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Export diversification and economic development: A dynamic spatial data analysis

Abstrakt

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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Copyright (2017 John Wiley & Sons Ltd)

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
Review of International Economics nr 26, strony 634 - 650,
ISSN: 0965-7576
Język:
angielski
Rok wydania:
2018
Opis bibliograficzny:
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:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1111/roie.12316
Bibliografia: test
  1. 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). otwiera się w nowej karcie
  2. 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.
  3. 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). otwiera się w nowej karcie
  4. 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. otwiera się w nowej karcie
  5. 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). otwiera się w nowej karcie
  6. 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"). otwiera się w nowej karcie
  7. 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). otwiera się w nowej karcie
  8. 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. otwiera się w nowej karcie
  9. 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. otwiera się w nowej karcie
  10. The countries are: Albania; Algeria; Angola; Armenia; Australia; Austria; Azerbaijan; Bangladesh; Belarus; Benin; otwiera się w nowej karcie
  11. Bolivia; Brazil; Bulgaria; Burkina Faso; Burundi; Cameroon; Canada; Central African Republic; Chad; Chile; China; otwiera się w nowej karcie
  12. Colombia; otwiera się w nowej karcie
  13. Congo, Rep.; Costa Rica; otwiera się w nowej karcie
  14. Cote d'Ivoire; Denmark; Dominican Republic; Ecuador; Egypt, Arab Rep.; El Salvador; Finland; France; Gabon; otwiera się w nowej karcie
  15. Gambia, The; Georgia; Germany; Ghana; Greece; Guatemala; Guinea; Guinea- Bissau; Honduras; otwiera się w nowej karcie
  16. Hong Kong SAR, China; Hungary; India; Indonesia; Israel; Italy; Japan; Jordan; Kazakhstan; otwiera się w nowej karcie
  17. Kenya;
  18. Korea, Rep.; Kyrgyz Republic; Lao PDR; Latvia; Lebanon; Lithuania; Madagascar; Malawi; Malaysia; Mali;
  19. Mauritania; Mauritius; Mexico; Moldova; Mongolia; Morocco; Nepal; Netherlands; New Zealand; Nicaragua; Niger; otwiera się w nowej karcie
  20. Nigeria; Norway; Pakistan; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Poland; Portugal; Romania; otwiera się w nowej karcie
  21. 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. otwiera się w nowej karcie
  22. 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. otwiera się w nowej karcie
  23. 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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