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The Balassa–Samuelson Effect in Central Europe: A Disaggregated Analysis

Abstract

This paper aims to explain differences in inflation between six central European economies – Croatia, the Czech Republic, Hungary, Poland, Slovakia and Slovenia – and the euro area in terms of differences in productivity growth between tradable and non-tradable sectors. The coverage of tradable and non-tradable sectors is broader and more detailed than in previous studies and the data samples are larger, as quarterly data for up to 10 years are used. The main conclusion is that productivity differentials explain on average only between 0.2 and 2.0 percentage points of annual inflation differentials vis-à-vis the euro area. Productivity differentials also explain only a small proportion of domestic inflation in central European economies. Earlier studies that estimated the Balassa–Samuelson effect to be larger have often neglected to consider the impact of productivity differentials on inflation relative to the euro area, focusing instead only on their impact on domestic inflation. Many studies have also neglected the relatively high productivity growth in non-tradable industries. The estimates in this paper suggest that differences in productivity growth between EU accession countries and the euro area are unlikely to widen sufficiently to become a determining factor in the ability of these countries to satisfy the Maastricht inflation criterion.

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Notes

  1. 1.

    Given the constant returns to scale production functions, equation (6) implies a unique level of K T/L T consistent with the world rate of return on capital R. Given K T/L T, equation 8 determines the economy-wide wage rate W. The remaining two equations then determine K NT/L NT and P NT/P T.

  2. 2.

    An equivalent expression can be derived within the Scandinavian model of inflation (Aukrust, 1977). This model ignores factor intensities and explains the domestic rate of inflation (π) and the increases in domestic money wages in the open (or ‘exposed’) and ‘sheltered’ sectors through an exogenously given rate of increase in the foreign price level, π *, and the development of labour productivity in the two sectors (a E and a S ): π=π *+α S (a E a S ).

  3. 3.

    This is also true of many studies of the Balassa–Samuelson effect in industrial countries, for example, De Gregorio et al. (1994) and Swagel (1999). One exception is Alberola-Ila and Tyrväinen (1998).

  4. 4.

    In a two-country model, the CPI-based REER corresponds to the real exchange rate in equation 2, which was used to derive the Balassa–Samuelson equation 11. Using the notation in equations 1, 1′ and 2, the evolution of the real exchange rate is e−[αp T+(1−α)p NT]+[αp T*+(1−α)p NT*], where the share of traded goods in consumption (α) is assumed to be the same at home and abroad. The real exchange rate then changes as −(1−α)[(p NTp T)−(p NT*p T*)], that is, it appreciates when p NTp T>p NT*p T*.

  5. 5.

    Fischer (2002) points out that, in a model with investment demand, rising productivity in the export sector (which is usually relatively capital intensive) raises the equilibrium capital stock and thus investment demand. This in turn increases prices and, ceteris paribus, leads to a real exchange rate appreciation. The estimated total effect of productivity on the real exchange rate may thus include not only the pure Balassa–Samuelson effect, but also the investment demand effect.

  6. 6.

    Egert (2002a, 2002b), Egert et al. (2002) and Flek et al. (2002) estimate the inflation differentials vis-à-vis Germany as a proxy for the European Union.

  7. 7.

    See Egert (2002a), 2002b), Egert (2003), however, provides a detailed analysis of the Balassa–Samuelson effect in Estonia based on highly disaggregated data.

  8. 8.

    See, for example, Coricelli and Jazbec (2001), De Broeck and Sløk (2001) and Jazbec (2001).

  9. 9.

    Under the assumptions of equal productivity growth in domestic and foreign non-tradable industries, equal shares of non-tradables and equal factor intensities, equation 11 simplifies to Δp t −Δp t *−Δe t =(1−α t ) (Δa t T−Δa t T*). The left-hand side of this equation is frequently used as the dependent variable in the empirical work using the CPI-based REERs.

  10. 10.

    Using a simple production function Y=AL α K (1−α), one can easily show that (dY/Y)/(dL/L)=α+(dA/A)/(dL/L)+(1−α)(dK/K)/(dL/L), that is, growth of average labour productivity is a sum of total factor productivity growth per worker and capital deepening.

  11. 11.

    To calculate the factor shares from national accounts data, one needs a breakdown of GDP by income component for different production sectors of the economy. In central Europe, only Hungary publishes such data.

  12. 12.

    See also Alberola-Ila and Tyrväinen (1998) and Swagel (1999). The expected sign of the wage differential coefficient [(w Tw NT)−(w T*w NT*)] is negative: the employers in the non-tradable sector are expected to react to wage pressures by increasing their prices. The estimated coefficient on wage differential for Slovakia is negative, in line with this hypothesis, but for Croatia it is positive. Both estimated coefficients are statistically highly significant.

  13. 13.

    For the two versions of the Balassa–Samuelson effect (equation 12, Table 3; and equation 14, Table 4) the long-term elasticities (calculated as β 2/(1−β 0)) are as follows: Croatia (1.5, 0.9); Czech Republic (1.1, 1.3); Hungary (3.1, 2.7); Poland (1.5, 3.9); Slovakia (2.9, 13.9); and Slovenia (1.5, 1.9). For the euro area, the long-term domestic Balassa–Samuelson elasticity is 0.16.

