starch products 10,1% 6,7% 4,4% 3,3% 1570 Prepared animal feeds 21,3% 16,2% 10,3% 5,5% 1580 Other food products 9,2% 7,9% 6,6% 3,9% 1590 Beverages 17,4% 12,6% 6,7% 13,3% Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 19/28
20 Normalised average standard deviation of monthly, quarterly, annual and pluriannual UVI / SPI discrepancies by product Green: < 2 % Blue: 2 to 4 % Black: 4 to 8 % Red: > 8 % 3200 Radio, television and communication equipment & apparatus 22,0% 17,0% 13,3% 20,8% 3210 Electronic valves & tubes & other electronic components 40,5% 29,8% 21,5% 15,1% 3220 TV & radio transmitters & apparatus for line telephony & line 33,4% 24,9% 20,7% 49,0% telegraphy 3230 TV & radio receivers, sound or video apparatus & associated goods 22,7% 15,3% 11,4% 7,8% 3300 Medical, precision and optical instruments, watches & clocks 17,5% 12,1% 7,4% 7,4% 3310 Medical & surgical equipment & orthopaedic appliances 29,1% 19,0% 11,1% 8,9% 3320 Precision instruments, except industrial process control equipment 24,5% 16,5% 9,2% 9,8% 3340 Optical instruments & photographic equipment 39,1% 25,3% 15,1% 6,3% 3350 Watches and clocks 70,4% 47,0% 23,5% 8,3% 3400 Motor vehicles, trailers and semi-trailers 7,8% 5,4% 3,5% 0,7% 3410 Motor vehicles 8,8% 5,6% 3,8% 1,4% 3420 Bodies (coachwork) for motor vehicles; trailers & semi-trailers 19,9% 12,9% 8,9% 6,6% 3430 Parts & accessories for motor vehicles & their engines 14,5% 11,2% 6,3% 3,0% 3500 Other transport equipment 52,1% 29,1% 18,6% 11,4% 3520 Railway & tramway locomotives & rolling stock 95,8% 72,1% 35,9% 23,4% 3530 Aircraft and spacecraft 126,6% 70,1% 47,5% 21,8% 3540 Motorcycles and bicycles 18,9% 12,6% 7,2% 5,1% 3550 Other transport equipment n.e.s. 39,3% 29,2% 24,5% 4,0% Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 20/28
21 3.2. Results (1) Stability SPIs more stable than UVIs Monthly UVIs often very unstable More discrepancies on the short-term than on the long-term Aggregation level less discrepancies at aggregated levels low-discrepancy product groups differ among MS Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 21/28
22 3.2. Results (cont d) Technological levels for high tech products more short-term discrepancies long-term systematic upward bias of UVIs Eurostat vs. national UVI data: sensitivity to methodology (detail, outliers) Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 22/28
23 3.2. Results (cont d) Overall conclusions of the study: Any list of product categories for which UVIs are a priori acceptable as proxies for SPIs would be very short, especially as regards monthly data. It would include almost only aggregates and raw materials, Apparently, any list of product categories for which short-term UVIs are acceptable proxies for SPIs seems country-specific. For a few low-tech products, for which quality changes are slow, UVI changes over the long term (several years) may be acceptable proxies for SPIs Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 23/28
24 3.3 Consequences in the EU Changes introduced in 2005 in the EU legislation on short term statistics: - the following variable is added to paragraph 1: Import prices - The information on output prices for non-domestic markets (No 312) and import prices (No 340) may be compiled using unit values for products originating from foreign trade or other sources only if there is no significant deterioration in quality compared to specific price information. The Commission shall determine, in accordance with the procedure laid down in Article 18, the conditions for assuring the necessary data quality. Most of EU countries have introduced surveys to measure import/export price indices, at least for industrial goods (hybrid indices). Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 24/28
25 4. The way forward (1) Strategies for the Measurement of External Trade Indices (UNSD 1981) Strategies for compiling index numbers (part VI) 1. Limited Budget a) Unit Value Indexes detailed Customs data selection of stable items - data screening 2. Average Budget a) Unit Value Indexes sophisticated data editing b) Commodity specialists possible use of a variety of sources to fill the gaps 3. Large Budget a) sophisticated Unit Values and Price surveys (dual or combined strategy) b) Commodity specialists Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 25/28
26 4. The way forward (2) a. More detailed specification for UVI? Country of origin or destination Point of export / import Size of shipment Individual trader Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 26/28
27 4. The way forward (3) b. Different formulas? c. Improving data editing? Lack of benchmark The risk of discarding all large price changes, even real ones Cooperation between countries? d. Hybrid solutions (EU countries) e. What about services? Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 27/28
28 Thank you Regional Seminar on International Trade Statistics, 3-6 November 2014, New Delhi, India 28/28
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223 POPULATION a, b Total population million; as of 1 July 5.704 6.156 6.665 6.744 6.731 6.784 6.813 6.857 Population density c persons per square kilometer 5296 5840 6200 6260 6240 6280 6310 6350 Population