a. A brief history of inflation in Poland, (1989-1994) ^1/

Chart II.1 illustrates the precipitous increase in inflation in Poland in 1989. By October 1989, CPI inflation had reached 55 percent per month and, in 1990, Poland was on the verge of hyperinflation (inflation averaged 585.8 percent). The stabilization program succeeded in bringing inflation down, but was accompanied by an unexpectedly high drop in output (see Ebrill et al. (1994)). That period also witnessed marked changes in the terms of trade and in real wages.

POLAND: INDICATORS OF REAL ECONOMIC ACTIVITY, 1989–95

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Sources: Polish authorities and staff estimates.

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POLAND: INDICATORS OF REAL ECONOMIC ACTIVITY, 1989–95

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Sources: Polish authorities and staff estimates.

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POLAND: INDICATORS OF REAL ECONOMIC ACTIVITY, 1989–95

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Sources: Polish authorities and staff estimates.

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During these chaotic developments on the macroeconomic front, movements in the overall CPI masked considerable divergences in relative prices. Past repressed inflation in the service sector suddenly materialized through a marked increase in relative prices. In particular, the drift in services prices persisted in 1992, even though overall price inflation was tamed (see Chart II.2). By 1993, however, it seemed that the bulk of relative price adjustment between food, services and manufactured goods had already taken place.

POLAND: INDICATORS OF REAL ECONOMIC ACTIVITY, 1989–95

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Sources: Polish authorities and staff estimates.

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POLAND: INDICATORS OF REAL ECONOMIC ACTIVITY, 1989–95

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Sources: Polish authorities and staff estimates.

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POLAND: INDICATORS OF REAL ECONOMIC ACTIVITY, 1989–95

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Sources: Polish authorities and staff estimates.

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Relative price adjustment was, however, not over. At a more disaggregated level, relative price changes remain substantial although less visible. As shown below, between January 1992 and May 1995, the relative cost of water has tripled. Rental prices have also largely exceeded average inflation. Substantial relative price adjustment can therefore take place not only in the atmosphere of hyperinflation and stabilization, but also during moderate inflation spells.

The causation between these relative price changes and inflation remains open to question. On the one hand, at low levels of inflation, even small changes in relative prices may have a large proportionate effect on inflation. On the other hand, annual inflation in the range of, say, 20 percent to 40 percent can also create large relative price shifts if certain prices are sticky.

The remainder of this section fleshes out the argument linking relative prices to inflation. First, it is shown how changes in relative prices might cause inflation. Specifically, following Ball and Mankiw (1995), it is shown that there is a relation between inflation and the skewness of the distribution of sectoral price changes, and that the latter helps explain future price inflation. Second, we use a comparison of international price levels to show that, notwithstanding the dramatic relative price changes associated with the “big bang”, considerable scope remains for further Polish relative price adjustment. To the extent that relative price changes are a cause of inflation, this could fuel more inflation. Finally, the Polish energy sector is considered in some detail, to illustrate the extent of relative price adjustment that may still be needed.

b. Relative price adjustment, asymmetric price adjustment, and inflation in Poland

In recent years, economists have paid renewed attention to relative price adjustments. This interest has been fueled by consistent statistical evidence that these adjustments are asymmetric (prices are more flexible upward than downward) and that relative price adjustments are correlated with the level of inflation. Fischer (1981) stressed, for instance, that U.S. inflation is highly correlated with a higher variance of sectoral price inflation. Similarly, Gerhaeusser (1988) found instantaneous Granger-causation between inflation and relative price volatility in Germany. ^1/

The empirical relationship between relative price changes and inflation has until recently been regarded as an analytical puzzle. Indeed, at first glance, there need be no relationship between relative price changes and the overall price level (for example, if money determines the overall level of prices). On this view, relative price increases in certain sectors would be offset by price decreases in other sectors, leaving the overall price level in the hands of monetary policy. Of course, if prices were sticky downward, relative price changes could only occur in an inflationary context. But downward price adjustment is difficult to justify on theoretical grounds (unless adjustment costs were, for some reason, asymmetric). A solution to this puzzle must therefore be based on different grounds.

Such a solution has recently been proposed by Ball and Mankiw (op. cit.) who argue that price stickiness (both upward and downward) arising from “menu costs” can cause higher inflation if the distribution of relative price changes is skewed. ^2/ Their argument runs as follows.

In the presence of menu costs, firms will only change their prices if the costs of inaction exceed the menu costs associated with changing prices. Accordingly, small shocks to the economy will have little effect on the price level, since individual firms will be reluctant to change prices. Large shocks will have a disproportionate effect on individual prices--but not necessarily on the aggregate price level: if the distribution of shocks is symmetric, price increases and decreases at the firm level cancel each other out. Thus, contrary to what is sometimes argued, greater variance of shocks need not lead to overall price inflation.

Rather, Ball and Mankiw’s approach can justify a relation between the skewness of price changes and overall inflation. In a skewed distribution, most outliers are on one side of the distribution. Suppose that the distribution of relative price shocks is skewed positive (that is, there is a small number of large positive price increases, offset by a large number of small relative price reductions). Owing to menu costs, the large number of small price reductions does not materialize, while the small number of large price increases will, resulting in an increase in the average price level.

Empirical analysis of price dispersion in Poland for the 1990-1994 period yields the following results.

First, the distribution of price changes is heavily skewed, and not only during the first months of the transition (Charts II.3a and II.3b). On average, once every two months there is some change in price more than two standard deviations away from the mean price increase. This fat-tailed distribution of price changes primarily reflects sizable price increases. It should also be noted that these increases are largely concentrated in particular sectors (e.g., utilities). Thus, there is considerable evidence of relative price adjustment within overall price level changes.

POLAND: MONTHLY PRICE CHANGES

(In percent)

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

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POLAND: MONTHLY PRICE CHANGES

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Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

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POLAND: MONTHLY PRICE CHANGES

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Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

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POLAND: HISTOGRAM OF MONTHLY PRICE CHANGES

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

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POLAND: HISTOGRAM OF MONTHLY PRICE CHANGES

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POLAND: HISTOGRAM OF MONTHLY PRICE CHANGES

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Second, inflation (CPI) and skewness (SK) are highly correlated. ^1/ This suggests that relative price shocks are closely linked to aggregate inflation. Note, also that the introduction of the asymmetry variable renders lagged inflation not statistically significant.

However, causation is uncertain. Just as menu costs can explain why relative price increases can cause inflation, they can also explain why trend inflation owing to other causes can lead, in any given period, to a positively skewed distribution of price changes (Ball and Mankiw (1994)). First, in the presence of menu costs and trend inflation, those sectors facing relative price declines can simply avoid paying the menu cost, keep nominal prices unchanged, and allow overall inflation to generate the desired erosion in relative prices. On the other hand, those sectors needing to establish relative price increases must increase prices, over and above the inflation rate. With no change in certain sectors and increases in others, the result is a positively skewed distribution of price changes. Second, menu costs may differ across sectors. Assume that average long-term inflation does not differ between sectors. Sectors with small menu costs will change prices continuously, but those with large menu costs will change prices infrequently--but by large amounts when they do. The result is apparent skewness in the distribution of price changes, without implication for the long term inflation rate.

