Gross trade, value-added trade and spatial autocorrelation: case of EU Member States

Transcription

1 Paweł Folfas, Warsaw School of Economics Gross trade, value-added trade and spatial autocorrelation: case of EU Member States Introduction This paper is aimed at checking the existence of absolute income (GDP per capita) betaconvergence among EU Member States over period and measuring the strength of spatial dependencies between them. The research is based on spatial Durbin models including spatial weight matrices based on the long-term bilateral trade (gross trade and value-added trade) relationships between EU Member States. The text includes three major sections: (1) motivation and literature review, (2) methods and data, and (3) estimation results. 1. Motivation and literature review From the beginning, there have been large territorial and demographic disparities in the European Community (now the European Union), with the potential to constitute obstacles to integration and development in Europe. From the outset, the Treaty of Rome (1957) established solidarity mechanisms in the form of two Structural Funds: the European Social Fund (ESF) and the European Agricultural Guidance and Guarantee Fund (EAGGF, Guidance Section). In 1975, regional aspects were introduced with the creation of the European Regional Development Fund (ERDF). In 1994, the Cohesion Fund was also created. However, for a long time these initiatives had only modest resources. With the Single European Act of 1986, economic and social cohesion became a competence of the European Community. Economic and social cohesion was implemented through the cohesion policy of the EU, which was incorporated into the EC Treaty by the Maastricht Treaty of Cohesion policy reduces structural disparities between regions and Member States through a variety of operations that are financed by the European Regional Development Fund (ERDF), the European Social Fund (ESF) and the Cohesion Fund. In 2008, the Treaty of Lisbon introduced a third dimension of EU cohesion: territorial cohesion. These three aspects of cohesion are supported through cohesion policy and the Structural Funds. According to the Treaty on the Functioning of the European Union (TFEU), the European Union 1

2 is strengthening its economic, social and territorial cohesion in order to promote its overall harmonious development 1. Table 1. Studies on economic convergence in the European Union models including spatial models without spatial components components EU regions (NUTS regions) Arbia et. al [2005], Baumont et. al. [2002], Breidenbach et al. [2016], Artelaris et al. [2010], Benedek and Veress [2013], Braga [2010], Herz Feldkircher [2006], Fischer and and Vogel [2003], Marques and Stirboeck [2004], Folfas [2015], Soukiazis [1998], Nevima and Paas et al. [2007], Šlander and Melecky [2011], Rosiek and Ogorevc [2010], Supińska [2013] Włodarczyk [2012], Soukiazis and Antunes [2004], Tsionas et. al [2014] EU Member States Próchniak and Witkowski [2014] Cuaresma et al. [2012], ECB[2015], Gligorić [2014], Jaakkola [2007], Kulhánek [2012], Marques and Soukiazis [1998], Matkowski et. al. [2016], Melner [2016] Source: own deliberation. Despite of the fact that cohesion policy aims at more than purely economic convergence, the researchers usually focus on the processes of convergence. Consequently, there is a lot of empirical studies on economic convergence among the EU regions and EU Member States see examples of these studies in Table 1. Some authors consider spatial dependencies between the EU regions or states but almost all of them apply quite standard spatial weights matrices such as first, second etc. neighbours matrices or the inverted distance matrices. In my study I use matrices based on the long-term bilateral trade flows between states. It is quite probably that economic growth in one of the trading partners may influence on the other ones growth. Additionally, I use two measures of international trade based on gross terms and value-added terms. 1 (accesed on ); (accessed on ). 2

3 Figure 1. Gross trade vs. value-added trade country B final good 110 country C value-added 10 semiproduct 100 country A value-added 100 Source: World Trade Report Factors shaping the future of world trade, WTO, Geneva 2013, p. 81. Studies on trade measured by value-added is a new form of analysing international trade. They reach the end of the first decade of the 21st century. It is not only a new approach but also important to empirical research in international economic cooperation, since analysing trade in value added differs substantially from analysing trade value measured by gross value. For instance, if country A exports to country B intermediate good worth 100, and B exports to C final good worth 110, then the export from B to C measured by value-added is 10 (rather than 110 by gross measure), and the export from A to C 100 (rather than 0) see Figure 1. Value-added trade seems to be a better than gross trade measure of economic links between countries which do not trade directly with each other (or do trade but the value of bilateral flow is low) see countries A and C on the Figure 1. As there can be substantial differences between gross trade and valueadded trade, thus I apply both methods of measuring international trade in construction of spatial weights matrices. 2. Methods and data I use spatial Durbin model which combines spatial lag and spatial error models (for more see for example Elhorst [2014, pp. 5-26]). The spatial Durbin model includes a spatial lag Wy j and a set of spatially lagged exogenous regressors WX i see equation (1): (1) y i = ρwy j + X i β+ WX i γ + ε i, 3

