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Appendix 1: Contribution of Explanatory Variables to CA norms in EU-10
Appendix 2: Robustness Check for Explanation of CA Divergence
Appendix 3: Data sources
I am grateful to Russell Kincaid who initiated this project and provided guidance at various stages. I am also thankful to Albert Jaeger, Alan MacArthur, Abdul Abiad, Philip Schellekens, Franziska Ohnsorge and Zsofia Arvai for insightful discussions and to desk economists of EU new member states and seminar participants at European and Policy Development and Review
Admittedly, this threshold is arbitrary. The idea behind using a relatively high threshold is to capture the impact of remittances only where they provide a stable and significant source of income.
The impact of FDI on the CA balance increases from -0.13 to -0.22 and becomes significant at 1 percent if the estimation is done with annual data. The higher negative impact on annual CA balance reflects imports that often accompany FDI in its initial phase, particularly if the investment is greenfield. Estimation with annual data also renders a highly significant coefficient for GDP growth, which is not observed at the estimation with 4-year averaged data implying its lesser influence on medium-term CA balance.
Regressing the CA balance on only country-specific dummy variables produces an R2 of 0.32.
The projected values for 2013 are used from the IMF’s WEO database for all explanatory variables except for NFA, for which data for 2009 is used. The drawback of this approach is that it uses projected values which may or may not materialize and, for NFA, may unduly allow for a lower CA norm for countries that are expected to run large CA deficits during 2008-09. The advantage of this approach is that it incorporates country-specific knowledge.
The medium-term (2013) value for the investment climate index used in the CA norm estimate is 4 for all and 4.3 for Hungary. In 2006, the value of this variable ranged between 3.56 in Romania and Slovenia to 4.0 in Hungary for EU-10. The highest possible value of the index is 4.3.
Another way of considering the impact of EU capital grants on the CA balance would be through the fiscal balance as these grants, recorded as revenues, allow for a higher fiscal balance. If we assume that in the medium-term, EU-10 countries are likely to receive EU capital grants of about 2 percentage point of GDP, taking these out of the fiscal balance would lower the fiscal balance by this magnitude. Using the coefficients on the fiscal balance reported in Table 4, this would allow for a decrease in the CA norm of half a percentage point of GDP. However, the channeling of at least part of the EU grants outside the budget and the explicit additionality rule for some grants would complicate such simple applications.
The regression coefficients from the EUR sample is chosen over those from the full sample pooled estimate in order to take account of possible non-homogeneity between EU-10 and other developing countries. At the same time, the FE estimates were disfavored as the country-specific coefficients take away much of our ability to identify factors that explain intra-EU differences.
An alternative measure of output gap obtained from applying the Hodrick-Prescott (HP) filter on actual output to estimate potential output gave different results. These results reported in Appendix 2 show a much higher impact of output gap on the CA balance, but no non-linearity. However, the use of HP filter to estimate output gap is problematic. In addition to the familiar end-point problem, its application may not be appropriate for certain transition economies that are going through structural changes.
The starting year in Figures 5a and 5b differs among countries reflecting differences in data availability for potential output/output gap. Potential output was available for 7 years for the Baltic countries and Hungary and for 4-5 years for Bulgaria, Romania and Slovakia.
Manufacturing exports of International Standard Industrial Classification (ISIC) products 15-29 were classified as follows: labor-intensive: 15-19, 36; resource-intensive: 20-28; and technology-intensive: 29-35.
Preliminary data for all three Baltic countries indicate a slowdown since the third quarter of 2007. For example, Latvia’s (seasonally unadjusted) CA deficit seemed to have peaked around 27 percent of GDP in 2006 Q4 and has fallen by 7.5 percentage points of GDP in the following four quarters.