The question is whether skewness causes inflation, or whether inflation causes skewness. On the first interpretation, skewness--and relative prices--are part of the inflation process; on the latter they are simply an epiphenomenon. If the relative price explanation is correct, one should observe in the same sectors skewed price increases and a lasting improvement in their relative prices. If the second explanation is correct, the skewed price increases should be essentially random, without any lasting effect. To distinguish between these explanations, the change in a sector’s relative price (RCPI_i) between December 1989 and June 1995 was regressed against the number of times that sector registered a skewed price increase (NSK_i) ^1/:

The regression indicates a strong correlation between a sector’s ability to generate a sustained increase in relative prices, and the number of times its price changes are “outliers”, consistent with the claim that skewness captures genuine relative price changes, and that these relative price changes are linked to Polish inflation.

c. Incomplete convergence toward world prices

While the previous section argued for a link from relative price changes to inflation, for this linkage to have continuing empirical significance it needs to be shown that considerable relative price adjustment has still to take place. Two attempts at this are made. First, it is shown that, in general, prices of Polish goods are lower than their international counterparts. Second, this undervaluation varies enormously for different goods. The implication is that exchange rate adjustment alone would not solve the problem of relative price disparities.

Description of the method Since 1994, Business Eastern Europe (BEE) has collected prices for basic consumer goods and services in various Eastern European countries. ^2/ In addition, the Polish statistical bulletin provides a breakdown of Polish households’ average expenditures (Table II.1 below) as well as some information on the volume of consumption of selected foodstuffs.

Table II.1.

Structure of Household Expenditures

(January-March 1994: in percent)

Source: Polish Statistical Office, Biuletyn Statystyczny.

Table II.1.

Structure of Household Expenditures

(January-March 1994: in percent)

Food		40.5
Alcoholic beverages		1.1
Tobacco products		1.5
Clothing and Footwear		6.0
Education		1.6
Health		3.9
Personal Hygiene		3.5
Dwelling		19.9
	Including rent payments	3.2
	Including fuel and electricity	11.0
	Including equipment	3.8
Transport and communication		9.6
Culture, Sports and recreation		4.9

Source: Polish Statistical Office, Biuletyn Statystyczny.

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Table II.1.

Structure of Household Expenditures

(January-March 1994: in percent)

Food		40.5
Alcoholic beverages		1.1
Tobacco products		1.5
Clothing and Footwear		6.0
Education		1.6
Health		3.9
Personal Hygiene		3.5
Dwelling		19.9
	Including rent payments	3.2
	Including fuel and electricity	11.0
	Including equipment	3.8
Transport and communication		9.6
Culture, Sports and recreation		4.9

Source: Polish Statistical Office, Biuletyn Statystyczny.

Table II.2.

Indices of Relative Prices in Visegrad Countries and Slovenia

(In percent, March 1994)

Sources: BEE (1994); and staff calculations.

Table II.2.

Indices of Relative Prices in Visegrad Countries and Slovenia

(In percent, March 1994)

		Poland	Hungary	Czech Rep.	Slovakia	Slovenia
Index I		37.7	52.3	37.0	40.4	81.3
	Including food	37.9	56.2	38.2	43.6	86.0
	Including utilities	20.6	34.1	29.8	12.1	70.1
Index S		50.9	35.2	38.6	59.7	35.1

Sources: BEE (1994); and staff calculations.

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Table II.2.

Indices of Relative Prices in Visegrad Countries and Slovenia

(In percent, March 1994)

		Poland	Hungary	Czech Rep.	Slovakia	Slovenia
Index I		37.7	52.3	37.0	40.4	81.3
	Including food	37.9	56.2	38.2	43.6	86.0
	Including utilities	20.6	34.1	29.8	12.1	70.1
Index S		50.9	35.2	38.6	59.7	35.1

Sources: BEE (1994); and staff calculations.

By combining these sets of data, one can calculate a cost of living index, with weights corresponding to the consumption basket for Polish households. To facilitate comparison, we calculated a relative price index (I), dividing each country’s cost-of-living index by its Austrian equivalent, assumed to be prototypical for a Western European country. Thus, I provides some information on the relative cost of living in Eastern European countries or, under certain conditions, about the possible undervaluation of the exchange rate.

Formally, index I is calculated as: ^1/

It is also of interest to measure the extent to which product prices depart from the average cost of living index. This can be done by calculating a (weighted) standard deviation of individual prices, and then dividing by the mean to derive the coefficient of variation. Formally, this weighted coefficient of variation is given by: ^1/

Results The main findings for the Visegrad group of countries and Slovenia are as follows. First, the cost-of-living index is clearly much lower in these countries than in Austria. However, prices in Slovenia and, to a lesser extent, Hungary come closer to their Austrian counterparts. Not surprisingly, these two countries were also relatively open to international trade during the communist era. In comparison, a recent (and much more detailed) study of Russian prices, De Masi and Koen (1995), found that, in 1994, Russian prices were only a fifth of the level of their French equivalents. Thus, while considerable distance remains, the group of countries surveyed here has made more progress toward Western price levels.

Second, relative prices (captured by index S) fluctuate significantly around the average price level, more than would be predicted if Eastern European prices were a constant fraction of Austrian prices. In a sense, the variation in relative prices gives a better indication of the extent of price liberalization. For Poland, much of this variation reflects the significant undervaluation (in relative terms) of utility prices: subsidized household utility prices have remained an important component of the social safety net. Other prices, and in particular those of manufacturing goods, come closer to world (Austrian) prices. ^2/

The low value of index I suggests that considerable currency undervaluation remains. However, currency appreciation would not eliminate the price dispersion and the need for corrective relative price adjustments.

d. The need to adjust relative prices: the case of energy

As a legacy of the communist safety net, certain goods are still highly subsidized in Poland. The comparison with Austrian price levels has underlined that the two single most important areas of subsidies are probably the housing and the energy sectors. This section focuses on the latter. ^1/

It would be incorrect to suggest that utility prices have not adjusted since the beginning of the transition. Indeed, as Table II.3 reveals, utility price growth has significantly outpaced CPI growth since 1990. Compared to the average CPI, their relative price has been multiplied by two to fifteen times. It is noteworthy, however, that most of the relative price hike took place before 1992.

Table II.3.

The Relative Price of Polish Utilities ^1/

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

Table II.3.

The Relative Price of Polish Utilities ^1/

	Fuel	Hot Water Central Heating	Electricity and Gas	Health	Public Transport	Rents
Dec. 1989	27.9	18.9	21.5	35.8	55.9	41.5
Jan. 1992	100.0	100.0	100.0	100.0	100.0	100.0
June 1995	103.0	331.5	124.1	114.3	118.4	137.1

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

View Table

Table II.3.

