This Selected Issues paper on Poland analyzes tax reform in the country. It highlights that in common with many countries, Poland’s personal income tax is based on a definition of global personal income, though some income sources (such as dividends and interest income) are taxed under separate schedules. In addition, agriculture, forestry, and inheritances are taxed under separate laws. The paper presents a medium-term perspective for capital flows to Poland. It highlights that Poland has developed a reputation for sound macroeconomic policies and openness both to trade and financial flows.


This Selected Issues paper on Poland analyzes tax reform in the country. It highlights that in common with many countries, Poland’s personal income tax is based on a definition of global personal income, though some income sources (such as dividends and interest income) are taxed under separate schedules. In addition, agriculture, forestry, and inheritances are taxed under separate laws. The paper presents a medium-term perspective for capital flows to Poland. It highlights that Poland has developed a reputation for sound macroeconomic policies and openness both to trade and financial flows.

IV. Inflation Targeting in Poland: Linkages Between Monetary Policy Instruments and Inflation1

141. With inflation in some advanced transition economies in Central and Eastern Europe now dropping to single-digit levels, monetary policy makers in several countries, including Poland, are showing a growing interest in analyzing inflation dynamics. How is inflation influenced by shifts in monetary policy instruments, such as interest rates, monetary aggregates, or the exchange rate? How does it respond to changes in other economic variables, such as wages, the unemployment rate, or capacity utilization? Given that Poland’s Monetary Policy Council (RPP) announced in the autumn of 1998 that it was adopting an inflation targeting framework for the conduct of monetary policy, these policymakers are particularly interested in knowing the characteristics and timing of the linkages between monetary policy instruments and the rate of inflation. Understanding the strength of these relationships and their lags will help them to better calibrate their monetary policy actions, improve their timing, and better achieve their inflation targets.

142. This chapter looks at the inflation process in Poland in the 1990s, and attempts to answer these questions by examining the statistical linkages between inflation and monetary policy instruments and other so-called leading indicators of inflation.2 Because transition economies have tended to experience large increases in administered prices, wide swings in relative prices, and the introduction of new Western-style taxes (like the VAT) that have tended to complicate the inflation story, it is important to look at measures of underlying inflation. In general, it appears that explicitly taking account of movements in individual components of the standard price indices over the transition period helps to reveal an underlying inflation process that is more stable and predictable than might appear at first. In addition, theoretically sound linkages between several monetary policy variables and underlying inflation are discernable in the data. In particular, the exchange value of the zloty and to some extent broad money seem to be statistically related to movements in inflation; in certain cases it can be shown that they Granger cause shifts in inflation. Even so, the statistical power of monetary policy instruments and leading indicators of inflation to “explain” inflation in Poland still appears to be modest.

A. Why Underlying Inflation is an Important Concept in Poland

143. The decline in CPI inflation in Poland during the period 1992-98—from roughly 50 percent a year to under 10 percent—has been impressive (Figure 1). Even in seasonally adjusted terms, however, substantial monthly fluctuations remain, as seen in the lower right panel of Figure 1. Most analyses of the inflation process presume that price changes for the main components of goods and services that make up the CPI are distributed normally for each period of time. This normality is important for at least two reasons. First, as Pujol and Griffiths (1996) and Ball and Mankiw (1995) argue, skewness or high variability of price movements across categories can impart a bias toward higher overall inflation because of menu effects.3 The argument runs as follows: if costs increase sharply in a few spending categories, higher prices are likely to be passed along to consumers because it is worth it for restaurant owners to pay the fixed costs of re-printing menus, but if costs decline slightly in a number of other spending categories (such that prices on average might otherwise remain unchanged), these prices may not be lowered to consumers because of the fixed costs of reprinting menus. That is, these authors argue that inflation could increase simply because of a non-normal distribution of price increases across the various categories of the CPI. Second, normality makes its easier to characterize and forecast CPI inflation. Unfortunately, however, evidence of skewness and excess kurtosis in the distribution of price changes across the categories of the CPI (for a given time period) is widespread in many countries, and Poland is no exception.4

Figure 1.
Figure 1.