  14. 14.

    The Maastricht inflation criterion is calculated on the basis of average inflation in the three euro area countries with the lowest inflation over 2000–2002, plus a margin of 1.5 percentage points.

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Acknowledgements

We acknowledge helpful comments from Palle Andersen, Claudio Borio, Balász Égert, Andrew Filardo, Renato Filosa, Christoph Fischer, Boštjan Jazbec, Ali Kutan, Jeffrey Miller and Velimir Šonje. We also thank central bank colleagues for supplying the data. Earlier versions of this paper were presented at the 8th Dubrovnik Economic Conference (June 2002); the International Centre for Economic Growth (European Centre) conference on Exchange rate strategies during the EU enlargement (November 2002); and a BIS seminar (June 2003); and appeared in working paper series of the International Centre for Economic Growth (European Centre) and the BIS.

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Appendices

APPENDIX A

Derivation of equation 10, domestic Balassa–Samuelson (or Baumol–Bowen) effect

Let k T=K T/L T, k NT=K NT/L NT, p N=P NT/P T. Then

Substitute equation A.1 into equation 8:

Substitute equation A.2 into equation 9:

Set equation A.3=A.4:

Take logs:

Collect the terms:

where c is a constant (note that R is given). Log differentiating this expression gives equation 10:

where lowercase letters denote logarithms and Δx=d logx/x.

APPENDIX B

Data description

Economies and periods covered

Euro area (1992:1–2001:3), Croatia (1995:1–2001:3), Czech Republic (1993–2001:3), Hungary (1994–2001:3), Poland (1994–2001:3), Slovakia (1995–2001:3) and Slovenia (1992–2001:3).

Traded and non-traded sectors

Traded goods and services: manufacturing, mining, hotels, transportation and communications.

Non-traded goods and services: electricity, gas and water supply, construction, wholesale and retail trade and repair services, financial intermediation, real estate, renting and business activities, education, health and social work, and other community, social and personal activities.

Not considered are, on the traded goods side, agriculture, forestry and fishing because trade in agricultural products is distorted by the Common Agricultural Policy and different agreements on agricultural trade between the European Union and accession countries; on the non-traded goods side, public administration, defence and compulsory social security are not considered because of the difficulty in interpreting labour productivity figures caused by large shifts in the number of public sector employees.

The above classification follows De Gregorio et al. (1994), who define a sector as ‘tradable’ if more than 10% of total production is exported. In this paper, hotels and restaurants are also included among tradables because of their large service export content in several central European countries (the Czech Republic, Hungary, Croatia, Slovenia).

Description of variables

  • Quarterly indices of value added (in constant prices) from the production side GDP estimates. The weights used to aggregate individual industries into traded and non-traded sectors are industries’ shares in total value added (corrected for agriculture and public administration). For Poland (and some years in a few other countries), only annual data on the GDP breakdown by industry were available. Quarterly data on industrial production were then used to determine quarterly growth rates for tradables; and quarterly GDP data to determine quarterly growth rates for non-tradables (as a ‘residual’ between GDP and tradables).

  • CPI rates of inflation with subcomponents enabling a breakdown into traded and non-traded goods and services; the subcomponents are aggregated into traded and non-traded goods inflation on the basis of respective weights in the CPI basket (quarterly averages).

  • Nominal exchange rates of domestic currency against the euro (quarterly averages).

  • Employment (quarterly averages) in traded and non-traded goods industries. The weights used to derive employment in traded and non-traded sectors are industries’ shares in total employment (corrected for agriculture and public administration).

  • Nominal wages (quarterly averages) by industry. The weights used to derive wages in traded and non-traded sectors are industries’ shares in total employment (corrected for agriculture and public administration).

Data sources

National central banks and statistical offices (data for six central European countries); European Central Bank (data for the euro area); BIS; and staff estimates (see Charts 7, 8, 9 and 10).

Chart 7
figure7

Croatia and Slovenia (Dotted lines represent linear time trends. Estimated regression lines show time trend (‘x’) regressed on the dependent variable (‘y’) shown in the legend on the left)

Chart 8
figure8

Czech Republic and Hungary (Dotted lines represent linear time trends. Estimated regression lines show time trend (‘x’) regressed on the dependent variable (‘y’) shown in the legend on the left)

Chart 9
figure9

Poland and Slovakia (Dotted lines represent linear time trends. Estimated regression lines show time trend (‘x’) regressed on the dependent variable (‘y’) shown in the legend on the left)

Chart 10
figure10

Euro area (Dotted lines represent linear time trends. Estimated regression lines show time trend (‘x’) regressed on the dependent variable (‘y’) shown in the legend on the left)

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Mihaljek, D., Klau, M. The Balassa–Samuelson Effect in Central Europe: A Disaggregated Analysis. Comp Econ Stud 46, 63–94 (2004). https://doi.org/10.1057/palgrave.ces.8100041

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Keywords

  • Balassa–Samuelson effect
  • productivity
  • inflation
  • EMU
  • transition

JEL Classifications

  • E31
  • F36
  • O11
  • P20