The Relative Price of Polish Utilities ^1/

	Fuel	Hot Water Central Heating	Electricity and Gas	Health	Public Transport	Rents
Dec. 1989	27.9	18.9	21.5	35.8	55.9	41.5
Jan. 1992	100.0	100.0	100.0	100.0	100.0	100.0
June 1995	103.0	331.5	124.1	114.3	118.4	137.1

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

Before the transition, energy subsidies in Poland were pervasive and clearly favored over-consumption. As stressed by Meyers et al. (1994a and 1994b), energy-intensive industries in Poland in 1991 reached a share in GDP that, among industrialized countries, only Norway exceeded. These authors estimate that energy use in the steel industry or in household central heating is comparable to that of Western countries in the early 1970s. As a striking example of the role of price distortions, energy subsidies allowed Poland to largely ignore the restructuring implications of the oil shock.

So far, the relative price changes already incurred have caused only minor shifts in consumption patterns. In the long run, one would expect higher energy prices to result in better insulation for houses and in a preference for low-energy appliances. However, such behavioral adjustments take time and, so far, the reduction in household energy consumption has been only minimal. Indeed, between 1989 and 1994, the share of energy in total household expenditure increased sixfold (Table II.4), matching the pattern of relative price increases. Unfortunately, the short observation period makes it difficult to disentangle the respective impact of price and income effects.

Table II.4.

Share of Energy in Total Household Expenditure, 1989-94 ^1/

(In percentage points)

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

Table II.4.

Share of Energy in Total Household Expenditure, 1989-94 ^1/

(In percentage points)

1989	1990	1991	1992	1993	1994
1.7	3.3	5.9	8.2	9.3	10.2

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

View Table

Table II.4.

Share of Energy in Total Household Expenditure, 1989-94 ^1/

(In percentage points)

1989	1990	1991	1992	1993	1994
1.7	3.3	5.9	8.2	9.3	10.2

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

Recent studies, however, suggest that past price increases, substantial as they were, fall short of the required adjustment (See Table II.5). Two different concerns have emerged. First, the cross-subsidization between firms and households is still worrisome. In Western countries, the high cost of energy distribution for households is used to justify lower energy prices for firms. Despite this, in most Eastern European countries, household energy prices are lower than those paid by firms, placing an undue burden on firms.

Table II.5.

Electricity and Natural Gas Pricing in 1994

(Absolute terms, and in percent of estimated LRMC)

Source: Freund and Wallich (1995).

^1/U.S. cents/kWh.

^2/U.S. cents/cubic meter.

Table II.5.

Electricity and Natural Gas Pricing in 1994

(Absolute terms, and in percent of estimated LRMC)

	Electricity 1/		Natural Gas 2/
Country	Industrial	Residential	Industrial	Residential
Poland	3.7 (60-70)	5.1 (50-50)	12 (90-100)	17 (60-70)
Hungary	5.2 (130)	5.5 (70-80)	28 (112)	24 (80)
OECD	7.4	14

Source: Freund and Wallich (1995).

^1/U.S. cents/kWh.

^2/U.S. cents/cubic meter.

View Table

Table II.5.

Electricity and Natural Gas Pricing in 1994

(Absolute terms, and in percent of estimated LRMC)

	Electricity 1/		Natural Gas 2/
Country	Industrial	Residential	Industrial	Residential
Poland	3.7 (60-70)	5.1 (50-50)	12 (90-100)	17 (60-70)
Hungary	5.2 (130)	5.5 (70-80)	28 (112)	24 (80)
OECD	7.4	14

Source: Freund and Wallich (1995).

^1/U.S. cents/kWh.

^2/U.S. cents/cubic meter.

Second, it remains to be seen whether current energy prices provide utility companies with adequate rates of return. Standard pricing strategies usually refer to long run marginal cost (LRMC), including current costs of existing investment as well as potential replacement costs. This stands in stark contrast with well-established practices in Eastern European economies where (short-term) average cost pricing is favored. Because the debt levels of utility companies are unusually low by Western standards, low energy prices do not necessarily bring these companies into the red. However, leaving aside the insufficient return on capital, ignoring replacement costs will become a growing impediment as the need for modernizing existing equipment becomes more pressing.

Thus there is evidence that the relative price of utilities is still in need of significant overhaul. According to Freund and Wallich (1995), in 1993 the extent of desirable price adjustment ranged from 60 percent for district heat to 80 percent for gas and 90 percent for electricity.

Implementing such an energy price increase could create profound social tensions. However, since the income elasticity of energy consumption exceeds one, the welfare gains from the current system of energy price subsidies are greater for the rich than for the poor. Overall, the current system is regressive (Freund and Wallich (op. cit.)). To maintain the safety net, while encouraging more efficient energy usage, the one-time increase in energy prices could be accompanied by cash or voucher distribution to compensate households. It is clear that, for the reform to succeed and to be genuinely welfare-improving, considerable attention needs to be paid to the design of such a reform. The point is more that the case of energy prices shows that further relative price adjustment still has to be made in Poland, and that this is likely to have significant implications for inflation.

a. The dynamics of wages, prices, and the exchange rate

To gauge the extent of wage and price indexation, we estimated a standard mark-up model of the Polish economy. Given that the estimation period is restricted to 1991:3-1995:5 ^1/, simple dynamic specifications were favored. This exercise sheds some light on the nature of the interplay between prices, wages and the exchange rate, as well as of the lags involved.

(i) The structure of the model

For the producer price index (PPI), the following mark-up pricing is assumed:

$\begin{array}{cc}\mathit{PPI}=(1+\mu ){\left(\frac{P_{\mathit{IC}}\mathit{IC}+\mathit{WL}}{Y}\right)}^{\alpha }& \left(5\right)\end{array}$

Thus, producer prices are a mark-up on unit variable costs, defined as the sum of wage costs (WL) and the costs of intermediate inputs (P_IC IC), divided by output (Y).

Output affects prices through at least two distinct channels. First, through the effect on marginal cost. Unless the production function has constant returns to scale, average and marginal productivity differ. For example, with increasing returns to scale and holding factor prices constant, marginal cost will decline as output increases. Second, through the effect on the mark-up. Some authors have argued that, in industrialized economies, price-competition fades somewhat during growth spurts. Therefore, the mark-up μ of prices on costs is a function of capacity utilization, simply proxied here by changes in output.

The impact of the exchange rate (exr) upon producer prices is also twofold. First, it directly affects the cost of imported intermediate inputs. Second, foreign competition may also influence the mark-up of domestic firms: when the currency appreciates, margins shrink in those sectors most exposed to foreign competition.