Poland: Consumer Price Index, 1992–1998

(percent change, as indicated)

Citation: IMF Staff Country Reports 1999, 032; 10.5089/9781451831818.002.A004

144. Prima facie evidence of the non-normality of Polish price changes is suggested by Figure 2. The top panel shows a histogram of seasonally-adjusted monthly price changes across the 33 main categories of goods and services in Poland’s CPI that were observed over 78 months (January 1992 to June 1998)—that is, 2,574 price changes. Each price change is standardized by subtracting the average monthly inflation across the 33 groups for each time period and dividing by the corresponding standard deviation. An alternative way of looking at the possible normality of these price changes is shown in the middle panel of Figure 2. Here, after seasonal adjustment, the price changes in each of the 33 categories is transformed by subtracting a third-degree polynominal time-trend to adjust for transition effects (i.e., the fact that inflation has been falling over time). After this adjustment, the remaining data values are standardized as in the top panel. For reference, the bottom histogram represents a standard normal distribution. Strong departures from normality are evident in the top and middle histograms, especially considering the existence of numerous observations four and five standard deviations away from the means. This suggests that there have been a surprisingly great number of very large price increases (and large price declines) in Poland.5

Figure 2.
Figure 2.

Poland: Histograms of Monthly Price Increases

Citation: IMF Staff Country Reports 1999, 032; 10.5089/9781451831818.002.A004

Note: See text for explanation of the construction of these histograms.

145. Transition dynamics, including large swings in relative prices, jumps in administered prices, and tax changes help to account for at least some of this non-normality, as does the heavy weight in the Polish CPI on foodstuffs, which are affected by weather conditions. The role played by administered prices and the prices of goods subject to tax changes, which together account for roughly one quarter of the CPI by weight in the period under consideration, are documented in Figure 3.6 Clearly many of these thirteen CPI categories show large discrete price movements. The cumulative effect is to make inflation appear less stable over time, less predictable, and harder to model.

Figure 3.
Figure 3.

Poland: Administered Prices (percent change, month-on-month)

Citation: IMF Staff Country Reports 1999, 032; 10.5089/9781451831818.002.A004

B. Estimating Underlying Inflation

146. The preceding discussion highlights the potential difficulties of trying to explain statistically the short-run movements in headline CPI inflation, as well as the possible drawbacks of using headline CPI inflation as a policy target. Because of these shortcomings, it is desirable to develop alternative statistical measures of inflation that behave in more a predictable manner. Often such measures are called “core inflation” or “underlying inflation.” This chapter follows closely the work of Bryan, Cecchetti and Wiggins (1997) on the concept of underlying inflation because their approach provides an operationally straightforward definition of underlying inflation as a trimmed measure of a long-run moving average of CPI inflation. Their concept is to remove from the aggregate monthly CPI measure price changes in categories that were either unusually large or unusually small to give a better picture of price trends without these outliers. Trimmed-mean underlying inflation is defined as:


where a is the percentage trimmed in each tail, ωi,t is the weight on commodity I at time t, and xi,t is the month-on-month price increase in commodity I at time t. Iα,t is the set of commodities left after trimming at time t—that is, the I’s remaining after the a smallest and a largest increases in the prices of individual index components at time t have been removed. Notice that the sample average corresponds to setting a to zero, and the sample median to setting a to 50. The weights in the trimmed mean are updated annually to adjust for the fact that the weights in the CPI are updated annually.

