Real Effective Exchange Rates and deindustrialization: Evidence from 25 Post-Communist Eastern European countries

ABSTRACT

For the past three decades, Eastern European countries have exhibited a noticeable decline in their share of the industrial production sector overall, but not uniformly. Simultaneously, trade liberalisation and integration in international production networks were intensified, bringing different export sophistication levels and economic development to countries in this region. This paper aims to examine the real effective exchange rate (REER) impact on the deindustrialisation or reindustrialisation process in 25 post-communist Eastern European countries. The paper employs a heterogeneous panel common factor approach for the period 1995–2018 to exploit the effect of diverse levels of export complexity, stage of economic development and intensity of participation in global value chains on the REER–industrial production relationship. The results establish a heterogeneous yet significant negative relationship between currency strengthening and industrial production. Our findings also indicate that this negative effect of appreciation is less pronounced with the country’s higher economic complexity and its broader participation in global value chains.

KEYWORDS:

• Global value chain
• economic complexity index
• Eastern Europe
• post-communist economies
• real effective exchange rate

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Albania, Armenia, Azerbaijan, Belarus, Bulgaria, Croatia, Czechia, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Poland, Republic of North Macedonia, Romania, Russian Federation, Serbia, Slovakia, Slovenia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan. For the other post-communist countries in the Eastern European region, the necessary data were not available for the observed period.

2. See https://www.bruegel.org/publications/datasets/real-effective-exchange-rates-for-178-countries-a-new-database/

3. As suggested by Pedroni (2007), to deal with possible reverse causality, we estimated Equation (2)(2) $lnREE{R}_{it}=\alpha +\beta lnGDPp{e}_{it}+u_{it}$(2) augmented with a common dynamic process variable using Group-Mean Fully-Modified Ordinary Least Square (GM-FMOLS) methodology. We obtain similar results as in AMG estimation, confirming that productivity estimate is not mis-specified.

4. Alternatively, a common dynamic process can be subtracted from the dependent variable, which means that the common process is imposed on each panel cross-section with a unit coefficient.

5. Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia.

6. Albania, Armenia, Azerbaijan, Belarus, Bulgaria, Georgia, Kazakhstan, Kyrgyzstan, Republic of North Macedonia, Romania, Russian Federation, Serbia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan.

7. Poland has an upstream position relative to the other CEE countries due to specialisation in intermediate goods and industrial equipment, and partially due to export of natural resources.