Overall, a reduced form equivalent of equation (5) can be expressed as

$\begin{array}{cc}L\mathit{og}\left(\mathit{PPI}\right)={\mathrm{a}}_{1}L\mathit{og}\left(W\right)+{\mathrm{a}}_{2}L\mathit{og}\left(\mathit{exr}\right)+{\mathrm{a}}_{3}L\mathit{og}\left(Y\right)+{\mathrm{a}}_4& \left(6\right)\end{array}$

The CPI equation is largely an accounting identity. Most models simply assume that the CPI is a weighted average of producer price indices and import prices. This CPI equation also incorporates wages. This could be justified on the following grounds. First, consumer goods are often more labor intensive than typical industrial goods. Hence, simply using an aggregate PPI would underestimate the influence of wages. Second, wages also represent an important variable cost for retailers. Thus, for consumer prices, the following equation is assumed:

$\begin{array}{cc}L\mathit{og}\left(\mathit{CPI}\right)=b_{1}L\mathit{og}\left(\mathit{PPI}\right)+b_{2}L\mathit{og}\left(\mathit{exr}\right)+b_{3}L\mathit{og}\left(W\right)+b_4& \left(7\right)\end{array}$

For the labor market, a standard relation between unemployment and real wages is assumed:

$\begin{array}{cc}L\mathit{og}\left(\mathit{Wn}\right)=L\mathit{og}\left(\mathit{CPI}\right)-c_{1}L\mathrm{og}\left(\mathit{UR}\right)+c_2& \left(8\right)\end{array}$

where UR represents the unemployment rate, and wages are considered net of tax. ^1/

To capture the dynamics associated with high frequency data, these equations were all estimated in an error-correction form. This involves estimating, first, a long-term (“cointegrating”) relation between the various variables and, second, a short-term dynamic equation which determines the speed of adjustment towards the long-term target. Econometric results are reported in Table II.6. ^2/

Table II.6.

Poland: Long Run Wage and Price Equations

Notation (all variables in logarithms):

lwnr	real wage (deflated by consumer price index)
lur	unemployment rate
lppi	producer price index
lip	industrial production
lcu	unit labor costs
lexr	exchange rate
lcpi	consumer price index
lwg	gross wages

Table II.6.

Poland: Long Run Wage and Price Equations

$\begin{array}{ll}\mathrm{lwnr}=\underset{26.3}{1.00}-\underset{\left(5.6\right)}{0.076}\mathrm{lur}+\mathrm{ecmw}& \left(1\right)\\ \begin{array}{cc}\begin{array}{c}{\mathrm{R}}^2=0.83\end{array}& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=0.44\end{array}& \end{array}$

$\begin{array}{ll}\mathrm{lppi}=\underset{\left(5.4\right)}{-1.37}+\underset{\left(11.0\right)}{0.81}\mathrm{lip}+\underset{\left(9.7\right)}{0.54}\mathrm{lcu}+\underset{\left(3.3\right)}{0.24}\mathrm{lexr}+\mathrm{ecmp}& \left(2\right)\\ \begin{array}{cc}{\mathrm{R}}^2=0.99& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=0.87\end{array}& \end{array}$

$\begin{array}{ll}\mathrm{lcpi}=\underset{\left(12.2\right)}{-0.74}+\underset{\left(14.3\right)}{0.43}\mathrm{lwg}+\underset{\left(9.8\right)}{0.56}\mathrm{lppi}+\underset{\left(1.9\right)}{0.07}\mathrm{lexr}+\mathrm{ecmc}& \left(3\right)\\ \begin{array}{cc}{\mathrm{R}}^2=0.99& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=0.96\end{array}& \end{array}$

Notation (all variables in logarithms):

lwnr	real wage (deflated by consumer price index)
lur	unemployment rate
lppi	producer price index
lip	industrial production
lcu	unit labor costs
lexr	exchange rate
lcpi	consumer price index
lwg	gross wages

View Table

Table II.6.

Poland: Long Run Wage and Price Equations

$\begin{array}{ll}\mathrm{lwnr}=\underset{26.3}{1.00}-\underset{\left(5.6\right)}{0.076}\mathrm{lur}+\mathrm{ecmw}& \left(1\right)\\ \begin{array}{cc}\begin{array}{c}{\mathrm{R}}^2=0.83\end{array}& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=0.44\end{array}& \end{array}$

$\begin{array}{ll}\mathrm{lppi}=\underset{\left(5.4\right)}{-1.37}+\underset{\left(11.0\right)}{0.81}\mathrm{lip}+\underset{\left(9.7\right)}{0.54}\mathrm{lcu}+\underset{\left(3.3\right)}{0.24}\mathrm{lexr}+\mathrm{ecmp}& \left(2\right)\\ \begin{array}{cc}{\mathrm{R}}^2=0.99& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=0.87\end{array}& \end{array}$

$\begin{array}{ll}\mathrm{lcpi}=\underset{\left(12.2\right)}{-0.74}+\underset{\left(14.3\right)}{0.43}\mathrm{lwg}+\underset{\left(9.8\right)}{0.56}\mathrm{lppi}+\underset{\left(1.9\right)}{0.07}\mathrm{lexr}+\mathrm{ecmc}& \left(3\right)\\ \begin{array}{cc}{\mathrm{R}}^2=0.99& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=0.96\end{array}& \end{array}$

Notation (all variables in logarithms):

lwnr	real wage (deflated by consumer price index)
lur	unemployment rate
lppi	producer price index
lip	industrial production
lcu	unit labor costs
lexr	exchange rate
lcpi	consumer price index
lwg	gross wages

Notation:Δ denotes first difference. Error-correction terms ecmw_-1, ecmp_-1, and ecmc_-1, are, respectively, the lagged residuals from the long run wage, producer price, and consumer price equations of the previous table.

$\begin{array}{l}\mathrm{\Delta lwn}=\underset{\left(14.5\right)}{-0.14}+\underset{\left(2.5\right)}{0.42}\mathrm{\Delta lcpi}-\underset{\left(2.3\right)}{0.21}{\mathrm{ecmw}}_{-1}\\ \begin{array}{cc}{\mathrm{R}}^2=0.95& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=2.02\end{array}\end{array}$

$\begin{array}{l}\mathrm{\Delta lppi}=\underset{\left(2.4\right)}{0.01}+\underset{\left(2.3\right)}{0.06}\mathrm{\Delta lcu}+\underset{\left(1.3\right)}{0.06}\mathrm{\Delta lexr}\underset{\left(2.7\right)}{-0.13}{\mathrm{ecmp}}_{-1}\\ \begin{array}{cc}{\mathrm{R}}^2=0.50& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=2.03\end{array}\end{array}$

$\begin{array}{l}\mathrm{\Delta lcpi}=\underset{\left(0.42\right)}{0.002}+\underset{\left(4.2\right)}{0.20}\mathrm{\Delta lwg}+\underset{\left(3.7\right)}{0.58}\mathrm{\Delta lppi}\underset{\left(2.6\right)}{-0.30}{\mathrm{ecmc}}_{-1}\\ \begin{array}{cc}{\mathrm{R}}^2=0.71& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=1.35\end{array}\end{array}$

Notation:Δ denotes first difference. Error-correction terms ecmw_-1, ecmp_-1, and ecmc_-1, are, respectively, the lagged residuals from the long run wage, producer price, and consumer price equations of the previous table.

