Abstract
This paper contributes to the empirical literature on the relationship between “export variety” (export diversification) and economic development by relaxing the assumption of cross-country independence and allowing for spatial diffusion of shocks in observed and unobserved factors. Export variety is measured for a balanced panel of 114 countries (1992–2012) using very detailed information on their exports (HS 6-digit product level). The estimation results of a dynamic spatial panel data model confirm the relevance of spatial network effects in export diversification: indirect effects (spatial spillovers) strongly reinforce direct effects, while spatial proximity to large countries accelerates the diversification process. In about 10 years the whole space–time diffusion of the diversification shock is widely completed. We reveal that the long-run spillover impact from European countries is much higher than from other countries such as the United States, Japan, or the BRICS (Brazil, Russia, India, China, and South Africa).
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- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
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Review of International Economics
no. 26,
pages 634 - 650,
ISSN: 0965-7576 - Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Basile R., Parteka A., Pittiglio R.: Export diversification and economic development: A dynamic spatial data analysis// Review of International Economics. -Vol. 26, nr. 3 (2018), s.634-650
- DOI:
- Digital Object Identifier (open in new tab) 10.1111/roie.12316
- Bibliography: test
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- their superior production techniques endow them with an absolute advantage in global markets, and there is no re-specialization. Regolo (2013) develops an extension of the Romalis (2004) framework and models how a country's export diversification varies across destination markets (export diversification is greater if the trade partner has similar endowments). open in new tab
- Theoretically, such a situation can take place when countries are "travelling across multiple cones of diversification" (Deardorff, 2000; Schott, 2003; Cadot et al., 2011). Countries initially diversify at the extensive margin, but when a high level of development is reached it is more profitable to abandon the production of labor-intensive goods and, thus, re-specialize.
- Absolute measures (such as the Herfindahl index or the Theil index), based on indices of concentration or inequality, were used by Imbs and Wacziarg (2003), Koren and Tenreyro (2007), Cadot et al. (2011), Agosin, Alvarez, and Bravo-Ortega (2012), and Klinger and Lederman (2006). Relative measures were employed by De Benedictis et al. (2008, 2009), Parteka & Tamberi (2013a,b) and Mau (2016). open in new tab
- Some authors only take into account the role played by distance between trade partners. For instance, Agosin et al. (2012) consider the GDP-weighted average distance of each country from its trading partners, whereas Dennis and Shepherd (2011) take into account the distance between the exporting country and Germany. These measures proxy for transportation costs. Theoretical framework by Regolo (2013) shows that trade costs matter for diversification. open in new tab
- Some diversification studies only address this issue indirectly by including in the set of additional explanatory variables the participation in common regional trade agreements (Parteka & Tamberi, 2013b). open in new tab
- Along these lines, the model by Regolo (2013) predicts that exports between similarly endowed countries ("South-South" and "North-North") are more diversified than exports between differently endowed countries ("South- North" and "North-South"). open in new tab
- Similar reasoning can be made in a multiregional NEG setting. See a thorough review of the state of the art in geo- graphical economics and spatial economic analysis, including multiregional framework, in Commendatore, Kubin, and Kayam (2015). open in new tab
- By a permanent change of x k at time t they mean: x kt 1D; x k;t11 1D; . . . ; x k;T 1D, so the values increase to a new level and remain there in future time periods. open in new tab
- The term spillover is referred to contemporaneous cross-partial derivatives, those that involve the same time period. Cross-partial derivatives involving different time periods are referred to as diffusion effects, since diffusion takes time. open in new tab
- The countries are: Albania; Algeria; Angola; Armenia; Australia; Austria; Azerbaijan; Bangladesh; Belarus; Benin; open in new tab
- Bolivia; Brazil; Bulgaria; Burkina Faso; Burundi; Cameroon; Canada; Central African Republic; Chad; Chile; China; open in new tab
- Colombia; open in new tab
- Congo, Rep.; Costa Rica; open in new tab
- Cote d'Ivoire; Denmark; Dominican Republic; Ecuador; Egypt, Arab Rep.; El Salvador; Finland; France; Gabon; open in new tab
- Gambia, The; Georgia; Germany; Ghana; Greece; Guatemala; Guinea; Guinea- Bissau; Honduras; open in new tab
- Hong Kong SAR, China; Hungary; India; Indonesia; Israel; Italy; Japan; Jordan; Kazakhstan; open in new tab
- Kenya;
- Korea, Rep.; Kyrgyz Republic; Lao PDR; Latvia; Lebanon; Lithuania; Madagascar; Malawi; Malaysia; Mali;
- Mauritania; Mauritius; Mexico; Moldova; Mongolia; Morocco; Nepal; Netherlands; New Zealand; Nicaragua; Niger; open in new tab
- Nigeria; Norway; Pakistan; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Poland; Portugal; Romania; open in new tab
- Russian Federation; Rwanda; Saudi Arabia; Senegal; Sierra Leone; Singapore; Slovenia; South Africa; Spain; Sri Lanka; Sweden; Switzerland; Tajikistan; Tanzania; Thailand; Togo; Trinidad and Tobago; Tunisia; Turkey; Turkmeni- stan; Uganda; Ukraine; United Kingdom; United States; Uruguay; Uzbekistan; Venezuela, RB; Vietnam; Yemen, Rep.; Zambia. open in new tab
- The estimation of dynamic spatial panel data models requires balanced data. The countries considered correspond to 90.7 percent of world trade (own calculations based on export data, 2012, from UN Comtrade). Microstates (defined as countries with a population below 1 million) are excluded from the analysis. open in new tab
- A similar level of detail is adopted by Klinger and Lederman (2006), Cadot et al. (2011), Parteka and Tamberi (2013a), and Mau (2016).
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Export diversification and economic development: a dynamic spatial data analysis
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