147. Note that it is categories with certain weights that are removed from each tail, not a specific number of categories. For example, with a=20, it is possible that in March 1995, the food category alone might represent 20 percent of the weight of the CPI and have the lowest inflation that month, so only food prices would be deleted from the lower tail in the computation of the CPI that month. On the other hand, it might be necessary to remove the price effects of say, fuels, education services, and telecommunications from the upper tail in the computation of the CPI if these three categories represented 20 percent of the weight in the CPI and these three categories had the highest inflation that month.

148. A key question is how much should be trimmed to develop the best measure of underlying inflation? In other words, which value should the parameter a take on? Bryan (1997) re-sample with replacement from each commodity group (picking a random month in a random year for each commodity) to build up a large Monte Carlo sample of trimmed means. This is repeated for different values of a and Athe mean squared error (MSE) for each a across the Monte Carlo replications is computed as:


149. Finally, the a corresponding to the lowest MSE is chosen. The same procedure is followed here. The data underlying the histograms in Figure 2 are sampled with replacement, and the result is that according to the MSE criterion, approximately 20 percent of the CPI weights should be trimmed from each tail, or a total of 40 percent of all weights in the CPI. In other words, this trimmed mean CPI concept relies on the pricing signal coming from the 60 percent of the weights of the CPI that have the most average price trends for any given month. The CPI categories whose price changes are included in or excluded from the trimmed inflation measures can change every month.

150. Turning specifically to the case of Poland, four alternative monthly time series of CPI inflation measures are apparent: headline CPI inflation (CPI), which is the sample average; a =20 percent trimmed mean inflation (T20); median inflation (MED), which is the inflation rate of the median CPI component at time t; and CPI inflation excluding administered prices (UPI). The month-on-month seasonally adjusted increases for these four CPI measures in Poland are plotted in Figure 4. Notice that even when administered prices are excluded in the UPI inflation measure, many outliers remain in the time series of monthly changes in the price index, while the 20 percent trim (T20) and the median inflation (MED) measures seem reasonably smooth except for large outliers in December 1993 and January 1994.7 Not surprisingly, the different inflation measures are quite correlated. Headline CPI is most highly correlated with the 20 percent trimmed inflation (0.90), followed by median inflation (0.83), and lastly, private-sector inflation (0.76).

Figure 4.
Figure 4.

Poland: Four Measures of Consumer Price Index

Citation: IMF Staff Country Reports 1999, 032; 10.5089/9781451831818.002.A004

Note: All measures are month-on-month increases in seasonally adjusted prices.

C. Linkages Between Monetary Policy Instruments and Inflation

151. In this section, the statistical linkages between monetary policy instruments and inflation are examined, as are the linkages between various so-called leading indicators of inflation and inflation. The variables investigated are listed in Table 1. The list includes monetary aggregates, interest rates, exchange rates, real activity variables, labor market variables, and foreign price indices. The data sources include the Polish Central Statistical Office and the National Bank of Poland. Most variables are seasonally adjusted and transformed by taking logarithms.8

Bivariate Relationships

152. To illustrate the bivariate relationships between the four candidate inflation measures and the monetary policy instruments and potential leading indicators of inflation, the P-values from bivariate Granger causality tests are presented in Table 2. Each of the four panels corresponds to one of the four inflation measures, and each column to an economic indicator.9 Each panel contains eight rows corresponding to 1 though n lags in the bivariate regressions, where n = 1, 2,.., 8. Each entry in the table gives the P-values for the null hypothesis that the indicator does not Granger-cause the inflation measure—that is, the probability of obtaining a sample which is even less likely to conform to the null-hypothesis of no Granger causality than the sample at hand. Values smaller than 5 percent are presented in bold italics. That is, these bold values indicate cases where there is evidence that movements in variables tend to Granger cause movements in inflation.

Table 1.

Poland: Variable Definitions and Transformations

article image
Note: All variables are observed at the monthly frequency. Based on the unit roots tests, all variables are applied in first differences. Real interest rates are computed by dividing one plus the nominal interest rate by one plus the 12-month percent change in the headline consumer price index, then subtracting one and multiplying by 100.
Table 2.