View Table

$\begin{array}{l}\mathrm{\Delta lwn}=\underset{\left(14.5\right)}{-0.14}+\underset{\left(2.5\right)}{0.42}\mathrm{\Delta lcpi}-\underset{\left(2.3\right)}{0.21}{\mathrm{ecmw}}_{-1}\\ \begin{array}{cc}{\mathrm{R}}^2=0.95& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=2.02\end{array}\end{array}$

$\begin{array}{l}\mathrm{\Delta lppi}=\underset{\left(2.4\right)}{0.01}+\underset{\left(2.3\right)}{0.06}\mathrm{\Delta lcu}+\underset{\left(1.3\right)}{0.06}\mathrm{\Delta lexr}\underset{\left(2.7\right)}{-0.13}{\mathrm{ecmp}}_{-1}\\ \begin{array}{cc}{\mathrm{R}}^2=0.50& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=2.03\end{array}\end{array}$

$\begin{array}{l}\mathrm{\Delta lcpi}=\underset{\left(0.42\right)}{0.002}+\underset{\left(4.2\right)}{0.20}\mathrm{\Delta lwg}+\underset{\left(3.7\right)}{0.58}\mathrm{\Delta lppi}\underset{\left(2.6\right)}{-0.30}{\mathrm{ecmc}}_{-1}\\ \begin{array}{cc}{\mathrm{R}}^2=0.71& \mathrm{D}\mathrm{.}\mathrm{W}\mathrm{.}=1.35\end{array}\end{array}$

Notation:Δ denotes first difference. Error-correction terms ecmw_-1, ecmp_-1, and ecmc_-1, are, respectively, the lagged residuals from the long run wage, producer price, and consumer price equations of the previous table.

(ii) A highly indexed economy

Poland is a well-indexed economy, therefore prone to relatively high inflation. Such findings are not unexpected after a hyperinflation spell (see Commander and Coricelli (1991)). However, certain aspects of the econometric results deserve specific comment.

First, wages are much more indexed than prices. After a consumer price shock, the mean lag of wage adjustment amounts to about two months. Hence, an inflationary shock to the CPI or wages is likely to reduce enterprises’ mark-ups rather than real wages. This result may seem counter-factual since, at the beginning of the transition, average real wages declined strongly. There are various ways to reconcile these two facts. For one, Polish households may have learned from the hyperinflation period how to protect themselves against price changes. Also, in addition to the precipitous decline in productivity, the initial real wage decline was facilitated by centralized negotiations, quite independently from any inflationary surprise. ^1/

Second, the producers’ mark-up reacts rapidly to changes in output, and is negatively affected by sudden changes in costs. The first feature may partly explain the PPI’s growth in early 1995 given the brisk pace of output growth. The magnitude of the effect of output on price is, however, surprising. It may have resulted from a shortage of capital in industry during the recent recovery and, subsequently, higher margins accompanying the boost in demand. As expected, exchange rate appreciation compresses producers’ margins. However, such an impact on inflation materializes only with a mean lag of 7-8 months. The pass-through from unit labor cost is somewhat more rapid.

Third, three factors drive CPI inflation: domestic PPI, the exchange rate--as a proxy for the cost of imported goods--and wages. Possible explanations for the strong direct impact of wages on the CPI have already been mentioned. The direct effect of the exchange rate appears to be relatively slow--the mean lag equals three to four months--and weak. A more disaggregated analysis shows that its impact is statistically significant only for manufactured consumption goods, but not for foodstuffs or services.

A casual Inspection of the structure of Polish imports provides an explanation for such findings (Table II.7). Before the transition, imports of consumer goods, as in other Eastern European countries, were constrained to fairly low levels in Poland. Since then, a strong appetite for Western products combined with a more liberal trade policy have boosted consumer goods imports. Their share in total imports has more than doubled since 1988 to reach approximately 20 percent. ^1/ As such, since imports and consumption represent respectively, 25 percent and 70 percent of GDP in 1994, an appreciation of the currency by 10 percent mechanically reduces CPI by 0.7 percent. ^2/

Table II.7.

The Structure of Polish Imports

Source: Polish Statistical Office.

Table II.7.

The Structure of Polish Imports

Goods		In Percent of Total Imports
Investment goods		13.6
Raw Materials		66.3
Consumer goods		19.3
Including chemicals		4.4
	Electro engineering	4.4
	Food	3.9
	Light products	2.3
	Agricultural products	2.5

Source: Polish Statistical Office.

View Table

Table II.7.

The Structure of Polish Imports

Goods		In Percent of Total Imports
Investment goods		13.6
Raw Materials		66.3
Consumer goods		19.3
Including chemicals		4.4
	Electro engineering	4.4
	Food	3.9
	Light products	2.3
	Agricultural products	2.5

Source: Polish Statistical Office.

This is in line with our econometric estimates. Overall, the stronger impact of the exchange rate on PPI than on CPI mainly reflects the large share of intermediate inputs in total imports (Table II.7).

It would be utterly misleading, however, to infer from this that the exchange rate is useless in controlling inflation. Indeed, the weak initial impact from the exchange rate on both CPI and PPI is magnified by the high indexation in the economy. To illustrate this point, this model was simulated in two different ways. The first simulation simply consists of a ten percent devaluation. Consumer prices increase by 2.5 percent after one year and by about 6 percent after three years. As shown in the second simulation, the effect could be even stronger if, after the initial shock, the exchange rate were to be depreciated in line with inflation. This would amount to adding to the previous three equations the following one:

$\begin{array}{cc}\mathrm{\Delta }Log\left(exr\right)=d_1\mathrm{\Delta }Log\left(CPI\right)& \left(9\right)\end{array}$

which represents an accommodating policy reaction function. With this addition to the model and assuming that d¿ equals 0.5, the inflationary effect of the devaluation is severely compounded and exceeds 10 percent.

Finally, the wage-price model was used to gauge the inflationary impact of the energy price adjustment discussed earlier in this chapter. At first glance, if utility prices increase by 60 percent, the immediate impact on CPI would amount to approximately 6 percent, given the share of energy in the consumption basket. This effect would be stronger if one takes into consideration the effects of utility price increases on the prices of other public services (e.g., transportation) and on household rents.

In the well-indexed Polish economy captured in our wage-price model, any such energy price shock would also induce a considerable multiplier effect. As shown in Chart II.4, such a shock would increase the overall level of prices by approximately 20 percent after a four year period. The inflationary impact would be compounded by additional exchange rate depreciation. With a partly accommodating exchange rate rule (d₁-0.5), consumer prices would increase by 40 percent after four years. ^1/

POLAND EFFECT OF A 60 PERCENT ENERGY PRICE INCREASE ON THE POLISH CPI

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Source: Staff estimates.

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POLAND EFFECT OF A 60 PERCENT ENERGY PRICE INCREASE ON THE POLISH CPI

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Source: Staff estimates.

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POLAND EFFECT OF A 60 PERCENT ENERGY PRICE INCREASE ON THE POLISH CPI

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Source: Staff estimates.

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This exercise in itself need not imply that inflation should be high if energy prices are liberalized. Indeed, one could argue that a tighter monetary policy would allow to keep overall inflation subdued. What is important, however, is that such an outcome would hinge on a severe price adjustment in other sectors. In the face of nominal price rigidity, changes in relative prices can only be achieved through higher inflation. In this case, the switch from moderate to low inflation would have to be postponed.

a. Statistical evidence on money and inflation

Since 1990, inflation and broad money growth have been closely related (Chart II.5). The relationship is clearest for six-month or annual growth rates, where both broad money growth and inflation have declined. The relationship is much less discernible for monthly growth rates. However, even for the six month and annual growth rates, it is difficult to ascertain whether monetary growth leads or lags the inflation rate. Finally, over this period broad money growth has, on average, exceeded measured CPI inflation. The result is a sizable trend increase in real money balances. This remonetization is not surprising given the decline in Poland’s inflation rate.

