Abstract
The authors construct an indicator of regional integration based on a Bayesian state-space approach. The state-space model is helpful in estimating the overall level of regional integration by using all information contained in a set of indicators. The authors apply this model to the level of regional integration between members of the OECD. The variables of the level of regional integration – i.e. the Current Economic Integration (AEI) (Mongelli FP, Dorrucci E, Agur I, What does European institutional integration tell us about trade integration. European Central Bank Occasional Paper Series 40, 2005) – are standardized and organized in four groups: flows of goods, flows of services, Foreign Direct Investment (FDI) and other financial flows, and migration. The AEI can also be used to construct a weighted directed network. By observing the weighted directed network, the authors found that the core players in the OECD are the US, Germany and the United Kingdom, and in second place, France, Italy and Japan. Finally, they conclude that the level of economic integration among OECD members has increased over the last 20 years, and that the European integration agreements and the NAFTA have had positive effects on economic integration.
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Notes
- 1.
For the sake of readability, the notation is sometimes simplified. y (j) is a single indicator of integration for all country-couples and all years. y i , t is the vector of all indicators in a given year and for a given country-couple, while this vector for all years and all country-couples is simply y.
- 2.
In network theory the link (or edge) going from country (node) A to country B is denoted XB,A (Newman 2010).
- 3.
Estonia (1991), Slovakia (1993), Slovenia (1993) and the Czech Republic (1991) are added later to the sample.
- 4.
The weighted indegree of a country is the sum of the AEI index of all incoming arrows.
- 5.
In Table 16.2, the following regression is estimated: AEI = βX + μ with μ ~ N(0, σ 2). This is done by drawing a value for β using randomly drawn values of the AEI index from the Gibbs sampler: \( {\beta}^{(j)}|X,AE{I}^{(j)}\operatorname{}\sim N[b;\overset{-}{\sigma }{({X}^{\mathrm{\prime}}X)}^{-1}] \), with b = (X ′ X)−1 X ′ AEI (j), \( \overset{-}{\sigma}={e}^{\prime } e/\left( n- k\right) \) and e = AEI (j) − Xb. The adjusted standard deviation is then computed using the drawn values of β (j).
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Acknowledgements
We are indebted to Gaspare Genna, Philippe De Lombaerde and the participants of UNU-CRIS’s Workshop on Indicator-Based Monitoring of Regional Economic Integration for their comments and suggestions. Funding provided by the Research Foundation – Flanders and the Belgian National Bank.
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Rayp, G., Standaert, S. (2017). Measuring Actual Economic Integration: A Bayesian State-Space Approach. In: De Lombaerde, P., Saucedo Acosta, E. (eds) Indicator-Based Monitoring of Regional Economic Integration. United Nations University Series on Regionalism, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-50860-3_16
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