Poland: P–values from Bivariate Granger Causality Tests

article image

153. A few features are common across the inflation measures in Table 2: the effective exchange rates and foreign price indices (neer, reerc, reerp and prcpizl, prppizl) are significant across lag orders for all four inflation measures. Other variables that are significant for some inflation measures for some lags include broad money (lur), the interest rate variables (fidbma and fitbl3w), administered prices (adm), and the retail sales activity variables (ars and ars_r). On the other hand, among the stock price index (wse), the fiscal deficit (gcbal), the nominal exchange rate (enda), and the labor cost variables (lulcm), none appear significant at any lag order. Some differences across inflation measures emerge. The unemployment rate (lur) appears highly significant for headline inflation, for example, but is much less significant for the other inflation measures. All in all, these tests lead us to the conclusion that foreign prices and the exchange rate seem to have had the strongest tendency to cause movements in Polish inflation during the past seven years, and that real sector activity and labor market factors—factors that are usually considered to be important in advanced economies–have played relatively little role in influencing inflation in Poland.

Impulse Responses

154. In Figure 5, the impulse responses from a 4th order bivariate VAR for the trimmed inflation measure (T20) are plotted as they relate to the monthly economic indicators.10 Each panel in Figure 5 gives the percentage point change in month-on-month trimmed inflation at time t+1,1=1, 2,.., 12, for a one standard deviation increase in the monetary policy instrument or the potential leading indicator of inflation at time t.11 Also depicted are plus/minus two (asymptotic) standard error bands. These impulse responses illustrate an important drawback of crude Granger causality testing: it provides no information about whether the sign of the (dynamic) bivariate relationship is correct from the point of view of economic theory. The unemployment example mentioned above illustrates this point. While the unemployment rate appeared to be significant in Granger causing headline inflation in Table 2, it is clear from Figure 5 that the dynamic relationship between unemployment and inflation is economically incorrect, because an increase in unemployment increases inflation.12

Figure 5.
Figure 5.

Poland: Impulse Responses of Trimmed Inflation to Indicator Shocks

Citation: IMF Staff Country Reports 1999, 032; 10.5089/9781451831818.002.A004

155. Unfortunately, much uncertainty surrounds the estimated impulse responses in Figure 5. The standard error bands usually contain zero, especially for the headline inflation measure, meaning that the absence of any statistical relationship cannot be ruled out. This is partly due to the high lag order chosen for the VAR. The strongest relationships between the monetary policy variables and the various CPI inflation measures again appear to come from the effective exchange rate (neer) and foreign inflation in zloty (prcpizl). The broad money measure (fmb) has the right sign and is marginally significant, although this is not the case for headline CPI inflation. The activity variables rarely appear to be significant in signaling movements in inflation. Only the retail sales variable is marginally relevant in signaling movements in median inflation. The interest rate variables generally show a positive relationship with inflation, which is counter-intuitive from the point of view of economic theory.

D. Conclusions

156. Large monthly price changes in various components of the CPI, often due to substantial changes in administered prices, have tended to cloud movements in inflation in Poland during the transition process and make inflation appear less regular and less explainable than it probably is in fact. For this reason, the concept of underlying inflation is helpful in trying to understand and quantify the inflation process in a transition economy like Poland. The construction of various underlying inflation measures seems to be particularly useful from an analytical view, especially an optimally mean-trimmed CPI inflation measure (such as the T20 inflation series developed in this chapter), which trims away large transitory price movements. This hypothesis is supported by Granger causality testing, which generally finds stronger statistical linkages between changes in monetary policy instruments and movements in underlying CPI inflation than between shifts in these policy instruments and movements in headline CPI inflation.