POLAND MONEY GROWTH AND INFLATION June 1991 - September 1995

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Source: Polish authorities.

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POLAND MONEY GROWTH AND INFLATION June 1991 - September 1995

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Source: Polish authorities.

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POLAND MONEY GROWTH AND INFLATION June 1991 - September 1995

Citation: IMF Staff Country Reports 1996, 019; 10.5089/9781451831788.002.A002

Source: Polish authorities.

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Inspecting the data suggests some relationship between money growth and inflation, but cannot pronounce on its statistical significance. Also, it is difficult to judge the direction of causality: is money leading inflation or lagging inflation? To answer these questions, econometric “Granger-causality” tests of the relationship between inflation and money were conducted.

Granger “causality” tests do not test for causality, strictly speaking: they are tests of temporal precedence. More precisely, X is said to Granger-cause Y if past values of X help predict future values of Y, over and above the predictive information contained in past values of Y. This is more a test for predictive content, than for causality. Thus, while such tests may only hint at “true” causality, they may be more useful for finding leading indicators of inflation or output growth.

Ideally, such tests would be employed on long data series, to increase degrees of freedom. But for Poland as a transition economy, there are barely five years of observations. To increase our degrees of freedom, we are forced to use monthly data. However, monthly inflation is extremely volatile, reflecting the effects of one-off shocks (such as changes in administered prices) or seasonal factors. This makes monthly inflation rates difficult to predict. Adding monthly dummy variables should help capture seasonal patterns in so far as they are regular, but will not incorporate the effects of irregular shocks.

One way round this data problem is to test whether money growth helps predict inflation not 1 month, but on average over a 6 or 12-month period. Annual and six-month inflation rates are far less volatile than monthly rates. Their coefficient of variation is far lower than that for monthly inflation (Table II.8). Also, even if inflation is a monetary phenomenon, monetarists would hardly claim that money growth explained every monthly change in prices. Rather, monetarists would claim that the relation holds in the medium term, and is subject to long and variable lags. For Western economies, a time lag of 18 months to 2 years between money growth and inflation would be thought common place. Indeed, predicting inflation only 6 or 12 months ahead might also be too short, save that inflation in transition economies is much higher and, presumably, the frequency of price changes that much greater, and the transmission to inflation that much more rapid.

Table II.8.

Consumer Price Inflation Basic Descriptive Statistics (1990:1 to 1995:9)

Table II.8.

Consumer Price Inflation Basic Descriptive Statistics (1990:1 to 1995:9)

	Mean Growth Rate	Standard Deviation	Coefficient of Variation
12 months ahead	38.4	16.6	0.4
6 months ahead	19.8	12.6	0.6
3 months ahead	10.5	10.5	1.0
1 month ahead	4.1	7.2	1.8

View Table

Table II.8.

Consumer Price Inflation Basic Descriptive Statistics (1990:1 to 1995:9)

	Mean Growth Rate	Standard Deviation	Coefficient of Variation
12 months ahead	38.4	16.6	0.4
6 months ahead	19.8	12.6	0.6
3 months ahead	10.5	10.5	1.0
1 month ahead	4.1	7.2	1.8

Table II.9 presents the results of various causality tests linking money growth to inflation. The table is divided into four sections, concerned with predicting inflation 12, 6, 3 and 1 month ahead, respectively. Each row of the table summarizes the results of a regression of future inflation on a constant and six lags of the first differences of the variables indicated in the first columns. Save for interest rates, all variables are in logarithms, so that the first differences measure growth rates.

Table II.9.

Predictive Content of Brood Honor and Interest Rates in Explaining Inflation, Inelading Six Legs of the Explanatory Variables

Table II.9.

Predictive Content of Brood Honor and Interest Rates in Explaining Inflation, Inelading Six Legs of the Explanatory Variables

Explanatory Variables		Sample Period	Standard Error	Adjusted R-Squared	F-Tests (P-Values) on Less of:			Sum of Coefficients on:
					DLCPI	DLFMB	DRD	DLCPI	DLFMB	DRD
Predicting inflation
	12 months ahead
	DLCPI	1990:07 1994:10	0.070	0.358	5.735			2.927
					(0.000)
DLCPI DLFMB		1990:07 1994:10	0.039	0.795	1.296	16.962		0.942	5.049
					(0.262)	(0.000)
DLCPI DRD		1990:07 1994.10	0.053	0.628	9.124		6.446	5.121		-0.016
					(0.000)		(0.000)
DLCPI DLFMB DRD		1990:07 1994:10	0.038	0.808	1.023	6.970	1.381	2.046	3.811	-0.005
					(0.428)	(0.000)	(0.251)
	6 months ahead
	DLCPI	1990:07 1995:04	0.071	0.035	1.344			1.137
					(0.255)
	DLCPI DLFMB	1990:07 1995:04	0.036	0.732	2.697	23.133		-1.170	5.608
					(0.025)	(0.000)
	DLCPI DRD	1990:07 1995:04	0.059	0.334	2.016		4.820	2.865		-0.014
					(0.083)		(0.001)
	DLCPI DLFMB DRD	1990:07 1995:04	0.037	0.734	1.416	12.273	1.050	-1.382	5.182	0.001
					(0.233)	(0.000)	(0.409)
3 months ahead
	DLCPI	1990:07 1995:07	0.042	0.115	2.300			1.073
					(0.048)
	DLCPI DLFMB	1990:07 1995:07	0.028	0.603	5.399	12.061		-0.312	2.963
					(0.000)	(0.000)
	DLCPI DRD	1990:07 1995:07	0.038	0.249	3.017		2.606	2.068		-0.007
					(0.014)		(0.029)
	DLCPI DLFMB DRD	1990:07 1995:07	0.028	0.593	3.906	7.754	0.800	-0.527	3.015	0.001
					(0.003)	(0.000)	(0.575)
	1 month ahead
	DLCPI	1990:07 1995:09	0.018	0.171	3.125			0.586
					(0.010)
	DLCPI DLFMB	1990:07 1995:09	0.015	0.384	5.245	4.243		0.145	0.746
					(0.000)	(0.002)
	DLCPI DRD	1990:07 1995:09	0.017	0.203	3.430		1.376	0.828		-0.002
					(0.006)		(0.243)
	DLCPI DLFMB DRD	1990:07 1995:09	0.015	0.430	3.951	4.323	1.665	0.202	0.637	-0.000
					(0.003)	(0.002)	(0.152)

View Table

Table II.9.