4 where: y i dependent variable for spatial unit i (i = 1,2..., N), an N 1 vector, ρ spatial lag coefficient, W spatial weights matrix, an N N matrix, y j dependent variable for spatial units j (j i), an N 1 vector, β K 1 vector of regression coefficients, X i set of independent variable, an N K matrix of regressors (corresponding to spatial unit i), γ K 1 vector of regression coefficients, ε i - N x1 vector of errors (corresponding to spatial unit i). In this study spatial units are countries EU Member States, thus N=28. Because of small number of spatial units, there is only one independent variable (regressor) natural logarithm of GDP per capita in the initial year of analysis (2000). The dependent variable is annual average change of GDP per capita measured by: (ln natural logarithm), where y 2000 and y 2014 correspond to the GDP per capita at the initial (2000) and the final (2014) year of analysis, and n=15 is the number of years in the analysed period. So because of simplicity and small sample of countries I use a static model. Two different weights matrices (see Appendix) are used for estimation. Both refer to the long-term trade relationships between EU Member States. The first one (W1) is based on gross trade and its general term is defined as:, where m ij is the average gross imports of country i coming from country j over the The second one (W2) is based on value-added trade and its general term is defined as:, where vam ij is the average value-added imports of country i coming from country j over the

5 Data concerning GDP per capita of are extracted from the UNCTAD statistical database 2. The value of gross trade and value-added trade was estimated based on the World Input-Output Database Estimation results Both models (with W1 and W2 matrix) confirm the existence of absolute income betaconvergence among EU Member States over the period (annual speed convergence at the level of about 2.35%) see Table 2. The value of the rho-parameter illustrating spatial dependencies is weakly statistically significant in both models. In the case of model including the spatial weights matrix based on gross trade (W1), growth in one country is explained in about 55% by the growth in other countries. In model with the spatial weights matrix based on valueadded trade (W2), the analogical number equals about 50%. Table 2. Spatial Durbin model W1 (gross trade) W2 (value-added trade) intercept β (ln y 2000 ) *** *** γ (spatially lagged exogenous regressor) ρ (spatial lag) * * *** p<0.01, ** p<0.05, *p<0.1 Source: own deliberation based on computations in STATA As in both models spatially lagged exogenous regressor is not statistically significant, I estimate also spatial lag model (simplified version of spatial Durbin model). Again both models confirm the existence of convergence at the similar level see Table 3. And again the value of rho-parameter is higher in model including matrix W1 than in model with matrix W2. According to the estimation results, growth in one country is explained in about 54% and 46% respectively by the growth in other countries. 2 (accessed on ). 3 Timmer et. al. [2015]; (accessed on ). 5

6 Table 3. Spatial lag model W1 (gross trade) W2 (value-added trade) intercept *** *** β (ln y 2000 ) *** *** ρ (spatial lag) ** * *** p<0.01, ** p<0.05, *p<0.1 Source: own deliberation based on computations in STATA Generally, spatial weights matrix W1 appreciates closest neighbouring countries and matrix W2 enhance the role of economic partners located farer from the reporting country. Thus, it seems that the closest neighborhoods and direct trade relationships are still more crucial than indirect links with other countries. Conclusion Spatial Durbin models including spatial weight matrices based on the long-term bilateral trade relationships between EU Member States confirm the existence of absolute income betaconvergence among EU Member States over the period The strength of spatial dependencies between EU Member states measured by value of rho-parameter is slightly higher in the case of spatial weights matrix based on gross trade than in the case of spatial weights matrix based on value-added trade. It suggests that direct trade links (better measured by gross trade) are more important than indirect economic links (better measured by value-added trade). 6

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10 Appendix Spatial weights matrices W1 (based on gross trade) and W2 (based on value-added trade) W1 AUT BEL BGR CYP CZE DNK EST FIN FRA DEU GRC HRV HUN IRL ITA LTU LVA LUX MLT NLD POL PRT ROU SVK SVN ESP SWE GBR AUT BEL BGR CYP CZE DNK EST FIN FRA DEU GRC HRV HUN IRL ITA LTU LVA LUX MLT NLD POL PRT ROU SVK SVN ESP SWE GBR

11 W2 AUT BEL BGR CYP CZE DNK EST FIN FRA DEU GRC HRV HUN IRL ITA LTU LVA LUX MLT NLD POL PRT ROU SVK SVN ESP SWE GBR AUT BEL BGR CYP CZE DNK EST FIN FRA DEU GRC HRV HUN IRL ITA LTU LVA LUX MLT NLD POL PRT ROU SVK SVN ESP SWE GBR