157. Impulse response tests indicate that only a few monetary policy variables seem to be significant in explaining CPI inflation in Poland during the 1990s. The most significant effects appear to come from movements in the exchange rate and in foreign inflation, although movements in broad money are marginally significant in explaining movements in CPI inflation. In most of these cases, leading indicators of inflation also are better at explaining movements in underlying CPI inflation than headline CPI inflation, presumably because underlying inflation is more stable.

158. The lack of a firm statistical linkage between policy interest rates and inflation is not surprising, given that both variables have been falling monotonically in Poland over the sample period (1992-98). Given such historical data trends, it would be extremely difficult for statistical tests to identify the kind of normal negative relationship between these two variables suggested by economic theory and monetary transmission channel analysis. As the Polish economy continues to mature in coming years, it is likely that the relationship between the policy interest rates and inflation will become more regular and will begin to illustrate the expected negative relationship. In the meantime, as the statistical basis for more normal relationships strengthen, it will be important for monetary policymakers in Poland to rely on economic theory as well as benchmarks from other countries as guides for their monetary policy decisions.


  • Andersen, Palle and Mar Gudmundsson, 1998, “Inflation and Disinflation in Iceland,” Manuscript, BIS.

  • Ball, Lawrence and N. Gregory Mankiw, 1995, “Relative Price Changes as Aggregate Supply Shocks,” The Quarterly Journal of Economics, Vol. CX, Issue No. 1.

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  • Bryan, Michael, Stephen Cecchetti, and Rodney Wiggins, 1997, “Efficient Inflation Estimation,” National Bureau of Economic Research (NBER) Working Paper No. 6183.

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  • Christoffersen, Peter, and Robert Wescott, (forthcoming), “Is Poland Ready for Inflation Targeting,” IMF Working Paper.

  • Debelle, Guy and Cheng Lim, 1998, “Preliminary Considerations of an Inflation Targeting Framework for the Philippines,” IMF Working Paper 98/39.

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  • Freeman, Donald, 1998, “Do Core Inflation Measures Help Forecast Inflation?Economics Letters, Vol 58, pp 143147.

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Prepared by Robert Wescott.


For a more complete analysis of the inflation process in Poland, including in-period and out-of-period simulations with a simple multivariate econometric model, see Peter Christoffersen and Robert Wescott, “Is Poland Ready for Inflation Targeting?” IMF Working Paper, (forthcoming).


In fact, Wozniak (1998), using a modeling framework suggested by Ball and Mankiw, has estimated that the large administered price increases associated with transition in Poland produced substantial upward inflationary pressures between 1989 and 1997.


See for example, Andersen and Gudmundsson (1998) on Iceland, and Debelle and Lim (1998) on the Philippines.


This non-normality is confirmed by statistical tests. Applying a Jarque-Bera test for the null hypothesis of normality leads to a rejection at the 1 percent level for both the top and middle panels of Figure 2.


The last panel in Figure 3 depicts an index of the thirteen government affected goods and service prices weighted together by their respective weights in the CPI (and re-based).


These outliers reflect the effects of a change of government and large expected movements in administered prices.


Using conventional augmented Dickey-Fuller (ADF) tests, the null hypothesis of a unit root cannot be rejected for most of the indicators. Taking first differences and reapplying the ADF tests, the presence of a unit root is typically rejected when including one lag. When including more than one lag on the right-hand-side, the power of the ADF tests drops, and the null hypothesis of a unit root again often cannot be rejected. Although an argument could be made for keeping the interest rates in levels, it was decided to work with first differences of all variables in the analysis below.


The variable mnemonics are listed in Table 1.


Figures for the impulse responses for the other three inflation measures are not presented in the interests of saving space, but generally they show slightly less significant results than for the mean-trimmed inflation case (T20).


The lag-length is fixed at 4 months in the bivariate regressions underlying Figure 5.


This is probably because both the unemployment rate and CPI inflation in Poland have been falling monotonically for most of the 1990s and labor markets have not yet reached equilibrium.

Republic of Poland: Selected Issues
Author: International Monetary Fund