Predictive Content of Brood Honor and Interest Rates in Explaining Inflation, Inelading Six Legs of the Explanatory Variables

Explanatory Variables		Sample Period	Standard Error	Adjusted R-Squared	F-Tests (P-Values) on Less of:			Sum of Coefficients on:
					DLCPI	DLFMB	DRD	DLCPI	DLFMB	DRD
Predicting inflation
	12 months ahead
	DLCPI	1990:07 1994:10	0.070	0.358	5.735			2.927
					(0.000)
DLCPI DLFMB		1990:07 1994:10	0.039	0.795	1.296	16.962		0.942	5.049
					(0.262)	(0.000)
DLCPI DRD		1990:07 1994.10	0.053	0.628	9.124		6.446	5.121		-0.016
					(0.000)		(0.000)
DLCPI DLFMB DRD		1990:07 1994:10	0.038	0.808	1.023	6.970	1.381	2.046	3.811	-0.005
					(0.428)	(0.000)	(0.251)
	6 months ahead
	DLCPI	1990:07 1995:04	0.071	0.035	1.344			1.137
					(0.255)
	DLCPI DLFMB	1990:07 1995:04	0.036	0.732	2.697	23.133		-1.170	5.608
					(0.025)	(0.000)
	DLCPI DRD	1990:07 1995:04	0.059	0.334	2.016		4.820	2.865		-0.014
					(0.083)		(0.001)
	DLCPI DLFMB DRD	1990:07 1995:04	0.037	0.734	1.416	12.273	1.050	-1.382	5.182	0.001
					(0.233)	(0.000)	(0.409)
3 months ahead
	DLCPI	1990:07 1995:07	0.042	0.115	2.300			1.073
					(0.048)
	DLCPI DLFMB	1990:07 1995:07	0.028	0.603	5.399	12.061		-0.312	2.963
					(0.000)	(0.000)
	DLCPI DRD	1990:07 1995:07	0.038	0.249	3.017		2.606	2.068		-0.007
					(0.014)		(0.029)
	DLCPI DLFMB DRD	1990:07 1995:07	0.028	0.593	3.906	7.754	0.800	-0.527	3.015	0.001
					(0.003)	(0.000)	(0.575)
	1 month ahead
	DLCPI	1990:07 1995:09	0.018	0.171	3.125			0.586
					(0.010)
	DLCPI DLFMB	1990:07 1995:09	0.015	0.384	5.245	4.243		0.145	0.746
					(0.000)	(0.002)
	DLCPI DRD	1990:07 1995:09	0.017	0.203	3.430		1.376	0.828		-0.002
					(0.006)		(0.243)
	DLCPI DLFMB DRD	1990:07 1995:09	0.015	0.430	3.951	4.323	1.665	0.202	0.637	-0.000
					(0.003)	(0.002)	(0.152)

The results suggest that broad money has considerable predictive power for future inflation. First, consider a simple autoregression of inflation on lagged monthly inflation rates (Row 1). Lagged monthly inflation explains only around a third of the variance of inflation over the next twelve months; the standard deviation of the forecast error is approximately 7 percentage points. Adding lagged broad money growth improves predictive power (Row 2). F-tests show statistical significance at even the 0.1 percent level, and the doubling of the R-squared and the near-halving of the standard error show that money has considerable explanatory power in economic terms. Results predicting inflation one, three and six months ahead were broadly similar.

However, proponents of alternative theories of inflation might argue that such tests of forecasting power reveal little about underlying economic causality. The arrival of seasonal greetings is usually a good predictor that the holiday season is about to arrive, but in no sense do Christmas cards “cause” Christmas. More moderate critics might accept that monetary growth leads to inflation, but would want to go one step further, and enquire what caused the monetary growth. Specifically, they would argue that monetary growth often accommodated inflationary impulses, such as the attempts of certain sectors to increase their relative prices, or of workers to raise their relative wages, to prevent a possible recession.

To test for this possibility, additional causality tests were conducted, this time from lagged inflation to future money growth. ^1/ Except when forecasting money growth one month ahead, lagged inflation proved statistically significant in explaining future broad money growth.

Thus, in general, the results are open to interpretation. There is evidence that financial factors, including monetary growth, are linked to future inflation, consistent with conventional monetary explanations. At the same time, there is evidence linking inflation to future monetary growth, consistent with less orthodox and more structural explanations of inflation, or with a monetary policy reaction function that seeks to offset the output costs of adverse supply shocks. Thus, tighter monetary policy should succeed in lowering inflation, but perhaps at significant cost in terms of lost output.

b. Inflation and monetary policy in a restructuring economy

So far, two potential sources of entrenched inflation in Poland have been isolated: a drift of certain relative prices and some evidence of an accommodating monetary policy. In this section, it is argued that the nature of Polish firms’ restructuring process may be an ultimate underlying cause. Indeed, certain firms have restructured largely through attrition, accompanied by gradual output contraction and accumulated losses. It is argued that, due to this slow restructuring, firms are likely to increase their relative prices or to run deficits. As a result, they are less responsive to traditional macro-policy instruments. Therefore, to the extent that such firms represent a sizable proportion of output, we show that a “hard-currency” strategy to reduce inflation may be unexpectedly costly. ^1/

The move to a market economy was accompanied by a relative price shock of unprecedented magnitude. This shock created a fault line between two groups of firms: those which benefitted from it and those for which it proved detrimental. ^2/ In the face of this shock, restructuring in the sectors adversely affected can be expected to follow one of three paths:

• (i) cutting wages to restore profitability;

• (ii) boosting productivity by shedding labor or, in certain (rare) instances, by investing in new equipment;

• (ill) a “wait-and-see” approach where firms merely shrink by attrition. In this situation they run losses, and possibly increase their prices.

In a sense (iii) is a slower version of (ii), leading through accumulated losses to a depletion of the firms’ assets.

The “wait-and-see” approach seems to capture certain traits of the restructuring process in some sectors of the Polish economy. Indeed, as argued in Chapter 4 of this background paper, Poland has adjusted employment rather than the structure of its relative (sectoral) wages. Relative wages have remained fairly stable, while employment has plunged most in loss-making sectors. So far, however, labor shedding has fallen short of restoring profits. In other words, for these sectors, the “hard” budget constraint has not been binding enough to force firms into sufficient adjustment.

However, it is important to bear in mind two things. First, this characterization only applies to sectors in need of restructuring and which have delayed it. Privatized or newly created firms have been less prone to procrastination and, in certain cases, have made giant strides toward Western productivity standards. In essence, the transition creates a dual economy. ^1/ Second, a pure “wait-and-see” approach is unlikely since no firm is totally immune from restructuring pressure. As we discuss below, firms are better sheltered if they can get additional financing (e.g., from rolling over loans, tax arrears or interenterprise credits). What the above description emphasizes, however, is that the process is relatively slow because managers try to shelter workers from painful wage adjustment or labor shedding. A similar point is made by Berg and Blanchard (1993).

