1. 1 Romania’s exports revealed. A trade and factor analysis MSc Student: VLAD Mihail Razvan Supervisor: Prof. Ph.D. Moisa ALTAR The Academy of Economic Studies DOFIN – Doctoral School of Finance and Banking Bucharest, July 2010

2. 2 Objectives  Identifying the goods for which Romania holds a revealed comparative advantage (RCA) for 2006 to 2009 - by means of the Balassa index  Pinpoint Romania and the EU27 countries in a bi-dimensional space having the axes capital intensity and labor skill intensity - by following Neven’s industry classification  Grouping EU27 countries by means of a cluster analysis. -by the level of they factor intensities  Determining the influences our EU partners have on Romania’s level of exports - by means of a Gravity model framework

3. 3 Model and methodology.1 Revealed comparative advantage.2 Factor analysis – Neven’s classification.3 The Gravity Model

4. 4.1 Revealed comparative advantage o 1965, Balassa in “Trade Liberalization and Revealed Comparative Advantage” This index compares the share of a given product in a country’s exports within another country or region, to the share of the same product in that country or region’s total exports. The Balassa’s index signification is: an index value over 1 for a product k denotes a comparative advantage in the export of goods k for country i on the market j or over county j

5. 5.2 Factor analysis – Neven’s classification 1995, Neven classified the factors intensity of production in which the countries have RCA in 5 groups characterized by different levels of capital and labor-skill intensities using the following criteria: the share of white-collar workers in total industry labor force the average wage in the industry, the ratio of labor costs to industry value added, and the ratio of fixed investment to value added in the industry Intensity Human capital Labor wages Physical capital Example Cat. 1Very highHighIntermediateHigh Tech 2High LowElectrical equipment 3LowHighLowTextiles; Apparel industry 4Low HighCar industry 5HighLowHighPaper industry; Food-processing Table 1. Source: Widgr é n (2005)

6. 6 … Factor analysis – Neven’s classification Category coordinates (2,0) for category 1 (1,-1) for category 2 (-1,-1) for category 3 (-1,1) for category 4 (1,1) for category 5 If a country’s RCA is equally distributed across all categories, it is located at (0.4,0) Countries’ that have RCA in relatively skill-intensive sectors have an x-coordinate larger than 0.4 and those having RCA in capital-intensive industries have a y- coordinate greater than 0.

7. 7.3 The Gravity Model Main presumption: Exports from one country to another are explained by their economic size (measured by GNP or GDP) and the geographical distance between them The bottom principle of the model is the Newtonian law of gravity for two objects For the purpose of this paper, two equations were taken into consideration:

8. 8 …The Gravity Model

9. 9 Results and Data description

10. 10 Data description The level of exports was extracted for each of the 27 : AT, BG, CZ, DK, DE, GR, IE,EE, ES, FR, IT, CY, LV, LT, LU, HU, MT, NL, PL, PT, RO, SL, SK, FI, SE and GB, for all of SITC’s Rev.3 level 4 products, in the period 2006 – 2009,-> 2700 products The GDP, the level of imports and exports was also extracted from Eurostat database. The GDP was extracted in constant prices with 2000 as reference year, whereas the level of imports and exports was extracted in Eur. The data for distance and population is taken from Centre D'etudes Prospectives Et D'informations Internationales. The weighted distance measure uses city-level data to to calculate distance between two countries based on bilateral distances between the largest cities of those two countries

11. 11.1 Revealed comparative advantage – Results Romania’s major trading good are those from category 3 which are mostly given by textile industry, shoes, leather or furniture industry.

12. 12.2 Factor analysis – Neven’s classification

13. 13 … Revealed comparative advantage – Results

14. 14 … Revealed comparative advantage – Results

15. 15.3 The Gravity Model - Results Romania’s aggregate exports: gdppartner –the GDP of Romania’s trading partners gdpro – Romania’s GDP, lndistance –the natural logarithm of the distance separating the countries, population~r (populationpartner) – partner’s population, population~a (populationromania) – Romania’s population, commoneume-p (commoneumembership) – dummy variable used to reflect the period past since Romania joined the EU (2007). As expected, in accordance with the economic theory, the influences of booth Romania’s GDP and its trading partners GDP have a positive effect in the variances in aggregate level of exports. Distance, not surprisingly, has a negative impact. This may be more due to the fact that Romania’s past is more related to its not so distant neighbors than with its partners more to the West. The model’s explanatory power is similar to the ones obtained by other authors that conducted similar studies

16. 16 … The Gravity Model - Results Romania’s trade intensity index for aggregate exports: The variances in partner’s GDP this time has a negative influence on the Trade Intensity index. The negative coefficient may be interpreted as a decrease in the share that Romania’s exports hold in its partner’s imports and not a decrease in the total volume of exports. Booth Romania’s population and the dummy variable are not significantly different from 0 and they exert no effect

17. 17 … The Gravity Model - Results Romania’s exports for goods with RCA in Neven’s category 1 Romania’s trade intensity index for goods with RCA in Neven’s category 1 As in the previous cases, the dummy variable and Romania’s population have no influence. For this group of products, it seems that the variances across partner’s GDP has no influence on the total volume of exports, but only Romania’s GDP variances

18. 18 … The Gravity Model - Results Romania’s exports for goods with RCA in Neven’s category 2 Romania’s trade intensity index for goods with RCA in Neven’s category 2 Romania’s exports for goods with RCA in Neven’s category 3 Romania’s trade intensity index for goods with RCA in Neven’s category 3

19. 19 … The Gravity Model - Results Romania’s exports for goods with RCA in Neven’s category 4 Romania’s trade intensity index for goods with RCA in Neven’s category 4 Romania’s exports for goods with RCA in Neven’s category 5 Romania’s trade intensity index for goods with RCA in Neven’s category 5

20. 20 Conclusions Neven’s framework reveals that Romania enjoys the advantages of having goods with RCA in all of Neven’s five categories, but with higher level of specialization in groups 3 and 4, the lowest being in group 1 The difference between Romania and the other EU27 countries were ameliorated since 2006. In this 4 year period it can be seen that a shift in specialization has occurred. Romania’s specialization shifted from an area of goods produced by industries with low levels of capital and labor skill intensities to goods produced by industries with relative high capital intensity but low labor skill intensities. The Gravity model has identified that the level of variance in aggregate export is not explained only by the variances between groups but also by the changes within groups. As such, for example, Romania’s aggregate export to one of its trading partner is determined not only by the level of that partner’s GDP but also by the increases/decreases from one period to another in the partners GDP. This effect, but with weaker force, is also visible in the level of exports of goods from Neven’s category 1 and 2 especially. The variances in TI are explained in a great measure, by the variables employed, for categories 1, 2,4 and 5 The explanatory power of the model estimation is similar with the ones reported by other authors who conducted similar studies, i.e. a value of R 2 ranging between 0.6 and 0.7. The main coefficients accounting for the influences of Romania’s and its trading partner’s GDP are significantly different from 0 in the analysis of the total trade, and the TI determined for this level of trade, and in analyzing goods from category 1 and 2.

21. 21 Thank you!

22. 22 Reference