Appendix III. Dataset Used in the Sectoral Approach
The sectoral analysis uses a disaggregation of the sectors at the two- or three-digit level of the ISIC Rev 3 classification depending on data availability. Precisely, 14 sub-sectors were considered:
Food products, beverages and tobacco
Textiles and textile products
Leather, leather products, and footwear
Wood and products of wood and cork
Pulp, paper, paper products, printing and publishing
Coke, refined petroleum products and nuclear fuel
Chemicals and chemical products
Rubber and plastic products
Other non-metallic mineral products
Basic metals and fabricated metal products
Machinery and equipment, n.e.c.
Electrical and optical equipment
Data were constructed for Poland, Hungary, and the Czech and Slovak Republics, with all data available between 1995-2003. Most of the data are from the OECD’s Structural Analysis (STAN) database. The following are the main variables (from STAN unless otherwise noted)
Value-added prices: defined as the ratio of the value of value added at basic prices and the volume of value-added in each sector. For Poland, since data for the volume of value-added was not available at that level of disaggregation in the STAN dataset, the Groningen Growth and Development Center database was used (http://www.ggdc.net/). Compiled by the faculty of economics at the University of Groningen (Netherlands), it uses for this variable, data published in the Statistical Yearbook of Industry by the polish Statistical Office (GUS).
Trade openness: defined as the ratio of the import value to the value-added in each sector. The imports referred to are those produced by foreign producers in the same sector.
Labor productivity: defined as the ratio of the volume of value-added in each sector to the number of employees.
Wages: defined as the ratio of the total labor costs of employees to the number of employees in each sector. For Poland, data are collected directly from the Polish Statistical Office (GUS), which at the time of the study, were only available until 2002.
Aggregate CPI index and unemployment rate: are extracted from the IMF World Economic Outlook (WEO) database for each country.
Nominal Effective Exchange Rate: is extracted from the IMF INS database, for each country.
Ball, L., G. Mankiw, 1995, “Relative-Price Changes as Aggregate Supply Shocks,” Quarterly Journal of Economics, Vol. 110 (1), pp.161 –193.
Chen, N., J. Imbs, and A. Scott, 2004, “Competition, Globalization, and the Decline of Inflation,” CEPR Discussion Paper No. 4695.
Van Elkan, R., N. Choueiri, F. Ohnsorge, and E. Stavrev, 2006, “A Factor Analysis of EU Inflation: Implications for New Members’ Euro Adoption Prospects,” IMF, forthcoming.
National Bank of Poland Monetary Policy Council, 2006, Inflation Report, available via internet: http://www.nbp.pl/Homen.aspx?f=en/publikacje/raport_inflacja/raport_inflacja.html, (Warsaw: National Bank of Poland).
Rogoff, K., 2003, “Globalization and the Global Disinflation,” paper presented for the Federal Reserve Bank of Kansas, 2003 Jackson Hole Conference.
Tytell, I., S. Wei, 2004, “Does Financial Globalization Induce Better Macroeconomic Policies,” IMF Working Paper 04/84, (Washington: IMF).
Prepared by Céline Allard.
HICP inflation published by Eurostat has been higher than headline inflation compiled by the Polish Statistical office recently, reflecting different weights in the index, in particular for food products. To facilitate comparability across countries, HICP data is used throughout section B.
These broad commonalities justify that the econometric analyses described in the following sections were conducted on panels including some or all of the other new member states, adjacent to Poland.
Additional IMF staff work, based on a general dynamic factor model approach, corroborates this finding (van Elkan and others, 2006). To analyze the co-movements of inflation within the European Union, their study decomposes inflation in each country into common trends, shared by the 25 members, and a residual, which reflects country-specific elements. This residual is found to have been negative by about one percentage point during the first half of 2005 in Poland, but is likely to gradually shrink going forward.
Here trade openness is not introduced as an explanatory variable by itself. At this level of aggregation, there is insufficient variability to see an independent effect on domestic prices. See also the discussion in section E.
Chen and others, as well as the authors of the WEO study, use production prices, but due to data availability constraints, value added prices were used here. This obviates the need to control for exchange rate movements related to imported intermediary consumptions. Chen’s paper considers EU 15 countries, while the WEO study extends the analysis to a larger OECD sample. This paper applies the methodology to four NMS.
The WEO study finds that the increased trade openness could have reduced relative producer prices in manufacturing by about 0.3 percentage points a year in industrial countries over the past 15 years.