The coal mining sector provides an interesting illustration of this behavior. First, the coal industry has restructured as evidenced by sizable productivity gains (Table II.10). These gains have certainly exceeded what a pure “wait-and-see” approach would have delivered and have significantly contributed to the improvement in the sectors’ profitability. However, despite substantial losses, coal miners have improved their relative wages which still stand strikingly above the average for industry as a whole. In other words, a large fraction of productivity gains have been erased by additional wage costs. These developments have three main implications. First, the price of Polish coal has increased despite a 15 percent decline in international prices between 1991 and 1994. Second, the sectors’ assets have been severely eroded, threatening its long-term viability. Third, the completion of restructuring has been delayed. A recent report issued by the Polish Ministry of Mining now considers that restructuring might take another five years and involve the loss of another 90,000 jobs.

Table II.10.

Restructuring in the Coal Industry, 1991-94 ^1/

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

Table II.10.

Restructuring in the Coal Industry, 1991-94 ^1/

		1991	1992	1993	1994
Change in employment
	(in percent)	9.5	-4.9	-14.3	-7.6
Output index		108.5	100.0	97.6	100.4
Relative wage index		164.5	158.3	70.1	190.6
Change in relative price index
	(in percent)	40.9	2.2	16.9	14.5
Profitability (in percent)		-4.9	-23	-10.4	-0.8
Assets/liabilities		1.4	1.0	0.6	0.75

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

View Table

Table II.10.

Restructuring in the Coal Industry, 1991-94 ^1/

		1991	1992	1993	1994
Change in employment
	(in percent)	9.5	-4.9	-14.3	-7.6
Output index		108.5	100.0	97.6	100.4
Relative wage index		164.5	158.3	70.1	190.6
Change in relative price index
	(in percent)	40.9	2.2	16.9	14.5
Profitability (in percent)		-4.9	-23	-10.4	-0.8
Assets/liabilities		1.4	1.0	0.6	0.75

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

How relevant is this example? One may certainly argue that the behavior of the mining industry is an extreme case, reflecting largely the historical and political clout of miners in the Polish society. In a sense, this is precisely the lesson drawn from this example: for whatever reasons, certain sectors have successfully circumvented the “hard-budget” constraint. And, because these sectors represented a substantial fraction of industrial employment before the transition (approximately 12 percent for the coal mining sector), they represent more than a statistical curiosity. Indeed, various studies have documented insiders’ resistance to painful restructuring. Aghion et al. (1994) have modeled how certain groups can impose the status quo to avoid mass layoffs. The companion study (Chapter 4 of the Background Papers) also documents various rigidities in the labor market, and the lack of reaction of relative wages to the financial situation of firms.

The slow adjustment to the new post-transition conditions is also illustrated by the financial results of Polish enterprises. Polish firms have restructured between 1992 and 1994, as evidenced by the recovery in profitability (Table II.11). However, during this period the proportion of loss making firms, as well as their losses as a percentage of total income, has remained sizable. ^1/ This is consistent with the view that some firms adjusted by slowly reducing activity rather than by raising profitability. For the economy as a whole, losses have dwindled partly because some firms are profitable again but also because loss-making firms have shrunk.

Table 11.11.

Financial Results of Polish Enterprises, 1989-94

(In percent)

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

^1/As a percentage of total income.

Table 11.11.

Financial Results of Polish Enterprises, 1989-94

(In percent)

		1989	1990	1991	1992	1993	1994
Gross profits 2/		34.6	23.1	4.6	2.1	2.8	4.5
Net profits 2/		22.3	10.6	-1.3	-1.5	-0.5	1.7
Loss-making firms
	Percentage of firms	n.a.	n.a.	46.3	49.2	45.3	39.6
	Share of income	n.a.	n.a.	28.6	29.5	29.4	19.6

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

^1/As a percentage of total income.

View Table

Table 11.11.

Financial Results of Polish Enterprises, 1989-94

(In percent)

		1989	1990	1991	1992	1993	1994
Gross profits 2/		34.6	23.1	4.6	2.1	2.8	4.5
Net profits 2/		22.3	10.6	-1.3	-1.5	-0.5	1.7
Loss-making firms
	Percentage of firms	n.a.	n.a.	46.3	49.2	45.3	39.6
	Share of income	n.a.	n.a.	28.6	29.5	29.4	19.6

Source: Polish Statistical Office, Biuletyn Statystyczny, various issues.

^1/As a percentage of total income.

What are the macroeconomic implications of sizable loss-making sectors undergoing only (relatively) slow transformation? To answer that fundamental question, a model is presented where relative price adjustment plays a key role. Such a model is useful--and somewhat unrealistic. However, it at least provides a useful way to synthesize certain stylized facts concerning the transition. ^3/ Its main conclusion is that the sector undergoing restructuring will gradually increase its relative prices, as it reduces output and labor hoarding, adding to medium-term inflationary pressures. True, if the slowly restructuring sector faces a high price elasticity of demand, achieving such a relative price increase is unlikely, and the inflationary pressure correspondingly lower. However, in these circumstances, the slowly restructuring sector will make higher losses, for longer. For given money supply growth, the credits that it will require to longer. For given money supply growth, the credits that it will require to stay afloat will unduly crowd out lending to the expanding sector, prolonging the restructuring process. If these credits remain in place, the burden of tighter monetary policy to reduce inflation would then fall disproportionately on the expanding sector.

Starting with the model, it is assumed that the Polish economy comprises two distinct groups of firms, say distressed (sector 1) and profitable (sector 2). ^1/ In sector 1, activity dwindles at a pace determined by the rate of attrition of the labor force. This rate of attrition includes an exogenous factor (say, retirement) and an endogenous one, linked to employment growth in sector 2. The intuition between such a linkage is as follows. Growing employment in the emerging sector is the main source of job creation. Therefore, separations from distressed firms are facilitated by an important pool of job creation. Conversely, quits tend to fall during a period of intense job destruction. As a result, during the transition in Poland, job-to-job movements have become increasingly important as a way to avoid unemployment spells (see Coricelli et al. (1994)). Also, for certain occupational groups, intense job creation has led to shortages and, in turn, to higher wages to lure workers away from sector 1. This equation may therefore capture this wage competition between the two sectors.

Consider supply. In sector 1, at any instant, employment is assumed to determine output:

Note that this implies that supply in sector 1 is exogenous: it does not respond to wage or price changes. For simplicity, it is assumed that this effect is minor (β=0).

In the profitable second sector, output is given by equating the real wage, w/P₂, to the marginal product of labor:

It is further assumed that nominal wages are given, so that increased prices of good 2 lower the real product wage, and increase employment and output in sector 2. ^1/ In contrast, in sector 1 we assume employment is excessive, so that the real wage always exceeds the marginal product in sector 1. Moreover, it is assumed that labor shedding has only a limited impact upon output. One measure of progress toward restructuring is the extent to which the marginal product of labor and the real wage in sector 1 can be brought closer together. ^2/ With fixed nominal wages, progress can only be achieved by reducing employment or by increasing output prices.

The demand side of the model is standard and simply assumes a certain substitution of domestic demand for the two goods depending upon their prices (see Blanchard and Kiyotaki (1987)). It is assumed that the output of sector 1 is consumed entirely at home, whereas the output of good 2 is both exported and consumed domestically. The share of domestic consumption of goods 1 and 2 as a proportion of domestic income depends on the relative price of the two goods: